Datasets:

florentgbelidji

/

ncbi_extracted_running

like 0

PMC9837725

544 www.cmj.hr Aim To identify physical, cognitive, and metabolic factors affecting gait speed in patients with type-2 diabetes mel- litus (T2DM) without neuropathy. Methods This cross-sectional study enrolled 71 diabetic patients without neuropathy (mean age 55.87 ± 7.74 years, 85.9% women). Neuropathy status was assessed with Douleur Neuropathique 4. We used a cut-off point for gait speed of 1 m/s to classify the participants into two groups: slow walkers (SW) and average and brisk walkers (ABW). The groups were compared in terms of age, sex, body mass index (BMI), hemoglobin A1c (HbA1c), fasting glucose, sys- tolic blood pressure, maximal aerobic capacity (VO2 max), percentage of muscle mass, percentage of lower extrem- ity muscle mass, Mini-Mental State Examination (MMSE) score, and years of education. Results Compared with the ABW group, the SW group had significantly lower VO2max (14.49 ± 2.95 vs 16.25 ± 2.94 mL/kg/min) and MMSE score (25.01 ± 3.21 vs 27.35 ± 1.97), fewer years of education, and these patients were more frequently women (P < 0.05). In the multivariate regression models, the combination of VO2 max, sex, and MMSE score explained only 23.5% of gait speed (P < 0.001). MMSE score and VO2 max independently determined gait speed after adjustment for age, BMI, HbA1c, fasting glucose, systolic blood pressure, percent of muscle mass, percent of lower extremity muscle mass, and years of education. Conclusion In diabetic patients without neuropathy, phys- ical impairment and disability could be prevented by an im- provement in aerobic capacity and cognitive function. ClinicalTrials.gov number: NCT04758364 Received: October 4, 2021 Accepted: November 30, 2022 Correspondence to: Gulin Findikoglu Pamukkale University, Faculty of Medicine Department of Physical Medicine and Rehabilitation Pamukkale-Denizli, Turkey [email protected] Gulin Findikoglu1, Abdurrahim Altinkapak1, Hakan Alkan1, Necmettin Yildiz1, Hande Senol2, Fusun Ardic1 1Department of Physical Medicine and Rehabilitation, Pamukkale University, Denizli, Turkey 2Department of Biostatistics, Pamukkale University, Denizli, Turkey Cognitive function and cardiorespiratory fitness affect gait speed in type-2 diabetic patients without neuropathy RESEARCH ARTICLE Croat Med J. 2022;63:544-52 https://doi.org/10.3325/cmj.2022.63.544 545 Findikoglu et al: Gait speed in type-2 diabetic patients without neuropathy www.cmj.hr In elderly and middle-aged adults, gait performance indi- cates health and functional status. Gait speed at the usu- al pace is a strong predictor for a range of adverse out- comes and denotes the multisystemic well-being of an individual (1). Patients with diabetes mellitus (DM) with neuropathy com- pared with individuals without DM have a slower walking speed, shorter step length, increased step width, prolonged stance phase, increased gait variability, and improper dis- tribution of foot pressure (2,3). These alterations have been attributed to an impairment of sensory or motor nerves or the central nervous system and to a decreased strength of lower extremity muscles (2,4). However, impaired gait, physical capacity (5), and functional mobility tests were also found in diabetic patients without neuropathy com- pared with individuals without DM (3). DM was also associated with an increased risk of cognitive deficits and dementia (6). Reduced cognitive function was identified even in early stages of DM. DM and hyperten- sion were also separate risk factors for dementia due to the development of cerebrovascular pathologies (7). There is a lack of studies on factors associated with gait speed in diabetic individuals without neuropathy (8). Therefore, the aim of this study was to compare possible factors affecting gait speed between slow walkers (SW) and average or brisk walkers (ABW) with DM without neu- ropathy. The second aim was to investigate the effect of age, sex, muscle mass, aerobic capacity, cognitive func- tion, blood pressure, metabolic measures, and years of education on gait speed in diabetic individuals without neuropathy. PATieNTS ANd meTHodS Patients This cross-sectional study was conducted at the Physical Medicine and Rehabilitation Clinic of Pamukkale University in February and March 2021. A total of 109 individuals with type-2 DM selected with computer-based randomization were interviewed and assessed for the presence of neu- ropathy. Participants self-reported a physician’s diagnosis of DM and time of onset of DM. All participants were under medical supervision and were taking anti-diabetic and/or antihypertensive agents. All could ambulate independent- ly. We also inquired about the presence of depression and hypothyroidism, factors that also affect gait speed. As DM duration longer than 10 years is strongly associat- ed with the development of diabetic neuropathy, we en- rolled patients with T2DM duration shorter than 10 years but longer than 1 year (5). These patients were assessed for the presence of neuropathic symptoms with the Douleur Neuropathique 4 (DN4) questionnaire. DN4 is used to as- sess neuropathic pain (9) and was validated for diabetic neuropathy (10). Its validity and reliability were confirmed for Turkish patients (11). Diabetic patients with scores less than 4 out of 10 points were included in the study. Exclusion criteria were insulin therapy, poor glycemic con- trol, manifesting cardiovascular disease, retinopathy or other visual problems, diabetic neuropathy, nephropathy, cerebrovascular disease, prominent cognitive impairment, alcohol dependence, cancer, chemo/radiotherapy, foot ul- cer, orthopedic or surgical problems interfering with gait, wheelchair or any assistive devices for ambulation, or knee or hip arthritis. Eighty participants met the inclusion and exclusion criteria, and 71 accepted to participate. Gait speed was assessed with G-walk (BTS Bioengineering, Quincy, MA, USA), a system with demonstrated validity and reliability (12) consisting of inertial sensors: a triaxial accel- erometer, magnetometer, and gyroscope. It was positioned on the S1 vertebra with a semi-elastic band. The participants walked on a smooth surface for 7 m at their usual pace and returned. The cut-off point for SW was 1.0 m/s (8,13). The factors related to both gait and DM were considered potential explanatory variables. These included age, sex, BMI, HbA1c level, fasting glucose, systolic blood pressure, maximal oxygen consumption (VO2max), percentage of muscle mass, percentage of lower extremity muscle mass, MMSE score, and years of education. Height was measured without shoes on a stadiometer. Body composition was evaluated with Tanita MC580 (Tan- ita, Arlington Heights, IL, USA), a valid and reliable bioelec- tric impedance analyzer (14). Weight, body mass index, percentage of muscle mass, and percentage of lower ex- tremity muscle mass were assessed. Muscle mass percent- age was expressed with respect to body weight. Before the analyses, participants did not eat or drink for more than three hours but were prompted to urinate. Blood glucose levels and HbA1c were detected in blood samples after overnight fasting. Blood pressure was mea- sured with a sphygmomanometer on the left arm in the sitting position after rest. RESEARCH ARTICLE 546 Croat Med J. 2022;63:544-52 www.cmj.hr VO2 max was measured with the cardiopulmonary exercise test on a bicycle (Bike-med, Technogym, Cesena, Italy) by an ergometer (CareFusion 234 Gmb 2011, Hoechberg Ger- many) using breath-by-breath technique. Exercise testing was made by a ramp protocol starting with 30 W and in- creasing 15 W per minute until respiratory exchange ratio ≥1.10, when VO2max was measured. Blood pressure, heart rate, and ECG were monitored during resting, exercise test- ing, and the recovery period. Exercise tests ended without any complications. MMSE, a questionnaire evaluating orientation, attention, calculation, memory recall, language, and visual-spatial skills (15), has been widely used for cognitive function as- sessment. The scores between 24 and 30 denote a normal cognitive function, scores between 18 and 23 indicate mild dementia, and scores below 17 indicate severe dementia. Its validity and reliability were confirmed for Turkish patients (16). Due to a close relationship of cognitive functions and education, education level was expressed in years. The study was approved by the Non-invasive Clinical Re- search Ethics Committee of Pamukkale University. This study conformed to the Declaration of Helsinki. All partici- pants provided written informed consent. Statistical analysis Continuous variables are expressed as means ± standard deviation (SD), and categorical data are expressed as fre- quencies and percentages. The normality of distributio was tested with the Shapiro-Wilk test. Independent sample t test or Mann-Whitney U test were used for comparison be- tween two groups. The differences in categorical variables were assessed with the χ2 test or Fisher exact test. The pow- er of the study was 90%, and beta was 0.10 with respect to VO2 max for the comparison between the groups. Multi- variate linear regression models were performed to deter- mine factors effecting gait speed. A multivariate regression models with backward elimination method was performed by entering all of the independent variables into the equa- tion first, then deleting one variable at a time if it did not contribute to the regression. P < 0.05 was considered signif- icant. The analysis was performed with SPSS 17.0 software (SPSS Inc., Chicago, IL, USA). ReSULTS The study enrolled 71 patients (61 women) with a mean age of 55.87 ± 7.74 years (min 38- max 74). The patients’ characteristics are presented in Table 1. The factors associated with gait speed in SW and ABW are shown in Table 2. Compared with the ABW group, the SW group had significantly lower VO 2max (14.49 ± 2.95 vs 16.25 ± 2.94 mL/kg/min) and MMSE score (25.01 ± 3.21 vs 27.35 ± 1.97), had fewer years of education, and these pa- tients were more frequently women (P < 0.05). The number of patients with depression and hypothyroidism did not significantly differ between the groups. Gait speed was re- lated to sex, VO2 max, muscle mass, MMSE score, and years of education (P < 0.05) (Figure 1). TAbLe 1. Characteristics of patients with type-2 diabetes mellitus (N = 71) mean ± standard deviation or number (%) min-max Age (years) 55.87 ± 7.74 38-74 Sex (male/female) 10/61 (14.1/85.9) - Body mass index (kg/cm2) 31.75 ± 4.63 Gait speed (m/s) 1.09 ± 0.18 0.76-1.55 Hemoglobin A1c (%) 6.91 ± 0.88 5.20-10.20 Fasting glucose (mg/dL) 114.93 ± 24.20 81-212 Systolic blood pressure (mmHg) 125.86 ± 10.14 90-140 VO2 max (kg/mL/min) 15.86 ± 3.09 Percent of muscle mass 61.35 ± 6.45 Percent of lower extremity muscle mass 33.69 ± 17.83 10.9-109.0 Hypertension 32 (39.5) - Hypothyroidism 13 (16) - Depression 6 (7.4) - Mini Mental State Examination Score 26.62 ± 2.71 18-30 Douleur Neuropathique 4 score 0.36 ± 0.12 0-1 Years of education 8.37 ± 3.72 547 Findikoglu et al: Gait speed in type-2 diabetic patients without neuropathy www.cmj.hr A series of multifactorial linear regression models was per- formed to examine the relationship between multiple fac- tors and gait speed (R) and assess how these factors po- tentially explained gait speed (R2). Sex, age, and years of education were included in the models as confounding factors (Table 3). Adjusted R2 was used to eliminate the ef- fect of several variables on R2. Model 1 included VO2max, sex, MMSE score, age, body mass index, fasting glucose, HbA1c, systolic blood pressure, percentage of muscle mass, percentage of muscle mass of lower extremities, and years of education (P < 0.05). All the models had a signifi- cant effect on gait speed. Significance progressively in- creased with each model, and Model 10, which included VO2 max, sex, and MMSE score, attained the lowest P value. VO2 max and MMSE score significantly positively correlated with gait speed after adjustment for age, BMI, HbA1c, fast- ing glucose, systolic blood pressure, percentage of mus- cle mass, percentage of lower extremity muscle mass, and years of education (Table 4). diSCUSSioN In this study, the SW group had significantly lower VO2 max and MMSE score, fewer years of education, and the patients were more frequently women. However, the com- bination of VO2 max, sex, and MMSE score explained only 23.5% of gait speed. VO2 max, and MMSE scores were mu- tually positively correlated and significantly contributed to gait speed. Older adults are known to have a slower gait speed (4,17). Older adults with T2DM have decreased stride length and FiGURe 1. Correlation matrix for the involved parameters. Lighter tones indicate negativa correlation and darker tones indicate positiva correlation. RESEARCH ARTICLE 548 Croat Med J. 2022;63:544-52 www.cmj.hr increased gait variability, particularly during dual-task con- ditions irrespective of the neuropathy status (18). Similar results were reported in middle-aged patients with diabe- tes (19). Our study involved mostly middle-aged patients, while other studies involved elderly or frail people, which might have obscured the effect of age on gait speed. In TAbLe 2. Comparison of factors associated with gait speed between slow and average/brisk walkers in patients with type-2 diabetes* Walkers slow (n = 21) average or brisk (n = 50) P Gait speed (m/s) 0.89 ± 0.65 1.18 ± 0.14 <0.001 Age (years) 56.45 ± 8.92 55.89 ± 6.74 0.723 Sex (male/female)(%) 0/21 (0/100) 10/40 (20/80) 0.027 body mass index (kg/cm2) 31.90 ± 4.46 31.46 ± 4.87 0.743 Hemoglobin A1c (%) 6.88 ± 0.79 6.93 ± 0.93 0.629 Fasting glucose (mg/dL) 118.45 ± 31.61 112.98 ± 20.28 0.643 Systolic blood pressure (mmHg) 127.0 ± 8.01 124.56 ± 10 0.556 maximal aerobic capacity (kg/mL/min) 14.49 ± 2.95 16.25 ± 2.94 0.029 Percentage of muscle mass 60.71 ± 5.28 61.75 ± 6.99 0.845 Percentage of lower extremity muscle mass 31.37 ± 0.74 34.67 ± 21.23 0.629 mini mental State examination Score 25.01 ± 3.21 27.35 ± 1.97 0.040 Year of education (years) 5.39 ± 3.73 9.54 ± 3.14 0.010 Comorbid diseases hypertension 9 (42.9) 23 (46) 0.808 hypothyroidism 5 (23.8) 8 (16.0) 0.437 depression 1 (4.8) 5 (10) 0.469 Pharmacological therapies (user/non-user) (user/non-user) metformin 20/1 (95.2/4.8) 46/4 (92/8) 0.999 dipeptidyl peptidase-4 inhibitors 6/15 (28.6/71.4) 18/32 (36/64) 0.546 sulphonylureas 3/18 (14.3/85.7) 8/42 (16.0/84) 0.855 SGLT2 inhibitors 2/19 (9.5/90.5) 7 /43(14.0/86) 0.716 angiotensin receptor blockers 6/15 (28.6/71.4) 11/39 (22.0/78) 0.554 calcium channel blockers 1/20 (4.8/95.2) 9/41 (18/82) 0.262 β blockers 4/17 (19/81) 3/47 (6/94) 0.184 diuretics 1/20 (4.8/95.2) 3/47 (6/94) 0.999 angiotensin converting enzyme inhibitors 2/19 (9.5/90.5) 5/45 (10/90) 0.999 *data are presented as mean ± standard deviation or number (%). TAbLe 3. multivariate linear regression models of each factor associated with gait speed corrected for age, sex, and education years for patients with type-2 diabetes mellitus Factors b (Standard error) Standardized beta p factor R Adjusted R2 p model 95% confidence interval Variance inflation factor Body mass index (kg/cm2) -0.002 (0.005) -0.048 0.671 0.519 0.269 0.01 -0.11 -0.007 1.080 Hemoglobin A1c -0.005 (0.024) -0.022 0.761 0.489 0.189 0.02 -0.53-0.43 1.010 Fasting glucose -0.001(0.001) -0.084 0.459 0.522 0.224 0.01 -0.002- 0.001 1.047 Systolic blood pressure -0.003(0.002) -0.136 0.281 0.533 0.284 0.01 -0.007- 0.002 1.334 Maximal aerobic capacity 0.012 (0.008) 0.192 0.158 0.542 0.294 0.01 -0.005-0.028 1.559 Percent of muscle mass 0.002 (0.004) 0.061 0.642 0.519 0.269 0.01 -0.006-0.009 1.429 Hypothyroidism -0.010 (0.054) -0.021 0.855 0.418 0.124 0.01 -0.117-0.098 1.034 Depression -0.023 (0.075) -0.034 0.736 0.419 0.125 0.01 -0.171- 0.126 1.026 Mini Mental State Examination score 0.007 (0.009) 0.099 0.451 0.523 0.227 0.01 -0.011- 0.24 1.446 Percent of lower extremity muscle mass 0 (0.001) -0.001 0.995 0.517 0.220 0.01 -0.002- 0.002 1.120 549 Findikoglu et al: Gait speed in type-2 diabetic patients without neuropathy www.cmj.hr our study, age did not differ between SW and ABW, but it was included in regression models due to its importance in the literature. Yavuzer et al showed that diabetic individuals without neu- ropathy and non-diabetic individuals significantly differed in gait speed of and step length, indicating that gait altera- tion can be encountered even in diabetic patients without neuropathy (3). Most of the population-based studies also did not take into account the neuropathy status of diabet- ic patients. One study showed that older women with DM duration of more than 10 years had a slower gait speed and smaller step length compared with women with DM dura- tion of less than 10 years (8). To exclude the effects of dia- betic neuropathy, our study involved participants who had DM for less than 10 years, and 29.6% of them were SW. Slow gait speed independently predicted MMSE score decline during seven years of follow-up (20). It also pre- dicted the onset of dementia, Alzheimer’s disease, or an increased cognitive decline (1). Although our participants had mild cognitive impairments, SW had significantly low- er MMSE scores. Additionally, MMSE score was one of the independent determinants of gait speed. In another study, gait speed was the only independent determinant of mild cognitive impairment in patients with DM (13). DM impairs psychomotor speed and processing, visual-spatial abilities, learning, memory, executive functioning, and attention (18). In diabetic individuals with and without neuropathy, dual-task conditions during gait reduced gait performance (18). In another study, derangements in cognition and gait were interrelated and common in individuals with DM and/or hypertension (21). Furthermore, non-demented older adults with hypertension (22) and non-demented older adults with DM (21) had a decreased cognitive per- formance. In our study, the MMSE scores were adjusted for several confounding factors including systolic blood pres- sure and metabolic factors. The mean resting systolic blood pressure in this study was 125.86 ± 10.14 mm Hg while the participants were on anti- hypertensive agents. Systolic blood pressure values did not differ between SW and ABW, and systolic blood pressure contributed non-significantly to all models except Model 10. This might be explained by a close-to-normal range of blood pressure in our patients. In other studies, hyperten- sive older adults had a slower gait speed than normoten- sive older patients (23,24). In our study, SW and ABW did not differ in either HbA1c or fasting glucose levels. These parameters also did not contribute significantly to the models. The literature re- sults on the relationship between gait and HbA1c are in- conclusive. Lower HbA1c and blood glucose levels were related to brisk walking pace (25,26). However, the Rot- terdam study found no relation between impaired fasting glucose and continuous glucose levels during gait (19). In another study, HbA1c level was not related to knee ex- tensor strength and gait speed after adjustment for body weight (17). In contrast, higher HbA1c levels were related to a worse physical, but not cognitive function, after ad- justment for several factors (27). A population-based study TAbLe 4. Highly significant multivariate linear regression models with predictive factors for gait speed in patients with type-2 diabe- tes mellitus Factors b (Standard error) Standardized beta p factor R Adjusted R2 p model 95% confidence interval Variance inflation factor model 8 0.551 0.241 0.01 Maximal aerobic capacity 0.015 (0.008) 0.242 0.069 -0.01-0.031 1.368 Sex -0.005 (0.024) -0.022 0.027 0.18-0.297 1.382 Mini Mental State Examination score -0.001(0.001) -0.084 0.059 -0.001-0.032 1.025 Fasting glucose -0.003(0.002) -0.136 0.326 -0.003-0.001 1.018 Systolic blood pressure 0.012 (0.008) 0.192 0.221 -0.007- 0.020 1.119 model 9 0.539 0.241 0.001 Maximal aerobic capacity 0.014 (0.008) 0.233 0.079 -0.002-0.030 1.360 Sex 0.154 (0.069) 0.291 0.030 -0.015-0.294 1.379 Mini Mental State Examination score 0.016 (0.008) 0.223 0.053 0-0.032 1.022 Systolic blood pressure -0.010 (0.054) -0.021 0.855 -0.07-0.002 1.117 model 10 0.523 0.235 0.0003 Maximal aerobic capacity 0.017 (0.008) 0.272 0.036 0.001-0.032 1.274 Sex 0.131 (0.067) 0.248 0.055 -0.03-0.266 1.274 Mini Mental State Examination score 0.017 (0.008) 0.239 0.038 0.001-0.033 1.008 RESEARCH ARTICLE 550 Croat Med J. 2022;63:544-52 www.cmj.hr found that HbA1c levels of >8% were related to a slower gait (28). Tight glucose control regimes might cause hypo- glycemic episodes leading to impaired cognition. Higher blood glucose levels, on the other hand, could also cause neuropathy or impaired cognition by leading to structur- al changes in the brain (28). In this study, neither HbA1c nor fasting blood glucose were correlated with any other factor. Fasting blood glucose was not below 70 mg/dL in any of the participants, thus hypoglycemia could not have been the factor affecting gait. VO2 max is a measure of cardiac, pulmonary, and muscu- lar functioning. Despite its well-known relation with gait speed, it has not been included in most of the population- based studies. Therefore, direct measurement of aerobic capacity is a strength of this study. In the present study, VO2 max strongly predicted gait speed and was related to the percentage of muscle mass and lower extremity mus- cle mass. In other studies, VO2max was associated with most of the self-selected walking speed options when corrected for age, weight, height, and fatness (29). Oth- er studies showed that individuals with T2DM had lower aerobic exercise capacity compared with healthy controls (30). Another regression model that included leg strength, VO2 max, weight, heigh t, and muscle strength predicted 26% of gait speed (31). We used the percentage of muscle mass as the muscle mass of the body was corrected by weight. The percentage of muscle mass and the percentage of lower extremity mus- cle mass did not significantly differ between SW and ABW. Although the percentage of muscle mass was associated with gait speed, BMI, sex, and VO2max, after adjustment it did not significantly contribute to gait speed. In some clini- cal and population-based studies, T2DM was related to loss in muscle mass and strength (17,22). Diabetic neuropathy might cause a loss of motor neurons and thus muscle mass. Diabetic patients over 65 years had lower muscle density, knee and ankle muscle strength, muscle power and qual- ity, and slower gait compared with non-diabetic individu- als (32). They also had decreased quadriceps muscle power, strength, and gait speed. Muscular strength loss was faster in people who had diabetes for over 3 years (17). Longer dis- ease duration (>6 years) and poor glycemic control (HbA1c >8.0%) were related to a low muscle quality (33). Muscle quality was significantly lower in the arms or legs of diabetic patients compared with non-diabetic people (17). This study suffers from several limitations. The fitness level and gait speed follow a nonlinear relation (31), which cannot be sufficiently explained by linear regression models. Second, due to a limited number of participants, men and women were not equally distributed across SW and ABW groups. This might have affected the significant contribution of sex in the models. In conclusion, gait is a highly integrated function of multi- ple coordinated physiological systems, all of which are pro- gressively impaired by DM. This study provides important information about alterations in gait in diabetic patients without neuropathy. In these patients, physical impair- ment and disability could be prevented by an improve- ment in aerobic capacity and cognitive function. Funding This study was funded by the Scientific Research Committee of Pamukkale University (2019TIPF003). ethical approval granted by the Ethics Committee of Pamukkale University (60116787-020/75097). declaration of authorship AA conceived and designed the study; AA, HA, NY acquired the data; GF, AA, HS, FA analyzed and interpreted the data; GF and FA drafted the manuscript; AA, HA, NY, HS, FA critically revised the man- uscript for important intellectual content; all authors gave approval of the version to be submitted; all authors agree to be accountable for all aspects of the work. Competing interests All authors have completed the Unified Competing Interest form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: no support from any organi- zation for the submitted work; no financial relationships with any organiza- tions that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influ- enced the submitted work. References 1 Abellan van Kan G, Rolland Y, Andrieu S, bauer J, beauchet o, bonnefoy m, et al. Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older people an international Academy on Nutrition and Aging (iANA) Task Force. J Nutr Health Aging. 2009;13:881-9. medline:19924348 doi:10.1007/ s12603-009-0246-z 2 Sacco iC, Amadio AC. A study of biomechanical parameters in gait analysis and sensitive cronaxie of diabetic neuropathic patients. Clin biomech (bristol, Avon). 2000;15:196-202. medline:10656981 doi:10.1016/S0268-0033(99)00060-1 3 Yavuzer G, Yetkin i, Toruner Fb, Koca N, bolukbasi N. Gait deviations of patients with diabetes mellitus: looking beyond peripheral neuropathy. eura medicophys. 2006;42:127-33. medline:16767059 4 brach JS, Talkowski Jb, Strotmeyer eS, Newman Ab. diabetes mellitus and gait dysfunction: possible explanatory factors. Phys Ther. 2008;88:1365-74. medline:18801861 doi:10.2522/ ptj.20080016 5 American diabetes Association. executive summary: Standards of medical care in diabetes–2014. diabetes Care. 2014;37 Suppl 1:S5- 13. doi:10.2337/dc14-S005 6 mcCrimmon RJ, Ryan Cm, Frier bm. diabetes and cognitive 551 Findikoglu et al: Gait speed in type-2 diabetic patients without neuropathy www.cmj.hr dysfunction. Lancet. 2012;379:2291-9. medline:22683129 doi:10.1016/S0140-6736(12)60360-2 7 Lu FP, Lin KP, Kuo HK. diabetes and the risk of multi-system aging phenotypes: a systematic review and meta-analysis. PLoS one. 2009;4:e4144. medline:19127292 doi:10.1371/journal. pone.0004144 8 da Cruz Anjos dm, de Souza moreira b, Pereira dS, Picorelli Am, Pereira dA, Kirkwood RN, et al. impact of Type-2 diabetes Time Since diagnosis on elderly Women Gait and Functional Status. Physiother Res Int. 2017;•••:22. medline:26284942 doi:10.1002/ pri.1651 9 bouhassira d, Attal N, Alchaar H, boureau F, brochet b, bruxelle J, et al. Comparison of pain syndromes associated with nervous or somatic lesions and development of a new neuropathic pain diagnostic questionnaire (dN4). Pain. 2005;114:29-36. medline:15733628 doi:10.1016/j.pain.2004.12.010 10 Spallone V, morganti R, d’Amato C, Greco C, Cacciotti L, marfia GA. Validation of dN4 as a screening tool for neuropathic pain in painful diabetic polyneuropathy. diabet med. 2012;29:578-85. medline:22023377 doi:10.1111/j.1464-5491.2011.03500.x 11 Unal-Cevik i, Sarioglu-Ay S, evcik d. A comparison of the dN4 and LANSS questionnaires in the assessment of neuropathic pain: validity and reliability of the Turkish version of dN4. J Pain. 2010;11:1129-35. medline:20418179 doi:10.1016/j. jpain.2010.02.003 12 de Ridder R, Lebleu J, Willems T, de blaiser C, detrembleur C, Roosen P. Concurrent Validity of a Commercial Wireless Trunk Triaxial Accelerometer System for Gait Analysis. J Sport Rehabil. 2019;•••:28. medline:30747572 doi:10.1123/jsr.2018-0295 13 machii N, Kudo A, Saito H, Tanabe H, iwasaki m, Hirai H, et al. Walking Speed is the Sole determinant Criterion of Sarcopenia of mild Cognitive impairment in Japanese elderly Patients with Type 2 Diabetes Mellitus. J Clin Med. 2020;•••:9. medline:32640726 doi:10.3390/jcm9072133 14 Vasold KL, Parks AC, Phelan dmL, Pontifex mb, Pivarnik Jm. Reliability and Validity of Commercially Available Low-Cost bioelectrical impedance Analysis. int J Sport Nutr exerc metab. 2019;29:406-10. medline:30507268 doi:10.1123/ijsnem.2018-0283 15 Folstein mF, Folstein Se, mcHugh PR. “mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189-98. medline:1202204 doi:10.1016/0022-3956(75)90026-6 16 Güngen CeT, eker e, Yaşar R, engin F. Standardize mini mental Test’in Türk Toplumunda Hafif demans Tanısında Geçerlik ve Güvenilirliği Türk Psikiyatri dergisi. 2002;13:273-81. medline:12794644 17 Kalyani RR, Tra Y, Yeh HC, egan Jm, Ferrucci L, brancati FL. Quadriceps strength, quadriceps power, and gait speed in older U.S. adults with diabetes mellitus: results from the National Health and Nutrition examination Survey, 1999-2002. J Am Geriatr Soc. 2013;61:769-75. medline:23617584 doi:10.1111/jgs.12204 18 Roman de mettelinge T, delbaere K, Calders P, Gysel T, Van den Noortgate N, Cambier d. The impact of peripheral neuropathy and cognitive decrements on gait in older adults with type 2 diabetes mellitus. Arch Phys med Rehabil. 2013;94:1074-9. medline:23385112 doi:10.1016/j.apmr.2013.01.018 19 maksimovic A, Hanewinckel R, Verlinden VJ, Ligthart S, Hofman A, Franco oH, et al. Gait characteristics in older adults with diabetes and impaired fasting glucose: The Rotterdam Study. J diabetes Complications. 2016;30:61-6. medline:26585369 doi:10.1016/j. jdiacomp.2015.10.006 20 Alfaro-Acha A, Al Snih S, Raji mA, markides KS, ottenbacher KJ. does 8-foot walk time predict cognitive decline in older mexicans Americans? J Am Geriatr Soc. 2007;55:245-51. medline:17302662 doi:10.1111/j.1532-5415.2007.01039.x 21 Palta P, Schneider AL, biessels GJ, Touradji P, Hill-briggs F. magnitude of cognitive dysfunction in adults with type 2 diabetes: a meta-analysis of six cognitive domains and the most frequently reported neuropsychological tests within domains. J int Neuropsychol Soc. 2014;20:278-91. medline:24555960 doi:10.1017/ S1355617713001483 22 Gifford KA, badaracco m, Liu d, Tripodis Y, Gentile A, Lu Z, et al. blood pressure and cognition among older adults: a meta-analysis. Arch Clin Neuropsychol. 2013;28:649-64. medline:23838685 doi:10.1093/arclin/act046 23 Rosano C, Longstreth WT Jr, boudreau R, Taylor CA, du Y, Kuller LH, et al. High blood pressure accelerates gait slowing in well- functioning older adults over 18-years of follow-up. J Am Geriatr Soc. 2011;59:390-7. medline:21391929 doi:10.1111/j.1532- 5415.2010.03282.x 24 dumurgier J, elbaz A, dufouil C, Tavernier b, Tzourio C. Hypertension and lower walking speed in the elderly: the Three- City study. J Hypertens. 2010;28:1506-14. medline:20404744 doi:10.1097/HJH.0b013e328338bbec 25 Cigarroa i, espinoza-Sanhueza mJ, Lasserre-Laso N, diaz-martinez X, Garrido-mendez A, matus-Castillo C, et al. Association between Walking Pace and diabetes: Findings from the Chilean National Health Survey 2016-2017. int J environ Res Public Health. 2020;•••:17. medline:32722215 doi:10.3390/ijerph17155341 26 Sugimoto K, ikegami H, Takata Y, Katsuya T, Fukuda m, Akasaka H, et al. Glycemic Control and insulin improve muscle mass and Gait Speed in Type 2 diabetes: The mUSCLeS-dm Study. J Am med dir Assoc. 2021;22:834-8. medline:33278348 doi:10.1016/j. jamda.2020.11.003 27 beavers Km, Leng i, Rapp SR, miller me, Houston dK, marsh AP, et al. effects of Longitudinal Glucose exposure on Cognitive and Physical Function: Results from the Action for Health in diabetes movement and memory Study. J Am Geriatr Soc. 2017;65:137-45. medline:27676466 doi:10.1111/jgs.14478 28 Zaslavsky o, Walker RL, Crane PK, Gray SL, Larson eb. Glucose RESEARCH ARTICLE 552 Croat Med J. 2022;63:544-52 www.cmj.hr control and cognitive and physical function in adults 80+ years of age with diabetes. Alzheimers dement (N Y). 2020;6:e12058. medline:32802933 doi:10.1002/trc2.12058 29 Cunningham dA, Rechnitzer PA, Pearce me, donner AP. determinants of self-selected walking pace across ages 19 to 66. J Gerontol. 1982;37:560-4. medline:7096927 doi:10.1093/ geronj/37.5.560 30 Fang ZY, Sharman J, Prins Jb, marwick TH. determinants of exercise capacity in patients with type 2 diabetes. diabetes Care. 2005;28:1643-8. medline:15983314 doi:10.2337/diacare.28.7.1643 31 buchner dm, Cress me, esselman PC, margherita AJ, de Lateur bJ, Campbell AJ, et al. Factors associated with changes in gait speed in older adults. J Gerontol A biol Sci med Sci. 1996;51:m297-302. medline:8914502 doi:10.1093/gerona/51A.6.m297 32 ble A, Volpato S, Zuliani G, Guralnik Jm, bandinelli S, Lauretani F, et al. executive function correlates with walking speed in older persons: the inCHiANTi study. J Am Geriatr Soc. 2005;53:410-5. medline:15743282 doi:10.1111/j.1532-5415.2005.53157.x 33 Park SW, Goodpaster bH, Strotmeyer eS, de Rekeneire N, Harris Tb, Schwartz AV, et al. decreased muscle strength and quality in older adults with type 2 diabetes: the health, aging, and body composition study. diabetes. 2006;55:1813-8. medline:16731847 doi:10.2337/db05-1183

Cognitive function and cardiorespiratory fitness affect gait speed in type-2 diabetic patients without neuropathy.

[]

Findikoglu, Gulin,Altinkapak, Abdurrahim,Alkan, Hakan,Yildiz, Necmettin,Senol, Hande,Ardic, Fusun

eng

PMC10688325

The potential impact of advanced footwear technology on the recent evolution of elite sprint performances Joel Mason1, Dominik Niedziela2, Jean-Benoit Morin3, Andreas Groll2 and Astrid Zech1 1Department of Human Movement Science and Exercise Physiology, Friedrich Schiller University Jena, Jena, Germany 2 Department of Statistics, TU Dortmund University, Dortmund, Germany 3 Inter-University Laboratory of Human Movement Biology, University Jean Monnet Saint- Etienne, Saint-Etienne, France ABSTRACT Background: Elite track and field sprint performances have reached a point of stability as we near the limits of human physiology, and further significant improvements may require technological intervention. Following the widely reported performance benefits of new advanced footwear technology (AFT) in road-running events, similar innovations have since been applied to sprint spikes in hope of providing similar performance enhancing benefits. However, it is not yet clear based on current evidence whether there have been subsequent improvements in sprint performance. Therefore, the aims of this study were to establish if there have been recent year-to-year improvements in the times of the annual top 100 and top 20 athletes in the men’s and women’s sprint events, and to establish if there is an association between the extensive use of AFT and potential recent improvements in sprint performances. Methods: For the years 2016–19 and 2021–2022, the season best performances of the top 100 athletes in each sprint event were extracted from the World Athletics Top lists. Independent t-tests with Holm corrections were performed using the season’s best performance of the top 100 and top 20 athletes in each year to identify significant differences between years for each sprint discipline. Following the classification of shoes worn by the top 20 athletes in each event during their annual best race (AFT or non-AFT), separate linear mixed-model regressions were performed to determine the influence of AFT on performance times. Results: For the top 100 and top 20 athletes, there were no significant differences year-to-year in any sprint event prior to the release of AFT (2016–2019). There were significant differences between AFT years (2021 or 2022) and pre-AFT years (2016–2019) in eight out of 10 events. These differences ranged from a 0.40% improvement (men’s 100 m) to a 1.52% improvement (women’s 400 m hurdles). In the second analysis, multiple linear mixed model regressions revealed that the use of AFT was associated with improved performance in six out of ten events, including the men’s and women’s 100 m, women’s 200 m, men’s 110 m hurdles, women’s 100 m hurdles and women’s 400 m hurdles (estimate range: −0.037 – 0.521, p = <0.001 – 0.021). Across both analyses, improvements were more pronounced in women’s sprint events than men’s sprint events. How to cite this article Mason J, Niedziela D, Morin J-B, Groll A, Zech A. 2023. The potential impact of advanced footwear technology on the recent evolution of elite sprint performances. PeerJ 11:e16433 DOI 10.7717/peerj.16433 Submitted 7 July 2023 Accepted 18 October 2023 Published 27 November 2023 Corresponding author Joel Mason, [email protected] Academic editor Ross Miller Additional Information and Declarations can be found on page 15 DOI 10.7717/peerj.16433 Copyright 2023 Mason et al. Distributed under Creative Commons CC-BY 4.0 Conclusion: Following a period of stability, there were significant improvements in most sprint events which may be partly explained by advances in footwear technology. These improvements appear to be mediated by event, sex and potentially level of athlete. Subjects Kinesiology, Biomechanics, Sports Medicine Keywords Superspikes, Track and field, Athletics, Innovation, Biomechanics, Supershoes, Running INTRODUCTION Track and field sprint events are among the most prominent and revered disciplines in the sporting world. The evolution of sprint performances over time reflects advancements in physiology and training methods, as well as technological innovation such as the introduction of synthetic tracks in the 1960s. Despite temporary regressions resulting from the implementation of automated timing and compulsory random drug testing, the 20th century was largely characterised by steady progress in elite sprint performances (Haake, Foster & James, 2014; Lippi et al., 2008). Following a century of progress, sprint times have now somewhat plateaued since the 1990s across most elite sprint disciplines as performances have approached their asymptotic limits (Berthelot et al., 2010, 2015; Weiss et al., 2016; Ganse & Degens, 2021). This plateau is particularly prominent in the women’s events. One model incorporating performances from 1896–2008 indicates that no meaningful progression has occurred in four out of five women’s sprint events since 1994 (Berthelot et al., 2010), which may be partially explained by the introduction of routine performance enhancing drug testing (Haake, Foster & James, 2014). Similar performance stagnations have been observed across field events and long-distance running events for both sexes (Berthelot et al., 2010; Haake, James & Foster, 2015), adding credence to the wider notion that we are approaching the limits of human physiology (Berthelot et al., 2008; Nevill & Whyte, 2005; Haugen, Tønnessen & Seiler, 2015). In order to further substantially improve human performances, exogenous measures to overcome the limits of our physiology may be required, including artificial conditions and new technologies (Marck et al., 2017). For road running events, the recent introduction of advanced footwear technology (AFT, Frederick, 2022) has marked a new era in long-distance running performance, headlined by new world records in every distance from 5-km to the marathon for both men and women. AFT’s combination of “lightweight resilient midsole foams with rigid moderators and pronounced rocker profiles in the sole” (Frederick, 2022) has been demonstrated to improve the metabolic cost of running compared to conventional marathon shoes (Hoogkamer et al., 2018). Analyses of the annual top 100 times worldwide across all road-running distances following the introduction of AFT confirm the paradigm shift, indicating that road-racing times have improved by 1–3% since their release (Senefeld et al., 2021; Rodrigo-Carranza et al., 2021; Bermon et al., 2021; Rodrigo-Carranza et al., 2022). Subsequent to this resounding success, similar innovative upgrades have since been introduced in track spikes for both sprint and middle-distance disciplines, with the ultimate ambition of inducing similar Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433 2/19 performance-enhancing effects. So-called superspikes use an analogous approach of a plated midsole (often carbon fibre or nylon) combined with a thick midsole of foam (or pods of air), which is a clear departure from preceding sprint spike designs emphasising slim midsoles to minimise weight. Carbon fibre plates are not a recent introduction to sprint spikes, and there is evidence of how this longitudinal bending stiffness may influence both acceleration and maximal velocity (Stefanyshyn & Fusco, 2004; Smith et al., 2016; Willwacher et al., 2016). However, there is no publicly available evidence demonstrating how changes in midsole material and midsole thickness may influence sprinting when paired with the increased longitudinal bending stiffness provided by a plated sole. Therefore, precisely how this new generation of spikes interacts with the biomechanical and metabolic determinants of sprinting to potentially augment performance remains unclear (Healey et al., 2022). Further, how these potential benefits may vary according to sex and ability level, both factors suggested to mediate the performance enhancing effects of AFT on long-distance running performance (Knopp et al., 2023; Senefeld et al., 2021; Bermon et al., 2021), is also unknown. Although high-quality evidence for the mechanisms and associated performance- enhancing effects is currently lacking, AFT sprint spikes have been widely adopted by both recreational and elite sprinters, and there are preliminary indications of a potential shift in elite sprint performances. Since the introduction of AFT to sprinting in 2020, there have been world records set in the men’s and women’s 400 m hurdles, women’s indoor 400 m and world junior records in the men’s 100 and 200 m. Further, although only 50% of gold medals in throwing events at the Tokyo Olympics exceeded the performance from the Rio Olympics five years earlier, 90% of sprinting gold medals exceeded the performances from Rio. Additionally, there is plausible theory underlying an AFT-induced improvement in sprint times (Healey et al., 2022). However despite these factors, there has yet to be a systematic appraisal of the influence of AFT on elite sprint performances, with only a pre-print available which provides no link between AFT and performance improvements (Willwacher et al., 2023). Therefore, the primary aims of this study were (1) to establish if there have been recent year-to-year improvements in the annual top 100 and top 20 athletes of men’s and women’s sprint events, and (2) to establish if there is an association between the introduction of AFT and the potential recent improvements in sprint performances in each event. We hypothesised that recent improvements in sprint times will be at least partially be explained by the use of AFT. MATERIALS AND METHODS All procedures adhered to the Declaration of Helsinki and were approved by the ethics committee of the Friedrich Schiller University Jena (approval number: FSV 23/057). Due to all analysis involving data available in the public domain, informed consent was not required. Database search and data selection For the years 2016–19 and 2021–2022, the season best performances of the top 100 athletes in each sprint event were extracted from the World Athletics Top lists (World Athletics, Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433 3/19 2023; accessed January 2023), including the men’s and women’s 100, 200, 400, 400 m hurdles, women’s 100 m hurdles and men’s 110 m hurdles. Only one performance per athlete was recorded, and only wind-legal times recorded electronically in an outdoor competition were included. The year 2020 was also excluded due to significant pandemic-induced interruptions to training and competition opportunities, including the postponement of the 2020 Olympic Games. We selected 2016 as a cut-off point to capture the most recent evolution in sprint performances, in line with time periods used by previous studies characterising the influence of AFT on road-racing times (Rodrigo- Carranza et al., 2021, 2022). Data from 2010 onwards is included as a supplementary file (Supplementary File 2), which, alongside of the results of Willwacher et al. (2023), indicates that altering the time period of our study has no bearing on our analysis and subsequent findings. Definition and identification of AFT For the top 20 performers in each event in the years 2021 and 2022, two investigators independently identified the shoes worn in each athlete’s season best race in order to classify the footwear worn as either AFT or non-AFT. As for Bermon et al. (2021), the identification of the footwear of the top 100 athletes was not feasible due to limited availability of information. Identification of spikes used in each race was completed through media content, including race footage or photos from athlete and event social media, YouTube, or other official event photography services available online. Any disagreement was resolved by consensus with the remaining authors. Previous studies have used the same method to identify AFT use in elite road-race athletes (Senefeld et al., 2021, Rodrigo-Carranza et al., 2021; Bermon et al., 2021). AFT was defined as per Healey et al. (2022), whereby a superspike incorporates “a combination of lightweight, compliant and resilient foams (and/or air pods) with a stiff (nylon, PEBA, carbon-fiber) plate”. Therefore, spikes which contained only a stiff plate or only a thick midsole of innovative foam without the presence of the other were not classified as AFT. Eligibility of models was assessed through manufacturer details of shoe composition available online. Data analysis and statistics Multiple one-sided independent t-tests with Holm correction (Holm, 1979) were performed using the season’s best performance of the top 100 athletes in each year to identify significant differences between the years 2016, 2017, 2018, 2019, 2021 and 2022 in each event. To verify the normal distribution assumption of our data for the t-test, visual analyses with kernel density estimations were completed. A Levene’s-Test was also conducted to test for unequal variances within the events (Levene, 1960). As the normality assumption appears to be somewhat critical in some events, particularly because the underlying top 20 or top 100 performance variables are cut off at the upper tails, we additionally performed Wilcoxon–Mann–Whitney tests in order to validate the findings from our t-test analysis. This approach tested for the null hypothesis that it is equally likely that a value chosen at Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433 4/19 random from one year is greater or less than a value chosen at random from another year’s population), and the findings can be found in Supplementary File 3. Identical analysis was performed using the season best performances of solely the top 20 athletes each year to provide the basis for the regression analyses assessing the impact of AFT on performances. Following the identification and classification of shoes worn by the top 20 athletes in each event during their season best race, separate linear mixed-model regressions were performed for each event to determine the influence of AFT on performance times. Use of AFT (or not) and year were used as fixed effects, and participant ID used as random effect predictors. For the linear mixed models, the normality assumption is also implicitly relevant. However, on the basis of a careful goodness-of-fit analysis using residual and QQ- plots, we found no mentionable violations here for all fitted models. All data analysis and visualisations were completed in R (R Core Team, 2023). The t-test analyses were performed using the pairwise.t.test function from the base R package. For the mixed effects regression analyses, the packages lme4 and lmerTest were applied. For the visualisations, the packages dplyr and ggplot2 were employed. Significance for all analyses was set at p < 0.05, and Cohen’s d to calculate effect size, with values of <0.5, 0.5–0.79 and >0.80 considered small, medium and large respectively (Cohen, 1992). RESULTS Comparison of the annual top 100 sprint performances in each sprint event Table 1 and Figs. 1–3 demonstrate the changes in the season best performances of the top 100 men and women in each sprint event between the years 2016–2022. For the pre-AFT period (2016–2019), no meaningful changes and no significant improvements were detected via t-test comparisons with Holm correction in the top 100 times in all sprint events for both sexes (Table 1). The sprint times of the AFT era years (2021 or 2022) were significantly faster compared to sprint times from the pre-AFT era in seven sprint events (women’s 100, 200, 400, 100 m hurdles and men’s 100, 200 and 110 m hurdles), with significant improvements ranging from 0.40% (men’s 100 m) to 0.90% (women’s 100 m) (Table 2). For the women’s 100 m, women’s 400 m and men’s 110 m hurdles, the year 2022 was significantly faster than all pre-AFT years. Figures 1–3 displays the raw data together with boxplots and kernel density estimates for all ten sprint events. In most events, the distributions of the year 2021 (magenta) and 2022 (blue) are clearly shifted down in comparison to earlier years, indicating an improvement in times. Comparison of the annual top 20 sprint performances in each sprint event Table 3 demonstrates the changes in the season best performances of the top 20 men and women in each sprint event between the years 2016–2022. For the pre-AFT period (2016–2019), no meaningful changes and no significant improvements were detected via Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433 5/19 Figure 1 Season best performances of the top 100 athletes from the years 2016–2022 in the men’s and women’s 100 and 200 m. Full-size  DOI: 10.7717/peerj.16433/fig-1 Table 1 Annual best performances of the top 100 athletes in each sprint event. Times listed as seconds (mean ± SD). Pre-AFT period AFT period Event (m) 2016 2017 2018 2019 2021 2022 W100 11.13 ± 0.14# 11.13 ± 0.14# 11.13 ± 0.11# 11.14 ± 0.12*# 11.09 ± 0.14 11.04 ± 0.13 M100 10.04 ± 0.08 10.07 ± 0.08# 10.06 ± 0.08# 10.06 ± 0.08# 10.04 ± 0.10 10.02 ± 0.08 W200 22.68 ± 0.27 22.73 ± 0.28* 22.69 ± 0.26* 22.77 ± 0.30*# 22.63 ± 0.35 22.57 ± 0.33 M200 20.25 ± 0.19 20.29 ± 0.16# 20.25 ± 0.20 20.26 ± 0.21# 20.25 ± 0.20 20.18 ± 0.22 W400 51.41 ± 0.68*# 51.46 ± 0.68*# 51.32 ± 0.72*# 51.39 ± 0.73*# 50.99 ± 0.75 51.07 ± 0.65 M400 45.18 ± 0.50 45.13 ± 0.46 45.10 ± 0.47 45.17 ± 0.53 45.15 ± 0.46 45.08 ± 0.43 W100H 12.87 ± 0.16 12.91 ± 0.19*# 12.89 ± 0.19* 12.88 ± 0.20* 12.83 ± 0.18 12.80 ± 0.22 M110H 13.43 ± 0.16# 13.44 ± 0.17*# 13.48 ± 0.16*# 13.46 ± 0.16*# 13.38 ± 0.17 13.36 ± 0.16 W400H 55.69 ± 0.91 55.78 ± 1.07 55.94 ± 1.00 55.90 ± 1.05 55.62 ± 1.19 55.55 ± 1.16 M400H 49.23 ± 0.53 49.22 ± 0.51 49.20 ± 0.59 49.25 ± 0.59 49.14 ± 0.80 49.07 ± 0.70 Notes: * Significantly slower than 2021 (via t-test comparison). # Significantly slower than 2022 (via t-test comparison). AFT, advanced footwear technology; W, women’s; M, men’s; H, hurdles; SD, standard deviation. Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433 6/19 t-test comparisons in the top 20 times in all sprint events for both sexes (Tables 3 and 4). The sprint times of the AFT era years (2021 or 2022) were significantly faster compared to sprint times from the pre-AFT era in eight sprint events (women’s 100, 200, 400, 100 m Figure 2 Season best performances of the top 100 athletes from the years 2016–2022 in the men’s and women’s hurdles. Full-size  DOI: 10.7717/peerj.16433/fig-2 Figure 3 Season best performances of the top 100 athletes from the years 2016–2022 in the men’s and women’s 400 m. Full-size  DOI: 10.7717/peerj.16433/fig-3 Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433 7/19 Table 2 Overview of significant year-to-year differences in the annual top 100 sprint performances. Event and comparison Performances (s) Δ p-value Effect size Women’s 100 m 2019–2021 11.14 ± 0.12 vs 11.09 ± 0.14 0.45% 0.027 −0.387 2016–2022 11.13 ± 0.14 vs 11.04 ± 0.13 0.81% <0.001 −0.662 2017–2022 11.13 ± 0.14 vs 11.04 ± 0.13 0.81% <0.001 −0.710 2018–2022 11.13 ± 0.11 vs 11.04 ± 0.13 0.81% <0.001 −0.661 2019–2022 11.14 ± 0.12 vs 11.04 ± 0.13 0.90% <0.001 −0.760 2021–2022 11.09 ± 0.14 vs 11.04 ± 0.13 0.45% 0.033 −0.373 Men’s 100 m 2017–2022 10.07 ± 0.08 vs 10.02 ± 0.08 0.50% <0.001 −0.553 2018–2022 10.06 ± 0.08 vs 10.02 ± 0.08 0.40% 0.034 −0.391 2019–2022 10.06 ± 0.08 vs 10.02 ± 0.08 0.40% 0.012 −0.430 Women’s 200 m 2019–2021 22.77 ± 0.30 vs 22.63 ± 0.35 0.62% 0.005 −0.470 2017–2022 22.73 ± 0.28 vs 22.57 ± 0.33 0.71% 0.002 0.517 2018–2022 22.69 ± 0.26 vs 22.57 ± 0.33 0.53% 0.028 −0.395 2019–2022 22.77 ± 0.30 vs 22.57 ± 0.33 0.88% <0.001 −0.666 Men’s 200 m 2017–2022 20.29 ± 0.16 vs 20.18 ± 0.22 0.54% 0.002 −0.525 2019–2022 20.26 ± 0.21 vs 20.18 ± 0.22 0.40% 0.045 −0.384 Women’s 400 m 2016–2021 51.41 ± 0.68 vs 50.99 ± 0.75 0.82% <0.001 −0.589 2017–2021 51.46 ± 0.68 vs 50.99 ± 0.75 0.92% <0.001 −0.654 2018–2021 51.32 ± 0.72 vs 50.99 ± 0.75 0.65% 0.004 −0.462 2019–2021 51.39 ± 0.73 vs 50.99 ± 0.75 0.78% <0.001 −0.553 2016–2022 51.41 ± 0.68 vs 51.07 ± 0.65 0.67% 0.003 −0.476 2017–2022 51.46 ± 0.68 vs 51.07 ± 0.65 0.76% 0.001 −0.541 2018–2022 51.32 ± 0.72 vs 51.07 ± 0.65 0.49% 0.046 −0.349 2019–2022 51.39 ± 0.73 vs 51.07 ± 0.65 0.62% 0.006 −0.440 Women’s 100 m H 2017–2021 12.91 ± 0.19 vs 12.83 ± 0.18 0.62% 0.028 −0.397 2017–2022 12.91 ± 0.19 vs 12.80 ± 0.22 0.86% 0.001 −0.541 2018–2022 12.89 ± 0.19 vs 12.80 ± 0.22 0.70% 0.001 −0.455 2019–2022 12.88 ± 0.20 vs 12.80 ± 0.22 0.62% 0.027 −0.401 Men’s 110 m H 2017–2021 13.44 ± 0.17 vs 13.38 ± 0.17 0.45% 0.043 −0.358 2018–2021 13.48 ± 0.16 vs 13.38 ± 0.17 0.74% <0.001 −0.570 2019–2021 13.46 ± 0.16 vs 13.38 ± 0.17 0.59% <0.001 −0.436 2016–2022 13.43 ± 0.16 vs 13.36 ± 0.16 0.52% 0.014 −0.414 2017–2022 13.44 ± 0.17 vs 13.36 ± 0.16 0.60% 0.003 −0.486 2018–2022 13.48 ± 0.16 vs 13.36 ± 0.16 0.89% <0.001 −0.698 2019–2022 13.46 ± 0.16 vs 13.36 ± 0.16 0.75% <0.001 −0.564 Note: M, metres; H, hurdles; Δ, percentage change. Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433 8/19 Table 3 Annual best performances of the top 20 athletes in each sprint event, reported in seconds (mean ± SD). Pre-AFT period AFT period Event (m) 2016 2017 2018 2019 2021 2022 W100 10.90 ± 0.11 10.92 ± 0.08* 10.96 ± 0.05*# 10.96 ± 0.11*# 10.86 ± 0.12 10.84 ± 0.08 M100 9.92 ± 0.04 9.95 ± 0.03*# 9.93 ± 0.04# 9.94 ± 0.04*# 9.89 ± 0.06 9.90 ± 0.05 W200 22.25 ± 0.21*# 22.29 ± 0.25*# 22.29 ± 0.18*# 22.28 ± 0.23*# 22.02 ± 0.23 22.02 ± 0.20 M200 19.94 ± 0.12 20.02 ± 0.12# 19.91 ± 12 19.92 ± 0.15 19.94 ± 0.18 19.83 ± 0.18 W400 50.27 ± 0.47# 50.27 ± 0.25# 50.15 ± 0.52 50.30 ± 0.84# 49.74 ± 0.48 50.00 ± 0.43 M400 44.38 ± 0.45 44.37 ± 0.37 44.35 ± 0.30 44.29 ± 0.37 44.39 ± 0.33 44.36 ± 0.32 W100H 12.61 ± 0.14# 12.59 ± 0.11# 12.60 ± 0.13# 12.57 ± 0.12# 12.53 ± 0.10 12.44 ± 0.13 M110H 13.17 ± 0.09 13.17 ± 0.12 13.23 ± 0.10*# 13.20 ± 0.11# 13.12 ± 0.10 13.10 ± 0.10 W400H 54.21 ± 0.52 53.98 ± 0.70 54.47 ± 0.69# 54.25 ± 0.88 53.76 ± 1.12 53.65 ± 0.90 M400H 48.48 ± 0.36 48.37 ± 0.23 48.25 ± 0.56 48.32 ± 0.59 47.89 ± 0.84 47.93 ± 0.67 Notes: * Significantly slower than 2021 (via t-test comparison). # Significantly slower than 2022 (via t-test comparison). AFT, advanced footwear technology; W, women’s; M, men’s; H, hurdles; SD, standard deviation. Table 4 Overview of significant year-to-year differences in the annual top 20 sprint performances (according to t-test comparison). Event and comparison Performances (s) Δ p-value Effect size Women’s 100 m 2018–2021 10.96 ± 0.05 vs 10.86 ± 0.12 0.92% 0.013 −0.920 2019–2021 10.96 ± 0.11 vs 10.86 ± 0.12 0.92% 0.013 −0.924 2017–2022 10.92 ± 0.08 vs 10.84 ± 0.08 0.74% 0.046 −0.781 2018–2022 10.96 ± 0.05 vs 10.84 ± 0.08 1.10% 0.001 −1.143 2019–2022 10.96 ± 0.11 vs 10.84 ± 0.08 1.10% 0.001 −1.148 Men’s 100 m 2017–2021 9.95 ± 0.03 vs 9.89 ± 0.06 0.60% 0.005 −1.059 2019–2021 9.94 ± 0.04 vs 9.89 ± 0.06 0.50% 0.039 −0.842 2017–2022 9.95 ± 0.03 vs 9.90 ± 0.05 0.50% 0.035 −0.860 Women’s 200 m 2016–2021 22.25 ± 0.21 vs 22.02 ± 0.23 1.04% 0.006 −0.917 2017–2021 22.29 ± 0.25 vs 22.02 ± 0.23 1.22% 0.001 −1.073 2018–2021 22.29 ± 0.18 vs 22.02 ± 0.23 1.29% 0.001 −1.065 2019–2021 22.28 ± 0.23 vs 22.02 ± 0.23 1.17% 0.001 −1.063 2016–2022 22.25 ± 0.21 vs 22.02 ± 0.20 1.04% 0.004 −0.949 2017–2022 22.29 ± 0.25 vs 22.02 ± 0.20 1.22% 0.001 −1.106 2018–2022 22.29 ± 0.18 vs 22.02 ± 0.20 1.29% 0.001 −1.098 2019–2022 22.28 ± 0.23 vs 22.02 ± 0.20 1.17% 0.001 −1.096 Men’s 200 m 2017–2022 20.02 ± 0.12 vs 19.83 ± 0.18 0.95% <0.001 −1.255 Women’s 400 m 2016−2021 50.27 ± 0.47 vs 49.74 ± 0.48 1.06% 0.020 −0.925 2017−2021 50.27 ± 0.25 vs 49.74 ± 0.48 1.06% 0.020 −0.921 (Continued) Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433 9/19 hurdles and 400 m hurdles and men’s 100, 200 and 110 m hurdles), with significant improvements ranging from 0.50% (men’s 100 m) to 1.52 % (women’s 400 m hurdles) (Table 4). For the women’s 200 m and women’s 100 m hurdles, the year 2022 was significantly faster than all pre-AFT years. The influence of AFT on recent sprint performances A total of 97.75% of shoes worn by the top 20 athletes of 2021 and 2022 in their season best performance were able to be identified via media content (Table 5). According to the mixed effects models, the use of AFT significantly improved performance in six out of ten events, including the men’s and women’s 100 m, women’s Table 4 (continued) Event and comparison Performances (s) Δ p-value Effect size 2019−2021 50.30 ± 0.84 vs 49.74 ± 0.48 1.12% 0.014 −0.967 Women’s 100 m H 2016−2022 12.61 ± 0.14 vs 12.44 ± 0.13 1.36% <0.001 −1.279 2017−2022 12.59 ± 0.11 vs 12.44 ± 0.13 1.20% 0.001 −1.159 2018−2022 12.60 ± 0.13 vs 12.44 ± 0.13 1.28% <0.001 −1.216 2019−2022 12.57 ± 0.12 vs 12.44 ± 0.13 1.04% 0.004 −1.010 Men’s 110 m H 2018−2021 13.23 ± 0.10 vs 13.12 ± 0.10 0.83% 0.007 −1.009 2018−2022 13.23 ± 0.10 vs 13.10 ± 0.10 0.98% 0.002 −1.112 2019–2022 13.20 ± 0.11 vs 13.10 ± 0.10 0.76% 0.026 −0.870 Women’s 400 m H 2018–2022 54.47 ± 0.69 vs 53.65 ± 0.90 1.52% 0.015 −0.960 Note: M, metres; H, hurdles; Δ, percentage change. Table 5 Number of top 20 athletes wearing AFT, non-AFT and unidentifiable spikes in the years 2021 and 2022 for each sprint event. Event (m) 2021 2022 Non-AFT AFT Unidentified Non-AFT AFT Unidentified W100 9 11 0 1 18 1 M100 8 12 0 2 18 0 W200 10 9 1 1 19 0 M200 13 7 0 3 16 1 W400 8 11 1 0 19 1 M400 11 9 0 3 17 0 W100H 15 5 0 1 17 2 M110H 15 4 1 4 16 0 W400H 9 11 0 0 20 0 M400H 14 6 0 3 16 1 Note: AFT, advanced footwear technology; W, women’s; M, men’s; H, hurdles. Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433 10/19 200 m, men’s 110 m hurdles, women’s 100 m hurdles and women’s 400 m hurdles (Table 6). DISCUSSION We sought to identify whether there have been recent changes in the annual top sprint performances, and to subsequently evaluate the influence of AFT on elite sprint times. Our key findings include that: (1) following a plateau in performances in all sprint events between 2016–2019, statistically significant and specific improvements were identified in most sprint disciplines which coincided with widespread adoption of AFT in 2021 and particularly 2022, (2) these significant improvements ranged from 0.40–1.52%, and were typically more pronounced in women’s events than men’s events, (3) the use of AFT may partially explain these recent improvements in sprint times, with a significant relationship identified in six out of ten events. This study provides the first peer-reviewed evidence suggesting that performances in some sprint events have significantly improved following a period of stability, and that this improvement has been at least partially driven by the widespread adoption of AFT. Although the changes in performance were less substantial, less consistent and less unanimous as the AFT-induced performance improvements in road-running events with longer distances (Rodrigo-Carranza et al., 2022, 2021; Bermon et al., 2021), our results provide initial evidence that along with the technological innovation there is meaningful advancement in sprint performances. This finding is also in line with a recent pre-print using a similar approach to characterise improvements in sprint times between 2010–2022 (Willwacher et al., 2023). A key cornerstone of our findings is that between 2016–2019, there were no significant differences in the season best performances of the top 100 or top 20 athletes in any of the Table 6 The estimated regression effect of AFT usage on performance times in each sprint event according to linear mixed effects models. Fixed effects Random effects Use of AFT Year Athlete Residual Event (m) Estimate Error p-value Estimate Error p-value Variance SD Variance SD W100 −0.106 0.027 <0.001* 0.004 0.006 0.493 0.004 0.060 0.005 0.072 M100 −0.053 0.016 0.001* 0.001 0.003 0.698 0.001 0.030 0.002 0.043 W200 −0.149 0.064 0.021* −0.031 0.013 0.022* 0.015 0.123 0.032 0.179 M200 −0.037 0.045 0.411 −0.014 0.009 0.100 0.006 0.078 0.016 0.127 W400 −0.084 0.171 0.623 −0.067 0.035 0.057 0.111 0.333 0.173 0.416 M400 −0.190 0.104 0.070 0.030 0.020 0.139 0.027 0.165 0.093 0.305 W100H −0.093 0.034 0.008* 0.017 0.007 0.014* 0.004 0.064 0.009 0.097 M110H −0.087 0.030 0.005* −0.003 0.005 0.621 0.004 0.060 0.007 0.086 W400H −0.521 0.216 0.018* −0.020 0.045 0.589 0.285 0.534 0.303 0.551 M400H −0.085 0.155 0.586 −0.081 0.030 0.007* 0.128 0.358 0.170 0.413 Notes: * Statistical significance (p = < 0.05). AFT, advanced footwear technology; W, women’s; M, men’s; H, hurdles; SD, standard deviation. Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433 11/19 sprint events, supporting the notion that performances had reached a plateau as we likely near the limits of human physiology (Berthelot et al., 2008; Nevill & Whyte, 2005; Haugen, Tønnessen & Seiler, 2015). This adds substantial weight to our finding that AFT is likely a factor explaining recent performance improvements. For example, in the annual top 100 performances in the women’s 100 m prior to the release of AFT (2016–2019), the average performance was stable between 11.13–11.14 s, with an average year-to-year variation of less than 0.1%, which underlines the significance of the 0.90% improvement in 2022 compared to 2019. There are a number of candidate mechanisms which potentially accrue and interact to underpin the performance benefits of AFT observed in the current study. Given that carbon fibre plates in isolation have existed in sprint spikes for an extended period of time, the presence of a stiff plate alone is likely insufficient to explain the significant improvements in sprint times, and instead it is more likely that innovative midsole materials and geometry are the key drivers alongside longitudinal bending stiffness. For example, new foams such as polyether block amide demonstrate far superior energy restitution than traditional midsoles made of ethylene–vinyl acetate (Hoogkamer et al., 2018). In addition to the composition of the midsole, the increased thickness/height of the midsole (and its spatial distribution beneath the foot) in the new generation of spikes compared to traditionally minimal racing spikes potentially provides several advantages. An increase in the midsole thickness, which is capped at 20 mm by World Athletics regulations (World Athletics, 2021), may create more beneficial lever arms, potentially creating favourable shifts in ratio of force during acceleration towards horizontal reaction ground force orientation, which is a central determinant of sprint performance (Morin, Edouard & Samozino, 2011; Rabita et al., 2015). Changes in both shank position and dorsiflexion range of movement, both of which may be achieved via a higher midsole stack height, have been recently linked with better ratio of force during acceleration (King et al., 2022). Further, an increase in midsole thickness may result in between a 1–3% increase in overall limb length and enhance stride length, the consequences of which are increasingly studied in the context of athletes with transtibial amputations. Although the topic is currently keenly debated (Taboga et al., 2020; Beck, Taboga & Grabowski, 2022; Zhang-Lea et al., 2023; Weyand et al., 2022.), there is evidence of an association between longer leg length and faster maximal velocity (Weyand et al., 2022). In the world’s best transtibial amputation 400 m runner, reducing limb length by 5 cm produced a substantial drop in maximal treadmill velocity from 11.4 to 10.9 m/s (Weyand et al., 2022), leading to substantial projected and actual reductions in race performance. Although reduced leg length in amputee athletes resulting in slower speed does not guarantee that increasing leg length results in higher speed in able-bodied athletes, there is also evidence from non-amputee athletes that longer leg lengths may be particularly beneficial for longer sprinting (i.e., 400 m) (Weyand & Davis, 2005; Tomita et al., 2020). These factors, combined with improvements in running economy (Hoogkamer et al., 2018), which are increasingly valuable in distances over 100 m, potentially explain some of the performance enhancing effects of AFT observed in this study. It should be noted that these mechanisms Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433 12/19 remain primarily speculative at this stage, based on studies which do not directly investigate midsole thickness. Importantly, an increase in midsole thickness alone is not sufficient to improve running economy over longer distances (Barrons, Wannop & Stefanyshyn, 2023), indicating that if midsole thickness is indeed involved in performance enhancements, then it likely acts in concert with other components of the footwear, including the longitudinal bending stiffness. A key example which further demonstrates the uncertainty of the mechanisms is that AFT potentially also creates a less beneficial lever arm, considering that for a constant hip torque, a longer effective leg length will result in a smaller propulsive force. Therefore, future studies should seek to clarify the precise mechanisms through which AFT ultimately contributes to enhanced sprint performance. This study also demonstrated via two unique methods that women’s sprint events have undergone more substantial and more widespread recent improvements than male events, and that AFT had a greater impact on women’s performances than men’s performances. This is consistent with road-running research indicating that women benefited more from AFT than men (Bermon et al., 2021), including the findings that AFT improved marathon finishing time by 0.8% for males and by 1.6% for females in a subsample of marathon finishers (Senefeld et al., 2021). Importantly, this finding may provide further insight into the potential mechanisms which may underpin the AFT-induced improvements in sprinting performance in some events. Firstly, the overall stature discrepancy between elite male and female sprinters is approximately 6% (Weyand & Davis, 2005), meaning that a similar absolute increase in midsole thickness (e.g., the maximum allowed 20 mm) affords a greater relative increase in leg length for female sprinters than male sprinters. Given the previously described relationship between leg length and maximal velocity (Weyand et al., 2022) and the relationship between stride length and sprinting performance, this may partially explain our observation that female sprinters generally benefit more from AFT than males. Similarly, the geometry of AFT may also influence the sex-specific results observed in this study. Although World Athletics rules stipulate that a marginally thicker sole beyond the 20 mm regulation is permitted in the case of larger shoe sizes (World Athletics, 2021), we understand that the 20 mm stack height is not scaled proportionately according to shoe size, and the 20 mm midsole stack is kept relatively consistent across shoe sizes. Theoretically, this creates a more advantageous lever for those with smaller shoe sizes than for those sprinters with larger shoe sizes, due to unique midsole thickness/foot length ratios. Given that continued horizontal force application to the ground at high velocities is a key discriminator of sprint performance between males and females (Slawinski et al., 2017), this potential creation of more advantageous levers via smaller shoe sizes may help to explain why female sprinters appear to accrue greater benefits from AFT than male sprinters. Finally, differences in body mass may interact with energy restitution and conformity of both the rigid plate and the midsole foam. Combined, these factors may help to explain the sex-specific improvements in performance achieved via AFT. Similarly, but more tentatively, we observed that performance improvements were generally more pronounced in the top 20 compared to the top 100 athletes. While this may be partly explained by statistical factors related to sample size, it may also suggest that AFT preferentially benefits sprinters with certain characteristics, such as technique or specific Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433 13/19 strength. Indeed, there is evidence indicating that optimal longitudinal bending stiffness of sprint spikes is specific to the individual (Stefanyshyn & Fusco, 2004), and may be mediated such as toe flexor strength, plantar flexor strength, rebound jump performance and body mass (Willwacher et al., 2016; Nagahara, Kanehisa & Fukunaga, 2017). Similar performance-level dependency has been reported with the AFT-induced enhancement of distance running performance, whereby large variations in the magnitude of performance enhancement have been observed which are partially mediated by the standard of the athlete (Knopp et al., 2023). However, given that our top 20 and top 100 cohorts have some overlap, this result should be interpreted with caution, and future studies are needed to more clearly elucidate the potential performance-level specific improvements associated with the use of AFT. Although we provide initial insight into the recent improvements in some sprint events and the potential performance-enhancing effects of AFT on sprint times, the consistency of our results warrants further discussion. For comparison, studies investigating the influence of AFT on annual long-distance road-race times in elite athletes have reported a universal benefit for all events assessed across both sexes (Rodrigo-Carranza et al., 2022, 2021; Bermon et al., 2021). Contrarily, we observed that recent improvements (regardless of AFT influence) were not consistent across all events or across all years, and that AFT was not a significant predictor of performance in four of the ten events analysed. Combined, our data suggests that although AFT in sprint spikes influences performance in some events, they do not discriminate sprint performance to the same extent as AFT discriminates road-racing performance. Some of this inconsistency may be explained by differences in the adoption of AFT in different events and different years. For example, in 2021 only 42.5% of the top 20 athletes (across all events) wore AFT, whereas in 2022, 88.5% of the top 20 athletes utilised AFT. However, our mixed model analyses revealed that other factors are likely also involved in recent sprint time improvements. Changes in factors such as athlete characteristics like age (Elmenshawy, Machin & Tanaka, 2015) and stature (Marck et al., 2017), weather conditions, career trajectories, changes in training methods and injury status, sex-based and event-based differences in proximity to physiological limits, and increased globalisation are all candidate mediators of performance changes. The COVID-19 pandemic also provided a unique set of circumstances which conceivably influenced the observed performance increases. For example, athletes were afforded the opportunity to train for a prolonged period of time without reducing load as they typically would to peak for major competitions. It is also noteworthy that there was a 46% reduction in drug testing worldwide in 2020 (World Anti-Doping Authority, 2021), allowing athletes more opportunity to enhance their performances exogenously (Negro, Di Trana & Marinelli, 2022; Lima et al., 2021). Given the history and prevalence of performance enhancing drugs in track and field (Faiss et al., 2020; Berthelot et al., 2015), this is a plausible explanation for some improvement. There are also limitations of our study which must be considered when interpreting the current results. This is perhaps most practically demonstrated by the unexpected finding of no significant improvements in the men’s 400 m hurdles, despite nine of the top 10 times in history being run since the introduction of AFT in 2020. This highlights the limitations Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433 14/19 of our initial statistical approach in dealing with outliers, such as Karsten Warholm’s 2021 world record (which he ran without AFT), likely due to large standard deviations. The authors do not propose that no meaningful change has occurred in this event, but rather concede the limitations of the initial statistical approach. Interestingly, our mixed model analysis detected that year was a significant predictor of men’s 400 m hurdles performance. Further, another weakness of this study was the failure to account for more deterministic factors in the mixed model analysis, such as age, training content and context, environmental conditions and athlete nationality. Importantly, annual changes in competition opportunity have been demonstrated to influence annual performances (Haake, Foster & James, 2014). In this specific case, the absence of a major global championship in 2018 may influence the results. Finally, the dataset is limited by size, with only two years of data where athletes had the opportunity to wear AFT. This may limit the interpretation of our mixed model results. CONCLUSION This is the first evidence indicating that sprint times have become significantly faster in some events in the last two years, and that these improvements may be partially driven by technological innovation with sprint footwear design, which aligns with our hypothesis. Further, these improvements appear to be mediated by event, sex and potentially the level of athlete. Future studies should seek to identify the precise mechanisms through which AFT may improve sprint performance in both sexes independently, and to elucidate the athlete characteristics which may moderate these performance enhancing effects, such as athlete stature, foot-length/midsole thickness ratio, sprinting mechanics and specific strength characteristics. Additional analysis of recent performance trends in events which do not have superspikes available (for example, shot put and discus) would also provide insight into whether recent track performance improvements have been driven by technology or by more general sport-wide improvements in training methodology and competition opportunities, for example. Further, given recent commentary on the potentially enhanced risk of injury with AFT (Tenforde et al., 2023), the long-term ramifications of repeated exposure to AFT in sprint spikes should be investigated, especially in youth and developing athletes. ACKNOWLEDGEMENTS The authors wish to sincerely thank Sean Whipp (Whipp Sports) for his assistance with identifying the spikes of athletes. ADDITIONAL INFORMATION AND DECLARATIONS Funding The authors received no funding for this research. We received support for the APC from the German Research Foundation Project No. 512648189 and the Open Access Publication Fund of the Thueringer Universitaets-und Landesbibliothek Jena. The funders had no role Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433 15/19 in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Grant Disclosures The following grant information was disclosed by the authors: APC from the German Research Foundation: 512648189. Competing Interests Astrid Zech is an Academic Editor for PeerJ. Author Contributions
Joel Mason conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.
Dominik Niedziela performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.
Jean-Benoit Morin analyzed the data, authored or reviewed drafts of the article, interpreted the data, and approved the final draft.
Andreas Groll performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.
Astrid Zech conceived and designed the experiments, authored or reviewed drafts of the article, and approved the final draft. Human Ethics The following information was supplied relating to ethical approvals (i.e., approving body and any reference numbers): The Ethics Commission of the Friedrich Schiller University Jena granted ethical approval to complete this study: FSV 23/057. Data Availability The following information was supplied regarding data availability: The raw data used for analysis are available in the Supplemental Files. Supplemental Information Supplemental information for this article can be found online at http://dx.doi.org/10.7717/ peerj.16433#supplemental-information. REFERENCES Barrons ZB, Wannop JW, Stefanyshyn DJ. 2023. The influence of footwear midsole thickness on running biomechanics and running economy in female and male runners. Footwear Science 15(1)S8–S9 DOI 10.1080/19424280.2023.2199257. Beck ON, Taboga P, Grabowski AM. 2022. Sprinting with prosthetic versus biological legs: insight from experimental data. Royal Society Open Science 9(1):211799 DOI 10.1098/rsos.211799. Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433 16/19 Bermon S, Garrandes F, Szabo A, Berkovics I, Adami PE. 2021. Effect of advanced shoe technology on the evolution of road race times in male and female elite runners. Frontiers in Sports and Active Living 3:653173 DOI 10.3389/fspor.2021.653173. Berthelot G, Sedeaud A, Marck A, Antero-Jacquemin J, Schipman J, Sauliere G, Marc A, Desgorces FD, Toussaint JF. 2015. Has athletic performance reached its peak? Sports Medicine 45:1263–1271 DOI 10.1007/s40279-015-0347-2. Berthelot G, Tafflet M, El Helou N, Len S, Escolano S, Guillaume M, Nassif H, Tolaïni J, Thibault V, Desgorces FD, Hermine O. 2010. Athlete atypicity on the edge of human achievement: performances stagnate after the last peak, in 1988. PLOS ONE 5(1):e8800 DOI 10.1371/journal.pone.0008800. Berthelot G, Thibault V, Tafflet M, Escolano S, El Helou N, Jouven X, Hermine O, Toussaint JF. 2008. The citius end: world records progression announces the completion of a brief ultra-physiological quest. PLOS ONE 3(2):e1552 DOI 10.1371/journal.pone.0001552. Cohen J. 1992. Statistical power analysis. Current Directions in Psychological Science 1(3):98–101 DOI 10.1111/1467-8721.ep10768783. Elmenshawy AR, Machin DR, Tanaka H. 2015. A rise in peak performance age in female athletes. Age 37:1–8 DOI 10.1007/s11357-015-9795-8. Faiss R, Saugy J, Zollinger A, Robinson N, Schuetz F, Saugy M, Garnier PY. 2020. Prevalence estimate of blood doping in elite track and field athletes during two major international events. Frontiers in Physiology 11:160 DOI 10.3389/fphys.2020.00160. Frederick EC. 2022. Let’s just call it advanced footwear technology (AFT). Footwear Science 14(3):131 DOI 10.1080/19424280.2022.2127526. Ganse B, Degens H. 2021. Declining track and field performance trends in recent years in the Austrian best results 1897–2019. Journal of Musculoskeletal & Neuronal Interactions 21(2):196. Haake SJ, Foster LI, James DM. 2014. An improvement index to quantify the evolution of performance in running. Journal of Sports Sciences 32(7):610–622 DOI 10.1080/02640414.2013.841974. Haake S, James D, Foster L. 2015. An improvement index to quantify the evolution of performance in field events. Journal of Sports Sciences 33(3):255–267 DOI 10.1080/02640414.2014.938099. Haugen T, Tønnessen E, Seiler S. 2015. 9.58 and 10.49: nearing the citius end for 100 m? International Journal of Sports Physiology and Performance 10(2):269–272 DOI 10.1123/ijspp.2014-0350. Healey L, Bertschy M, Kipp S, Hoogkamer W. 2022. Can we quantify the benefits of “super spikes” in track running? Sports Medicine 52(6):1211–1218 DOI 10.1007/s40279-022-01657-4. Holm S. 1979. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6:65–70. Hoogkamer W, Kipp S, Frank JH, Farina EM, Luo G, Kram R. 2018. A comparison of the energetic cost of running in marathon racing shoes. Sports Medicine 48(4):1009–1019 DOI 10.1007/s40279-017-0811-2. King D, Burnie L, Nagahara R, Bezodis NE. 2022. Relationships between kinematic characteristics and ratio of forces during initial sprint acceleration. Journal of Sports Sciences 40(22):2524–2532 DOI 10.1080/02640414.2023.2172797. Knopp M, Muñiz-Pardos B, Wackerhage H, Schönfelder M, Guppy F, Pitsiladis Y, Ruiz D. 2023. Variability in running economy of Kenyan world-class and European amateur male runners with advanced footwear running technology: experimental and meta-analysis results. Sports Medicine 53(6):1255–1271 DOI 10.1007/s40279-023-01816-1. Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433 17/19 Levene H. 1960. Robust tests for equality of variances. In: Olkin I, ed. Contributions to Probability and Statistics. Palo Alto: Stanford University Press, 278–292. Lima G, Muniz-Pardos B, Kolliari-Turner A, Hamilton B, Guppy FM, Grivas G, Bosch A, Borrione P, Fossati C, Pigozzi F, Pitsiladis Y. 2021. Anti-doping and other sport integrity challenges during the COVID-19 pandemic. The Journal of Sports Medicine and Physical Fitness 61(8):1173–1183 DOI 10.23736/S0022-4707.21.12777-X. Lippi G, Banfi G, Favaloro EJ, Rittweger J, Maffulli N. 2008. Updates on improvement of human athletic performance: focus on world records in athletics. British Medical Bulletin 87(1):7–15 DOI 10.1093/bmb/ldn029. Marck A, Antero J, Berthelot G, Saulière G, Jancovici JM, Masson-Delmotte V, Boeuf G, Spedding M, Le Bourg É, Toussaint JF. 2017. Are we reaching the limits of homo sapiens? Frontiers in Physiology 8:812 DOI 10.3389/fphys.2017.00812. Morin JB, Edouard P, Samozino P. 2011. Technical ability of force application as a determinant factor of sprint performance. Medicine & Science in Sports & Exercise 43(9):1680–1688 DOI 10.1249/MSS.0b013e318216ea37. Nagahara R, Kanehisa H, Fukunaga T. 2017. Influence of shoe sole bending stiffness on sprinting performance. The Journal of Sports Medicine and Physical Fitness 58(12):1735–1740 DOI 10.23736/S0022-4707.17.07834-3. Negro F, Di Trana A, Marinelli S. 2022. The effects of the COVID-19 pandemic on the use of the performance-enhancing drugs. Acta Bio Medica: Atenei Parmensis 92(6):e2021401 DOI 10.23750/abm.v92i6.12377. Nevill AM, Whyte G. 2005. Are there limits to running world records? Medicine & Science in Sports & Exercise 37(10):1785–1788 DOI 10.1249/01.mss.0000181676.62054.79. R Core Team. 2023. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Available at https://www.R-project.org/. Rabita G, Dorel S, Slawinski J, Sàez-de-Villarreal E, Couturier A, Samozino P, Morin JB. 2015. Sprint mechanics in world-class athletes: a new insight into the limits of human locomotion. Scandinavian Journal of Medicine & Science in Sports 25(5):583–594 DOI 10.1111/sms.12389. Rodrigo-Carranza V, González-Mohíno F, Santos del Cerro J, Santos-Concejero J, González- Ravé JM. 2021. Influence of advanced shoe technology on the top 100 annual performances in men’s marathon from 2015 to 2019. Scientific Reports 11(1):22458 DOI 10.1038/s41598-021-01807-0. Rodrigo-Carranza V, González-Mohíno F, Santos-Concejero J, González-Ravé JM. 2022. Impact of advanced footwear technology on elite men’s in the evolution of road race performance. Journal of Sports Sciences 40(23):2661–2668 DOI 10.1080/02640414.2023.2183103. Senefeld JW, Haischer MH, Jones AM, Wiggins CC, Beilfuss R, Joyner MJ, Hunter SK. 2021. Technological advances in elite marathon performance. Journal of Applied Physiology 130(6):2002–2008 DOI 10.1152/japplphysiol.00002.2021. Slawinski J, Termoz N, Rabita G, Guilhem G, Dorel S, Morin JB, Samozino P. 2017. How 100-m event analyses improve our understanding of world-class men’s and women’s sprint performance. Scandinavian Journal of Medicine & Science in Sports 27(1):45–54 DOI 10.1111/sms.12627. Smith G, Lake M, Sterzing T, Milani T. 2016. The influence of sprint spike bending stiffness on sprinting performance and metatarsophalangeal joint function. Footwear Science 8(2):109–118 DOI 10.1080/19424280.2016.1143038. Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433 18/19 Stefanyshyn D, Fusco C. 2004. Athletics: increased shoe bending stiffness increases sprint performance. Sports Biomechanics 3(1):55–66 DOI 10.1080/14763140408522830. Taboga P, Drees EK, Beck ON, Grabowski AM. 2020. Prosthetic model, but not stiffness or height, affects maximum running velocity in athletes with unilateral transtibial amputations. Scientific Reports 10(1):1763 DOI 10.1038/s41598-019-56479-8. Tenforde A, Hoenig T, Saxena A, Hollander K. 2023. Bone stress injuries in runners using carbon fiber plate footwear. Sports Medicine 53:1499–1505 DOI 10.1007/s40279-023-01818-z. Tomita D, Suga T, Terada M, Tanaka T, Miyake Y, Ueno H, Otsuka M, Nagano A, Isaka T. 2020. A pilot study on a potential relationship between leg bone length and sprint performance in sprinters; are there any event-related differences in 100-m and 400-m sprints? BMC Research Notes 13(1):1–6 DOI 10.1186/s13104-020-05140-z. Weiss M, Newman A, Whitmore C, Weiss S. 2016. One hundred and fifty years of sprint and distance running-past trends and future prospects. European Journal of Sport Science 16(4):393–401 DOI 10.1080/17461391.2015.1042526. Weyand PG, Brooks LC, Prajapati S, McClelland EL, Hatcher SK, Callier QM, Bundle MW. 2022. Artificially long legs directly enhance long sprint running performance. Royal Society Open Science 9(8):220397 DOI 10.1098/rsos.220397. Weyand PG, Davis JA. 2005. Running performance has a structural basis. Journal of Experimental Biology 208(14):2625–2631 DOI 10.1242/jeb.01609. Willwacher S, Kurz M, Menne C, Schrödter E, Brüggemann GP. 2016. Biomechanical response to altered footwear longitudinal bending stiffness in the early acceleration phase of sprinting. Footwear Science 8(2):99–108 DOI 10.1080/19424280.2016.1144653. Willwacher S, Mai P, Helwig J, Hipper M, Utku B, Robbin J. 2023. Does advanced footwear technology improve track and road racing performance? An explorative analysis based on the 100 best yearly performances in the world between 2010 and 2022: the effects of super-footwear on world-class track and road racing performances. SportRxiv DOI 10.51224/SRXIV.297. World Anti-Doping Authority. 2021. Anti-doping testing figures report. Available at https://www. wada-ama.org/en/news/wada-publishes-2020-testing-figures-report (accessed 13 February 2023). World Athletics. 2021. Athletic shoe regulations. Available at https://worldathletics.org/news/press- releases/new-athletic-shoe-regulations-approved-2022 (accessed 10 June 2023). World Athletics. 2023. Season top lists. Available at https://worldathletics.org/records/toplists/ sprints/100-metres/outdoor/women/senior/2022?regionType=world&timing=electronic& windReading=regular&page=1&bestResultsOnly=true (accessed 28 January 2023). Zhang-Lea JH, Tacca JR, Beck ON, Taboga P, Grabowski AM. 2023. Equivalent running leg lengths require prosthetic legs to be longer than biological legs during standing. Scientific Reports 13(1):7679 DOI 10.1038/s41598-023-34346-x. Mason et al. (2023), PeerJ, DOI 10.7717/peerj.16433 19/19

The potential impact of advanced footwear technology on the recent evolution of elite sprint performances.

11-27-2023

Mason, Joel,Niedziela, Dominik,Morin, Jean-Benoit,Groll, Andreas,Zech, Astrid

eng

PMC8755824

1 Vol.:(0123456789) Scientific Reports | (2022) 12:645 | https://doi.org/10.1038/s41598-021-04215-6 www.nature.com/scientificreports Mechanical work accounts for most of the energetic cost in human running R. C. Riddick1,2,3* & A. D. Kuo2 The metabolic cost of human running is not well explained, in part because the amount of work performed actively by muscles is largely unknown. Series elastic tissues such as tendon can save energy by performing work passively, but there are few direct measurements of the active versus passive contributions to work in running. There are, however, indirect biomechanical measures that can help estimate the relative contributions to overall metabolic cost. We developed a simple cost estimate for muscle work in humans running (N = 8) at moderate speeds (2.2–4.6 m/s) based on measured joint mechanics and passive dissipation from soft tissue deformations. We found that even if 50% of the work observed at the lower extremity joints is performed passively, active muscle work still accounts for 76% of the net energetic cost. Up to 24% of this cost compensates for the energy lost in soft tissue deformations. The estimated cost of active work may be adjusted based on assumptions of multi-articular energy transfer, elasticity, and muscle efficiency, but even conservative assumptions yield active work costs of at least 60%. Passive elasticity can reduce the active work of running, but muscle work still explains most of the overall energetic cost. Abbreviations M Body mass (kg) L Leg length (m) g Gravitational acceleration (m/s2) c+, c− Metabolic costs for positive and negative work respectively (metabolic/mechanical energy, dimensionless) c± Metabolic cost for net work (metabolic/mechanical energy, dimensionless) fM Muscle work fraction (muscle work divided by total joint work by muscle & tendon, dimensionless) W+ M, W− M Muscle positive and negative work, respectively (J) W+ MT, W− MT Muscle–tendon positive and negative work, respectively (J) WST Soft tissue work (J) Ework Metabolic energy cost due to mechanical work (J) SI Summed Ipsilateral work, an estimate of muscle–tendon work assuming no energy transfer across the pelvis SB Summed bilateral work, an underestimate of muscle–tendon work assuming full energy transfer across all joints of the body IJ Independent Joint work, an overestimate of muscle–tendon work assuming no energy transfer across all joints of the body KT cost The model proposed by Kram and Taylor1 to estimate the metabolic cost of generating muscle force The metabolic cost of human running is not well explained, in part because the work and forces of the muscles are largely unknown. There is little energy dissipated by the environment, and so almost all of the action occurs within a cyclic stride, with equal amounts of positive and negative work by muscles2–4, at substantial levels of force and therefore energy cost. Although it is difficult to directly measure this information, there is nevertheless nearly a century of evidence5 about important factors such as the energetic cost of work performed by muscle, elastic energy return by tendon, and multi-joint energy transfer by muscle6–10. These factors could potentially OPEN 1Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA. 2Faculty of Kinesiology & Biomedical Engineering Program, University of Calgary, Calgary T2N 1N4, AB, UK. 3Centre for Sensorimotor Performance, University of Queensland, Brisbane, QLD 4072, Australia. *email: [email protected] 2 Vol:.(1234567890) Scientific Reports | (2022) 12:645 | https://doi.org/10.1038/s41598-021-04215-6 www.nature.com/scientificreports/ be combined to synthesize a plausible estimate for how much work muscles perform. This might in turn explain a substantial fraction of the overall energetic cost of running. A first step is to quantify the mechanical work performed by the body, both to redirect the body as it moves across the ground, as well as to move its limbs in relation to its center of mass11–13. Muscles expend positive metabolic energy to perform positive and negative work, with efficiencies of about 25% and − 120%, respectively (e.g., ex vivo5, for pedaling9, and for running up or down steep slopes8 where work is largely performed against gravity). The cost of positive work is also supported by the biochemical cost of producing and using ATP for muscle cross bridges to perform work, with a net effi- ciency (in aerobic conditions, excluding resting metabolism) in the muscles of various animals at about 25%14. However, during steady, level human running, work is not readily measurable at the muscles, but rather at the body joints, as with the “inverse dynamics” technique (e.g.,15). Joint work does not account for multi-articular muscles, which can appear to perform positive work at one joint and negative work at another, yet actually per- form no work6,16,17. The estimation of muscle work from joint work therefore depends on the assumed degree of multi-articular energy transfer6. Joint work can be used to estimate the work done by muscles only with careful consideration of these mechanisms, and is therefore better suited for giving bounds on muscle work as opposed to precise estimates. A second issue is elastic energy return. Muscles act in series with elastic tendons, which along with other tissues such as the plantar fascia, can store and return energy passively18–20. With some of the work performed on the body due to passive elasticity, running can appear to have high positive work efficiencies of 40%21–23 or more. At a comfortable aerobic running speed (2.8–4 m/s) Cavagna and Kaneko24 reported an efficiency of 50%. Since the efficiency of positive muscle work is about 25%, these higher efficiencies must be due to the passive return of energy in elastic tissues of the body. In vivo measurements of elastic contributions in the gastrocnemius of a turkey25 suggest that tendon could account for about 60% of the observed joint work. But the contribution of elastic tissues to human running has been estimated for a select few tendons under specific types and speeds of locomotion26–29, leaving elastic contributions unknown for the majority of muscles and tendons of the body. Elastic energy return has led to alternative measures that correlate with energy cost. For example, Kram and Taylor1 proposed that the cost of running is inversely proportional to the amount of time spent on the ground during each step, scaled by body weight. Referred to here as the KT cost, it presumes that much of the work observed at joints is performed passively by elastic tendon, with muscle largely acting isometrically and at high cost30,31. This is largely based on the mass-spring model of running, widely used to suggest that the leg acts purely elastically as it hits the ground32,33, with tendons doing most of the work. Indeed, the KT cost correlates well with metabolic cost for a variety of animals at different scales1, albeit with differing proportionalities for each case. But its proposed independence from work is also problematic. For example, the KT cost cannot explain the cost of running on an incline34, where net work is certainly performed against gravity8. Even on level ground, in vivo measurements reveal muscles that do not act isometrically, but perform substantial work26,27,35. In addition, soft tissue deformations during running may dissipate substantial mechanical energy2, which can only be restored through active muscle work. Thus, work by muscle fascicles is likely still relevant to the overall energetic cost of human running. The present study therefore re-evaluates the contribution of muscle work to running (Fig. 1). This is based on previous estimates for the metabolic cost of work7,22,34, but expanded to clarify the upper and lower bounds on each parameter. We account for the effects of multi-articular energy transfer, elastic energy return, and muscle efficiency, and consider how energy dissipation from soft tissues can account for a significant amount of meta- bolic cost. Recognizing that the assumptions are inexact, our goal is to determine reasonable bounds, rather than an exact estimate, for the cost of work. We then test the degree to which mechanical work can explain the overall energetic cost of running. We hypothesize that even by using the lowest possible bound on the cost of muscle work (taking into account the uncertainty of the model parameters), that muscle work will account for the majority of metabolic cost in running. Methods We estimated the active mechanical work performed by the body during running, and its potential contribu- tion to metabolic cost. We started with joint work measures using standard procedures, supplemented it with recently developed measures of soft tissue dissipation, and then applied simple estimates of multi-articular energy transfer and elastic energy return. Measurement were performed on healthy adult subjects ( N = 8 , 7 male, 1 female; 20–34 years) who ran at seven speeds according to each person’s comfort, in randomized order, ranging 2.2–4.6 m/s. Body mass M was 74.9 ± 13.0 kg (mean ± s.d.), and leg length L was 0.94 ± 0.04 m. Subjects ran for a continuous period of 6 min at each speed. This study was approved by the University of Michigan Institutional Review Board and all subjects gave informed consent prior to their participation. All methods and techniques used in the experiment followed the guidelines set forth by the Michigan Institutional Review Board. The kinematic and dynamic data used for this study is the same as presented previously2, and briefly summa- rized here again. Kinematics and ground reaction forces were recorded on a split-belt instrumented treadmill at the University of Michigan. Forces (980 Hz sampling; Bertec, Columbus, OH, USA) and motion capture (480 Hz; PhaseSpace Inc., San Leandro, CA, USA) were collected concurrently, with markers placed bilaterally on the ankle (lateral mallelous), knee (lateral epicondyle), hip (greater trochanter), shoulder (acromion of scapula), elbow (lateral epicondyle of humerus), and wrist (trapezium). Additional tracking markers were placed on the shanks, thighs, trunk, upper arm, lower arm, and upper arm, with three markers on the pelvis (sacrum, left/ right anterior superior iliac spine) and two markers on each foot (calcaneus, fifth metatarsal). These data were collected for at least 1 min per trial, with force data filtered at 25 Hz and marker motion at 10 Hz (second-order low-pass Butterworth), and then applied to inverse dynamics calculations (Fig. 2) using standard commercial software (Visual3D, C-Motion, Germantown, MD, USA). 3 Vol.:(0123456789) Scientific Reports | (2022) 12:645 | https://doi.org/10.1038/s41598-021-04215-6 www.nature.com/scientificreports/ These data were used to compute two kinds of mechanical work. The first was standard rigid-body joint powers, as the work per time needed to rotate and translate (via joint torque and intersegmental reaction forces, respectively) two connected segments relative to each other. We used the so-called 6-D joint power, considered robust to errors such as in joint center locations13,36,37. The second quantity was the dissipative work performed by soft tissue deformations. Briefly, this is the dif- ference between rigid-body joint power and the total mechanical work2,13,24,38. The total mechanical work is defined as the rate of work performed on the COM (evaluated using ground reaction forces with no rigid-body assumptions12) plus the rate of work performed to move rigid-body segments relative to the COM. In running, this quantity is similar in magnitude to the difference between the positive and negative joint work over a stride2, which itself implies that rigid body work does not capture all of the work of running. The term “total mechanical work” is defined as the summation of soft tissue and joint work. Metabolic cost was estimated through respirometry (Oxycon; CareFusion Inc., San Diego, CA). Both O2 consumption and CO2 production were recorded on a breath by breath basis and averaged over the final three minutes of each 6-min trial, and converted to gross metabolic rate (in W). Net metabolic rate was found by subtracting each subject’s cost for standing quietly, collected before running. The subjects’ respiratory exchange ratio (RER) was measured to be 0.85 ± 0.09 across subjects, with each individual trial having an average RER of less than 1, indicating mostly aerobic conditions. Mechanical work and energy transfer by muscle–tendon. The work performed by joints and soft tissue deformation was used to estimate that done by the series combination of muscle and tendon. To illustrate energy transfer assumptions, we initially consider two opposing sets of assumptions—an Overestimate and an Underestimate—before introducing our intermediate measure. The Overestimate assumes no multiarticular energy transfer between joints, as if all muscles acted uniarticularly. Positive work is thus evaluated by integrat- ing the positive intervals of each joint’s power over a stride (Fig. 2A), and then summing across all joints in both sides of the body, as if they were independent joints (IJ). Multiplying by stride frequency then yields the average rate of positive independent-joint work, ˙W+ IJ . We consider this quantity to be an Overestimate because it disre- gards energy transfer by multi-articular muscle. The Underestimate of work takes the opposite extreme, and assumes that simultaneous positive and negative work always cancel each other. This entails summing the powers from all the body joints at each instance in time, yielding summed joint power13, and then integrating the positive summed joint power over a stride. Multiply- ing by stride frequency yields the average rate of positive summed-bilateral (SB) joint work, ˙W+ SB (Fig. 2B). This is considered an Underestimate of actual muscle–tendon work, because it assumes energy transfer can occur between any two joints, regardless of whether a muscle crosses those joints. The Over- and Under-estimates, ˙W+ IJ and ˙W+ SB , are roughly analogous to the terms “no between-segment transfer” and “total transfer between all segments” of Williams and Cavanagh7, except applied here to transfer between joints rather than body segments. We introduce our own intermediate muscle–tendon work estimate, termed Summed Ipsilateral (SI) work. It assumes full energy transfer across the joints on each side of the body, but not between the two sides. This Figure 1. A depiction of the sources of mechanical work in the body during locomotion. Muscle fascicles perform active work in series with passive elastic tendon, and the two together perform work about joints. Soft tissues such as the heel pad and the viscera also deform and dissipate energy over a stride. Passive contributions from series elasticity and deformable soft tissues, along with the structure of multi-articular muscles spanning more than one joint, play an important role in estimating the amount of work performed by muscles. 4 Vol:.(1234567890) Scientific Reports | (2022) 12:645 | https://doi.org/10.1038/s41598-021-04215-6 www.nature.com/scientificreports/ has previously been justified based on inter-segmental energetic analysis39. This is mostly because there are no muscles that cross the legs and could transfer negative work from one leg into positive work at the other. The average rate of work ˙W+ SI entails summing the joint powers on one side of the body at each point in time, inte- grating the positive intervals of this power (Fig. 2B), and then multiplying by step frequency. Of course, further examination of musculoskeletal geometry, neural activation patterns, and loading conditions could yield more intricate estimates of muscle–tendon work. But without full knowledge of individual muscle forces and displace- ments, we use the Summed Ipsilateral estimate as a simple and not unreasonable set of assumptions, between the aforementioned extremes. Figure 2. Mechanical work contributions to metabolic energy expenditure, for a representative subject (3.10 m/s, mass = 70.8 kg, leg length = 0.89 m). (A) Instantaneous mechanical power of the joints (ankle, knee, and hip), and from soft tissue deformations, over one-half running stride (beginning with heelstrike). Also shown is the summation of all joint powers from both sides of the body, which is an underestimate of power (B) Four summary measures of work per step: Overestimate, Estimate (Summed Ipsilateral work), Underestimate, and Soft Tissue work. Positive (negative) work refers to integrated intervals of positive (negative) power. Soft tissue work shown includes positive and negative work per step, and the net (negative, dissipative) work. (C) Work costs illustrate metabolic cost contributions. The magnitude of Summed Ipsilateral negative work is treated as an estimate of the joint positive and negative work performed on rigid body segments. This is multiplied by muscle work fraction fM (provisionally 0.5) to yield work due to muscle. Active muscle work includes positive work to offset net soft tissue dissipation. Active muscle work is multiplied by the cost of positive and negative muscle work ( c+ and c− ) to estimate the energetic cost due to active muscle work. 5 Vol.:(0123456789) Scientific Reports | (2022) 12:645 | https://doi.org/10.1038/s41598-021-04215-6 www.nature.com/scientificreports/ Metabolic cost of muscle work. We define two quantitative parameters to link muscle–tendon mechani- cal work to energy expenditure. The first is the proportion of work performed actively by muscle vs. passively by tendon, and second is the metabolic cost at which the active work is performed. The proportion is defined as fm , the fraction (ranging 0–1) of muscle–tendon work performed by muscle fascicles, such that where W+ M is the positive work of muscle fascicles and W+ MT is the positive work of muscle–tendon (applying the proposed Summed Ipsilateral measure, or the Over- or Under-estimate assumptions), and analogously for negative work. In vivo measurements suggest a variety of possible values for fm , for example 0.40 for turkey gastrocnemius25, and 0.26–0.56 for two muscles of running dogs40. For humans, cadaver data suggest 0.52 for the Achilles tendon and foot arch19. Other indirect data suggest a range of 0.4–0.62522,24, depending on energy transfer assumptions. The correct value is unknown, and almost certainly varies with muscle group, loading conditions, and speed. We use a single parameter fm to summarize an overall effect for all muscles, and adopt a provisional value of 0.5, while allowing for other possible values (see Table 1). We characterize the metabolic cost of muscle work with separate parameters for positive and negative work. The positive work cost c+ is defined as the metabolic energy cost of producing a unit of active positive work, equivalent to the inverse efficiency of pure positive work. An analogous cost c− is defined for the metabolic cost of negative work. We adopt provisional values for c+ and c_ of 4.00 and − 0.83, respectively, equivalent to efficien- cies of 25% and −120%41, again allowing a range for c_ (see Table 1). The overall energetic cost of this work Ework is summed for rigid body and soft tissue contributions (graphi- cally depicted in Fig. 2C). Soft tissues dissipate net energy (yielding negative ˙WST ), and muscles must actively perform net positive work to compensate for those losses. The positive cost of making up for such dissipation is therefore c+|WST| . The cost of rigid body work is estimated from the magnitude of negative work from inverse dynamics W− M  , multiplied by the costs for both positive and negative work. These summed contributions yield This energetic cost per stride is then multiplied by stride frequency to yield metabolic power ˙Ework due to active work. To account for differences in subject size42, data were non-dimensionalized using body mass M leg length L , and gravitational acceleration g as base variables. Mean power and work normalization constants were Mg3/2L1/2 = 2184W and MgL = 678J , respectively. The mean running speed normalization constant was g1/2L1/2 = 3.04 m/s. All averaging and statistical tests were performed with dimensionless quantities. In figures, data were plotted with dimensional scales in SI units, using the mean normalization constants. Statistical tests were performed as follows. We used a linear least-squares fit to relate running speed to mechanical or metabolic rates, and then used Eq. (2) to estimate the metabolic cost attributable to work. We also used the linear least-squares fit to test how other work measures and the KT cost are related to metabolic rate. All regressions were performed allowing each subject an individual constant offset, while constraining them all to a single linear coefficient. The relationship between the predictor and response variables were considered significant when p < 0.05 for the F-statistic. Measures are reported in the form Y ± C.I. for α = 0.05 where Y is the predicted response of the linear regression model. Results We found that all measures of mechanical work rate and metabolic rate exhibited typical and fairly linear increases with running speed. Mechanical work data are summarized here, with more comprehensive measures reported previously2. In terms of standard joint powers (Fig. 2A, representative data), the ankle, knee, and hip powers far exceeded that for the upper body. Soft tissues produced power similar to a damped oscillation (reported previously2), and the Over- and Under-estimates of power bracketed the intermediate estimate, as expected. This was also true for the overall Over- and Under-estimates of positive and negative work per stride (Fig. 2B); soft tissues produced net negative work. These observations were consistent across the range of run- ning speeds measured (Fig. 3). As expected, the proposed Summed Ipsilateral work rate increased with running speed (Fig. 3A), and was between the expected Overestimate and Underestimate. Net soft tissue work rates were negative and increased in magnitude with speed. The regression coefficients and statistical outcomes for the relationship between these measures of power and running speed can be found in Table 2. (1) W+ M = fMW+ MT (2) Ework = (c+ + c−)  ˙W− M  + c+  ˙WST . Table 1. The cost coefficient represents how much metabolic energy a unit of mechanical work costs. The cost coefficient is calculated by taking into account the amount of work performed by tendon relative to muscle, and the efficiency of positive and negative muscle work. A range of cost coefficients between 1.8 and 3.2 were found by consulting experimental data from the literature. Positive work cost c+ Negative work cost c− Net work cost c± = c+ − c− Muscle work fraction fM Cost coefficient fMc± Upper bound 4.008,9 − 0.838,9 4.83 0.6521 3.14 Lower bound 4.008,9 0 4.00 0.3822 1.52 6 Vol:.(1234567890) Scientific Reports | (2022) 12:645 | https://doi.org/10.1038/s41598-021-04215-6 www.nature.com/scientificreports/ The estimated metabolic cost for performing that work was substantial. Applying elastic contributions, the metabolic cost for performing active work (Eqs. 1 and 2) ranged about 500–1000 W over the speeds examined, compared to an overall net metabolic rate of 700–1500 W (Fig. 3B). In relative terms (Fig. 2C), work accounted for about 76% of net metabolic rate (Fig. 3C), with little dependence on running speed (slope = 0.10% per 1 m/s change in speed). In contrast, the Overestimate of work yielded a much higher proportion (slope = 7.1% per 1 m/s change in speed), of 106% at 3 m/s, and actually exceeding 100% of net metabolic rate at most speeds considered. The Underestimate yielded a fairly constant proportions of about 61% (slope = 0.62% per 1 m/s change in speed). These results are next illustrated as a function of parameters, to facilitate evaluation of assumptions (Fig. 4). Here we use an overall cost for combined positive and negative work, c± = c+ − c− , with nominal value 4.83. This is nominally paired with muscle work fraction fm of 50%. With these values, the proportion of metabolic cost explained by work was 61% for the Underestimate, 76% for Summed Ipsilateral, and 106% for Overestimate, respectively, across the observed running speeds. Here we also examine two extremes for alternative assumptions. One is to assume a considerably lower fraction of muscle work, fm = 0.38 , which would yield a lower fraction of metabolic cost explained, of 43%. On the other hand, assuming that muscle performs more work, fm = 0.65 , yields an unrealistic explained amount of 135% (Fig. 4). Using the nominal efficiency of c± along with the Summed Ipsilateral cost for work, active work to compensate for soft tissue dissipation accounted for an increasingly larger proportion of the metabolic cost due to work. At the nominal speed of 3 m/s, soft tissue compensation increased the metabolic cost due to work (as predicted by the linear regression) by 23.3%, from 3.00 to 3.70 J/kg. Whereas at the highest speed of 4.6 m/s, soft tissue compensation increased the estimate of cost due to work by 31.5%, from 3.82 to 5.03 J/kg. Discussion We had sought to re-evaluate the degree to which mechanical work performed by muscle can explained the net metabolic cost of running. We considered three sets of assumptions to translate joint work estimates into metabolic cost: how energy is transferred between joints by muscle, how much work is performed passively by tendon, and how much metabolic energy is expended to perform muscle work. Using nominal assumptions for muscle vs. tendon work and muscle efficiency from the literature, we found that about 76% of the metabolic cost Figure 3. Mechanical work and estimates of absolute and relative metabolic cost vs. speed ( N = 8 ). (A) Average positive work rates: Mechanical work (using Summed Ipsilateral estimate), net Metabolic rate, and net Soft tissue work rate. Also shown are Over- and Under-estimates of work (dashed gray lines) assuming no work transferred between joints by multiarticular muscles, and full transfer, respectively. (B) Estimated metabolic power for mechanical work, based on each work rate, along with soft tissue deformations, muscle work fraction, and muscle work cost. (C) Relative metabolic cost for mechanical work, showing each cost as a fraction of net metabolic rate. Axes shown include dimensional units, as well as dimensionless units (top and right-hand axes) using body mass, leg length, and gravitational acceleration as base units. Table 2. Linear relationships between measurements of power (metabolic and mechanical) vs running speed. The slope and offset from the linear regression (in dimensionless units) are reported, along with r2. Measurements of power Slope ± 95% CI Offset r2 p Net metabolic 0.48 ± 0.033 − 0.01 0.98 2E − 31 Summed ipsilateral 0.08 ± 0.011 0.05 0.96 2E − 25 Summed bilateral (underestimate) 0.09 ± 0.014 0.03 0.93 3E − 21 Independent joint (overestimate) 0.19 ± 0.0098 0.01 0.99 3E − 38 Soft tissue − 0.04 ± 0.020 0.03 0.80 4E − 12 7 Vol.:(0123456789) Scientific Reports | (2022) 12:645 | https://doi.org/10.1038/s41598-021-04215-6 www.nature.com/scientificreports/ of running is attributable to muscle work. We next discuss how our estimates may be interpreted, and how they could be affected by alternate assumptions. One contributor to the high work cost is dissipation by soft tissues. The dissipation is not typically measured in inverse dynamics analysis, nor incorporated into estimates of metabolic cost. In a typical inverse dynamics analysis, the only work is performed about joints acting between rigid segments, leading to an imbalance of work2,43, with more positive than negative work. In fact, soft tissue deformation largely explains this joint work discrepancy2. For example, (representative subject, Fig. 2), soft tissues dissipated 0.18 J/kg, explaining much of the positive/negative work discrepancy of 0.16 J/kg at 3.1 m/s. Active work to make up for this dissipation accounted for 0.7 J/kg (16%) of the entire 4.27 J/kg of the net metabolic rate. And at faster speed of 4.6 m/s, that fraction increases to about 31%. Faster speeds entail higher impact between leg and ground, and more energy dissipation. The work to compensate for soft tissue energy dissipation costs substantial metabolic energy. Another contributor is active work in tandem with passive elasticity. Series elasticity is recognized to per- form substantial work passively, and thus to play an important role in running energetics. But even with passive elasticity, our results suggest that the remaining work attributable to muscle accounts for much of the overall energetic cost. This is based on an assumed muscle work fraction fm , provisionally set to a nominal value of 50%, for which far different values might be appropriate. For example, the plantaris and gastrocnemius of hopping wallabies have a range from only 3–8%44. In human, the Achilles tendon appears to facilitate a low muscle work fraction23,35. However, many other muscles also participate in running, not all under conditions ideal for tendon elastic work. It is therefore helpful to use the parameter study (Fig. 4) to evaluate other candidate assumptions that lie between these two extremes. Another factor in our energy estimate is the energetic cost of muscle work. This is mainly for positive work, and is attributable to crossbridge cycling45. Thermodynamic principles dictate that this cost likely exceeds c+ = 4 (or efficiency does not exceed 25%), due to the biochemical costs of ATP production and for the work of cross- bridge cycling46. We did not include other effects such as frictional work47, muscle co-contraction, isometric force production, or calcium pumping48, which would generally be expected to cost energy, and could be lumped into the remaining fraction of energy cost (24%) not explained by fascicle work. We also assumed a small but posi- tive energetic cost to negative work. An extreme assumption would be zero cost for negative work, which would reduce the estimated metabolic cost for work from 76 to about 63%, still a majority of overall metabolic cost. We also examined alternative assumptions for energy transfer by multi-articular muscles. Although generally unknown in humans, measurement of muscle forces in cat locomotion show significant energy transfer from Figure 4. Average work cost as a function of cost coefficient for running at 3 m/s. Relative work cost is estimated metabolic cost of mechanical work divided by overall net metabolic cost. Cost coefficient is defined as fraction of work attributable to muscle from overall muscle–tendon work, multiplied by cost of active work c± . Boundaries are shown for extreme assumptions. Overestimate is for Independent Joints assumption, where muscles only act uniarticularly; underestimate is for Summed Bilateral joint assumption, where work can be transferred from one side of the body to the other. Left and right boundaries are for extremes in muscle work fraction, 38% and 65%, respectively, with constant cost of work. The proposed work estimate (Summed Ipsilateral joints), along with a muscle fraction of 50%, yields 76% of the metabolic cost of running is attributable to active work by muscle. For the same parameters, the Underestimate yields 61% and the Overestimate 106%. 8 Vol:.(1234567890) Scientific Reports | (2022) 12:645 | https://doi.org/10.1038/s41598-021-04215-6 www.nature.com/scientificreports/ the ankle to the knee during collision, and from the knee to the ankle during push-off49. We therefore consider it unrealistic to assume no such transfer in humans, hence the label of Overestimate for the individual joints (IJ) estimate of work. Indeed, the IJ estimate would yield an entirely unrealistic apparent mechanical efficiency of 102% for running at 3 m/s (Fig. 4). On the other hand, the Underestimate is too low since it assumes that negative work at any joint could be transferred perfectly to positive work at any other joint in the body. We have therefore presented the Summed Ipsilateral (SI) assumption as a better, yet likely low, estimate for work performed by muscles. This model of energy transfer was previously proposed by Willems et al.39, although without including the contributions of soft tissues, which we have found important for metabolic cost. It has long been recognized that energy transfer can occur between joints of an individual leg6,49–51. Our own estimates summarize the bounds from the possible assumptions and could be improved with more direct muscle measurements from humans. Our findings could inform other estimates of mechanical work. Others have used independent joint work to evaluate apparent efficiency during locomotion23,52, for example yielding unusually high running efficiencies of 35–40%23, which they largely attributed to series elasticity at the ankle. But we also believe some of their observed work may be an Overestimate, due to multi-articular energy transfer. Our preferred estimate using summed ipsilateral joint work is more similar to the segmental energy transfer approach of Williams and Cavanagh7, except using work at joints rather than between segments, and including soft tissue work not been previously considered. This facilitates estimation of metabolic cost contributions (Eq. 2) with only two main parameters ( fM and c± ) lumped into the cost coefficient. We anticipate that further measurements of muscle and tendon action in vivo will inform better estimates of cost contributions such as work. There are certainly other costs for running, not attributable to work. Examples include a cost for producing force in the absence or regardless of mechanical work1,30,31, or due to the rate at which force is generated53. We evaluated the KT cost (Fig. 5) proportional to body weight divided by ground contact time1, which correlates quite well with metabolic cost. But several measures, including various estimates of work, also correlate well (Fig. 5, Table 3). We consider it more mechanistic for a cost to depend on applied muscle force or work, rather than general parameters such as body weight. For example, “Groucho running” on flexed knees54 costs 50% more energy than normal running, whereas the KT cost would predict a decrease, due to increased ground contact time. We suspect that the high cost of Groucho running is due to greater muscle forces and work with flexed Figure 5. Sample correlates of metabolic cost. (A) Correlates: Summed Ipsilateral (SI) work, positive COM work rate, Total mechanical work, Underestimate of joint work (assumes full energy transfer), and the Overestimate of joint work (assumes joint independence). (B) The KT measure of body weight divided by ground contact time (Kram and Taylor29) compared to metabolic cost. All measures correlate well (r2 > 0.9) with metabolic cost. Power is plotted in terms of normalization units, Mg3/2L1/2. Table 3. Linear relationships between running measurements and metabolic cost. The slope and offset from the linear regression (in dimensionless units) are reported, along with r2. Each measurement is a measure of mechanical work performed at the joints or on the COM. Measurement Slope ± 95% CI Offset r2 p Estimate: summed ipsilateral (SI) 3.55 ± 0.50 − 0.03 0.92 7E − 20 Overestimate: individual joints (IJ) 2.10 ± 0.20 0.06 0.96 9E − 26 Underestimate: summed bilateral (SB) 4.45 ± 0.73 − 0.02 0.90 7E − 18 COM work rate 4.75 ± 0.40 − 0.13 0.97 7E − 28 Total mechanical work rate 2.71 ± 0.23 − 0.01 0.97 1E − 27 Kram and Taylor1 (KT) cost 3.58E − 4 ± 3.1E − 5 − 0.48 0.97 7E − 28 9 Vol.:(0123456789) Scientific Reports | (2022) 12:645 | https://doi.org/10.1038/s41598-021-04215-6 www.nature.com/scientificreports/ knees55 even though body weight remains unchanged. Furthermore, reinterpretation of the KT cost reveals that it could be equivalent to a cost of performing mechanical work under appropriate assumptions (see Supplementary Appendix S1). Work is certainly needed to accelerate during running, or to ascend an incline, and it appears to account for a majority of the cost for level ground. We do acknowledge other costs, potentially for isometric force production, but mainly for the 24% of energy not explained by work. The present study has a number of limitations. Our results are specific to humans running at a limited range of speeds, and it remains to be seen how well work can explain energy cost over a wider range of speeds. In particular, work may be less explanatory for other animals, particularly smaller ones where muscles are turned on or off more quickly56. Such force cycling costs may be applicable to humans as well53,57,58. And our model for the cost of mechanical work could be applied to other activities such as walking43,59 and hopping, not considered here. Similar assumptions for the cost of work in incline running reported by Minetti et al.34 (without accounting for soft tissue deformations) suggest that this approach could be applicable beyond level-ground running. The present model for estimating metabolic cost is mostly based on motion data, whereas a more comprehensive and mechanistic model would include body dynamics and predict both motion and energy cost. But the primary limitation is in the cost coefficient, which attempts to aggregate information from empirical data. Better estimates could be obtained as in vivo measurements of muscle state (e.g. ultrasound60) and series elastic energy storage (e.g.29,60) become available. Still better would be to dispense with the cost coefficient in favor of detailed information about each individual muscle61,62, including differences in fiber type and function. We expect improved estimates of elastic contributions, energy transfer, and the cost of performing work to lead to better explanation of the cost of running. However, based on current evidence, it appears that even though series elasticity performs a major role in running, active mechanical work still explains a majority of the metabolic cost in running. Accurate models for estimating this cost are important for understanding human preferences in dynamic activities and can inform the design of devices that interface with the body such as prostheses and orthotics. Received: 25 September 2020; Accepted: 31 May 2021 References 1. Kram, R. & Taylor, C. R. Energetics of running: A new perspective. Nature 346, 265–267 (1990). 2. Riddick, R. C. & Kuo, A. D. Soft tissues store and return mechanical energy in human running. J. Biomech. 49, 436–441 (2016). 3. Winter, D. A. Energy generation and absorption at the ankle and knee during fast, natural, and slow cadences. Clin. Orthop. 175, 147–154 (1983). 4. Winter, D. A. Moments of force and mechanical power in jogging. J. Biomech. 16, 91–97 (1983). 5. Fenn, W. O. A quantitative comparison between the energy liberated and the work performed by the isolated sartorius muscle of the frog. J. Physiol. 58, 175–203 (1923). 6. Williams, K. R. The relationship between mechanical and physiological energy estimates. Med. Sci. Sports Exerc. 17, 317–325 (1985). 7. Williams, K. R. & Cavanagh, P. R. A model for the calculation of mechanical power during distance running. J. Biomech. 16, 115–128 (1983). 8. Margaria, R. Positive and negative work performances and their efficiencies in human locomotion. Int. Z. Für Angew. Physiol. Einschließlich Arbeitsphysiologie 25, 339–351 (1968). 9. Abbott, B. C., Bigland, B. & Ritchie, J. M. The physiological cost of negative work. J. Physiol. 117, 380–390 (1952). 10. DeVita, P., Helseth, J. & Hortobagyi, T. Muscles do more positive than negative work in human locomotion. J. Exp. Biol. 210, 3361–3373 (2007). 11. Cavagna, G. A., Thys, H. & Zamboni, A. The sources of external work in level walking and running. J. Physiol. 262, 639–657 (1976). 12. Donelan, J., Kram, R. & Kuo, A. D. Simultaneous positive and negative external mechanical work in human walking. J. Biomech. 35, 117–124 (2002). 13. Zelik, K. E., Takahashi, K. Z. & Sawicki, G. S. Six degree-of-freedom analysis of hip, knee, ankle and foot provides updated under- standing of biomechanical work during human walking. J. Exp. Biol. 218, 876–886 (2015). 14. Smith, N. P., Barclay, C. J. & Loiselle, D. S. The efficiency of muscle contraction. Prog. Biophys. Mol. Biol. 88, 1–58 (2005). 15. Belli, A., Kyröläinen, H. & Komi, P. V. Moment and power of lower limb joints in running. Int. J. Sports Med. 23, 136–141 (2002). 16. Kuo, A. D. A mechanical analysis of force distribution between redundant, multiple degree-of-freedom actuators in the human: Implications for the central nervous system. Hum. Mov. Sci. 13, 635–663 (1994). 17. Kuo, A.D. The action of two-joint muscles: The legacy of W. P. Lombard. In Classical Papers in Movement Science vol. Ch. 10 (eds Latash, M. & Zatsiorsky, V.) 289–316 (Human Kinetics, 2000). 18. Cheng, H.-Y.K., Lin, C.-L., Wang, H.-W. & Chou, S.-W. Finite element analysis of plantar fascia under stretch—The relative con- tribution of windlass mechanism and Achilles tendon force. J. Biomech. 41, 1937–1944 (2008). 19. Ker, R. F., Bennett, M. B., Bibby, S. R., Kester, R. C. & Alexander, R. M. The spring in the arch of the human foot. Nature 325, 147–149 (1987). 20. McDonald, K. A. et al. The role of arch compression and metatarsophalangeal joint dynamics in modulating plantar fascia strain in running. PLoS ONE 11, e0152602 (2016). 21. Asmussen, E. & Bonde-Petersen, F. Apparent efficiency and storage of elastic energy in human muscles during exercise. Acta Physiol. Scand. 92, 537–545 (1974). 22. Cavagna, G. A., Saibene, F. P. & Margaria, R. Mechanical work in running. J. Appl. Physiol. 19, 249–256 (1964). 23. Farris, D. J. & Sawicki, G. S. The mechanics and energetics of human walking and running: A joint level perspective. J. R. Soc. Interface 13, rsif20110182 (2011). 24. Cavagna, G. A. & Kaneko, M. Mechanical work and efficiency in level walking and running. J. Physiol. 268, 467–481 (1977). 25. Roberts, T. J., Marsh, R. L., Weyand, P. G. & Taylor, C. R. Muscular force in running turkeys: The economy of minimizing work. Science 275, 1113–1115 (1997). 26. Bohm, S., Marzilger, R., Mersmann, F., Santuz, A. & Arampatzis, A. Operating length and velocity of human vastus lateralis muscle during walking and running. Sci. Rep. 8, 5066 (2018). 27. Hof, A. L., Van Zandwijk, J. P. & Bobbert, M. F. Mechanics of human triceps surae muscle in walking, running and jumping. Acta Physiol. Scand. 174, 17–30 (2002). 10 Vol:.(1234567890) Scientific Reports | (2022) 12:645 | https://doi.org/10.1038/s41598-021-04215-6 www.nature.com/scientificreports/ 28. Lichtwark, G. A., Bougoulias, K. & Wilson, A. M. Muscle fascicle and series elastic element length changes along the length of the human gastrocnemius during walking and running. J. Biomech. 40, 157–164 (2007). 29. Monte, A., Maganaris, C., Baltzopoulos, V. & Zamparo, P. The influence of Achilles tendon mechanical behaviour on “apparent” efficiency during running at different speeds. Eur. J. Appl. Physiol. 120, 2495–2505 (2020). 30. Arellano, C. J. & Kram, R. Partitioning the metabolic cost of human running: A task-by-task approach. Integr. Comp. Biol. 54, 1084–1098 (2014). 31. Kram, R. Muscular force or work: What determines the metabolic energy cost of running. Exerc. Sport Sci. Rev. 28, 138–143 (2000). 32. Blickhan, R. The spring-mass model for running and hopping. J. Biomech. 22, 1217–1227 (1989). 33. Geyer, H., Seyfarth, A. & Blickhan, R. Spring-mass running: Simple approximate solution and application to gait stability. J. Theor. Biol. 232, 315–328 (2005). 34. Minetti, A. E., Ardigò, L. P. & Saibene, F. Mechanical determinants of the minimum energy cost of gradient running in humans. J. Exp. Biol. 195, 211–225 (1994). 35. Lichtwark, G. A., Cresswell, A. G. & Newsham-West, R. J. Effects of running on human Achilles tendon length–tension properties in the free and gastrocnemius components. J. Exp. Biol. 216, 4388–4394 (2013). 36. Buczek, F. L., Kepple, T. M., Siegel, K. L. & Stanhope, S. J. Translational and rotational joint power terms in a six degree-of-freedom model of the normal ankle complex. J. Biomech. 27, 1447–1457 (1994). 37. Honert, E. C. & Zelik, K. E. Foot and shoe responsible for majority of soft tissue work in early stance of walking. Hum. Mov. Sci. 64, 191–202 (2019). 38. Fenn, W. O. Frictional and kinetic factors in the work of sprint running. Am. J. Physiol. Leg. Content 92, 583–611 (1930). 39. Willems, P. A., Cavagna, G. A. & Heglund, N. C. External, internal and total work in human locomotion. J. Exp. Biol. 198, 379–393 (1995). 40. Gregersen, C. S., Silverton, N. A. & Carrier, D. R. External work and potential for elastic storage at the limb joints of running dogs. J. Exp. Biol. 201, 3197–3210 (1998). 41. Margaria, R., Cerretelli, P., Aghemo, P. & Sassi, G. Energy cost of running. J. Appl. Physiol. 18, 367–370 (1963). 42. Hof, A. L. Scaling gait data to body size. Gait Posture 4, 222–223 (1996). 43. Zelik, K. E. & Kuo, A. D. Human walking isn’t all hard work: Evidence of soft tissue contributions to energy dissipation and return. J. Exp. Biol. 213, 4257–4264 (2010). 44. Biewener, A. A. Muscle function in vivo: A comparison of muscles used for elastic energy savings versus muscles used to generate mechanical power1. Am. Zool. 38, 703–717 (1998). 45. Woledge, R., Curtin, N. & Hsiao-Wecksler, E. T. Energetic aspects of muscle contraction. Monogr. Physiol. Soc. 41, 1–357 (1984). 46. Barclay, C. J. Energetics of contraction. Compr. Physiol. 5, 961–995 (2015). 47. Minetti, A. E., Moorhead, A. P. & Pavei, G. Frictional internal work of damped limbs oscillation in human locomotion. Proc. R. Soc. B Biol. Sci. 287, 20201410 (2020). 48. Bergstrom, M. & Hultman, E. Energy cost and fatigue during intermittent electrical stimulation of human skeletal muscle. J. Appl. Physiol. 65, 1500–1505 (1988). 49. Prilutsky, B. I., Herzog, W. & Leonard, T. Transfer of mechanical energy between ankle and knee joints by gastrocnemius and plantaris muscles during cat locomotion. J. Biomech. 29, 391–403 (1996). 50. Prilutsky, B. I. & Zatsiorsky, V. M. Tendon action of two-joint muscles: Transfer of mechanical energy between joints during jump- ing, landing, and running. J. Biomech. 27, 25–34 (1994). 51. van Ingen Schenau, G. J., Bobbert, M. F. & Rozendal, R. H. The unique action of bi-articular muscles in complex movements. J. Anat. 155, 1–5 (1987). 52. Umberger, B. R. & Martin, P. E. Mechanical power and efficiency of level walking with different stride rates. J. Exp. Biol. 210, 3255–3265 (2007). 53. Rebula, J. R. & Kuo, A. D. The cost of leg forces in bipedal locomotion: A simple optimization study. PLoS ONE 10, e0117384 (2015). 54. McMahon, T. A., Valiant, G. & Frederick, E. C. Groucho running. J. Appl. Physiol. 62, 2326–2337 (1987). 55. Gordon, K. E., Ferris, D. P. & Kuo, A. D. Metabolic and mechanical energy costs of reducing vertical center of mass movement during gait. Arch. Phys. Med. Rehabil. 90, 136–144 (2009). 56. Heglund, N. C., Fedak, M. A., Taylor, C. R. & Cavagna, G. A. Energetics and mechanics of terrestrial locomotion. IV. Total mechani- cal energy changes as a function of speed and body size in birds and mammals. J. Exp. Biol. 97, 57–66 (1982). 57. Dean, J. C. & Kuo, A. D. Energetic costs of producing muscle work and force in a cyclical human bouncing task. J. Appl. Physiol. 110, 873–880 (2011). 58. Doke, J. & Kuo, A. D. Energetic cost of producing cyclic muscle force, rather than work, to swing the human leg. J. Exp. Biol. 210, 2390–2398 (2007). 59. Donelan, J. M., Kram, R. & Kuo, A. D. Mechanical work for step-to-step transitions is a major determinant of the metabolic cost of human walking. J. Exp. Biol. 205, 3717–3727 (2002). 60. Brennan, S. F., Cresswell, A. G., Farris, D. J. & Lichtwark, G. A. In vivo fascicle length measurements via B-mode ultrasound imag- ing with single vs dual transducer arrangements. J. Biomech. 64, 240–244 (2017). 61. Biewener, A. & Roberts, T. Muscle and tendon contributions to force, work, and elastic energy savings: A comparative perspective. Exerc. Sport Sci. Rev. 28, 99 (2000). 62. Beck, O. N., Punith, L. K., Nuckols, R. W. & Sawicki, G. S. Exoskeletons improve locomotion economy by reducing active muscle volume. Exerc. Sport Sci. Rev. 47, 237–245 (2019). Acknowledgements This work supported in part by Natural Sciences and Engineering Research Council of Canada (NSERC Discov- ery Award, Canada Research Chair Tier 1) and Dr. Benno Nigg Research Chair. Author contributions R.R. collected the data, analyzed, and wrote the main text of the manuscript. A.D.K. conceived the experiment, guided the analysis of the data, and edited and wrote portions of the manuscript. Competing interests The authors declare no competing interests. Additional information Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1038/ s41598- 021- 04215-6. 11 Vol.:(0123456789) Scientific Reports | (2022) 12:645 | https://doi.org/10.1038/s41598-021-04215-6 www.nature.com/scientificreports/ Correspondence and requests for materials should be addressed to R.C.R. Reprints and permissions information is available at www.nature.com/reprints. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. © The Author(s) 2022

Mechanical work accounts for most of the energetic cost in human running.

01-12-2022

Riddick, R C,Kuo, A D

eng

PMC7503581

International Journal of Environmental Research and Public Health Article Runner’s Perceptions of Reasons to Quit Running: Influence of Gender, Age and Running-Related Characteristics Daphne Menheere 1,*, Mark Janssen 1,2 , Mathias Funk 1 , Erik van der Spek 1, Carine Lallemand 1,3 and Steven Vos 1,2 1 Department of Industrial Design, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; [email protected] (M.J.); [email protected] (M.F.); [email protected] (E.v.d.S.); [email protected] (C.L.); [email protected] (S.V.) 2 School of Sport Studies, Fontys University of Applied Sciences, 5644 HZ Eindhoven, The Netherlands 3 HCI Research Group, University of Luxembourg, Esch-sur-Alzette, 4365 Luxembourg, Luxembourg * Correspondence: [email protected] Received: 15 July 2020; Accepted: 18 August 2020; Published: 20 August 2020



 Abstract: Physical inactivity has become a major public health concern and, consequently, the awareness of striving for a healthy lifestyle has increased. As a result, the popularity of recreational sports, such as running, has increased. Running is known for its low threshold to start and its attractiveness for a heterogeneous group of people. Yet, one can still observe high drop-out rates among (novice) runners. To understand the reasons for drop-out as perceived by runners, we investigate potential reasons to quit running among short distance runners (5 km and 10 km) (n = 898). Data used in this study were drawn from the standardized online Eindhoven Running Survey 2016 (ERS16). Binary logistic regressions were used to investigate the relation between reasons to quit running and different variables like socio-demographic variables, running habits and attitudes, interests, and opinions (AIOs) on running. Our results indicate that, not only people of different gender and age show significant differences in perceived reasons to quit running, also running habits, (e.g., running context and frequency) and AIOs are related to perceived reasons to quit running too. With insights into these related variables, potential drop-out reasons could help health professionals in understanding and lowering drop-out rates among recreational runners. Keywords: running drop-out; novice runners; gender; age; running habits; attitudes; interests; motives 1. Introduction Physical inactivity has become a major public health concern as it is associated with the development of chronic diseases [1,2]. Consequently, the awareness and importance of striving for an active and healthy lifestyle within our society have increased [3]. This is notably reflected in the increased popularity of unorganized recreational sports such as running [4,5]. Running is known for its low threshold to start: it is relatively inexpensive and easy to practice [6] and is associated with many health benefits (i.e., musculoskeletal and cardiovascular health, body composition, and psychological state) [7–14] and is therefore a popular recreational sport. This popularity is especially apparent in the increasing number of commercial running events, and their growing number of participants. In terms of event participation, running is even one of the most popular recreational sports in the world [15,16]. Therefore, since the begin of the 21st century, we can speak of the second wave of running [15]. The growing number and diversity of specialized running events (e.g., ladies runs, color runs, survival runs) are aligned with the development of the heterogeneous profile of ‘the runner’ over the years [17–19]. During the first wave of running starting in the 1960s, running used to be dominated by Int. J. Environ. Res. Public Health 2020, 17, 6046; doi:10.3390/ijerph17176046 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2020, 17, 6046 2 of 12 young males [20,21] as it was considered outrageous for women to engage in running [22]. It was not until almost 25 years later, the first Olympic marathon for women was introduced [15]. This partake of women in running continued to develop, where a strong growth is notably visible during the second wave of running, resulting in an almost equal distribution of men and women in recent years [15,19,23]. Similar to data of other Western countries [15], 11.3% of women and 13.2% of men within Dutch adults (i.e., the context of the present study), between the ages 20–79 years, expressed to run at least monthly in 2012 [23], also indicating the age diversity of running participants [15,24]. Besides some socio-demographic characteristics (i.e., gender and age) representing the heterogeneous population of runners, studies showed a variety in terms of motives to partake in running (e.g., health, social and competition elements, performance) [4,25,26]. Furthermore, one can also observe a broader range of different experienced runners (e.g., recreational, competitive) [27] but also running context (e.g., small groups, running partner, individually) [4,19,28]. This diverse profile of ‘the runner’ illustrates that running can appeal to many people (regardless of age, gender, motives, experience or running context) and illustrates the potential of making running even more accessible for an even larger group of people. Despite the increasing popularity and the growing heterogeneity in runners, one can observe high drop-out rates due to running-related injuries and motivational loss, which is often noticeable among novice runners [29–31]. What type of runners are affected by running-related injuries and how this affects a potential drop-out, and how long this drop-out lasts, has been studied extensively in previous literature [29–33]. Although there is evidence on motivations to partake in running [17,25,34,35], reasons to quit running are rather unexplored. Previous studies on reasons to start running, show the influence of the different type of characteristics. Indicating the influence of socio-demographic variables (i.e., gender and age), running habits (e.g., experience, frequency, relative performance) and in the runners’ attitudes, interests and opinions (AIOs). In a study of Hanson et al. women seemed to be more motivated by AIOs on weight concern, self-esteem, affiliation and psychological coping compared to men and less by AIOs with regards to competition and goal achievement [36]. This is in line with a study by Deaner et al., indicating men reported higher levels of competitiveness compared to women [37]. Motivational differences in age were investigated by Ogles and Masters, indicating young marathon participants (20–28 years) were more motivated by personal goal achievements, compared to older marathon runners (≥50 years). Furthermore, the older participants were more motivated by weight concerns, life meaning, health orientation and affiliation. Besides gender and age, running experience also impacts AIOs towards running. For example, Forsberg et al. showed that more experienced runners, those who run for more than eight years, were more likely to run for social motives and just ‘for the love of running’. Whereas lesser experienced runners, those who run up to three years were more health orientated. Although motives for running can influence running drop-out [38–40], to the best of our knowledge, there is limited evidence about reasons to quit running. An important step toward expanding the evidence base is to understand the reasons for drop-out as perceived by runners. Hence, the scope of this paper is on the perceived reasons to quit running. Janssen et al. distinguish two groups of perceived reasons to quit running: individual (e.g., time management, injuries) and social (e.g., running partner/trainer quits) [4,41]. These reasons are covered by the items of the Leuven Running Survey 2009 [42] and adapted to event runners. Whether these are related to socio-demographic characteristics as gender and age, as they are for motives to running [17,36,37], or running-related characteristics is, however unknown. With the present study, we aimed to: (i) gain insights in perceived reasons to quit running, and (ii) how this is affected by socio-demographics (i.e., gender and age) running habits, and AIOs on running. Int. J. Environ. Res. Public Health 2020, 17, 6046 3 of 12 2. Materials and Methods 2.1. Study Design and Respondents The data used in this study were drawn from the Eindhoven Running Survey 2016 (ERS2016). We collected data through an online standardized questionnaire among runners at the Eindhoven Marathon Running Event, which offered races at 5, 10, 21.1 and 42.2 km. For this paper, a sub-dataset was drawn with only those runners that participated in the 5 and 10 km races. These distances were selected because of the heterogeneity of the participants, including both more experienced, less and unexperienced runners. The items used in this questionnaire were directly derived from the standardized questionnaire from previous editions of this event (ERS2014 and ERS2015). In total, 18,261 runners participated in this event, who agreed upon registration that they could be contacted for research purposes. After finishing the event, all runners received an email with an explanation of the study, informed consent and our guarantee that their data would be processed anonymously. If they agreed upon participation in this study, they could click the link to the online questionnaire. The email contained all needed information and was in line with the ethical principles of the Declaration of Helsinki and the American Psychological Association. Thereby, the Research Board of the Fontys School of Sport Studies was consulted prior to initiation of this study, and approval for the study design was obtained. Of the 18,261 runners, 3727 runners completed the questionnaire (overall response rate of 20.4%) of which 7.9% in the 5 km and 16.2% in the 10 km run. Since this study focused on the 5 and 10 km distances, the subset used here consists of 898 runners (603 who ran the 10 km and 295 the 5 km). The average age of the runners in the present study was 40.7 years, with the youngest runner at 18 years and the oldest 78 years old. 52.7% per cent of the participants were women (n = 474 runners). These socio-demographic backgrounds are comparable to other running samples in previous large-scale running studies in Western Europe [4,15,43]. 2.2. Questionnaire The online questionnaire consisted of three sections. The first section included attitudes, interests, and opinions (AIOs) on running, the second focused on socio-demographics and the last on running habits. The questionnaire is provided in the Supplementary Materials of a previous study of Janssen et al. (File S1, questionnaire ERS2016) [41], in which Figure S1 shows a flowchart of the questionnaire. The first section of the questionnaire consists of items on running AIOs and was adopted from previous studies [4,19,41,43]. Runners were asked to rate the extent to which they agreed with the items, using a 5-point Likert scale (ranging from 1 = totally disagree, to 5 = totally agree). The second section of the questionnaire includes questions on sociodemographic characteristics. We asked for gender (male/female); age (years); professional status (student/unemployed/employed part-time/employed full-time); and level of education (lower and middle/higher/university). The third section covered running habits included running frequency (number of runs per week) years of running experience (<1 year: novice; 1–5 years: moderately experienced; >5 years: experienced); and preferred running context (individual/with friends/colleagues, small running groups/clubs). 2.3. Measurements 2.3.1. Creating Scales of Running AIOs First, we created scales of the items on running AIOs by replicating the questionnaire used by Janssen et al. [41]. We ran reliability analyses for all scales. Items were assessed (Cronbach’s Alpha’s scores of >0.700 were considered acceptable) and reconsidered whether they substantively contributed to the component or not, and no changes were made. Finally, scales were constructed by calculating the average scores for the reliable items per component, resulting in average scale scores. Table 1 gives Int. J. Environ. Res. Public Health 2020, 17, 6046 4 of 12 an overview of these components (i.e., scales), including the number of items, Cronbach’s Alpha’s and average score (ranging from 1 to 5). Eventually, five AIO-scales were formed and used in this study: (1) Perceived advantages of running (e.g., ‘running gives me energy’, or ‘running is good for my health’); (2) Identification with running (e.g., ‘I am proud to be a runner’, or ‘I feel myself to be a real runner’); (3) Running is a sport that is easy to practice (e.g., ‘I can practice running anytime, anywhere’); (4) Social motives for quitting (e.g., I would quit running ‘if my trainer quit’ or ‘if my running friends quit’); (5) Individual motives for quitting (e.g., I would quit running if ‘I got injured’, or if ‘my spare time was decreased’). Table 1. Components including the number of items, Cronbach α, average scores and standard deviations. Scale Attitudes toward Running Items Cronbach α N Mean SD 1 Perceived advantages of running 4 0.794 853 4.29 0.458 2 Identification with running 5 0.738 853 3.33 0.640 3 Running as a sport that is easy to practice 3 0.781 853 4.22 0.623 4 Social motives for quitting 3 0.941 853 1.79 0.722 5 Individual motives for quitting 4 0.712 853 3.33 0.784 2.3.2. Dependent Variables In this study, we used two dependent variables: social motives for quitting and individual motives for quitting. As they do not follow a normal distribution, both scales were recoded into binary variables. All scores below the scale average (i.e., M = 1.79) were coded as ‘0 below’ and all scores above the average were coded as ‘1 above’. In this way, we were able to interpret the data relative to the sample and able to see if there are variables that could explain why runners score lower or higher compared to their fellow runners. 2.3.3. Independent Variables As independent variables, we included three groups of variables: (i) socio-demographic variables; (ii) running habits; and (iii) running AIOs. The socio-demographic characteristics included gender, age, and level of education. The group of running habits consisted of variables that are directly related to running and which define the level of running involvement: years of running experience, training frequency and running context. The three-remaining scale on running AIOs perceived advantages of running, identification with running and running as a sport that is easy to practice complete the list of independent variables. Table 2 gives the descriptive statistics of the sample for the dependent and independent variables. 2.4. Analysis All results were analyzed using SPSS 26.0 (IBM Corp., Armonk, NY, USA). First, descriptive statistics (i.e., mean scores, standard deviations, minimum and maximum values) were collected to provide an overview of the sample structure, and the items and variables used. Second, two binary logistic regression models (method = enter) were created with the two dependent variables: social motives for quitting and individual motives for quitting. As aforementioned, both scales were recoded into binary variables. Nagelkerke R2 was used as a measure of goodness of fit. Values between 0.10 and 0.20 were considered as satisfactory and above 0.20 as very satisfactory [44,45]. The different models were tested for multicollinearity, outliers, and leverage points by calculating the variance inflation factors and influence statistics (Cook’s). No problems with the data were found concerning these aspects. Int. J. Environ. Res. Public Health 2020, 17, 6046 5 of 12 Table 2. The descriptive statistics of the sample for the dependent and independent variables. Variable Measurement n % Individual Motives Binary Below 399 46.8 Above 454 53.2 Social Motives Binary Below 390 45.7 Above 463 54.3 Gender Male 387 47.8 Female 422 52.2 Age ≤35 year 261 32.1 36–45 year 239 29.4 ≥46 year 313 38.5 Education Lower or middle education 273 33.5 Higher education 332 40.8 University 209 25.7 Experience <1 years 248 29.2 1–5 years 364 42.8 >5 years 238 28.0 Running frequency ≤1x/week 384 45.1 2x/week 350 41.1 ≥3x/week 117 13.7 Running context Individual 526 61.8 Friends, colleagues, small groups 226 26.6 Clubs 99 11.6 3. Results 3.1. Descriptive Analysis First, descriptive analysis shows that the social motives for quitting scores an average of 1.79 (SD = 0.72) on a 5-point Likert scale. From the 853 runners, 390 (45.7%) runners score below the group average, and the remaining 54.3% scores above and perceive relatively more social reasons to quit running. For the individual motives for quitting a mean of 3.33 (SD = 0.78) on a 5-point Likert scale was given. Here, of the 853 runners, 399 (46.8%) runners scored below this relative average, and the remaining 46.8% perceived relatively more individual reasons to quit running. In Table 3, the mean scores on the items that form both scales are presented. If we compare these items, it is clear to see that ‘physical constraints or injuries’ are the most important reason to quit running (M = 4.14 SD = 0.77), followed by item 6; ‘tired of running’ (M = 3.20; SD = 1.05). The items that are related to ‘social motives to quit running’, score the lowest (M = 1.82 or lower). Table 3. Mean scores, standard deviations, minimum and maximum values of the items. Item No. Item Mean SD Min Max 1 My running partners quit running 1 1.82 0.85 1 5 2 My running group falls apart 1 1.80 0.84 1 5 3 My trainer/coach is leaving 1 1.76 0.80 1 5 4 Preference for another sport 2 3.06 1.04 1 5 5 Reduction of leisure time 2 2.95 1.05 1 5 6 Tired of running 2 3.20 1.06 1 5 7 Physical constraints or injuries 2 4.14 0.77 1 5 Superscript number indicate to which scale, the items belong to. Social reasons to quit running indicated with a 1, and individual reasons indicated with 2. Second, the results of the binary logistic regression are presented in Table 4. The binary logistic regression with social motives for quitting running as a dependent variable showed significant Int. J. Environ. Res. Public Health 2020, 17, 6046 6 of 12 differences (p < 0.05, p < 0.01 or p < 0.001) for gender, experience with running, running context and on the AIOs towards running, viz. running as a sport that is easy to practice, perceived advantage of running and identification with running. The binary logistic regression with individual motives for quitting running as a dependent variable revealed significant differences for age, education level, experience with running, running frequency and one of the AIOs towards running, viz. identification with running. Table 4. Results of the binary logistic regression, in odds ratios (Exp (β)) with regards to the reference group (ref.). Social Reasons (n = 803) Individual Reasons (n = 803) Constant 646,050 *** 42,827 *** Gender Male Ref. Ref. Female 1.642 ** 1.234 Age ≤35 year Ref. Ref. 36–45 year 1.018 0.777 ≥46 year 1.402 0.498 *** Education Lower or middle education Ref. Ref. *** Higher education 1.193 2.012 *** University 0.972 2.721 *** Experience <1 years Ref. Ref. 1–5 years 0.829 0.888 >5 years 0.610 * 0.610 * Running frequency ≤1x/week Ref. Ref. 2x/week 0.717 0.654 * ≥3x/week 0.734 0.799 Running context Individual Ref. *** Ref. Friends, colleagues, small groups 3.352 *** 1.203 Clubs 4.541 *** 1.361 AIO toward running Running as a sport that is easy to practice 0.502 *** 0.985 Perceived advantages of running 0.314 *** 0.992 Identification 1.366 * 0.352 *** Nagelkerke R2 0.278 0.244 * = p < 0.05; ** = p < 0.01; *** = p < 0.001. 3.2. Binary Logistic Regression Social Reasons for Quitting In the model for ‘social motives for quitting running’, female runners were more likely (OR = 1.642; p < 0.01) to perceive social motives to quit running than male runners. No effect was found for age and education. With regards to the running habits, runners with more than 5 years of running experience, were less likely (OR = 0.610; p < 0.05) to perceive social motives to quit running compared to runners with less than 1 year of running experience. Thereby, runners who run with other runners are more likely to perceive social motives to quit running. Those who run with friends, colleagues and in small groups have an odds ratio of 3.352 (p < 0.001) and those who run in clubs have an odds ratio of 4.541 (p < 0.01), both compared to runners that participate individually. The third running habit; running frequency did not show significant differences. In the final set of independent variables, significant differences for all included AIOs towards running were found. Those who see running as a sport that is easy to practice (OR = 0.502; p < 0.01) and those who perceive advantages of running (OR = 0.314; p < 0.01) were less likely to perceive social motives to quit running, whereas runners who identify themselves with running (OR = 1.366; p < 0.05) were more likely to perceive social motives to quit running. 3.3. Binary Logistic Regression Individual Reasons for Quitting In the model for individual motives for quitting running, gender was not found to be associated with the individual motives, were the other socio-demographic variables was. Runners that were older (>46 years) are less likely to perceive individual motives to quit running than younger runners (<35 years) did (OR = 0.498; p < 0.001). Runners with higher education or who finished university, were Int. J. Environ. Res. Public Health 2020, 17, 6046 7 of 12 more likely to quit running based on individual motives compared to runners with a lower of middle education (resp. OR = 2.012; p < 0.001 and OR = 2.721; p < 0.001). Similarly, to the model on social motives for quitting, runners with more than 5 years of running experience, were less likely (OR = 0.610; p < 0.05) to perceive individual motives to quit running compared to runners with less than 1 year of running experience. The running frequency was also found to be significant, those who run twice a week (OR = 0.654; p < 0.05) were less likely to perceive individual motives for quitting compared to runners who run once (or less) a week. Furthermore, runners who identify themselves with running (OR = 0.352; p < 0.001) were less to perceive individual motives to quit running. No significant differences were found for running context, and AIO-items running as a sport that is easy to practice and perceived advantages of running. 4. Discussion The aim of this study was to gain insight among short-distance event runners into the perceived reasons to quit running, and to identify how these reasons are affected by socio-demographics (i.e., gender and age), running habits and AIOs on running. This is an important step toward expanding the evidence base to understand the reasons for dropout as perceived by runners. This is key to support runners in continued running and to address the barriers runners perceive adequately. The limitations of this study, such as the treatment of the data and its implications, are discussed at the end of the discussion section. Our findings show that runners are more likely to perceive individual reasons to quit running than social reasons (Table 3). Physical constraints or injuries (item 7) is the most important reason to quit running, which is in line with previous studies [29–33], followed by being tired of running (item 6). Socials reasons to quit running because ‘my trainer is leaving’, or ‘my buddy quit running’ were less likely to be perceived as important. A possible explanation for this might be that a large group of the participants (approx. 60%) does not run in a social context but runs individually. This is in line with studies showing that running is an activity that is mostly practiced individually, outside the organized context of clubs [4,28,46]. For individual runners, individual reasons to quit running might be more applicable and easier to identify with, as compared to social reasons. For individual reasons to quit running, significant differences were found for age, education level, experience with running, running frequency and one of the AIOs towards running; identification with running (Table 4). Furthermore, results showed that social reasons to quit running are significantly different depending on the gender, experience with running, running context and on the AIOs towards running; running as a sport that is easy to practice, perceived advantage of running and identification with running. Compared to male runners, our results show that female runners perceive more social reasons to quit running. This result may be explained by the fact that women appear to attach greater value to social support [47–49]. A previous study by Vos et al. [19], in which a typology of female runners was constructed, did show that women valued connectedness with others. This finding was also reported by Pridgeon and Grogan [49], stating that loss of social support contributed to exercise dropout, especially among women. Another possible assumption would be that female runners, compared to male runners, run more often in a social context and therefore experience social reasons to quit running more often. However, this explanation is not supported by a previous study (N = 3727) on running typologies, which does not suggest that women are more likely to run in social contexts but often run in individual context as well [41]. Notably, in the present study, we did not found significant differences for individual reasons to quit running for gender. So, although female runners run in both social and individual contexts, social reasons to quit running are perceived more often by women than men. Runners aged above 45 years, perceive fewer individual reasons to quit running as compared to younger runners below 35 years. This result might hint at the idea of people feeling more in control of their own time when ageing, as compared to having difficulties in seeking a way to incorporate running in their daily lives [47,48,50]. This might also be related to the fact that people over 45 are in a Int. J. Environ. Res. Public Health 2020, 17, 6046 8 of 12 less exploratory phase of their lives, and thus do not perceive reasons to seek for different types of sports to practice [50]. Another explanation might be that these ‘older’ runners are more experienced and therefore, more aware of their bodies and potential injuries [30,51]. This is in line with a previous study, indicating that the most experienced runners included most runners being older than 45 [41]. What is notable is that there is no significant difference found for social reasons to quit running for age, indicating that reasons to quit from a social perspective are not dependent on age. Our results suggest that runners who have a higher education or university degree perceive more individual reasons to quit running compared to runners with a low or middle educational degree. Runners with a university degree perceive these reasons about three times as much, and runners with a higher education twice as much. This is not the case for social reasons to quit running. The reason for this might be that runners with a higher or university degree have more trouble in finding a good work-life-sports balance, and thus have more trouble in prioritizing running on a day to day basis. Running experience influenced both social and individual reasons to quit negatively, where runners who run for more than 5 years perceive less (social and individual) reasons to quit as compared to runners running for less than a year. We can hypothesize that runners who already have been running for more than 5 years have already been able to overcome obstacles and barriers (e.g., injuries or motivational loss) throughout the years and kept pursuing running [30,51]. On the other hand, participants running for less than a year might have a lower self-efficacy, i.e., confidence in one’s ability to overcome potential obstacles [52]. Another possible explanation is that experienced runners might feel more competent, and therefore are less afraid of getting injured or being dependent on external factors like a coach or a running group. A previous study, for instance, indicated that the more experienced runners (>7 years) were more likely to run “for the love of running” [25], which might indicate that regardless of some obstacles, their love for running helps them overcome these. When looking at running frequency, the results suggest that runners who run twice a week perceive fewer individual reasons to quit running as compared to runners who run once a week or less. Notably, this is not the case for social reasons to quit running, nor for runners who run three times a week or more. Although these runners who run twice a week have a higher time investment compared to runners who run once a week or less, they might be able to better incorporate this activity in their schedule on a weekly basis [39]. For those running ≤1 per week, the involvement into running is lower, as compared to runners who dedicate to run twice a week. These ‘occasional’ runners might perceive more reasons to quit since they have not been able to commit to the sport that often on a training basis yet [39,41]. Additionally, a lower running frequency might also affect the feeling of competence or experience, which in turn might increase the fear of getting injured [30]. Although runners in our sample generally experienced more individual reasons to quit running, the running context positively influenced social reasons to quit running. Runners who run in a running group perceive more than three times as many social reasons to quit running compared to runners who run individual, and runners running at a running club more than four times as much. It might seem obvious that when one runs individually, fewer social reasons to quit can be observed. Interestingly though, individual runners do not perceive more individual reasons to quit running, as compared to social runners. Individual reasons to quit running might thus not be dependent on the running context but on other variables (e.g., age, running experience, running frequency) as stated in earlier studies [17,36,37]. Runners who do not think of running as a sport that is easy to practice, and do not perceive many advantages of running, perceive more social reasons to quit running. Instead of these advantages of running, these runners might value and need other AIOs (e.g., social support) to go running and therefore, experience more social reasons to quit running [49]. When one identifies as being a runner, our results indicate that this affects both social and individual reasons to quit running. Runners who identify themselves as a runner perceive more social reasons to quit running. This might indicate that runners who run in a social context (e.g., club or running group), identify themselves as being a ‘real’ runner and therefore might also depend more Int. J. Environ. Res. Public Health 2020, 17, 6046 9 of 12 on their fellow runners (as a community) and social support. When for example a fellow runner quits, this might act as a trigger to quit running [49]. Contrary to this, runners who identify as being a runner perceive less individual reasons to quit running. A possible explanation might be that these are less likely to get tired of running, or running is their main sport. This is in line with previous studies indicating that runners who identify strongly with running are the more experienced, long-distance runners [41,43], hinting they might have been able to overcome these possible reasons to quit previously. Based on our results, we argue that although we see significant differences related to gender in social reasons to quit running and significant ones related to age in individual reasons to quit running, these should not be considered conclusive. Our results showed that running characteristics (e.g., running experience, context, frequency, running AIOs) also influence one’s perceived reasons to quit running. We thus contribute to knowledge on running dropouts by drawing a more accurate picture of the situation. Limitations Our study has some limitations. As part of our sampling strategy, we selected a subset of the dataset and included runners who participated in the 5 and 10 km distances of the running event. Through this, we purposively focused on novice and less experienced runners, who are more likely to drop-out. Although these runners might not be representative of all runners who perceive reasons to quit running, participants of large running events have been considered a representative selection of the broader recreational running community in previous studies [41,53]. In this study, we investigated runners’ perceived reasons to quit running. By asking perceived reasons, this study relies on self-reported data and the perception of the participants. We do not know if these reasons would be an actual reason to quit running. However, knowing more about the perception of runners might indicate possible solutions or interventions to lower drop-out rates. Finally, some methodological limitations related to the dependent variables should be mentioned. As aforementioned, we had to recode our two dependent variables into binary variables because both scales were not normally distributed. We thus lost some information about individual differences. Yet, we were able to interpret the data relativity to the sample. Second, we used 7 items to construct the 2 independent variables. Next to these seven possible reasons to quit running, there are other reasons why runners may quit running. Here we decided to build further on previous studies and hence could benefit from items which have an acceptable internal consistency. 5. Conclusions Our survey study shows that although gender and age have shown significant differences in perceived reasons to quit running, these should not be considered conclusive. Our findings implicate that running characteristics (e.g., running experience, context, frequency, running AIOs) also influence one’s perceived reasons to quit running. These insights could help policymakers to understand novice runners and their perceived reasons for a potential drop-out. This insight can be used to match public health policies to the motives and barriers of novice runners. Sports professionals (e.g., trainers and, coaches) could use this insight to lower drop-out rates among novice runners and eliminate potential perceived reasons to quit running. Supplementary Materials: The following are available online at http://www.mdpi.com/1660-4601/17/17/6046/s1, File S1: Figure of Descriptive Statistics and File S2: Mean scores and SD of the Items. Author Contributions: Conceptualization, D.M., M.J. and S.V.; methodology, M.J. and S.V.; formal analysis, M.J. and S.V., investigation, M.J. and S.V.; writing—original draft preparation, D.M., M.J.; writing—review and editing, D.M., M.J., M.F., E.v.d.S., C.L. and S.V. All authors have read and agreed to the published version of the manuscript. Funding: This work is part of the project Nano4Sports which is financed by Europees Fonds voor Regionale Ontwikkeling Interreg Vlaanderen Nederland award number(s): 0271. Int. J. Environ. Res. Public Health 2020, 17, 6046 10 of 12 Acknowledgments: We would like to thank the organization and the runners of the Marathon Eindhoven 2016, for their help and time to take part in our online survey. Without their involvement, this study would not have been possible. Conflicts of Interest: The authors declare no conflict of interest. References 1. Warburton, D.E.R.; Charlesworth, S.; Ivey, A.; Nettlefold, L.; Bredin, S.S.D. A systematic review of the evidence for Canada’s Physical Activity Guidelines for Adults. Int. J. Behav. Nutr. Phys. Act. 2010, 7, 39. [CrossRef] [PubMed] 2. World Health Organization. World Health Statistics 2016: Monitoring Health for the SDGs Sustainable Development Goals; World Health Organization: Geneva, Switzerland, 2016; ISBN 9241565268. 3. Trost, S.; Blair, S.N.; Khan, K.M. Physical inactivity remains the greatest public health problem of the 21st century: Evidence, improved methods and solutions using the ‘7 investments that work’ as a framework. Br. J. Sports Med. 2014, 48, 394–401. [CrossRef] [PubMed] 4. Janssen, M.; Scheerder, J.; Thibaut, E.; Brombacher, A.; Vos, S. Who uses running apps and sports watches? Determinants and consumer profiles of event runners’ usage of running-related smartphone applications and sports watches. PLoS ONE 2017, 12, e0181167. [CrossRef] [PubMed] 5. Hulteen, R.M.; Smith, J.J.; Morgan, P.J.; Barnett, L.M.; Hallal, P.C.; Colyvas, K.; Lubans, D.R. Global participation in sport and leisure-time physical activities: A systematic review and meta-analysis. Prev. Med. (Baltim) 2017, 95, 14–25. [CrossRef] 6. Eime, R.M.; Sawyer, N.; Harvey, J.T.; Casey, M.M.; Westerbeek, H.; Payne, W.R. Integrating public health and sport management: SPORT participation trends 2001–2010. Sport Manag. Rev. 2015, 18, 207–217. [CrossRef] 7. Borgers, J.; Vanreusel, B.; Vos, S.; Forsberg, P.; Scheerder, J. Do light sport facilities foster sports participation? A case study on the use of bark running tracks. Int. J. Sport Policy 2016, 8, 287–304. [CrossRef] 8. Shipway, R.; Holloway, I. Health and the running body: Notes from an ethnography. Int. Rev. Sociol. Sport 2016, 51, 78–96. [CrossRef] 9. Grunseit, A.; Richards, J.; Merom, D. Running on a high: Parkrun and personal well-being. BMC Public Health 2017, 18, 1–11. [CrossRef] 10. Nezlek, J.B.; Cyprya´nska, M.; Cyprya´nski, P.; Chlebosz, K.; Jenczylik, K.; Sztacha´nska, J.; Zalewska, A.M. Within-Person Relationships Between Recreational Running and Psychological Well-Being. J. Sport Exerc. Psychol. 2018, 40, 146–152. [CrossRef] 11. Pedisic, Z.; Shrestha, N.; Kovalchik, S.; Stamatakis, E.; Liangruenrom, N.; Grgic, J.; Titze, S.; Biddle, S.J.H.; Bauman, A.E.; Oja, P. Is running associated with a lower risk of all-cause, cardiovascular and cancer mortality, and is the more the better? A systematic review and meta-analysis. Br. J. Sports Med. 2020, 54, 898–905. [CrossRef] 12. van Iperen, L.P.; de Jonge, J.; Gevers, J.M.; Vos, S.B. Running-related demands and vigor in long-distance runners: The moderating role of resources and recovery. Curr. Psychol. 2020, 1–15. [CrossRef] 13. Schoemaker, J.; van Genderen, S.; de Boer, W.I.J. Increased physical activity in preparation for a women-only mass participation sport event: A framework for estimating the health impact. Int. J. Environ. Res. Public Health 2020, 17, 98. [CrossRef] [PubMed] 14. Junior, L.C.H.; Pillay, J.D.; van Mechelen, W.; Verhagen, E. Meta-Analyses of the Effects of Habitual Running on Indices of Health in Physically Inactive Adults. Sport. Med. 2015, 45, 1455–1468. [CrossRef] [PubMed] 15. Scheerder, J.; Breedveld, K.; Borgers, J. Running across Europe: The Rise and Size of One of the Largest Sport Markets; Springer: New York, NY, USA, 2015; ISBN 1137446374. 16. State of the Sport—Part II: Running Industry Report. 2014. Available online: http://runinfinity.com/2014/06/ 2014-state-of-the-sport-part-ii-running-industry-report.html (accessed on 19 August 2002). 17. Ogles, B.; Masters, K. A Typology of Marathon Runners Based on Cluster Analysis of Motivations. J. Sport Behav. 2003, 26, 69. 18. Rohm, A.J. A mixed-method approach for developing market segmentation typologies in the sports industry. Sport Mark. Q. 2006, 15, 29–39. 19. Vos, S.; Walravens, R.; Hover, P.; Borgers, J.; Scheerder, J. Voor de pret of de prestatie? Typologieen van evenementenloopsters. Vrijetijdsstudies 2014, 32, 19–34. Int. J. Environ. Res. Public Health 2020, 17, 6046 11 of 12 20. Scheerder, J.; Vanreusel, B.; Taks, M.; Renson, R. Social sports stratification in Flanders 1969–1999: Intergenerational reproduction of social inequalities? Int. Rev. Sociol. Sport 2002, 37, 219–245. [CrossRef] 21. Van Bottenburg, M. De Tweede Loopgolf; W.J.H. Mulier Instituut: ‘s-Hertogenbosch, The Netherlands, 2006. 22. Sosienski, S. Women Who Run; Seal Press: New York, NY, USA, 2006; ISBN 1580051839. 23. Hover, P.; van der Werff, H.; Breedveld, K. Rising Participation Rates, Shifting Segments. In Running across Europe. The Rise and Size of One of the Largest Sport Markets; Springer: New York, NY, USA, 2015; pp. 187–207. 24. Wilder, B. On the run: A Physician’s perspective. J. Orthop. Sports Phys. Ther. 2014, 44, 731–732. [CrossRef] 25. Forsberg, P. Denmark: Running for the Sake of Running? A Profile and Segmentation of Danish Runners. In Running across Europe; Springer: New York, NY, USA, 2015; pp. 59–80. 26. Scheerder, J.; Vos, S.; Taks, M. Expenditures on sport apparel: Creating consumer profiles through interval regression modelling. Eur. Sport Manag. Q. 2011, 11, 251–274. [CrossRef] 27. Clermont, C.A.; Duffett-Leger, L.; Hettinga, B.A.; Ferber, R. Runners’ Perspectives on “Smart” Wearable Technology and Its Use for Preventing Injury. Int. J. Hum. Comput. Interact. 2020, 36, 31–40. [CrossRef] 28. Hallmann, K.; Wicker, P. Consumer profiles of runners at marathon races. Int. J. Event Festiv. Manag. 2012, 3, 171–187. [CrossRef] 29. Videbæk, S.; Bueno, A.M.; Nielsen, R.O.; Rasmussen, S. Incidence of Running-Related Injuries Per 1000◦ h of running in Different Types of Runners: A Systematic Review and Meta-Analysis. Sport. Med. 2015, 45, 1017–1026. [CrossRef] 30. Kemler, E.; Blokland, D.; Backx, F.; Huisstede, B. Differences in injury risk and characteristics of injuries between novice and experienced runners over a 4-year period. Phys. Sportsmed. 2018, 46, 485–491. [CrossRef] 31. Junior, L.C.H.; Costa, L.O.P.; Lopes, A.D. Previous injuries and some training characteristics predict running-related injuries in recreational runners: A prospective cohort study. J. Physiother. 2013, 59, 263–269. [CrossRef] 32. Van Poppel, D.; Scholten-Peeters, G.G.M.; van Middelkoop, M.; Koes, B.W.; Verhagen, A.P. Risk models for lower extremity injuries among short and long distance runners: A prospective cohort study. Musculoskelet. Sci. Pract. 2018, 36, 48–53. [CrossRef] 33. Fokkema, T.; Hartgens, F.; Kluitenberg, B.; Verhagen, E.; Backx, F.J.G.; van der Worp, H.; Bierma-Zeinstra, S.M.A.; Koes, B.W.; van Middelkoop, M. Reasons and predictors of discontinuation of running after a running program for novice runners. J. Sci. Med. Sport 2019, 22, 106–111. [CrossRef] 34. Parra-Camacho, D.; González-Serrano, M.H.; González-García, R.J.; Moreno, F.C. Sporting habits of urban runners: Classification according to their motivation. Int. J. Environ. Res. Public Health 2019, 16, 4990. [CrossRef] 35. Parra-Camacho, D.; Dos Santos, M.A.; González-Serrano, M.H. Amateur runners’ commitment: An analysis of sociodemographic and sports habit profiles. Int. J. Environ. Res. Public Health 2020, 17, 925. [CrossRef] 36. Hanson, N.; Madaras, L.; Dicke, J.; Buckworth, J. Motivational Differences between Half, Full and Ultramarathoners. J. Sport Behav. 2015, 38, 180. 37. Deaner, R.O.; Masters, K.S.; Ogles, B.M.; Lacaille, R.A. Marathon Performance as a Predictor of Competitiveness and Training in Men and Women. J. Sport Behav. 2011, 34, 325–342. 38. Mueller, F.F.; Tan, C.T.; Byrne, R.; Jones, M. 13 Game Lenses for Designing Diverse Interactive Jogging Systems. In Proceedings of the Annual Symposium on Computer-Human Interaction in Play, Amsterdam, The Netherlands, 15–18 October 2017; pp. 43–56. 39. Rocha, C.M.; Gratao, O.A. The process toward commitment to running—The role of different motives, involvement, and coaching. Sport Manag. Rev. 2017, 21, 459–472. [CrossRef] 40. Havenar, J.; Lochbaum, M. Differences in Participation Motives of First-time Marathon Finishers and Pre-Race Dropouts. J. Sport Behav. 2007, 30, 270. 41. Janssen, M.; Walravens, R.; Thibaut, E.; Scheerder, J.; Brombacher, A.; Vos, S. Understanding Different Types of Recreational Runners and How They Use Running-Related Technology. Int. J. Environ. Res. Public Health 2020, 17, 2276. [CrossRef] 42. Scheerder, J.; Boen, F. The Flanders Run! Social-scientific research on the market of running. In Vlaanderen Loopt! Sociaal-Wetenschappelijk Onderzoek Naar de Loopsportmarkt; Academia Press: Cambridge, MA, USA, 2010; ISBN 9038214847. Int. J. Environ. Res. Public Health 2020, 17, 6046 12 of 12 43. Deelen, I.; Janssen, M.; Vos, S.; Kamphuis, C.B.M.; Ettema, D. Attractive running environments for all? A cross-sectional study on physical environmental characteristics and runners’ motives and attitudes, in relation to the experience of the running environment. BMC Public Health 2019, 19, 366. [CrossRef] 44. Hosmer, D.W., Jr.; Lemeshow, S. Applied Logistic Regression; John Wiley: New York, NY, USA, 2000; ISBN 0471654027. 45. Menard, S. Applied Logistic Regression Analysis, 2nd ed.; Quantitative Applications in the Social Sciences No. 106; Sage Publications: Newbury Park, CA, USA, 2002; p. 111. 46. Stragier, J.; Vanden Abeele, M.; Marez, L. De Recreational athletes’ running motivations as predictors of their use of online fitness community features. Behav. Inf. Technol. 2018, 37, 815–827. [CrossRef] 47. James, D.V.B.; Johnston, L.H.; Crone, D.; Sidford, A.H.; Gidlow, C.; Morris, C.; Foster, C. Factors associated with physical activity referral uptake and participation. J. Sports Sci. 2008, 26, 217–224. [CrossRef] 48. McArthur, D.; Dumas, A.; Woodend, K.; Beach, S.; Stacey, D. Factors influencing adherence to regular exercise in middle-aged women: A qualitative study to inform clinical practice. N. Engl. J. Med. 2014, 300, 1056–1057. [CrossRef] 49. Pridgeon, L.; Grogan, S. Understanding exercise adherence and dropout: An interpretative phenomenological analysis of men and women’s accounts of gym attendance and non-attendance. Qual. Res. Sport. Exerc. Heal. 2012, 4, 382–399. [CrossRef] 50. Hickey, M.; Mason, S. Age and gender differences in particpation rates, motivators for, and barriers to exercise. Mod. Psychol. Stud. 2017, 22, 3. 51. Stoggl, T.; Wunsch, T. Biomechanics of Marathon Running. In Marathon Running: Physiology, Psychology, Nutrition and Training Aspects; Zinner, C., Sperlich, B., Eds.; Springer International Publishing: Cham, Switzerland, 2016. 52. Barz, M.; Lange, D.; Parschau, L.; Lonsdale, C.; Knoll, N.; Schwarzer, R. Self-efficacy, planning, and preparatory behaviours as joint predictors of physical activity: A conditional process analysis. Psychol. Heal. 2016, 31, 65–78. [CrossRef] [PubMed] 53. Breedveld, K.; Scheerder, J.; Borgers, J. Running across Europe: The way forward. In Running across Europe; Springer: New York City, NY, USA, 2015; pp. 241–264. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Runner's Perceptions of Reasons to Quit Running: Influence of Gender, Age and Running-Related Characteristics.

08-20-2020

Menheere, Daphne,Janssen, Mark,Funk, Mathias,van der Spek, Erik,Lallemand, Carine,Vos, Steven

eng

PMC7897453

Physiological Reports. 2021;9:e14760. | 1 of 11 https://doi.org/10.14814/phy2.14760 wileyonlinelibrary.com/journal/phy2 Received: 18 November 2020 | Revised: 22 January 2021 | Accepted: 23 January 2021 DOI: 10.14814/phy2.14760 O R I G I N A L A R T I C L E Four weeks of high- intensity training in moderate, but not mild hypoxia improves performance and running economy more than normoxic training in horses Kazutaka Mukai1 | Hajime Ohmura1 | Yuji Takahashi1 | Yu Kitaoka2 | Toshiyuki Takahashi1 This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2021 The Authors. Physiological Reports published by Wiley Periodicals LLC on behalf of The Physiological Society and the American Physiological Society These data were presented in abstract form and as a poster presentation at the American College of Sports Medicine Conference on Integrative Physiology of Exercise, San Diego, USA, September 2018. 1Equine Research Institute, Japan Racing Association, Shimotsuke, Japan 2Kanagawa University, Yokohama, Kanagawa, Japan Correspondence Kazutaka Mukai, Equine Research Institute, Japan Racing Association, 1400- 4 Shiba, Shimotsuke, Tochigi 329- 0412, Japan. Email: [email protected] Funding information This study was funded by the Japan Racing Association. Abstract We investigated whether horses trained in moderate and mild hypoxia demonstrate greater improvement in performance and aerobic capacity compared to horses trained in normoxia and whether the acquired training effects are maintained after 2 weeks of post- hypoxic training in normoxia. Seven untrained Thoroughbred horses completed 4 weeks (3 sessions/week) of three training protocols, consisting of 2- min cantering at 95% maximal oxygen consumption ( ̇VO2max ) under two hypoxic conditions (H16, FIO2 = 16%; H18, FIO2 = 18%) and in normoxia (N21, FIO2 = 21%), followed by 2 weeks of post- hypoxic training in normoxia, using a randomized crossover study design with a 3- month washout period. Incremental treadmill tests (IET) were con- ducted at week 0, 4, and 6. The effects of time and groups were analyzed using mixed models. Run time at IET increased in H16 and H18 compared to N21, while speed at ̇VO2max was increased significantly only in H16. ̇VO2max in all groups and cardiac output at exhaustion in H16 and H18 increased after 4 weeks of training, but were not significantly different between the three groups. In all groups, run time, ̇VO2max, V ̇VO2max, ̇Qmax, and lactate threshold did not decrease after 2 weeks of post- hypoxic training in normoxia. These results suggest that 4  weeks of training in moderate (H16), but not mild (H18) hypoxia elicits greater improvements in performance and running economy than normoxic training and that these effects are maintained for 2 weeks of post- hypoxic training in normoxia. K E Y W O R D S horse, hypoxic training, performance, running economy 1 | INTRODUCTION Altitude/hypoxic training is popular in endurance athletes and has been used recently in middle- distance runners, swimmers, and speed skaters. Although the efficacy of al- titude/hypoxic training for sea- level exercise performance remains controversial from a research perspective, athletes continue to use it to train for competitions. Most commonly, 2 of 11 | MUKAI et Al. athletes both live and train at moderate to high altitude (live high- train high, LHTH) or live at altitude and train at sea level (live high- train low, LHTL). Previous reports and reviews have shown increases in exercise performance, maximal oxygen consumption ( ̇VO2max ) , and hemoglobin mass after several weeks of LHTH and/or LHTL training (Bonetti & Hopkins, 2009; Millet et al., 2010; Robertson et al., 2010). Another hypoxic training program gaining popularity is the live low- train high (LLTH) model. This model involves athletes living in normoxia and performing some training sessions in hypoxia. While several LLTH studies failed to demonstrate benefits in LLTH compared with equivalent normoxic training (McLean et al., 2014), some studies demonstrated that LLTH training can enhance exercise per- formance, maximal workload, and ( ̇VO2max ) (Czuba et al., 2011), and can augment skeletal muscle mitochondrial density, capillary- to- fiber ratio, and fiber cross- sectional area (Desplanches et al., 1993; Vogt et al., 2001), likely via up- regulation of hypoxia- inducible factor 1α (HIF- 1α) (Vogt et al., 2001). Some authors also suggest that LLTH may improve anaerobic exercise performance (Hamlin et al., 2010; Hendriksen & Meeuwsen, 2003), possibly via increases in muscle buffering capacity (Gore et al., 2001) and increased glycolytic enzyme activity (Puype et al., 2013). When athletes and coaches use hypoxic training in prac- tical situations, a key question is when is the best timing to return to sea level before a race to optimize performance. The general consensus among top coaches suggests that endur- ance performance is optimized after 14 days at sea level after altitude/hypoxic training (Dick, 1992), but there is limited scientific evidence to support this opinion. While some re- searchers suggest that repeated sprint ability and hemoglobin mass are higher at 3 weeks after hypoxic training compared with pre- hypoxic training levels (Brocherie et al., 2015), an- other group reported that most hematological adaptations after altitude training are lost in 9 days (Pottgiesser et al., 2012). In addition, previous studies on the maintenance of post- hypoxic training use mostly LHTL training and not LLTH. Thoroughbred horses have high ̇VO2max, exceeding 180  mL/kg/min in trained individuals, and the aerobic contribution to total energy expenditure for a 120- s sprint is estimated to reach >70% (Eaton et al., 1995; Ohmura et al., 2010). Furthermore, Thoroughbred horses have large amounts of glycogen (>600 mmol/kg dwt) in their muscle (Davie et al., 1999) and the lactate concentration in plasma and skeletal muscle during maximal exercise increases to more than 20 mmol/L and 20 mmol/kg, re- spectively (Kitaoka et al., 2014), which suggests that Thoroughbred horses also utilize the glycolytic pathway maximally for energy resources during high- intensity exercise. Therefore, improvements in both aerobic and anaerobic capacity are needed to enhance equine racing performance. Previously, we reported that high- intensity training (100% ̇VO2max for 3  min, 3 sessions/week for 4  weeks) in moderate hypoxia (15% O2) improves run time and ̇VO2max at incremental exercise tests (IET) in normoxia to a greater extent than the same training in nor- moxia (Mukai et al., 2020), which indicates that hypoxic training may be a strong strategy for better exercise performance without increasing absolute training speed and/or distance. However, very few studies have examined hypoxic training in horses (Davie et al., 2017; Ohmura et al., 2017), and further studies are needed to determine the optimal severity of hypoxia and the intensity, duration, and volume of training in hypoxia. The purpose of this study was to investigate the hypoth- esis that horses trained in moderate and mild hypoxia for 4  weeks experience greater improvements in performance and aerobic capacity compared with horses trained in nor- moxia. In addition, we examined whether acquired training effects are maintained after 2 weeks of post- hypoxic training in normoxia. 2 | MATERIALS AND METHODS Protocols for the study were reviewed and approved by the Animal Welfare and Ethics Committee of the Japan Racing Association (JRA) Equine Research Institute (Permit number: 2017– 1, 2018– 1). All surgery was performed under sevoflu- rane anesthesia and all incisions for catheter placements were performed under local anesthesia using lidocaine. All efforts were made to minimize animal suffering. 2.1 | Horses Seven untrained Thoroughbreds (2 geldings and 5 females; mean ±SE age, 7.9 ± 0.7 years; body weight, 512 ± 11 kg at the onset of the study) were used in this study. Each horse had a carotid artery moved surgically from the ca- rotid sheath to a subcutaneous location under sevoflu- rane anesthesia to facilitate arterial catheterization. After recovery from surgery, the horses were trained to run on a treadmill (Sato I, Sato AB, Uppsala, Sweden) while wear- ing an open- flow mask (Pascoe et al., 1999). After surgery, each horse was kept in a 17 x 22 m yard for approximately 6 h/day every day for at least 4 months before treadmill experiments began. All horses received 1 kg of oats, 1 kg of pelleted feed, and 3 kg of timothy hay in the morning, and 1 kg of oats, 2 kg of pelleted feed, and 3 kg of timothy hay in the afternoon. Water was available ad libitum during the study.  | 3 of 11 MUKAI et Al. 2.2 | Experimental design In a randomized crossover design, horses were trained in moderate hypoxia (H16; 16% inspired O2), mild hypoxia (H18; 18% inspired O2), or normoxia (N21; 21% inspired O2) for 3 days/week on a treadmill at a 6% incline. The horses were pastured in 17 × 22 m yards for approximately 6 h/day and walked for 1 h/day in a walker on the other 4 days during the training period. The training session con- sisted of a warm- up (walking at 1.7 m/s for 30 min and trotting at 4 m/s for 2 min), cantering at 7 m/s for 1 min and for 2 min at the speed previously determined to elicit 95% ̇VO2max measured in normoxia, followed by a cool- down (1.7 m/s for 30 min) in all groups. In hypoxic groups, horses wore an open- flow mask after walking for 30 min and were exposed to hypoxia during trotting for 2 min and cantering at 7 m/s for 1 min and at 95% ̇VO2max for 2 min. After 4  weeks of hypoxic/normoxic training, all groups continued the same training protocols in normoxia for 2 weeks. Each training period was separated by 3 months to ensure a sufficient detraining interval. 2.3 | Incremental exercise tests (IET) in normoxia Incremental exercise tests in normoxia were conducted at weeks 0, 4, and 6. The procedure for the incremental exercise test, including oxygen consumption measurements and blood sampling, has been described previously (Mukai et al., 2017). Briefly, after catheters and transducers were connected and tested, the horse began its exercise. The horse warmed up by trotting at 4 m/s for 3 min, then cantering up a 6% incline for 2 min each at 1.7, 4, 6, 8, 10, 12, 13, and 14 m/s until the horse could not maintain its position at the front of the treadmill with humane encouragement. This condition was defined as exhaustion. Run time to exhaustion was meas- ured with a stopwatch. For each speed, the horse ran on the treadmill for 90 s to allow the O2 transport system to come to steady- state (equine ̇VO2 comes to steady- state faster than human ̇VO2 does), then ̇VO2 was calculated for the final 30 s of each step. Heart rate was recorded using a commercial heart rate monitor (S810, Polar, Kempele, Finland) and mean heart rate was calculated for the final 30 s of each step. 2.4 | Oxygen consumption Horses wore an open- flow mask on the treadmill through which a rheostat- controlled blower drew air. Air flowed through 25- cm diameter tubing and across a pneumotacho- graph (LF- 150B, Vise Medical, Chiba, Japan) connected to a differential pressure transducer (TF- 5, Vise Medical) to ensure that bias flows during measurements were identi- cal to those used during calibrations. Bias flow was set to keep changes in O2 concentration and CO2 concentrations at <1.5% to avoid having the horses rebreathe CO2. Oxygen and CO2 concentrations were measured with an O2 and CO2 analyzer (MG- 360, Vise Medical), and calibrations were used to calculate rates of O2 consumption and CO2 produc- tion with mass flowmeters (CR- 300, Kofloc, Kyoto, Japan) using the N2- dilution/CO2- addition mass- balance technique (Fedak et al., 1981). Gas analyzer and mass flowmeter out- puts were also recorded on personal computers using com- mercial hardware and software (DI- 720 and Windaq Pro+, DATAQ, Akron, OH) sampling at 200 Hz. 2.5 | Blood sampling Before leading a horse onto the treadmill, an 18- gauge cath- eter (Surflow, Terumo, Tokyo, Japan) was placed in the horse's left carotid artery, and an 8- F introducer (MO95H- 8, Baxter International, Deerfield, IL) was placed in the right jugular vein. A Swan- Ganz catheter (SP5107  U, Becton, Dickinson and Company, Franklin Lakes, NJ) was passed via the jugular vein so that its tip was positioned in the pulmo- nary artery, confirmed by measuring pressure at its tip with a pressure transducer (P23XL, Becton, Dickinson and Company, Franklin Lakes, NJ). Mixed- venous blood samples were drawn from the tip of the Swan- Ganz catheter and arterial samples from the 18- gauge carotid catheter into heparinized syringes at timed intervals for the final 30 s of each step and at 1, 3, and 5 min after exhaustion. Samples were stored on ice until measured immediately following the experiment. Blood sam- ples were analyzed with a blood gas analyzer (ABL800 FLEX, Radiometer, Copenhagen, Denmark) and O2 saturation (SO2) and concentration (CO2) were determined using a hemoximeter (ABL80 FLEX- CO- OX, Radiometer, Copenhagen, Denmark). Following measurement of blood gases and oximetry, the blood was sampled for plasma lactate concentration using a lactate analyzer (Biosen S- Line, EKF- diagnostic GmbH, Barleben, Germany) after being centrifuged at 1870 × g for 10 min. The Swan– Ganz catheter in the pulmonary artery was connected to a cardiac output computer (COM- 2, Baxter International, Deerfield, IL) so that its thermistor registered pulmonary arte- rial temperature, could be recorded at each blood sampling and used to correct the blood gas measurements. 2.6 | Hypoxic training protocol and measurements during exercise in the first week of each training period The procedure for producing the hypoxic condition was slightly modified from the method previously described 4 of 11 | MUKAI et Al. (Ohmura et al., 2010). Briefly, a mixing chamber was con- nected to the upstream flexible tube on a 25- cm diameter open- flow mask through which a flow of N2 was blown into the upstream end of the flow system and mixed with a bias- flow of air of 80– 120 L/s to create the desired inspired O2 concentration. Nitrogen gas flow was controlled with a mass flow meter (Model DPM3, Kofloc, Kyoto, Japan) connected to compressed gas cylinders through a gas mani- fold. Nitrogen gas flow was adjusted to maintain 16% or 18% O2 by monitoring the O2 concentration in the down- stream arm of the mass flowmeter with an O2 analyzer (LC- 240UW, Vise Medical, Chiba, Japan) when horses ran in hypoxia. In the first week of training for all groups, we collected arterial blood samples in the final 15 s of cantering at 95% ̇VO2max during the exercise session to measure arterial blood gas variables (ABL800 FLEX and ABL80 FLEX- CO- OX, Radiometer, Copenhagen, Denmark) and plasma lactate concentration (Biosen S- Line, EKF- diagnostic GmbH, Barleben, Germany). We also recorded heart rate (S810, Polar, Kempele, Finland) during cantering. 2.7 | Statistical analysis Data are presented as mean ± standard error (SE). Differences in the variables between H16, H18, and N21 during training sessions in the first week were analyzed using mixed models with a group as a fixed effect and horse as a random effect. Post hoc testing was performed by Tukey's test. After train- ing, the with- in subject changes were analyzed using mixed models for differences between groups with a group as a fixed effect and horse as a random effect. Tukey's tests were used as post hoc tests. Pearson correlation was used to determine the relation- ship between the changes in the run time and body weight at IET after training and arterial O2 saturation (SaO2), peak plasma lactate concentration, and heart rate during exercise sessions in the first week of training periods. Statistical anal- yses were performed with commercial software (JMP 13.1.0, SAS Institute Inc, Cary, NC) with significance defined as p < 0.05. 3 | RESULTS 3.1 | Blood gas variables, heart rate, and plasma lactate concentration during exercise sessions in the first week of training SaO2 was lowest at the last 15 s of a 2- min run at 95% ̇VO2max in H16 and highest in N21 (p < 0.0001, Table 1). Arterial O2 partial pressure (PaO2) in H16 and H18 was lower than that in N21 (p < 0.0001, Table 1), and arterial carbon dioxide partial pressure (PaCO2) in H16 and H18 was higher than that in N21 during exercise (p = 0.0013, Table 1). There were no differences in heart rate at the last 15 s of a 2- min run at 95% ̇VO2max between all groups (p = 0.96, Table 1). Arterial pH of H16 was lower than that of N21 (p = 0.0038, Table 1), and peak plasma lactate concentration of H16 was higher than that of H18 (p = 0.032, Table 1). 3.2 | Effects of normoxic and hypoxic training on exercise performance and aerobic capacity at IET After 4 weeks of training, run time (H16, +20.6%, p < 0.0001; H18, +11.7%, p = 0.017) and maximal cardiac output ( ̇Qmax : H16, +8.1%, p = 0.024; H18, +9.5%, p = 0.012) at IET increased in H16 and H18, ̇VO2max increased in all groups (H16, +9.8%, p = 0.0039; H18, +10.5%, p = 0.0025; N21, +8.8%, p = 0.025), and speed at ̇VO2max (V ̇VO2max ) increased only in H16 (+7.7%, p = 0.010)(Figure 1, Figure 2 and Table 2). Blood gas variables including hemoglobin concentration, O2 and CO2 partial pressures, and arterial- mixed venous O2 concentration did not change in all groups during the train- ing period (Figure 2 and Table 2). Changes in run time and V ̇VO2max after 4 weeks of training were different between H16 and N21 (run time, p = 0.040; V ̇VO2max, p = 0.014), while the H16 H18 N21 SaO2 (%) 66.5 ± 1.7 a 74.1 ± 1.7 b 90.9 ± 1.3 c PaO2 (Torr) 38.8 ± 0.6 a 44.8 ± 2.4 a 68.8 ± 3.3 b PaCO2 (Torr) 59.1 ± 1.5 a 55.3 ± 3.5 a 42.3 ± 1.3 b Heart rate (bpm) 203 ± 4 a 202 ± 3 a 202 ± 4 a Arterial pH 7.210 ± 0.015 a 7.247 ± 0.017 ab 7.281 ± 0.011 b Peak lactate (mmol/L) 22.3 ± 2.7 a 17.7 ± 1.4 b 18.5 ± 1.0 ab Arterial O2 saturation (SaO2), arterial O2 partial pressure (PaO2), arterial carbon dioxide partial pressure (PaCO2), heart rate, arterial pH, and peak plasma lactate concentration. Values are means ± SE for 7 horses. Different letters indicate significant differences between groups (p < 0.05). TABLE 1 Parameters on aerobic capacity and blood gas analysis during the exercise session at the 1st week of training  | 5 of 11 MUKAI et Al. changes in ̇VO2max, ̇Qmax, SVmax, and blood gas variables were not different between the groups ( ̇VO2max, p  =  0.87; ̇Qmax, p = 0.74; SVmax, p = 0.99) (Figure 1 and Figure 2). Run time, ̇VO2max, V ̇VO2max, ̇Qmax, SVmax, and lactate threshold did not change after 2 weeks of post- hypoxic training in normoxia in all groups compared with those at week 4 (Figure 1 and Table FIGURE 1 Changes in run time (a), ̇VO2max (b), speed eliciting ̇VO2max (V ̇VO2max ; c) and speed at which plasma lactate concentration reached 4 mmol/L (VLA4; d) in IET from pre- training to immediate post- training (4 weeks) and 2 weeks of post- hypoxic training in normoxia (6 weeks) either after moderate hypoxia (H16, red), mild hypoxia (H18, green), or normoxia (N21, blue). Values are mean ±SE. * Significant changes from pre- training (p < 0.05). † Significant differences between groups (p < 0.05) (a) (b) (c) (d) FIGURE 2 Changes in cardiac output (a), stroke volume (b), arterial- mixed venous O2 difference (c), and hemoglobin concentration (d) at exhaustion in IET from pre- training to immediate post- training (4 weeks) and 2 weeks of post- hypoxic training in normoxia (6 weeks) either after moderate (H16, red), mild hypoxia (H18, green), or normoxia (N21, blue). Values are mean ±SE. * Significant changes from pre- training (p < 0.05). † Significant differences between groups (p < 0.05) (a) (b) (c) (d) 6 of 11 | MUKAI et Al. TABLE 2 Parameters on exercise performance, aerobic capacity, and blood gas analysis in normoxic incremental exercise tests at week 0, 4, and 6 H16 H18 N21 0 week 4 weeks 6 weeks 0 week 4 weeks 6 weeks 0 week 4 weeks 6 weeks Run time (s) 416 ± 26 501 ± 36 * 498 ± 36 * 438 ± 25 490 ± 34 * 487 ± 34* 433 ± 18 454 ± 25 464 ± 23 ̇VO2max (mL/(min kg)) 164 ± 3 180 ± 3 * 180 ± 4 * 161 ± 4 177 ± 3 * 175 ± 6 * 161 ± 4 175 ± 3 * 177 ± 3 * Body weight (kg) 517 ± 11 493 ± 9 * 494 ± 10 * 518 ± 9 497 ± 11 * 498 ± 12 * 514 ± 10 502 ± 9 * 501 ± 8 * V ̇VO2max (m/s) 11.1 ± 0.4 12.0 ± 0.4 * 12.0 ± 0.4 * 11.5 ± 0.4 12.1 ± 0.4 12.0 ± 0.5 11.7 ± 0.2 11.6 ± 0.3 11.7 ± 0.3 ̇Qmax (mL/(min kg)) 666 ± 10 720 ± 19 * 743 ± 19 * 654 ± 16 713 ± 8 * 724 ± 15 * 662 ± 20 703 ± 11 735 ± 16 * SVmax (mL/kg) 3.20 ± 0.08 3.39 ± 0.12 3.51 ± 0.13 * 3.13 ± 0.09 3.35 ± 0.07 * 3.42 ± 0.10 * 3.18 ± 0.13 3.32 ± 0.10 3.48 ± 0.14 * HRmax (bpm) 209 ± 5 215 ± 5 213 ± 3 209 ± 4 213 ± 3 212 ± 4 209 ± 5 213 ± 4 214 ± 4 VHRmax (m/s) 10.2 ± 0.4 10.9 ± 0.5 11.0 ± 0.4 10.4 ± 0.5 11.0 ± 0.4 11.1 ± 0.4 10.5 ± 0.3 10.6 ± 0.4 10.7 ± 0.4 Ca- vO2 (mL/dL) 24.6 ± 0.2 24.6 ± 0.2 24.2 ± 0.2 24.8 ± 0.2 24.8 ± 0.2 24.1 ± 0.2 24.3 ± 0.1 24.8 ± 0.2 24.1 ± 0.2 [Hb] (g/dL) 23.9 ± 0.5 23.7 ± 0.6 23.6 ± 0.6 24.0 ± 0.4 24.1 ± 0.5 23.7 ± 0.5 23.5 ± 0.5 23.8 ± 0.4 23.5 ± 0.6 PaO2 (Torr) 78.8 ± 0.7 79.0 ± 0.5 80.4 ± 1.0 83.0 ± 1.0 81.0 ± 1.0 79.2 ± 1.2 84.0 ± 0.9 79.8 ± 1.0 79.2 ± 0.8 PvO2 (Torr) 21.2 ± 0.1 19.7 ± 0.3 20.6 ± 0.3 22.3 ± 0.2 20.8 ± 0.2 21.0 ± 0.3 22.4 ± 0.2 19.9 ± 0.2 20.4 ± 0.3 PaCO2 (Torr) 52.9 ± 0.5 55.8 ± 0.6 55.0 ± 0.8 53.1 ± 0.6 56.7 ± 0.6 56.6 ± 0.6 52.9 ± 0.4 54.4 ± 0.6 54.7 ± 0.5 PvCO2 (Torr) 112.2 ± 1.6 126.7 ± 2.9 122.2 ± 2.2 110.9 ± 1.7 121.3 ± 2.2 120.0 ± 2.2 114.2 ± 1.7 119.5 ± 2.6 122.1 ± 2.2 SaO2 (%) 87.7 ± 0.4 86.5 ± 0.4 86.5 ± 0.5 88.9 ± 0.4 86.8 ± 0.5 85.9 ± 0.5 89.5 ± 0.4 87.5 ± 0.5 86.7 ± 0.4 SvO2 (%) 13.5 ± 0.3 11.7 ± 0.6 11.7 ± 0.4 14.9 ± 0.4 12.7 ± 0.5 12.5 ± 0.6 14.7 ± 0.5 12.5 ± 0.5 12.0 ± 0.5 pHa 7.207 ± 0.005 7.200 ± 0.009 7.203 ± 0.008 7.235 ± 0.006 7.218 ± 0.009 7.213 ± 0.010 7.227 ± 0.007 7.215 ± 0.010 7.220 ± 0.009 pHv 7.084 ± 0.006 7.070 ± 0.011 7.073 ± 0.008 7.101 ± 0.007 7.094 ± 0.010 7.073 ± 0.009 7.097 ± 0.011 7.085 ± 0.011 7.087 ± 0.009 Run time, maximal oxygen consumption ( ̇VO2max), body weight, speed eliciting ̇VO2max (V ̇VO2max), cardiac output ( ̇Qmax), cardiac stroke volume (SVmax), maximal heart rate (HRmax), speed eliciting HRmax (VHRmax), arterial and mixed- venous O2 difference (Ca- vO2), arterial and mixed- venous O2 partial pressure (PaO2, PvO2), arterial and mixed- venous carbon dioxide partial pressure (PaCO2, PvCO2), hemoglobin concentration ([Hb]), arterial and mixed venous O2 saturation (SaO2, SvO2), and arterial and mixed venous pH (pHa, pHv) at exhaustion during normoxic incremental exercise tests. Values are means ±SE for seven horses. *Significant changes from 0 week (p < 0.05).  | 7 of 11 MUKAI et Al. 2). Body weight decreased after 4 weeks of training in all groups (H16, −4.7%, p < 0.0001; H18, −4.1%, p < 0.0001; N21, −2.4%, p = 0.0003) and there was a significantly greater weight loss in H16 compared to N21 (p = 0.021), but not be- tween H18 and N21. These reductions in body weight lasted for 2 weeks after a switch to normoxic training (Table 2). 3.3 | Correlations between the variables during the exercise session and the changes of variables at IET after 4 weeks of training There were significant correlations between SaO2 during ex- ercise and the changes in run time (r = −0.59, p = 0.0067; Figure 3a), between peak plasma lactate concentration during exercise and the changes in run time (r = 0.66, p = 0.0017; Figure 3b), and between SaO2 during exercise and the changes in body weight (r = 0.61, p = 0.0040; Figure 3d). No sig- nificant correlations were observed between heart rate during exercise and the changes in run time (r = −0.077, p = 0.75; Figure 3c). 4 | DISCUSSION The purpose of this study was to determine whether high- intensity training in moderate and mild hypoxia could improve exercise performance and aerobic capacity to a greater extent than the same training in normoxia. In addition, we sought to determine if two weeks of post- hypoxic training in normoxia could maintain the benefit of hypoxic training. First, we dem- onstrated that horses trained in 16% O2 enhanced run time and V ̇VO2max at IET more than horses trained in normoxia and that horses trained in 18% O2 showed a similar adaptation as H16, but there was no statistical significance between H18 and N21. In addition, the acquired hypoxic training effects on performance and aerobic capacity were sustained after 2 weeks of post- hypoxic training in normoxia. 4.1 | Training effects on exercise performance and aerobic capacity after hypoxic training Despite several human studies reported no additional ben- efits in LLTH training (Faiss et al., 2013), 4 weeks of hy- poxic training in H16 showed greater improvements in run time and V ̇VO2max at IET than normoxic training. Vogt and Hoppeler (2010) stated that there is no clear trend in the ef- fects of LLTH training on performance at sea level and no conclusive recommendations can be made as to which al- titude, exposure duration, and exercise intensity might be beneficial. In contrast, (McLean et al., 2014) indicated that enhancements in normoxic performance appear most likely FIGURE 3 Correlations between SaO2 (a), peak plasma lactate concentration (b), and heart rate (c) during exercise session in the first week and the change in run time at IET after 4 weeks of training, and between SaO2 during exercise session and the change in body weight (d) at IET after 4 weeks of training. Red, green, and blue dots indicate moderate hypoxia (H16), mild hypoxia (H18), and normoxia (N21), respectively. Solid lines indicate regression lines and pink areas indicate 95% confidence intervals. (a) (b) (c) (d) 8 of 11 | MUKAI et Al. following high- intensity and short- term training in hypoxia. Despite our hypoxic settings (FIO2: 16% and 18%) being con- sidered as moderate and/or mild condition for human hypoxic training, horses exercised in H16 and H18 experienced se- vere arterial hypoxemia in this study, and their end- exercise SaO2 declined to 66.5 ± 1.7% and 74.1 ± 1.7%, respectively (Table 1). Thoroughbred horses often exhibit arterial hypox- emia during high- intensity exercise even in normoxia mostly due to diffusion limitations in the lungs (Wagner et al., 1989). Previous literature has demonstrated that exercise- induced arterial hypoxemia also occurs in highly- trained human ath- letes during heavy exercise in normoxia and hypoxia, and the end- exercise SaO2 at FIO2 of 21% was similar to that ob- served in horses (91 ± 1%), while SaO2 at FIO2 of 17% was not as low as that observed horses (83 ± 1%) (Vogiatzis et al., 2007). These findings suggest that hypoxia may cause more severe exercise- induced arterial hypoxemia in horses than in humans, and these differences between horses and humans in the severity of exercise- induced arterial hypoxemia during hypoxic training might induce different training adaptations on performance and aerobic capacity. In equine studies, whereas (Davie et al., 2017) reported no additional improvements in heart rate and blood lactate con- centration during incremental treadmill tests after 6 weeks of moderate- intensity hypoxic training (3 hypoxic and 3 nor- moxic sessions/week, total 30 min/session, 15% inspired O2), Ohmura et al. (2017) demonstrated that all- out running for 2– 3 min in hypoxia (15.1% inspired O2) twice a week for 3 weeks increased ̇VO2max of well- trained horses in normoxia. Our previous study in horses also showed that 4 weeks of high- intensity training in hypoxia (100% ̇VO2max 2 min, 3 ses- sions/week, 15% inspired O2) improved performance, ̇VO2max , and maximal cardiac output to a greater extent than nor- moxic training (Mukai et al., 2020). Training programs var- ied among these studies, including intensity and duration of training, training status of horses (untrained or trained), and hypoxic exposure duration, but these interventions used sim- ilar O2 concentrations of hypoxic gas. Given that the program of Davie's group, which used a longer duration of training and hypoxic exposure, but lower training intensity, showed no benefit in hypoxic training. The key factors for hypoxic training adaptations in horses may also be high- intensity and short- term training as McLean et al. (2014) suggested. The changes in V ̇VO2max in all groups were very similar to those in run time at IET in our study (Figure 1). While V ̇VO2max is not a direct parameter for running economy, Billat et al. (2003) reported that V ̇VO2max is highly correlated with 10- km performance time (r = −0.86 in men, r = −0.95 in women) and V ̇VO2max predicts performance better than ̇VO2max since V ̇VO2max integrates the energy cost of running in addition to ̇VO2max. Given that the changes in ̇VO2max after 4 weeks of training were similar in all groups in this study, the improve- ments in V ̇VO2max may reflect enhanced running economy in submaximal exercise at IET. Several researchers also demon- strated that hypoxic training improves the running economy compared with normoxic training in humans (Katayama et al., 2003; Park et al., 2018; Saunders et al., 2004; Sinex & Chapman, 2015). Barnes and Kilding (2015) stated that altitude acclimatization induces both central and peripheral adaptations that improve oxygen delivery and utilization, mechanisms that may improve running economy. In contrast, Saunders et al. (2004) suggested that the lower aerobic cost of running is not related to ventilation, heart rate, respiratory exchange ratio, or hemoglobin mass. These conflicting re- sults indicate that the mechanism of improved running econ- omy after hypoxic training is unclear and further studies are needed. ̇VO2max, ̇Qmax, and SVmax at IET increased similarly in all groups throughout the study. In our previous study (Mukai et al., 2020), however, we observed greater ̇VO2max, ̇Qmax, and SVmax in the hypoxic group compared to that of the normoxic group in a similar study design. The causes for these differ- ences are not clear, but the minor differences in the training intensity (100% ̇VO2max vs. 95% ̇VO2max), the degree of hy- poxia (15% O2 vs. 16% or 18% O2), and the age of horses (6.5 years vs. 7.9 years) might affect training adaptation on aerobic capacity. On the other hand, Ca- vO2 was unchanged during the training period in all groups, indicating that the consumed O2 in working muscle, that is mitochondrial oxidative capacity, did not change after training. Therefore, the majority of the increase in ̇VO2maxseems to be induced by the increase in O2 delivery. These results suggest that the mitochondrial oxida- tive capacity is not a limiting factor of ̇VO2maxin horses and O2 delivery is implicated as the primary limitation for ̇VO2max , as previously described (Jones & Lindstedt, 1993). 4.2 | Correlations between SaO2 during exercise session and the changes of variables at IET after 4 weeks of training We observed a moderate negative correlation (r  =  −0.59) between SaO2 during the training session and the increase in run time at IET, and also a moderate positive correla- tion (r = 0.61) between SaO2 and body weight loss (Figure 3), which suggests that a greater reduction in SaO2 during the exercise session induces a greater improvement in per- formance and greater weight loss after 4 weeks of training. Given that the lower SaO2 during the training session could simultaneously induce both positive and negative effects, trainers should understand the possibility of hypoxia- induced weight loss and set an optimal training program for peak racing performance. As monitoring each horse's SaO2 dur- ing exercise is not practical at the training track, we recom- mend monitoring peak lactate concentration instead, which  | 9 of 11 MUKAI et Al. correlated moderately with the change in run time after train- ing (r = 0.66), as well as SaO2 (Figure 3). 4.3 | Effect of hypoxic training on body weight loss At the same absolute exercise intensity, exercise in hypoxia is perceived as harder (i.e., lower SaO2 and/or higher lactate concentration) and the relative exercise intensity is higher in hypoxia due to the lower ̇VO2maxthan in normoxia (Ohmura et al., 2020). Consequently, hypoxic training can lead to in- creased energy expenditure, decreased energy intake, and greater body weight loss compared to normoxic training at the same absolute intensity. Furthermore, Katayama et al. (2010) reported that carbohydrate utilization increased dur- ing exercise and recovery period in moderate hypoxia com- pared with normoxic exercise at the same relative intensity. These findings suggest that a shift in substrate utilization may also occur during hypoxic training in horses. Contrary to these results, our previous study demonstrated that well- trained horses did not reduce their body weight after 3 weeks of high- intensity training (5 sessions/3  weeks) in hypoxia (Ohmura et al., 2017). These contradictory data indicate that the training status at the beginning of training, as well as the intensity, frequency, and volume of training, may affect the extent of body weight loss during hypoxic training. However, the mechanism of body weight loss during hypoxic training is still unclear and further studies are needed to investigate the relationship between hypoxic training and body weight loss. 4.4 | Hematological changes with hypoxic training Consistent with human studies (Roels et al., 2005; Truijens et al., 2003) and our previous study (Mukai et al., 2020), hemoglobin concentrations both at rest and at exhaustion in all groups did not increase after training in our present study. While LHTH and/or LHTL training usually aims to enhance athletic performance by stimulating an increase in serum erythropoietin and erythrocyte volume, only a few well- controlled LLTH studies on trained or elite athletes have reported increments in hemoglobin concentration (Bonetti et al., 2006), and none have reported any increases in eryth- rocyte volume and/or hemoglobin mass. Some studies showed that intermittent hypoxic exposure at rest (3 h/day, 5 days/week for 4 weeks at 4000– 5500 m altitude) increases serum erythropoietin only immediately after a 3 h hypoxic exposure, but no significant differences were observed in erythrocyte volume or hemoglobin mass compared with the normoxic control (Abellán et al., 2005; Gore et al., 2006). Millet et al. (2010) reported that the minimum daily dose for stimulating erythropoiesis seems to be 12 h/day. These previ- ous reports in humans suggest that the exposure duration to hypoxia (approximately 3 min/session) in this study was too short to increase erythrocyte volume and hemoglobin mass/ concentration. 4.5 | Maintenance of post- hypoxic training effects There is contradictory evidence concerning how long the ac- quired benefits of hypoxic training last after a return to sea level. Pottgiesser et al. (2012) reported that nearly hypoxia- induced hematological changes observed after 4  weeks of LHTL training may be lost within 9 days, while Brocherie et al. (2015) showed that 14 days of hypoxic training im- proved repeated sprint performance and hemoglobin mass, with the benefits lasting for at least 3 weeks post- intervention. Another study also reported that the increase in total hemo- globin mass of elite runners at altitude training camp is stable for 14 days after returning to sea level (Prommer et al., 2010). Consistent with the findings of Brocherie et al. (2015), exer- cise performance and aerobic capacity after 2 weeks of post- hypoxic training in normoxia were very similar to those after 4 weeks of hypoxic training in our study, which indicates that most adaptations induced by hypoxic training are main- tained after 2 weeks of normoxic training. LLTH training in our study did not induce any changes in hemoglobin concen- tration throughout the study, which suggests that horses did not gain or lose any benefits from hematological adaptations. This may be one of the reasons that horses maintained their performance and aerobic capacity after post- hypoxic training in normoxia. 4.6 | Experimental design of this study Our study design altered two factors: the ̇VO2- based relative training intensity and FIO2. In some human studies, subjects trained at matched relative intensity either in normoxia or hy- poxia experience similar adaptations after training (McLean et al., 2014; Truijens et al., 2003). In our design, horses trained in moderate hypoxia showed a greater adaptation in performance without increasing absolute speed. Given that Thoroughbred horses often experience musculoskeletal in- juries, we consider that this model of hypoxic training may have benefits of no additional speed/mechanical load. Even if we match the relative training intensity in both normoxia and hypoxia, the absolute speed or mechanical load will decrease in the hypoxic training program, which indicates that we are changing two factors again: the absolute speed/ mechanical load and FIO2. In addition, we were concerned 10 of 11 | MUKAI et Al. about performing two incremental exercise tests for pre- training measurements in normoxia and for the relative in- tensity in hypoxia during a relatively short period. Indeed, we understand that further studies that match the relative training intensity in normoxia and hypoxia are needed to clarify the mechanism of hypoxic training in horses. 5 | CONCLUSION In this study, we demonstrated that 4  weeks of training in moderate (H16), but not mild hypoxia (H18) was sufficient to elicit greater improvements in performance and running econ- omy than normoxic training and that the effects of the hypoxic training were maintained over 2 weeks of post- hypoxic train- ing. Although trainers should monitor weight loss, hypoxic training may be a strategic option for an equine training pro- gram without increasing locomotory mechanical stress. ACKNOWLEDGMENTS The authors thank the technical staff of the JRA Equine Research Institute for expert technical assistance, training, and husbandry throughout the study. CONFLICT OF INTEREST This study was funded by the Japan Racing Association. KM, HO, YT, and TT are employees of the Japan Racing Association. AUTHOR CONTRIBUTIONS Conceptualization: KM, HO, YK, and TT. Investigation: KM, HO, YT, and TT. Formal analysis: KM and TT. Methodology: KM, HO, and TT. Writing— original draft: KM. Writing— review & editing: KM, HO, YT, and YK. ORCID Kazutaka Mukai  https://orcid.org/0000-0002-1992-6634 Yuji Takahashi  https://orcid.org/0000-0002-2139-6142 Yu Kitaoka  https://orcid.org/0000-0001-6932-2735 REFERENCES Abellán, R., Remacha, A. F., Ventura, R., Sardà, M. P., Segura, J., & Rodríguez, F. A. (2005). Hematologic response to four weeks of intermittent hypobaric hypoxia in highly trained athletes. Haematologica, 90(1), 126– 127. Barnes, K. R., & Kilding, A. E. (2015). Strategies to improve running economy. Sports Medicine (Auckland, N. Z.), 45(1), 37– 56. Billat, V., Lepretre, P. M., Heugas, A. M., Laurence, M. H., Salim, D., & Koralsztein, J. P. (2003). Training and bioenergetic characteristics in elite male and female Kenyan runners. Medicine and Science in Sports and Exercise, 35(2), pp. 297– 304; discussion 5– 6. Bonetti, D. L., & Hopkins, W. G. (2009). Sea- level exercise perfor- mance following adaptation to hypoxia: a meta- analysis. Sports Medicine (Auckland, N. Z.), 39(2), 107– 127. Bonetti, D. L., Hopkins, W. G., & Kilding, A. E. (2006). High- intensity kayak performance after adaptation to intermittent hypoxia. International Journal of Sports Physiology and Performance, 1(3), 246– 260. Brocherie, F., Millet, G. P., Hauser, A., Steiner, T., Rysman, J., Wehrlin, J. P., & Girard, O. (2015). "Live High- Train Low and High" Hypoxic Training Improves Team- Sport Performance. Medicine and Science in Sports and Exercise, 47(10), 2140– 2149. Czuba, M., Waskiewicz, Z., Zajac, A., Poprzecki, S., Cholewa, J., & Roczniok, R. (2011). The effects of intermittent hypoxic train- ing on aerobic capacity and endurance performance in cyclists. Journal of Sports Science and Medicine, 10(1), 175– 183. Davie, A. J., Evans, D. L., Hodgson, D. R., & Rose, R. J. (1999). Effects of muscle glycogen depletion on some metabolic and physiologi- cal responses to submaximal treadmill exercise. Canadian Journal of Veterinary Research, 63(4), 241– 247. Davie, A. J., Wen, L., Cust, A. R. E., Beavers, R., Fyfe, T., & Zhou, S. (2017). The effects of moderate intensity training in a hypoxic environment on transcriptional responses in Thoroughbred horses. Biology Open, 6(7), 1035– 1040. Desplanches, D., Hoppeler, H., Linossier, M. T., Denis, C., Claassen, H., Dormois, D., Lacour, J. R., & Geyssant, A. (1993). Effects of training in normoxia and normobaric hypoxia on human muscle ultrastructure. Pflugers Archiv European Journal of Physiology, 425(3– 4), 263– 267. Dick, F. W. (1992). Training at altitude in practice. International Journal of Sports Medicine, 13(Suppl 1), S203– S206. Eaton, M. D., Evans, D. L., Hodgson, D. R., & Rose, R. J. (1995). Maximal accumulated oxygen deficit in thoroughbred horses. Journal of Applied Physiology, 78(4), 1564– 1568. Faiss, R., Girard, O., & Millet, G. P. (2013). Advancing hypoxic train- ing in team sports: from intermittent hypoxic training to repeated sprint training in hypoxia. British Journal of Sports Medicine, 47(Suppl 1), i45– 50. Fedak, M. A., Rome, L., & Seeherman, H. J. (1981). One- step N₂- dilution technique for calibrating open- circuit VO₂ measuring sys- tems. Journal of Applied Physiology, 51(3), 772– 776. Gore, C. J., Hahn, A. G., Aughey, R. J., Martin, D. T., Ashenden, M. J., Clark, S. A., Garnham, A. P., Roberts, A. D., Slater, G. J., & Mckenna, M. J. (2001). Live high:train low increases muscle buf- fer capacity and submaximal cycling efficiency. Acta Physiologica Scandinavica, 173(3), 275– 286. Gore, C. J., Rodriguez, F. A., Truijens, M. J., Townsend, N. E., Stray- Gundersen, J., & Levine, B. D. (2006). Increased serum erythropoietin but not red cell production after 4 wk of inter- mittent hypobaric hypoxia (4,000– 5,500 m). Journal of Applied Physiology, 101(5), 1386– 1393. Hamlin, M. J., Marshall, H. C., Hellemans, J., Ainslie, P. N., & Anglem, N. (2010). Effect of intermittent hypoxic training on 20 km time trial and 30 s anaerobic performance. Scandinavian Journal of Medicine and Science in Sports, 20(4), 651– 661. Hendriksen, I. J., & Meeuwsen, T. (2003). The effect of intermittent training in hypobaric hypoxia on sea- level exercise: a cross- over study in humans. European Journal of Applied Physiology, 88(4– 5), 396– 403. Jones, J. H., & Lindstedt, S. L. (1993). Limits to maximal performance. Annual Review of Physiology, 55, 547– 569. Katayama, K., Goto, K., Ishida, K., & Ogita, F. (2010). Substrate utiliza- tion during exercise and recovery at moderate altitude. Metabolism, 59(7), 959– 966.  | 11 of 11 MUKAI et Al. Katayama, K., Matsuo, H., Ishida, K., Mori, S., & Miyamura, M. (2003). Intermittent hypoxia improves endurance performance and submaximal exercise efficiency. High Altitude Medicine & Biology, 4(3), 291– 304. Kitaoka, Y., Endo, Y., Mukai, K., Aida, H., Hiraga, A., & Hatta, H. (2014). Muscle glycogen breakdown and lactate metabolism during intensive exercise in Thoroughbred horses. The Journal of Physical Fitness and Sports Medicine, 3(4), 451– 456. McLean, B. D., Gore, C. J., & Kemp, J. (2014). Application of ‘live low- train high’ for enhancing normoxic exercise performance in team sport athletes. Sports Medicine (Auckland, N. Z.), 44(9), 1275– 1287. Millet, G. P., Roels, B., Schmitt, L., Woorons, X., & Richalet, J. P. (2010). Combining hypoxic methods for peak performance. Sports Medicine (Auckland, N. Z.), 40(1), 1– 25. Mukai, K., Hiraga, A., Takahashi, T., Matsui, A., Ohmura, H., Aida, H., & Jones, James H. (2017). Effects of maintaining different exercise intensities during detraining on aerobic capacity in Thoroughbreds. American Journal of Veterinary Research, 78(2), 215– 222. Mukai, K., Ohmura, H., Matsui, A., Aida, H., Takahashi, T., & Jones, J. (2020). High- intensity training in normobaric hypoxia enhances exercise performance and aerobic capacity in Thoroughbred horses: A randomized crossover study. Physiological Reports, 8(10), e14442. Ohmura, H., Mukai, K., Matsui, A., Takahashi, T., & Jones, J. H. (2020). Cardiopulmonary function during supramaximal exercise in hy- poxia, normoxia and hyperoxia in Thoroughbred horses. Journal of Equine Science, 31(4), 67– 73. Ohmura, H., Mukai, K., Takahashi, T., Matsui, A., Hiraga, A., & Jones, J. H. (2010). Comparison of net anaerobic energy utilisation esti- mated by plasma lactate accumulation rate and accumulated oxy- gen deficit in Thoroughbred horses. Equine Veterinary Journal, 42(Suppl 38), 62– 69. Ohmura, H., Mukai, K., Takahashi, Y., Takahashi, T., & Jones, J. H. (2017). Hypoxic training increases maximal oxygen consump- tion in Thoroughbred horses well- trained in normoxia. Journal of Equine Science, 28(2), 41– 45. Park, H. Y., Shin, C., & Lim, K. (2018). Intermittent hypoxic training for 6 weeks in 3000 m hypobaric hypoxia conditions enhances ex- ercise economy and aerobic exercise performance in moderately trained swimmers. Biology of sport, 35(1), 49– 56. Pascoe, J. R., Hiraga, A., Hobo, S., Birks, E. K., Yarbrough, T. B., Takahashi, T., Hada, T., Aida, H., Steffey, E. P., & Jones, J. H. (1999). Cardiac output measurements using sonomicrometer crys- tals on the left ventricle at rest and exercise. Equine Veterinary Journal. Supplement, 30, 148– 152. Pottgiesser, T., Garvican, L. A., Martin, D. T., Featonby, J. M., Gore, C. J., & Schumacher, Y. O. (2012). Short- term hematological effects upon completion of a four- week simulated altitude camp. International Journal of Sports Physiology and Performance, 7(1), 79– 83. Prommer, N., Thoma, S., Quecke, L., Gutekunst, T., Völzke, C., Wachsmuth, N., Niess, A. M., & Schmidt, W. (2010). Total hemo- globin mass and blood volume of elite Kenyan runners. Medicine and Science in Sports and Exercise, 42(4), 791– 797. Puype, J., Van Proeyen, K., Raymackers, J. M., Deldicque, L., & Hespel, P. (2013). Sprint interval training in hypoxia stimulates glycolytic enzyme activity. Medicine and Science in Sports and Exercise, 45(11), 2166– 2174. Robertson, E. Y., Saunders, P. U., Pyne, D. B., Aughey, R. J., Anson, J. M., & Gore, C. J. (2010). Reproducibility of performance changes to simulated live high/train low altitude. Medicine and Science in Sports and Exercise, 42(2), 394– 401. Roels, B., Millet, G. P., Marcoux, C. J., Coste, O., Bentley, D. J., & Candau, R. B. (2005). Effects of hypoxic interval training on cy- cling performance. Medicine and Science in Sports and Exercise, 37(1), 138– 146. Saunders, P. U., Telford, R. D., Pyne, D. B., Cunningham, R. B., Gore, C. J., Hahn, A. G., & Hawley, J. A. (2004). Improved running economy in elite runners after 20 days of simulated moderate- altitude exposure. Journal of Applied Physiology, 96(3), 931– 937. Sinex, J. A., & Chapman, R. F. (2015). Hypoxic training methods for improving endurance exercise performance. Journal of Sport and Health Science, 4(4), 325– 332. Truijens, M. J., Toussaint, H. M., Dow, J., & Levine, B. D. (2003). Effect of high- intensity hypoxic training on sea- level swimming performances. Journal of Applied Physiology, 94(2), 733– 743. Vogiatzis, I., Georgiadou, O., Koskolou, M., Athanasopoulos, D., Kostikas, K., Golemati, S., Wagner, H., Roussos, C., Wagner, P. D., & Zakynthinos, S. (2007). Effects of hypoxia on diaphragmatic fatigue in highly trained athletes. Journal of Physiology, 581(Pt 1), 299– 308. Vogt, M., & Hoppeler, H. (2010). Is hypoxia training good for muscles and exercise performance? Progress in Cardiovascular Diseases, 52(6), 525– 533. Vogt, M., Puntschart, A., Geiser, J., Zuleger, C., Billeter, R., & Hoppeler, H. (2001). Molecular adaptations in human skeletal muscle to en- durance training under simulated hypoxic conditions. Journal of Applied Physiology, 91(1), 173– 182. Wagner, P. D., Gillespie, J. R., Landgren, G. L., Fedde, M. R., Jones, B. W., DeBowes, R. M., Pieschl, R. l., & Erickson, H. H. (1989). Mechanism of exercise- induced hypoxemia in horses. Journal of Applied Physiology, 66(3), 1227– 1233. How to cite this article: Mukai K, Ohmura H, Takahashi Y, Kitaoka Y, Takahashi T. Four weeks of high- intensity training in moderate, but not mild hypoxia improves performance and running economy more than normoxic training in horses. Physiol Rep. 2021;9:e14760. https://doi.org/10.14814/phy2.14760

Four weeks of high-intensity training in moderate, but not mild hypoxia improves performance and running economy more than normoxic training in horses.

[]

Mukai, Kazutaka,Ohmura, Hajime,Takahashi, Yuji,Kitaoka, Yu,Takahashi, Toshiyuki

eng

PMC6719209

International Journal of Environmental Research and Public Health Article The Effect of Static and Dynamic Stretching Exercises on Sprint Ability of Recreational Male Volleyball Players Foteini Alipasali 1, Sophia D. Papadopoulou 2 , Ioannis Gissis 1, Georgios Komsis 1, Stergios Komsis 1, Angelos Kyranoudis 3, Beat Knechtle 4,* and Pantelis T. Nikolaidis 5 1 Department of Physical Education & Sport Science, Aristotle University of Thessaloniki, 62100 Serres, Greece 2 Laboratory of Evaluation of Human Biological Performance, Department of Physical Education & Sport Science, Aristotle University of Thessaloniki, 57001 Thessaloniki, Greece 3 Department of Physical Education & Sport Science, Democritus University of Thrace, 69100 Komotini, Greece 4 Institute of Primary Care, University of Zurich, 8091 Zurich, Switzerland 5 Exercise Physiology Laboratory, 18450 Nikaia, Greece * Correspondence: [email protected]; Tel.: +41-(0)71-226-9300 Received: 29 June 2019; Accepted: 4 August 2019; Published: 8 August 2019



 Abstract: The aim of the present trial was to investigate the effect of two stretching programs, a dynamic and a static one, on the sprint ability of recreational volleyball players. The sample consisted of 27 male recreational volleyball players (age 21.6 ± 2.1 years, mean ± standard deviation, body mass 80.3 ± 8.9 kg, height 1.82 ± 0.06 m, body mass index 24.3 ± 2.5 kg.m−2, volleyball experience 7.7 ± 2.9 years). Participants were randomly divided into three groups: (a) the first performing dynamic stretching exercises three times per week, (b) the second following a static stretching protocol on the same frequency, and (c) the third being the control group, abstaining from any stretching protocol. The duration of the stretching exercise intervention period was 6 weeks, with all groups performing baseline and final field sprinting tests at 4.5 and 9 m. The post-test sprint times were faster in both the 4.5 (p = 0.027, η2 = 0.188) and 9 m tests (p < 0.001, η2 = 0.605) compared to the pre-test values. A large time × group interaction was shown in both the 4.5 (p = 0.007, η2 = 0.341) and 9 m tests (p = 0.004, η2 = 0.363) with the static and dynamic stretching groups being faster in the post-test than in the pre-test, whereas no change was found in the control group. The percentage change in the 4.5 m sprint time correlated with volleyball experience (r = −0.38, p = 0.050), i.e., the longer the volleyball experience, the larger the improvement in the 4.5 m sprint. Thus, it is concluded that both stretching techniques have a positive effect on the velocity of recreational male volleyball players, when performed at a frequency of three times per week for 6 weeks under the same conditions as defined in the study protocol. Keywords: dynamic stretching; static stretching; velocity; volleyball; warm-up 1. Introduction Today, the main goal of athletic training and sports participation is to ameliorate performance. Performance however, is multifactorial, depending on several parameters, including warm-up practices. The purpose of warming up is to prepare the athlete for the upcoming sports event in a physiological view point, making the transition from the resting state to the state of preparedness needed for sports competition [1,2]. It is common for stretching exercises to be performed between the general and specialized parts of the warm-up session, with dynamic stretching being more preferred lately as opposed to static stretching. Stretching exercises are considered a pivotal effector of joint flexibility [2–4], Int. J. Environ. Res. Public Health 2019, 16, 2835; doi:10.3390/ijerph16162835 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2019, 16, 2835 2 of 10 adding biomechanical precision to an athlete’s movement while offering the opportunity to perform at maximum force throughout the range of motion [5,6]. Although the literature provides ample evidence on the acute effects of static and dynamic stretching exercises on performance [1,2,7,8], the number of studies on the chronic effects of both static [9–11] and dynamic stretching are limited and appear inconclusive [12–15]. Passive stretching is associated with an eccentric elongation of the muscle [16], while on the other hand, energetic stretching induces concentric elongation with parallel increments in the muscle perimeter. It is hypothesized that new sarcomeres are formed in line during passive stretching [17,18], whereas when adhering to a dynamic stretching protocol new muscle fibers are produced, with a parallel sarcomere formation. It should be noted, however, that flexibility improvements associated with muscle elongation have an additional effect on muscle performance [19]. Volleyball is one of the sports where stretching is usually incorporated in the warming up procedure. During a volleyball match, the high vertical jump and the explosive movements performed to cover court space are considered of utmost importance, and are highly intercorrelated [20]. During the match, a volleyball player tends to cover distances ranging between 4.5 and 9 m [21], and due to these small distances as compared to other sports, sprint and acceleration are pivotal acquisitions of a successful volleyball player. Additionally, only few seconds or milliseconds are required when moving towards the ball, and this is why accurate sprint measurements are performed, using photocells with a precision of milliseconds [22]. Sprints are important components of team sports, with the majority of research reporting reductions in speed immediately after the performance of static stretching exercises [23–25]. Nevertheless, research examining the sprinting ability of athletes after a long-term adherence to static stretching protocols has been limited and has provided conflicting findings [9,12,26]. According to the research, no differences were observed in the sprinting ability with agility changes after the implementation of either a 4 week [12] or a 6 week [9] lower-limb static stretching protocol, whereas the 20 m sprint time was significantly improved after performing static stretching exercises for a total of 10 weeks [26]. On the other hand, as far as dynamic stretching is concerned, it is reported to acutely improve the sprint time [23,27]. Research assessing sprinting ability post the implementation of dynamic stretching protocols lasting for a few weeks is limited, providing controversial results [12,14]. For example, when a 4 week lower-limb dynamic stretching program was followed, improvements in the sprinting ability with agility changes have been reported by some [12], whereas others [14] failed to record differences in the sprinting ability after an 8 week protocol. Given the controversial literature findings, the aim of the present trial was to investigate the effect of two stretching programs, a dynamic and a static one, on the sprint ability of recreational volleyball players. 2. Materials and Methods 2.1. Participants A total of 50 male, apparently healthy physical education undergraduate students, all recreational volleyball players, participated in the study. The participants were randomly assigned into three groups (static, n = 17; dynamic, n = 17; control group, n = 16). The term “recreational” denotes that participants were volleyball players of teams competing at the regional level. Two participants were excluded due to injury during the course of the trial and six were excluded for not completing the trial, leaving a total sample of 42 participants. Among them, complete data of demographic characteristics (age, body mass, height, volleyball experience) and sprint ability (4.5 and 9 m sprint times) were available for 27 participants (Table 1), who were included in the present analysis. Participants volunteered for study participation during the volleyball module offered by the Aristotle University of Thessaloniki. Their volleyball experience was defined as the years they had been practicing volleyball as members of sport clubs that involved three to four training units during weekdays and an official match during the weekend. All participants were informed of the exact nature, procedures, and aim of the trial Int. J. Environ. Res. Public Health 2019, 16, 2835 3 of 10 before providing informed consent to participate. Ethical permission was granted from the Aristotle University’s Ethics Committee and all procedures were in accordance with the Declaration of Helsinki for research on human subjects. Table 1. Demographic characteristics of participants in the experimental group. Variable Total (n = 27) Static Group (n = 11) Dynamic Group (n = 7) Control Group (n = 9) Age (years) 21.6 ± 2.1 21.4 ± 2.0 22.4 ± 2.1 21.3 ± 2.3 Weight (kg) 80.3 ± 8.9 76.5 ± 7.9 84.5 ± 10.4 81.7 ± 8.0 Height (m) 1.82 ± 0.06 1.79 ± 0.04 1.85 ± 0.07 1.83 ± 0.05 BMI (kg.m-2) 24.3 ± 2.5 24.0 ± 2.6 24.6 ± 1.9 24.6 ± 3.1 Volleyball experience (years) 7.7 ± 2.9 7.5 ± 3.6 9.1 ± 2.3 6.8 ± 2.0 BMI = body mass index. 2.2. Design and Procedures The study was conducted from the middle of February 2015 until the end of March 2015. Both testing and stretching exercise sessions were performed in the indoor court of the School of Physical Education and Sport Sciences of Aristotle University of Thessaloniki. All stretching exercise sessions of both the static and dynamic groups were supervised by the principal investigator of this study (F.A.) and were administered individually, i.e., one-by-one. During the 6 week period of the study, participants were strictly instructed to maintain their regular physical activity and nutritional habits. Participants were randomized into three groups, each following a different protocol, with every protocol lasting for a total of 6 weeks as, according to the literature, this is the minimum time required to produce effective changes in the joint range of motion (ROM) [13]. The baseline characteristics of participants were presented in Table 1. The first group adhered to a static stretching protocol performed three times per week, the second followed a dynamic stretching protocol performed in the same frequency, and the last one abstained from any stretching exercises for the duration of the trial, forming the control group. During the trial, all participants continued to follow their everyday activities, but additionally incorporated the protocol of the group in which they were placed for the duration of the trial. All three groups participated in baseline and post-protocol 4.5 and 9 m sprint tests. The static stretching protocol included static stretching exercises of the lower limbs (posterior tibial muscles, front and posterior crural muscles, topside and iliopsoas muscles), for a total duration of 4 min. Each stretching exercise lasted for 10 s and was repeated twice (2 × 10 s), with a 10 s break between exercises using both limbs simultaneously and without any break for exercises using one limb at a time. All exercises were performed in the maximum joint ROM, while avoiding muscle pain (Figure 1). The second protocol involved 6 weeks of dynamic stretching exercises, implemented in the same frequency as the first one (three times per week). It involved dynamic stretching exercises performed in the exact same manner as the first protocol (Figure 2). Finally, the third protocol (control protocol) involved abstaining from stretching exercises for the total duration of the trial (6 weeks). The sprint tests were performed inside the volleyball court, in line with the parallel end of the court. Two maximal sprint tests were carried out at 4.5 m, and the one with the best result was kept for each participant (Figure 3). Initially, participants warmed up by performing submaximal intensity sprints towards different directions, including side movements, for a total duration of 5 minutes without any stretching exercises. Then, sprint tests were carried out on the side of the court (Figure 3). Participants were asked to start the sprint in random order, with their body in standing position and their knees slightly bent, with one leg (right or left) approximately 40 cm behind the starting line, entering from the beam gate where the first pair of photocells was placed. Then they ran towards the finishing line where the second pair of photocells was placed. Instructions were provided on running as fast as possible, without slowing down towards the finishing line. Each participant initiated the trial alone, without receiving any signal from the examiners. Int. J. Environ. Res. Public Health 2019, 16, 2835 4 of 10 The static stretching protocol included static stretching exercises of the lower limbs (posterior tibial muscles, front and posterior crural muscles, topside and iliopsoas muscles), for a total duration of 4 min. Each stretching exercise lasted for 10 s and was repeated twice (2 × 10 s), with a 10 s break between exercises using both limbs simultaneously and without any break for exercises using one limb at a time. All exercises were performed in the maximum joint ROM, while avoiding muscle pain (Figure 1). Figure 1. Static stretching protocol exercises of the (a) posterior tibial, (b) front crural, (c) posterior crural, (d) gluteus, (e) iliopsoas, and (f) topside muscles. a b c d e f Figure 1. Static stretching protocol exercises of the (a) posterior tibial, (b) front crural, (c) posterior crural, (d) gluteus, (e) iliopsoas, and (f) topside muscles. Int. J. Environ. Res. Public Health 2019, 16, x 4 of 9 The second protocol involved 6 weeks of dynamic stretching exercises, implemented in the same frequency as the first one (three times per week). It involved dynamic stretching exercises performed in the exact same manner as the first protocol (Figure 2). Finally, the third protocol (control protocol) involved abstaining from stretching exercises for the total duration of the trial (6 weeks). Figure 2. Dynamic stretching protocol exercises of the (a) posterior tibial, (b,c) topside, (d,e) iliopsoas, (f,g) front and (h,i) posterior crural, and (j,k) gluteal muscles. The sprint tests were performed inside the volleyball court, in line with the parallel end of the court. Two maximal sprint tests were carried out at 4.5 m, and the one with the best result was kept for each participant (Figure 3). Initially, participants warmed up by performing submaximal intensity sprints towards different directions, including side movements, for a total duration of 5 minutes without any stretching exercises. Then, sprint tests were carried out on the side of the court (Figure 3). Participants were asked to start the sprint in random order, with their body in standing position and their knees slightly bent, with one leg (right or left) approximately 40 cm behind the starting line, entering from the beam gate where the first pair of photocells was placed. Then they ran towards the finishing line where the second pair of photocells was placed. Instructions were provided on running as fast as possible, without slowing down towards the finishing line. Each participant initiated the trial alone, without receiving any signal from the examiners. a b c d e f g h i j k Figure 2. Dynamic stretching protocol exercises of the (a) posterior tibial, (b,c) topside, (d,e) iliopsoas, (f,g) front and (h,i) posterior crural, and (j,k) gluteal muscles. Int. J. Environ. Res. Public Health 2019, 16, x 4 of 9 The second protocol involved 6 weeks of dynamic stretching exercises, implemented in the same frequency as the first one (three times per week). It involved dynamic stretching exercises performed in the exact same manner as the first protocol (Figure 2). Finally, the third protocol (control protocol) involved abstaining from stretching exercises for the total duration of the trial (6 weeks). Figure 2. Dynamic stretching protocol exercises of the (a) posterior tibial, (b,c) topside, (d,e) iliopsoas, (f,g) front and (h,i) posterior crural, and (j,k) gluteal muscles. The sprint tests were performed inside the volleyball court, in line with the parallel end of the court. Two maximal sprint tests were carried out at 4.5 m, and the one with the best result was kept for each participant (Figure 3). Initially, participants warmed up by performing submaximal intensity sprints towards different directions, including side movements, for a total duration of 5 minutes without any stretching exercises. Then, sprint tests were carried out on the side of the court (Figure 3). Participants were asked to start the sprint in random order, with their body in standing position and their knees slightly bent, with one leg (right or left) approximately 40 cm behind the starting line, entering from the beam gate where the first pair of photocells was placed. Then they ran towards the finishing line where the second pair of photocells was placed. Instructions were provided on running as fast as possible, without slowing down towards the finishing line. Each participant initiated the trial alone, without receiving any signal from the examiners. Figure 3. Sprint tests procedure. a b c d e f g h i j k Figure 3. Sprint tests procedure. Int. J. Environ. Res. Public Health 2019, 16, 2835 5 of 10 The same procedure was followed for the 9 m sprint test, which was also performed on the side of the court. A break lasting for more than 3 min intervened between each sprint [28]. The running speed was measured using the two pairs of photocell shutters and a digital chronometer [28]. The velocity assessment was carried out with a dual-beam photocell system (Autonics Beam Sensor BL5M-MFR) and a digital timer (Saint Wien Digital Timer Type H5K). 2.3. Statistical Analysis Statistical analysis was carried out using SPSS software (IBM, New York, NY, United States of America) and the level of significance was set at α = 0.05. Between- and within-subjects analyses of variance examined the main effects of group (static, dynamic, and control), time (pre- and post-test), and group × time interaction on sprint times of 4.5 and 9 m. A post hoc Bonferroni test examined differences among the static, dynamic, and control groups. The percentage difference (∆%) in sprint time from pre- to post-test was calculated using the formula ‘100 × (sprint time at post-test − sprint time at pre-test)/sprint time at pre-test’. The relationship of ∆% in sprint time with demographic characteristics was examined using Pearson correlation coefficient r, whose magnitude was interpreted as trivial (r < 0.10), small (0.10 ≤ r < 0.30), moderate (0.30 ≤ r < 0.50), large (0.50 ≤ r < 0.70), very large (0.70 ≤ r < 0.90), nearly perfect (r ≥ 0.90), or perfect (r = 1.00) [29]. 3. Results In the 4.5 m sprint time, a large main effect of time was observed (p = 0.027, η2 = 0.188), where overall the post-test was faster than the pre-test sprint time (1.03 ± 0.11 s and 1.08 ± 0.07 s, respectively; mean difference −0.05 s; 95% confidence intervals, CI, −0.09, −0.01) (Figure 4). A large time × group interaction was shown (p = 0.007, η2 = 0.341), with the static and dynamic stretching groups being faster in the post-test than in the pre-test, whereas no change was found in the control group. Overall, the static and dynamic stretching groups were faster than the control group by −0.07 s (95% CI, −0.13, −0.01) and −0.09 s (95% CI, −0.16, −0.02), respectively. Int. J. Environ. Res. Public Health 2019, 16, x 5 of 9 The same procedure was followed for the 9 m sprint test, which was also performed on the side of the court. A break lasting for more than 3 min intervened between each sprint [28]. The running speed was measured using the two pairs of photocell shutters and a digital chronometer [28]. Τhe velocity assessment was carried out with a dual-beam photocell system (Autonics Beam Sensor BL5M-MFR) and a digital timer (Saint Wien Digital Timer Type H5K). 2.3. Statistical Analysis Statistical analysis was carried out using SPSS software (IBM, New York, NY, United States of America) and the level of significance was set at α = 0.05. Between- and within-subjects analyses of variance examined the main effects of group (static, dynamic, and control), time (pre- and post-test), and group × time interaction on sprint times of 4.5 and 9 m. A post hoc Bonferroni test examined differences among the static, dynamic, and control groups. The percentage difference (Δ%) in sprint time from pre- to post-test was calculated using the formula ‘100 × (sprint time at post-test − sprint time at pre-test)/sprint time at pre-test’. The relationship of Δ% in sprint time with demographic characteristics was examined using Pearson correlation coefficient r, whose magnitude was interpreted as trivial (r < 0.10), small (0.10 ≤ r < 0.30), moderate (0.30 ≤ r < 0.50), large (0.50 ≤ r < 0.70), very large (0.70 ≤ r < 0.90), nearly perfect (r ≥ 0.90), or perfect (r = 1.00) [29]. 3. Results In the 4.5 m sprint time, a large main effect of time was observed (p = 0.027, η2 = 0.188), where overall the post-test was faster than the pre-test sprint time (1.03 ± 0.11 s and 1.08 ± 0.07 s, respectively; mean difference −0.05 s; 95% confidence intervals, CI, −0.09, −0.01) (Figure 4). A large time × group interaction was shown (p = 0.007, η2 = 0.341), with the static and dynamic stretching groups being faster in the post-test than in the pre-test, whereas no change was found in the control group. Overall, the static and dynamic stretching groups were faster than the control group by −0.07 s (95% CI, −0.13, −0.01) and −0.09 s (95% CI, −0.16, −0.02), respectively. Figure 4. Individual changes in the 4.5 m sprint time by experimental group and percentage change (Δ%). Figure 4. Individual changes in the 4.5 m sprint time by experimental group and percentage change (∆%). In the 9 m sprint time, a large main effect of time was observed (p < 0.001, η2 = 0.605), where overall the post-test was faster than the pre-test sprint time (1.72 ± 0.12 s and 1.81 ± 0.08 s, respectively; mean Int. J. Environ. Res. Public Health 2019, 16, 2835 6 of 10 difference −0.08 s; 95% CI, −0.11, −0.06) (Figure 5). A large time × group interaction was shown (p = 0.004, η2 = 0.363), with the static and dynamic stretching groups being faster in the post-test than in the pre-test, whereas no change was found in the control group. Overall, the static and dynamic stretching groups were faster than the control group by −0.09 s (95% CI, −0.18, 0) and −0.11 s (95% CI, −0.21, −0.01), respectively. Int. J. Environ. Res. Public Health 2019, 16, x 6 of 9 In the 9 m sprint time, a large main effect of time was observed (p < 0.001, η2 = 0.605), where overall the post-test was faster than the pre-test sprint time (1.72 ± 0.12 s and 1.81 ± 0.08 s, respectively; mean difference −0.08 s; 95% CI, −0.11, −0.06) (Figure 5). A large time × group interaction was shown (p = 0.004, η2 = 0.363), with the static and dynamic stretching groups being faster in the post-test than in the pre-test, whereas no change was found in the control group. Overall, the static and dynamic stretching groups were faster than the control group by −0.09 s (95% CI, −0.18, 0) and −0.11 s (95% CI, −0.21, −0.01), respectively. Figure 5. Individual changes in the 9 m sprint time by experimental group and percentage change (Δ%). With regard to the relationship of changes in the sprint ability from pre- to post-test with demographic characteristics of participants, a moderate negative correlation of percentage change in the 4.5 m sprint with volleyball experience was observed; i.e., the longer the volleyball experience, the larger the improvement in the 4.5 m sprint (Figure 6). The percentage change in the 4.5 m sprint correlated largely with the percentage change in the 9 m sprint. No relationship was observed in the relationship of age, weight, height, or body mass index with percentage changes in sprint ability (p > 0.05). Figure 6. Relationship of percentage change (Δ%) from pre-test to post-test between sprint ability and volleyball experience. 4. Discussion Figure 5. Individual changes in the 9 m sprint time by experimental group and percentage change (∆%). With regard to the relationship of changes in the sprint ability from pre- to post-test with demographic characteristics of participants, a moderate negative correlation of percentage change in the 4.5 m sprint with volleyball experience was observed; i.e., the longer the volleyball experience, the larger the improvement in the 4.5 m sprint (Figure 6). The percentage change in the 4.5 m sprint correlated largely with the percentage change in the 9 m sprint. No relationship was observed in the relationship of age, weight, height, or body mass index with percentage changes in sprint ability (p > 0.05). Int. J. Environ. Res. Public Health 2019, 16, x 6 of 9 In the 9 m sprint time, a large main effect of time was observed (p < 0.001, η2 = 0.605), where overall the post-test was faster than the pre-test sprint time (1.72 ± 0.12 s and 1.81 ± 0.08 s, respectively; mean difference −0.08 s; 95% CI, −0.11, −0.06) (Figure 5). A large time × group interaction was shown (p = 0.004, η2 = 0.363), with the static and dynamic stretching groups being faster in the post-test than in the pre-test, whereas no change was found in the control group. Overall, the static and dynamic stretching groups were faster than the control group by −0.09 s (95% CI, −0.18, 0) and −0.11 s (95% CI, −0.21, −0.01), respectively. Figure 5. Individual changes in the 9 m sprint time by experimental group and percentage change (Δ%). With regard to the relationship of changes in the sprint ability from pre- to post-test with demographic characteristics of participants, a moderate negative correlation of percentage change in the 4.5 m sprint with volleyball experience was observed; i.e., the longer the volleyball experience, the larger the improvement in the 4.5 m sprint (Figure 6). The percentage change in the 4.5 m sprint correlated largely with the percentage change in the 9 m sprint. No relationship was observed in the relationship of age, weight, height, or body mass index with percentage changes in sprint ability (p > 0.05). Figure 6. Relationship of percentage change (Δ%) from pre-test to post-test between sprint ability and volleyball experience. 4. Discussion Figure 6. Relationship of percentage change (∆%) from pre-test to post-test between sprint ability and volleyball experience. 4. Discussion The present study examined the effects of 6 week static and dynamic stretching exercise protocols on the sprint speed of recreational volleyball players. The main finding of the study was that the time Int. J. Environ. Res. Public Health 2019, 16, 2835 7 of 10 to complete the 4.5 and 9 m sprint tests significantly improved after the implementation of dynamic and static stretching protocols. A secondary finding was that both 4.5 and 9 m sprint tests had similar sensitivity to evaluate chronic adaptations to stretching exercise programs. Similar findings have been reported among wrestlers performing dynamic stretching five times per week for a total of 4 weeks [12]. Adherence to long-term dynamic stretching appears to improve sprinting time as a result of dynamic muscle elongation and coordination improvement [30], reducing energy cost [31] while paving the way for re-usage of the elastic strain energy [32]. Time to complete the 4.5 and 9 m sprint tests was also improved in the static stretching protocol team. Similar improvements were reported by Kokkonen et al. [26] on men and women performing static stretching three times per week for a total of 10 weeks. Bazett-Jones et al. [9], on the other hand, failed to record any improvements in sprinting ability 6 weeks after a static stretching warm-up scheme, performed at a frequency of four times per week. Their sample included female athletes of classic sports; however, it is well known that women are less affected by static stretching due to the already high flexibility they attain as a result of the inner gastrocnemius muscle tendon [33]. According to Earp et al. [34], muscle contraction speed and the ability to perform power exercises are both improved in line with muscle fiber elongation. Thus, the improvement in the sprinting tests herein could be attributed to an improvement in muscle fiber length. On the other hand, the control group failed to demonstrate any improvements. This was expected, given that participants of this group did not adhere to any exercise/warm-up protocol affecting muscle elongation during the 6 week trial. In addition, it should be highlighted that both tests (4.5 and 9 m) indicated improvement of sprint ability at 6 weeks of dynamic and static stretching protocols. This similarity between these two sprint tests suggested their physiological affinity. Previous research in soccer showed that sprint tests—e.g., 10 versus 20 m—are related to similar anthropometric and physiological characteristics [35,36]. For instance, both the 10 and 20 m sprints correlated positively with body mass and height, and negatively with squat jump, countermovement jump, and peak power in the Wingate anaerobic test [35]. With regard to the relationship of change in the 4.5 m sprint time from pre- to post-test with volleyball experience, the larger improvements in sprint time observed in the more experienced participants compared to their less experienced counterparts highlighted the relationship between trainability and volleyball experience. A limitation of the present study was that it used a specific set of either dynamic or static stretching exercises; thus, the findings should be generalized with caution to stretching exercise programs consisting of different stretching exercises or exercise characteristics (e.g., exercise intensity, volume, and frequency). Moreover, further research could examine—using larger sample sizes—the relationship of longitudinal changes in sprinting ability and anthropometric characteristics, as well as the role of nutrition, since it has been shown that physical performances in volleyball are related to anthropometric characteristics [37]. On the other hand, the strength of the study was its novelty considering the relatively small number of previous research works on the chronic adaptations of sprint ability to dynamic and static stretching exercise. Since stretching exercises are a major component of exercise programs, knowledge of their impact would be of great practical importance for professionals (e.g., physicians, sport scientists) who prescribe exercise. 5. Conclusions The present study shows that both static and dynamic stretching protocols have a positive effect on sprinting time when implemented for a total of 6 weeks, three times per week. Additionally, the protocols used herein could be of use to trainers for systematic implementation among athletes of different sports, including volleyball, in an effort to improve sprint ability. Int. J. Environ. Res. Public Health 2019, 16, 2835 8 of 10 Author Contributions: Conceptualization, F.A., S.D.P., and I.G.; methodology, F.A., S.D.P., I.G., and S.K.; software, F.A., S.D.P., G.K., and S.K.; validation, F.A., S.D.P., I.G., G.K., and A.K.; formal analysis, F.A., S.D.P., I.G., and G.K.; investigation, F.A., S.D.P., I.G., and S.K.; resources, F.A., S.D.P., G.K., and A.K.; data curation, F.A., S.D.P., I.G., G.K., S.K., and A.K.; writing—original draft preparation, F.A., S.D.P., I.G., and G.K.; writing—review and editing, F.A., S.D.P., I.G., G.K., S.K., P.T.N., B.K., and A.K.; visualization, F.A., I.G., G.K., and S.K.; supervision, P.T.N. and B.K.; project administration, P.T.N. and B.K. Funding: This research received no external funding. Acknowledgments: The voluntary participation of all athletes in the present study is gratefully acknowledged. Conflicts of Interest: The authors declare no conflict of interest. References 1. Zakas, A.; Galazoulas, C.; Grammatikopoulou, M.G.; Vergou, A. Effects of stretching exercise during strength training in prepubertal, pubertal and adolescent boys. J. Bodyw. Mov. Ther. 2002, 6, 170–176. [CrossRef] 2. Zakas, A.; Grammatikopoulou, M.G.; Zakas, N.; Zahariadis, P.; Vamvakoudis, E. The effect of active warm-up and stretching on the flexibility of adolescent soccer players. J. Sports Med. Phys. Fit. 2006, 46, 57–61. 3. Chaouachi, A.; Padulo, J.; Kasmi, S.; Othmen, A.B.; Chatra, M.; Behm, D.G. Unilateral static and dynamic hamstrings stretching increases contralateral hip flexion range of motion. Clin. Physiol. Funct. Imaging 2017, 37, 23–29. [CrossRef] [PubMed] 4. Harvey, L.; Herbert, R.; Crosbie, J. Does stretching induce lasting increases in joint rom? A systematic review. Physiother. Res. Int. J. Res. Clin. Phys. Ther. 2002, 7, 1–13. [CrossRef] 5. Van Gyn, G.H. Contemporary stretching techniques: Theory and application. In The Dancer as Athlete; Shell, C., Ed.; Human Kinetics: Champaign, IL, USA, 1986. 6. Zakas, A.; Balaska, P.; Grammatikopoulou, M.G.; Zakas, N.; Vergou, A. Acute effects of stretching duration on the range of motion of elderly women. J. Bodyw. Mov. Ther. 2005, 9, 270–276. [CrossRef] 7. Kyranoudis, A.; Nikolaidis, V.; Ispirlidis, I.; Galazoulas, C.; Alipasali, F.; Famisis, K. Acute effect of specific warm-up exercises on sprint performance after static and dynamic stretching in amateur soccer players. J. Phys. Educ. Sport 2018, 18, 825–830. 8. Zakas, A.; Vergou, A.; Grammatikopoulou, M.G.; Zakas, N.; Sentelidis, T.; Vamvakoudis, S. The effect of stretching during warming-up on the flexibility of junior handball players. J. Sports Med. Phys. Fit. 2003, 43, 145–149. 9. Bazett-Jones, D.M.; Gibson, M.H.; McBride, J.M. Sprint and vertical jump performances are not affected by sixweeks of static hamstring stretching. J. Strength Cond. Res. 2008, 22, 25–31. [CrossRef] 10. Yuktasir, B.; Kaya, F. Investigation into the long-term effects of static and pnf stretching exercises on range of motion and jump performance. J. Bodyw. Mov. Ther. 2009, 13, 11–21. [CrossRef] 11. Worrell, T.W.; Smith, T.L.; Winegardner, J. Effect of hamstring stretching on hamstring muscle performance. J. Orthop. Sports Phys. Ther. 1994, 20, 154–159. [CrossRef] 12. Herman, S.L.; Smith, D.T. Four-week dynamic stretching warm-up intervention elicits longer-term performance benefits. J. Strength Cond. Res. 2008, 22, 1286–1297. [CrossRef] 13. Laroche, D.P.; Lussier, M.V.; Roy, S.J. Chronic stretching and voluntary muscle force. J. Strength Cond. Res. 2008, 22, 589–596. [CrossRef] 14. Turki-Belkhiria, L.; Chaouachi, A.; Turki, O.; Chtourou, H.; Chtara, M.; Chamari, K.; Amri, M.; Behm, D.G. Eight weeks of dynamic stretching during warm-ups improves jump power but not repeated or single sprint performance. Eur. J. Sport Sci. 2014, 14, 19–27. [CrossRef] 15. Woolstenhulme, M.T.; Griffiths, C.M.; Woolstenhulme, E.M.; Parcell, A.C. Ballistic stretching increases flexibility and acute vertical jump height when combined with basketball activity. J. Strength Cond. Res. 2006, 20, 799–803. 16. Taber, L.A. Biomechanics of growth, remodeling, and morphogenesis. Appl. Mech. Rev. 1995, 48, 487–545. [CrossRef] 17. Göktepe, S.; Abilez, O.J.; Parker, K.K.; Kuhl, E. A multiscale model for eccentric and concentric cardiac growth through sarcomerogenesis. J. Theor. Biol. 2010, 265, 433–442. [CrossRef] Int. J. Environ. Res. Public Health 2019, 16, 2835 9 of 10 18. Kerckhoffs, R.C.P.; Omens, J.H.; McCulloch, A.D. A single strain-based growth law predicts concentric and eccentric cardiac growth during pressure and volume overload. Mech. Res. Commun. 2012, 42, 40–50. [CrossRef] 19. Medeiros, D.M.; Lima, C.S. Influence of chronic stretching on muscle performance: Systematic review. Hum. Mov. Sci. 2017, 54, 220–229. [CrossRef] 20. Barnes, J.L.; Schilling, B.K.; Falvo, M.J.; Weiss, L.W.; Creasy, A.K.; Fry, A.C. Relationship of jumping and agility performance in female volleyball athletes. J. Strength Cond. Res. 2007, 21, 1192–1196. 21. Gabbett, T.; Georgieff, B. Physiological and anthropometric characteristics of australian junior national, state, and novice volleyball players. J. Strength Cond. Res. 2007, 21, 902–908. 22. Johnson, T.M.; Brown, L.E.; Coburn, J.W.; Judelson, D.A.; Khamoui, A.V.; Tran, T.T.; Uribe, B.P. Effect of four different starting stances on sprint time in collegiate volleyball players. J. Strength Cond. Res. 2010, 24, 2641–2646. [CrossRef] 23. Fletcher, I.M.; Jones, B. The effect of different warm-up stretch protocols on 20 meter sprint performance in trained rugby union players. J. Strength Cond. Res. 2004, 18, 885–888. 24. Sayers, A.L.; Farley, R.S.; Fuller, D.K.; Jubenville, C.B.; Caputo, J.L. The effect of static stretching on phases of sprint performance in elite soccer players. J. Strength Cond. Res. 2008, 22, 1416–1421. [CrossRef] 25. Paradisis, G.P.; Pappas, P.T.; Theodorou, A.S.; Zacharogiannis, E.G.; Skordilis, E.K.; Smirniotou, A.S. Effects of static and dynamic stretching on sprint and jump performance in boys and girls. J. Strength Cond. Res. 2014, 28, 154–160. [CrossRef] 26. Kokkonen, J.; Nelson, A.G.; Eldredge, C.; Winchester, J.B. Chronic static stretching improves exercise performance. Med. Sci. Sports Exerc. 2007, 39, 1825–1831. 27. Turki, O.; Chaouachi, A.; Behm, D.G.; Chtara, H.; Chtara, M.; Bishop, D.; Chamari, K.; Amri, M. The effect of warm-ups incorporating different volumes of dynamic stretching on 10- and 20-m sprint performance in highly trained male athletes. J. Strength Cond. Res. 2012, 26, 63–72. [CrossRef] 28. Gissis, I.; Papadopoulos, C.; Kalapotharakos, V.; Sotiropoulos, A.; Komsis, G.; Manolopoulos, E. Strength and speed characteristics of elite, subelite, and recreational young soccer players. Res. Sports Med. 2006, 14, 205–214. [CrossRef] 29. Batterham, A.M.; Hopkins, W.G. Making meaningful inferences about magnitudes. Int. J. Sports Physiol. Perform. 2006, 1, 50–57. [CrossRef] 30. Behm, D.G.; Blazevich, A.J.; Kay, A.D.; McHugh, M. Acute effects of muscle stretching on physical performance, range of motion, and injury incidence in healthy active individuals: A systematic review. Appl. Physiol. Nutr. Metab. 2015, 41, 1–11. [CrossRef] 31. Wilson, J.M.; Hornbuckle, L.M.; Kim, J.S.; Ugrinowitsch, C.; Lee, S.R.; Zourdos, M.C.; Sommer, B.; Panton, L.B. Effects of static stretching on energy cost and running endurance performance. J. Strength Cond. Res. 2010, 24, 2274–2279. [CrossRef] 32. Wilson, G.; Elliott, B.C.; Wood, G.A. Stretch shorten cycle performance enhancement through flexibility training. Med. Sci. Sports Exerc. 1992, 24, 116–123. [CrossRef] 33. Dalrymple, K.J.; Davis, S.E.; Dwyer, G.B.; Moir, G.L. Effect of static and dynamic stretching on vertical jump performance in collegiate women volleyball players. J. Strength Cond. Res. 2010, 24, 149–155. [CrossRef] 34. Earp, J.E.; Kraemer, W.J.; Newton, R.U.; Comstock, B.A.; Fragala, M.S.; Dunn-Lewis, C.; Solomon-Hill, G.; Penwell, Z.R.; Powell, M.D.; Volek, J.S.; et al. Lower-body muscle structure and its role in jump performance during squat, countermovement, and depth drop jumps. J. Strength Cond. Res. 2010, 24, 722–729. [CrossRef] 35. Nikolaidis, P.T.; Ruano, M.A.G.; de Oliveira, N.C.; Portes, L.A.; Freiwald, J.; Leprêtre, P.M.; Knechtle, B. Who runs the fastest? Anthropometric and physiological correlates of 20 m sprint performance in male soccer players. Res. Sports Med. 2016, 24, 341–351. [CrossRef] Int. J. Environ. Res. Public Health 2019, 16, 2835 10 of 10 36. Nikolaidis, P.T.; Dellal, A.; Torres-Luque, G.; Ingebrigtsen, J. Determinants of acceleration and maximum speed phase of repeated sprint ability in soccer players: A cross-sectional study. Sci. Sports 2015, 30, e7–e16. [CrossRef] 37. Mielgo-Ayuso, J.; Calleja-Gonzalez, J.; Clemente-Suarez, V.J.; Zourdos, M.C. Influence of anthropometric profile on physical performance in elite female volleyballers in relation to playing position. Nutr. Hosp. 2014, 31, 849–857. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

The Effect of Static and Dynamic Stretching Exercises on Sprint Ability of Recreational Male Volleyball Players.

08-08-2019

Alipasali, Foteini,Papadopoulou, Sophia D,Gissis, Ioannis,Komsis, Georgios,Komsis, Stergios,Kyranoudis, Angelos,Knechtle, Beat,Nikolaidis, Pantelis T

eng

PMC9819466

Citation: Zacharko, M.; Cichowicz, R.; Depta, A.; Chmura, P.; Konefał, M. High Levels of PM10 Reduce the Physical Activity of Professional Soccer Players. Int. J. Environ. Res. Public Health 2023, 20, 692. https://doi.org/10.3390/ ijerph20010692 Academic Editor: Paul B. Tchounwou Received: 22 November 2022 Revised: 20 December 2022 Accepted: 27 December 2022 Published: 30 December 2022 Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). International Journal of Environmental Research and Public Health Article High Levels of PM10 Reduce the Physical Activity of Professional Soccer Players Michał Zacharko 1,* , Robert Cichowicz 2 , Adam Depta 3,4, Paweł Chmura 5 and Marek Konefał 1 1 Department of Human Motor Skills, Wroclaw University of Health and Sport Sciences, I.J. Paderewskiego 35, 51-612 Wrocław, Poland 2 Institute of Environmental Engineering and Building Installations, Faculty of Civil Engineering, Architekture and Environmental Engineering, Lodz University of Technology, Al. Politechniki 6, 90-924 Lodz, Poland 3 Department of Forecasts and Quantitative Analyses, Faculty of Organization and Management, Institute of Management, Lodz University of Technology, Wolczanska Street 221, 93-005 Lodz, Poland 4 Department of Medical Insurance and Health Care Financing, Medical University of Lodz, Lindleya 6, 90-131 Lodz, Poland 5 Department of Team Games, Wroclaw University of Health and Sport Sciences, I.J. Paderewskiego 35, 51-612 Wrocław, Poland * Correspondence: [email protected] Abstract: The aim of this study is to determine the impact of air quality, analyzed on the basis of the PM10 parameter in three regions of Poland, on the physical activity of soccer players from the Polish Ekstraklasa. The study material consisted of 4294 individual match observations of 362 players during the 2019/2020 domestic season. The measured indices included the parameter of air quality— PM10—and players’ physical activities: total distance (TD) and high-speed running (HSR). Poland was divided into three regions (North, Central, South). The statistical analysis of particulate matter (PM) and athletes’ physical activities, compared by region, revealed the effects in relation to the PM10 (H = 215.6566(2); p = 0.0001) and TD (H = 28.2682(2); p = 0.0001). Players performed better in regards to physical parameters in the North Region, where air pollution is significantly lower than in other regions. This means that even a short stay in more polluted regions can reduce the performance of professional footballers, which can indirectly affect the outcome of the match. Therefore, greater actions should be taken to improve air quality, especially through changes in daily physical activity, as this will reduce the carbon footprint. Keywords: football; total distances covered; high speed running; intensity; air quality; particulate matter; regions 1. Introduction Air pollution is a factor that is currently attracting greater attention because of its threat to human health (Schraufnagel et al., 2019) [1]. According to the Lancet Commission on Pollution and Health, pollution is currently the principal environmental cause of disease and premature death in the world. Pollution-induced diseases were responsible for around 9 million premature deaths in 2015 (Landrigan et al., 2018) [2] and 790,000 additional deaths in Europe alone (Lelieveld et al., 2019) [3]. Moreover, air pollution is the most important risk factor among all environmental pollutants (Cohen et al., 2017) [4]. One of the most harmful parameters is particulate matter (PM), which is produced by burning wood and fossil fuels, especially due to construction work and traffic (Cichowicz and Stel˛egowski, 2019) [5]. Its concentration depends on several factors, including the season of the year, time of day, and location (Nieckarz and Zoladz, 2020) [6]. It is worth noting that in large urban agglomerations in many cities around the world, increasingly higher concentrations of PM are observed (Gupta et al., 2006; Khilnani and Tiwari, 2018; Tian and Sun, 2017) [7–9]. In Poland, the concentration of PM and other parameters of air Int. J. Environ. Res. Public Health 2023, 20, 692. https://doi.org/10.3390/ijerph20010692 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2023, 20, 692 2 of 9 pollutants varies depending on the region (Lubi´nski et al., 2005) [10]. Breathing air with a high concentration of pollutants has a negative effect on health (Orru et al., 2017) [11]. This effect has been extensively studied, and subsequent studies have consistently documented the negative effects of pollution on people’s physical and mental health (Landrigan et al., 2018; Welsch, 2007) [2,12]. Particulate matter is especially dangerous because it recruits immune cells, increases oxidative stress in both the vascular system and the brain, and makes the vascular system hypersensitive to vasoconstrictors, contributing to vascular (endothelial) dysfunction (Münzel et al., 2018) [13]. It is also worth emphasising that air pollution has a negative impact on several components of an individual’s mental health, including subjective well-being (Li et al., 2018) [14], life satisfaction (Welsch, 2006) [15], happiness (Welsch, 2007) [12], and even depressive symptoms (Zhang et al., 2017) [16]. These harmful effects of air pollution also apply to sports and physical activity, which is why attention should be paid to examining the impact that pollution can have on an individual’s health and level of physical activity (Roberts et al., 2014) [17]. Previous research has shown that there are many other factors affect an individual’s physical (Marmot, 2005) [18] and mental health (Dolan et al., 2008) [19]. An example of such a factor is physical activity, which has a positive impact on both physical (Downward et al., 2016; Humphreys et al., 2014) [20,21] and mental health (Downward and Dawson, 2016; Wicker and Frick, 2017) [22,23]. For this reason, a topic that is currently receiving special at- tention is the impact of air pollution on the health of people who engage in physical activity (An et al., 2018; Giles and Koehle, 2014) [24,25] and practice outdoor sports, including ath- letes (Kuskowska et al., 2019; Reche et al., 2020) [26,27]. However, it should be emphasized that all forms of physical activity increase the amount of air ventilated through the lungs (minute ventilation—VE), which is several times greater during moderate-intensity exercise than at rest (Bowen et al., 2019; Zoladz et al., 2019) [28,29]. For example, minute ventilation (VE), which is about 6–8 L of air for a person at rest, can reach 30–50 L per minute during moderate exertion and may even exceed 100 L per minute during very intense exercise (Wasserman et al., 2011) [30]. Some athletes are able to exceed the VE value by up to 200 L per minute, which is about 30 times more than at rest levels (Allen, 2004) [31]. During increased intake of air, the amount of suspended solid particles inhaled is greater. This, in turn, results in the deposition of more these substances in the respiratory tract and other body organs (Nieckarz and Zoladz, 2020) [6]. Therefore, the study of soccer players is indicated, because they are particularly exposed to the negative health effects of air pollution. The number and frequency of professional soccer matches is large. Match schedules are very exhausting and teams need to be ready to play up to 60 matches per season (Carling et al., 2018) [32]. During a 90 min game, elite-level players run approximately 10 km and perform numerous explosive bursts of activities, such as kicking, jumping, tackling, sprinting, changing direction, turning, and sustaining forceful contractions to maintain balance and control of the ball against defensive pressure (Stølen et al., 2005) [33]. Therefore, every match requires players to be in top physical condition. The parameters of physical activity most frequently studied and described in the literature are total distance covered (TD) and high-speed running (HSR) (Aquino et al., 2021; Konefał et al., 2021) [34,35]. For example, Andrzejewski et al., (2018) [36] proved that total distance covered is significantly greater for winning teams. In other studies, both Chmura et al., (2018) [37] and Modric et al., (2019) [38] indicated that high-intensity efforts (sprinting and fast running) should be included among the most important measures of physical activity in soccer. The aim of this study is to determine the impact of air quality, analyzed on the basis of the PM10 parameter in three regions of Poland, on the physical activity of soccer players from the Polish Ekstraklasa. 2. Materials and Methods 2.1. Match Sample and Data Collection Match performance data were collected from 362 soccer players competing in the Polish Ekstraklasa during the 2019/2020 season. The league featured 16 teams, who faced Int. J. Environ. Res. Public Health 2023, 20, 692 3 of 9 each opponent twice during each season, at home and away. Additionally, after playing 30 matches, two groups were formed: the championship and the relegation group, and in each of these, 7 additional matches were played. Thus, a season included 37 match days and 296 matches. A total of 4294 individual match observations of outfield players (excluding goalkeepers, due to the specificity of the position) were made (Konefał et al., 2019) [39]. Only data on players who completed entire matches (i.e., remained on the pitch for the entire 90 min) were taken into account. The physical activity data were collected using the previously-validated (Linke et al., 2020) [40] TRACAB system (ChyronHego, NY, USA). This system consists of two multi- camera units, each consisting of three HD-SDI cameras with a resolution of 1920 × 1080 pixels. The sampling frequency of this system was 25 Hz. Two variables were analyzed: total dis- tance (TD), distance covered in high-speed running (HSR; 19.8–25.1 km·h−1). The TRACAB tracking system has been verified by passing the official FIFA (Fédération Internationale de Football Association) test protocol for electronic performance and tracking systems (EPTS). This study maintains the anonymity of the players (following data protection laws), is conducted in compliance with the Declaration of Helsinki, and was approved by the Senate Committee on Ethics of Scientific Research at the Academy of Physical Education in Wroclaw (No. 12/2021). 2.2. Procedures Data on air quality were collected on the basis of information from automatic air monitoring stations, which were made available by the Voivodship Inspectorate for Envi- ronmental Protection (WIO´S) and by the Main Inspectorate for Environmental Protection (GIO´S) in Poland, whereas the meteorological data used in this analysis come from the In- stitute of Meteorology and Water Management-National Research Institute. The parameter PM10 was analyzed because its concentration is one of the basic parameters examined in the assessment of air quality (Anderson et al., 2012; Zaric et al., 2021) [41,42]. However, according to the European Union (Directive 2008/50/EC), the permissible annual average concentration of PM10 is 40 µg·m−3, and the daily average concentration is 50 µg·m−3. However, according to WHO (WHO, 2021) [43], the permissible annual average concentra- tion of PM10 is 15 µg·m−3, and the daily average is 45 µg·m−3. Data were collected from air quality measurement stations located closest to the stadiums where matches were played, and all measurements were read with an accuracy of 0.01 µg·m−3. In each analyzed match, three measurements of air pollution were made (at the beginning, during the break, and at the end of the match). The arithmetic mean and standard deviation were then calculated from these air pollution values. Data on the analyzed pollutants came from a total of 15 monitoring stations, which were divided into three regions of Poland (North Region, Central Region, South Region). As a result, regions with different levels of air pollution were obtained (Lubi´nski et al., 2005) [10]. Regions have been designated based on latitudes (Cox and Popken, 2020) [44], with each region extending 2◦ north latitude (N). Regarding the North Region (latitude 53◦ N–55◦ N), the players play matches in the cities of Bialystok, Gdansk, Gdynia, and Szczecin (1038 observations). In the Central Region (latitude 51◦ N–53◦ N), teams play matches in the cities of Lodz, Lubin, Plock, Poznan, Wroclaw, and Warsaw (1624 observations). In the South Region (latitude 49◦ N–51◦ N), the teams perform in the cities of Cracow, Czestochowa, Gliwice, Kielce, and Zabrze (1632 observations). Thus, the locations were obtained and marked with a combination of symbols relating to the region and the city (Table 1 and Figure 1). Int. J. Environ. Res. Public Health 2023, 20, 692 4 of 9 Table 1. Measuring station symbols. Symbol Region City Name Population NBia North Bialystok 293,413 NGda North Gdansk 486,271 NGdy North Gdynia 244,676 NSzc North Szczecin 394,482 CLod Central Lodz 664,860 CLub Central Lubin 69,267 CPlo Central Plock 113,660 CPoz Central Poznan 545,073 CWro Central Wroclaw 674,312 CWar Central Warsaw 1,863,056 SCra South Cracow 802,583 SCze South Czestochowa 210,773 SGli South Gliwice 172,628 SKie South Kielce 184,520 SZab South Zabrze 156,935 Population status as of 31 December 2021. Source: Central Statistical Office. Int. J. Environ. Res. Public Health 2023, 20, x FOR PEER REVIEW 4 of 10 Figure 1. Location of selected measuring stations in selected cities of Poland. Table 1. Measuring station symbols. Symbol Region City Name Population NBia North Bialystok 293,413 NGda North Gdansk 486,271 NGdy North Gdynia 244,676 NSzc North Szczecin 394,482 CLod Central Lodz 664,860 CLub Central Lubin 69,267 CPlo Central Plock 113,660 CPoz Central Poznan 545,073 CWro Central Wroclaw 674,312 CWar Central Warsaw 1,863,056 SCra South Cracow 802,583 SCze South Czestochowa 210,773 SGli South Gliwice 172 628 Figure 1. Location of selected measuring stations in selected cities of Poland. 2.3. Statistical Analyses In the research, several methods of statistical inference were used, including the Shapiro–Wilk normality test, tests of homogeneity of variance, i.e., Bartlett’s, Cochran’s, and Hartley’s tests (used to verify the assumptions: about the normality of the explained variable distribution and the homogeneity of its variance in the studied populations), and the Kruskal–Wallis test, in the case of the data failing to meet the above assumptions. In order to apply the analysis of variance for the variables—HSR, TD, PM10—initially, the assumption regarding the normality of the distribution of the above-mentioned vari- ables was checked using the Shapiro–Wilk test. In order to verify the null hypothesis regarding the distribution normality of the results of the analyzed variables, the null hy- pothesis that the examined feature of the population has a normal distribution was checked Int. J. Environ. Res. Public Health 2023, 20, 692 5 of 9 against the alternative hypothesis that the feature of the population does not have a normal distribution. At the significance level of α = 0.05, the verified null hypothesis was rejected, so it could not be concluded that the distribution of the variables was normal. In the next stage, the assumption regarding the homogeneity of the variance of variables in the regions was checked. At the significance level of α = 0.05, the verified null hypothesis that the variances in individual regions are the same for the analyzed variables was rejected. Due to the failure of the above assumptions regarding the classical analysis of variance, the non-parametric Kruskal–Wallis test was used. All statistical analyses were performed using the Statistica ver. 13.3 software package (Dell Inc., Tulsa, OK, USA). 3. Results Based on the results presented in Table 2, it can be concluded that the variables TD and PM10 depend on the regions (p < 0.05). Table 2. Value of air pollution and physical activity parameters by region (mean ± SD). Parameter Region SSD p < 0.05 North (N) Central (C) South (S) PM10 [µg·m−3] 18.16 ± 11.70 22.20 ± 12.62 27.33 ± 20.32 N-C; N-S; C-S Total Distance [km] 10.78 ± 0.83 10.61 ± 0.87 10.59 ± 0.90 N-C; N-S High Speed Running [m] 669.84 ± 204.55 656.10 ± 214.83 661.43 ± 214.26 - SSD—statistically significant differences. The statistical analysis of PM and the physical activity of players, compared by regions (North, Central, South), revealed the effects in relation to the PM10 (H = 215.6566(2); p = 0.0001), TD (H = 28.2682(2); p = 0.0001). No significant effect was found for HSR (H = 3.411(2); p = 0.1817); Table 2. 4. Discussion The aim of the study is to determine the impact of air quality, analyzed on the basis of the PM10 parameter in three regions of Poland, on the physical activity of soccer players from the Polish Ekstraklasa. On the basis of the literature reviewed, the authors noted that only two studies have been published on the impact of air quality on the activity of professional soccer players (Lichter et al., 2017; Zacharko et al., 2021) [45,46]. This study is a continuation of an important observation described in the publication entitled, “Air Pollutants Reduce the Physical Activity of Professional Soccer Players” (Zacharko et al., 2021) [46]. Continuing the research in this area is very important, as it concerns the physical activity of professional athletes, but it can also support the health-promoting nature of the daily physical activity of the whole society. The quantitative and qualitative analysis of soccer performance is currently very popular, as it can maximize the chances for team success (Maneiro et al., 2020) [47]. Lichter et al. (2017) [45] proved that air pollution negatively affects footballers in German stadiums. However, the performance indicator was limited to only the number of passes attempted during the match. As soccer is considered a high-intensity sport, the total distance covered and the running speed are more valuable parameters for assessing the performance of the athletes in order to evaluate the impact of air quality (Barnes et al., 2014) [48]. Therefore, in another study, based on the example of the German Bundesliga, the topic of physical activity was discussed, and it was proved that the reduction in the level of air quality during the match had a negative impact not only on technical activities, such as passing, but also on total distance covered (TD) and high-speed running (HSR) (Zacharko et al., 2021) [46]. In order to investigate the problem in more detail, research was carried out in Poland, i.e., a country with more polluted air and characterized by large regional differences in ambient air pollution (Kocot and Zejda, 2022) [49]. Int. J. Environ. Res. Public Health 2023, 20, 692 6 of 9 Analyzing the average level of particulate matter in three regions of Poland, our study found that the more northern the region, the lower the level of PM10 pollution. The difference may be due to the fact that the South Region is the most industrial region in Poland, which includes the Silesian Agglomeration and Cracow, which in turn are of the cities with the worst air quality in Europe (Traczyk and Gruszecka-Kosowska, 2020) [50]. On the other hand, in the Central Region, there are large cities such as Warsaw, a city more polluted than, for example, Bialystok or Gdansk, which are included in the North Region (Slama et al., 2020) [51]. This is also due to the terrain and its roughness, as well as meteorological conditions. Additionally, attention should be paid to the fact that in Poland, the dominant wind directions are western and south-western, which may result in both the transboundary and local displacement of pollutants, and the consequence of this may be increased levels of pollution in a given area. Under the best air quality conditions (the lowest levels of PM10), i.e., in the North Region of Poland, the players exhibited a significantly longer average TD compared to those noted in the Central Region and South Region. In addition, when analyzing the HSR, it was noticed that in the North Region, players also achieved the best results, although this is not supported by statistical significance. Thus, it can be seen that by playing matches in a less polluted environment, soccer players can achieve better results regarding the physical parameters. This may be caused mainly by geographic and meteorological conditions, as a consequence of lower population density, higher average wind speed, and more green areas. In addition, it is also worth analyzing the human body’s response to breathing polluted air and its impact on the exercise capacity of the players. All forms of physical activity increase the amount of air ventilated through the lungs (minute ventilation—VE), which is several times higher than during rest, even during moderate-intensity exercise (Allen, 2004; Bowen et al., 2019; Zoladz et al., 2019) [28,29,31]. Additionally, this causes a greater intake of particulate matter into the lungs and increases the amount of particulate matter deposited in the respiratory tract. That is why air quality is especially important during a soccer match because the effect of increased physical exertion causes the absorption of harmful substances from the air into the body (Duda et al., 2020) [52]. Moreover, Kargarfard et al. (2015) [53] showed that hematological parameters and cardiovascular functions during exercise are disturbed by high concentrations of air pollution. In athletes, the consequence is worsening lung function, which in turn results in reduced peak expiratory flow and increased airway inflammation (Qin et al., 2019) [54]. Moreover, elevated blood pressure caused by air pollution can even impaired exercise capacity and decrease athletic performance (An et al., 2018; Tainio et al., 2021) [24,55]. Considering the negative impact of air pollution on the human body, it is worth determining specific actions that should be taken so that athletes and fans are less exposed to the harmful health effects related to poor air quality. Several very interesting concepts were presented by Nieuwenhuijsen (2021) [56], the main suggestion of which was to increase active transport (walking and cycling). By walking or cycling, the carbon footprint produced by daily trips is reduced by up to 84%, compared to that created by car users (Brand et al., 2021) [57]. At the same time, active transport will lead to an increase in physical activity and, as a result, the promotion and improvement of health. This method of movement may be supported by the concept of the so-called 15 min city, in which schools, work, sports, shopping, and entertainment are all within a 15 min walking or cycling distance from home (Moreno et al., 2021) [58]. Another solution could be a car-free city that relies heavily on public, pedestrian, or bicycle transport (Nieuwenhuijsen and Khreis, 2016) [59]. By applying the above concepts, the society (fans) can contribute to the improvement of air quality in cities, and at the same time, affect the performance of players during matches. The authors are fully aware of numerous factors that could have influenced the results of the presented analyses. The measuring stations were located close to the stadiums; how- ever, to obtain more accurate measurements, the meters should be placed in the stadiums themselves. In addition, as the meteorological conditions and the type of development Int. J. Environ. Res. Public Health 2023, 20, 692 7 of 9 between the stadium and the measuring station were not taken into account, the air quality data may have been inaccurate. Another limitation is the failure to take into account other parameters that make it possible to characterize the external load, such as acceleration, deceleration, and player load, which also are used to express the demands of matches in non-cyclical team sports. Additionally, the HSR parameter was not individualised based on the percentage of maximum sprint speed, and the match results were not taken into account. In addition, the time spent by the players before the match in a given zone, as well as the diversity of the schedule of the games, were not taken into account. The above limitations are worth considering in future research. Moreover, in subsequent studies, the dynamics of regeneration processes in various air quality should be considered. 5. Conclusions Air pollution is an important situational factor during soccer matches. Even a short- term stay in a more polluted region can reduce the performance of professional soccer players, which can indirectly affect the match outcome. Moreover, it seems that every fan can take action in everyday life to improve air quality. Supporting one’s favorite players and soccer teams should not be limited only to activity in the stadium, but should also extend to daily physical activity, which will reduce the carbon footprint. As a result, this change in daily activity will improve air quality, which will translate into significant health benefits for both athletes and fans. Author Contributions: Conceptualization, M.Z., R.C. and M.K.; Methodology, M.Z., R.C. and M.K.; Software, M.Z. and M.K.; Validation, M.Z., R.C. and M.K.; Formal analysis, M.Z., R.C., A.D., P.C. and M.K.; Investigation, M.Z., R.C., A.D., P.C. and M.K.; Resources, M.Z., R.C., A.D. and M.K.; Data curation, M.Z., R.C., A.D. and M.K.; Writing—original draft, M.Z. and M.K.; Writing—review & editing, M.Z., R.C., A.D., P.C. and M.K.; Visualization, M.Z., R.C., A.D., P.C. and M.K.; Supervision, M.Z., R.C., A.D., P.C. and M.K.; Project administration, M.Z., A.D., P.C. and M.K.; Funding acquisition, M.Z. and M.K. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: This study maintains the anonymity of the players (following data protection laws), is conducted in compliance with the Declaration of Helsinki, and was approved by the Senate Committee on Ethics of Scientific Research at the Academy of Physical Education in Wroclaw (No. 12/2021). Informed Consent Statement: Not applicable. Data Availability Statement: The data used for this study was acquired from a third-party, https: //tracabportal.azurewebsites.net/login (access on 1 April 2021). The data was provided under scientific cooperation with a football clubs currently appearing in Ekstraklasa. The authors’ ethical approval also prevents them from sharing any data in any way that could be re-identified. The metadata would allow someone else to re-identify teams and possibly players. However, access to the data should be possible from the third-party. The data acquired were so called ‘excel dumps’ of player statistics per match. Access to the data can be organized by contacting Match Analysis Hub: [email protected]. Conflicts of Interest: The authors declare no conflict of interest. References 1. Schraufnagel, D.E.; Balmes, J.R.; De Matteis, S.; Hoffman, B.; Kim, W.J.; Perez-Padilla, R.; Rice, M.; Sood, A.; Vanker, A.; Wuebbles, D.J. Health Benefits of Air Pollution Reduction. Ann. Am. Thorac. Soc. 2019, 16, 1478–1487. [CrossRef] [PubMed] 2. Landrigan, P.J.; Fuller, R.; Acosta, N.J.R.; Adeyi, O.; Arnold, R.; Basu, N.; Baldé, A.B.; Bertollini, R.; Bose-O’Reilly, S.; Boufford, J.I.; et al. The Lancet Commission on pollution and health. Lancet 2018, 391, 462–512. [CrossRef] [PubMed] 3. Lelieveld, J.; Klingmüller, K.; Pozzer, A.; Pöschl, U.; Fnais, M.; Daiber, A.; Münzel, T. Cardiovascular disease burden from ambient air pollution in Europe reassessed using novel hazard ratio functions. Eur. Heart J. 2019, 40, 1590–1596. [CrossRef] [PubMed] 4. Cohen, A.J.; Brauer, M.; Burnett, R.; Anderson, H.R.; Frostad, J.; Estep, K.; Balakrishnan, K.; Brunekreef, B.; Dandona, L.; Dandona, R.; et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: An analysis of data from the Global Burden of Diseases Study 2015. Lancet 2017, 389, 1907–1918. [CrossRef] [PubMed] Int. J. Environ. Res. Public Health 2023, 20, 692 8 of 9 5. Cichowicz, R.; Stel˛egowski, A. Average Hourly Concentrations of Air Contaminants in Selected Urban, Town, and Rural Sites. Arch. Environ. Contam. Toxicol. 2019, 77, 197–213. [CrossRef] [PubMed] 6. Nieckarz, Z.; Zoladz, J.A. Low-cost air pollution monitoring system—An opportunity for reducing the health risk associated with physical activity in polluted air. PeerJ 2020, 8, e10041. [CrossRef] 7. Gupta, P.; Christopher, S.A.; Wang, J.; Gehrig, R.; Lee, Y.; Kumar, N. Satellite remote sensing of particulate matter and air quality assessment over global cities. Atmos. Environ. 2006, 40, 5880–5892. [CrossRef] 8. Khilnani, G.C.; Tiwari, P. Air pollution in India and related adverse respiratory health effects: Past, present, and future directions. Curr. Opin. Pulm. Med. 2018, 24, 108–116. [CrossRef] 9. Tian, L.; Sun, S. Comparison of Health Impact of Air Pollution between China and other Countries. Adv. Exp. Med. Biol. 2017, 1017, 215–232. [CrossRef] 10. Lubi´nski, W.; Toczyska, I.; Chciałowski, A.; Płusa, T. Influence of air pollution on pulmonary function in healthy young men from different regions of Poland. Ann. Agric. Environ. Med. 2005, 12, 1–4. 11. Orru, H.; Ebi, K.L.; Forsberg, B. The Interplay of Climate Change and Air Pollution on Health. Curr. Environ. Health Rep. 2017, 4, 504–513. [CrossRef] [PubMed] 12. Welsch, H. Environmental welfare analysis: A life satisfaction approach. Ecol. Econ. 2007, 62, 544–551. [CrossRef] 13. Münzel, T.; Gori, T.; Al-Kindi, S.; Deanfield, J.; Lelieveld, J.; Daiber, A.; Rajagopalan, S. Effects of gaseous and solid constituents of air pollution on endothelial function. Eur. Heart J. 2018, 39, 3543–3550. [CrossRef] [PubMed] 14. Li, Y.; Guan, D.; Tao, S.; Wang, X.; He, K. A review of air pollution impact on subjective well-being: Survey versus visual psychophysics. J. Clean. Prod. 2018, 184, 959–968. [CrossRef] 15. Welsch, H. Environment and happiness: Valuation of air pollution using life satisfaction data. Ecol. Econ. 2006, 58, 801–813. [CrossRef] 16. Zhang, X.; Zhang, X.; Chen, X. Happiness in the Air: How Does a Dirty Sky Affect Mental Health and Subjective Well-being? J. Environ. Econ. Manag. 2017, 85, 81–94. [CrossRef] [PubMed] 17. Roberts, J.D.; Voss, J.D.; Knight, B. The Association of Ambient Air Pollution and Physical Inactivity in the United States. PLoS ONE 2014, 9, e90143. [CrossRef] 18. Marmot, M. Social determinants of health inequalities. Lancet 2005, 365, 1099–1104. [CrossRef] 19. Dolan, P.; Peasgood, T.; White, M. Do we really know what makes us happy? A review of the economic literature on the factors associated with subjective well-being. J. Econ. Psychol. 2008, 29, 94–122. [CrossRef] 20. Downward, P.; Dawson, P.; Mills, T.C. Sports participation as an investment in (subjective) health: A time series analysis of the life course. J. Public Health 2016, 38, e504–e510. [CrossRef] 21. Humphreys, B.R.; McLeod, L.; Ruseski, J.E. Physical activity and health outcomes: Evidence from Canada. Health Econ. 2014, 23, 33–54. [CrossRef] [PubMed] 22. Downward, P.; Dawson, P. Is it Pleasure or Health from Leisure that We Benefit from Most? An Analysis of Well-Being Alternatives and Implications for Policy. Soc. Indic. Res. 2016, 126, 443–465. [CrossRef] 23. Wicker, P.; Frick, B. Intensity of physical activity and subjective well-being: An empirical analysis of the WHO recommendations. J. Public Health 2017, 39, e19–e26. [CrossRef] [PubMed] 24. An, R.; Zhang, S.; Ji, M.; Guan, C. Impact of ambient air pollution on physical activity among adults: A systematic review and meta-analysis. Perspect. Public Health 2018, 138, 111–121. [CrossRef] 25. Giles, L.V.; Koehle, M.S. The Health Effects of Exercising in Air Pollution. Sports Med. 2014, 44, 223–249. [CrossRef] [PubMed] 26. Kuskowska, K.; Rogula-Kozłowska, W.; Widziewicz, K. A preliminary study of the concentrations and mass size distributions of particulate matter in indoor sports facilities before and during athlete training. Environ. Prot. Eng. 2019, 45, 103–112. [CrossRef] 27. Reche, C.; Viana, M.; van Drooge, B.L.; Fernández, F.J.; Escribano, M.; Castaño-Vinyals, G.; Nieuwenhuijsen, M.; Adami, P.E.; Bermon, S. Athletes’ exposure to air pollution during World Athletics Relays: A pilot study. Sci. Total Environ. 2020, 717, 137161. [CrossRef] 28. Bowen, T.S.; Benson, A.P.; Rossiter, H.B. The Coupling of Internal and External Gas Exchange during Exercise. In Muscle and Exercise Physiology; Elsevier: Amsterdam, The Netherlands, 2019; pp. 217–249. [CrossRef] 29. Zoladz, J.A.; Szkutnik, Z.; Grassi, B. Metabolic Transitions and Muscle Metabolic Stability: Effects of Exercise Training. In Muscle and Exercise Physiology; Elsevier: Amsterdam, The Netherlands, 2019; pp. 391–422. [CrossRef] 30. Wasserman, K.; Hansen, J.; Sietsema, K.; Sue, D.; Stringer, W.W.; Whipp, B. Principles of Exercise Testing and Interpretation: Including Pathophysiology and Clinical Applications; Lippincott Williams & Wilkins: Philadelphia, PA, USA, 2011. 31. Allen, T.J. Textbook of Work Physiology: Physiological Bases of Exercise, Fourth Edition. Physiother. Can. 2004, 56, 248. [CrossRef] 32. Carling, C.; Lacome, M.; McCall, A.; Dupont, G.; Le Gall, F.; Simpson, B.; Buchheit, M. Monitoring of Post-match Fatigue in Professional Soccer: Welcome to the Real World. Sports Med. 2018, 48, 2695–2702. [CrossRef] 33. Stølen, T.; Chamari, K.; Castagna, C.; Wisløff, U. Physiology of soccer: An update. Sports Med. 2005, 35, 501–536. [CrossRef] 34. Aquino, R.; Gonçalves, L.G.; Galgaro, M.; Maria, T.S.; Rostaiser, E.; Pastor, A.; Nobari, H.; Garcia, G.R.; Moraes-Neto, M.V.; Nakamura, F.Y. Match running performance in Brazilian professional soccer players: Comparisons between successful and unsuccessful teams. BMC Sport. Sci. Med. Rehabil. 2021, 13, 93. [CrossRef] [PubMed] 35. Konefał, M.; Andrzejewski, M.; Chmura, P.; Zacharko, M.; Radzimi´nski, Ł. Physical Activity of the Right- and Left-Footed Professional Soccer Players from Symmetrical Defensive Positions. Symmetry 2021, 13, 1551. [CrossRef] Int. J. Environ. Res. Public Health 2023, 20, 692 9 of 9 36. Andrzejewski, M.; Chmura, P.; Konefał, M.; Kowalczuk, E.; Chmura, J. Match outcome and sprinting activities in match play by elite German soccer players. J. Sport. Med. Phys. Fit. 2018, 58, 785–792. [CrossRef] [PubMed] 37. Chmura, P.; Konefał, M.; Chmura, J.; Kowalczuk, E.; Zaj ˛ac, T.; Rokita, A.; Andrzejewski, M. Match outcome and running performance in different intensityranges among elite soccer players. Biol. Sport 2018, 35, 197–203. [CrossRef] [PubMed] 38. Modric, T.; Versic, S.; Sekulic, D.; Liposek, S. Analysis of the Association between Running Performance and Game Performance Indicators in Professional Soccer Players. Int. J. Environ. Res. Public Health 2019, 16, 4032. [CrossRef] 39. Konefał, M.; Chmura, P.; Zaj ˛ac, T.; Chmura, J.; Kowalczuk, E.; Andrzejewski, M. A New Approach to the Analysis of Pitch- Positions in Professional Soccer. J. Hum. Kinet. 2019, 66, 143–153. [CrossRef] 40. Linke, D.; Link, D.; Lames, M. Football-specific validity of TRACAB’s optical video tracking systems. PLoS ONE 2020, 15, e0230179. [CrossRef] 41. Anderson, J.O.; Thundiyil, J.G.; Stolbach, A. Clearing the Air: A Review of the Effects of Particulate Matter Air Pollution on Human Health. J. Med. Toxicol. 2012, 8, 166–175. [CrossRef] 42. Zaric, N.; Spalevic, V.; Bulatovic, N.; Pavlicevic, N.; Dudic, B. Measurement of Air Pollution Parameters in Montenegro Using the Ecomar System. Int. J. Environ. Res. Public Health 2021, 18, 6565. [CrossRef] 43. World Health Organization. WHO Global Air Quality Guidelines: Particulate Matter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulfur Dioxide and Carbon Monoxide; World Health Organization: Geneva, Switzerland, 2021; Available online: https://apps.who. int/iris/handle/10665/345329 (accessed on 30 September 2022). 44. Cox, L.A.; Popken, D.A. Should air pollution health effects assumptions be tested? Fine particulate matter and COVID-19 mortality as an example. Glob. Epidemiol. 2020, 2, 100033. [CrossRef] 45. Lichter, A.; Pestel, N.; Sommer, E. Productivity effects of air pollution: Evidence from professional soccer. Labour Econ. 2017, 48, 54–66. [CrossRef] 46. Zacharko, M.; Cichowicz, R.; Andrzejewski, M.; Chmura, P.; Kowalczuk, E.; Chmura, J.; Konefał, M. Air Pollutants Reduce the Physical Activity of Professional Soccer Players. Int. J. Environ. Res. Public Health 2021, 18, 12928. [CrossRef] [PubMed] 47. Maneiro, R.; Losada, J.L.; Casal, C.A.; Ardá, A. The Influence of Match Status on Ball Possession in High Performance Women’s Football. Front. Psychol. 2020, 11, 487. [CrossRef] [PubMed] 48. Barnes, C.; Archer, D.T.; Hogg, B.; Bush, M.; Bradley, P.S. The evolution of physical and technical performance parameters in the English Premier League. Int. J. Sports Med. 2014, 35, 1095–1100. [CrossRef] [PubMed] 49. Kocot, K.; Zejda, J.E. The relationship between ambient air pollution and life expectancy—An ecological fallacy revisted. Prz. Epidemiol. 2022, 76, 58–66. [CrossRef] 50. Traczyk, P.; Gruszecka-Kosowska, A. The Condition of Air Pollution in Kraków, Poland, in 2005–2020, with Health Risk Assessment. Int. J. Environ. Res. Public Health 2020, 17, 6063. [CrossRef] [PubMed] 51. Slama, A.; ´Sliwczy´nski, A.; Wo´znica-Pyzikiewicz, J.; Zdrolik, M.; Wi´snicki, B.; Kubajek, J.; Tur˙za´nska-Wieczorek, O.; Studnicki, M.; Wierzba, W.; Franek, E. The short-term effects of air pollution on respiratory disease hospitalizations in 5 cities in Poland: Comparison of time-series and case-crossover analyses. Environ. Sci. Pollut. Res. 2020, 27, 24582–24590. [CrossRef] 52. Duda, H.; Rydzik, Ł.; Czarny, W.; Błach, W.; Görner, K.; Ambro˙zy, T. Reaction of the Organisms of Young Football Players to City Smog in the Sports Training. Int. J. Environ. Res. Public Health 2020, 17, 5510. [CrossRef] 53. Kargarfard, M.; Shariat, A.; Shaw, B.S.; Shaw, I.; Lam, E.T.C.; Kheiri, A.; Eatemadyboroujeni, A.; Tamrin, S.B.M. Effects of polluted air on cardiovascular and hematological parameters after progressive maximal aerobic exercise. Lung 2015, 193, 275–281. [CrossRef] 54. Qin, F.; Yang, Y.; Wang, S.; Dong, Y.; Xu, M.; Wang, Z.; Zhao, J. Exercise and air pollutants exposure: A systematic review and meta-analysis. Life Sci. 2019, 218, 153–164. [CrossRef] 55. Tainio, M.; Jovanovic Andersen, Z.; Nieuwenhuijsen, M.J.; Hu, L.; de Nazelle, A.; An, R.; Garcia, L.M.T.; Goenka, S.; Zapata- Diomedi, B.; Bull, F.; et al. Air pollution, physical activity and health: A mapping review of the evidence. Environ. Int. 2021, 147, 105954. [CrossRef] [PubMed] 56. Nieuwenhuijsen, M.J. New urban models for more sustainable, liveable and healthier cities post covid19; reducing air pollution, noise and heat island effects and increasing green space and physical activity. Environ. Int. 2021, 157, 106850. [CrossRef] [PubMed] 57. Brand, C.; Dons, E.; Anaya-Boig, E.; Avila-Palencia, I.; Clark, A.; de Nazelle, A.; Gascon, M.; Gaupp-Berghausen, M.; Gerike, R.; Götschi, T.; et al. The climate change mitigation effects of daily active travel in cities. Transp. Res. Part D Transp. Environ. 2021, 93, 102764. [CrossRef] 58. Moreno, C.; Allam, Z.; Chabaud, D.; Gall, C.; Pratlong, F. Introducing the “15-Minute City”: Sustainability, Resilience and Place Identity in Future Post-Pandemic Cities. Smart Cities 2021, 4, 93–111. [CrossRef] 59. Nieuwenhuijsen, M.J.; Khreis, H. Car free cities: Pathway to healthy urban living. Environ. Int. 2016, 94, 251–262. [CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

High Levels of PM10 Reduce the Physical Activity of Professional Soccer Players.

12-30-2022

Zacharko, Michał,Cichowicz, Robert,Depta, Adam,Chmura, Paweł,Konefał, Marek

eng

PMC7312918

International Journal of Environmental Research and Public Health Article Sprint Interval Running and Continuous Running Produce Training Specific Adaptations, Despite a Similar Improvement of Aerobic Endurance Capacity—A Randomized Trial of Healthy Adults Sigbjørn Litleskare 1,2 , Eystein Enoksen 1, Marit Sandvei 1, Line Støen 1, Trine Stensrud 1 , Egil Johansen 1 and Jørgen Jensen 1,* 1 Department of Physical Performance, Norwegian School of Sport Sciences, 0863 Oslo, Norway; [email protected] (S.L.); [email protected] (E.E.); [email protected] (M.S.); [email protected] (L.S.); [email protected] (T.S.); [email protected] (E.J.) 2 Department of Sports and Physical Education, Inland Norway University of Applied Sciences, 2406 Elverum, Norway * Correspondence: [email protected] Received: 9 April 2020; Accepted: 26 May 2020; Published: 29 May 2020



 Abstract: The purpose of the present study was to investigate training-specific adaptations to eight weeks of moderate intensity continuous training (CT) and sprint interval training (SIT). Young healthy subjects (n = 25; 9 males and 16 females) performed either continuous training (30–60 min, 70–80% peak heart rate) or sprint interval training (5–10 near maximal 30 s sprints, 3 min recovery) three times per week for eight weeks. Maximal oxygen consumption, 20 m shuttle run test and 5·60 m sprint test were performed before and after the intervention. Furthermore, heart rate, oxygen pulse, respiratory exchange ratio, lactate and running economy were assessed at five submaximal intensities, before and after the training interventions. Maximal oxygen uptake increased after CT (before: 47.9 ± 1.5; after: 49.7 ± 1.5 mL·kg−1·min−1, p < 0.05) and SIT (before: 50.5 ± 1.6; after: 53.3 ± 1.5 mL·kg−1·min−1, p < 0.01), with no statistically significant differences between groups. Both groups increased 20 m shuttle run performance and 60 m sprint performance, but SIT performed better than CT at the 4th and 5th 60 m sprint after the intervention (p < 0.05). At submaximal intensities, CT, but not SIT, reduced heart rate (p < 0.05), whereas lactate decreased in both groups. In conclusion, both groups demonstrated similar improvements of several performance measures including VO2max, but sprint performance was better after SIT, and CT caused training-specific adaptations at submaximal intensities. Keywords: maximal oxygen consumption; heart rate; oxygen pulse; shuttle run; repeated sprint ability 1. Introduction Manipulation of duration and intensity of exercise bouts change the demands of metabolic pathways within muscle cells, as well as oxygen delivery to exercising muscles [1]. The training adaptations that occur after repeated bouts of exercise are to some degree specific to that particular exercise [1,2], but both high intensity interval training and continuous training bouts increase VO2max and oxidative capacity in skeletal muscles [1–3]. Within this context, it is of interest to clarify the specific adaptations of different training protocols to optimize endurance training, health and performance. There has recently been a lot of interest in a type of high intensity interval training known as sprint interval training (SIT). SIT is (often) performed as 30 s of “all out” sprints with 2.5–4.5 min of rest between sprints [4–6]. Several cycling studies have reported that this type of training Int. J. Environ. Res. Public Health 2020, 17, 3865; doi:10.3390/ijerph17113865 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2020, 17, 3865 2 of 12 improves maximal oxygen consumption (VO2max), endurance performance and the oxidative capacity of skeletal muscle [3–12]. Previous studies have also demonstrated that the magnitude of improvement in endurance performance and VO2max after SIT is comparable to continuous cycling at moderate intensity [3,4]. Furthermore, research also suggest that SIT is an efficient approach to improve several important health parameters in addition to VO2max, such as insulin sensitivity, blood pressure, cardiovascular function, and body composition [13]. Because most previous studies on SIT adaptations have used a cycling protocol, there is limited knowledge about sprint interval running [14]. This is unfortunate, as running is a basic and popular type of exercise. More importantly, there are several fundamental differences between cycling and running exercise. Power output during sprint exercise is substantially higher in cycling than in running [15]. There are also several physiological differences, such as higher heart rate (HR), higher fat oxidation and higher muscle mass activation in running than in cycling [16,17]. Thus, results from sprint interval cycling may not be directly applicable to sprint interval running [18]. Only a few previous studies have investigated the effects of sprint interval running. In most of these studies, SIT is added to the training program of trained endurance athletes [19–21]. However, one previous study has compared the effect of sprint interval and traditional endurance running in healthy untrained subjects [22]. Macpherson et al. [22] reported similar improvements of VO2max and endurance performance after SIT and continuous running at moderate intensity. Interestingly, VO2max improved in the SIT group without affecting cardiac output, whereas continuous running increased cardiac output, as expected. The study by Macpherson et al. [22] revealed that sprint interval running and continuous running produced similar improvements of aerobic performance, but still caused training-specific physiological adaptations. Because there is limited data available on this topic, it is of great interest to investigate training-specific adaptations of sprint interval running and continuous running. The purpose of this study was therefore to compare performance and health related adaptations of continuous training (CT) and SIT, performed as running, on VO2max, 20 m shuttle run performance, repeated sprint ability (RSA) and the physiological response to submaximal exercise. We hypothesized that both types of training would improve VO2max and 20 m shuttle run similarly, and that training-specific adaptations would occur at submaximal exercise in favor of CT and during RSA in favor of SIT. 2. Materials and Methods 2.1. Participants Participants were recruited through the official webpage of the Norwegian School of Sport Sciences, and printed and electronic flyers posted in various places in the local area of northern Oslo and in social media, respectively. Forty-eight subjects volunteered and were screened for participation. The inclusion criteria for participation were: (1) non-smokers; (2) body mass index (BMI) < 30 kg·m−2; (3) no cardiovascular or metabolic disease; (4) no systematic endurance training during the last two years (≤2 sessions per week). Twenty-nine subjects met these criteria and were invited to participate. Subjects were matched based on gender and VO2max, and then randomly assigned by coin toss to either CT or SIT. Four subjects dropped out during the training intervention; One dropped out during week 1 due to receiving a job offer (CT, male 21 years), one, during week 2, after realizing that participation in the intervention was not compatible with his life situation (SIT, male, 21 years), one during week 5, due to unspecified reasons (CT, male, 22 years), and one during week 8, due to moving to a different region (SIT, female, 22 years). Thus, 25 subjects (9 males and 16 females) completed the training intervention. Int. J. Environ. Res. Public Health 2020, 17, 3865 3 of 12 2.2. Training Protocol Both groups completed eight weeks of training. Each week consisted of three training sessions, separated by at least one resting day. Training sessions were organized and supervised by qualified instructors. Subjects were occasionally allowed to perform sessions at home if participation in organized sessions was problematic. The training intensity was controlled during all sessions by heart rate monitors (Polar Sport Tester RS800CX, Polar Electro, OY, Kempele, Finland). An adherence of >85% (19 of 24 training sessions, including sessions performed at home) was required. Subjects were instructed to maintain their normal diet and lifestyle throughout the intervention. The CT group was instructed to maintain an intensity corresponding to 70–80% HRpeak at all training sessions. Organized training sessions were performed on slightly undulating terrain. During the first week, the CT group performed 30 min of running. The time then increased by five minutes per week, up to a total of 60 min. The SIT group consisted of 30 s sprints at near maximal effort, with three minutes of rest between each sprint. The training intensity of SIT sessions was evaluated subjectively during sessions, while the HR data was used to verify that the individual participant did not have a session or interval that deviated from their usual level of effort. During the first week, the SIT group performed five sprints per session. The number of sprints then increased gradually, until a total of 10 sprints per session in weeks 7 and 8. When the number of sprints reached seven, subjects were given six minutes of rest midway through the training session. All sprints were performed on slightly uphill terrain. Prior to all training sessions, the CT group performed a ten-minute warm-up at an intensity corresponding to 60–75% of HRpeak. The SIT group performed a ten-minute warm-up at an intensity corresponding to 60–85% of HRpeak, followed by three incremental strides of about 80 m. After each training session, all subjects performed five minutes of walking or running at intensities below 70% of HRpeak. The training volume in CT and SIT was not matched. 2.3. Measures Incremental treadmill test to exhaustion. The test was performed on a motorized treadmill (Woodway pps55 sport, Woodway Gmbh, Weil an Rhein, Germany). Oxygen consumption (VO2) was measured through a 2-way mouthpiece (Hans Rudolph Instr., Shawnee, KS, USA) and a sling, which was connected to an O2 and CO2 analyzer (Oxycon Champion, Jaeger Instruments, Hoechberg, Germany). Samples of O2 and CO2 were collected continuously from a mixing chamber, with average values obtained over 30-s intervals. The gas analyzer was calibrated before each test with ventilated indoor air and standardized gas concentrations, to span the concentration range observed during exercise. The expired volume was measured with a turbine (Triple V volume transducer, Leipzig, Germany), and volume calibration was performed regularly with a 3-L syringe. The incremental test to exhaustion followed current recommendations for test duration [23], and was performed according to the standard protocol of the Norwegian Olympic Sports Centre (see e.g., [24]). Prior to the pre-test, subjects performed two familiarization tests to reduce the learning effect, following the recommendations of Edgett et al. [25]. Identical procedures were conducted for familiarization, pre- and post-test. All subjects performed a 15-min warm-up of gradually increasing intensity. The last five minutes of the warm-up were performed with an inclination of 5.3%, as was the incremental test. The starting speed was chosen in order to exhaust the subjects after ~5 min. Running speed was initially increased by 1 km·h−1 every minute. At the end of the test, running speed was either maintained or increased by 0.5 km·h−1, to allow at least one minute running at the final speed. VO2max was determined as the average of the highest values achieved over two subsequent 30-s measurements. Verbal encouragement was given throughout the test. Two minutes after completion, a capillary blood sample was obtained and 20 µl of blood was injected into a lactate analyzer (1500 SPORT, YSI Inc., Yellow Springs Instr., Yellow spring, OH, USA), with the help of a standard injector. The lactate analyzer was calibrated before each test with a 5.0 mM lactate standard. The main criterion for evaluating whether VO2max was achieved was a plateau in Int. J. Environ. Res. Public Health 2020, 17, 3865 4 of 12 oxygen consumption. A levelling-off of the VO2 curve was used in conjunction with a lactate value ≥ 6 mmol·l−1 and respiratory exchange ratio (RER) > 1.10 as secondary criteria. HR was monitored throughout the test (Polar Sport Tester RS800CX, Polar Electro, OY, Kempele, Finland) and the highest value achieved was defined as HRpeak. Submaximal treadmill test. The submaximal treadmill test was conducted with the same equipment as described above and consisted of four stages of five minutes on a motorized treadmill. The running speed at each stage was individualized based on each subject’s VO2max and a general relationship between running speed and VO2. This relationship was estimated based on data from a pilot study. The purpose of the procedure was to establish four individualized stages of gradually increasing running velocities at approximate intensities of 50%, 60%, 70% and 80% of VO2max. The same velocity (absolute intensity) was used for both the pre- and post-test. Measurements of VO2 and RER were made between the third and fourth minute. After the fourth minute, the mouthpiece was removed and HR was monitored until the end of the stage. Between each stage, the subjects were given one minute rest for measurement of lactate, as described above. The post-test was conducted at the same running velocities as the pre-test. Running economy (RE; mL·kg−1·km−1) was defined as VO2 divided by body mass and running speed. O2 pulse (mL·beat−1) was calculated by dividing VO2 (mL·min−1) by HR (beat·min−1). Training adaptations at the same relative intensity were evaluated by examining the running speed that elicited the VO2 value closest to 70% of the individual subject’s VO2max. This intensity was chosen because it produced the least variation in VO2 values. Repeated sprint test. After completing the submaximal treadmill test, all subjects performed a 5·60 m repeated sprint test in an indoor sports hall. The test was considered appropriate to induce the performance decrement associated with repeated sprint exercise [26]. All subjects performed a test-specific warm-up prior to the sprint test consisting of 3·60 m incremental runs. The sprints were performed with a 1 m flying start and each sprint was separated by 30 s of rest. Time was measured by photoelectric detectors (Brower Speed Trap II Timing system, Brower Timing system, Salt Lake USA). Verbal encouragement was given throughout the test. 20 m shuttle run test. The 20 m shuttle run test procedure was the same as previously described [27]. In short, subjects ran repeatedly between two lines, 20 m apart. The test started at a running speed of 8.5 km·h−1, which then increased by 0.5 km·h−1 per minute. The test was terminated when subjects failed to reach the 20 m line before the signal on two successive occasions. To stimulate competition, the subjects ran in groups. 2.4. Procedures All tests were performed before and after the training interventions. The submaximal treadmill test and the repeated sprint test were performed on the same day, and only separated by the time to relocate from the laboratory to the sports hall. All other tests were separated by at least one resting day. Subjects were familiarized with testing procedures to minimize any potential learning effect. The data for this study were collected in relation to a larger study [28]. The study was approved by the Regional Ethics Committee of Oslo, Norway (ref. number 2010/1567-1) and was performed according to the Declaration of Helsinki. All subjects were informed about the purpose of the study and associated risks before they gave their written informed consent to participate. A few subjects did not obtain valid results for all tests due to sickness, injury and unspecified withdrawal from the study. These subjects were excluded from both pre and post analysis for these particular tests. The number of participants for each test is stated in the captions of tables and figures. 2.5. Analysis Data are presented as group means ± SEM. All statistical analyses were performed in SPSS version 18 (SPSS inc., Chicago, IL, USA). The assumption of normality was evaluated by a Shapiro–Wilk test. Student’s paired t-test was used to investigate within-group differences, and a Student’s Int. J. Environ. Res. Public Health 2020, 17, 3865 5 of 12 unpaired t-test was used to investigate between-group differences. A repeated measures ANOVA with a Greenhouse–Geisser correction was used to evaluate a potential increase in VO2max as a function of the number of tests performed before the intervention. In cases where data was not normally distributed, a Wilcoxon signed-rank test was used to verify within-group differences, and a Mann–Whitney test was used to verify between-group differences. Statistical significance was accepted at the p < 0.05 level. 3. Results The number of females in each group was eight, while the number of males was four in CT and five in SIT. The mean age, height, weight and BMI was 25 ± 1 years, 175 ± 2 cm, 72.6 ± 3.8 kg and 23.6 ± 0.9 kg·m−2 in CT at the start of the intervention. In SIT, the mean age, height, weight and BMI was 25 ± 1 years, 173 ± 3 cm, 71.2 ± 4.1 kg and 24.0 ± 0.8 kg·m−2. There was no statistical difference between groups and these characteristics did not change during the intervention. Heart rate registrations at all training sessions confirmed that the subjects performed the training as recommended, including the sessions performed at home (19% of sessions). Three participants experienced minor injuries during the training intervention, including one injury unrelated to the intervention. All three were in the SIT group, and all managed to complete > 85% of training sessions. Maximal oxygen consumption and 20 m shuttle run performance. Maximal oxygen uptake was measured three times prior to the intervention, and VO2max increased from test to test. The repeated measures ANOVA revealed that VO2max increased from 48.2 ± 1.1 at the first familiarization test to 49.3 ± 1.3 in the second, and eventually to 49.9 ± 1.3 mL·kg−1·min−1 at the third test when combining both groups (F(1.434, 28.683) = 10.320, p < 0.01). VO2max was improved in both CT (p < 0.05) and SIT (p < 0.01) after training (Table 1). The improvement of VO2max corresponded to a 3.8% increase in CT and 5.5% in SIT. The increase in VO2max varied between subjects and five subjects did not increase VO2max (Figure 1). In accordance with the improved VO2max, both groups also increased maximal O2 pulse (p < 0.05) and the number of laps performed on the 20 m shuttle run test (CT p < 0.05; SIT p < 0.01). Figure 1. Individual change in maximal oxygen consumption (VO2max) after eight weeks of either continuous training (CT) or sprint interval training (SIT) (one subject in CT did not experience any change). Repeated sprint test. Both the CT and SIT groups improved sprint performance for the first sprint (Table 2) and thereby improved maximal 60 m sprint performance. Both groups also improved performance on all successive sprints. However, the SIT group performed better than the CT group on sprints number four (p < 0.05) and five (p < 0.05) after the intervention (Table 2). Int. J. Environ. Res. Public Health 2020, 17, 3865 6 of 12 Table 1. Parameters of maximal endurance performance before and after eight weeks of continuous training (CT) and sprint interval training (SIT). CT SIT Pre Post Pre Post VO2max (mL·kg−1·min−1) 47.9 ± 1.5 49.7 ± 1.5 * 50.5 ± 1.6 53.3 ± 1.5 * Maximal O2 pulse 17.4 ± 1.0 18.1 ± 1.0 * 18.0 ± 1.0 19.2 ± 1.0 * Laps 71.5 ± 6.1 79.4 ± 5.2 * 69.5 ± 3.8 81.7 ± 4.0 * Values are mean ± SEM. CT, n = 12 (4 males, 8 females). SIT, n = 13 (5 males, 8 females). VO2max, maximal oxygen consumption; O2 pulse, oxygen pulse; Laps, number of laps completed during the 20 m shuttle run test. * Statistically significant difference from pre (student’s t-test), p < 0.05. There were no statistically significant differences between groups. Table 2. Performance on the repeated sprint test before and after eight weeks of continuous training (CT) and sprint interval training (SIT). CT SIT Pre Post Pre Post Time (s) %dec. Time (s) %dec Time (s) %dec Time (s) %dec 1. 60 m 9.92 ± 0.25 9.69 ± 0.26 * 9.64 ± 0.26 9.20 ± 0.21 * 2. 60 m 10.44 ± 0.33 5.2 10.06 ± 0.27 * 3.8 9.98 ± 0.23 3.5 9.48 ± 0.18 * 3.0 3. 60 m 10.76 ± 0.29 8.5 10.31 ± 0.23 * 6.4 10.27 ± 0.22 6.5 9.89 ± 0.20 * 7.5 4. 60 m 10.87 ± 0.30 9.6 10.54 ± 0.23 * 8.8 10.37 ± 0.25 7.6 9.91 ± 0.19 *,† 7.7 5. 60 m 10.93 ± 0.21 10.2 10.70 ± 0.22 * 10.4 10.53 ± 0.25 9.2 9.96 ± 0.20 *,† 8.3 Values are mean ± SEM. CT, n = 10. SIT, n = 11. %dec = percent performance decrement compared to the fastest sprint time * Statistically significant difference from pre.† Statistically significant difference from CT (student’s t-test), p < 0.05. Physiological response to submaximal exercise at the same absolute intensity. The submaximal treadmill test was performed at the same velocity, before and after the intervention. Both groups ran at a lower percentage of VO2max after eight weeks of training (Table 3). The CT group decreased VO2 at all stages (i.e., running economy), while the SIT group decreased VO2 at stage 4 and RE at stages 2 and 4 (Table 3). HR was lower after CT at all stages (p < 0.01), but remained unchanged after SIT (Table 3). O2 pulse at submaximal intensities did not change in any group (Table 3). Physiological response to submaximal exercise at the same relative intensity. Adaptations to running, performed at the same relative intensity before and after the intervention, were evaluated at the velocity closest to 70% VO2max. At this intensity, HR remained unchanged after CT, while O2 pulse increased by 0.6 mL·beat−1 (p < 0.05; Table 4). In contrast, HR increased (p < 0.05) and O2 pulse remained unchanged after SIT (Table 4). RER was reduced at 70% VO2max after CT (p < 0.05), but not statistically significant after SIT (p = 0.07; Table 4). Lactate was reduced at 70% of VO2max in both groups after the intervention. Int. J. Environ. Res. Public Health 2020, 17, 3865 7 of 12 Table 3. Physiological responses at submaximal velocities, before (pre) and after (post) eight weeks of either continuous training (CT) or sprint interval training (SIT). 1 2 3 4 Pre Post Pre Post Pre Post Pre Post CT VO2 (mL·min−1) 1553 ± 139 1381 ± 159 *,# 2307 ± 177 1876 ± 157 * 2414 ± 175 2275 ± 174 * 2754 ± 186 2620 ± 172 * % VO2max 45.1 ± 3.4 37.8 ± 2.1 * 58.4 ± 3.1 52.4 ± 2.8 * 71.1 ± 2.3 64.6 ± 2.4 * 79.4 ± 2.0 73.3 ± 1.8 * RE (mL·kg−1·km−1) 213 ± 16 186 ± 10 *,# 229 ± 12 213 ± 10 * 238 ± 8 224 ± 8 * 232 ± 6 223 ± 6 * % HRpeak 66.9 ± 2.1 59.6 ± 2.3 * 76.8 ± 2.2 70.8 ± 2.5 * 85.3 ± 1.5 80.2 ± 2.1 * 90.0 ± 1.0 86.3 ± 1.4 * O2pulse (mL·beat−1) 11.5 ± 0.7 11.5 ± 1.0 13.1 ± 0.8 13.3 ± 0.9 14.2 ± 0.8 14.1 ± 0.9 15.3 ± 0.9 15.2 ± 0.9 RER (VCO2·VO2−1) 0.89 ± 0.01 0.84 ± 0.01 * 0.93 ± 0.01 0.88 ± 0.01 * 0.94 ± 0.01 0.90 ± 0.01 * 0.97 ± 0.01 0.93 ± 0.01 * Lactate (mmol·l−1) 1.22 ± 0.13 0.77 ± 0.06 * 1.76 ± 0.26 1.16 ± 0.13 * 2.39 ± 0.25 1.75 ± 0.17 * 3.84 ± 0.30 2.66 ± 0.24 * SIT VO2 (mL·min−1) 1544 ± 152 1523 ± 150 2221 ± 168 2076 ± 158 2574 ± 181 2500 ± 165 2909 ± 204 2832 ± 196 *,# % VO2max 42.6 ± 2.2 40.1 ± 2.5 * 61.9 ± 1.6 55.3 ± 2.0 * 71.8 ± 1.2 66.5 ± 1.5 * 81.2 ± 0.9 75.2 ± 1.2 * RE (mL·kg−1·km−1) 201 ± 10 199 ± 10 243 ± 8 228 ± 8 * 240 ± 5 234 ± 4 237 ± 4 231 ± 4 * % HRpeak 61.6 ± 2.4 61.6 ± 2.5 75.0 ± 1.6 72.0 ± 2.0 82.6 ± 1.2 81.1 ± 1.5 88.6 ± 0.9 87.2 ± 1.2 O2pulse (mL·beat−1) 12.8 ± 1.0 12.6 ± 1.0 14.9 ± 1.0 14.5 ± 1.1 15.8 ± 1.0 15.6 ± 0.9 16.6 ± 1.0 16.4 ± 1.0 RER (VCO2·VO2−1) 0.86 ± 0.02 0.83 ± 0.02 0.91 ± 0.01 0.87 ± 0.02 *,# 0.92 ± 0.01 0.89 ± 0.01 * 0.96 ± 0.01 0.92 ± 0.01 * Lactate (mmol·l−1) 1.12 ± 0.08 0.89 ± 0.06 * 1.79 ± 0.15 1.20 ± 0.07 *,# 2.38 ± 0.17 1.68 ± 0.12 * 3.45 ± 0.24 2.66 ± 0.19 *,# Values are mean ± SEM. VO2, oxygen consumption; % VO2max, percent of maximal oxygen consumption; RE, running economy; % HFpeak, percent of peak heart rate; O2 pulse, oxygen pulse; RER, respiratory exchange ratio. Velocities at stage 1, 2, 3 and 4 equaled 6.2 ± 0.2, 7.5 ± 0.2, 8.8 ± 0.3 and 10.1 ± 0.3 km·h−1 in CT, and 6.4 ± 0.2, 7.7 ± 0.2, 9.1 ± 0.3 and 10.4 ± 0.3 km·h−1 in SIT. CT, n = 11. SIT, n = 13. Values for % HFpeak and O2 pulse represents only 12 subjects in SIT. * Statistically significant difference from pre (student’s t-test), p < 0.05. # Statistically significant difference from pre (verified by Wilcoxon signed rank test), p < 0.05. There were no statistically significant differences between groups. Int. J. Environ. Res. Public Health 2020, 17, 3865 8 of 12 Table 4. Physiological responses to running at the velocity closest to 70 percent of maximal oxygen consumption, before (pre) and after (post) eight weeks of either continuous training (CT) or sprint interval training (SIT). CT SIT Pre Post Pre Post Velocity (km·h−1) 8.7 ± 0.4 9.7 ± 0.3 * 8.8 ± 0.4 9.6 ± 0.3 * % VO2max 70.5 ± 0.9 71.4 ± 0.7 70.3 ± 0.7 70.2 ± 0.6 % HRpeak 84.3 ± 1.0 85.1 ± 0.9 81.9 ± 1.6 84.9 ± 1.4 * O2 pulse (mL·beat−1) 14.5 ± 0.9 15.1 ± 0.9 * 15.7 ± 1.0 16.0 ± 0.9 RER (VCO2·VO2−1) 0.94 ± 0.01 0.91 ± 0.01 * 0.93 ± 0.01 0.91 ± 0.01 Lactate (mmol·l−1) 2.62 ± 0.16 2.18 ± 0.19 *,# 2.33 ± 0.12 2.03 ± 0.15 *,# Values are mean ± SEM. % VO2max, percent of maximal oxygen consumption; % HFpeak, percent of peak heart rate; O2 pulse, oxygen pulse; RER, respiratory exchange ratio. CT, n = 11. SIT, n = 13. Values for % HFpeak and O2 pulse represents only 12 subjects in SIT. * Statistically significant difference from pre (student’s t-test), p < 0.05. # Statistically significant difference from pre (verified by Wilcoxon signed rank test), p < 0.05. There were no statistically significant differences between groups. 4. Discussion The main findings of the present study were that both training protocols increased VO2max and shuttle run performance, but also produced training-specific adaptations. The SIT group performed better than the CT group on the last two 60 m sprints, while only CT improved HR and O2 pulse adaptations at submaximal intensities. The higher VO2max in both groups after eight weeks of training holds implications for both performance and health, and is supported by previous research, showing a comparable improvement of VO2max after sprint interval running and cycling [4,22]. Interestingly, previous research suggests that the adaptations that lead to the comparable improvement of VO2max are different in the two types of training interventions. Macpherson et al. [22] showed that continuous endurance running improved maximal cardiac output, while sprint interval running did not. These reports suggest that the improvement of VO2max in the present study was due to peripheral adaptations [5,6]. Importantly, the increase in VO2max varied substantially between participants whether they performed CT or SIT, and five subjects did not increase their VO2max, even though the training was supervised by qualified instructors, and heart rate recordings confirmed that the training was performed with the recommended HR. These data agree with previous studies showing large variation in the increase in VO2max after endurance training [29,30]. Genetic variation has been suggested to explain differences in the increase of VO2max, but research also suggests that a large number of genetic variations collectively determine increases in VO2max [31]. No genetic variation predicting has so far been validated. The increase of VO2max observed after endurance exercise is caused by an improvement of cardiac output and/or arteriovenous oxygen difference [32], which results in higher VO2 per heartbeat (i.e., O2 pulse). At submaximal intensities, an increased O2 pulse results in lower HR [32]. In the present study, HR was reduced after CT at all submaximal velocities, while it remained unchanged after SIT. However, the participants improved running economy, which precludes the comparison of cardio-respiratory adaptations at the same absolute intensities. Therefore, to investigate the submaximal training adaptations independent of running economy, we examined HR and O2 pulse at the same relative intensity (~70% VO2max), pre and post training. At ~70% VO2max, O2 pulse increases after CT as expected (please see Table 4). In contrast, SIT did not change O2 pulse at ~70% VO2max and HR was higher after the training intervention, supporting Macpherson et al. [22], who reported unchanged cardiac output after sprint interval running. Increased cardiac output leads to higher O2 pulse and decreased HR at submaximal intensities [31]. The limited cardiac adaptations after SIT in the present study suggest that CT is a superior option for cardiac adaptations, which holds implications for the health benefits of SIT, as improved cardiac function is an important part of the health benefits of exercise [33]. Int. J. Environ. Res. Public Health 2020, 17, 3865 9 of 12 Several studies have shown that SIT increases the expression of oxidative enzymes in skeletal muscle [3,5,6]. In the present study, blood lactate and RER were reduced after both CT and SIT (although p = 0.07 for RER in SIT at 70% VO2max). It is well known that lactate production and RER are influenced by the oxidative capacity of skeletal muscle [34] and, thus, our results suggest that both CT and SIT improved the oxidative capacity of skeletal muscle. Results from the 20 m shuttle run test also revealed that both groups improved endurance performance and that the improvement was similar in both groups. These results are in accordance with previous investigations of both sprint interval cycling and running [3,22]. Endurance performance is a complex characteristic that is dependent on several factors, which makes it difficult to identify any single factor responsible for improved performance. Several adaptions could potentially contribute to the improved endurance performance observed in this study, but the correlation between the change of VO2max and the change of 20 m shuttle run performance (r = 0.56, p = 0.01) suggest that VO2max is central. Results from the test of repeated sprints showed that both CT and SIT improved the performance of the first sprint and thereby improved maximal sprint performance. Improved maximal 60 m sprint performance after CT may be surprising based on the “slow paced” nature of the training intervention. However, previous research has reported similar results for untrained people, including two studies of endurance cycling reporting improved sprint performance after continuous training [34,35]. The mechanisms behind these improvements are uncertain, but mechanical efficiency has been suggested as the most important factor [35]. In the present study, CT improved RE, which is a common measure of mechanical efficiency [36]. Improved RE could therefore offer an explanation for improved maximal sprint performance after CT. Improvements of mechanical efficiency is often associated with increased stiffness of muscles and tendons, but improved running technique by wasting less energy on braking forces and excessive vertical oscillation may be a likely cause for the improvement in CT [36], since the participants were inexperienced runners with a high potential for improving running technique. Improved maximal running velocity after SIT has previously been reported [22], and is supported by findings of improved peak power after sprint interval cycling [4,8,9,19]. Both groups also improved repeated sprint ability. These results are in accordance with previous studies that have investigated RSA after continuous training and high intensity interval training [34,35,37]. Interestingly, the SIT group performed better than the CT group on sprints number four and five after the intervention, thus demonstrating a superior ability to resist fatigue. The reason for the improved performance on the last two sprints could be related to the ability of SIT to increase muscle buffer capacity and levels of anaerobic enzymes [3,6], and to prevent metabolic and ionic perturbation during high-intensity exercise [8]. All of these adaptations can potentially improve performance during repeated sprint exercise [26]. The benefit of improved buffer capacity and ability to prevent ionic and metabolic perturbations would be progressively more beneficial during repeated sprint exercise, which may explain why SIT performed better at sprint number 4 and 5, and not 1, 2 and 3. Some limitations in the present study need to be recognized. The number of participants included in this study was based on an a priori power analysis for the between group comparison of VO2max. However, the statistical power may still be limited for the other comparisons in this study, in particular for tests with missing data. The results at the 70% intensity should be considered carefully. As explained in the methods, running speed was not adjusted to exactly 70% VO2max and there was some individual variation in running intensity from pre- to post-test. However, these variations were small, and mean relative intensity varied by less than one percentage-point between pre- and post-test (Table 4). The majority of participants were female, and although training groups were gender matched, we did not control for oral contraceptive use and menstrual cycle phase. Furthermore, high intensity exercise is commonly associated with increased risk of injury [38], and in the present study, we were unable to prevent the occurrence of injuries in the SIT group, despite a standardized warm-up and supervision of highly qualified personnel. Strengths of this study include the fact that heart rate was recorded at all training sessions, and that both females and males were included. Furthermore, a substantial Int. J. Environ. Res. Public Health 2020, 17, 3865 10 of 12 effort was made during familiarization, to reduce the potential impact of a learning effect on VO2max from pre- to post-test. VO2max did increase during the familiarization process, but levelled off from the last familiarization test to the pre-test, which indicates that the efforts was successful at minimizing the learning effect in this study. However, the inclusion of the familiarization tests in addition to an already high number of pre-tests may have resulted in some minor training adaptations before the onset of the training interventions. 5. Conclusions In conclusion, both types of training produced similar improvements of VO2max, endurance capacity and sprint performance. Despite these similarities, O2 pulse and HR during submaximal exercise was improved after CT only, which suggests superior adaptations of cardiac health after CT compared to SIT. In addition, SIT improved RSA significantly more compared to CT. The present study therefore suggest that training-specific adaptations occur after sprint interval running and continuous running with moderate intensity. The presumption of training-specific adaptations should be taken into consideration when composing an optimal endurance training program. Author Contributions: Conceptualization, S.L., E.E., M.S., L.S., E.J., T.S. and J.J.; methodology, S.L., E.E., M.S., L.S., E.J., and J.J.; formal analysis, S.L.; investigation, S.L., E.E., M.S., L.S., E.J. and J.J.; writing—original draft preparation, S.L.; writing—review and editing, E.E., M.S., L.S., E.J., T.S. and J.J.; supervision, E.E. and J.J.; project administration, S.L., M.S. and L.S. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Acknowledgments: The authors thank Puma, Norway, for supplying subjects with training equipment. We also thank Line Hårklau for technical assistance and Kristoffer Jensen Kolnes for help as training instructor. Conflicts of Interest: The authors declare no conflict of interest. References 1. Laursen, P.B.; Jenkins, D.G. The scientific basis for high-intensity interval training: Optimising training programmes and maximising performance in highly trained endurance athletes. Sports Med. 2002, 32, 53–73. [CrossRef] [PubMed] 2. Seiler, S.; Tønnessen, E. Intervals, Thresholds, and Long Slow Distance: The Role of Intensity and Duration in Endurance Training. Sport Sci. 2009, 13, 32–53. 3. Gibala, M.J.; Little, J.P.; Van Essen, M.; Wilkin, G.P.; Burgomaster, K.A.; Safdar, A.; Raha, S.; Tarnopolsky, M.A. Short-term sprint interval versus traditional endurance training: Similar initial adaptations in human skeletal muscle and exercise performance. J. Physiol. 2006, 575 Pt 3, 901–911. [CrossRef] 4. Burgomaster, K.A.; Howarth, K.R.; Phillips, S.M.; Rakobowchuk, M.; MacDonald, M.J.; McGee, S.L.; Gibala, M.J. Similar metabolic adaptations during exercise after low volume sprint interval and traditional endurance training in humans. J. Physiol. 2008, 586, 151–160. [CrossRef] [PubMed] 5. Burgomaster, K.A.; Hughes, S.C.; Heigenhauser, G.J.; Bradwell, S.N.; Gibala, M.J. Six sessions of sprint interval training increases muscle oxidative potential and cycle endurance capacity in humans. J. Appl. Physiol. 2005, 98, 1985–1990. [CrossRef] 6. MacDougall, J.D.; Hicks, A.L.; MacDonald, J.R.; McKelvie, R.S.; Green, H.J.; Smith, K.M. Muscle performance and enzymatic adaptations to sprint interval training. J. Appl. Physiol. 1998, 84, 2138–2142. [CrossRef] 7. Burgomaster, K.A.; Heigenhauser, G.J.; Gibala, M.J. Effect of short-term sprint interval training on human skeletal muscle carbohydrate metabolism during exercise and time-trial performance. J. Appl. Physiol. 2006, 100, 2041–2047. 8. Harmer, A.R.; McKenna, M.J.; Sutton, J.R.; Snow, R.J.; Ruell, P.A.; Booth, J.; Thompson, M.W.; Mackay, N.A.; Stathis, C.G.; Crameri, R.M.; et al. Skeletal muscle metabolic and ionic adaptations during intense exercise following sprint training in humans. J. Appl. Physiol. 2000, 89, 1793–1803. [CrossRef] 9. Hazell, T.J.; MacPherson, R.E.; Gravelle, B.M.; Lemon, P.W. 10 or 30-s sprint interval training bouts enhance both aerobic and anaerobic performance. Eur. J. Appl. Physiol. 2010, 110, 153–160. Int. J. Environ. Res. Public Health 2020, 17, 3865 11 of 12 10. Macpherson, T.W.; Weston, M. The effect of low-volume sprint interval training on the development and subsequent maintenance of aerobic fitness in soccer players. Int. J. Sports Physiol. Perform. 2015, 10, 332–338. 11. Naves, J.P.A.; Viana, R.B.; Rebelo, A.C.S.; de Lira, C.A.B.; Pimentel, G.D.; Lobo, P.C.B.; de Oliveira, J.C.; Ramirez-Campillo, R.; Gentil, P. Effects of High-Intensity Interval Training vs. Sprint Interval Training on Anthropometric Measures and Cardiorespiratory Fitness in Healthy Young Women. Front. Physiol. 2018, 9, 1738. [CrossRef] [PubMed] 12. Yamagishi, T.; Babraj, J. Effects of reduced-volume of sprint interval training and the time course of physiological and performance adaptations. Scand. J. Med. Sci. Sports 2017, 27, 1662–1672. [CrossRef] [PubMed] 13. Vollaard, N.B.J.; Metcalfe, R.S. Research into the Health Benefits of Sprint Interval Training Should Focus on Protocols with Fewer and Shorter Sprints. Sports Med. 2017, 47, 2443–2451. [CrossRef] [PubMed] 14. Sloth, M.; Sloth, D.; Overgaard, K.; Dalgas, U. Effects of sprint interval training on VO2max and aerobic exercise performance: A systematic review and meta-analysis. Scand. J. Med. Sci. Sports 2013, 23, e341–e352. [CrossRef] 15. Ratel, S.; Williams, C.A.; Oliver, J.; Armstrong, N. Effects of age and mode of exercise on power output profiles during repeated sprints. Eur. J. Appl. Physiol. 2004, 92, 204–210. [CrossRef] 16. Achten, J.; Venables, M.C.; Jeukendrup, A.E. Fat oxidation rates are higher during running compared with cycling over a wide range of intensities. Metabolism 2003, 52, 747–752. [CrossRef] 17. Millet, G.P.; Vleck, V.E.; Bentley, D.J. Physiological differences between cycling and running: Lessons from triathletes. Sports Med. 2009, 39, 179–206. [CrossRef] 18. Kavaliauskas, M.; Jakeman, J.; Babraj, J. Early Adaptations to a Two-Week Uphill Run Sprint Interval Training and Cycle Sprint Interval Training. Sports 2018, 6, 72. [CrossRef] 19. Bangsbo, J.; Gunnarsson, T.P.; Wendell, J.; Nybo, L.; Thomassen, M. Reduced volume and increased training intensity elevate muscle Na+-K+ pump alpha2-subunit expression as well as short- and long-term work capacity in humans. J. Appl. Physiol. 2009, 107, 1771–1780. [CrossRef] 20. Iaia, F.M.; Hellsten, Y.; Nielsen, J.J.; Fernström, M.; Sahlin, K.; Bangsbo, J. Four weeks of speed endurance training reduces energy expenditure during exercise and maintains muscle oxidative capacity despite a reduction in training volume. J. Appl. Physiol. 2009, 106, 73–80. [CrossRef] 21. Iaia, F.M.; Thomassen, M.; Kolding, H.; Gunnarsson, T.; Wendell, J.; Rostgaard, T.; Nordsborg, N.; Krustrup, P.; Nybo, L.; Hellsten, Y.; et al. Reduced volume but increased training intensity elevates muscle Na+-K+ pump alpha1-subunit and NHE1 expression as well as short-term work capacity in humans. Am. J. Physiol. Regul. Integr. Comp. Physiol. 2008, 294, R966–R974. [CrossRef] [PubMed] 22. Macpherson, R.E.; Hazell, T.J.; Olver, T.D.; Paterson, D.H.; Lemon, P.W. Run sprint interval training improves aerobic performance but not maximal cardiac output. Med. Sci. Sports Exerc. 2011, 43, 115–122. 23. Midgley, A.W.; Bentley, D.J.; Luttikholt, H.; McNaughton, L.R.; Millet, G.P. Challenging a dogma of exercise physiology: Does an incremental exercise test for valid VO 2 max determination really need to last between 8 and 12 minutes? Sports Med. 2008, 38, 441–447. 24. Grendstad, H.; Nilsen, A.K.; Rygh, C.B.; Hafstad, A.; Kristoffersen, M.; Iversen, V.V.; Nybakken, T.; Vestbøstad, M.; Algrøy, E.A.; Sandbakk, Ø.; et al. Physical capacity, not skeletal maturity, distinguishes competitive levels in male Norwegian U14 soccer players. Scand. J. Med. Sci. Sports 2020, 30, 254–263. [CrossRef] [PubMed] 25. Edgett, B.A.; Bonafiglia, J.T.; Raleigh, J.P.; Rotundo, M.P.; Giles, M.D.; Whittall, J.P.; Gurd, B.J. Reproducibility of peak oxygen consumption and the impact of test variability on classification of individual training responses in young recreationally active adults. Clin. Physiol. Funct. Imaging 2018, 38, 630–638. [CrossRef] [PubMed] 26. Girard, O.L.; Mendez-Villanueva, A.; Bishop, D. Repeated-sprint ability—Part I: Factors contributing to fatigue. Sports Med. 2011, 41, 673–694. [CrossRef] [PubMed] 27. Liu, N.Y.; Plowman, S.A.; Looney, M.A. The reliability and validity of the 20-meter shuttle test in American students 12 to 15 years old. Res. Q. Exerc. Sport 1992, 63, 360–365. 28. Sandvei, M.; Jeppesen, P.B.; Stoen, L.; Litleskare, S.; Johansen, E.; Stensrud, T.; Enoksen, E.; Hautala, A.; Martinmäki, K.; Kinnunen, H.; et al. Sprint interval running increases insulin sensitivity in young healthy subjects. Arch. Physiol. Biochem. 2012, 118, 139–147. [CrossRef] Int. J. Environ. Res. Public Health 2020, 17, 3865 12 of 12 29. Bouchard, C.; Rankinen, T. Individual differences in response to regular physical activity. Med. Sci. Sports Exerc. 2001, 33 (Suppl. 6), S446–S451, discussion S452–S453. [CrossRef] 30. Hautala, A.J.; Kiviniemi, A.M.; Mäkikallio, T.H.; Kinnunen, H.; Nissilä, S.; Huikuri, H.V.; Tulppo, M.P. Individual differences in the responses to endurance and resistance training. Eur. J. Appl. Physiol. 2006, 96, 535–542. 31. Bouchard, C.; Sarzynski, M.A.; Rice, T.K.; Kraus, W.E.; Church, T.S.; Sung, Y.J.; Rao, D.C.; Rankinen, T. Genomic predictors of the maximal O2 uptake response to standardized exercise training programs. J. Appl. Physiol. 2011, 110, 1160–1170. [CrossRef] [PubMed] 32. Jones, A.M.; Carter, H. The effect of endurance training on parameters of aerobic fitness. Sports Med. 2000, 29, 373–386. [CrossRef] [PubMed] 33. Seo, D.Y.; Kwak, H.B.; Kim, A.H.; Park, S.H.; Heo, J.W.; Kim, H.K.; Ko, J.R.; Lee, S.J.; Bang, H.S.; Sim, J.W.; et al. Cardiac adaptation to exercise training in health and disease. Pflug. Arch. 2020, 472, 155–168. [CrossRef] [PubMed] 34. Edge, J.; Bishop, D.; Goodman, C.; Dawson, B. Effects of high- and moderate-intensity training on metabolism and repeated sprints. Med. Sci. Sports Exerc. 2005, 37, 1975–1982. [CrossRef] 35. Glaister, M.; Stone, M.H.; Stewart, A.M.; Hughes, M.G.; Moir, G.L. The influence of endurance training on multiple sprint cycling performance. J. Strength Cond. Res. 2007, 21, 606–612. 36. Saunders, P.U.; Pyne, D.B.; Telford, R.D.; Hawley, J.A. Factors affecting running economy in trained distance runners. Sports Med. 2004, 34, 465–485. 37. Iaia, F.M.; Bangsbo, J. Speed endurance training is a powerful stimulus for physiological adaptations and performance improvements of athletes. Scand. J. Med. Sci. Sports 2010, 20 (Suppl. 2), 11–23. 38. Rynecki, N.D.; Siracuse, B.L.; Ippolito, J.A.; Beebe, K.S. Injuries sustained during high intensity interval training: Are modern fitness trends contributing to increased injury rates? J. Sports Med. Phys. Fit. 2019, 59, 1206–1212. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Sprint Interval Running and Continuous Running Produce Training Specific Adaptations, Despite a Similar Improvement of Aerobic Endurance Capacity-A Randomized Trial of Healthy Adults.

05-29-2020

Litleskare, Sigbjørn,Enoksen, Eystein,Sandvei, Marit,Støen, Line,Stensrud, Trine,Johansen, Egil,Jensen, Jørgen

eng

PMC8505335

Vol.:(0123456789) 1 3 European Journal of Applied Physiology (2021) 121:3083–3093 https://doi.org/10.1007/s00421-021-04763-9 ORIGINAL ARTICLE Training status affects between‑protocols differences in the assessment of maximal aerobic velocity Andrea Riboli1  · Susanna Rampichini1  · Emiliano Cè1  · Eloisa Limonta1  · Marta Borrelli1  · Giuseppe Coratella1  · Fabio Esposito1,2 Received: 15 February 2021 / Accepted: 2 July 2021 / Published online: 28 July 2021 © The Author(s) 2021 Abstract Purpose Continuous incremental protocols (CP) may misestimate the maximum aerobic velocity (Vmax) due to increases in running speed faster than cardiorespiratory/metabolic adjustments. A higher aerobic capacity may mitigate this issue due to faster pulmonary oxygen uptake ( ̇VO2) kinetics. Therefore, this study aimed to compare three different protocols to assess Vmax in athletes with higher or lower training status. Methods Sixteen well-trained runners were classified according to higher (HI) or lower (LO) ̇VO2max ̇VO2-kinetics was calculated across four 5-min running bouts at 10 km·h−1. Two CPs [1 km·h−1 per min (CP1) and 1 km·h−1 every 2-min (CP2)] were performed to determine Vmax ̇VO2max, lactate-threshold and submaximal ̇VO2/velocity relationship. Results were compared to the discontinuous incremental protocol (DP). Results Vmax, ̇VO2max, ̇VCO2 and VE were higher [(P < 0.05,(ES:0.22/2.59)] in HI than in LO. ̇VO2-kinetics was faster [P < 0.05,(ES:-2.74/ − 1.76)] in HI than in LO. ̇VO2/velocity slope was lower in HI than in LO [(P < 0.05,(ES:-1.63/ − 0.18)]. Vmax and ̇VO2/velocity slope were CP1 > CP2 = DP for HI and CP1 > CP2 > DP for LO. A lower [P < 0.05,(ES:0.53/0.75)] Vmax-difference for both CP1 and CP2 vs DP was found in HI than in LO. Vmax-differences in CP1 vs DP showed a large inverse correlation with Vmax, ̇VO2max and lactate-threshold and a very large correlation with ̇VO2-kinetics. Conclusions Higher aerobic training status witnessed by faster ̇VO2 kinetics led to lower between-protocol Vmax differences, particularly between CP2 vs DP. Faster kinetics may minimize the mismatch issues between metabolic and mechanical power that may occur in CP. This should be considered for exercise prescription at different percentages of Vmax. Keywords ̇VO2 kinetics · Maximal aerobic power · Maximum oxygen uptake · Incremental test · Running velocity · Aerobic capacity Abbreviations HI Group with high ̇VO2max LO Group with low ̇VO2max CP1 Continuous incremental protocol [1 km·h-1 per min] CP2 Continuous incremental protocol [1 km·h-1 every 2 min] DP Discontinuous incremental protocol ̇VO2max Maximum oxygen uptake ̇VO2/Velocity slope Regression analysis of the ̇VO2 vs velocity relationship at submaximal workloads ̇VO2 kinetics ̇VO2-transition from rest to steady-condition Vmax The velocity associated with maxi- mum oxygen uptake ̇VCO2 Carbon dioxide production RER Respiratory exchange ratio SaO2 Arterial O2 saturation ̇VE Expiratory ventilation BLa- Blood lactate concentration RPE Rate of perceived exertion ANOVA Analysis of variance ES Effect size 95% CI 95% Confidence intervals Communicated by Guido ferrati. * Andrea Riboli [email protected] 1 Department of Biomedical Sciences for Health (SCIBIS), University of Milan, Via G. Colombo 71, 20133 Milan, Italy 2 IRCCS, Istituto Ortopedico Galeazzi, Via R. Galeazzi 4, 20161 Milan, Italy 3084 European Journal of Applied Physiology (2021) 121:3083–3093 1 3 Introduction A successful aerobic performance depends on several physiological, biomechanical, and psychological factors (Bentley et al. 2007; Coyle 1995). Among physiological aspects, a high maximum pulmonary oxygen uptake ( ̇V O2max), the ability to maintain a long time to exhaustion at V̇O2max, a faster ̇VO2-transition from rest to steady- condition ( ̇VO2 kinetics), a higher lactate threshold and a low O2 cost of running are the main parameters of aerobic performance (Poole and Richardson 1997; Coyle 1995; Poole and Jones 2012). Also the maximum aerobic velocity (Vmax), defined as the minimum velocity capable to elicit ̇VO2max when considering only the completion of the primary phase of ̇VO2-on kinetics (Ferretti 2015), is reported as a strong marker of running performance (Bentley et al. 2007) and it integrates both metabolic and biomechanical aspects of running into a single factor (Buchheit and Laursen 2013). In elite aerobic athletes, a higher Vmax reflects a greater capacity to utilize the aerobic metabolic pathways across several sports (Noakes 1988; Pedro et al. 2013; Ziogas et al. 2011; Rampinini et al. 2007). ̇VO2max and Vmax are generally determined using dif- ferent incremental running protocols (Kuipers et al. 2003; Riboli et al. 2017), among which continuous or discontinu- ous tests that may vary in work rate increments and stage duration (Billat et al. 1996; Kuipers et al. 2003; Riboli et al. 2017). Discontinuous incremental protocols (DP) are characterized by constant work rates interspersed by resting periods (Duncan et al. 1997; Riboli et al. 2017). DP permits to reach an equilibrium between the cardiorespira- tory and metabolic systems and the work rate when lasting at least three minutes to achieve a steady-state condition (Poole and Jones 2012). However, the long overall dura- tion of DP would markedly lengthen the whole testing phase, thus affecting the possibility to test several athletes within one single session, as often required in sports prac- tice. Conversely, incremental continuous protocols (CP) last short overall duration and they have been shown as a valid and reliable method to determine ̇VO2max despite the submaximal physiological adjustments cannot be reached as in DP due to increments in work rate faster than cardi- orespiratory and metabolic adjustments (Riboli et al. 2017, 2021). Despite in some intermittent protocols with very low workload vs recovery ratio ̇VO2max may not be reached (Vinetti et al. 2017), previous studies using CP and DP showed that ̇VO2max was found to be independent from the protocol adopted (Kuipers et al. 2003; Riboli et al. 2017, 2021). Conversely, testing protocols with shorter stage duration may lead to higher Vmax (Riboli et al. 2017; Kuipers et al. 2003; Adami et al. 2013). Given that Vmax is currently utilized to prescribe or monitor training routines (Buchheit and Laursen 2013; Riboli et al. 2021), a precise Vmax assessment may allow coaches to manipulate accu- rately the physiological load during running exercises as a percentage of Vmax (Buchheit and Laursen 2013; Riboli et al. 2021). For instance, 90–110% of Vmax are suggested for long-interval exercises, 110–130% Vmax for short-inter- vals exercises, 130–160% Vmax for repeated sprint train- ing and > 160% Vmax for sprint interval training (Buchheit and Laursen 2013). Therefore, a precise Vmax assessment should be carefully taken into account for athletes’ test- ing and training prescription (Riboli et al. 2017; Bentley et al. 2007). Athletes with a high aerobic capacity (HI), such as long- and middle-distance runners, are qualified by greater physi- ological characteristics in terms of high ̇VO2max and fast ̇VO2 kinetics (Coyle 1995) than in individuals with lower aerobic capacity (LO). A high ̇VO2max represents, indeed, a pronounced maximal pulmonary, cardiovascular, metabolic and muscular capacity to uptake, transport and utilize O2 (Poole and Richardson 1997). Moreover, rapid ̇VO2 kinetics may lead to a smaller O2 deficit and a reduced intracellu- lar perturbation, thus reflecting greater exercise tolerance (Poole and Jones 2012; Dupont et al. 2005) and endurance performance (Poole and Jones 2012). These characteristics in HI may therefore lower or even minimize the misestimat- ing issue that may occur in CP because of their faster ̇VO2 kinetics. With this in mind, the present study aimed to investi- gate how aerobic training status may affect Vmax assessment during CPs vs DP in two groups of athletes, characterized by different aerobic training conditions. Should HI in the investigated group demonstrate faster ̇VO2 kinetics due to their greater ability of the cardiorespiratory and metabolic systems to adjust to continuous increases in work rate typi- cal of CP, the Vmax misestimating issue may be minimized, when comparing their CPs to DP results. Materials and methods Participants Sixteen well-trained middle and long-distance runners (age: 22.1 ± 1.8 years; stature: 1.75 ± 0.05 m; body mass: 70.3.7 ± 3.7 kg; mean ± standard deviation) volunteered to participate in the study and were classified into two groups, according to their higher (HI) or lower (LO) ̇VO2max and the International Physical Activity Questionnaire (IPAQ). All participants met the following criteria: (a) more than four years of systematic training and (b) no injuries in the last year. The ethics committee of the local University approved the study (protocol #102/14) which was performed in 3085 European Journal of Applied Physiology (2021) 121:3083–3093 1 3 accordance with the principles of the Declaration of Hel- sinki (1964 and updates). All participants gave their written consent after a full explanation of the purpose of the study and the experimental design. Study design To test the current hypothesis, two incremental continuous protocols with different stage durations (CP) were performed and compared to a discontinuous incremental protocol (DP). The present study spanned over a maximum of 3 weeks. The participants reported to the laboratory five times, separated by at least 72 h. During the first visit, they were familiar- ized with the experimental procedures. During the second session, they performed a continuous incremental protocol (1 km⋅h−1 per minute) to determine ̇VO2max and to complete the IPAQ. Within the remaining three sessions, the partici- pants randomly underwent the three experimental condi- tions (two continuous and one discontinuous incremental protocols). Within each testing-session, an initial 5-min submaximal bout at 10 km⋅h−1 was modelled to determine the on-transient ̇VO2 kinetics. Participants were instructed to avoid any form of strenuous exercise in the three days before each session. In addition, they were asked to have their last standardized meal at least three hours before each session. Finally, they were requested to abstain from ergogenic and caffeinated beverages before testing. Participants were split subsequently into two groups, according to their ̇VO2max normalized per body mass (ml·kg−1·min−1) and their training routines (i.e., n of train- ing sessions per week). The first HI group was characterized by a higher ̇VO2max and more than five training sessions per week. The second LO group was characterized by a lower ̇VO2max and no more than three training sessions per week. Experimental procedures All tests were conducted approximately at the same time of the day in a climate-controlled laboratory (constant tem- perature of 20 ± 1 °C and relative humidity of 50 ± 5%). All tests were carried out on a treadmill ergometer (RAM s.r.l., mod. 770 S, Padova, Italy) with a 1% positive slope. Blood lactate concentration (BLa−) was assessed by a spectrophotometric system (Lactate Pro LT-1710, Arkray, Kyoto, Japan). The lactate analyzer was calibrated before each protocol to guarantee consistent data. ̇VO2max, expira- tory ventilation, carbon dioxide production and respiratory exchange ratio were measured during each protocol by a gas analyzer cart (Cosmed, mod. Quark b2, Rome, Italy). The device was calibrated before each test with gas mixtures of known concentration (O2 16%, CO2 5%, balance N2). Heart rate was monitored continuously using a heart rate monitor (Polar Electro Oy, mod. S810i, Kempele, Finland). Arterial O2 saturation was determined by a finger-tip infrared oxym- eter (NONIN Medical, mod. 3011, Minneapolis, MN). At the end of the test, the rate of perceived exertion (RPE) was determined using the 6–20 Borg scale for general, respira- tory and muscular fatigue. The participants were strongly encouraged by the operators to perform each test up to their maximum exercise capacity. Continuous Incremental Protocol 1 (CP1). After 5 min of baseline measurements, while standing on the treadmill, the participants warmed up at 10 km⋅h−1 for 5 min. Then, the running speed was increased progressively by 1 km⋅h−1 per minute until volitional exhaustion. BLa− was meas- ured at baseline, at the end of each stage and after 1, 3 and 5 min of passive recovery. The achievement of V̇O2max was identified as the plateauing of ̇VO2 (< 2.1 ml·kg−1·min−1 increase) despite an increase in workload (Poole and Rich- ardson 1997). If the above-stated criterion and/or second- ary criteria to establish ̇VO2max (Poole et al. 2008) were not fulfilled, the participants were asked to perform a further constant-speed test equal or higher than the highest speed achieved at the end of the incremental test, as strongly rec- ommended (Rossiter et al. 2006). ̇VO2, carbon dioxide pro- duction, expiratory ventilation, O2 saturation and respiratory exchange ratio were averaged during the last 30 s of each step at submaximal workload and over the last 30 s before exhaustion. Vmax was determined as the minimal running velocity that elicited ̇VO2max over a period of 30 s (Billat et al. 1996). If a stage could not be completed, the Vmax was calculated according to a previously published equation (Kuipers et al. 2003) [Vmax = Vcompleted + t/T x speed incre- ment], in which Vcompleted is the running speed of the last stage that was completed, t the number of seconds that the uncompleted running stage could be sustained, T the number of seconds required to complete the stage, and speed incre- ment is the speed load increment in km⋅h−1. Continuous Incremental Protocol 2 (CP2). CP2 followed the same experimental procedures as CP1, but with the increases in treadmill running speed of 1 km⋅h−1 every two minutes. As for CP1, ̇VO2, carbon dioxide production, expir- atory ventilation, O2 saturation, and respiratory exchange ratio were averaged during the last 30 s of each step at sub- maximal workload and over the last 30 s before exhaustion. Vmax was determined as the minimal running velocity that elicited ̇VO2max over a period of 30 s (Billat et al. 1996). Discontinuous Incremental Protocol (DP). DP protocol involved five workloads of 4 min each, interspersed by at least 5 min of recovery (Bernard et al. 2000). The optimal stage duration suggested for DPs is still questioned (Bernard et al. 2000). Although some authors suggested that it should be around 6–8 min (Bernard et al. 2000), it was criticized that relatively long stage duration could result in prema- ture fatigue and suggested that 4–6 min could be suitable for this purpose (Bentley et al. 2007; Kuipers et al. 2003; 3086 European Journal of Applied Physiology (2021) 121:3083–3093 1 3 Bernard et al. 2000). Since shorter test duration is strongly advocated during in-field practice, a 4-min stage duration was used here. Baseline measurements were recorded with the partici- pants standing on the treadmill. The first two workloads were set at 8 and 10 km·h−1 for all participants. The following three workloads were tailored for each participant according to the individual cardiorespiratory responses to the first two workloads and considering the theoretical maximum heart- rate determined (Bernard et al. 2000). Firstly, based on the ̇VO2 and the heart-rate recorded during the first two stages, a sub-maximal linear regression was determined up to the predicted peak heart rate, to predict the speed correspond- ing to possible exhaustion (Bernard et al. 2000). Then, the third, the fourth and the fifth workloads corresponded to approximately 80%, 90% and 105% of the predicted peak workload, respectively. The fourth and the fifth workloads were recalculated using the heart-rate and ̇VO2 recorded dur- ing the third and the fourth stage, respectively. The last stage was tailored to let the participants maintain the task for at least four minutes (Bernard et al. 2000). The blood lactate concentration was measured at baseline and after 1, 3 and 5 min of passive recovery for each workload, and the peak blood lactate was inserted into the data analysis. ̇VO2, car- bon dioxide production, expiratory ventilation, O2 saturation and respiratory exchange ratio were determined as the aver- age value of the last (fourth) minute during each workload (Poole and Richardson 1997). Vmax was extrapolated from the regression analysis equation of ̇VO2 as a function of run- ning velocity at submaximal workloads below the lactate threshold (Bernard et al. 2000; Riboli et al. 2017). Lactate threshold, ̇VO2/Velocity slope at submaximal exercise and ̇VO2 kinetics Lactate threshold was determined by the DMAX method, according to which it was identified as the point on the third-order polynomial curve that yielded the maximal per- pendicular distance to the straight line formed by the two end data points (Riboli et al. 2019). Similar to the previous study, lactate threshold calculated from CP1 was utilized to limit the range of exercise during which the ̇VO2 vs running velocity relationship at submaximal exercise was considered (Riboli et al. 2017). ̇VO2/Velocity slope: the ̇VO2/Velocity slope was calcu- lated as the regression analysis of the ̇VO2 vs velocity rela- tionship at submaximal workloads below lactate threshold for CP1, CP2 and DP (Anderson 1996; Fletcher et al. 2009). ̇VO2 kinetics. The on-transient ̇VO2 kinetics were mod- elled after four different bouts of 5-min submaximal exer- cise (10 km·h−1, moderate intensity, below lactate thresh- old) to avoid any effect of the slow component phenomenon (Jones et al. 2011). The influence of the inter-breath noise was reduced averaging the results of four identical tests in each participant (Lamarra et al. 1987). Each abnormal breath (e.g., different from the mean of the adjacent four data point by more than three times the standard-deviation of those four point, were excluded (Dupont et al. 2005). To increase the time resolution the breath-by-breath ̇VO2 data were sub- sequently linearly interpolated, and the four data sets were averaged together to produce a single response for each sub- ject. This procedure was previously established to reduce the noise of the ̇VO2 signal and to provide the highest confident results (Poole and Jones 2012). The on-transient of the ̇V O2 kinetics were modelled as previously proposed (Barstow and Mole 1991). The time-delay of the cardiodynamic-phase and the time-constant of the primary-phase (i.e., the time to reach 63% of the ̇VO2 steady-state of the ̇VO2 kinetics were calculated to determine the amplitude of ̇VO2 from baseline to steady-state (Poole and Jones 2012). Then, the mean response time of the on-transition ̇VO2 kinetics as the sum of time-delay and time-constant was calculated. The time-delay, the time-constant and the mean response time were thereafter inserted into data analysis. Statistical analysis Statistical analysis was performed using a statistical software package (Sigma Plot for Windows, v 12.5, Systat Software Inc., San Jose, CA, USA). To check the normal distribution of the sampling, a Kolgomorov-Smirnov test was applied. A one-way analysis of variance (ANOVA) for repeated meas- ures was used also to assess significant differences in Vmax, ̇VO2max, carbon dioxide production, respiratory exchange ratio, arterial O2 saturation, heart-rate, expiratory ventila- tion, blood lactate concentration, ̇VO2/Velocity slope (for both slope and intercept of the submaximal regression analy- sis equation), ̇VO2 kinetics (time-delay, time-constant and mean-response time), general-, muscular-, and respiratory- RPE between CP1, CP2 and DP. For all pairwise multiple comparisons, a post-hoc Shapiro–Wilk test was applied. A regression analysis was used to assess the relationship between ̇VO2 and running velocity at submaximal exercise. The magnitude of the changes was assessed using Cohen’s standardized effect size (ES) with 95% confidence inter- vals (95% CI). Effect size with 95% CI was calculated and interpreted as follows: < 0.20: trivial; 0.20–0.59: small; 0.60–1.19: moderate; 1.20–1.99: large; ≥ 2.00: very large (Hopkins et al. 2009). Pearson’s product moment and 95% CI were utilized to assess the relationship among protocols for Vmax. The correlation coefficients were interpreted as follows: r < 0.1 trivial; 0.1 ≤ r < 0.3 small; 0.3 ≤ r < 0.5 mod- erate; 0.5 ≤ r < 0.7 large; 0.7 ≤ r < 0.9 very large; 0.9 ≤ r < 1 nearly perfect. Statistical significance was set at an α level of 0.05. Unless otherwise stated, all values are presented as mean ± standard deviation (SD). 3087 European Journal of Applied Physiology (2021) 121:3083–3093 1 3 Results Between‑groups differences As shown in Table 1, Vmax [P < 0.001, (ES:1.85/2.59)], ̇VO2max [P < 0.001, (ES:0.85/1.07)], VCO2 [P < 0.001, (ES:0.22/0.61)] and VE [P < 0.001, (ES:0.57/0.82] were small to very largely higher in HI than LO within-each protocol (CP1, CP2 and DP) (Table 1). No between-groups differences (P > 0.05) in respiratory exchange ratio, arte- rial O2 saturation, heart rate, BLa− peak, general-, respira- tory-, and muscular-RPE were found. The lactate threshold calculated in CP1 was moder- ately [ES:1.99(CI:0.79/3.19)] higher (P < 0.001) in HI [17.8(1.1)] than LO [16.1(0.3)]. Overall, the submaxi- mal regression analysis of ̇VO2/velocity relationship for CP1, CP2 and DP was less steep (P < 0.05) in HI than LO (Fig. 1); in details, the intercept of the submaximal regression analysis in ̇VO2/velocity relationship ( ̇VO2/ velocity intercept) was moderately to largely (ES:-0.86/- 1.63) lower (P < 0.05) in HI than LO within-each protocol (CP1, CP2 and DP). The slope of the submaximal regres- sion analysis in ̇VO2/velocity relationship ( ̇VO2/velocity slope) showed trivial to moderate (ES:-0.18/0.83) not significant (P > 0.05) differences between HI and LO in CP1, CP2 and DP. The ̇VO2 kinetics was largely to very largely (ES: -2.74/-1.76) faster (P > 0.05) in HI than LO: despite small [ES:-0.36(CI: -1.35/0.63] non-significant differences (P > 0.05) in time-delay, HIGH showed a large [ES:-1.76(CI:−2.92/−0.61] and very-large [ES:- 2.74(−4.10/−1.37)] difference with a faster time-constant and mean-response time than LO, respectively (Fig. 2). Between‑protocols differences at maximal exercise As shown in Table 1, Vmax was largely higher in CP1 vs DP for both HI [P < 0.001, ES:1.96(0.77/3.16)] and LO [P < 0.001, ES: 1.84(0.67/3.01)]. In CP1 vs CP2, Vmax was largely higher for HI [P < 0.001, ES: 1.73, CI: 0.58/2.88)] and moderately higher for LO [P = 0.006, ES: 1.11(0.06/2.17]. In CP2 vs DP, Vmax was moderately higher for LO [P = 0.039, ES: 0.75(−0.26/1.76)], while small not significant Vmax-difference for HI [P = 0.102, ES: 0.30(−0.68/1.29)] were retrieved. No between-protocol (CP1 vs CP2 vs DP) differences for maximum ̇VO2, VCO2, RER, SaO2, fH, VE and BLa− peak were found for both HI and LO. Similarly, no between-pro- tocol differences in general-, respiratory- and muscular-RPE were found. Between‑protocols differences at submaximal exercise As shown in Fig.  1, ̇VO2/velocity slope showed a moderate difference in CP1 vs DP for HI [P = 0.003, ES:−0.85(−1.88/−0.17)] and a large difference for LO [P = 0.002, ES: −1.75(−2.91/−0.60)]. In CP1 vs CP2, ̇VO2/ velocity slope showed a small difference for HI [P = 0.003, Table 1 Cardiorespiratory, metabolic, and perceptual variables at maximum exercise for HI and LO groups. Mean (SD) Vmax velocity associated with maximum oxygen uptake; ̇VO2 oxygen uptake; ̇VCO2 carbon dioxide production; RER respiratory exchange ratio; SaO2 arterial O2 saturation; fH heart rate frequency; ̇VE, expiratory ventilation; BLa− peak peak blood lactate concentration; and rate of perceived exertion (RPE) at general, respiratory, and muscular level. Variables were determined at maximum exercise in the three testing conditions (CP1, continuous ramp 1; CP2, continuous ramp 2; DP, discontinuous protocol). * P < 0.05 vs DP; **P < 0.05 vs CP1; ***P < 0.05 vs HI HI LO CP1 CP2 DP CP1 CP2 DP Vmax (km·h−1) 22.1 (1.2)* 19.9 (1.2)*, ** 19.5 (1.3) 19.1 (1.8)*, *** 17.2 (1.4) *,**,*** 16.2 (1.1)*** ̇VO2 (ml·min−1) 4169.6 (478.9) 4132.8 (134.2) 4158.8 (473.5) 3912.0 (442.6)*** 3907.8 (356.4) *** 3895.3 (424.9) § ̇VO2 (ml·kg·min−1) 59.2 (5.2) 58.7 (5.4) 59.1 (5.2) 54.6 (4.8)*** 54.4 (4.1) *** 54.5(2.5)*** ̇VCO2 (ml·min−1) 4581.9 (510.4) 4492.8 (110.8) 4665.2 (442.0) 4465.8 (494.7)*** 4366.4 (473.0) *** 4371.7 (463.0) *** RER 1.10 (0.09) 1.09 (0.03) 1.13 (0.04) 1.13 (0.06) 1.11 (0.06) 1.12 (0.06) SaO2 (%) 89.8 (2.7) 89.6 (1.8) 89.8 (2.7) 91.0 (1.7) 90.6 (2.7) 90.1 (2.7) fH (beats·min−1) 188.0 (10.0) 188 (10.0) 186.0 (7.0) 189.0 (1.0) 188.0 (5.0) 187.0 (7.0) ̇V E (l·min−1) 166.9 (19.4) 164.1 (4.2) 163.3 (10.9) 155.1 (19.4)*** 156.2 (14.9) *** 155.4 (7.0) *** BLa− peak (mM) 13.0 (4.0) 11.4 (2.3) 12.5 (2.1) 11.4 (1.3) 11.9 (1.0) 11.8 (0.8) General RPE (au) 18.2 (1.2) 17.9 (1.3) 18.0 (1.3) 18.1 (2.1) 18.3 (1.5) 18.9 (1.2) Respiratory RPE (au) 18.5 (1.2) 17.7 (1.4) 17.7 (1.4) 17.6 (3.1) 17.8 (1.7) 18.8 (1.0) Muscular RPE (au) 17.4 (1.5) 17.9 (1.8) 18.4 (1.5) 17.8 (1.7) 17.9 (2.6) 18.1 (1.9) 3088 European Journal of Applied Physiology (2021) 121:3083–3093 1 3 ES:−.48(−1.48/0.51)] and very large difference for LO [P = 0.007, ES: -5.97(−8.26/-3.68)]. In CP2 vs DP, ̇VO2/ velocity slope showed a trivial no-significant difference for HI [P = 0.283, ES: −0.20(−1.18/0.79)] and a very large dif- ference for LO [P = 0.016, ES: −2.33(−3.60/−1.06)]. In CP1 vs DP, ̇VO2/velocity intercept showed a small dif- ference for HI [P < 0.001, ES:0.21(−0.78/1.19)] and a mod- erate difference for LO [P = 0.002, ES: 0.99(0.05/2.03)]. In CP1 vs CP2, ̇VO2/velocity intercept showed a trivial differ- ence for HI [P = 0.010, ES:0.10(-0.88/1.08)] and a moderate difference for LO [P = 0.015, ES:0.61 (-0.36/1.60)]. In CP2 vs DP, ̇VO2/velocity intercept showed a trivial no-signifi- cant differences for HI [P = 0.348, ES: 0.00(−0.98/0.98)] and a very large difference for LO [P < 0.001, ES: 1.51(0.40/2.62)]. Between‑protocol Vmax correlations Very large between-protocol correlations for Vmax were cal- culated for HI (r = 0.73, r = 0.84, and r = 0.73 for CP1 vs DP, CP2 vs DP and CP1 vs CP2, respectively P < 0.05). Mod- erate to large between-protocol correlations for Vmax were calculated for LO (r = 0.49, r = 0.68, and r = 0.79 for CP1 vs DP, CP2 vs DP and CP1 vs CP2, respectively P < 0.05). Relationship between training status and between‑protocol differences The percentage of the Vmax in CP1 vs DP showed a small [P = 0.045, ES: -0.53 (-1.56/0.46)] difference between HI and LO [+ 13.3(5.4)% and + 17.9(10.2)%,, respectively] and a moderate [P = 0.032, ES:−0.75 (−1.76/0.26)] difference for CP2 vs DP [+ 6.2(6.6) and + 2.1(3.7)% for HI and LO, respectively]. As shown in Fig. 3, the percentage of the Vmax-difference in CP1 than DP showed an inversely large correlation with Vmax, ̇VO2max and the velocity at lactate threshold. Con- versely, the percentage of the Vmax-difference in CP1 than DP was largely correlated with the time-constant of the ̇V O2 kinetics. Discussion The main finding of the present study was that HI, with faster ̇VO2 kinetics, had lower differences in Vmax between CP and DP than LO. This observation may confirm the experimental hypothesis stating that athletes with higher aerobic capacity and faster ̇VO2 kinetics are able to adjust better to work rate increments typical of CP with short stage duration. Noticeably, HI had a similar Vmax in DP and CP2 (i.e., the continuous protocol with slower work rate incre- ments) and the difference in Vmax between CP1 and DP was lower than in LO. Lastly, the percentage of the Vmax differ- ences between CP1 and DP were inversely correlated with Vmax, ̇VO2max and directly correlated to the time-constant of the ̇VO2 kinetics, providing further evidence that between- protocol Vmax differences in HI are minimized likely because of their faster ̇VO2 kinetics. Fig. 1 The ̇VO2 as a function of running velocity at submaximal work rates (below the velocity corresponding to the lactate threshold cal- culated in CP1 condition) for both HI and LO. The solid, dashed and dotted lines represent the regression lines for the discontinuous (DP), continuous protocol with 1 km·h−1 increment per minute (CP1) and 2 km·h−1 increment every 2 min (CP2), respectively. Panel A and B show HIGH and LOW group, respectively. Regression equations (y = a · bx) and correlation coefficients are also reported. *P < 0.05 vs DP for slope and intercept of the regression equation, #P < 0.05 vs CP1 for slope of the regression equation, §P < 0.05 vs HI for the inter- cept of the regression equation 3089 European Journal of Applied Physiology (2021) 121:3083–3093 1 3 Preliminary considerations The present results came with no between-protocol differ- ences in ̇VO2max and in the other main cardiorespiratory and metabolic parameters in both HI and LO. Despite some previous findings about the effects of protocol (i.e. workload vs recovery ratio) on ̇VO2max (Vinetti et al. 2017), these findings reinforce previous data demonstrating that ̇VO2max was independent of the protocol adopted across different incremental testing procedures (Bentley et al. 2007; Billat et al. 1996; Riboli et al. 2017). The present outcomes are in line with previous literature, in which no differences in ̇V O2max were observed between protocols in different popu- lations, such as recreationally-active men (Kirkeberg et al. 2011), physically-active young adults (Riboli et al. 2017), semi-professional soccer players (Riboli et al. 2021) and competitive middle- and long-distance runners (Billat et al. 1996; Kuipers et al. 2003). Similar results were also found in moderately-active cyclists during cycle-ergometric evalu- ation (Adami et al. 2013). Maximum exercise The present findings demonstrate that Vmax was protocol- dependent, as also previously observed (Kuipers et al. 2003; Riboli et al. 2017, 2021). The steeper the work rate increase, the higher the Vmax in both groups. In LO Vmax differed in each protocol (i.e., CP1 > CP2 > DP). Conversely, in HI the Vmax differences between CP2 and DP were not present (i.e., CP1 > CP2 = DP). These findings suggest that higher aerobic capacity may minimize the between-protocol Vmax Fig. 2 The rate of ̇V O2 increase at submaximal exercise for both HI and LO. Panel A shows the rate of ̇VO2 increase ( ̇VO2 kinetic) for two representative subjects (HI: white circles; LO: black circles). The time-delay (Panel B), the time-constant (Panel C) and the mean- response time (Panel D) are illustrated for each subject (white circles) in HI (white bar) and LO (dark-grey bar) group. #P < 0.05 vs HI 3090 European Journal of Applied Physiology (2021) 121:3083–3093 1 3 differences due to the faster cardiorespiratory and metabolic adjustments to match the increasing mechanical power in CP. This explanation was further supported by the faster ̇VO2 kinetics in HI, in which no difference was found between CP2 and DP. On the contrary, in LO Vmax in CP2 was higher than in DP due to the slower ̇VO2 kinetics. A direct comparison with previous studies is challenging, as this was the first study investigating the effect of aerobic training status on Vmax. Previous studies observed a greater between-protocol difference when steeper work rate vs time increments were utilized (Kuipers et al. 2003; Riboli et al. 2017, 2021). Indeed, when comparing three CPs with 1-, 3- or 6-min stage duration in competitive middle-distance runners, Vmax was related to the slope of the work rate vs velocity increments (Kuipers et al. 2003). Similar results were found when a CP with different work rate vs veloc- ity increments was used during cycle ergometry in active people (Adami et al. 2013) or international competitive tri- athletes (Bentley and McNaughton 2003). Recently, greater peak mechanical power output was found also in healthy participants using a synchronous arm crank ergometry when work rate increments were steeper (Kouwijzer et al. 2019). Interestingly, when long-distance runners were tested using CP with different stage duration but similar slope in the velocity vs time increments (e.g., 1 km·h−1 increments every 2 min vs 0.5 km·h−1 increments every min), no difference in Vmax was detected (Billat et al. 1996). Similar findings were observed also in sedentary men on cycle ergometer (Zhang et al. 1991). Submaximal exercise A faster ̇V02 kinetics was observed in HI than in LO partici- pants during the test at 10 km/h, implying a more rapid car- diorespiratory and metabolic adjustment capacity to match mechanical power increase during incremental exercise. Previous investigations observed that athletes with a high aerobic capacity, such as long- and middle-distance run- ners, were qualified by greater physiological characteristics in terms of faster ̇VO2 kinetics (Poole and Jones 2012; Coyle 1995). In top-level aerobic athletes, indeed, an extremely short time (i.e., ~ 30 to ~ 40 s) is required to achieve a ̇VO2 steady-state (Poole and Jones 2012), while in trained healthy individuals at least 2–3 min or even more are required (Rob- ergs 2014; Poole and Jones 2012). The present results con- firm the current hypothesis demonstrating a lower between- protocol Vmax difference in HI than in LO likely due to the changes in running velocity faster than cardiorespiratory and metabolic adjustments. This was remarkably highlighted by no-differences in Vmax between CP2 and DP for HI. The between-protocol difference in the ̇VO2/velocity slope, was greater in LO (large to very large) than in HI (trivial to moderate), leading the slope to CP1 > CP2 > DP Fig. 3 Relationship between training status and the between-protocol Vmax dif- ference. The percentage of the individual Vmax-difference in CP1 than DP is related with the velocity associated with maximum oxygen uptake (Vmax, Panel A), maximum oxygen uptake ( ̇VO2max, Panel B) and lactate threshold (LaT, Panel C). Regression equations (y = a · bx), 95% confidence intervals and cor- relation coefficients are also reported 3091 European Journal of Applied Physiology (2021) 121:3083–3093 1 3 and CP1 > CP2 = DP in LO and HI, respectively. High-level aerobic athletes are also qualified by better biomechanical characteristics matching with a faster ̇V O kinetics and a higher running economy (Coyle 1995). In the present study, LO showed a reduced ̇VO2/velocity slope in both CP1 and CP2 than DP, while in HI the difference between CP2 and DP disappeared. This condition typically occurs when the time to reach cardiorespiratory and metabolic equilibrium matches the change in work rate across CPs. Training status and between‑protocol differences The between-protocol Vmax differences were inversely cor- related with training status. A higher ̇VO2max, Vmax, lactate threshold and faster ̇VO2 kinetics provided further evidence that between-protocol Vmax differences in HI may be likely counteracted by their higher aerobic training status. There- fore, a more consistent Vmax across different protocols in athletes with a higher aerobic capacity was found. The knowledge of the between-protocols Vmax differences could have practical implications for testing, exercise prescriptions and physiological outcomes during running activities. Dif- ferent % Vmax were shown to lead different physiological responses by increasing or decreasing the time spent at ~ ̇V O2max, a crucial factor for chronic adaptations and perfor- mance development (Buchheit and Laursen 2013). There- fore, a more consistent Vmax determination should permit a more accurate running exercise prescription in both HI and LO athletes. Methodological considerations Some methodological considerations should accompany the present investigation. First, the study of the dynamic response of metabolic and pulmonary variables upon exer- cise onset is strongly affected by the recording technique (Ferretti 2015). The Auchincloss algorithm (Auchincloss et al. 1966) utilized to calculate dynamic ̇VO2 responses requires a correct determination of the change in the amount of gas stored in the lungs over each breath. However, the algorithm estimated the end-expiratory lung volume impos- ing fixed pre-defined values of end-expiratory lung volumes (Ferretti 2015) leading to an impossibility of attaining a cor- rect estimation (di Prampero and Lafortuna 1989). Subse- quently, it was demonstrated a two-time improvement of the signal-to-noise ratio in breath-by-breath alveolar gas transfer (Capelli et al. 2001) and a lower dynamic response (Cautero et al. 2002) using Grønlund algorithm. However, despite such algorithm improvements, the aforementioned issue could not be fixed (Ferretti 2015). Secondly, despite a step- wise interpolation procedure was proposed to improve the time-constant calculation (Lamarra et al. 1987), a slightly higher time-constant than the interpolation interval still remains. Therefore, at least in the light exercise domain, mere stacking of multiple repetitions was proposed if the data were from the same ̇VO2 on rest-to-exercise transient (Bringard et al. 2014; Francescato et al. 2014b, a). As such, attempts at improving the time resolution beyond the single- breath duration could rely only on computational manipula- tions, such as superimposition of several trials and interpola- tion procedures (Francescato et al. 2014a; Francescato and Cettolo 2020). Lastly, the present findings open to new future perspec- tives. During submaximal running bouts, the time shift between velocity and ̇VO2 could be calculated knowing the time constant of the ̇VO2-on kinetics. Therefore, a mathe- matical modeling would possibly provide a calibration equa- tion for Vmax correction in CP1 and CP2 with respect to DP. Practical considerations The between-protocol Vmax differences in CP1 (+ 18% and + 13% than DP in LO and HI, respectively) and CP2 (+ 6% than DP in LO) should be considered for both athletes aerobic profiling and exercise prescription. These results suggest that in LO a protocol with more than 2 min stage durations is required for the metabolic power to match the mechanical power. In HI, a 2-min stage duration may be suitable and can be consistently utilized within sport con- texts. When shorter stage durations are mandatorily required (e.g., 1-min), a misestimate Vmax should be considered to plan accurately high-intensity exercises in both HI and LO. Indeed, different %- Vmax are suggested to increase the time spent at ~ ̇VO2max during high-intensity interval or intermit- tent exercises (e.g., 110% to 130%-Vmax for short-intervals exercises or 130% to 160%-Vmax for repeated sprint train- ings) (Buchheit and Laursen 2013). Therefore, when short intervals exercises (e.g., ~ 110% Vmax) are prescribed, ~ 18% of Vmax difference in CP1 vs DP for LO should induce an unexpected greater anaerobic involvement leading to acute physiological responses similar to a running exercise at ~ 130%-Vmax (i.e., ~ 25 km·h−1 instead of ~ 21 km·h−1). Similar differences between desired and actual physiologi- cal responses could be found across any %-Vmax within both longer and shorter running exercises. Neglected between- protocol Vmax differences may mislead acute physiological responses (e.g., more aerobic or anaerobic contribution) and possibly negatively affect the training adaptations, especially within-athletes with lower training status. Therefore, the knowledge of the between-protocol differences may help practitioners to properly manage different testing modali- ties and to adjust the %-Vmax when intermittent or interval running-based exercises are prescribed. 3092 European Journal of Applied Physiology (2021) 121:3083–3093 1 3 Conclusions As previously observed, CP and DP can be used inter- changeably to assess ̇VO2max, but not Vmax (Riboli et al. 2017, 2021). We demonstrate here that aerobic training status can influence the magnitude of the between-protocol differences in Vmax assessment. When different protocols are utilized to determine Vmax, between-protocol differ- ences exist, especially in CPs vs DP in which a matching between metabolic and mechanical power clearly occurs. These Vmax differences should be considered when ath- letes with different aerobic training status are tested. The Vmax difference between CPs and DP disappeared in HI during CP2, suggesting that a protocol with at least 2-min stage duration may be sensitive enough in athletes with a greater aerobic capacity, while differences still exist across participants with lower aerobic training status for which at least 3-min stage duration seems required. These between-protocol Vmax differences should be considered when athletes with different aerobic capacity are tested because they may affect the testing outcomes and training prescriptions. Acknowledgements The authors would like to thank all the partici- pants for their commitment. Author contribution All authors contributed to the study. Conceptu- alization: AR, FE, Data collection: AR, SR, EL, MB, EC, GC. Data analysis: AR, SR. Methodology: AR, FE. Visualization: AR, GC. Writ- ing – original draft: AR, FE. Writing – review & editing: AR, FE. Funding Open access funding provided by Università degli Studi di Milano within the CRUI-CARE Agreement. The authors have no fund- ing supports to declare. Availability of data and material Data and materials are available on request to the corresponding author. Code availability Protocol #102/14. Declarations Conflict of interest The authors have no conflicts of interest/competi- tive interests to declare. Ethical approval The ethics committee of the local University approved the study (protocol #102/14) which was performed in accordance with the principles of the Declaration of Helsinki (1964 and updates). Consent to participate All participants gave their written consent after a full explanation of the purpose of the study and the experimental design. Consent for publication All Authors give their consensus for publica- tion. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. References Adami A, Sivieri A, Moia C, Perini R, Ferretti G (2013) Effects of step duration in incremental ramp protocols on peak power and maxi- mal oxygen consumption. Eur J Appl Physiol 113(10):2647–2653. https:// doi. org/ 10. 1007/ s00421- 013- 2705-9 Anderson T (1996) Biomechanics and running economy. Sports Med 22(2):76–89. https:// doi. org/ 10. 2165/ 00007 256- 19962 2020- 00003 Auchincloss JH Jr, Gilbert R, Baule GH (1966) Effect of ventilation on oxygen transfer during early exercise. J Appl Physiol 21(3):810– 818. https:// doi. org/ 10. 1152/ jappl. 1966. 21.3. 810 Barstow TJ (1991) Mole PA (1991) Linear and nonlinear characteristics of oxygen uptake kinetics during heavy exercise. J Appl Physiol 71(6):2099–2106. https:// doi. org/ 10. 1152/ jappl. 1991. 71.6. 2099 Bentley DJ, McNaughton LR (2003) Comparison of W(peak), VO2(peak) and the ventilation threshold from two different incre- mental exercise tests: relationship to endurance performance. J Sci Med Sport 6(4):422–435 Bentley DJ, Newell J, Bishop D (2007) Incremental exercise test design and analysis: implications for performance diagnostics in endur- ance athletes. Sports Med 37(7):575–586 Bernard O, Ouattara S, Maddio F, Jimenez C, Charpenet A, Melin B, Bittel J (2000) Determination of the velocity associated with VO2max. Med Sci Sports Exerc 32(2):464–470 Billat VL, Hill DW, Pinoteau J, Petit B, Koralsztein JP (1996) Effect of protocol on determination of velocity at VO2 max and on its time to exhaustion. Arch Physiol Biochem 104(3):313–321. https:// doi. org/ 10. 1076/ apab. 104.3. 313. 12908 Bringard A, Adami A, Moia C, Ferretti G (2014) A new interpolation- free procedure for breath-by-breath analysis of oxygen uptake in exercise transients. Eur J Appl Physiol 114(9):1983–1994. https:// doi. org/ 10. 1007/ s00421- 014- 2920-z Buchheit M, Laursen PB (2013) High-intensity interval training, solutions to the programming puzzle: Part I: cardiopulmonary emphasis. Sports Med 43(5):313–338. https:// doi. org/ 10. 1007/ s40279- 013- 0029-x Capelli C, Cautero M, di Prampero PE (2001) New perspectives in breath-by-breath determination of alveolar gas exchange in humans. Pflugers Arch 441(4):566–577. https:// doi. org/ 10. 1007/ s0042 40000 429 Cautero M, Beltrami AP, di Prampero PE, Capelli C (2002) Breath- by-breath alveolar oxygen transfer at the onset of step exercise in humans: methodological implications. Eur J Appl Physiol 88(3):203–213. https:// doi. org/ 10. 1007/ s00421- 002- 0671-8 Coyle EF (1995) Integration of the physiological factors determining endurance performance ability. Exerc Sport Sci Rev 23(1):25–64 di Prampero PE, Lafortuna CL (1989) Breath-by-breath estimate of alveolar gas transfer variability in man at rest and during exercise. 3093 European Journal of Applied Physiology (2021) 121:3083–3093 1 3 J Physiol 415:459–475. https:// doi. org/ 10. 1113/ jphys iol. 1989. sp017 731 Duncan GE, Howley ET, Johnson BN (1997) Applicability of VO2max criteria: discontinuous versus continuous protocols. Med Sci Sports Exerc 29(2):273–278 Dupont G, Millet GP, Guinhouya C, Berthoin S (2005) Relationship between oxygen uptake kinetics and performance in repeated run- ning sprints. Eur J Appl Physiol 95(1):27–34. https:// doi. org/ 10. 1007/ s00421- 005- 1382-8 Ferretti G (2015) Energetics of muscular exercise. Springer, Heidel- berg. https:// doi. org/ 10. 1007/ 978-3- 319- 05636-4 Fletcher JR, Esau SP (2009) Macintosh BR (2009) Economy of run- ning: beyond the measurement of oxygen uptake. J Appl Physiol 107(6):1918–1922. https:// doi. org/ 10. 1152/ jappl physi ol. 00307. 2009 Francescato MP, Cettolo V (2020) The 1-s interpolation of breth-by- breath O2 uptake data to determine kinetic parameters: the mis- leading procedure. Sport Sci Health 16:193. https:// doi. org/ 10. 1007/ s11332- 019- 00602-9 Francescato MP, Cettolo V, Bellio R (2014a) Assembling more O(2) uptake responses: is it possible to merely stack the repeated tran- sitions? Respir Physiol Neurobiol 200:46–49. https:// doi. org/ 10. 1016/j. resp. 2014. 06. 004 Francescato MP, Cettolo V, Bellio R (2014b) Confidence intervals for the parameters estimated from simulated O2 uptake kinetics: effects of different data treatments. Exp Physiol 99(1):187–195. https:// doi. org/ 10. 1113/ expph ysiol. 2013. 076208 Hopkins WG, Marshall SW, Batterham AM, Hanin J (2009) Progres- sive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc 41(1):3–13. https:// doi. org/ 10. 1249/ MSS. 0b013 e3181 8cb278 Jones AM, Grassi B, Christensen PM, Krustrup P, Bangsbo J, Poole DC (2011) Slow component of VO2 kinetics: mechanistic bases and practical applications. Med Sci Sports Exerc 43(11):2046– 2062. https:// doi. org/ 10. 1249/ MSS. 0b013 e3182 1fcfc1 Kirkeberg JM, Dalleck LC, Kamphoff CS, Pettitt RW (2011) Validity of 3 protocols for verifying VO2 max. Int J Sports Med 32(4):266– 270. https:// doi. org/ 10. 1055/s- 0030- 12699 14 Kouwijzer I, Valize M, Valent LJM, Grandjean Perrenod Comtesse P, van der Woude LHV, de Groot S (2019) The influence of protocol design on the identification of ventilatory thresholds and the attainment of peak physiological responses during syn- chronous arm crank ergometry in able-bodied participants. Eur J Appl Physiol 119(10):2275–2286. https:// doi. org/ 10. 1007/ s00421- 019- 04211-9 Kuipers H, Rietjens G, Verstappen F, Schoenmakers H, Hofman G (2003) Effects of stage duration in incremental running tests on physiological variables. Int J Sports Med 24(7):486–491. https:// doi. org/ 10. 1055/s- 2003- 42020 Lamarra N, Whipp BJ, Ward SA (1987) Wasserman K (1987) Effect of interbreath fluctuations on characterizing exercise gas exchange kinetics. J Appl Physiol 62(5):2003–2012. https:// doi. org/ 10. 1152/ jappl. 1987. 62.5. 2003 Noakes TD (1988) Implications of exercise testing for prediction of athletic performance: a contemporary perspective. Med Sci Sports Exerc 20(4):319–330. https:// doi. org/ 10. 1249/ 00005 768- 19880 8000- 00001 Pedro RE, Milanez VF, Boullosa DA, Nakamura FY (2013) Running speeds at ventilatory threshold and maximal oxygen consump- tion discriminate futsal competitive level. J Strength Cond Res 27(2):514–518. https:// doi. org/ 10. 1519/ JSC. 0b013 e3182 542661 Poole DC, Jones AM (2012) Oxygen Uptake Kinetics Compr Physiol 2(2):933–996. https:// doi. org/ 10. 1002/ cphy. c1000 72 Poole DC, Richardson RS (1997) Determinants of oxygen uptake implications for exercise testing. Sports Med 24(5):308–320 Poole DC, Wilkerson DP, Jones AM (2008) Validity of criteria for establishing maximal O2 uptake during ramp exercise tests. Eur J Appl Physiol 102(4):403–410. https:// doi. org/ 10. 1007/ s00421- 007- 0596-3 Rampinini E, Bishop D, Marcora SM, Ferrari Bravo D, Sassi R, Impel- lizzeri FM (2007) Validity of simple field tests as indicators of match-related physical performance in top-level professional soc- cer players. Int J Sports Med 28(3):228–235. https:// doi. org/ 10. 1055/s- 2006- 924340 Riboli A, Ce E, Rampichini S, Venturelli M, Alberti G, Limonta E, Veicsteinas A, Esposito F (2017) Comparison between continuous and discontinuous incremental treadmill test to assess velocity at VO2max. J Sports Med Phys Fitness 57(9):1119–1125. https:// doi. org/ 10. 23736/ S0022- 4707. 16. 06393-3 Riboli A, Rampichini S, Ce E, Limonta E, Coratella G, Esposito F (2019) Effect of ramp slope on different methods to determine lac- tate threshold in semi-professional soccer players. Res Sports Med 27(3):326–338. https:// doi. org/ 10. 1080/ 15438 627. 2018. 15237 90 Riboli A, Coratella G, Rampichini S, Limonta E, Esposito F (2021) Testing protocol affects the velocity at VO2max in semi-profes- sional soccer players. Res SportsMed.https://doi.org/10.1080/15 438627.2021.1878460.https:// doi. org/ 10. 1080/ 15438 627. 2021. 18784 60 Robergs RA (2014) A critical review of the history of low- to moder- ate-intensity steady-state VO2 kinetics. Sports Med 44(5):641– 653. https:// doi. org/ 10. 1007/ s40279- 014- 0161-2 Rossiter HB, Kowalchuk JM (2006) Whipp BJ (2006) A test to establish maximum O2 uptake despite no plateau in the O2 uptake response to ramp incremental exercise. J Appl Physiol 100(3):764–770. https:// doi. org/ 10. 1152/ jappl physi ol. 00932. 2005 Vinetti G, Fagoni N, Taboni A, Camelio S, di Prampero PE, Ferretti G (2017) Effects of recovery interval duration on the param- eters of the critical power model for incremental exercise. Eur J Appl Physiol 117(9):1859–1867. https:// doi. org/ 10. 1007/ s00421- 017- 3662-5 Zhang YY, Johnson MC 2nd, Chow N, Wasserman K (1991) Effect of exercise testing protocol on parameters of aerobic function. Med Sci Sports Exerc 23(5):625–630 Ziogas GG, Patras KN, Stergiou N, Georgoulis AD (2011) Velocity at lactate threshold and running economy must also be considered along with maximal oxygen uptake when testing elite soccer play- ers during preseason. J Strength Cond Res 25(2):414–419. https:// doi. org/ 10. 1519/ JSC. 0b013 e3181 bac3b9 Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Training status affects between-protocols differences in the assessment of maximal aerobic velocity.

07-28-2021

Riboli, Andrea,Rampichini, Susanna,Cè, Emiliano,Limonta, Eloisa,Borrelli, Marta,Coratella, Giuseppe,Esposito, Fabio

eng

PMC7828502

International Journal of Environmental Research and Public Health Article The Effect of Eight-Week Sprint Interval Training on Aerobic Performance of Elite Badminton Players Haochong Liu 1, Bo Leng 2, Qian Li 2, Ye Liu 1, Dapeng Bao 1,* and Yixiong Cui 3,*



 Citation: Liu, H.; Leng, B.; Li, Q.; Liu, Y.; Bao, D.; Cui, Y. The Effect of Eight-Week Sprint Interval Training on Aerobic Performance of Elite Badminton Players. Int. J. Environ. Res. Public Health 2021, 18, 638. https://doi.org/10.3390/ijerph 18020638 Received: 27 November 2020 Accepted: 8 January 2021 Published: 13 January 2021 Publisher’s Note: MDPI stays neu- tral with regard to jurisdictional clai- ms in published maps and institutio- nal affiliations. Copyright: © 2021 by the authors. Li- censee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and con- ditions of the Creative Commons At- tribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 China Institute of Sport and Health Science, Beijing Sport University, Beijing 100084, China; [email protected] (H.L.); [email protected] (Y.L.) 2 Sports Coaching College, Beijing Sport University, Beijing 100084, China; [email protected] (B.L.); [email protected] (Q.L.) 3 AI Sports Engineering Lab, School of Sports Engineering, Beijing Sport University, Beijing 100084, China * Correspondence: [email protected] (D.B.); [email protected] (Y.C.) Abstract: This study was aimed to: (1) investigate the effects of physiological functions of sprint interval training (SIT) on the aerobic capacity of elite badminton players; and (2) explore the potential mechanisms of oxygen uptake, transport and recovery within the process. Thirty-two elite badminton players volunteered to participate and were randomly divided into experimental (Male-SIT and Female-SIT group) and control groups (Male-CON and Female-CON) within each gender. During a total of eight weeks, SIT group performed three times of SIT training per week, including two power bike trainings and one multi-ball training, while the CON group undertook two Fartlek runs and one regular multi-ball training. The distance of YO-YO IR2 test (which evaluates player’s ability to recover between high intensity intermittent exercises) for Male-SIT and Female-SIT groups increased from 1083.0 ± 205.8 m to 1217.5 ± 190.5 m, and from 725 ± 132.9 m to 840 ± 126.5 m (p < 0.05), respectively, which were significantly higher than both CON groups (p < 0.05). For the Male- SIT group, the ventilatory anaerobic threshold and ventilatory anaerobic threshold in percentage of VO2max significantly increased from 3088.4 ± 450.9 mL/min to 3665.3 ± 263.5 mL/min (p < 0.05),and from 74 ± 10% to 85 ± 3% (p < 0.05) after the intervention, and the increases were significantly higher than the Male-CON group (p < 0.05); for the Female-SIT group, the ventilatory anaerobic threshold and ventilatory anaerobic threshold in percentage of VO2max were significantly elevated from 1940.1 ± 112.8 mL/min to 2176.9 ± 78.6 mL/min, and from 75 ± 4% to 82 ± 4% (p < 0.05) after the intervention, which also were significantly higher than those of the Female-CON group (p < 0.05). Finally, the lactate clearance rate was raised from 13 ± 3% to 21 ± 4% (p < 0.05) and from 21 ± 5% to 27 ± 4% for both Male-SIT and Female-SIT groups when compared to the pre-test, and this increase was significantly higher than the control groups (p < 0.05). As a training method, SIT could substantially improve maximum aerobic capacity and aerobic recovery ability by improving the oxygen uptake and delivery, thus enhancing their rapid repeated sprinting ability. Keywords: interval training; badminton; aerobic; repeated sprint; testing 1. Introduction Badminton is a fast and dynamic sport, which has high requirements for the player’s rapid reaction, fast action and high-speed hitting ability. Studies have shown that there are on average 5–9 strokes in badminton games [1]. Due to its fastball speed, high swing frequency and short interval time, badminton requires players to mainly compete with fast running, sudden acceleration, abrupt stop, change of direction and continuously high inten- sity of multiple rallies, which requires a player’s well-developed aerobic endurance [2,3]. Due to the influence of players’ strength level, badminton competition is easy to form a multi-round competition, which requires higher aerobic working capacity. Particularly, a relevant study has indicated that badminton players usually reach an average heart rate of over 90% of their HRmax during competitive games, which is demanding to both aerobic Int. J. Environ. Res. Public Health 2021, 18, 638. https://doi.org/10.3390/ijerph18020638 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2021, 18, 638 2 of 11 and anaerobic systems: 60–70% on the aerobic system and 30% on the anaerobic system, with a greater demand on alactic metabolism [3]. Previous research [3,4] has shown that various physiological parameters had a strong correlation with badminton performance. Particularly, aerobic capacity and intermit- tent exercise performance are positively correlated, involving VO2max, lactate/anaerobic threshold and running efficiency [5]. However, practically, due to the limited training time, the traditional long-period aerobic endurance training would not be the most suitable modality for the actual needs of current competition. Therefore, it is essential to explore time-efficient and badminton-specific fitness training programs. As one of the advocated alternatives to traditional continuous aerobic training, Sprint Interval Training (SIT) is a training approach that asks athletes to complete the required actions at maximum effort in a short period of time, and takes active rest with limited recovery time between two sets of training. Meanwhile, as the active rest often lasts only 3–5 min between multiple short and full sprint training, SIT can effectively improve the performance of athletes in intermittent sports with substantially lower overall training volume [6]. In recent years, there have been many studies on the training effect of SIT on sports performance in other intermittent sports such as soccer, basketball, volleyball and field hockey [7]. Among them, Buchan [8] and Bayati et al. [9] conducted a 6-week 30-s full- speed sprint running and rowing training experiment. Each training was carried out in 4–6 rounds, with 4 min of low-intensity activities serving as an active rest interval be- tween groups. The results showed that the maximum oxygen uptake, peak power, average power and aerobic capacity significantly improved compared with those of the control group. Meanwhile, the study by Jone et al. [10] proved that field hockey players’ muscle oxygenation kinetics and performance during the 30–15 intermittent fitness test (30-s shut- tle runs with 15-s passive recovery) were significantly improved after 6-week of Sprint Interval Cycling. Additionally, Burgomaster [11] and Gibala et al. [12] also conducted similar comparative experiments between traditional endurance training and SIT on young healthy individuals. Their results showed that the SIT group shortened the training time by about 80%, and the participants’ aerobic capacity was significantly improved. Nonetheless, currently, there are few attempts to investigate the application of SIT to badminton training. This study was, therefore aimed to explore the effect of SIT on improving players’ aerobic capacity, as well as the mechanism of oxygen uptake and transport, by testing the changes of badminton players’ rapid and repeated sprinting ability and related aerobic capacity parameters before and after 8 weeks of SIT. It was hypothesized that such training would induce greater improvement in before-mentioned parameters compared to traditional continuous aerobic training. 2. Materials and Methods 2.1. Participants Thirty-two elite players from who had played in or beyond the semi-finals of Bad- minton Championship at National level volunteered to participate in the study. There were sixteen male and female players, respectively, and they were randomly divided into male Sprint Interval Training (SIT) group (n = 8) and control (CON) group (n = 8), and female SIT group (n = 8) and CON group (n = 8). Detailed information about different groups can be found in Table 1. All subjects were in good health and had no severe injuries during the last six months before the study. Prior to the formal experiment and test, the nature and possible risks were explained to the participants, and they provided their written informed consent to participate. The tests were conducted at least 48 h after competitive match or heavy training session. The subjects participated in all the training sessions as well as pre- and post- training tests. All procedures were approved by Research Ethics Committee of Beijing Sport University (Approval number: 2020008H). All procedures were conducted in accordance with the Declaration of Helsinki. Int. J. Environ. Res. Public Health 2021, 18, 638 3 of 11 Table 1. Personal information of participating players. Group N Age (year) Height (cm) Weight (kg) Training Age (year) HRmax (bpm) Male-SIT 8 20.0 ± 1.3 179.6 ± 3.6 73.8 ± 6.9 12.1 ± 2.2 190.7 ± 8.8 Male-CON 8 21.5 ± 2.2 177.1 ± 7.1 72.4 ± 6.7 13.2 ± 3.2 191.8 ± 6.2 Female-SIT 8 20.5 ± 1.4 168.5 ± 4.2 62.6 ± 4.2 9.5 ± 1.2 181.9 ± 8.9 Female-CON 8 19.4 ± 1.5 168.2 ± 4.8 61.3 ± 4.2 9.8 ± 1.5 180.4 ± 8.5 Note: SIT = Sprint Interval Training; CON = Control; HRmax = maximum heart rate. 2.2. Procedures For eight weeks, the CON group followed previous training routines of two Fartlek running sessions and one regular multi-ball feeding training per week, which was a tra- ditionally employed aerobic training protocol for these badminton players. Meanwhile, the SIT group carried out sprint interval training three times a week, including two power bicycle training sessions with a Monark 894E exercise bike (Monark Exercise AB, Vansbro, Sweden), which has high reliability of weight loading for anaerobic testing or training [13], and one SIT-specific multi-ball training session. Pedaling is a closed-chain exercise, and is relatively easier for the players to acquire correct technique and to achieve expected training effect from an injury-prevention perspective. Moreover, it is practically applicable to indoor badminton courts training during winter or in bad weather condition. The train- ing intervention was designed and modified based on the previous literature [10,14,15]. The detailed training plan and description are shown in Table 2. The Polar Team2 System (Polar Electro Oy, Kemple, Finland) was used to monitor the heart rate of each player throughout each training session, with data later extracted from custom-specific software (Polar Team2, Electro Oy, Kemple, Finland), in order to obtain maximum heart rate (HRmax), time spent in each HRmax% zone and Training impulse (TRIMP). TRIMP takes into account the training duration and intensity at the same time, and reflects the comprehensive effect of training on the internal and external load of the athlete’s body, as well as the load of medium and high intensity training. The method to determine the athlete’s TRIMP in the current study is based on the formula proposed by Edwards [16], where the time in each HRmax% zone is multiplied by the corresponding weighting factor for that zone and the results summated (see Table 3 for detailed description of the zone and factors). The HRmax of each player was established using the peak value recorded by the monitoring system during the training. Table 2. Weekly training plan for two groups during the study. Group Monday Wednesday Friday SIT SIT Cycling Training 1–2 min 50 W cycling, prepare to 30 s cycling with full force, the load is 0.075/kg of weight individualized to each player’s body weight [17,18], between-group rest: 5 min 5 groups in total SIT-specific Multiple Balls Training 30 s × 8 groups × 2 rounds of multi-ball training, intensity: > 90% HRmax between-group rest: 5 min between-round rest: 8 min CON Traditional Training: 40 min of Fartlek Run (Intensity: 65–79% HRmax) Traditional Multiple Ball Training: 1 min × 4 groups × 2 rounds of continuous multi-ball training between-group rest: 5 min between-round rest: 8 min Int. J. Environ. Res. Public Health 2021, 18, 638 4 of 11 Table 3. HRmax% zones and corresponding weighting factors. Zone Weighting Factor HRmax% I 1 50–60% II 2 60–70% III 3 70–80% IV 4 80–90% V 5 90–100% 2.3. Test Program Before and after 8 weeks of training, four groups all participated in a set of testing, which included YO-YO IR2 intermittent recovery test, analysis of the increasing load gas metabolism and lactate clearance rate test. 2.3.1. YO-YO Intermittent Recovery Test Level 2 (YO-YO IR2 Test) Speed endurance level is generally reflected by short bursts of repetitive sprints (RS), which requires subjects to try their best to accomplish the fastest speed in each repetitive sprint, and this ability is generally evaluated via the YO-YO IR2 test during field-test [6]. The test is based on increasing and intermittent load protocol, and evaluates player’s ability to recover between high intensity intermittent exercises. Moreover, it has been proven to validly monitor training effects [19]. After dynamic warm-up, players perform a combination of running to and fro on a 20 m course and a 10-s interval of active rest after 40 m, and players quit the test when the subjective exhaustion occurs or when they drop behind the required pace or make one of the errors listed below for a second time: (i) does not come to a complete stop before starting the next 40 m run; (ii) starts the run before the audio signal; (iii) does not reach/either line before the audio signal; (iv) turns at the 20 m mark without touching or crossing the line (therefore running short). The starting speed starts at 13 km/h, and increases to 15 km/h, 16 km/h, and then increases by 0.5 km/h thereafter. The final running distance is then recorded. The speed of each bout is controlled by an audio recorder. All subjects were familiarized with the test within a one-minute trial. 2.3.2. Analysis of Increasing Load of Gas Metabolism and Test of Lactate Clearance Rate An incremental load test was performed using an incremental load treadmill (H/P Cosmos, Germany). Warm-up exercises should be performed for 5–10 min before each test. At the beginning of the test, the starting speed of the treadmill was set at 6 km/h, increasing by 1 km/h per minute, until 16 km/h, when the speed was stopped and the slope increased by 1.5% per minute, until the subject was exhausted. Relevant ventilation indicators such as maximum oxygen uptake (VO2max), ventilatory anaerobic threshold (VT-VO2) and ventilatory anaerobic threshold in percentage of VO2max (VT/VO2max) were measured using a gas metabolism analyzer (Max I, Physio-Dyne Instrument Corp., New York, USA). Among them, VT is determined according to the following criteria: In the incremental load test, the VT value is determined (i) when the ratio of ventilation (VE) to carbon dioxide production (VCO2) shows a non-linear increase in the inflection point, and (ii) when the load intensity reaches a certain level, and the ratio of VE to oxygen consumption (VO2) increases sharply [20]. VT is determined by two independent investigators. When they are not coherent, and if the difference between the two selected results is remarkable, the value of VT needs to be determined again, while, if the difference could be overlooked, the average value is taken. Next, in order to analyze the aerobic recovery speed of athletes after increasing load and to evaluate their recovery ability after aerobic exercise, blood samples were collected for rest (before testing with players being seated) and 0, 1, 3, 5, 7 and 10 min immediately Int. J. Environ. Res. Public Health 2021, 18, 638 5 of 11 after the increasing load test via a volume of 20 microliters of fingertip blood. The EKF Biosen s-line automatic blood lactate analyzer (EKF-diagnostic GmbH, Barleben, Germany) was used to measure blood lactate, with the results being later recorded with the lactate clearance rate being calculated using the following formula [21]: LA10% = LAmax − LA10 LAmax − LArest × 100% where LA10% means the lactate clearance rate at 10 min after testing, LAmax represents the peak lactic value after testing, LA10 is the lactate value at 10 min after testing and LArest the value of lactate before testing. 2.4. Statistical Analysis Experimental data were processed by SPSS statistical software package (version 23.0, Chicago, IL, USA); all test results before and after training were presented using the average ± standard deviation (x ± s). The normality of the tests results was checked before the subsequent analysis. A repetitive measure analysis of variance was then used to compare the within and between group difference in test outcomes for both genders, with the statistical significance level defined as p < 0.05. Pairwise differences and post hoc comparisons were tested with the Bonferroni post hoc test. Besides, the effect size (ES) was calculated using Cohen’s d to quantify the amount of change before and after each group of training and to reflect the comparison of training effects between SIT and CON groups based on the following scales: <0.2 trivial, 0.2–0.6 small, 0.6–1.2 moderate, 1.2–2.0 large and >2.0 very large [22]. 3. Results 3.1. Training Intensity and Time Used During Training Table 4 shows the descriptive statistics of heartrate and time within the 8-week training, and the results show that the average heart rate and maximum heart rate of both male and female SIT groups during training were significantly higher than those of the CON groups (p < 0.05). Moreover, the effective training time of the former was significantly less than that of the latter (p < 0.05). Table 4. Intensity monitoring during training. Group N Avg HR (bpm) HRmax (bpm) Total Training Time (min) Effective Training Time (min) Male-SIT 8 132.7 ± 7.3 * 190.7 ± 8.8 * 52.7 ± 4.1 19.8 ± 3.0 * Male-CON 8 126.0 ± 10.2 169.8 ± 6.2 78.5 ± 4.5 40.2 ± 1.8 Female-SIT 8 134.1 ± 6.0 * 181.9 ± 8.9 * 52.7 ± 4.1 19.8 ± 3.0 * Female-CON 8 115.4 ± 8.4 169.4 ± 8.5 78.5 ± 4.5 40.2 ± 1.8 Note: Values are expressed as means ± SD. * indicates significant difference between SIT and CON group, p < 0.05. During the 8-week training, the mean weekly effective training time (time spent within 50–100% HRmax zone) and TRIMP in the 80–100% HRmax intensity range of the Male-SIT group were significantly higher than those in the Male-CON group (p < 0.05), while the total weekly effective training time and TRIMP for the former were significantly lower than the latter (p < 0.05). As for female players, the average weekly effective training time and TRIMP in the 90–100%HRmax intensity range for the Female-SIT group were significantly higher than those in the Female-CON group (p < 0.05). However, in the intensity range of 80–90% HRmax, no differences were found between the F-SIT and F-CON groups. The overall effective training time and TRIMP for Female-SIT were significantly lower than those in Female-CON as well (p < 0.05), as is shown in Figure 1. Int. J. Environ. Res. Public Health 2021, 18, 638 6 of 11 Figure 1. Comparisons of weekly effective training time and training impulse (TRIMP) between SIT and CON groups. Note: * Indicates a significant difference between SIT and CON group, p < 0.05. 3.2. Comparisons of Testing Results Before and After Training Intervention After training, the running distance of the YO-YO IR2 and the lactate clearance rate at 10 min after testing (LA10%) significantly increased in both the Male-SIT and the Female-SIT group (p < 0.05), and such improvement was significantly higher than that of the CON groups (p < 0.05), as is shown in Figure 2. Meanwhile, as Table 5 demonstrates, VO2max, VT-VO2 and VT/VO2max for the SIT group significantly improved after the intervention (p < 0.05), and the improvement was significantly higher than that in the Male-CON group and Female-CON group (p < 0.05), as is shown in Table 4. Int. J. Environ. Res. Public Health 2021, 18, 638 7 of 11 Figure 2. Within- and between-group differences in YO-YO Intermittent Recovery Test Level 2 (IR2) distance and lactate clearance rate for SIT and CON groups before and after intervention. Note: * Indicates significant difference between SIT and CON group, p < 0.05; # indicates significant within-group difference before and after intervention, p < 0.05; ES: effect size. Table 5. Within- and between-group differences in gas metabolism analysis for SIT and CON groups before and after intervention. Group VO2max (mL/kg/min) VT-VO2 (mL/min) VT/VO2max (%) Male-SIT pre 56.8 ± 7.0 3088.4 ± 450.9 73.8 ± 9.7 post 63.6 ± 4.7 #,* 3665.3 ± 263.5 #,* 84.8 ± 3.37 #,* ES 1.14 1.56 1.51 Male-CON pre 55.8 ± 8.0 2962.5 ± 743.4 80.2 ± 1.5 post 57.7 ± 6.7 3004.1 ± 738.1 80.8 ± 2.3 ES 0.26 0.06 0.31 Female-SIT pre 42.5 ± 2.9 1940.1 ± 112.9 0.75 ± 0.04 post 46.2 ± 3.0 *,# 2176.9 ± 78.6 *,# 0.82 ± 0.04 *,# ES 1.28 1.11 1.75 Female-CON pre 42.9 ± 1.6 1930.7 ± 151.8 0.75 ± 0.06 post 43.3 ± 2.1 2055.3 ± 160.7 0.78 ± 0.08 ES 0.21 0.79 0.42 Note: * Indicates significant difference between SIT and CON group, p < 0.05; # indicates significant within-group difference before and after intervention, p < 0.05; ES: effect size. 4. Discussion This study was aimed to explore the effect of 8-weeks of SIT on the aerobic capacity of badminton players. The results showed that their performance in the YO-YO IR2 test, the lactate clearance rate, VO2max, VT-VO2 and VT/VO2max were significantly enhanced in a time-efficient manner, compared to the control group, which confirms the hypothesis of this research. The badminton match is highly demanding to a player’s aerobic capacity due to the dif- ferences in individual physical fitness and the appearance of the new scoring model [4,5]. Under such situation, the competition rhythm is obviously accelerated and the proportion of multiple rallies is gradually increased, which forces players to endure longer periods Int. J. Environ. Res. Public Health 2021, 18, 638 8 of 11 of rapid and repeated accelerations and decelerations [23]. In the Male-SIT and Female- SIT groups, the time spent and TRIMP values in the 80–100% HRmax intensity interval accounted for the highest proportion, suggesting that SIT enables the body to complete multiple short-time and high-intensity outputs under the continuous incomplete recov- ery state, which is more in line with the current badminton competition characteristics and demands. In contrast to the previous training programs where all subjects routinely undertook Fartlek running, the 30-s SIT is a training mode closer to the maximum phys- iological load intensity of players, and the features of its time structure are also closer to the actual combat of badminton competition. It is an effective training program to improve a player’s aerobic capacity. Previously, it was reported that SIT could induce skeletal muscle metabolism, increase capillaries and mitochondrial proliferation, enhance oxidase activity and improve peripheral vascular function and peripheral fitness of skeletal muscle [24]. When training intensity exceeded 90%VO2max, SIT could simultaneously improve oxygen uptake and transport ability of the cardiopulmonary system and skeletal muscle [25]. Ermanno et al., found that intermittent exercise could activate the energy supply of the aerobic system in advance and reduce the proportion of the energy sup- ply of the anaerobic system, thus delaying the generation of fatigue [25]. These changes in the body were physiological feedback for SIT. While the training improved the player’s ability to maintain high-intensity exercise for a long time in competition and training, their ability to recover from fast running could be improved, consequently achieving the goal of improving aerobic capacity. Moreover, this study found that after 8 weeks of SIT, players’ VO2max, VT-VO2 and VT/VO2max increased significantly, implying that the proportion of exercise intensity lower than the anaerobic threshold for the body was increased under the same testing protocol. The time players take to enter the anaerobic glycolytic process would be post- poned, thus reducing the consumption of glycogen. At the same time, the movement of the body would become more efficient, and eventually the maximum aerobic capacity of the players would be improved [26]. Previous studies showed that by inducing skeletal muscle metabolism, SIT could increase capillary proliferation, mitochondrial prolifera- tion, enhance the activity and oxidation of glycolytic oxidase and improve peripheral vascular function and skeletal muscle peripheral adaptability [14,27]. Studies with similar schemes applied to the general population showed that the oxygen uptake and transport capacity were improved via a series of changes, such as increased capillary density and blood volume, decreased heart rate and increased stroke output, when the same exercise intensity was completed. At this time, the body showed certain adaptability. In practice, with the improvement of the body’s oxygen uptake ability, elite badminton players could prolong the time of oxygen supply and enter the hypoxemia state later in the competition, which could effectively improve their match performance during the competition. Besides, although some research showed certain discrepancies in results, we found that after the SIT, no significant increase in VO2max was indicated. It would be inferred that the effect of such modality would be conditioned by factors such as the level of training, whether the subjects undertake regular training and the body weight. Aerobic recovery ability has a direct impact on players’ on-court performance. High- intensity and high-load activity during competition would produce physiological fatigue and large amount of lactate accumulation in the skeletal muscle. Changes in the internal responses of the body may cause players’ physical dysfunction and decline in athletic per- formance [28,29]. Therefore, rapid recovery ability is the key prerequisite for decent physi- cal and technical performance during the competition. This study analyzed the changes in skeletal muscle’s oxygen recovery ability from both physiological and biochemical perspectives. Blood lactate is one of the most commonly used biochemical indicators to detect the body fatigue recovery status [30], and the accumulation of lactate may indirectly lead to reduced performance, because the conversion of lactic acid to lactate releases H+ that leads to a metabolic acidosis with subsequent inhibition of glycolytic rate-limiting enzymes, Int. J. Environ. Res. Public Health 2021, 18, 638 9 of 11 lipolysis and contractility of the skeletal muscles [31]. From the results, it was shown that SIT performed at a higher level of intensity could positively influence the clearance of lactate after exercise, increasing intra-cellular alkali reserve and slowing the pH reduction in muscles, and delaying the onset of fatigue [21]. Consequently, players’ ability to recover from intermittent activities was enhanced and they would be better prepared for the next point and game [32]. In particular, at the final game and the last points of each game, each point would be ended with prolonged multiple-strokes and high-intensity movements, which might even last couple of minutes. Under such circumstances, possessing rapid aerobic recovery would become a key factor determining elite player’s aerobic endurance and technical-tactical performance in the next point [33]. In the study conducted by Jones et al. [10], near-infrared spectroscopy was used to measure muscle oxygenation of the vastus lateralis of elite women hockey players for SIT groups and endurance training groups. Their results showed that there were significant increases in tissue deoxyhaemoglobin + deoxymyoglobin (HHb + HMb) and tissue oxygenation (TSI%), and a significant decrease in tissue oxyhaemoglobin + oxymyoglobin (HbO2 + MbO2), which indicated ‘positive peripheral muscle oxygen adaptations’ occurring in response to SIT training. Moreover, existing literature also stated that the higher exercise intensity provided during SIT would increase the probability of favorable adaptations in both type one and type two fibers as opposed to the generally lower intensity of endurance training [34]. Although as a limitation, the current study was unable to measure blood saturation, it could be implied that the SIT protocol might promote the skeletal muscle oxidative capacity of badminton players after the training. Nonetheless, future studies should look into the changes of EPOC (excess post-exercise oxygen consumption), body temperature and ventilation to comprehensively verify the improvement in recovery after such intervention. 5. Conclusions Eight-week SIT effectively improved the aerobic exercise capacity of elite badminton players, particularly considering oxygen uptake and recovery ability, and the adaptability of skeletal muscle to exercising load. Eventually, the rapidly repeated sprint ability and physical performance of players were enhanced. The study has provided evidence-based findings that as a time-efficient training alternative, SIT could be suitable to be included in the training routine for badminton players. However, it is acknowledged that this study also has certain limitations. The technical and tactical performance was not considered, which might be another representative indicator of improved aerobic capacity. Moreover, anaerobic endurance training, strength training and functional training are also of vital importance for badminton players and their joint effect on aerobic training was not investigated within the current program. Future research is suggested to look into these aspects to better inform sport-specific training prescription. Author Contributions: Conceptualization, H.L., Q.L. and D.B.; methodology, H.L., B.L., Q.L. and D.B.; software, Q.L. and Y.C.; validation, H.L., Q.L., D.B. and Y.C.; formal analysis, H.L., Q.L. and Y.C.; investigation, H.L., Q.L. and Y.L.; resources, B.L. and D.B.; data curation, H.L. and Q.L.; writing— original draft preparation, H.L., Q.L., D.B. and Y.C.; writing—review and editing, H.L., Q.L. and Y.C.; visualization, Q.L. and Y.C.; supervision, D.B. and Y.C.; project administration, D.B. and Y.C.; funding acquisition, D.B. and Y.C. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported in part by the National Key Research and Development Program of China under grants 2020AAA0103404 and 2018YFC2000600, and by National Natural Science Foundation of China under grant 72071018. The corresponding author (Y.C.) was supported by the China Postdoctoral Science Foundation (2020T130067). Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Beijing Sport University (2020008H, 17/01/2020). Int. J. Environ. Res. Public Health 2021, 18, 638 10 of 11 Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Conflicts of Interest: The authors declare no conflict of interest. References 1. Jan, C.; Petr, S. Serve and Return in Badminton: Gender Differences of Elite Badminton Players. Int. J. Phys. Educ. Fit. Sports 2020, 9, 44–48. [CrossRef] 2. Nhan, D.T.; Klyce, W.; Lee, R.J. Epidemiological Patterns of Alternative Racquet-Sport Injuries in the United States, 1997–2016. Orthop. J. Sports Med. 2018, 6, 2325967118786237. [CrossRef] [PubMed] 3. Phomsoupha, M.; Laffaye, G. The science of badminton: Game characteristics, anthropometry, physiology, visual fitness and biomechanics. Sports Med. 2015, 45, 473–495. [CrossRef] [PubMed] 4. Laffaye, G.; Phomsoupha, M.; Dor, F. Changes in the Game Characteristics of a Badminton Match: A Longitudinal Study through the Olympic Game Finals Analysis in Men’s Singles. J. Sports Sci. Med. 2015, 14, 584–590. [PubMed] 5. Faude, O.; Meyer, T.; Rosenberger, F.; Fries, M.; Huber, G.; Kindermann, W. Physiological characteristics of badminton match play. Eur. J. Appl. Physiol. 2007, 100, 479–485. [CrossRef] 6. Bangsbo, J.; Iaia, F.M.; Krustrup, P. The Yo-Yo intermittent recovery test: A useful tool for evaluation of physical performance in intermittent sports. Sports Med. 2008, 38, 37–51. [CrossRef] 7. Kelly, D.T.; Tobin, C.; Egan, B.; McCarren, A.; O’Connor, P.L.; McCaffrey, N.; Moyna, N.M. Comparison of Sprint Interval and Endurance Training in Team Sport Athletes. J. Strength Cond. Res. 2018, 32, 3051–3058. [CrossRef] 8. Buchan, D.S.; Ollis, S.; Young, J.D.; Thomas, N.E.; Cooper, S.M.; Tong, T.K.; Nie, J.; Malina, R.M.; Baker, J.S. The effects of time and intensity of exercise on novel and established markers of CVD in adolescent youth. Am. J. Hum. Biol. 2011, 23, 517–526. [CrossRef] 9. Bayati, M.; Farzad, B.; Gharakhanlou, R.; Agha-Alinejad, H. A practical model of low-volume high-intensity interval training induces performance and metabolic adaptations that resemble ‘all-out’ sprint interval training. J. Sports Sci. Med. 2011, 10, 571–576. 10. Jones, B.; Hamilton, D.K.; Cooper, C.E. Muscle Oxygen Changes following Sprint Interval Cycling Training in Elite Field Hockey Players. PLoS ONE 2015, 10, e0120338. [CrossRef] 11. Burgomaster, K.A.; Heigenhauser, G.J.; Gibala, M.J. Effect of short-term sprint interval training on human skeletal muscle carbohydrate metabolism during exercise and time-trial performance. J. Appl. Physiol. 2006, 100, 2041–2047. [CrossRef] [PubMed] 12. Gibala, M.J.; Little, J.P.; van Essen, M.; Wilkin, G.P.; Burgomaster, K.A.; Safdar, A.; Raha, S.; Tarnopolsky, M.A. Short-term sprint interval versus traditional endurance training: Similar initial adaptations in human skeletal muscle and exercise performance. J. Physiol. 2006, 575, 901–911. [CrossRef] [PubMed] 13. Lunn, W.R.; Axtell, R.S. Validity and Reliability of the Lode Excalibur Sport Cycle Ergometer for the Wingate Anaerobic Test. J. Strength Cond. Res. 2019. [CrossRef] [PubMed] 14. Gist, N.H.; Fedewa, M.V.; Dishman, R.K.; Cureton, K.J. Sprint interval training effects on aerobic capacity: A systematic review and meta-analysis. Sports Med. 2014, 44, 269–279. [CrossRef] 15. Hostrup, M.; Gunnarsson, T.P.; Fiorenza, M.; Mørch, K.; Onslev, J.; Pedersen, K.M.; Bangsbo, J. In-season adaptations to intense intermittent training and sprint interval training in sub-elite football players. Scand. J. Med. Sci. Sports 2019, 29, 669–677. [CrossRef] 16. Edwards, S. The Heart Rate Monitor Book. Med. Sci. Sports Exerc. 1994, 26, 647. [CrossRef] 17. Bar-Or, O.; Dotan, R.; Inbar, O. A 30 second all-out ergometric test-its reliability and validity for anaerobic capacity. Isr. J. Med. Sci. 1977, 13, 126. 18. Laurent, C.M.; Meyers, M.C.; Robinson, C.A.; Green, J.M. Cross-validation of the 20-versus 30-s Wingate anaerobic test. Eur. J. Appl. Physiol. 2007, 100, 645–651. [CrossRef] 19. Schmitz, B.; Pfeifer, C.; Kreitz, K.; Borowski, M.; Faldum, A.; Brand, S.M. The Yo-Yo Intermittent Tests: A Systematic Review and Structured Compendium of Test Results. Front. Physiol. 2018, 9, 870. [CrossRef] 20. Binder, R.K.; Wonisch, M.; Corra, U.; Cohen-Solal, A.; Vanhees, L.; Saner, H.; Schmid, J.-P. Methodological approach to the first and second lactate threshold in incremental cardiopulmonary exercise testing. Eur. J. Cardiovasc. Prev. Rehabil. 2008, 15, 726–734. [CrossRef] 21. Wang, J.; Qiu, J.; Yi, L.; Hou, Z.; Benardot, D.; Cao, W. Effect of sodium bicarbonate ingestion during 6 weeks of HIIT on anaerobic performance of college students. J. Int. Soc. Sports Nutr. 2019, 16, 1–10. [CrossRef] 22. Hopkins, W.; Marshall, S.; Batterham, A.; Hanin, J. Progressive statistics for studies in sports medicine and exercise science. Med. Sci. Sports Exerc. 2009, 41, 3–12. [CrossRef] [PubMed] 23. Gomez, M.A.; Rivas, F.; Connor, J.D.; Leicht, A.S. Performance Differences of Temporal Parameters and Point Outcome between Elite Men’s and Women’s Badminton Players According to Match-Related Contexts. Int. J. Environ. Res. Public Health 2019, 16. [CrossRef] 24. Buchheit, M.; Abbiss, C.R.; Peiffer, J.J.; Laursen, P.B. Performance and physiological responses during a sprint interval training session: Relationships with muscle oxygenation and pulmonary oxygen uptake kinetics. Eur. J. Appl. Physiol. 2012, 112, 767–779. [CrossRef] [PubMed] 25. Koral, J.; Oranchuk, D.J.; Herrera, R.; Millet, G.Y. Six Sessions of Sprint Interval Training Improves Running Performance in Trained Athletes. J. Strength Cond. Res. 2018, 32, 617–623. [CrossRef] [PubMed] Int. J. Environ. Res. Public Health 2021, 18, 638 11 of 11 26. Connolly, D.A. The anaerobic threshold: Over-valued or under-utilized? A novel concept to enhance lipid optimization! Curr Opin. Clin. Nutr. Metab. Care 2012, 15, 430–435. [CrossRef] [PubMed] 27. Phillips, S.M.; Sproule, J.; Turner, A.P. Carbohydrate ingestion during team games exercise: Current knowledge and areas for future investigation. Sports Med. 2011, 41, 559–585. [CrossRef] [PubMed] 28. Meeusen, R.; Watson, P.; Hasegawa, H.; Roelands, B.; Piacentini, M.F. Central fatigue: The serotonin hypothesis and beyond. Sports Med. 2006, 36, 881–909. 29. Welsh, R.S.; Davis, J.M.; Burke, J.R.; Williams, H.G. Carbohydrates and physical/mental performance during intermittent exercise to fatigue. Med. Sci. Sports Exerc. 2002, 34, 723–731. [CrossRef] 30. Robergs, R.A.; McNulty, C.R.; Minett, G.M.; Holland, J.; Trajano, G. Lactate, not lactic acid, is produced by cellular cytosolic energy catabolism. Physiology 2018, 33, 10–12. 31. Menzies, P.; Menzies, C.; McIntyre, L.; Paterson, P.; Wilson, J.; Kemi, O.J. Blood lactate clearance during active recovery after an intense running bout depends on the intensity of the active recovery. J. Sports Sci. 2010, 28, 975–982. [CrossRef] [PubMed] 32. Fukuoka, Y.; Iihoshi, M.; Nazunin, J.T.; Abe, D.; Fukuba, Y. Dynamic Characteristics of Ventilatory and Gas Exchange during Sinusoidal Walking in Humans. PLoS ONE 2017, 12, e0168517. [CrossRef] [PubMed] 33. Andersen, L.L.; Larsson, B.; Overgaard, H.; Aagaard, P. Torque–velocity characteristics and contractile rate of force development in elite badminton players. Eur. J. Sport Sci. 2007, 7, 127–134. [CrossRef] 34. Hood, D.A. Invited Review: Contractile activity-induced mitochondrial biogenesis in skeletal muscle. J. Appl. Physiol. 2001, 90, 1137–1157. [CrossRef]

The Effect of Eight-Week Sprint Interval Training on Aerobic Performance of Elite Badminton Players.

01-13-2021

Liu, Haochong,Leng, Bo,Li, Qian,Liu, Ye,Bao, Dapeng,Cui, Yixiong

eng

PMC8803780

Vol.:(0123456789) Sports Medicine (2022) 52:257–286 https://doi.org/10.1007/s40279-021-01552-4 SYSTEMATIC REVIEW The Training of Medium‑ to Long‑Distance Sprint Performance in Football Code Athletes: A Systematic Review and Meta‑analysis Ben Nicholson1 · Alex Dinsdale1 · Ben Jones1,2,3,4,5 · Kevin Till1,2 Accepted: 24 August 2021 / Published online: 9 September 2021 © The Author(s) 2021 Abstract Background Within the football codes, medium-distance (i.e., > 20 m and ≤ 40 m) and long-distance (i.e., > 40 m) sprint performance and maximum velocity sprinting are important capacities for success. Despite this, no research has identified the most effective training methods for enhancing medium- to long-distance sprint outcomes. Objectives This systematic review with meta-analysis aimed to (1) analyse the ability of different methods to enhance medium- to long-distance sprint performance outcomes (0–30 m, 0 to > 30 m, and the maximum sprinting velocity phase [Vmax]) within football code athletes and (2) identify how moderator variables (i.e., football code, sex, age, playing standard, phase of season) affected the training response. Methods We conducted a systematic search of electronic databases and performed a random-effects meta-analysis (within- group changes and pairwise between-group differences) to establish standardised mean differences (SMDs) with 95% con- fidence intervals and 95% prediction intervals. This identified the magnitude and direction of the individual training effects of intervention subgroups (sport only; primary, secondary, tertiary, and combined training methods) on medium- to long- distance sprint performance while considering moderator variables. Results In total, 60 studies met the inclusion criteria (26 with a sport-only control group), totalling 111 intervention groups and 1500 athletes. The within-group changes design reported significant performance improvements (small–moderate) between pre- and post-training for the combined, secondary (0–30 and 0 to > 30 m), and tertiary training methods (0–30 m). A significant moderate improvement was found in the Vmax phase performance only for tertiary training methods, with no significant effect found for sport only or primary training methods. The pairwise between-group differences design (experi- mental vs. control) reported favourable performance improvements (large SMD) for the combined (0 to > 30 m), primary (Vmax phase), secondary (0–30 m), and tertiary methods (all outcomes) when compared with the sport-only control groups. Subgroup analysis showed that the significant differences between the meta-analysis designs consistently demonstrated a larger effect in the pairwise between-group differences than the within-group change. No individual training mode was found to be the most effective. Subgroup analysis identified that football code, age, and phase of season moderated the overall magnitude of training effects. Conclusions This review provides the first systematic review and meta-analysis of all sprint performance development meth- ods exclusively in football code athletes. Secondary, tertiary, and combined training methods appeared to improve medium- long sprint performance of football code athletes. Tertiary training methods should be implemented to enhance Vmax phase performance. Nether sport-only nor primary training methods appeared to enhance medium to long sprint performance. Performance changes may be attributed to either adaptations specific to the acceleration or Vmax phases, or both, but not exclusively Vmax. Regardless of the population characteristics, sprint performance can be enhanced by increasing either the magnitude or the orientation of force an athlete can generate in the sprinting action, or both. Trial Registration OSF registration https:// osf. io/ kshqn/. Extended author information available on the last page of the article 258 B. Nicholson et al. Key Points Research evaluating the medium- to long-distance sprint performance in the football codes is biased towards male soccer athletes involved in tertiary training methods (e.g., strength, power, and plyometrics training). Medium- to long-distance sprint performance of football code athletes can be enhanced through secondary (i.e., resisted or assisted sprinting), combined (i.e., primary or secondary and tertiary methods) (0–30 and 0–>30 m), and tertiary training methods (0–30 m). Tertiary training methods were the only mode to significantly enhance the maximum velocity phase performance. However, sport-only training or primary training methods did not enhance performance. Despite the use of performance outcomes >20 m as a proxy measure of maximum veloc- ity performance, performance changes may be attributed to either or both adaptations specific to the acceleration or maximum velocity phases, not exclusively maximum velocity. Independent of the population characteristics, findings suggest that practitioners should develop either the magnitude or the orientation of forces, or both, that an athlete can generate and express in the sprinting action to improve medium- to long-distance sprint performance. 1 Introduction Football athletes are defined as those who are competing within a football code. These typically include soccer, American football, Canadian football, Australian football, rugby union, rugby league, rugby sevens, Gaelic football, and futsal. Football code athletes should be proficient at sprinting both short (i.e., 5–20 m) and medium–long (> 20 m) distances [1–5]. Although less frequent, play- ers also perform medium- (i.e., > 20 and ≤ 40 m) to long- distance sprints (e.g., > 40 m), enabling athletes to express maximum sprinting velocity (Vmax) capabilities, particularly from moving starts [4, 6–14]. Very large associations have been demonstrated between Vmax and sprint performance (0–36.6 m, r = 0.94; 18.3–36.6 m, r = 0.97) in football code athletes, whereas the relative rate of acceleration remained the same irrespective of sprinting performance, indicating that a higher Vmax enables a superior acceleration perfor- mance [8]. Given that most athletes accelerate in a similar manner relative to Vmax, it may be that Vmax serves as the upper threshold or limiting factor in the acceleration phase performance. Therefore, improving an athlete’s sprinting Vmax may indirectly improve acceleration [8]. Hence, the development of Vmax and medium–long sprint performance is a vital component of athletic performance within the foot- ball codes [15–18]. Sprint performance over distances greater than 20 m (i.e., 0–30 and 0–40 m split time or velocity) has been shown to be a differentiating factor between playing standards [19–21] and age categories [19, 21, 22] and is associated with suc- cess in key attacking and defensive performance indicators in football code athletes (e.g., rugby sevens [16], rugby league [17, 18], soccer [23]). This body of evidence emphasises the importance of sprint performance for football performance and player development. Unlike sprinters or non-athletic populations, sprint performance development programmes in football code athletes are typically performed concur- rently with multiple other potentially contrasting physical capacities (e.g., endurance) alongside the code’s specific technical–tactical skills. Therefore, developing sprint per- formance is a challenge for all practitioners involved in the football codes [15, 19, 24]. The review by Nicholson et al. [25] reported that short-sprint performance outcomes (0–5, 0–10, and 0–20 m) were enhanced concurrently with code- specific training in football code athletes, but no research has identified the most effective training methods for enhancing medium- to long-distance sprint outcomes in football code athletes (e.g., 0–30, 0–40, 0–50 m). This highlights the need for specifically targeted sprint-based research to understand the most effective, evidence-based methods for developing sprint performance over medium to long sprint distances (e.g., 30–50 m). Sprinting is a multidimensional skill with distinct phases (e.g., acceleration and Vmax). The sequential phases present shifting kinetic and kinematic outcomes as running velocity increases [26]. The kinetic changes include a reduction in the relative contribution of horizontal and increasing con- tribution of vertical ground reaction forces [26]. Kinematic outcomes include progressively greater stride length and fre- quency, reduced contact times, and the trunk lean becoming closer to vertical [26]. As a population, football code ath- letes exhibit different physical and technical approaches to sprinting [27, 28] when compared with well-trained sprint- ers. Notably, Vmax is achieved at shorter distances (e.g., 15–40 vs. 40–60 m, respectively) with a lower Vmax (~ 7–10 vs. > 12 m·s−1) compared with well-trained elite male sprint- ers [8, 9, 27, 29–31]. Furthermore, a higher Vmax percent- age is attained at shorter distances (e.g., 90% at 13.7 m in American football [8]; 96% at 21 m in rugby [9]). This high- lights the need for specifically targeted sprint-based research within this population. 259 Training Medium to Long Sprint Performance in Football Athletes Previous reviews of the literature and meta-analyses [32, 33] assessing mixed population cohorts (i.e., sprint- ers, team sport, and non-athletic populations) and several training studies evaluating the effectiveness of sprint training interventions [34–36] reported that sprint performance is a trainable capacity. However, the responses to sprint develop- ment were reported to be highly variable [32, 34, 37, 38]. Training effects appear to be mode specific, with distance- specific performance changes (e.g., 0–30 and 0 to > 30 m) associated with phase-specific adaptations (i.e., accelera- tion vs. Vmax [32, 33]). Training modes are typically classi- fied based on task specificity into the following subgroups: primary (e.g., sprint technique, sprinting), secondary (e.g., resisted or assisted sprinting), or tertiary (e.g., non-specific methods, including resistance training and plyometrics) [39]. Limitations in the literature mean that the best method of enhancing medium to long sprint performance, both indi- vidually and across football codes, is currently unclear. These limitations include (1) a lack of reviews exclusively including football code athletes, instead including sprint- ers and non-athletes [32, 33, 40–49]; (2) a lack of studies examining all training modalities across football code ath- letes [32, 33, 40–49]; and (3) previous systematic reviews and meta-analyses [32, 33, 41] have misclassified training modes by failing to account for the normal training practices undertaken by training intervention groups (e.g., training categorised as a resisted sled intervention also including two strength sessions per week). These limitations heavily influence the interpretation and knowledge associated with sprint training interventions for applying evidence-based practices within football code athletes. Hence, the effec- tive development of medium to long sprint performance is a collective problem across codes. A cross-football codes systematic review would provide a more comprehensive overview of the available literature than one focusing on an individual sport, while also comparing best methods of developing medium to long sprint performance. However, the magnitude and direction of the training response may be affected by ‘moderator’ variables, presenting changes based on population characteristics such as the sport [50], age [42], and sex [51] of the athlete and on training phase (e.g., pre- season [33]). Therefore, it is important to also identify the moderator variables and evaluate the extent that they may affect the resultant training effect [52]. This systematic review and meta-analysis aimed to (1) analyse the impact of different methods to enhance medium- to long-distance sprint performance outcomes (0–30 m, 0 to > 30 m, and the Vmax phase) within football code athletes and (2) identify how moderator variables (i.e., football code, sex, age, playing standard, phase of season) affect the train- ing response. 2 Methods 2.1 Design and Search Strategy A systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for System- atic Reviews and Meta-Analyses (PRISMA) statement [53] and followed the PROSPERO guidelines. Given the nature of the project, the review protocol was prospectively regis- tered on the database for Open Science Framework (OSF: https:// osf. io/ kshqn/). A systematic search of electronic data- bases (PubMed, The Cochrane Library, MEDLINE, SPORT- Discus, and CINAHL, via EBSCOhost) was conducted to identify original research articles published from the earli- est available records up to and including 4 December 2019. Boolean search phrases were used to include search terms relevant to football code athletes (population), the training intervention (dependent variable), and the sprint perfor- mance outcomes (independent variable). Relevant keywords for each search term were determined through pilot search- ing (screening of titles/abstracts/keywords/full texts of pre- viously known articles). Keywords were combined within terms using the ‘OR’ operator, and the final search phrase was constructed by combining the three search terms using the ‘AND’ operator (Table 1). 2.2 Study Selection Duplicate records were identified and removed, and the remaining records were screened against the predefined inclusion and exclusion criteria (Table 2). Studies were Table 1 Database literature search strategy Search term Keywords 1. Sports population “soccer” OR “football” OR “rugby” OR “futsal” (NOT/- “sprinters” OR “swimming” OR “cycling” OR “Paralympic”) 2. Training intervention “sprinting” OR “sprint” OR “training” OR “speed” OR “resisted” OR “assisted” OR “resistance” OR “power” OR “strength” OR “plyo- metric” OR “weightlifting” OR “strongman” OR “technique” OR “weight” OR “sled” OR “intervention” OR “sprint mechanics” 3. Outcome measures “sprint performance” OR “acceleration” OR “velocity” Search phrase: 1 AND 2 AND 3 260 B. Nicholson et al. screened independently by two researchers (BN, AD). The screening of the journal articles was completed over two phases. Studies were initially excluded based on the content of the titles and abstracts, followed by a full-text review. If the reviewers’ decisions differed, reviewers met to come to an agreed decision on the paper. Disparities in study selec- tion were resolved by a third reviewer (KT). 2.3 Data Extraction One author (BN) extracted the following data using a spe- cifically designed standardised Microsoft Excel spread- sheet: general study information (i.e., author, year), sub- ject characteristics (i.e., sample size, sex, age, body mass, height, sport, training status, performance level), training intervention characteristics (i.e., training methods, control group information, number of sessions per week, duration of training intervention, total amount of training sessions, training intensity, training volume, testing distances, test- ing equipment, training surface, other training, reported training-related injuries), and primary outcome measures (i.e., pre- and post-training intervention means and stand- ard deviations [SDs]). All studies that included the time or velocity achieved from the initial start position (0 m) to between > 20 and ≤ 30 m and between 0 and > 30 m were categorised into the 0–30 m and 0 to > 30 m subgroups, respectively. The Vmax-phase subgroup included directly measured Vmax achieved or time to completion for dis- tances > 20 m with a maximum intensity run-in distance of ≥ 20 m before recording time (e.g., 20–30 or 30–40 m). These outcomes aimed to identify distance-specific changes, whilst representing the longer sprint distances (0 to 30–50 m) performed by football code athletes and those commonly measured by researchers/practitioners. Descriptive information relating to the training activi- ties performed in the studies was used to categorise each intervention into the training mode subgroups outlined in Table 3. If the pre- and post-outcome measure data were not available from the tables or the results section, the data were requested from the author(s). If the authors did not have access to these data, we extracted data on outcome measures from figures using WebPlotDigitizer version 4.1 software (2018). Means and SDs/standard error of the mean were measured manually at the pixel level to the scale provided in the study’s figures. 2.4 Study Quality Assessment The quality of the included studies was assessed using the same scale as in McMaster et al. [54]. This scale is designed to evaluate research conducted in athletic-based training environments from a combination of items from the Cochrane, Delphi, and PEDRO scales. The methodologi- cal scale assesses the study in the following ten domains: inclusion criteria stated, subject assignment, intervention description, control groups, dependent variables definition, assessment methods, study duration, statistics, results sec- tion, and conclusions. Each domain was assigned a score of either 0 indicating clearly no, 1 indicating maybe, or 2 indi- cating clearly yes. The scores were then summed to assess the total study quality out of a maximum of 20. Table 2 Inclusion/exclusion criteria (title/abstract screening and full screening) Criteria Inclusion Exclusion 1 Studies with human subjects and a pre- and post-outcome measure(s) identifying sprint performance > 20 m Studies with non-human subjects and/or no pre- and post-outcome measure(s) identifying sprint performance ≤ 20 m or performance outcomes measured using stopwatches 2 Training intervention study with the training programme clearly outlined, designed to produce chronic adaptations (not acute). Interventions including specific sprint training (resisted, assisted, unresisted sprinting, sprint mechanics, and technique training), non-specific sprint training (strength, power, plyo- metric training, and non-traditional methods), and combined sprint training (combined specific, combined non-specific, and combined mixed methods) Inappropriate study design: not an intervention study or an acute/ post-activation study 3 Original research article Reviews, surveys, opinion pieces, books, periodicals, editorials 4 Population: football code athletes. Football athletes defined as those who are competing within a football code. Football codes for inclusion: soccer, American football, Canadian football, Australian football, rugby union, rugby league, rugby sevens, Gaelic football, futsal Non-football code sports (e.g., solo, racquet/bat, or combat sports), match officials, or non-athletic populations 5 Healthy, able-bodied, non-injured athletes Special populations (e.g., clinical, patients), athletes with a physi- cal or mental disability, and athletes considered to be injured or returning from injury 261 Training Medium to Long Sprint Performance in Football Athletes 2.5 Data Analysis and Meta‑analyses Data extracted from the systematic search were included in the meta-analyses. Improvements in sprint performance are typically identified by a reduction in time taken to cover a given distance or an increase in Vmax achieved for a given time point and or distance [55, 56]. Therefore, pre- and post- time changes were reversed before conducting the analysis. This enabled both time and velocity changes to represent the same direction, thus identifying a reduction in time or an increase in velocity for a given distance as a positive change. A random-effects meta-analysis was performed using Comprehensive Meta-Analysis Version 3.0 software (Bio- stat, Englewood, NJ, USA) to assess the magnitude of change in the outcomes across the relevant primary studies and to explore the effect of moderator variables on the vari- ation among study outcomes [57]. This included implement- ing two meta-analysis approaches: (1) pre-and post-training within-group changes and (2) pairwise between-group effect difference designs. This approach provides an extensive review of all the available training intervention literature for developing sprint performance in football code athletes, including multiple research designs with and without sport- only control groups. In the between-group pairwise analysis, for the studies with multiple intervention groups and single control groups [35, 36, 58–68], the control samples were split into two or more groups of smaller sample sizes to enable two or more (reasonably independent) experimental comparisons [69]. This aligns with our extensive design to evaluate all available literature without combining or remov- ing distinct subgroups (e.g., primary and tertiary methods [67]). Overall summary estimates were calculated for each of the training type subgroups: primary, secondary, combined specific, tertiary, combined methods, and sport-only train- ing (Table 3). We conducted a meta-analysis to identify the between-comparator group (e.g., primary vs. sport only, ter- tiary vs. sport only) adjusted mean performance effects when a sport-only comparator group was available. Combining a within-group pre-post change design and pairwise between- group differences enabled an evaluation of both high-quality controlled trial studies to evaluate training causality and to explore the breadth of the available literature using a range of research designs. Outcome measures were converted into standardised mean differences (SMDs) with 95% confidence intervals (CI) (used as the summary statistic) and 95% prediction intervals (PI). The SMD represents the size of the effect of the intervention relative to the variability observed in that intervention. An inverse-variance random-effects model was used for the meta-analysis because it allocated a propor- tionate weight to trials based on the size of their individual standard errors and facilitated analysis while controlling for heterogeneity across studies [70]. The inputted data included Table 3 Subgroup categorisation Subgroup categories are based on previous definitions from Plisk [39] and Rumpf et al. [32] Specific sprint training: training methods in which the athlete is simulating/performing the sprint movement pattern (see pri- mary and secondary methods) Tertiary methods (non-specific sprint training): training meth- ods not involving the athlete sprinting, that have a transfer into sprint performance as a result of the subsequent training adaptations (e.g., strength, power, plyometric training). These may be performed individually (e.g., strength training) or in combination with other tertiary methods (e.g., strength, power, and plyometric training) Combined specific methods: training methods that included both primary and secondary methods (e.g., sprinting + resisted sled sprinting) Primary methods: training methods simulating the sprint movement pattern (sprint-technique drills, stride length and frequency exercises, and sprints of varying distances and intensities) Secondary methods: training methods simulating the sprint action but applying overload by reducing or increasing the speed of the movement by applying additional resistance (e.g., sledges, resistance bands, weighted garments or incline sprints [gravity resisted]) or assistance (e.g., pulley systems, partner assisted or decline sprints [gravity assisted]) Combined training: training methods that included both specific sprint training (primary and or secondary methods) and tertiary methods in combination (e.g., strength, power, resisted, and unresisted sprint training) Sport only training: training methods not including any specific or non-specific sprint training. This is described as a format of offensive, defensive, and match simulation technical and tactical drills, which may include some form of endurance training and or competitive games 262 B. Nicholson et al. sample sizes, outcome measures with their respective SDs, and a correlation coefficient for within-subject measure- ments. These correlation coefficients (0–30 m, r = 0.92; 0 to > 30 m, r = 0.92; and Vmax phase, r = 0.95) were estimated from prior field testing. The SMD values were interpreted as follows: < 0.20 as trivial, 0.20–0.39 as small, 0.40–0.80 as moderate, and > 0.80 as large [71]. A positive SMD indi- cated that the training intervention was associated with an improvement in sprint performance, whereas a negative SMD indicated a decrease in the respective performance outcome. Accompanying p values tested the null hypothesis that there was no statistically significant change in sprint performance regardless of the training method. Statistical significance was considered for p < 0.05. Heterogeneity between trials was assessed using the I2 statistic, with mod- erate (> 50%) to high (> 75%) values used to indicate poten- tial heterogeneity sources [72]. The I2 statistic was supported by reporting the Tau-squared statistic and the Chi-squared statistic. Sensitivity analyses were conducted for each sub- group by repeating the analyses with each study omitted in turn; this examined whether any conclusions were dependent on a single study. Subgroup analyses were performed to (1) compare the within-group change in pre- and post-sprint performance and pairwise between-group effects from comparative tri- als and (2) evaluate the potential moderator variables. The moderator variables were determined a priori: sex (male vs. female), football code, playing standard (elite vs. sub-elite [from Swann et al. [73], the highest reported standard of performance]), age category (senior [mean age ≥ 18 years] vs. youth [mean age < 18 years]), and training phase (pre- season vs. in-season vs. off-season). 2.6 Evaluation of Small Study Effects Small study effects were explored through visual interpre- tation of funnel plots of SMD versus standard errors and by quantifying Egger’s linear regression intercept [74] to evaluate potential bias. A statistically significant Egger’s statistic (p value < 0.05) indicated the presence of a small study effect. 3 Results 3.1 Overview After duplicates were removed, 1801 studies remained. The study selection inclusion criteria identified 60 studies for inclusion in the within-group change meta-analysis and 26/60 studies for inclusion in the pairwise between-group analysis (Fig. 1). The 60 studies [34–36, 58–68, 75–120] included multiple different research designs (with and without experimental control groups), providing 111 inter- vention groups and 27 sport-only groups. Training groups were sub-grouped into six training classifications (sport only, n = 27; combined methods, n = 35; primary methods, n = 8; secondary methods, n = 9; tertiary methods, n = 59; and combined specific n = 0) to differentiate between find- ings for distinct sprint performance outcomes (Table 3). The 26 identified studies compared a training intervention with a sport-only (i.e., control) comparator group [35, 36, 58–68, 75, 82, 88, 90, 92, 95–97, 104, 106, 113, 114, 119]. This pro- vided 41 eligible training groups for pairwise between-group comparisons (sport-only training vs. combined methods, n = 9; primary methods, n = 3; secondary methods, n = 2; and tertiary methods, n = 27). Table S1 (non-specific/tertiary, n = 59), Table S2 (com- bined, n = 35), and Table S3 (specific, n = 17) (all in the electronic supplementary material [ESM]) present the indi- vidual training group study descriptives, training interven- tions, and sprint outcomes for the included studies. The 60 studies [34–36, 58–68, 75–120] represented a total sample of 1500 football code athletes with a mean sample size of 11.1 ± 3.9 participants per training group. In total, 56 stud- ies were conducted in male athletes, three studies were in female athletes [86, 106, 116], and one was in a mixed popu- lation [65]. The mean age of the participants included in the studies ranged from 11 to 26.8 years. The athlete popula- tions ranged from sub-elite to elite [73]. Collectively, the training intervention durations ranged from 3 to 22 weeks (7.4 ± 3.1 weeks), with the intervention training frequency ranging between one and four sessions per week (2.1 ± 0.6) over 6–32 individual sessions. Studies were conducted in soccer (n = 43), rugby league (n = 4), rugby union (n = 4), rugby sevens (n = 3), Ameri- can football (n = 1), Australian football (n = 1), and mixed football codes (n = 4). No studies in futsal or Gaelic foot- ball players satisfied the inclusion criteria. Studies were conducted in pre-season (n = 21), in-season (n = 26), or off- season (n = 3) periods, and across pre-season and in-season periods (n = 2). Eight studies did not report the phase of the season. Sprint assessment distances ranged from 22.9 to 50 m (0–30 m [n = 46], 0 to > 30 m [n = 20], and Vmax phase [n = 13]). Timing devices included electronic timing gate systems (n = 52), high-speed video cameras (n = 3), radar measurement devices (n = 2), 1080 sprint device (n = 2), a digital timing device (n = 1), a laser measurement device (n = 1), a kinematic measurement system (n = 1), and a mobile application (mysprint; n = 1). Sport-only training groups were described as some format of offensive or defensive match simulation and technical and tactical drills performed over two to ten sessions per week across 2–6 days per week lasting between 30 and 120 min per session as well as some form of endurance training and one to two competitive or friendly games per week. Various 263 Training Medium to Long Sprint Performance in Football Athletes methods of endurance training were described, including simulated games performed in small-, medium-, or large- sided games formats (e.g., 3 vs. 3–11 vs. 11), low-intensity aerobic conditioning, high-intensity interval training, and recreational or cardiovascular activities (e.g., basketball, bik- ing, running, aerobics). Sport-only training was conducted in both pre-season and in-season periods over a duration of 6–16 weeks. Specific sprint-training groups completed sprinting, resisted and assisted sprinting, and technical sprint drills as individual modalities and/or in combination (e.g., complex and contrast sets). The training was performed 1–3 days per week, with intervention periods lasting from 4 to 8 weeks (8–21 sessions). The primary sprint-training methods included single-set interventions ranging from 8–10 rep- etitions of short-distance sprints (18.3–20 m; 160–183 m session totals) to 4–6 repetitions of long-distance springs (200 m; 800–1200 m session totals). Multiple-set methods ranged from 2–6 sets of 2–8 repetitions of medium- to long- distance sprints (30–50 m; 120–1200 m session totals). One study performed submaximal sprint efforts (85% Vmax), involving 4–6 sets of 4 repetitions of long sprints (50 m; 800–1200 m session totals) [102]. Resisted sprinting was performed as either a single set of 3–10 repetitions of Fig. 1 Flow diagram of the process of study selection Records idenfied through database searching (n=5788) Addional records idenfied through other sources (n=34) Records aer duplicates removed (n=1801) Full-text arcles assessed for eligibility (n=245) Full-text arcles excluded (n=185) 73 sprint performance outcome measures ≤20m 30 Inappropriate outcome measure - no sprinng performance outcome 30 Training programme - not clearly outlined 16 Inappropriate populaon - not football code athletes 10 Inappropriate study design - irrelevant intervenon design 6 Full text not available 6 Inappropriate study design - not an intervenon study 7 Measured using stopwatches 4 Not published in English language 3 Inappropriate study design - acute/post acvaon study Studies included in the within-group change meta-analysis (n=60) Titles and abstracts screened (n=1801) Records excluded (n=1556) Idenficaon Screening Eligibility Included Studies with a sport only comparator group included in the pairwise between-group effect meta-analysis (n=26) Studies excluded with no sport only comparator group (n=34) 264 B. Nicholson et al. short-distance sprints (18.3–20 m; 60–200 m session total) or multiple-set methods, ranging from 2 to 7 sets of 3–5 repetitions of short-medium distance resisted sled sprints (5–40 m; 130–455 m session totals). Resisted sprint loads ranged from light to very heavy loads [44]. Loads were pre- scribed based on percentage body mass (BM) (i.e., 10–80% BM). Assisted sprinting methods involved both single and multi-set methods. The single-set intervention included 1 set of 10 repetitions of short sprints over 18.3 m with a bungee cord assistive load at 14.7% BM (183 m session total [116]). Multi-set methods ranged from 1 to 3 sets of 3 repetitions of medium-distance sprints (40 m) with towing eliciting a 0.5- to 1-s faster 0–40 m time using a sprint master towing device (120–360 m session total) [101]. The same study used a combined study arm using the same assistance load while also wearing a 10-lb weighted vest. Tertiary sprint-training groups consisted of strength, power, and/or plyometrics training performed as individual modalities and/or in combination (e.g., complex and con- trast sets). The training was performed 1–4 days per week, with intervention periods lasting from 4 to 22 weeks (8–32 sessions). Lower body strength training (e.g., squat, hip hinge, and calf raise variations) ranged from moderate to supramaximal loads (55–110% one-repetition maximum [1RM]) with low- to high-volume training (e.g., 2–6 sets of 2–6 repetitions and/or 2–6 sets of 8–30 repetitions). Power sessions consisted of ballistic (e.g., squat jump) and Olym- pic weightlifting-type exercises (e.g., clean/snatch deriva- tives) at low to heavy loads (15–80% 1RM to + 30% BM) and velocity-based training using loads corresponding to the mass at which optimal power is produced (1–1.1 × optimal power load). Volume ranged from 2 to 5 sets of 2–12 repeti- tions. Plyometrics sessions involved low- to high-intensity plyometrics (e.g., ankle hops to 50 cm accentuated eccen- tric loading drop jump at + 20% BM) for 1–12 sets of 4–20 repetitions (20–260 foot contacts session totals). The only type of surface identified was a grass surface. Several of the sessions were performed in combination with upper body training. Combined methods training groups consisted of various formats of both specific sprint training (primary and/or sec- ondary methods) and tertiary methods in combination (e.g., strength, power, resisted and unresisted sprint training). These were completed as individual modalities and/or in combination (e.g., complex and contrast sets). The training was performed 1–4 days per week, with intervention periods lasting from 3 to 15 weeks (6–22 sessions). Strength train- ing ranged from moderate to supramaximal loads (70–120% 1RM) with low to high volume (e.g., 2–6 sets of 2–6 rep- etitions and/or 3–4 sets of 8–12 repetitions). Power train- ing consisted of ballistic (e.g., squat jump) and Olympic weightlifting-type exercises (e.g., clean/snatch derivatives) at light to heavy loads (20–86% 1RM) and/or velocity-based training using loads corresponding to the mass at which optimal power is produced (1–1.1 × optimal power load or 0.8–1.2 m·s−1 loads). This also included medicine ball throws of 3–12 kg. Volume ranges were from 2 to 6 sets of 2–8 repetitions per set. Plyometrics sessions involved low- to high-intensity plyometrics (e.g., ankle hops to 75 cm hurdle jumps), with 2–5 sets of 1–10 repetitions (9–250 foot con- tacts session totals). The only type of surface identified was a synthetic grass pitch. The specific sprint-training methods included single-set interventions ranging from 1 to 8 repeti- tions of short- to long-distance sprints (5–45.72 m) or mul- tiple-set methods, ranging from 1 to 5 sets of 3–7 repetitions of short- to medium-distance sprints (5–40 m; 30–800 m session totals) from various starting positions. Resisted sprint loads ranged from light to very heavy loads. Loads were prescribed based on absolute loads (i.e., 10–30 kg), percentage BM, i.e., 5–20% BM or reduction in Vmax cor- responding to the additional resistance applied (10–60% reduction in Vmax). One training study used assisted sprints, involving 1 set of five medium-distance sprints (40 m) with 25 m of each sprint including a 2% gradient decline (200 m session total [83]). Several of the sessions were performed in combination with upper body training. 3.2 Study Quality The scores for the assessment of study quality [54] are shown in Table 4 and ranged from 11 to 20 with a mean score of 18 ± 1.9, demonstrating high study quality. Items 2 (subjects assigned appropriately [random/equal baseline]), 4 (control group inclusion), and 9 (results detailed [mea n ± SD, percent change, effect size]) were the most decisive factors in separating high-quality and low-quality studies. 3.3 Meta‑analysis Tables S1–S3 in the ESM provide the individual study statistics. 3.4 Standardised Mean Difference (SMD) for 0–30 m Performance For 0–30 m performance, 103 within-training group effects were analysed from 45 original studies [34, 36, 58–60, 62–66, 75, 77–80, 82, 85–88, 90–100, 102–108, 113, 115–120]. In total, 32 training and control groups from 21 studies were eligible for a pairwise between-group analysis (sport-only control vs. experimental) [36, 58–60, 62–66, 75, 82, 88, 90, 92, 95–97, 104, 106, 113, 119]. In nine studies [36, 58–60, 62–66], the 21 available control groups were split to allow comparison between the multiple training groups in the studies [69]. Figures 2, 3 show the SMD for each training type. 265 Training Medium to Long Sprint Performance in Football Athletes Table 4 Methodological quality scale scores Study Question number Score 1 2 3 4 5 6 7 8 9 10 Alptekin et al. [75] 2 2 2 2 2 1 2 2 0 2 17 Barr et al. [76] 2 2 2 2 2 2 2 2 2 2 20 Beato et al. [77] 2 2 2 0 2 2 2 2 2 2 18 Bianchi et al. [78] 2 2 2 0 2 2 2 2 2 2 18 Borges et al. [79] 2 2 2 0 2 2 2 2 0 2 16 Bouguezzi et al. [80] 2 2 2 0 2 2 2 2 2 2 18 Bremec [58] 2 2 2 2 2 2 2 2 2 2 20 Chelly et al. [81] 2 2 2 2 2 2 2 2 2 2 20 Christou et al. [82] 2 2 2 2 2 2 2 2 2 2 20 Cook et al. [83] 2 2 2 0 2 2 2 2 2 2 18 Coratella et al. [59] 2 2 2 2 2 2 2 2 2 2 20 Coutts et al. [84] 2 0 2 0 2 2 2 2 0 2 14 de Hoyo et al. [85] 2 2 2 0 2 2 2 2 2 2 18 Derakhti [60] 2 2 2 2 2 2 2 2 2 2 20 Douglas et al. [34] 2 2 2 2 2 2 2 2 2 2 20 Enoksen et al. [35] 2 2 2 2 2 2 2 2 2 2 20 Escobar-Álvarez et al. [86] 2 0 2 2 2 2 2 2 1 1 16 Escobar-Álvarez et al. [87] 1 0 2 0 1 2 2 2 1 0 11 Faude et al. [88] 2 2 2 2 2 2 2 2 2 2 20 Gabbett et al. [89] 2 0 2 0 2 2 2 2 1 2 15 García-Pinillos et al. [90] 2 2 2 2 2 2 2 2 0 2 18 Gil et al. [91] 2 2 2 0 2 2 2 2 2 2 18 Hammami et al. [92] 2 2 2 2 2 2 2 2 0 2 18 Hammami et al. [93] 2 2 2 0 2 2 2 2 2 2 18 Hammami et al. [61] 2 2 2 2 2 2 2 2 2 2 20 Harris et al. [94] 2 2 2 0 2 0 2 2 0 2 14 Karsten et al. [95] 2 2 2 2 2 2 2 2 0 2 18 Krommes et al. [96] 2 2 2 2 2 2 2 2 2 2 20 Lahti et al. [36] 2 2 2 2 2 2 2 2 2 2 20 López-Segovia et al. [97] 2 2 2 2 2 2 2 2 2 2 20 Loturco et al. [98] 2 2 2 0 2 2 2 2 0 2 16 Loturco et al. [99] 2 2 2 0 2 2 2 2 2 2 18 Loturco et al. [100] 2 2 2 0 2 2 2 2 0 2 16 Majdell and Alexander [101] 2 2 2 0 2 2 2 2 0 2 16 Manouras et al. [62] 2 2 2 2 2 2 2 2 0 2 18 McMaster et al. [120] 2 2 2 0 2 2 2 2 2 2 18 Meckel et al. [102] 2 2 2 0 2 2 2 2 0 2 16 Michailidis et al. [103] 2 2 2 2 2 2 2 2 0 2 18 Negra et al. [104] 2 2 2 2 2 2 2 2 0 2 18 Orange et al. [105] 2 2 2 2 2 2 2 2 2 2 20 Ozbar [106] 2 2 2 2 2 2 2 2 2 2 20 Ramírez-Campillo et al. [63] 2 2 2 2 2 2 2 2 2 2 20 Ramírez-Campillo et al. [64] 2 2 2 2 2 2 2 2 2 2 20 Ramírez-Campillo et al. [65] 2 2 2 2 2 2 2 2 2 2 20 Ramírez-Campillo et al. [66] 2 2 2 2 2 2 2 2 1 2 19 Randell et al. [107] 2 2 2 2 2 2 2 2 2 2 20 Rey et al. [108] 2 2 2 2 2 2 2 2 2 2 20 Rimmer and Sleivert [67] 2 2 2 2 2 2 2 2 0 2 18 Rønnestad et al. [68] 2 2 2 2 2 2 2 2 0 2 18 266 B. Nicholson et al. Table 4 (continued) Study Question number Score 1 2 3 4 5 6 7 8 9 10 Rønnestad et al. [109] 2 2 2 0 2 2 2 2 0 2 16 Ross et al. [110] 2 2 2 0 2 2 2 2 2 2 18 Scott et al. [111] 2 2 2 2 2 2 2 2 0 2 18 Shalfawi et al. [112] 2 2 2 2 2 2 2 2 2 2 20 Söhnlein et al. [113] 2 0 2 2 2 2 2 2 2 2 18 Tønnessen et al. [114] 2 2 2 2 2 2 2 2 2 2 20 Tous-Fajardo et al. [115] 2 0 2 2 2 2 2 2 0 2 16 Upton [116] 2 2 2 0 2 2 2 2 0 2 16 West et al. [117] 2 2 2 0 2 2 2 2 0 2 16 Winwood et al. [118] 2 2 2 0 2 2 2 2 0 2 16 Wong et al. [119] 2 0 2 2 2 2 2 2 0 2 16 0 = clear no, 1 = maybe, 2 = clear yes Training type Training groups (n) SMD (95% CI) 95% PI p- value Sport only 22 0.02 [-0.11, 0.15] [-0.60, 0.64] 0.78 Combined methods 23 0.43 [0.21, 0.65] [0.69, 1.55] <0.001 Primary methods 6 0.20 [-0.01, 0.42] [-0.51, 0.92] 0.06 Secondary methods 7 0.61 [0.31, 0.91] [-0.43, 1.65] <0.001 Ter ary methods 45 0.39 [0.24, 0.54] [-0.63, 1.41] <0.001 -1 -0.5 0 0.5 1 1.5 2 Heterogeneity: I2 = 92.84%; Q = 1424.88; t2 = 0.21 and df =102 ← Reduced sprint performance Increased sprint performance→ a a a,b Standardised mean difference (mean ± 95% CI and 95% PI) Fig. 2 Forest plots showing the SMD (mean [95% CI and 95% PI]) for the studies evaluating the between-training-group effects on 0–30  m sprint performance. aSignificantly different to sport-only training, p < 0.05; bSignificantly different to primary training meth- ods, p < 0.05. Bold formatting indicates p < 0.05. CI confidence inter- val, PI prediction interval, SMD standardised mean difference Training type Training groups (n) SMD (95% CI) 95% PI p- value Combined methods 5 0.58 [-0.93, 2.10] [-5.20, 6.37] 0.45 Primary methods 2 0.33 [-0.58, 1.25] N/A 0.48 Secondary methods 2 2.78 [1.53, 4.03] N/A <0.001 Ter ary methods 23 1.49 [0.95, 2.03] [-1.06, 4.03] <0.001 Heterogeneity: I2 = 85.25%; Q = 210.11; t2 = 1.68 and df =31 ← Favours control Favours experimental→ Standardised mean difference (mean ± 95% CI) -7.5 -5.0 -2.5 0.0 2.5 5.0 7.5 and 95% PI) Fig. 3 Forest plots showing the SMD (mean [95% CI and 95% PI]) in post-intervention 0–30 m sprint performance between intervention and control athletes. Bold formatting indicates p < 0.05. CI confi- dence interval, N/A fewer than three training groups available, PI pre- diction interval, SMD standardised mean difference 267 Training Medium to Long Sprint Performance in Football Athletes 3.4.1 Within‑Group Changes (0–30 m) The sport-only and primary methods training failed to show statistical significance for change in 0–30 m performance. Significant performance improvements were observed in the combined and secondary methods training groups (moderate SMD) and tertiary methods (small SMD). The combined, secondary, and tertiary methods demon- strated a significantly larger training effect than sport-only training. Only secondary methods reported a significantly larger training effect than primary training methods. 3.4.2 Pairwise Between‑Group Differences (0–30 m) The combined and primary training methods failed to show statistical significance to sprint performance changes compared with sport-only training. Significant performance improvements were observed (large SMD) for the secondary and tertiary training groups compared with the sport-only control groups. Between-experimental subgroups analysis failed to show statistical significance between training methods. Between-experimental-sub- group analysis was not conducted on the primary or sec- ondary subgroups with control groups because only two training groups were available. 3.5 SMD for 0 to > 30 m Performance For 0 to > 30 m performance, 43 within-training group effects were analysed from 18 original studies [35, 61, 68, 76–78, 83–85, 89, 92, 101, 109–112, 114, 116]. Eight train- ing and control groups from five studies were eligible for a pairwise between-group analysis (sport-only control vs. experimental) [35, 61, 68, 92, 114]. The five available con- trol groups were split in three studies [35, 61, 68] to allow comparison between multiple training groups in the studies [69]. Figures 4, 5 show the SMD for each training type. Training type Training groups (n) SMD (95% CI) 95% PI p-value Sport only 5 0.22 [-0.10, 0.54] [-0.97, 1.41] 0.18 Combined methods 18 0.33 [0.14, 0.52] [-0.50, 1.16] <0.001 Primary methods 2 0.06 [-0.14, 0.25] N/A 0.57 Secondary methods 5 0.37 [0.25, 0.50] N/A* <0.001 Ter ary methods 12 0.22 [-0.26, 0.70] [-1.73, 2.17] 0.37 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 ← Reduced sprint performance Increased sprint performance→ Standardised mean difference (mean ± 95% CI and 95% PI) Heterogeneity: I2 = 93.16%; Q = 599.021; t2 = 0.24 and df = 42 a a Fig. 4 Forest plots showing the SMD (mean [95% CI and 95% PI]) for the studies evaluating the between-training-group effects on 0 to > 30 m sprint performance. aSignificantly different to primary train- ing methods, p < 0.05. Bold formatting indicates p < 0.05. CI confi- dence interval, N/A fewer than three training groups available, N/A* all studies show a common effect size, PI prediction interval, SMD standardised mean difference Training type Training groups (n) SMD (95% CI) 95% PI p- value Combined methods 4 1.51 [0.21, 2.80] [-4.26, 7.28] 0.02 Ter ary methods 4 1.12 [0.56, 1.68] [-0.29, 2.52] <0.001 Heterogeneity: I2 = 60.25%; Q = 17.61; t2 = 0.50 and df =7 ← Favours control Favours experimental→ Standardised mean difference (mean ± 95% CI -5 -2.5 0 2.5 5 7.5 and 95% PI) Fig. 5 Forest plots showing the SMD (mean [95% CI and 95% PI]) in post-intervention 0 to > 30 m sprint performance between intervention and control athletes. Bold formatting indicates p < 0.05. CI confidence interval, PI prediction interval, SMD standardised mean difference 268 B. Nicholson et al. 3.5.1 Within‑Group Changes (0 to > 30 m) The sport-only training, primary, and tertiary methods failed to show statistical significance for change in 0 to > 30 m sprint performance. Significant performance improvements were observed in the combined and secondary methods training groups (small SMD). Between-subgroups analy- sis failed to show statistical significance between training methods. Between-subgroup analysis was not conducted on the primary training methods subgroup as only two training groups were available. 3.5.2 Pairwise Between‑Group Differences (0 to > 30 m) Significant performance improvements were observed (large SMD) for the combined and tertiary training groups compared with the sport-only control groups. Between- experimental subgroups analysis failed to show statistical significance between training methods. 3.6 SMD for Maximum‑Velocity Phase Performance For Vmax-phase performance, 31 within-training group effects were analysed from 13 original studies [34, 58, 67, 68, 76, 81, 93, 97, 110–112, 114, 116]. Eight training and control groups from five studies were eligible for a pairwise between-group analysis (sport-only control vs. experimen- tal) [58, 67, 68, 97, 114]. The five available control groups were split in three studies [58, 67, 68] to allow comparison between the multiple training groups in the studies [69]. Fig- ures 6, 7 show the SMD for each training type. Training type Training groups (n) SMD (95% CI) 95% PI p- value Sport only 5 0.19 [-0.45, 0.83] [-2.33, 2.71] 0.57 Combined methods 9 0.05 [-0.23, 0.34] [-1.01, 1.11] 0.73 Primary methods 3 -0.07 [-0.24, 0.10] [-1.75, 1.61] 0.43 Secondary methods 3 0.07 [-0.10, 0.23] [-1.57, 1.71] 0.43 Ter ary methods 11 0.45 [0.08, 0.81] [-0.97, 1.83] 0.02 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 ← Reduced sprint performance Increased sprint performance→ Heterogeneity: I2 = 94.82%; Q = 578.74; t2 = 0.23 and df = 30 a Standardised mean difference (mean ± 95% CI and 95% PI) Fig. 6 Forest plots showing the SMD (mean [95% CI and 95% PI]) for the studies evaluating the between-training-group effects on Vmax- phase sprint performance. aSignificantly different to primary training methods, p < 0.05. Bold formatting indicates p < 0.05. CI confidence interval, PI prediction interval, SMD standardised mean difference, Vmax maximum sprinting velocity Training type Training groups (n) SMD (95% CI) 95% PI p- value Combined methods 2 -0.83 [-4.33, 2.68] N/A 0.64 Primary methods 2 1.13 [0.17, 2.09] N/A 0.02 Secondary methods 1 1.27 [-0.11, 2.65] N/A 0.07 Ter ary methods 3 1.95 [0.75, 3.15] [-10.13, 14.03] <0.001 Heterogeneity: I2 = 89.47%; Q = 66.49; t2 = 3.40 and df =7 ← Favours control Favours experimental→ Standardised mean difference (mean ± 95% CI) -12.5 -10 -7.5 -5 -2.5 0 2.5 5 7.5 10 12.5 15 and 95% PI) Fig. 7 Forest plots showing the SMD (mean [95% CI and 95% PI]) in post-intervention Vmax-phase sprint performance between interven- tion and control athletes. Bold formatting indicates p < 0.05. CI confi- dence interval, N/A fewer than three training groups available, PI pre- diction interval, SMD standardised mean difference, Vmax maximum sprinting velocity 269 Training Medium to Long Sprint Performance in Football Athletes 3.6.1 Maximum‑Velocity Phase Within‑Group Changes The sport-only training, primary, secondary, and combined methods failed to show statistical significance for change in Vmax-phase performance. The tertiary training methods showed a significant moderate performance improvement. The tertiary training methods demonstrated a significantly larger training effect than primary training methods. 3.6.2 Maximum‑Velocity Phase Pairwise Between‑Group Differences The secondary and combined training methods failed to show statistical significance to sprint performance change to sport-only training. Significant performance improve- ments were observed (large SMD) for the primary and tertiary methods training groups compared with the sport- only control groups. Between-subgroup analysis was not conducted as the tertiary methods were the only training group with more than two training groups available. 3.7 Within‑Group Change Design vs. Pairwise Between‑Group Effect No significant difference was observed for the combined methods subgroups (all distance outcomes). Both signifi- cant (Vmax phase) and non-significant (0–30 m) differences were found for the primary training between-subgroup analysis. The between-group effect from comparative trials was significantly larger for both tertiary (all distance out- comes) and secondary methods (0–30 m and Vmax phase) (Table 5). 3.8 Heterogeneity The degree of overall heterogeneity was high for all out- come measures between studies I2 (> 75%). 3.9 Sensitivity Analysis Omitting each study separately identified the effect that each study had on the mean effect. This revealed minor changes only for the secondary training methods. These changes did not have a substantial impact on the statistical significance of the overall mean effect. Sport-only, combined, primary, and tertiary training methods were sensitive to the exclusion of one or more studies independently and, in turn, moder- ated the statistical interpretation of the results. Removal of one of the five 0 to > 30 m studies [35] from the sport-only methods subgroup moderated the within-group change sta- tistical significance from non-significant (p > 0.05) to signifi- cant (p < 0.05). Removal of one of the five 0–30 m studies [97] and one of two Vmax-phase studies from the pairwise between-group differences (sport-only vs. combined train- ing methods) moderated the statistical significance from non-significant (p > 0.05) to significant (p < 0.05). Removal of one of the four 0 to > 30 m studies [35] from the pair- wise between-group differences (sport-only vs. combined training methods) moderated the statistical significance from significant (p < 0.05) to non-significant (p > 0.05). Removal of two of the five 0–30 m studies [58, 60] and one of three Vmax-phase studies [58] from the within-group change primary methods subgroup moderated the statisti- cal significance from non-significant (p > 0.05) to signifi- cant (p < 0.05). Removing one of two 0–30 m Vmax-phase primary methods subgroup studies [58] from the pairwise between-group differences (primary vs. combined training methods) moderated the statistical significance from non- significant (p > 0.05) to significant (p < 0.05). Removing one of the eight within-group 0 to > 30 m studies [89] and one of the six Vmax-phase studies [81] from the tertiary training method subgroup moderated the statistical significance from non-significant to significant and from significant to non- significant, respectively. 3.10 Evaluation of Small Study Effects Inspection of the funnel plots for the within-group change revealed the presence of a statistically significant Egger’s regression intercept, showing evidence of small study effects for the 0–30 m (intercept 9.36; 95% CI 5.68–13.04; p < 0.001) and Vmax-phase (intercept 11.38; 95% CI − 4.88 to 17.87; p < 0.01). For studies included in the pairwise between-group differences comparison, evidence indicated small study effects for the 0–30 m (intercept 8.90; 95% CI 4.22–13.21; p < 0.001), 0 to > 30 m (intercept 6.60; 95% CI − 0.10 to 13.27; p = 0.05), and Vmax-phase (intercept 15.83; 95% CI − 3.15 to 28.14; p = 0.02). The SMD between pre- and post-intervention sprint performance was therefore not considered symmetrical, suggesting the presence of sig- nificant publication bias [121]. However, there was little Table 5 Subgroup analysis comparing the within-group change standardised mean difference in sprint performance and pairwise between-group effect from comparative trials ↑ indicates that the pairwise between-group effect standardised mean difference was significantly larger (p < 0.05) than the within-group change in sprint performance Subgroup within study 0–30 m 0 to > 30 m Vmax phase Combined methods p = 0.85 p = 0.08 p = 0.63 Primary methods p = 0.79 NA ↑p = 0.02 Secondary methods ↑p < 0.01 NA ↑p = 0.01 Tertiary methods ↑p < 0.001 ↑p = 0.02 ↑p = 0.02 270 B. Nicholson et al. evidence to indicate a small study effect for the within-group change in the 0 to > 30 m outcome studies (intercept 3.69; 95% CI − 1.90 to 9.28; p = 0.19). Table 6 Summary of moderator variable analysis for football code, sex, playing standard, age, and phase of training meta-analysis by subgroup with the sport-only training groups removed Between-group differences Subgroup standardised mean difference Football code 0–30 m Soccer vs. rugby league, p = 0.07 Soccer vs. rugby union, p = 0.98 Rugby league vs. rugby union, p = 0.10 American footballa Rugby sevensa 0 to > 30 m American football vs. rugby league, p = 0.47 American football vs. rugby sevens, p = 0.31 American football vs. rugby union, p = 0.08 American football vs. soccer, p = 0.34 Rugby league vs. rugby union, p = 0.59 Rugby league vs. rugby sevens, p = 0.64 Rugby league vs. soccer, p = 0.37 Rugby sevens vs. rugby union, p = 0.49 Rugby sevens vs. soccer, p = 0.02* Rugby union vs. soccer, p < 0.001* Australian footballa Vmax phase Rugby sevens vs. soccer, p = 0.16 Australian footballa Soccer 0–30 m (n = 62; SMD 0.47; 95% CI 0.34–0.59; 95% PI − 0.55 to 1.48); p < 0.001* 0 to > 30 m (n = 21; SMD 0.49; 95% CI 0.30–0.68; 95% PI − 0.41 to 1.39); p < 0.001* Vmax (n = 14; SMD 0.32; 95% CI 0.02–0.62; 95% PI − 0.45 to 1.43); p = 0.04* Rugby union 0–30 m (n = 6; SMD 0.46; 95% CI 0.18–0.74; 95% PI − 0.50 to 1.42); p < 0.001* 0 to > 30 m (n = 4; SMD 0.07; 95% CI − 0.02 to 0.16; 95% PI − 0.12 to 0.26); p = 0.12 Vmax (NA) American football 0–30 m (NA) 0 to > 30 m (n = 3; SMD 0.33; 95% CI 0.06–0.60; 95% PI − 2.43 to 3.08); p = 0.02* Vmax (NA) Rugby league 0–30 m (n = 4; SMD − 0.06; 95% CI − 0.60 to 0.48; 95% PI − 2.64 to 2.53); p = 0.84 0 to > 30 m (n = 3; SMD − 0.39; 95% CI − 2.30 to 1.53; 95% PI − 25.17 to 24.39); p = 0.69 Vmax (NA) Rugby sevens *0–30 m (n = 1; SMD 0.43; 95% CI 0.17–0.69); p < 0.01* 0 to > 30 m (n = 4; SMD 0.15; 95% CI − 0.06 to 0.36; 95% PI − 0.58 to 0.88); p = 0.16 Vmax (n = 4; SMD 0.08; 95% CI − 0.06 to 0.22; 95% PI − 1.37 to 2.34); p = 0.27 Australian Football 0–30 m (NA) 0 to > 30 ma (n = 2; SMD − 0.14; 95% CI − 0.39 to 0.12); p = 0.29 Vmax a (n = 2; SMD 0.09; 95% CI − 0.07 to 0.24); p = 0.27 Sex 0–30 m Male vs. female, p = 0.15 0 to > 30 m Male vs. female, p = 0.77 Vmax phase Male vs. female, p = 0.17 Male 0–30 m (n = 74; SMD 0.38; 95% CI 0.26–0.49; 95% PI − 0.59 to 1.35); p < 0.001* 0 to > 30 m (n = 34; SMD 0.30; 95% CI 0.11–0.48; 95% PI − 0.81 to 1.41); p < 0.001* Vmax (n = 23; SMD 0.22; 95% CI 0.02–0.42; 95% PI − 0.41 to 1.38); p = 0.03* Female 0–30 m (n = 7; SMD 0.64; 95% CI 0.30–0.97; 95% PI − 0.54 to 1.81); p < 0.001* 0 to > 30 m (n = 3; SMD 0.25; 95% CI 0.00–0.50; 95% PI 2.53–3.03); p = 0.05 Vmax (n = 3; SMD 0.02; 95% CI − 0.18 to 0.22; 95% PI − 4.99 to 5.96); p = 0.84 Playing standard 0–30 m Elite vs. sub-elite, p = 0.21 0 to > 30 m Elite vs. sub-elite, NA Vmax phase Elite vs. sub-elite, p = 0.55 Elite 0–30 m (n = 52; SMD 0.39; 95% CI 0.25–0.53; 95% PI − 0.60 to 1.38); p < 0.001* 0 to > 30 m (n = 36; SMD 0.28; 95% CI 0.10–0.45; 95% PI − 0.39 to 1.36); p < 0.001* Vmax (n = 22; SMD 0.21; 95% CI 0.00–0.42; 95% PI …); p = 0.04* Sub-elite 0–30 m (n = 16; SMD 0.58; 95% CI 0.32–0.85; 95% PI − 0.59 to 1.75); p < 0.001* 0 to > 30 m (NA) Vmax (n = 4; SMD 0.10; 95% CI − 0.18 to 0.22; 95% PI − 1.37 to 2.34); p = 0.48 Age 0–30 m Senior vs. youth, p = 0.07 0 to > 30 m Senior vs. youth, p = 0.24 Vmax phase Senior vs. youth, p = 0.37 Senior 0–30 m (n = 44; SMD 0.51; 95% CI 0.34–0.68; 95% PI − 0.63 to 1.65); p < 0.001* 0 to > 30 m (n = 25; SMD 0.19; 95% CI 0.08–0.31; 95% PI − 0.40 to 1.38); p < 0.001* Vmax (n = 21; SMD 0.25; 95% CI 0.03–0.47; 95% PI − 0.41 to 1.39); p = 0.03* Youth 0–30 m (n = 35; SMD 0.32; 95% CI 0.20–0.44; 95% PI − 0.41 to 1.05); p < 0.001* 0 to > 30 m (n = 12; SMD 0.48; 95% CI 0.03–0.94; 95% PI − 0.47 to 1.45); p = 0.04* Vmax (n = 5; SMD 0.00; 95% CI − 0.23 to 0.23; 95% PI − 0.88 to 1.86); p = 0.98* 271 Training Medium to Long Sprint Performance in Football Athletes 3.11 Moderator Variables Table 6 presents the subgroup analysis assessing potential moderating factors for sprint performance (0–30 m, 0 to > 30 m performance, and Vmax-phase). The between-sub- group analysis showed significant (p < 0.05) differences for football code, age, and phase of training; all moderated the overall magnitude of training effects (either smaller or larger SMD). However, the between-subgroup differences were not consistent across distance outcomes. Both playing standard and sex consistently demonstrated no significant difference between subgroups. 4 Discussion 4.1 Overview of the Main Findings Multiple training methods are recommended for improving medium- to long-distance sprint performance because of its importance in the football codes [32, 33, 40–49]. This sys- tematic review with meta-analysis is the first to (1) analyse the impact of different methods in enhancing medium- to long-distance sprint performance outcomes (0–30 m, 0 to > 30 m, and the Vmax phase) within football code athletes and (2) identify how moderator variables (i.e., football code, sex, playing standard, age, and phase of season) affected the training response. This review analysed 60 studies [34–36, 58–68, 75–120], totalling sprint performance measurements from 1500 athletes, thus providing the largest systematic evidence base for enhancing medium- to long-distance sprint performance over distances > 20 m exclusively including football code athletes. In summary, the meta-analysis of all the included studies showed enhanced sprint performance in the combined, sec- ondary, and tertiary training methods groups. Combined and secondary methods showed small to moderate improvements in 0–30 m and 0 to > 30 m performance. Tertiary methods showed small and moderate performance improvements in both 0–30 m and Vmax-phase outcomes, respectively. Signifi- cant performance improvements (large SMD) were observed for the combined (0 to > 30 m), primary (Vmax phase), sec- ondary (0–30 m), and tertiary methods (all outcomes) when compared pairwise with the sport-only control groups. These findings support previous literature that stated that the medium to long sprint performance of football code ath- letes can be enhanced concurrently alongside football code- specific training [25, 41]. Despite several training methods demonstrating significant improvement in sprint perfor- mance, it is important to note that the PIs contained both null and negative effects in all training groups. This indi- cates that, for all training subgroups and assuming a normal distribution of the data, some athletes experienced null or negative performance effects even though the point estimate suggested benefit. Sport-only training showed no significant change in medium to long sprint performance, suggesting such training alone is insufficient to improve performance. The significant differences in between-group effect compari- sons for studies with control groups and the within-group change consistently demonstrated a larger effect. Despite Subgroup analyses showing the SMD (mean; 95% CI and 95% PI) between post and pre-intervention sprint performance outcomes. Some stud- ies were not included because the value used for subgroup analysis was not reported or did not match the appropriate categories. PI were not included for subgroups with fewer than three training groups CI confidence interval, NA no training group met the inclusion criteria, PI prediction interval, SMD standardised mean difference, Vmax maxi- mum velocity-phase sprint performance outcome a Fewer than three training groups *p < 0.05 Table 6 (continued) Between-group differences Subgroup standardised mean difference Phase 0–30 m In-season vs. off-season, p = 0.91 In-season vs. pre-season, p = 0.13 Pre-season vs. off-season, p = 0.33 0 to > 30 m In-season vs. off-season, p < 0.10 In-season vs. pre-season, p < 0.09 Pre-season vs. off-season, p = 0.11 Vmax phase In-season vs. pre-seaso,n p = 0.36 In-season 0–30 m (n = 41; SMD 0.32; 95% CI 0.16–0.48; 95% PI − 0.72 to 1.36); p < 0.001* 0 to > 30 m (n = 11; SMD 0.64; 95% CI 0.38–0.89; 95% PI − 0.49 to 1.46); p < 0.001* Vmax (n = 10; SMD 0.28; 95% CI − 0.14 to 0.71; 95% PI − 0.51 to 1.48); p = 0.19 Off-season 0–30 m (n = 4; SMD 0.29; 95% CI − 0.13 to 0.71; 95% PI − 1.73 to 2.31); p = 0.18* 0 to > 30 m (n = 3; SMD 0.33; 95% CI 0.06–0.60; 95% PI − 4.99 to 5.96); p = 0.02* Vmax (NA) Pre-season 0–30 m (n = 26; SMD 0.52; 95% CI 0.31–0.73; 95% PI − 0.58 to 1.62); p < 0.001* 0 to > 30 m (n = 17; SMD 0.02; 95% CI − 0.23 to 0.26; 95% PI − 0.43 to 1.41); p = 0.94 Vmax (n = 8; SMD 0.08; 95% CI − 0.04 to 0.19; 95% PI − 0.57 to 1.54); p = 0.19 272 B. Nicholson et al. sprint measures over > 20 m being a proxy measure of Vmax improvements, changes in performance may not result exclu- sively from Vmax-specific adaptations. Instead, performance changes in outcomes > 20 m may be attributed to either or both adaptations specific to the acceleration or Vmax phases. Between-subgroup analysis identified that football code, age, and phase of training all moderated the overall magnitude of training effects (either smaller or larger SMD). However, the between-subgroup differences were not consistent across distance outcomes. The increase in performance was signifi- cantly greater for soccer than for rugby union, rugby sevens, and American football for 0 to > 30 m, whereas the improve- ment in performance was significantly greater for American football than for rugby union (0 to > 30 m). The increase in performance was significantly greater for youth athletes than for senior athletes (0 to > 30 m). In-season performance changes were significantly greater than in the pre-season and off-season periods in the 0 to > 30 m outcomes only. Playing standard and sex consistently demonstrated no significant difference between subgroups. The lack of consistency may suggest greater importance of other moderator variables, such as training and load prescription (e.g., mode, volume, intensity, and frequency), over the described individual population characteristics. 4.2 Summary of Interventions to Develop Sprint Performance The 60 studies were categorised into five training modes, resulting in 111 training groups (i.e., sport only, n = 27; com- bined methods, n = 35; primary methods, n = 8; secondary methods, n = 9; tertiary methods, n = 59). Of the 60 studies, 26 had sport-only comparator groups [35, 36, 58–68, 75, 82, 88, 90, 92, 95–97, 104, 106, 113, 114, 119], which provided 41 training groups for between-group effect comparisons (combined methods, n = 9; primary methods, n = 3; second- ary methods, n = 2; tertiary methods, n = 27). No research met the inclusion criteria for the combined specific training methods group, which combined both primary and second- ary training methods. These findings highlight the volume of tertiary method training studies and the reported gap in the available literature to support specific sprint-training methods (primary, secondary, and combined specific train- ing methods) in football code athletes [33, 44]. This also fur- ther supports the requirement for the within-group analysis, including a greater range of study designs given the small number of studies with a sport-only control group avail- able. The scarcity of specific sprint-training method studies is most probably because football code training typically consists of tertiary training methods to develop the multiple physical capacities (e.g., strength, speed, power) required within these sports. This is a strength of the current study, as previous reviews [32, 33] did not include all training undertaken by the intervention groups within their analysis (e.g., primary or secondary training groups also completing tertiary training methods or vice versa [94, 117, 122, 123]). The current degree of overall heterogeneity was high for all outcome measures between studies (I2 > 75% [124]). Het- erogeneity is to be expected in systematic reviews given the grouping of both clinically and methodologically diverse studies [124]. The high degree of heterogeneity reflects the diversity of the training effects presented. This is likely due to the wide variation in the intervention characteristics, including training frequency [78, 80], intensity [34, 36, 59, 125], duration [76], volume [109], other training completed [62, 100]), population characteristics (e.g., sex [65], base- line physical characteristics [60, 110], training experience [34, 80]), sprint monitoring methods (e.g., start position, environmental factors [56]), and technology (e.g., equipment [58]). Therefore, these findings should be interpreted care- fully as the variation of the effect sizes demonstrates that training response is highly individualised. The quality of the studies was high (18 ± 1.9; range 11–20) because most studies provided clearly described research methodology, enabling practitioners and/or researchers to replicate or build on research findings reli- ably [126]. A methodological study scale used to evaluate research conducted in athletic-based training environments [54] showed that, to increase the quality of future studies, researchers should randomise participants, include a control group, and provide a detailed results section. The inclusion of detailed information on additional training conducted in applied settings is important for the understanding of the training intervention undertaken and to fully assess whether any outside interactions with any adaptations were seen fol- lowing a training intervention [127]. Most training interventions reported positive effects on sprinting capabilities, which suggests that sprint perfor- mance outcomes can easily be improved with a variety of methods. However, this needs to be considered from the context of the literature base and the relative importance of phase-specific adaptations. Included studies represented both youth and senior athletes from elite and sub-elite cohorts, with the majority having limited previous system- atic exposure to the intervention methods [58, 80, 82, 85, 89, 95, 114]. Based on the dose–response relationship and the principle of diminishing returns, athletes with a rela- tively low training age are more likely to have greater train- ing responses [128–130]. However, as previously reported [33], this does not appear to be the case for the Vmax phase or highly trained populations. Highly trained athletes have demonstrated that mean annual within-athlete sprint per- formance differences are lower than typical variations, or smallest worthwhile change, and the influence of external conditions (e.g., wind, temperature, altitude, timing meth- ods/procedures [56, 130]). Inspection of the funnel plot and 273 Training Medium to Long Sprint Performance in Football Athletes Egger’s regression intercept identified evidence of small study effects in the 0–30 m and Vmax-phase performance outcomes. The SMDs between pre- and post-intervention sprint outcomes were not considered symmetrical, suggest- ing the presence of significant publication bias. While publi- cation bias towards studies reporting positive outcomes may be involved, another plausible explanation is the lack of a control group in many studies, as the results might have been affected by learning effects or the football code training in the intervention period. 4.3 Subgroup Analyses of Training Methods The principle of specificity [137, 138] was used to categorise the training intervention subgroups (i.e., sport only, primary, secondary, tertiary, and combined). Primary methods pre- sent the greatest specificity by simulating the sprint move- ment pattern [131], whereas the secondary methods are less specific, involving overloaded sprinting actions. The tertiary training methods included strength, power, and plyometric training, which are considered the least ‘specific’ to sprint performance as these methods are commonly performed to target neuromuscular adaptations rather than simulating movement mechanics [132]. The extent to which the method impacts on and ‘transfers’ to sprint performance ultimately determines the quality of a training programme to improve athletic performance [133]. The factors underpinning the development of sprint per- formance appear to be consistent across sports [134–140]. Practitioners can target the determinants of performance, such as optimising the sequencing of stride length and fre- quency, enhancing the athlete’s physical capacities relative to BM (e.g., lower limb force–velocity–power; stiffness) and increasing the mechanical effectiveness of force appli- cation [134, 136, 138, 140–145]. These methods provide practitioners with multiple methods of developing sprinting performance [130, 144, 146]. Performance improvements result from specific transferable training adaptations typi- cally categorised as neural or morphological (architectural or structural) factors [26, 146–149]. However, training effects appear to be mode specific, with distance-specific performance changes (e.g., 0–30 and 0 to > 30 m) associated with phase-specific adaptations [32]. Although the factors underpinning sprint development are consistent, phase-spe- cific differences in both kinetic and kinematic variables are clear [26]. The importance of mechanical variables appears to shift as sprint distance increases (e.g., greater associa- tion between theoretical maximal force generation in shorter sprints vs. greater associations in maximum theoretical velocity force can be applied in longer sprints [150]). There- fore, it is important to consider the phase-specific adapta- tions that may be present across medium- to long-distance sprint outcomes. Despite researchers and practitioners typically using out- come measures over distances > 20 m as a proxy measure of Vmax-phase capabilities, performance changes may be attrib- uted to either or both adaptations specific to the acceleration or Vmax phases, not the Vmax phase exclusively. This is evi- dent as the Vmax phase presented performance changes that were distinctly different from both the 0–30 m and the 0 to > 30 m outcomes. Although the acceleration and Vmax phases are related [8, 132, 150–153], separate physical capacities and mechanical parameters determine sprint performance [27, 29, 129, 137, 140, 154–156]. Research has demon- strated that football code athletes can attain Vmax-phase sprinting patterns at distances ≤ 20 m [6–10, 29]. Therefore, after 20 m, there is likely an increasing influence of the Vmax phase, with the time spent increasing with distance. There- fore, given the sequential phases of sprinting, both 0–30 and 0 to > 30 m outcomes will be influenced by changes in acceleration, with the 0 to > 30 m outcome influenced to a lesser extent (more time performing Vmax sprinting patterns), whereas the Vmax-phase flying sprint split times and Vmax assessments do not include, or include a limited, acceleration phase. Hence, it is important to emphasize that, although the sequential phases are related, different factors affect performance in each phase. Therefore, training pro- tocols to develop each of these phases must also differ [33]. This was evident in both the secondary and the combined methods training groups. Hence, when including all stud- ies, both training methods presented a significant improve- ment in both 0–30 and 0 to > 30 m performance, whereas they produced non-significant trivial changes in Vmax-phase performance. Therefore, practitioners should also consider the mechanical and neuromuscular requirements that shift across the sub-phases (acceleration, maximal speed, and maintenance) of medium- to long-distance sprint outcomes and the implications of these for training phase-specific per- formance [26, 150, 154, 157]. 4.3.1 Sport‑Only Training Sport-only training focuses on the development of techni- cal and tactical performance within football and does not include any specific or non-specific sprint training. The meta-analysis showed that sport-only training groups did not significantly change sprint performance [35, 36, 58–68, 75, 82, 88, 90, 95–97, 104, 106, 113, 114, 119]. Football code training is characterised by multidirectional and intermittent bouts of high-intensity running and sprinting interspersed with bouts of moderate- and low-intensity activity (e.g., jogging, walking, and repositioning [158–161]). Therefore, although football code training may involve athletes repeat- edly performing short sprints (e.g., 5–20 m, 2–3 s) during and between sport-specific actions [2, 23, 158, 159, 162], this most likely has limited or no very-high-speed or sprint 274 B. Nicholson et al. threshold running [160, 161, 163]. Such training methods do not meet the recommendations that athletes be exposed to multiple sprints where they maximally accelerate to achieve and maintain Vmax with complete recovery between efforts to effectively enhance sprint performance [130]. Further explanations could include residual fatigue and the interfer- ence effect affecting maximal force and velocity outcomes within sport-only practices [130, 164–166]. Therefore, evi- dence suggests that sport-only training alone is insufficient to improve medium to long sprint performance, and football code practitioners should consider this within their planning and delivery of training. 4.3.2 Primary Methods Primary methods simulate the sprint movement pattern (e.g., sprint-technique drills, stride length and frequency exercises, and sprints of varying distances and intensities). The com- bined exposure of large forces (> 2 × BM) produced over short ground-contact periods (~ 0.08 to ~ 0.20 s) performed at high movement velocities (7–10 m·s−1) while maxi- mally sprinting results in both a coordinative overload and high neuromuscular stimulation [134, 136–138, 140, 155, 167]. Therefore, exposure to maximal sprinting is expected to facilitate chronic physical adaptations and enhanced mechanical efficiency to improve sprint performance [133, 134, 136–138, 140, 167]. However, no studies have meas- ured chronic kinematic changes over distances > 20 m in response to primary training methods (no additional tertiary methods) to support their use in football code athletes [67, 101]. Our findings suggest that primary training methods [58, 60, 67, 86, 101, 102, 116] may not significantly improve sprint performance and—in some cases—may impair per- formance. The primary methods within-group changes presented no significant change in sprint performance (i.e., 0–30 m, SMD 0.20 [95% CI − 0.01 to 0.42; 95% PI − 0.51 to 0.92]; 0 to > 30 m, SMD 0.06 [95% CI − 0.14 to 0.25; 95% PI not applicable as n < 3]; Vmax SMD − 0.07 [95% CI − 0.24 to 0.10; 95% PI − 1.75 to 1.61]). This was further supported by the pairwise between-group comparisons (sport only vs. primary), which confirmed no significant difference was evi- dent in the 0–30 m: SMD 0.33 (95% CI − 0.58 to 1.25; 95% PI not applicable as n < 3). Despite the Vmax-phase outcome reporting, the primary methods were superior (large SMD) to sport-only training (SMD 1.13 [95% CI 0.17–2.09; 95% PI not applicable as n < 3]), and this difference reflects the maintenance of sprint performance rather than the reduced performances reported in the sport-only groups [58, 67]. The contradictions between our findings and previous reviews supporting primary training methods is likely because other studies misclassified training methods by not including addi- tional training (e.g., resistance training), most probably as part of their usual training programme [38, 117, 168–171]. Therefore, previous review findings may support a combined approach of both specific and non-specific training, not pri- mary training alone [32, 33]. Football code athletes have high chronic exposure to short sprints (< 20 m) with incomplete recovery between sprints as part of the demands of training and matches; therefore, replicating these exposures is unlikely sufficient stimulus for neurological or morphological adaptations [158–161, 172]. Prescribing short-sprint repetition distances (e.g., 18.7–20 m [58, 60, 116]) limits athlete exposure to sprinting at Vmax (typically achieved at > 20 m in football code athletes [8, 9, 27, 29–31]), performed at submaximal efforts (< 95% Vmax [102]) and/or with incomplete recovery (e.g., 2–3 min between repetitions [< 1–2 min of activity−1]) for medium to long sprints (e.g., 30–55 m sprints, ~ 4–7 s duration [67, 86, 102]). Furthermore, incomplete rest between sprint efforts may reduce maximal sprint intensity, causing meta- bolic stress and reduction in energy substrates [173–175]. However, it is worth noting that the removal of two studies [58, 60] that prescribed short sprints moderated the statisti- cal significance for the 0–30 m and Vmax-phase outcomes from non-significant to significant. These findings contrast with the findings in short-sprint performance, indicating that longer sprints and Vmax-phase outcomes may be more susceptible to performance changes from primary training methods when prescribed appropriately [25]. Future stud- ies should provide complete rest periods between maximal intensity sprints reaching and maintaining Vmax. Running technique drills that simulate the sprinting action by isolating specific movements into more manageable com- ponents [130, 176] are a component of primary training. For positive reinforcement of the technique, sprinting biome- chanics must closely resemble the action and develop the athlete’s limiting factor(s) [131, 177]. However, technique drills (e.g., A and B drills) are often performed at much slower velocities than sprinting, potentially not replicating sprinting from a kinematic standpoint [178]. It has been questioned whether running drills have value, especially when performed incorrectly [179, 180]. However, as with short-distance sprint outcomes [25], no study has evaluated the effects of including/excluding sprint-technique drills in football code athletes, and explanations of the training pre- scription are often limited. Therefore, sprint training that addresses the magnitude and rate of force production on the ground and the mechanical efficiency (e.g., tertiary or sec- ondary methods) may be more appropriate [180]. 4.3.3 Secondary Methods Secondary training modalities apply overload to the sprint- ing action by reducing (e.g., resisted sprinting) or increas- ing (e.g., assisted sprinting) the movement speed, allowing athletes to reach supramaximal velocities. Across the seven 275 Training Medium to Long Sprint Performance in Football Athletes studies [58, 60, 79, 85, 86, 101, 116], findings showed a significant moderate within-group improvement in 0–30 m (SMD 0.61 [95% CI 0.31–0.91; 95% PI − 0.43 to 1.65]) and small improvements in 0 to > 30 m (SMD 0.37 [95% CI 0.25–0.50; 95% PI: all studies shared a common effect size]), with no significant changes in Vmax phase (SMD 0.07 [95% CI − 0.10 to 0.23; 95% PI − 1.57 to 1.71]). These find- ings are supported by the pairwise between-group analysis (sport only vs. control), confirming the effectiveness of the secondary methods (large SMD) in enhancing or maintain- ing medium to long sprint performance, respectively, com- pared with reductions in sport-only training groups (0–30 m, SMD 2.78 [95% CI 1.53–4.03; 95% PI not applicable as n < 3]) and Vmax phase (SMD 1.27 [95% CI − 0.11 to 2.65; 95% PI not applicable as n < 3]). Training adaptations have been reported as being velocity change specific (%Vmax increase vs. reduction [181]), with variations in distance- specific improvements for secondary methods (i.e., assisted vs. resisted) [116]. This is evident in both our findings and those of another review, reporting no significant improve- ments in Vmax-phase outcomes in secondary training meth- ods [33]. Hence, the improvements in 0–30 and 0 to > 30 m performance may be a result of acceleration-specific adap- tations reflected in short-sprint improvements included in the sprint outcome. The overload of the secondary training methods results in neurological or morphological adapta- tions, allowing greater generation of ground reaction forces and improved mechanical efficiency to enhance performance [33, 44]. Resisted sprints (i.e., loaded sleds) were shown to increase both stride length and frequency and lead to an acute increase in forward trunk lean (improved position to generate horizontal impulse) during sprints < 20 m in team sport athletes and university students [182–185]. In contrast, assisted methods demonstrated increased stride length and decreased stride frequency in track athletes [33, 44], whereas reduced ground contact times were reported in football code athletes [101]. Studies measuring chronic temporospatial changes in response to secondary training methods (no additional tertiary methods) to support these in football code athletes are currently limited [101]. Of the two overload strategies, resisted sprint training [58, 60, 79, 85, 86, 116] has received the greatest attention in the research on football code athletes despite significant improvements in both training methods (resisted [58, 60, 85, 86, 116], assisted [116], and a combination of both [101]). Currently, no study has reported a statistically superior training effect between assisted and resisted training modes, so which train- ing mode is the most effective for developing sprint per- formance remains unclear. Therefore, secondary training methods appear to be an effective method for coaches and athletes to improve 0–30 and 0 to > 30 m sprint performance outcomes. However, if the aim is to develop the Vmax-phase performance, then training strategies other than sled towing (e.g., weighted vests) may be needed to develop phase-spe- cific adaptations. For example, vertical forces have a greater relative contribution to the Vmax phase [136, 137]. Acute kinematic differences suggest vertical force production when sprinting could be developed by undertaking training strate- gies utilising weighted vests by providing a greater load on the eccentric braking phase at the beginning of the stance phase [185, 186], whereas sled towing is expected to pro- vide a superior adaptation in horizontal force production [185, 187, 188]. Further research is required to determine the optimal load, loading strategy, and dose for performance enhancement, particularly for Vmax development. 4.3.4 Tertiary Methods Tertiary training methods represent a wide range of training methods (e.g. strength, power, plyometrics [32, 189]) that are commonly performed to target neuromuscular adapta- tions that determine sprint performance (e.g., force–veloc- ity–power and force–velocity profile) rather than simulat- ing movement mechanics [26, 130, 146, 150]. Using the load–velocity relationship, the appropriate resistance (body- weight or external loads) limits either the maximum velocity or the force at which the maximum effort will occur, or both [190]. Therefore, practitioners are able to use force–veloc- ity–power-orientated exercises in isolation or in combination (e.g., high force/low velocity vs. low force/high velocity vs. peak power load) to target load-specific adaptations [26, 130, 146, 150]. Despite previous criticisms of tertiary training methods questioning the effectiveness of developing sprint perfor- mance, significant within-group moderate improvements were found for the 0–30 m (SMD 0.38 [95% CI 0.23–0.53; 95% PI − 0.63 to 1.41]) and Vmax-phase outcomes (SMD 0.45 [95% CI 0.08–0.81; 95% PI − 0.97 to 1.83]). No sig- nificant change was found for the 0 to > 30 m outcome when all studies were included (SMD 0.22 [95% CI − 0.26 to 0.70; 95% PI − 1.73 to 2.17]). The significant within- group changes in point estimate in the 0–30 m and Vmax outcomes were supported by significant findings in the pairwise between-group analysis (sport only vs. tertiary), with observed performance improvements (large SMD) confirming the effectiveness of the tertiary training meth- ods in enhancing medium to long sprint performance com- pared with sport-only training: 0–30 m (SMD 1.49 [95% CI 0.95–2.03; 95% PI − 1.06 to 4.03]), 0 to > 30 m (SMD 1.12 [95% CI 0.56–1.68; 95% PI − 0.29 to 2.52]), and Vmax phase (SMD 1.95 [95% CI − 0.75 to 3.15; 95% PI − 10.13 to 14.03]). Therefore, phase-specific adaptations may be pre- sent. However, the presence of significant improvements in both 0–30 m (likely a greater influence of the acceleration phase) and the Vmax-phase performance changes are likely 276 B. Nicholson et al. a result of both acceleration- and Vmax-phase-specific adap- tations. Research comparing the kinetic factors underlying differences between athletes with higher Vmax capabilities (sprinters) and slower athletes (soccer players), found that, at the same touchdown velocity, the sprinters attenuated the eccentric forces to a greater extent in the late braking phase and produced a higher antero‐posterior component of force, yet ground contact durations were similar across groups [27]. Therefore, training methods such as strength, power, or plyometrics training that increase an athlete’s ability to produce sufficient vertical force, to withstand and reverse eccentric braking forces, and to generate high antero‐pos- terior propulsive force may be required to enhance an ath- lete’s ability to accelerate more rapidly while also attaining a greater Vmax [27, 130]. The improved physical capacities developed during tertiary training methods have previously been shown to manifest in significant improvements in sprint performance with associated reductions in contact time or changes in stride frequency and length [34, 67, 169, 170]. Therefore, correspondence between the larger ground reac- tion forces produced across medium- to long-distance sprints and the neural and morphological adaptations induced by these training methods is likely to be high [140]. Hence, the lack of significance in the 0–30 m outcomes is likely due to large significant reductions in sprint performance as presented by Gabbett et al. [89], moderating the statistical interpretation of the results and therefore supporting previ- ous research [32] for the use of tertiary training methods (i.e., strength, power, and plyometric training) performed individually or in combination (e.g., strength power and plyometrics training) for improving sprint performance. Considerations should be made when training for increased mass development, which is often associated with tertiary methods: as an athlete gets heavier they may not produce higher maximal force characteristics when normal- ised for BM [132]. Therefore, the force requirements in the stance leg increase with BM to minimise the braking forces and maximise propulsive forces to attain Vmax, as does the aerodynamic drag resulting from a larger frontal surface area [132, 191]. Hence, increases in BM may be counterproduc- tive for sprinting, at least when not moving an external mass [132]. 4.3.5 Combined Methods Combined methods training includes both specific sprint training (primary and or secondary methods) and tertiary methods, recommended by researchers and sprint and football code practitioners to develop sprint performance [24, 32, 133, 192–194]. This combination of both training methods enables practitioners to provide stimuli to develop both mechanical efficiency and the maximal physical capa- bilities of the lower limb concurrently [110, 169, 170, 195]. Previous studies of combined specific and tertiary training methods demonstrated significant improvements in physi- cal capacities (e.g., force, velocity, and power [36, 110]), increased stride lengths, reduced stride frequencies, and reduced stance contact times [76, 169, 170]. However, the changes in spatiotemporal variables are limited to short dis- tances, with no significant changes presented in medium-dis- tance sprints (e.g., stride length or frequency and contact or flight times [36, 76]). This review found significant within- group moderate improvement at 0–30 m (SMD 0.43 [95% CI 0.21–0.65; 95% PI − 0.69 to 1.55]) and small improvements in 0 to > 30 m (SMD 0.33 [95% CI 0.15–0.51; 95% PI − 0.50 to 1.16]), with no significant change in the Vmax phase (SMD 0.05 [95% CI − 0.23 to 0.34; 95% PI − 1.01 to 1.11]). Pair- wise within-group analysis (sport only vs. combined) indi- cated significant performance improvements in favour of combined methods (large SMD): 0 to > 30 m, SMD 1.51 [95% CI 0.21–2.80; 95% PI − 4.26 to 7.28]). Interestingly, the 0–30 m and Vmax-phase contrasted with these findings, suggesting the combined methods were no more effective than sport-only training: 0–30 m (SMD 0.58 [95% CI − 0.93 to 2.10; 95% PI − 5.20 to 6.37]); Vmax phase (SMD − 0.83 [95% CI − 4.33 to 2.68; 95% PI not applicable as n < 3]). Sensitivity analysis appeared to indicate that the single study demonstrating a large reduction in the Vmax-phase sprint per- formance changed both the statistical significance and the direction of the reported effect [97]. The negative effects reported in this study were attributed to the interference of the volume of aerobic training and thus is an important con- sideration when attempting to develop medium to long sprint performance. As discussed in Sect. 4.3.3, phase-specific adaptations appear to be present, with performance changes likely a result of acceleration-specific adaptations reflected in short-sprint improvements included in the sprint outcome. Despite presenting significant training effects, each method presented different training methods (see Table S3 in the ESM). Therefore, combined specific methods appear to be an effective training method for football code athletes for 0–30 and 0 to > 30 m sprint performance outcomes. How- ever, if the aim is to develop the Vmax-phase performance, training strategies may be modified to develop phase-spe- cific adaptations (e.g., increase vertical ground reaction in reduced stance phases). Further research is required to iden- tify the optimal combination of exercises and training loads to improve phase-specific performance. 4.4 Moderator Variables It is important to identify the moderator variables (i.e., foot- ball code, sex, age, playing standard, stage of the season) that may impact upon sprint training outcomes. Studies were excluded from the analysis if the value used for subgroup analysis was not reported, if they did not provide sufficient 277 Training Medium to Long Sprint Performance in Football Athletes detail, or if they did not match the appropriate moderator categories. 4.4.1 Sex The meta-analysis of the intervention training groups found that the sprint performance of both male and female football code athletes could be improved. However, the improvements for the 0 to > 30 m and Vmax-phase outcomes in females were not significant. When comparing male and female athletes, there was no significant difference between the training effects. This should be taken within the context of the scarcity of the available information on female athlete training compared with that for males [196]. The limited research comparing sex differences in training response in football code athletes found no significant effect of sex on changes in sprint performance [65]. Therefore, despite the demonstrated differences between physical characteristics [21, 132] and endocrine response [197] to training between males and females, evidence is currently insufficient to sug- gest that practitioners should approach developing sprint performance differently based on an athlete’s sex. 4.4.2 Playing Standard Both elite and sub-elite subgroups improved sprint perfor- mance. However, there was no significant improvement in sub-elite Vmax-phase performance. The between-group comparison identified no significant difference between the training effects for elite and sub-elite groups. Despite sprint performance differentiating between performance standards [19–21], no study has explored whether sub-elite athletes are more sensitive to training than elite populations. However, research has demonstrated a decrement in the magnitude of the correlations with increasing levels of practice between the lower limb neuromuscular maximal capabilities and the ability to generate force during sprinting for sub-elite athletes compared with elite athletes [129, 150]. Therefore, further improvements may be represented by the ability to effectively apply force into the ground at progressively increasing velocities (mechanical effectiveness) to achieve either a greater rate of acceleration or enhanced Vmax perfor- mance, or both. Hence, for medium- to long-distance sprints, a greater focus on developing the mechanical capabilities contributing to the athlete’ s ability to generate propulsive impulse (force × time) and their application at higher veloci- ties and decreasing ground contact times (i.e., mechanical efficiency, theoretical maximal horizontal velocity and maxi- mal power) is required [146, 150]. Theoretically, this may be achieved by using resisted sprints that enable athletes to apply force at high velocities (low loads or assisted sprint- ing), by training at loads that correspond with optimal load for maximal power as well as low load (BM) or assisted horizontal or vertical jumps [146]. However, further research is required to demonstrate the effectiveness of these training strategies. Therefore, despite the demonstrated differences between physical characteristics between elite and sub-elite athletes [132] when considered independent of training status, evidence is insufficient to suggest that practitioners should approach developing sprint performance differently based on athlete’s playing standard within the football codes. 4.4.3 Age The sprint performance of both senior and youth cohort subgroups was enhanced following training interventions, apart from the youth Vmax phase. Between-group compari- sons identified that youth athletes enhanced sprint per- formance more than senior athletes at 0 to > 30 m, which supports research stating that training response is typically greater in younger athletes than in their older counterparts [89]. Factors such as chronological age may have moder- ated the training effects of the tertiary training methods in male youth athletes, with a greater training effect in younger (< 15 years) than in older (< 18 years) athletes [89]. Youth athletes experience multiple morphological and neural changes as a result of growth and maturation [198], which has implications for sprinting performance changes [48, 50, 199]. The stage of maturation has been shown to moderate the training effect, with youth athletes training at pre-peak height velocity presenting lesser improvements than those at mid- and post-peak height velocity [48, 50]. Changes in youth cohorts may have been affected by the inclusion of pre-pubescent athletes and ineffective training exposures [93], which was not considered in the current analysis. These training effects suggest that coaches of youth athletes should take into consideration chronological and maturational age, increased baseline performance levels, and greater training experience [89, 200]. However, further research is required to understand sprint performance outcomes by age, which could include maturity grouping. 4.4.4 Sport All sprint performance outcomes were improved in the soccer subgroup. Rugby union and American football pre- sented significant improvements in 0–30 and 0 to > 30 m, respectively. No significant improvement was found in rugby league, rugby sevens, or Australian Rules football. Football codes training subgroups with limited representa- tion in the literature (one to two training groups for a given distance outcome) were not considered for subgroup analy- sis (e.g., 0–30 m rugby sevens [n = 1] [87]). Despite differ- ences in physical characteristics [129, 132] and movement demands [158, 159], there were limited between-subgroup differences. The between-group comparison showed that 278 B. Nicholson et al. the increase in performance was significantly greater in soccer than in rugby union, rugby sevens, and American football (0 to > 30 m). The improvement in performance was significantly greater in American football than in rugby union (0 to > 30 m). No significant differences were found between the training effects for the football code subgroups for the Vmax-phase outcome. Although several factors may have contributed to the significant differences (e.g., training content, duration, frequency), the greater training experience in various forms of resistance training in the rugby codes and American football (e.g., ≥ 2 years’ systematic resistance training [76, 83, 105, 117, 118, 201]) may have resulted in lower morphological or neurological adaptability to the training stressors, resulting in lower training responses com- pared with the less training-experienced soccer subgroups [130, 132]. However, the literature is insufficient to dem- onstrate the between-subgroup differences across all sprint performance outcomes, and it remains unclear whether these are specific to training methods or distance outcomes. No study has compared the difference in training effects between football codes implementing matched training interventions in football code athletes on sprint performance. Therefore, evidence is insufficient to support coaches adapting sprint- training methods based on football code. 4.4.5 Season The in-season and off-season subgroups presented signifi- cant improvements in 0–30 and 0 to > 30 m, despite prac- titioners typically having less time available to develop physical or movement capacities during the in-season and off-season periods [51]. Pre-season subgroups only sig- nificantly enhanced 0–30 m performance, and no signifi- cant improvement in the Vmax phase was observed at any phase of the season. It is generally reported that fitness improvements are observed in the pre-season, with sub- sequent stabilisation of such fitness variables in-season [202]. Consequently, greater benefits are expected in trials performed during the pre-season period than in those in the in-season [203, 204]. The between-group comparison found significantly greater improvements in-season com- pared with pre-season and off-season in the 0 to > 30 m outcome only. Therefore, with appropriate prescribed training methods, 0–30 and 0 to > 30 m sprint performance can be enhanced in-season. The 0 to > 30 m pre-season subgroup was sensitive to the large reduction in training performance presented by Gabbett et al. [89], explain- ing the lack of significant improvement. The Vmax phase appeared to present greater resistance to change based on the current training programmes. None of the included studies compared the difference between training effects between the phase of the season, implementing matched training interventions in football code athletes on sprint performance. Therefore, despite the differences in training demands between training phases, evidence is insufficient to support coaches adapting sprint-training methods based on the phase of the season. 4.5 Limitations Whilst this work represents the largest systematic review and meta-analysis of medium- to long-distance sprint performance, limitations do exist. First, this review clas- sified training into groups (i.e., sport-only, primary, sec- ondary, tertiary, combined, and combined specific meth- ods), which improved on previous classifications [32, 39] but also did not consider the complexity of sprint per- formance development within the training prescription, the population, and the assessment methodologies. The broad within-group change approach taken was used to review all available literature; however, this method rep- resents a suboptimal method of exploring training cau- sality while also providing additional areas of bias to the interpretation (e.g., regression to the mean [205]). However, we attempted to address this by combining a within-group pre-post change design and a pairwise between-group design, enabling an evaluation of both high-quality controlled trial comparisons and an explora- tion of the breadth of the available literature using a range of research designs. Despite the important influence of prior training status and physical capacities [128–130], it was not possible to include these as moderators for several reasons: (1) most studies did not report physical capacity and/or training experience within their descriptive statis- tics; (2) those that did were inconsistent in how they were reported and the testing methods used; and (3) studies were often limited to years of football code-specific train- ing or resistance training, with little consideration of how the stimulus was provided. Therefore, the level of detail to fully understand sprint development is lacking, but this is difficult in the context of understanding sprint develop- ment and the multiple factors that interact. However, the review attempted to analyse several moderator variables (i.e., football code, sex, playing standard, age, and phase of the season), highlighting a limitation that most research is undertaken using parallel-group trials within male soc- cer athletes involving mainly tertiary training methods. Therefore, research including randomised controlled trials across the football codes and female cohorts using multi- ple training methods is limited, which may affect the meta- analysis and moderator variable analysis and subsequent interpretation. Despite these limitations, the information gathered from the current review with meta-analysis may support practitioners in making evidence-informed deci- sions when organising and evaluating training. 279 Training Medium to Long Sprint Performance in Football Athletes 4.6 Future Research Directions This review presented similar research directions to those presented in the short-sprint training literature as the limi- tations were consistent across all outcomes [25]. Where possible, future research should use high-quality research designs (e.g., randomised controlled trials) to expand and reaffirm the current findings whilst addressing the multiple gaps in specific populations. Research is required to exam- ine the training effects in football codes other than soccer (e.g., rugby codes, American football, Australian Rules, Gaelic football, and futsal), in world-class and successful elite athletes, in trained populations with systematic training exposures, in youth athletes of various growth and matura- tional status, and in female athlete cohorts. It is worth high- lighting that, although several effective training methods are reported, it may be inappropriate to try to define the best methods for enhancing sprint performance in football (e.g., exercises, set and repetition schemes). Instead, the integration of different methods based on the training back- ground, individual requirements, and progression over the training process needs to be further analysed to inform the optimal stimulus and organisation of training. It is essential that future research designs include pairing subjects based on resistance training experience and/or physical capacities (i.e., lower limb force characteristics) to establish a greater understanding of whether training changes and adaptations are dependent upon these variables. Both researchers and practitioners should consider the combined modelling of velocity–time curves with kinematic and kinetic changes assessed at more frequent intervals. This would enable prac- titioners to isolate and confirm a time course of adaptations and the underlying causes of changes in performance [21, 129, 150] whilst also reducing the limitations associated with pre- and post-sprint times or velocities [55]. Given the respective importance of repeated sprint ability and non- linear sprint outcomes in the football codes, future research should explore their development, providing practitioners with a more comprehensive overview of developing athletes’ sprint characteristics. Research identified that the majority of elite sprint and football code coaches reported utilising and advocating for an integrated approach using the combined training methods approach [24, 32, 133, 192–194]. This is performed both individually and in separate sessions and combinations (e.g., complex or contrast sets), enabling the development of mul- tiple physical capacities and skills simultaneously [24, 32, 133, 192–194]. Therefore, further research would be better suited to manipulating the combination, sequencing, and loading parameters of combined specific and non-specific methods to enhance sprint performance longitudinally as, ultimately within the football codes, combined training is implemented. This should be combined with methods of profiling that allow optimisation and individualisation of training exposures [150, 189, 206–208], which may reduce the variability in performance change [189]. While exercise specificity is certainly an important principle when develop- ing a training programme, it is only one of several princi- ples that will influence the effectiveness of the programme. Therefore, future research should continue to explore within and between subgroups the effects of overload, variation, and reversibility and the effect on sprint performance change [26]. Furthermore, this needs to be supported with determin- ing the minimal and optimal training doses to retain and develop sprint performance in football code athletes. This will directly influence practitioners’ organisation of training and the prescribed loading variables. 5 Conclusions Establishing the most effective methods to improve medium- to long-distance performance outcomes is an important consideration for practitioners working across the football codes. This work represents the first systematic review and meta-analysis of sprint performance development using medium- to long-distance outcomes that include all training modalities while exclusively assessing within- and across- football code athletes. The results indicate that medium to long sprint performance outcomes can be enhanced through secondary (i.e., resisted or assisted sprinting), combined (i.e., primary or secondary and tertiary training methods) (0–30 m and 0 to > 30 m), and tertiary training methods (0–30 m). In addition, tertiary training methods were the only method that enhanced Vmax-phase performance signifi- cantly. Performance changes in outcomes > 20 m may be attributed to either or both adaptations specific to the accel- eration or Vmax phases, and not Vmax exclusively. Despite this, when comparing training typology, no individual mode was found to be the most effective. However, both sport- only training and primary training methods appeared to be insufficient to develop medium- to long-distance sprint performance outcomes. The null and negative performance effects present in all training group PIs warrant caution, as— regardless of training mode-specific point estimate—factors such as athlete’s capacities, previous training exposures, and the programme design may moderate positive performance adaptations. Moderator effects, although not mode specific, suggested that there is no consistent effect of age, sex, play- ing standard, and phase of the season on sprint performance change across outcomes. Regardless of the population char- acteristics, medium- to long-distance sprint performance can be enhanced by increasing either the magnitude or the ori- entation of force an athlete can generate and express in the sprinting action, or both. These findings present practitioners 280 B. Nicholson et al. with several options to suit their programme to enhance medium- to long-distance sprint performance. Supplementary Information The online version contains supplemen- tary material available at https:// doi. org/ 10. 1007/ s40279- 021- 01552-4. Declarations Funding No sources of funding were used to assist in the preparation of this article. Conflict of interest Ben Nicholson, Alex Dinsdale, Ben Jones, and Kevin Till have no conflicts of interest that are directly relevant to the content of this article. Availability of data and materials The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics approval Approval was obtained from the ethics committee of Leeds Beckett University. The procedures used in this study comply with the ethical standards of the Declaration of Helsinki. Consent for publication Not applicable. Author contributions All the authors contributed to the manuscript, including the conception and design of the study, analysis and inter- pretation of the data, drafting and critical revision of the manuscript, and approval for publication. All authors read and approved the final manuscript. Open Access This article is licensed under a Creative Commons Attri- bution 4.0 International License, which permits use, sharing, adapta- tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. References 1. Deutsch M, Kearney G, Rehrer N. Time–motion analysis of pro- fessional rugby union players during match-play. J Sport Sci. 2007;25(4):461–72. 2. Barnes C, Archer D, Hogg B, Bush M, Bradley P. The evolution of physical and technical performance parameters in the English Premier League. Int J Sport Med. 2014;35(13):1095–100. 3. Johnston RD, Gabbett TJ, Jenkins DG. Applied sport science of rugby league. Sport Med. 2014;44(8):1087–100. 4. Veale JP, Pearce AJ, Carlson JS. Player movement patterns in an elite junior Australian Rules Football Team: an exploratory study. J Sport Sci Med. 2007;6(2):254. 5. NFL. Top plays: Fastest ball carriers https:// nextg ensta ts. nfl. com/ stats/ top- plays/ faste st- ball- carri ers/ 2018/ all: NFL next gen stats; 2018. 6. Duthie GM, Pyne DB, Marsh DJ, Hooper SL. Sprint patterns in rugby union players during competition. J Strength Cond Res. 2006;20(1):208–14. 7. Benton D. Sprint running needs of field sport athletes: a new perspective. Sports Coach. 2001;24(2):12–4. 8. Clark KP, Rieger RH, Bruno RF, Stearne DJ. The national foot- ball league combine 40-yd dash: how important is maximum velocity? J Strength Cond Res. 2019;33(6):1542–50. 9. Barr MJ, Sheppard JM, Newton RU. Sprinting kinematics of elite rugby players. J Aust Strength Cond. 2013;21(4):14–20. 10. Young WB, Duthie GM, James LP, Talpey SW, Benton DT, Kilfoyle A. Gradual vs. maximal acceleration: their influence on the prescription of maximal speed sprinting in team sport athletes. Sports. 2018;6(3):66. 11. Gabbett TG. Sprinting patterns of national rugby league com- petition. J Strength Cond Res. 2012;26(1):121–30. 12. Suarez-Arrones L, Arenas C, López G, Requena B, Terrill O, Mendez-Villanueva A. Positional differences in match running performance and physical collisions in men rugby sevens. Int J Sports Physiol Perform. 2014;9(2):316–23. 13. Di Salvo V, Baron R, González-Haro C, Gormasz C, Pigozzi F, Bachl N. Sprinting analysis of elite soccer players during European Champions League and UEFA Cup matches. J Sport Sci. 2010;28(14):1489–94. 14. Di Salvo V, Baron R, Tschan H, Montero FC, Bachl N, Pigozzi F. Performance characteristics according to playing position in elite soccer. Int J Sport Med. 2007;28(03):222–7. 15. Bishop DJ, Girard O. Determinants of team-sport performance: implications for altitude training by team-sport athletes. Br J Sports Med. 2013;47:i17–21. 16. Ross A, Gill N, Cronin J, Malcata R. The relationship between physical characteristics and match performance in rugby sev- ens. Eur J Sports Sci. 2015;15(6):565–71. 17. Gabbett TJ, Jenkins DG, Abernethy B. Relationships between physiological, anthropometric, and skill qualities and playing performance in professional rugby league players. J Sport Sci. 2011;29(15):1655–64. 18. Gabbett T, Kelly J, Pezet T. Relationship between physical fitness and playing ability in rugby league players. J Strength Cond Res. 2007;21(4):1126. 19. Till K, Scantlebury S, Jones B. Anthropometric and physical qualities of elite male youth rugby league players. Sport Med. 2017;47(11):2171–86. 20. Delaney JA, Olson TM, Morin J-B. Sprint acceleration mechanical profiling for the NFL draft. Sports Perform Sci Rep. 2018;27:1–3. 21. Baumgart C, Freiwald J, Hoppe M. Sprint mechanical proper- ties of female and different aged male top-level german soccer players. Sports. 2018;6(4):161. 22. Al Haddad H, Simpson BM, Buchheit M, Di Salvo V, Mendez- Villanueva A. Peak match speed and maximal sprinting speed in young soccer players: effect of age and playing position. Int J Sports Physiol Perform. 2015;10(7):888–96. 23. Faude O, Koch T, Meyer T. Straight sprinting is the most fre- quent action in goal situations in professional football. J Sport Sci. 2012;30(7):625–31. 24. Jones TW, Smith A, Macnaughton LS, French DN. Strength and conditioning and concurrent training practices in elite rugby union. J Strength Cond Res. 2016;30(12):3354–66. 25. Nicholson B, Dinsdale A, Jones B, Till K. The training of short distance sprint performance in football code ath- letes: a systematic review and meta-analysis. Sport Med. 2020;Forthcoming:1–29. 26. Moir GL, Brimmer SM, Snyder BW, Connaboy C, Lamont HS. Mechanical limitations to sprinting and biomechanical solutions: a constraints-led framework for the incorporation of 281 Training Medium to Long Sprint Performance in Football Athletes resistance training to develop sprinting speed. Strength Cond J. 2018;40(1):47–67. 27. Colyer SL, Nagahara R, Takai Y, Salo AI. How sprinters accel- erate beyond the velocity plateau of soccer players: Waveform analysis of ground reaction forces. Scand J Med Sci Sports. 2018;28(12):2527–35. 28. Wild JJ, Bezodis IN, North JS, Bezodis NE. Differences in step characteristics and linear kinematics between rugby players and sprinters during initial sprint acceleration. Eur J Sports Sci. 2018;18(10):1327–37. 29. Buchheit M, Samozino P, Glynn JA, Michael BS, Al Haddad H, Mendez-Villanueva A, et al. Mechanical determinants of accel- eration and maximal sprinting speed in highly trained young soc- cer players. J Sport Sci. 2014;32(20):1906–13. 30. Sides D. Kinematics and kinetics of maximal velocity sprinting and specificity of training in elite athletes. Salford: University of Salford; 2015. 31. Bissas A, Walker J, Tucker C, Paradisis G, Merlino S. Biome- chanical report for the IAAF World Championships London 2017: 100 m men’s. Monte Carlo: IAAF. 2018. 32. Rumpf MC, Lockie RG, Cronin JB, Jalilvand F. Effect of different sprint training methods on sprint performance over various dis- tances: a brief review. J Strength Cond Res. 2016;30(6):1767–85. 33. Alcaraz PE, Carlos-Vivas J, Oponjuru BO, Martínez-Rodríguez A. The effectiveness of resisted sled training (RST) for sprint performance: a systematic review and meta-analysis. Sport Med. 2018;48(9):2143–65. 34. Douglas J, Pearson S, Ross A, McGuigan M. Effects of accentu- ated eccentric loading on muscle properties, strength, power, and speed in resistance-trained rugby players. J Strength Cond Res. 2018;32(10):2750–61. 35. Enoksen E, Staxrud M, Tønnessen E, Shalfawi S. The effect of supervised strength training on young elite male soccer players’ physical performance. Serbian J Sport Sci. 2013;7(4):173–9. 36. Lahti J, Huuhka T, Romero V, Bezodis I, Morin J, Hakkinen K. Changes in sprint performance and sagittal plane kinematics after heavy resisted sprint training in professional soccer players [Pre-print]. 2019. https:// osf. io/ prepr ints/ sport rxiv/ neh5m/ 2019 Updated 27 Aug 2020. 37. Loturco I, Nakamura F, Kobal R, Gil S, Pivetti B, Pereira L, et al. Traditional periodization versus optimum training load applied to soccer players: effects on neuromuscular abilities. Int J Sport Med. 2016;37(13):1051–9. 38. Spinks CD, Murphy AJ, Spinks WL, Lockie RG. The effects of resisted sprint training on acceleration performance and kin- ematics in soccer, rugby union, and Australian football players. J Strength Cond Res. 2007;21(1):77–85. 39. Plisk S (2000) Speed, agility, and speed-endurance development. In: Baechle TR, Earle RW. (eds.) Human Kinetics, 2nd edition. Champaign IL, p 471–91. 40. Bauer P, Uebellacker F, Mitter B, Aigner AJ, Hasenoehrl T, Ristl R, et al. Combining higher-load and lower-load resist- ance training exercises: a systematic review and meta-analysis of findings from complex training studies. J Sci Med Sport. 2019;22(7):838–51. 41. García-Ramos A, Haff GG, Feriche B, Jaric S. Effects of different conditioning programmes on the performance of high-velocity soccer-related tasks: systematic review and meta-analysis of con- trolled trials. Int J Sports Sci Coach. 2018;13(1):129–51. 42. Freitas TT, Martinez-Rodriguez A, Calleja-González J, Alcaraz PE. Short-term adaptations following complex training in team- sports: a meta-analysis. PLoS ONE. 2017;12(6):180223. 43. Moran JJ, Sandercock GR, Ramírez-Campillo R, Meylan CM, Collison JA, Parry DA. Age-related variation in male youth athletes’ countermovement jump after plyometric training: a meta-analysis of controlled trials. J Strength Cond Res. 2017;31(2):552–65. 44. Petrakos G, Morin J-B, Egan B. Resisted sled sprint training to improve sprint performance: a systematic review. Sport Med. 2016;46(3):381–400. 45. Bolger R, Lyons M, Harrison AJ, Kenny IC. Sprinting perfor- mance and resistance-based training interventions: a systematissc review. J Strength Cond Res. 2015;29(4):1146–56. 46. Hrysomallis C. The effectiveness of resisted movement training on sprinting and jumping performance. J Strength Cond Res. 2012;26(1):299–306. 47. Harries SK, Lubans DR, Callister R. Resistance training to improve power and sports performance in adolescent ath- letes: a systematic review and meta-analysis. J Sci Med Sport. 2012;15(6):532–40. 48. Rumpf MC, Cronin JB, Pinder SD, Oliver J, Hughes M. Effect of different training methods on running sprint times in male youth. Pediatr Exerc Sci. 2012;24(2):170–86. 49. de Villarreal ES, Requena B, Cronin JB. The effects of plyomet- ric training on sprint performance: a meta-analysis. J Strength Cond Res. 2012;26(2):575–84. 50. Moran J, Sandercock G, Rumpf MC, Parry DA. Variation in responses to sprint training in male youth athletes: a meta-anal- ysis. Int J Sport Med. 2017;38(01):1–11. 51. Haugen T. Sprint conditioning of elite soccer players: worth the effort or lets just buy faster players? Sport Perform Sci Rep. 2017;1:1–2. 52. Ryan R. Heterogeneity and subgroup analyses in Cochrane Con- sumers and Communication Review Group reviews: planning the analysis at protocol stage. Cochrane Consumers Communication Review Group. 2014. 53. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting sys- tematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. PLoS Med. 2009;6(7):e1000100. 54. McMaster DT, Gill N, Cronin J, McGuigan M. The develop- ment, retention and decay rates of strength and power in elite rugby union, rugby league and American football. Sport Med. 2013;43(5):367–84. 55. Haugen TA, Breitschädel F, Samozino P. Power-force-velocity profiling of sprinting athletes: methodological and practical considerations when using timing gates. J Strength Cond Res. 2018;34(6):1769–73. 56. Haugen T, Buchheit M. Sprint running performance monitor- ing: methodological and practical considerations. Sport Med. 2016;46(5):641–56. 57. Gurevitch J, Koricheva J, Nakagawa S, Stewart G. Meta- analysis and the science of research synthesis. Nature. 2018;555(7695):175. 58. Bremec D. Very heavy resisted sprinting: a better way to improve acceleration? Effects of a 4-week very heavy resisted sprinting intervention on acceleration, sprint and jump performance in youth soccer players [Masters Thesis]. Swedish School of Sport and Health Sciences; 2018. 59. Coratella G, Beato M, Milanese C, Longo S, Limonta E, Rampi- chini S, et al. Specific adaptations in performance and muscle architecture after weighted jump-squat vs body mass squat jump training in recreational soccer players. J Strength Cond Res. 2019;32(4):921–9. 60. Derakhti M. Very heavy resisted sprint training for adolescent football players: a training intervention on acceleration, sprint and jump performance in late pubertal adolescent athletes [Mas- ters of sports science]. Swedish School of sport and health sci- ence, 2018. 282 B. Nicholson et al. 61. Hammami M, Gaamouri N, Shephard RJ, Chelly MS. Effects of contrast strength vs plyometric training on lower limb explosive performance, ability to change direction and neuro- muscular adaptation in soccer players. J Strength Cond Res. 2018;33(8):2094–103. 62. Manouras N, Papanikolaou Z, Karatrantou K, Kouvarakis P, Gerodimos V. The efficacy of vertical vs horizontal plyometric training on speed, jumping performance and agility in soccer players. Int J Sport Sci Coach. 2016;11(5):702–9. 63. Ramírez-Campillo R, Burgos CH, Henríquez-Olguín C, Andrade DC, Martínez C, Álvarez C, et al. Effect of unilateral, bilateral, and combined plyometric training on explosive and endurance performance of young soccer players. J Strength Cond Res. 2015;29(5):1317–28. 64. Ramírez-Campillo R, Gallardo F, Henriquez-Olguín C, Meylan CM, Martínez C, Álvarez C, et al. Effect of vertical, horizontal, and combined plyometric training on explosive, balance, and endurance performance of young soccer players. J Strength Cond Res. 2015;29(7):1784–95. 65. Ramírez-Campillo R, Vergara-Pedreros M, Henríquez-Olguín C, Martínez-Salazar C, Alvarez C, Nakamura FY, et  al. Effects of plyometric training on maximal-intensity exercise and endurance in male and female soccer players. J Sport Sci. 2016;34(8):687–93. 66. Ramirez-Campillo R, Álvarez C, García-Pinillos F, García- Ramos A, Loturco I, Chaabene H, et al. Effects of combined surfaces vs. single-surface plyometric training on soccer players' physical fitness. J Strength Cond Res. 2019. (Publish Ahead of Print). 67. Rimmer E, Sleivert G. Effects of a plyometrics interven- tion program on sprint performance. J Strength Cond Res. 2000;14(3):295–301. 68. Rønnestad BR, Kvamme NH, Sunde A, Raastad T. Short-term effects of strength and plyometric training on sprint and jump performance in professional soccer players. J Strength Cond Res. 2008;22(3):773–80. 69. Higgins JP, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al. Cochrane handbook for systematic reviews of interventions. Hoboken: John Wiley & Sons; 2019. 70. Ades AE, Lu G, Higgins JPT. The interpretation of ran- dom-effects meta-analysis in decision models. MDM. 2005;25(6):646–54. 71. Hopkins W, Marshall S, Batterham A, Hanin J. Progressive sta- tistics for studies in sports medicine and exercise science. Med Sci Sports Exerc. 2009;41(1):3. 72. Higgins J, Thompson SG. Quantifying heterogeneity in a meta- analysis. Stat Med. 2002;21(11):1539–58. 73. Swann C, Moran A, Piggott D. Defining elite athletes: Issues in the study of expert performance in sport psychology. Psychol Sport Exerc. 2015;16:3–14. 74. Sterne JA, Egger M, Moher D. Addressing reporting biases. Cochrane handbook for systematic reviews of interventions: Cochrane book series. 2008:297–333. 75. Alptekin A, Kılıç Ö, Maviş M. The effect of an 8-week plyomet- ric training program on sprint and jumping performance. Serbian J Sport Sci. 2013;7(2):45–50. 76. Barr MJ, Gabbett TJ, Newton RU, Sheppard JM. Effect of 8 days of a hypergravity condition on the sprinting speed and lower-body power of elite rugby players. J Strength Cond Res. 2015;29(3):722–9. 77. Beato M, Bianchi M, Coratella G, Merlini M, Drust B. Effects of plyometric and directional training on speed and jump per- formance in elite youth soccer players. J Strength Cond Res. 2018;32(2):289–96. 78. Bianchi M, Coratella G, Dello IA, Beato M. Comparative effects of single vs double weekly plyometric training sessions on jump, sprint and change of directions abilities of elite youth football players. J Sports Med Phys Fit. 2019;59(6):910. 79. Borges JH, Conceição MS, Vechin FC, Pascoal EHF, Silva RP, Borin JP. The effects of resisted sprint vs plyometric training on sprint performance and repeated sprint ability during the final weeks of the youth soccer season. Sci Sports. 2016;31(4):101–5. 80. Bouguezzi R, Chaabene H, Negra Y, Ramirez-Campillo R, Jlalia Z, Mkaouer B, et al. Effects of different plyometric training fre- quency on measures of athletic performance in prepuberal male soccer players. J Strength Cond Res. 2020;34(6):1609–17. 81. Chelly MS, Ghenem MA, Abid K, Hermassi S, Tabka Z, Shephard RJ. Effects of in-season short-term plyometric training program on leg power, jump-and sprint performance of soccer players. J Strength Cond Res. 2010;24(10):2670–6. 82. Christou M, Smilios I, Sotiropoulos K, Volaklis K, Pilianidis T, Tokmakidis SP. Effects of resistance training on the physical capacities of adolescent soccer players. J Strength Cond Res. 2006;20(4):783–91. 83. Cook CJ, Beaven CM, Kilduff LP. Three weeks of eccentric training combined with overspeed exercises enhances power and running speed performance gains in trained athletes. J Strength Cond Res. 2013;27(5):1280–6. 84. Coutts A, Reaburn P, Piva TJ, Murphy A. Changes in selected biochemical, muscular strength, power, and endurance meas- ures during deliberate overreaching and tapering in rugby league players. Int J Sport Med. 2007;28(2):116–24. 85. de Hoyo M, Gonzalo-Skok O, Sañudo B, Carrascal C, Plaza- Armas JR, Camacho-Candil F, et al. Comparative effects of in- season full-back squat, resisted sprint training, and plyometric training on explosive performance in U-19 elite soccer players. J Strength Cond Res. 2016;30(2):368–77. 86. Escobar-Álvarez J, Fuentes-García J, Pérez-Sousa M, Calleja- González J. Effect of very heavy sled training in speed per- formance (30m) in female rugby union players. In: ICTS VI International Conference in Team Sports; España2018. p 1. 87. Escobar-Alvarez J, Pérez-Sousa MA, Fuentes J, Jimenez-Reyes P. Effect of velocity-based training on strength, CMJ height and sprint performance in professional rugby 7 players. I Congreso Internacional de Innovación en el Deporte; Cáceres, Spain. 2019. 88. Faude O, Roth R, Di Giovine D, Zahner L, Donath L. Combined strength and power training in high-level amateur football during the competitive season: a randomised-controlled trial. J Sport Sci. 2013;31(13):1460–7. 89. Gabbett TJ, Johns J, Riemann M. Performance changes follow- ing training in junior rugby league players. J Strength Cond Res. 2008;22(3):910–7. 90. García-Pinillos F, Martínez-Amat A, Hita-Contreras F, Martínez- López EJ, Latorre-Román PA. Effects of a contrast training pro- gram without external load on vertical jump, kicking speed, sprint, and agility of young soccer players. J Strength Cond Res. 2014;28(9):2452–60. 91. Gil S, Barroso R, Crivoi do Carmo E, Loturco I, Kobal R, Tricoli V, et al. Effects of resisted sprint training on sprinting ability and change of direction speed in professional soccer players. J Sport Sci. 2018;36(17):1923–9. 92. Hammami M, Negra Y, Aouadi R, Shephard RJ, Chelly MS. Effects of an in-season plyometric training program on repeated change of direction and sprint performance in the junior soccer player. J Strength Cond Res. 2016;30(12):3312–20. 93. Hammami R, Granacher URS, Makhlouf I, Behm DG, Chaouachi A. Sequencing effects of balance and plyometric training on physical performance in youth soccer athletes. J Strength Cond Res. 2016;30(12):3278–89. 283 Training Medium to Long Sprint Performance in Football Athletes 94. Harris NK, Cronin JB, Hopkins WG, Hansen KT. Squat jump training at maximal power loads vs heavy loads: effect on sprint ability. J Strength Cond Res. 2008;22(6):1742–9. 95. Karsten B, Larumbe-Zabala E, Kandemir G, Hazir T, Klose A, Naclerio F. The effects of a 6-week strength training on criti- cal velocity, anaerobic running distance, 30-m sprint and Yo-Yo intermittent running test performances in male soccer players. PLoS ONE. 2016;11(3):e0151448. 96. Krommes K, Petersen J, Nielsen MB, Aagaard P, Hölmich P, Thorborg K. Sprint and jump performance in elite male soccer players following a 10-week Nordic Hamstring exercise protocol: a randomised pilot study. BMC Res Notes. 2017;10(1):669. 97. López-Segovia M, Andrés JMP, González-Badillo JJ. Effect of 4 months of training on aerobic power, strength, and accel- eration in two under-19 soccer teams. J Strength Cond Res. 2010;24(10):2705–14. 98. Loturco I, Ugrinowitsch C, Tricoli V, Pivetti B, Roschel H. Dif- ferent loading schemes in power training during the preseason promote similar performance improvements in brazilian elite soccer players. J Strength Cond Res. 2013;27(7):1791–7. 99. Loturco I, Pereira LA, Kobal R, Maldonado T, Piazzi AF, Bot- tino A, et al. Improving sprint performance in soccer: effective- ness of jump squat and olympic push press exercises. PLoS ONE. 2016;11(4):153958. 100. Loturco I, Kobal R, Kitamura K, Cal Abad CC, Faust B, Almeida L, et al. Mixed training methods: effects of combining resisted sprints or plyometrics with optimum power loads on sprint and agility performance in professional soccer players. Front Physiol. 2017;8:1034. 101. Majdell R, Alexander M. The effect of overspeed train- ing on kinematic variables in sprinting. J Hum Mov Stud. 1991;21(1):19–39. 102. Meckel Y, Gefen Y, Nemet D, Eliakim A. Influence of short vs long repetition sprint training on selected fitness components in young soccer players. J Strength Cond Res. 2012;26(7):1845–51. 103. Michailidis Y, Tabouris A, Metaxas T. Effects of plyometric and directional training on physical fitness parameters in youth soccer players. Int J Sports Physiol Perform. 2019;14(3):392–8. 104. Negra Y, Chaabene H, Hammami M, Hachana Y, Granacher URS. Effects of high-velocity resistance training on athletic per- formance in prepuberal male soccer athletes. J Strength Cond Res. 2016;30(12):3290–7. 105. Orange ST, Metcalfe JW, Robinson A, Applegarth MJ, Liefeith A. Effects of in-season velocity-versus percentage-based training in academy rugby league players. Int J Sports Physiol Perform. 2019;1(2):1–8. 106. Ozbar N. Effects of plyometric training on explosive strength, speed and kicking speed in female soccer players. Anthropol. 2015;19(2):333–9. 107. Randell AD, Cronin JB, Keogh JWL, Gill ND, Pedersen MC. Effect of instantaneous performance feedback during 6 weeks of velocity-based resistance training on sport-specific performance tests. J Strength Cond Res. 2011;25(1):87–93. 108. Rey E, PadrÓN-Cabo A, FernÁNdez-Penedo D. Effects of sprint training with and without weighted vest on speed and repeated sprint ability in male soccer players. J Strength Cond Res. 2017;31(10):2659–66. 109. Rønnestad BR, Nymark BS, Raastad T. Effects of in-season strength maintainance training frequency in professional scocer players. J Strength Cond Res. 2011;25(10):2653–60. 110. Ross A, Gill N, Cronin J, Cross M. The effects of two power training programmes on the sprint speed, mechanical sprint char- acteristics, and lower body power of rugby sevens players [Doc- tor of Philosophy (PhD)]. New Zealand: Auckland University of Technology; 2015. 111. Scott BR, Peiffer JJ, Goods PSR. The effects of supplementary low-load blood flow restriction training on morphological and performance-based adaptations in team sport athletes. J Strength Cond Res. 2017;31(8):2147–54. 112. Shalfawi SA, Ingebrigtsen J, Dillern T, Tønnessen E, Delp TK, Enoksen E. The effect of 40 m repeated sprint training on physi- cal performance in young elite male soccer players. Serbian J Sport Sci. 2012;6(3). 113. Söhnlein Q, Müller E, Stöggl TL. The effect of 16-week plyo- metric training on explosive actions in early to mid-puberty elite soccer players. J Strength Cond Res. 2014;28(8):2105–14. 114. Tønnessen E, Shalfawi SA, Haugen T, Enoksen E. The effect of 40-m repeated sprint training on maximum sprinting speed, repeated sprint speed endurance, vertical jump, and aerobic capacity in young elite male soccer players. J Strength Cond Res. 2011;25(9):2364–70. 115. Tous-Fajardo J, Gonzalo-Skok O, Arjol-Serrano JL, Tesch P. Enhancing change-of-direction speed in soccer players by functional inertial eccentric overload and vibration training. Int J Sports Physiol Perform. 2016;11(1):66–73. 116. Upton DE. The effect of assisted and resisted sprint training on acceleration and velocity in Division IA female soccer athletes. J Strength Cond Res. 2011;25(10):2645–52. 117. West DJ, Cunningham DJ, Bracken RM, Bevan HR, Crewther BT, Cook CJ, et al. Effects of resisted sprint training on accel- eration in professional rugby union players. J Strength Cond Res. 2013;27(4):1014–8. 118. Winwood PW, Cronin JB, Posthumus LR, Finlayson SJ, Gill ND, Keogh JW. Strongman vs. traditional resistance training effects on muscular function and performance. J Strength Cond Res. 2015;29(2):429–39. 119. Wong P, Chaouachi A, Chamari K, Dellal A, Wisloff U. Effect of preseason concurrent muscular strength and high-intensity interval training in professional soccer players. J Strength Cond Res. 2010;24(3):653–60. 120. McMaster D, Gill N, McGuigan M, Cronin J. Effects of com- plex strength and ballistic training on maximum strength, sprint ability and force-velocity-power profiles of semi-professional rugby union players. J Aust Strength Cond. 2014;22(1):17–30. 121. Sterne JA, Gavaghan D, Egger M. Publication and related bias in meta-analysis: power of statistical tests and prevalence in the literature. J Clin Epidemiol. 2000;53(11):1119–29. 122. Harris GR, Stone MH, Obryant HS, Proulx CM, Johnson RL. Short-term performance effects of high power, high force, or combined weight-training methods. J Strength Cond Res. 2000;14(1):14–20. 123. Hoffman JR, Cooper J, Wendell M, Kang J. Comparison of olympic vs traditional power lifting training programs in foot- ball players. J Strength Cond Res. 2004;18(1):129–35. 124. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557–60. 125. Kobal R, Pereira LA, Zanetti V, Ramirez-Campillo R, Loturco I. Effects of unloaded vs loaded plyometrics on speed and power performance of elite young soccer players. Front Phys- iol. 2017;8:742. 126. Hoffmann TC, Glasziou PP, Boutron I, Milne R, Perera R, Moher D, et al. Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide. BMJ. 2014;348:g1687. 127. Cuthbert M, Ripley N, McMahon JJ, Evans M, Haff GG, Com- fort P. The effect of Nordic hamstring exercise intervention volume on eccentric strength and muscle architecture adap- tations: a systematic review and meta-analyses. Sport Med. 2019;50(1):83–99. 284 B. Nicholson et al. 128. Medicine ACoS. American College of Sports Medicine posi- tion stand progression models in resistance training for healthy adults. Med Sci Sports Exerc. 2009;41(3):687. 129. Jimenez-Reyes P, Samozino P, García Ramos A, Cuadrado V, Brughelli M, Morin J-B. Relationship between vertical and horizontal force-velocity-power profiles in various sports and levels of practice. PeerJ. 2018;6:e5937. 130. Haugen T, Seiler S, Sandbakk O, Tønnessen E. The training and development of elite sprint performance: an integration of scientific and best practice literature. Sport Med Open. 2019;5:1–6. 131. Sale D, MacDougall D. Specificity in strength training: a review for the coach and athlete. Can J Appl Sport Sci. 1981;6(2):87–92. 132. Haugen TA, Breitschädel F, Seiler S. Sprint mechanical vari- ables in elite athletes: are force-velocity profiles sport specific or individual? Int J Sports Physiol. 2019;14(7):215551. 133. Brearley S, Bishop C. Transfer of training: how specific should we be? Strength Cond J. 2019;41(3):97–109. 134. Kanehisa H, Matsuo A, Fukunaga T. Are peak ground reaction forces related to better sprint acceleration performance? Sports Biomech. 2019:1–10. 135. Nagahara R, Takai Y, Kanehisa H, Fukunaga T. Vertical impulse as a determinant of combination of step length and fre- quency during sprinting. Int J Sport Med. 2018;39(4):282–90. 136. Weyand PG, Sternlight DB, Bellizzi MJ, Wright S. Faster top running speeds are achieved with greater ground forces not more rapid leg movements. J Appl Physiol. 2000;89(5):1991–9. 137. Weyand PG, Sandell RF, Prime DN, Bundle MW. The biologi- cal limits to running speed are imposed from the ground up. J Appl Physiol. 2010;108(4):950–61. 138. Morin J-B, Edouard P, Samozino P. Technical ability of force application as a determinant factor of sprint performance. Med Sci Sports Exerc. 2011;43(9):1680–8. 139. Morin J-B, Slawinski J, Dorel S, Couturier A, Samozino P, Brughelli M, et al. Acceleration capability in elite sprint- ers and ground impulse: push more, brake less? J Biomech. 2015;48(12):3149–54. 140. Morin J-B, Bourdin M, Edouard P, Peyrot N, Samozino P, Lacour J-R. Mechanical determinants of 100-m sprint running performance. Eur J Appl Physiol. 2012;112(11):3921–30. 141. Rabita G, Dorel S, Slawinski J, Sàez-de-Villarreal E, Couturier A, Samozino P, et al. Sprint mechanics in world-class athletes: a new insight into the limits of human locomotion. Scand J Med Sci Sports. 2015;25(5):583–94. 142. Morin J-B, Gimenez P, Edouard P, Arnal P, Jiménez-Reyes P, Samozino P, et al. Sprint acceleration mechanics: the major role of hamstrings in horizontal force production. Front Phys- iol. 2015;6:404. 143. Clark K, Weyand P. Sprint running research speeds up: a first look at the mechanics of elite acceleration. Scand J Med Sci Sports. 2015;25(5):581–2. 144. Bezodis IN, Kerwin DG, Cooper S-M, Salo AI. Sprint running performance and technique changes in athletes during perio- dized training: an elite training group case study. Int J Sports Physiol Perform. 2018;13(6):755–62. 145. Salo AI, Bezodis IN, Batterham AM, Kerwin DG. Elite sprint- ing: are athletes individually step-frequency or step-length reli- ant? Med Sci Sports Exerc. 2011;43(6):1055–62. 146. Hicks D, Schuster J, Samozino P, Morin J-B. Improving mechanical effectiveness during sprint acceleration: prac- tical recommendations and guidelines. Strength Cond J. 2019;42(2):45–62. 147. Moir G. Strength and conditioning. A biomechanical approach. Burlington: Jones & Bartlett Publishers; 2015. 148. Eston R, Reilly T. Kinanthropometry and exercise physiology laboratory manual: tests, procedures and data: volume two: physiology. Routledge; 2013. p. 362. 149. Douglas J, Pearson S, Ross A, McGuigan M. Reactive and eccen- tric strength contribute to stiffness regulation during maximum velocity sprinting in team sport athletes and highly trained sprint- ers. J Sport Sci. 2019:1–9. 150. Morin J-B, Samozino P. Interpreting power-force-velocity pro- files for individualized and specific training. Int J Sports Physiol Perform. 2016;11(2):267–72. 151. Mendez-Villanueva A, Buchheit M, Kuitunen S, Douglas A, Pel- tola ESA, Bourdon P. Age-related differences in acceleration, maximum running speed, and repeated-sprint performance in young soccer players. J Sport Sci. 2011;29(5):477–84. 152. Vescovi JD, Mcguigan MR. Relationships between sprint- ing, agility, and jump ability in female athletes. J Sport Sci. 2008;26(1):97–107. 153. Little T, Williams AG. Specificity of acceleration, maximum speed, and agility in professional soccer players. J Strength Cond Res. 2005;19(1):76–8. 154. Colyer SL, Nagahara R, Salo AI. Kinetic demands of sprinting shift across the acceleration phase: novel analysis of entire force waveforms. Scand J Med Sci Sports. 2018;28(7):1784–92. 155. Wild J, Bezodis NE, Blagrove R, Bezodis I. A biomechanical comparison of accelerative and maximum velocity sprinting: Specific strength training considerations. Prof Strength Cond. 2011;21:23–37. 156. Slawinski J, Termoz N, Rabita G, Guilhem G, Dorel S, Morin JB, et al. How 100-m event analyses improve our understanding of world-class men’s and women’s sprint performance. Scand J Med Sci Sports. 2017;27(1):45–54. 157. Schache AG, Brown NA, Pandy MG. Modulation of work and power by the human lower-limb joints with increasing steady- state locomotion speed. J Exp Biol. 2015;218(15):2472–81. 158. Whitehead S, Till K, Weaving D, Jones B. The use of microtech- nology to quantify the peak match demands of the football codes: a systematic review. Sport Med. 2018;48(11):2549–75. 159. Taylor JB, Wright AA, Dischiavi SL, Townsend MA, Marmon AR. Activity demands during multi-directional team sports: a systematic review. Sport Med. 2017;47(12):2533–51. 160. Phibbs PJ, Jones B, Read DB, Roe GA, Darrall-Jones J, Weakley JJ, et al. The appropriateness of training exposures for match- play preparation in adolescent schoolboy and academy rugby union players. J Sport Sci. 2018;36(6):704–9. 161. Phibbs PJ, Jones B, Roe GA, Read DB, Darrall-Jones J, Weakley JJ, et al. We know they train, but what do they do? Implications for coaches working with adolescent rugby union players. Int J Sports Sci Coach. 2017;12(2):175–82. 162. Wellman AD, Coad SC, Goulet GC, McLellan CP. Quantification of competitive game demands of ncaa division i college football players using global positioning systems. J Strength Cond Res. 2016;30(1):11–9. 163. Hartwig TB, Naughton G, Searl J. Motion analyses of adoles- cent rugby union players: a comparison of training and game demands. J Strength Cond Res. 2011;25(4):966–72. 164. Jiménez-Reyes P, Cross M, Ross A, Samozino P, Brughelli M, Gill N, et al. Changes in mechanical properties of sprinting dur- ing repeated sprint in elite rugby sevens athletes. Eur J Sports Sci. 2019;19(5):585–94. 165. Nagahara R, Morin J-B, Koido M. Impairment of sprint mechani- cal properties in an actual soccer match: a pilot study. Int J Sports Physiol Perform. 2016;11(7):893–8. 166. de Souza E. Molecular adaptations to concurrent strength and endurance training. In: Schumann M, Rønnestad BR, editors. Concurrent aerobic and strength training: scientific basics and 285 Training Medium to Long Sprint Performance in Football Athletes practical applications. Cham: Springer International Publishing; 2019. p. 99–123. 167. Hunter JP, Marshall RN, McNair PJ. Relationships between ground reaction force impulse and kinematics of sprint-running acceleration. J Appl Biomech. 2005;21(1):31–43. 168. Lockie RG, Murphy AJ, Scott BR, Janse de Jonge XA. Quanti- fying session ratings of perceived exertion for field-based speed training methods in team sport athletes. J Strength Cond Res. 2012;26(10):2721–8. 169. Lockie RG, Murphy AJ, Callaghan SJ, Jeffriess MD. Effects of sprint and plyometrics training on field sport acceleration tech- nique. J Strength Cond Res. 2014;28(7):1790–801. 170. Lockie RG, Murphy AJ, Schultz AB, Knight TJ, de Jonge XAJ. The effects of different speed training protocols on sprint accel- eration kinematics and muscle strength and power in field sport athletes. J Strength Cond Res. 2012;26(6):1539–50. 171. Harrison AJ, Bourke G. The effect of resisted sprint training on speed and strength performance in male rugby players. J Strength Cond Res. 2009;23(1):275–83. 172. Selye H. Stress and the general adaptation syndrome. BMJ. 1950;1(4667):1383. 173. Billaut F, Bishop D. Muscle fatigue in males and females during multiple-sprint exercise. Sport Med. 2009;39(4):257–78. 174. Perrey S, Racinais S, Saimouaa K, Girard O. Neural and muscu- lar adjustments following repeated running sprints. Eur J Appl Physiol. 2010;109(6):1027–36. 175. Jimenez-Reyes P, Pareja-Blanco F, Cuadrado-Peñafiel V, Mor- cillo J, Párraga J, González-Badillo J. Mechanical, metabolic and perceptual response during sprint training. Int J Sport Med. 2016;37(10):807–12. 176. Cissik JM. Means and methods of speed training. Part I Strength Cond J. 2004;26(4):24–9. 177. Schmidt RA, Wrisberg CA. Motor learning and performance: a situation-based learning approach. Human kinetics; 2008. 178. Kivi DM. A kinematic comparison of the running A and B drills with sprinting [Master of science]. National Library of Canada: The University of Manitoba; 1997. 179. West T, Robson S. Running drills-are we reaping the benefits. 5th ed. Mountain View: TAFNEWS Press; 2000. p. 64–7. 180. Jakalski K. Contemporary research and sprinting: Reconsider- ing the conceptual paradigm of running mechanics. Track Field Coach Rev. 2002;75(1):21–2. 181. Cahill M, Cronin JB, Oliver JL, Clark K, Cross MR, Lloyd RS. Sled pushing and pulling to enhance speed capability. Strength Cond J. 2019;41(4):94–104. 182. Kawamori N, Newton RU, Hori N, Nosaka K. Effects of weighted sled towing with heavy versus light load on sprint acceleration ability. J Strength Cond Res. 2014;28(10):2738–45. 183. Zafeiridis A, Saraslanidis P, Manou V, Ioakimidis P. The effects of resisted sled-pulling sprint training on acceleration and maxi- mum speed performance. J Sports Med Phys Fit. 2005;45(3):284. 184. Makaruk B, Sozański H, Makaruk H, Sacewicz T. The effects of resisted sprint training on speed performance in women. Hum Mov. 2013;14(2):116–22. 185. Cronin J, Hansen K, Kawamori N, Mcnair P. Effects of weighted vests and sled towing on sprint kinematics. Sports Biomech. 2008;7(2):160–72. 186. Alcaraz PE, Palao JM, Elvira JL, Linthorne NP. Effects of three types of resisted sprint training devices on the kinemat- ics of sprinting at maximum velocity. J Strength Cond Res. 2008;22(3):890–7. 187. Kawamori N, Newton R, Nosaka K. Effects of weighted sled towing on ground reaction force during the acceleration phase of sprint running. J Sport Sci. 2014;32(12):1139–45. 188. Cross MR, Brughelli ME, Cronin JB. Effects of vest load- ing on sprint kinetics and kinematics. J Strength Cond Res. 2014;28(7):1867–74. 189. Jiménez-Reyes P, Samozino P, Brughelli M, Morin J-B. Effec- tiveness of an individualized training based on force-velocity profiling during jumping. Front Physiol. 2017;7:677. 190. Hill A, Long C, Lupton H. The effect of fatigue on the relation between work and speed, in contraction of human arm muscles. J Physiol. 1924;58(4–5):334–7. 191. Haugen T, Tønnessen E, Seiler S. 958 and 1049: nearing the citius end for 100 m? Int J Sports Physiol Perform. 2015;10(2):269–72. 192. Bolger R, Lyons M, Harrison AJ, Kenny IC. Coaching sprint- ing: expert coaches’ perception of resistance-based training. Int J Sports Sci Coach. 2016;11(5):746–54. 193. Healy R, Kenny IC, Harrison AJ. Resistance training practices of sprint coaches. J Strength Cond Res. 2019. (Publish Ahead of Print) 194. Whelan N, Kenny IC, Harrison AJ. An insight into track and field coaches’ knowledge and use of sprinting drills to improve performance. Int J Sports Sci Coach. 2016;11(2):182–90. 195. Mendiguchia J, Martinez-Ruiz E, Morin JB, Samozino P, Edouard P, Alcaraz PE, et al. Effects of hamstring-emphasized neuromuscular training on strength and sprinting mechanics in football players. Scand J Med Sci Sports. 2015;25(6):e621–9. 196. Emmonds S, Heyward O, Jones B. The challenge of applying and undertaking research in female sport. Sport Med Open. 2019;5(1):51. 197. Kraemer WJ, Ratamess NA, Nindl BC. Recovery responses of testosterone, growth hormone, and IGF-1 after resistance exer- cise. J Appl Physiol. 2016;122(3):549–58. 198. Oliver JL, Lloyd RS, Rumpf MC. Developing speed throughout childhood and adolescence: the role of growth, maturation and training. Strength Cond J. 2013;35(3):42–8. 199. Rumpf MC, Cronin JB, Oliver JL, Hughes M. Assessing youth sprint ability–Methodological issues, reliability and performance data. Pediatr Exerc Sci. 2011;23(4):442–67. 200. Rodríguez-Rosell D, Franco-MÁrquez F, Mora-Custodio R, GonzÁlez-Badillo JJ. Effect of high-speed strength training on physical performance in young soccer players of different ages. J Strength Cond Res. 2017;31(9):2498–508. 201. Coutts AJ, Reaburn P, Piva TJ, Rowsell GJ. Monitoring for overreaching in rugby league players. Eur J Appl Physiol. 2007;99(3):313–24. 202. McMillan K, Helgerud J, Grant S, Newell J, Wilson J, Macdonald R, et al. Lactate threshold responses to a season of professional British youth soccer. Br J Sports Med. 2005;39(7):432–6. 203. Markovic G, Mikulic P. Neuro-musculoskeletal and performance adaptations to lower-extremity plyometric training. Sport Med. 2010;40(10):859–95. 204. Yanci J, Los Arcos A, Camara J, Castillo D, García A, Castagna C. Effects of horizontal plyometric training volume on soccer players’ performance. Res Sport Med. 2016;24(4):308–19. 205. Wewege M, Devonshire J, Hansford H, McAuley J, Jones M. Comment on: “The training of short distance sprint performance in football code athletes: a systematic review and meta-analysis". Sport Med. 2021;51:1–5. 206. Cross MR, Brughelli M, Samozino P, Brown SR, Morin J-B. Optimal loading for maximising power during sled-resisted sprinting. Int J Sports Physiol Perform. 2017:1–25. 207. Samozino P, Morin J-B, Hintzy F, Belli A. A simple method for measuring force, velocity and power output during squat jump. J Biomech. 2008;41(14):2940–5. 208. Samozino P, Rabita G, Dorel S, Slawinski J, Peyrot N, Saezdeil- larreal E, et al. A simple method for measuring power, force, velocity properties, and mechanical effectiveness in sprint run- ning. Scand J Med Sci Sport. 2016;26(6):648–58. 286 B. Nicholson et al. Authors and Affiliations Ben Nicholson1 · Alex Dinsdale1 · Ben Jones1,2,3,4,5 · Kevin Till1,2 * Ben Nicholson [email protected] 1 Carnegie Applied Rugby Research (CARR) Centre, Carnegie School of Sport, Leeds Beckett University, Headingley Campus, Leeds LS6 3QS, UK 2 Leeds Rhinos Rugby League Club, Leeds, UK 3 England Performance Unit, The Rugby Football League, Leeds, UK 4 School of Science and Technology, University of New England, Armidale, NSW, Australia 5 Division of Exercise Science and Sports Medicine, Department of Human Biology, Faculty of Health Sciences, The University of Cape Town and the Sports Science Institute of South Africa, Cape Town, South Africa

The Training of Medium- to Long-Distance Sprint Performance in Football Code Athletes: A Systematic Review and Meta-analysis.

09-09-2021

Nicholson, Ben,Dinsdale, Alex,Jones, Ben,Till, Kevin

eng

PMC8751030




 Citation: Ouyang, Y.; Cai, X.; Li, J.; Gao, Q. Investigating the “Embodied Spaces of Health” in Marathon Running: The Roles of Embodiment, Wearable Technology, and Affective Atmospheres. Int. J. Environ. Res. Public Health 2022, 19, 43. https:// doi.org/10.3390/ijerph19010043 Academic Editor: Paul B. Tchounwou Received: 12 November 2021 Accepted: 20 December 2021 Published: 21 December 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). International Journal of Environmental Research and Public Health Article Investigating the “Embodied Spaces of Health” in Marathon Running: The Roles of Embodiment, Wearable Technology, and Affective Atmospheres Yi Ouyang 1, Xiaomei Cai 2, Jie Li 1 and Quan Gao 3,* 1 School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China; [email protected] (Y.O.); [email protected] (J.L.) 2 School of Tourism Management, South China Normal University, Guangzhou 510006, China; [email protected] 3 School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510006, China * Correspondence: [email protected] Abstract: This paper examines how spaces of health are produced through embodied and affective practices in marathon running in China. While the social-cultural effects of distance running have gained increasing attention among public health scholars and policymakers, there has been little effort paid to the spatiality of running and its contributions to producing healthy spaces for the general public. This paper therefore fills the lacuna through a qualitative study that was conducted with 29 amateur marathon runners in China. Drawing on the Gioia Methodology in coding and analyzing qualitative data, we highlight the interactive effects of body, wearable technology, and affective atmospheres in producing what we call “embodied space of health.” We suggest that the embodied space of health is not simply the bodily experience per se but rather a relational space constituted through the co-production of body, non-human objects, and space/place. It is through these relational spaces that the effects of health and well-being (e.g., self-exploration and therapeutic feelings) emerge in marathon. Keywords: running; bodily space; affective atmospheres; health 1. Introduction Running has gradually become an important inquiry to public health researchers and policymakers since the late 1970s given its health-promoted benefits [1,2]. In particular, over the past one decade or so, scholars have shown growing interests in exploring the cultures of distance running and how they relate to the issues of health and wellbeing [3–7]. This cultural approach to running studies pay attention to the ways in which healthy lifestyles are produced and maintained through running cultures and practices [6,8]. They contend that running is a form of embodied practice through which people’s health consciousness and subjectivity are shaped. For example, distance running is increasingly perceived by people as a way to attain self-realisation and a self-disciplined lifestyle [4]. However, existing research in the cultural studies of running largely neglects the spatial dimension of running and especially how the consequence of health emerges from the interaction be- tween body, object, and space/place in running. Some scholars have noted that space/place matters in health studies not only because some places (e.g., therapeutic landscape) have health-promoted effects but because space conditions and mediates people’s practices of health making [9,10]. This is particularly the case in running, an inherently spatial practice that calls upon the body to move across/through spaces [4]. Nevertheless, the relationship between running, space, and health warrants a closer examination. This paper therefore fills the lacuna through elaborating on the idea of what we call “embodied space of health” and through a qualitative study of marathon running in China. Over the past decade, marathon running has become one of the most popular sports in Int. J. Environ. Res. Public Health 2022, 19, 43. https://doi.org/10.3390/ijerph19010043 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2022, 19, 43 2 of 13 China. The number of marathon races held on the country has dramatically grown from 22 in 2011 to 1581 in 2018 [11]. During the same period, the number of participants has also increased from 410 thousand to more than 7 million [11]. Marathons have gained much attention from not only the public but also the policymakers. In 2017, the Chinese state launched a Marathon Development Plan, which signified that the marathon was promoted to a state-sanctioned program [11]. According to Ronkainen et al., the growing demands and enthusiasm for marathon running in China is fueled by the country’s socio-political reform and the dramatic economic growth over the past two decades [12]. The significant improvement on people’s life quality has led to “a rapidly growing, health-conscious, and affluent class” who have paid increasing attentions to their bodies and health [12] (p. 42). In the ideological sphere, the market-oriented and neoliberal reforms in China have released people from the constraints of collectivist ideologies, which instead enables individuals’ pursuit of self-enrichment and self-making [13]. In particular, health and health-making are viewed as an important facet of desired citizenship and particularly individuals’ utility in a market-oriented society [13]. The popularity of marathon in China has opened up new spaces for health practices. These social contexts provide an important entry to examine the social-cultural logics of marathon and especially the interaction of body, space, and health in the marathon. Against this backdrop, this paper therefore aims to explore how spaces of health are produced through the embodied and affective practices in running. It is noteworthy that we utilise “space” rather “place” in this paper because space addresses a more relational account of health-making, while place is often associated with particular qualities of health (e.g., therapeutic landscape). Nevertheless, we suggest that space can become place through embodied and affective practices in running. Drawing on Gioia Methodology [14], we emphasise the interactive effects of body, wearable technology, and affective atmospheres in producing the “embodied space of health.” This paper therefore moves away from a biophysical study of health in running studies to an embodied and relational account of running and health. 2. Recreational Running and the Embodied Space of Health 2.1. Recreational Running, Space, and Health Public health scholars in recent years have shown growing interest in exploring the role of recreational running in shaping the practices and subjectivities of health [4]. Running is perhaps one of the most popular form of sports that contributes to a healthy lifestyle [2]. In a medical sense, running positively facilitates the achieving of healthy body through tackling obesity, improving well-being and mental health status, inspiring individuals’ desires for participating in sports activities, and increasing people’s capacity of self-management [15]. The popularity of running is not only due to its health-related benefits but also the low costs of entry that enable individuals to easily and inexpensively practice at flexible space- times [16,17]. Moreover, running as a widely participated sport is increasingly promoted by the state to build up a “healthy society,” which is integral to the neoliberal health policy that channels the accountability for health from state to individuals [18]. Therefore, running is an important topic for public health policymakers around the world. In particular, the cultural studies of recreational running has gained growing attentions in recent years [3,6,7,19]. This line of research primarily focuses on how health practices and subjectivities are constituted through social construction of running. For example, Collinson and Hockey suggested that distance running can create subculture and collective identity within runners in which particular values and dispositions, such as self-discipline, stoicism, and sportsmanship, are valorized [3]. As they note, the “affective community of friends and fellow runners” influences runners’ social-psychological capacity to manage their bodies and especially injuries and restoration [3] (p. 394). Yet, the sociality of running is not only shaped by the collective practices and interactions of runners but also the spatial-temporal arrangements and the collective physical infrastructures that structures runners’ health practices [20,21]. In general, running is argued to be a key element of the self-realisation and identity-making of its practitioners who seek to demonstrate their Int. J. Environ. Res. Public Health 2022, 19, 43 3 of 13 skills and affirm their beliefs in healthy lifestyle: “this identity extends beyond immediate benefits, such as body tone, weight loss, and overall fitness, and comes to include a more intrinsic self-identification as a runner” [7] (p. 338). Another strand of research relates running to the production of healthism and health-related consciousness [6,14]. Runners’ health practices and desires for fitness is also influenced by the discourses of healthism constructed by society and media on the “internalized body-ideals—on what the healthy and fit body should look like, and how to gain body-satisfaction” [6] (p. 18). The promotion of healthism in many societies has become a device to achieve social control and to produce productive, healthy, and self-responsible citizens. In this sense, social and political contexts are also crucial in shaping the health practices and subjectivities of runners, as running is a site through which healthism is internalised into runners’ body. Although existing scholarship has acknowledged the cultural significance of running, little attention has been paid to the spatiality of running and especially how running can create health-promoting spaces [4]. In essence, running is an inherently spatial practice because it calls upon the body to move across/through space, place, and landscape and influences the way individuals make sense of the environments around them [18]. Some research has revealed that space/place matters in eliciting running practices and public health [22–24]. This primarily manifests in the studies that examine the significance of events (e.g., road runs and fun runs) and particular place/landscape (e.g., parks, forests, and hill) in stimulating the health effects of running [22–24]. Nevertheless, a more nuanced understanding of running, space, and health remains piecemeal [21]. Attending to the spatiality of running and health should focus on not only the body as a site through which health subjectivities are formed but also how the embodied practices of running may create new spaces of health [4,21]. In other words, embodiment is not only a crucial dimension of health but also influences the ways that runners make sense of the space and place around them [4]. This paper therefore contributes to this inquiry by elaborating on what we call the “embodied spaces of health” in recreational running. 2.2. Theorising the “Embodied Space of Health” in Running It has been widely recognised that running is an embodied and mobile practice that moves in/across space/place [4]; yet, how health subjects form and emerge from the interaction between body and space is largely under-theorised. Some research has revealed that health and well-being are not simply acquired through the management of medical body but also cultivated through the encounters with particular space/place, for instance, the therapeutic landscapes [10]. The importance of the concept of space/place in health studies is further acknowledged by the relational approach to health studies that draws upon the merits of actor-network theory [25]. This approach argues that effects of health “emerge from relationalities, interactions and assemblages of body/self, social discourses, more-than-human subjects, and the broader social-environmental setting” [26] (p. 1). Bearing this in mind, running is better understood as an embodied encounter between people and space/place [27]. In running, people and space are actually in “a mutually reinforcing and reciprocal relationship” [27] (p. 1825). For example, Little’s study of running in nature suggested that people’s intimacy with nature is integral to their project of self-caring, while the running practices in turn produce an authentic natural space that is emotionally perceived as health-promoted [4]. This paper therefore advances the relational and spatial approach to running and health by introducing the concept of what we call “embodied space of health.” There are at least three lines of insights that contribute to our theorisation of the “embodied space of health.” First, the “embodied space of health” recognises the role of bodies and bodily practices at the centre of health making and the production of health-promoted space/place. The health effects of running are not instinctive but rather learned and disciplined through running bodies such that particular health subjectivities, consciousness, dispositions, and lifestyles can become part of the self [4,28]. Hanold pointed out that the bodily experiences of pain during marathon running provide a way for the runners to explore the capacity of Int. J. Environ. Res. Public Health 2022, 19, 43 4 of 13 the body and to achieve self-realisation [29]. He also suggested that the desires for healthy and disciplined bodies reproduce the social norms that associate marathon running with middle-classness. Similarly, inspired by Lefebvre’s rhythm analysis, Edensor and Larsen examined the bodily rhythms of marathon practitioners [5]. They argued that marathon running is a rhythm that is collectively achieved by the spatial-temporal arrangements of the body, place, environment, and various actors. Therefore, runners need to train and manage their body in order to mobile to attain mobile rhythms and “experience a collective eurhythmia with fellow runners” [5] (p. 731). In short, marathon running involves management of the desires, capacity, pain, and rhythm of the body through which the health subjects can emerge. Second, existing literature of running has begun to acknowledge the role of wearable technologies (e.g., smartwatch, self-tracking devices) in the production of health-promoting places/spaces [4,30,31]. The literature on the “quantified self” has suggested that digital technologies can help quantify bodies and their interaction with places to facilitate self- betterment and self-reflection [32]. This is particularly the case in the wide utilisation of wearable technologies, such as self-tracking devices in jogging and running. Esmonde’s study of women’s use of fitness tracking technologies indicated that digital technologies can enhance, reframe, or even undermine the pleasure that runners derive from their body’s movement through space. She further acknowledges the non-human agency of digital technologies that data collection in turn disciplines individuals’ feelings of health [31]. Little [4], however, argued that some runners’ use of digital technology may influence personal values of health that cannot be quantified, such as sociality and escape from regular life patterns. Nevertheless, human, non-human, and other types of objects are capable of acting and shaping the social-spatial relations of health. Wearable technologies therefore can be considered as an important factor in shaping the “embodied space of health” in running. Third, the “embodied space of health” captures not only the bodily experiences per se but also the diffused, ambiguous, and non-representational spaces of health. This is partic- ularly the case in the studies of “atmospheres,” “affect,” and “moods” of running [33,34]. Atmosphere is often understood as the “spatially extended quality of feeling” and “some- thing distributed yet palpable, a quality of environmental immersion that registers in and through sensing bodies whilst also remaining diffuse, in the air, ethereal” [35] (p. 413). Researchers often use the concept of “affective atmospheres” to understand this diffused forms of spatiality. “Affective atmospheres” are not feelings and bodily experiences per se but something formed through the interactions between bodies, objects, and environments, which in turn have the capacity to shape and condition people’s behaviours and subjec- tivities [36,37]. Lupton contended that affective atmospheres can profoundly influence the ways in which people “sense the spaces they inhabit and through which they move and the other actors in those spaces;” therefore, affective atmospheres also shape how health is felt and performed in specific spaces [32] (p. 1). For example, Larsen and Jensen considered weather as an important affective atmosphere in distance running [37]. They contended that “concrete and situated weather conditions are felt in our multi-sensorial embodied relations to the ‘outer environment’” so that the experiences of running bodies can be animated [37]. In general, existing literature has indicated that body, technology, and atmospheres are important in shaping the space of health. However, there is scant research that examine how body, technology, and atmospheres mutually shape one another in a way that may engender new subjectivities and spaces of health in running. This paper therefore aims to explore how embodied spaces of health are produced through the interaction of body, non-human actors, and environments. Int. J. Environ. Res. Public Health 2022, 19, 43 5 of 13 3. Materials and Methods 3.1. Data Collection The materials of this paper are based on a project that explores the embodied prac- tices of marathon runners in China. Our data were collected through a qualitative study conducted from September 2019 to January 2020. The methods utilised in this study in- cluded participant observation and in-depth interviews. We also collected four runners’ dairies that recorded their experience during marathons. The dairies were copied, with respondents’ permission, and analysed as important data sources of this research project. The interviews were conducted with 29 amateur marathon runners, including 20 men and 9 women, with ages varying from 22 to 55 (see Table 1). All respondents had participated in at least two marathons in one year. Most respondents were well-educated university students, managers, or professionals who can be roughly categorised as middle class in China. The interviews were largely unstructured to encourage participants to freely narrate their experiences of running and how they make sense of the environments around them during running. Interviews lasted from 30 min to 2 h and were recorded and transcribed in full. Pseudonyms are utilised to protect the anonymity of respondents. Table 1. Demographic information of participants. Number Gender Age Occupation Number Gender Age Occupation M1 Male 30 Manager M16 Male 27 Freelancer M2 Male 30 IT developer * M17 Male 41 Doctor M3 Male 28 Architect M18 Male 30 Company staff M4 Male 40 Teacher M19 Male 50 Constructor M5 Male 22 Student M20 Male 28 Entrepreneur M6 Male 25 Student F1 Female 40 Manager M7 Male 40 Teacher F2 Female 28 Company staff M8 Male 23 Student F3 Female 50 Accountant M9 Male 38 Manager F4 Female 35 Manager M10 Male 48 Manager F5 Female 34 Banker M11 Male 25 Company staff F6 Female 26 Banker M12 Male 24 Student F7 Female 23 Teacher M13 Male 29 Company staff F8 Female 29 Researcher M14 Male 34 Manager F9 Female 28 Company staff M15 Male 28 Teacher * Note: IT refers to information technology. 3.2. Data Coding and Analysis This paper engages the Gioia Methodology to analyse and interpret interview and dairies data so as to increase the “qualitative rigor” in inductive research [14]. This method- ology is a modified version of grounded theory that aims to reveal the structure and connection of qualitative data through conceptualisation and coding. There are some basic steps of this method. First, researcher should “start looking for similarities and differences among emerging categories” and “bend over backward to give those categories labels that retain informants’ terms” [38] (p. 286). Second, we consider the constellation of 1st-order codes, which should adhere faithfully to the terms ustilised by the informants. Third, if there are some deeper process or structures underlying the 1st-order codes, we then can proceed to 2nd-order themes and aggregate dimensions that form the basis for building a data structure [14]. The interview and dairies data were coded with the assistance of the qualitative data analysis software NVivo 11. As a result, we generated 522 codes that relate to “body, space, and health” in marathon running. Based on these 522 codes, we conducted 1st-order analysis, which resulted in eleven 1st-order concepts and six 2nd-order themes. An overview of the coding is presented below in Table 2. Int. J. Environ. Res. Public Health 2022, 19, 43 6 of 13 Table 2. The data coding of marathon running. Aggregate Dimensions 2nd-Order Concepts 1st-Order Concepts Examples of Illustrative Quote Bodily experience of health Capacity of the body Pursuing the healthy and desired body “I have hyperlipidaemia and fatty liver. So, I decided to change by running marathon.” Exploring the potentials and limits of the running body “That felt like riding a roller coaster, which made you addicted and kept you pushing the limits of your body.” Autonomy of the body Cultivating self-disciplined bodies “You paid more attention to manage your body and time and stopped staying up late.” Resisting social norms “Running has enabled me to break up this patterned life trajectory.” Digitally-mediated experience of health Self-betterment through wearable technology Establishing quantified self “I can see the number and intensity of trainings that I have done and I intend to reach.” Self-monitoring “After training, these devices can help you monitor your body.” Negotiation of digital agency Constraints of the wearable technology “Without the device, I can’t ensure whether I was leading a scientific running. It made me uncomfortable”. Atmospheric experience of health Affective atmosphere Sense of ritual “You feel validated because of this sense of ritual.” Interaction of the bodies “We would encourage and take care each other on the road.” Aesthetic place and landscape “When you are running, you can experience different beautiful landscapes across China”. Therapeutic environments Nature, urban environment, and weather “This [environment] somewhat purified me and brought me peacefulness at that comment.” 4. Results 4.1. Marathon Running and the Bodily Experience of Health The perception and exploration of the body is central to the health effects of marathon running. Our coding processes show that the bodily experience of health can be divided into two conceptulisations of the body: First, runners attempt to build up the capacity of the body by cultivating healthy body and exploring the potentials of their body; second, through marathon running, practitioners reclaim the autonomy of the body that was thwarted by the programmatic lifestyles and social norms in the city. In general, the body is a crucial site through which not only the biophysical presence of health but also the health subjectivities are formed. Pursuing a healthy body or desired body shape is often one of the main motivations for participating into marathon running. However, marathon is nevertheless an intensive endurance sport that may not be suitable for those who are not ready for a full marathon. Therefore, many practitioners would engage in normal running first as pre-marathon training and consider participating into marathon as the impetus to push them to build up a healthy lifestyle and disciplined body. For example, M4, a 40-year-old teacher who was troubled by obesity, told us: You know, medically speaking, running is the best cure for illness. I have hyper- lipidaemia and fatty liver. So, I decided to change by running marathon. But I am not ready for a full marathon. I just ran around in the playground and hopefully I will be ready to participate one day. Despite this, I can see significant change that took place on myself. I became more self-disciplined. To prepare for the marathon, I have a morning jog and regular diet every day. Int. J. Environ. Res. Public Health 2022, 19, 43 7 of 13 When we re-interviewed M4 four months later, he had completed his first marathon attempt. However, we suggest that people’s engagements with marathon often goes beyond concerns for the biophysical sense of health but also contributes to the construction of a running body through which to achieve self-exploration. Many runners highlight that marathon is a journey of self-exploration and transformation in which you can truly experience the potentials and limits of your body. For example, M3, a 28-year-old architect, recorded his experience of one marathon race in his diary: Marathon is normally considered as a boring and physically-intensive sport in outsiders’ eyes. But after you have participated in it, you would know that it’s a process of communication between you and your body. While your body was extremely tired, it persuaded you to give up. But simultaneously, your brain would generate endorphin that made you excited and joyful. Gradually, you would be addicted to these complex feelings . . . I think marathon has changed me from within, which manifested in not only the body shape but also the spirit and the energetic state of life. It is a systematic transformation of the self. M3’s experience is consistent with Shipway and Holloway’s argument [6] that running provides people a source of meaning and a life-changing experience that enable runners to cultivate a confident self. Yet, we further suggest that running as a project of self-exploration is animated by the situated bodily experience in particular “moments.” For example, the bodily experience of “tiredness” and “painfulness” frequently appears in the interviews and dairies. In particular, the negotiation of painfulness during marathon describes the bodily experience of most practitioners. F5, a 34-year-old banker, contended that the painful experience offered her a way to explore the limits of her body, through which she can attain a new understanding of painfulness and the self: When I reached the last 10 km in my first marathon attempt, I intensively felt that I had pushed my body to its limits. I heavily and slowly moved my legs that went into convulsions. I could clearly hear my breaths and heartbeats. It was definitely painful . . . But, when I came back from it, I couldn’t help having a second attempt of marathon, to continue experiencing this kind of pain. I realised that pain was just a part of my experience that I didn’t need to avoid. I learned to attain happiness from being in pain during a marathon. Apart from self-exploration, M14, a 34-year-old manager, explains that the embodied experience of marathon enabled him to reclaim the autonomy of the body and to escape from the patterned lifestyle in the city. For M14, marathon running helped him to rediscover the “true potential” of his body and attain an state of transcendence, in which he detached from the patterned and ordinary self: After a few years into the job, I had led an increasingly patterned and program- matic lifestyle, nothing had changed. I couldn’t find any passion until I took up marathon running. Running has enabled me to break up this patterned life trajectory that may constrain me in the expected future . . . Running a marathon felt like riding a roller coaster; it is painful yet exciting while you keep pushing the limits of your body. It seemed masochistic, but If you didn’t do this, you would never know your true potential. Although marathon is a sport that requires the management and discipline of the body, many correspondents report that self-discipline instead enabled them to reclaim control of their body/life. For example, F9, a 28-year-old company staff member, told us that it helped her attain higher degree of freedom by leading an ordered and self-disciplined lifestyle: “When you engage in marathon, your life becomes more ordered because you pay more attention to managing your body and time and stop staying up late. To maintain a healthy lifestyle, I also had courage to reject many unnecessary parties.” In general, the running body is an important site through which they can transform themselves to attain a certain state of freedom, control, and self-realisation that form the basis for a healthy lifestyle. Int. J. Environ. Res. Public Health 2022, 19, 43 8 of 13 4.2. Wearable Technology and Digitally-Mediated Body As Esmonde [31] (p. 809) noted, “the practice of self-tracking can influence a person’s movement through the world while running or walking in important ways.” In particular, self-tracking and wearable technologies can reconfigure the ways runners make sense of the relationship between space and self through a quantitative lens. In this section, we reveal more complex ways of how running body and space are digitally mediated and negotiated. We suggest that running bodies are also shaped by wearable technologies that facilitate, condition, and even structure the ways in which marathon runners manage their own bodies, conduct, and ways of being and extent to which they exercise their agency. According to the interviews and dairies, wearable technologies, such as self-tracking devices, GPS, and running-oriented apps, are widely used among runners. Twenty-five of 29 participants reported that they frequently used wearable devices or other running- oriented apps in running. On the one hand, for most runners, the use of self-tracking and wearable digital devices is a crucial requirement of scientific running. That is, it is through the digital quantification of the body that runners can scientifically monitor their bodies, avoid risks, and achieve self-betterment. As F1, a 40-year-old manager, noted: I must use the watch from which I can see the indexes of my body because I think I am a scientific runner. It can help me more efficiently set up my own training plans. I can see the number and intensity of trainings that I have done and I intend to reach. After training, these devices can help you monitor your body—whether your body has re-energised or whether it is ready for the next race. Wearable technology not only provides an quantitative account of runners’ body but also shapes the ways and rhythms that they interact with the space/place around them while they are running. The word “rhythms” (jiezhou, 节奏) were frequently mentioned by many marathon runners. For them, wearable technology can help them significantly build up the rhythms of running. Edensor and Larsen [5] note that running rhythms is a body’s harmonious relation with the situated environments and the spatial-temporal arrangements in marathon running. These include but are not limited to the control of the speed, breathing, and pulse in accordance with particular phases/environments in marathon [34]. In this sense, wearable technology plays a crucial role in establishing the rhythms. For example, M5, a 23-year-old college student, explained to us the importance of the digitally-mediated rhythms in marathon running: Sometimes losing your rhythms (jiezhou, 节奏) of running would really affect your mood and lead to frustration . . . You need to know where you can speed up and where you should preserve your strength. You may face topographies during marathon, so you need to adjust your paces accordingly. The running watch can help you achieve this by offering you in-situ data. On the other hand, the construction of a quantified body also means that data and dig- ital technologies are not simply “tools” but rather an extension of the body. In other words, digital technologies have homeostatic autonomy that in turn conditions and disciplines human affective capacities [39]. To a certain extent, the digital normalises the disciplined bodies by quantitively guiding runners to overcome or be cautious of the whims (and basically laziness) of the self. As M15, a 28-year-old teacher, suggested, the data becomes an integral part of his body: I rely on the data stored in my watch. If I forget to wear it, I feel anxious when I am running, because the device can tell you the locations, track your footprints, and record your heartbeat. They are very important for a runner. Without these data, you may be in a dangerous condition that you don’t realise. So, mastering this information is also being responsible to your own body. Similarly, F4, a 35-year-old manager, highlighted her reliance on the wearable running devices and what counts as “scientific” running: Int. J. Environ. Res. Public Health 2022, 19, 43 9 of 13 If you run without the device, you can’t find the problems that may harm you. It’s unscientific. For example, you cannot know whether the strength from your two legs are equal. This may harm your legs if you don’t realise . . . Without the device, I will feel uncomfortable. M15 and F4’s running practices indicate that reliance on digital technologies may also lead to an emphasis on data over feelings, confidence, and corporeal sensations. In this sense, wearable technologies can offer a scientific approach to running but also simul- taneously structure and limit individuals’ perception and imagination of the embodied potential of the body. In a few situations, digital technologies may decrease the pleasure of running, as data do not always follow runners’ desires [31,32]. As a few correspondents noted, failing to achieve the expectations they set up (e.g., the amount of training) would always upset them. For example, F6 told us that unsatisfied data leads to a feeling of loss and frustration because it influences her confidence and rhythms in marathon running: “When the data shows that you didn’t finish the first half in the expected time, it will definitely thwart your confidence because the second half will be more challenging.” In this sense, the stress imposed by the data also took their focus away from the pleasure in running. Overall, the embodied and digital practices of marathon running are important “tech- nologies of the self” through which runners constitute a desired and scientific way of health. However, the relationship between body and wearable technology are always mutually constitutive: on the one hand, wearable technology can enhance the potential of the body that help runners to achieve self-betterment and self-exploration; on the other hand, the running body is also shaped and structured by both technological forces that may limit its agency. In what follows, we further elaborate on the collective running body and the formation of embodied atmospheres of health. 4.3. Atmospheric Experience of Health Marathon running is not an isolating sport but rather an atmospheric space in which different bodies, objects, and environments co-produce runners’ situated experience. Many marathon runners highlight that the “atmosphere” (qifen, 气氛) is an important source of their enjoyment that enables them to participate repeatedly. Drawing on the episte- mology of actor-network theory, we therefore reveal how the atmospheric space of health is constructed through runners’ interaction with other human bodies (e.g., runners and audiences), non-human objects, and situated environments/landscape (e.g., nature and weather). These atmospheres in turn shape runners’ embodied practices of health. Ac- cording to our coding, we particularly emphasise the ritualistic, aesthetic, and therapeutic nature of place/nature and how it contributes to generating the “affective atmosphere” of marathons. The affective atmospheres of marathons are co-produced by, for example, the opening ceremony, the chants and cheers from the audiences, the particular place/landscape that people run across, and the interactions of different runners. These collectively create a sense of ritual that distinguishes marathons from the ordinary. For example, M2, a 30-year-old IT developer, explained how he attains a strong sense of ritual in marathons: I think the atmosphere of a marathon is something that really puts you in motion. It make you excited immediately. This is quite different from the situations that you ran individually because you can’t feel these atmospheres and especially the sense of ritual—you feel like you are participating in a very especial event. Similarly, M5, a 22-year-old college student, recorded his accounts of the ritualised atmospheres in a marathon staged in Beijing: I have been to Beijing three times, but this time is quite different. The starting point of marathon was set up at Tiananmen Square. That really gave me a sense of spectacle and ritual. It made you felt that this particular moments and the spectacular architectures were exclusively designed for you . . . Int. J. Environ. Res. Public Health 2022, 19, 43 10 of 13 As Collins [40] (p. 340) notes, ritualised atmospheres or spaces are generated from the assemblage of the collective bodies in a physical attunement: “When human bodies are together in the same place, there is a physical attunement: currents of feeling, a sense of wariness or interest, a palpable change in the atmosphere.” This is particularly the case in marathons in which different bodies are immerged into a collective affective atmosphere. For example, F9, a 28-year-old company staff, described how this atmosphere serves as an affective force that pushes her body: When you went to that mood and atmosphere, you would never easily quit even though you were extremely tired. There were quite a lot people around you. Not matter how fast and slow you ran, there were always people that accompanied you. We called each other running fellows regardless of age and gender. We would encourage and take care each other on the road. So, there was an atmosphere there. The atmospheres of marathons are also formed through runners’ embodied encounter/ interaction with particular place, nature, landscape, and environmental conditions. When participants run across/through spaces, they also experience and attach meanings to the situated space/place around them. For example, M4, a 40-year-old teacher, considered marathon as a journey in which he can view the aesthetic landscape in different places across China. Yet, For M4, marathon is not simply a journey because it enables him to interact with the place in a mobile way that he cannot experience in normal tourism: Marathon is like a journey in that you can view different landscape and experi- ence different cultures in different places of China. But the difference [between marathon and travel] is that you are embracing the landscape while you are run- ning, you are using your foot to measure the land you ran through. For example, I participated a marathon in Yangzhou. That was in March, as the Chinese ancient poetry says: “In the mist and flowers of spring, I journeyed south to Yangzhou” (烟花三月下扬州). When I ran along the West Lake, I can feel the connection with this place. This experience was quite different from that of tourism visitors. M4’s experiences suggest that marathon running can be seem as the embodied encoun- ters with places. On the one hand, we acknowledge the body’s crucial roles in generating aesthetic experience of place [33]. Yet, on the other hand, the embodied encounter in marathon running is not simply a sensual and visionary “tourist gaze” [41] but rather a mo- bile practice that can engender new atmospheric and aesthetic perception of space/place. Many runners also emphasis the role of situated natural environments and especially weather in creating different atmospheric feelings in marathons. As Larsen and Jensen [37] (p. 1) argued, the atmospheres of running is also “mediated by the material sensations of what Ingold [42] terms ‘weather-worlds,’” as “people move in and through the air, sunshine, heat, rain, wind, snow, fog, or icy roads.” For example, M1, a 30-year-old manager, described in his diary how he attained a sense of purification and a therapeutic feeling when he was running in cold and rainy environments: It was a cold and rainy morning, around nine degrees Celsius. My body hadn’t warmed up even though I had ran away from the starting point for 20 min. When I ran across the city centre, it’s strange that I didn’t see the streets thronged with people and traffics as I expected. At this moment, the city hadn’t yet revived from the night time, peaceful and cool. This somewhat purified me and brought me peacefulness at that moment. As Schusterman [43] (p. 8) argued, “to focus on feeling one’s body is to foreground it against its environmental background, which must be somehow felt in order to constitute that experienced background.” It is through the situated environments that “an essentially situated, relational, and symbolic self” can be animated and felt. In this case, it is through the ritualistic, atmospheric, and environmental atmospheres from which the marathon, as a journey of self-exploration, is affectively engendered and constituted. The experience and Int. J. Environ. Res. Public Health 2022, 19, 43 11 of 13 subjectivity of health therefore emerge from these embodied encounters between body and space/place in marathon running. 5. Discussion In general, this paper suggests that the embodied space of health in marathons emerges through interaction of body, non-human objects (wearable technology), and atmospheres. As we see in Figure 1, the construction of running body provides marathon participants a way to build up the capacity of the body and to reclaim the autonomy of the body. This bodily capacity including not only the biophysical presence of health but also individuals’ project of self-exploration and self-realisation, which are achieved through mobile and disciplined running practice. Running also helps participants to escape from or resist to the patterned and programmatic “social body” so as to reclaim the autonomy of their bodies. Overall, the body can be viewed as a basic spatial unit through individuals act on themselves to attain healthy or desired being of the body. Figure 1. The model of the embodied space of health in marathon running. The qualitative data also show the ways that wearable technology interacts with body and space. Given the intimacy between body and technology, the self-tracking devices can be considered as the extended body of runners. These wearable technologies enhance runners’ bodily capacity by establishing a “quantified self” and by refiguring the ways they make sense of the spaces around them. Yet, wearable technology also in turn constrains runners’ bodily autonomy, as they heavily rely on data and technology to achieve self-betterment. Digital technology, as Esmonde noted, can induce runners to particular assumptions and expectations of their own: “that running should have a purpose beyond pleasure in movement that one can shape their body through data collection and the type of body towards which people aspire, and that improving one’s numbers by running faster and longer is a common-sense goal” [31] (p. 814). This is particularly the case in this paper: that these assumptions instead distract runner away from the pleasure in running. This paper also offers an account of the diffused and atmospheric spaces of health. We outline the ritualistic, aesthetic, and therapeutic atmospheres that emerge from the interactions between different bodies and between the body and situated environments in marathons. In this sense, we therefore argue that health experience is not simply constructed through biophysical (e.g., the medical definition of health) and discursive processes (e.g., healthism) but also can be captured through the lens of atmospheres. In general, drawing Int. J. Environ. Res. Public Health 2022, 19, 43 12 of 13 on an actor-network theory and the relational accounts of health, we suggest that the embodied space of health in marathon running is not simply bodily experience per se but rather is the relational space constituted through the interactive effects of body, technology, and atmospheres. 6. Conclusions In this paper, we have shown what is the “embodied space of health” in marathon running and how it is formed through the interactions between body, wearable technology, and space (especially the atmospheres). This paper advances the research of public and environmental health studies by offering a relational and non-representational approach to capturing the spatiality of health experiences. We argue that the effects of health emerges from the bodily, digitally mediated, and atmospheric experiences of running. Different from the research on health and place that tends to associate health with particular qualities of place, the “embodied space of health” highlights the relational and non-representational nature of health that emerges from the relationalities of different bodies and objects. We also note that through particular atmospheres in running, space becomes meaningful places such that the therapeutic effects of places are engendered. Yet, the limitations of this research are also noticeable. Our study cannot fully capture and understand the bodily and atmospheric experiences in running due to the limitation of interviews. Therefore, future studies can use innovative methods, such as mobile methods and qualitative GIS [44], to explore the more complex spatiality of health experience. Author Contributions: Conceptualization, Q.G., Y.O., and X.C.; methodology, Q.G. and J.L.; formal analysis, Y.O. and Q.G.; investigation, Y.O., J.L., and Q.G.; resources, Q.G.; writing—original draft preparation, Y.O.; writing—review and editing, Y.O. and X.C.; supervision, Q.G. and X.C.; project administration, Q.G.; funding acquisition, Q.G. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the National Natural Science Foundation of China, grant number 42071191 and 41829101. Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Newcastle University (protocol code 14813, 7 September 2017). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Conflicts of Interest: The authors declare no conflict of interest. References 1. Scheerder, J.; Breedveld, K.; Borgers, J. Running across Europe: The Rise and Size of One of the Largest Sport Markets; Palgrave Macmillan: Basingstoke, UK, 2015. 2. Janssen, M.; Walravens, R.; Thibaut, E.; Scheerder, J.; Brombacher, A.; Vos, S. Understanding Different Types of Recreational Runners and How They Use Running-Related Technology. Int. J. Environ. Res. Public Health 2020, 17, 2276. [CrossRef] [PubMed] 3. Allen Collinson, J.; Hockey, J. ‘Working out’ identity: Distance runners and the management of disrupted identity. Leis. Stud. 2007, 26, 381–398. [CrossRef] 4. Little, J. Running, health and the disciplining of women’s bodies: The influence of technology and nature. Health Place 2017, 46, 322–327. [CrossRef] 5. Edensor, T.; Larsen, J. Rhythmanalysing marathon running: ‘A drama of rhythms’. Environ. Plann. Econ. Space 2018, 50, 730–746. [CrossRef] 6. Shipway, R.; Holloway, I. Health and the running body Notes from an ethnography. Int. Rev. Sociol. Sport 2016, 51, 78–96. [CrossRef] 7. Hitchings, R.; Latham, A. How ‘social’is recreational running? Findings from a qualitative study in London and implications for public health promotion. Health Place 2017, 46, 337–343. [CrossRef] [PubMed] 8. Bridel, W.; Markula, P.; Denison, J. (Eds.) Endurance Running: A Socio-Cultural Examination; Routledge: London, UK, 2015. 9. Bell, S.L.; Foley, R.; Houghton, F.; Maddrell, A.; Williams, A.M. From therapeutic landscapes to healthy spaces, places and practices: A scoping review. Soc. Sci. Med. 2018, 196, 123–130. [CrossRef] 10. Milligan, C.; Gatrell, A.; Bingley, A. ‘Cultivating health’: Therapeutic landscapes and older people in northern England. Soc. Sci. Med. 2004, 58, 1781–1793. [CrossRef] Int. J. Environ. Res. Public Health 2022, 19, 43 13 of 13 11. Document Collection of China’s Marathon Development. Available online: http://www.athletics.org.cn/ (accessed on 17 Decem- ber 2021). 12. Ronkainen, N.J.; Shuman, A.; Ding, T.; You, S.; Xu, L. ‘Running Fever’: Understanding runner identities in Shanghai through turning point narratives. Leis. Stud. 2018, 37, 211–222. [CrossRef] 13. Zhang, L. Afterword: Flexible Postsocialist Assemblages from the Margin. Positions East Asia Cult. Crit. 2012, 20, 659–667. [CrossRef] 14. Gioia, D.A.; Corley, K.G.; Hamilton, A.L. Seeking Qualitative Rigor in Inductive Research: Notes on the Gioia Methodology. Organ. Res. Methods 2013, 16, 15–31. [CrossRef] 15. United Kingdom Department of Health. Choosing Health? Choosing Activity: A Consultation on How to Increase Physical Activity; Department of Health: London, UK, 2004. 16. Borgers, J.; Vanreusel, B.; Vos, S.; Forsberg, P.; Scheerder, J. Light sport facilities to foster sports participation? A case study on the use of bark running tracks. Int. J. Sport Policy 2015, 8, 287–304. [CrossRef] 17. Hespanhol Junior, L.C.; Pillay, J.D.; van Mechelen, W.; Verhagen, E. Meta-analyses of the effects of habitual running on indices of health in physically inactive adults. Sport Med. 2015, 45, 1455–1468. [CrossRef] 18. Crawford, R. Health as meaningful social practice. Health 2006, 10, 401–420. [CrossRef] [PubMed] 19. Cook, S.; Shaw, J.; Simpson, P. Jography: Exploring Meanings, Experiences and Spatialities of Recreational Road-running. Mobilities 2016, 11, 744–769. [CrossRef] 20. Maller, C.; Nicholls, L.; Strengers, Y. Understanding the Materiality of Neighbourhoods in ‘Healthy Practices’: Outdoor Exercise Practices in a New Masterplanned Estate. Urban Policy Res. 2016, 34, 55–72. [CrossRef] 21. Qviström, M.; Fridell, L.; Kärrholm, M. Differentiating the time-geography of recreational running. Mobilities 2020, 15, 575–587. [CrossRef] 22. Howe, P.D.; Morris, C. An exploration of the co-production of performance running bodies and natures within “running taskscapes”. J. Sport Soc. 2009, 33, 308–330. [CrossRef] 23. Stevinson, C.; Wiltshire, G.; Hickson, M. Facilitating Participation in Health-Enhancing Physical Activity: A Qualitative Study of Parkrun. Int. J. Behav. Med. 2015, 22, 170–177. [CrossRef] [PubMed] 24. Ettema, D. Runnable Cities: How Does the Running Environment Influence Perceived Attractiveness, Restorativeness, and Running Frequency? Environ. Behav. 2016, 48, 1127–1147. [CrossRef] 25. Conradson, D. Landscape, care and the relational self: Therapeutic encounters in rural England. Health Place 2005, 11, 337–348. [CrossRef] 26. Cai, X.; Woods, O.; Gao, Q. Running bodies and the affective spaces of health in and beyond marathon running in China. Health Place 2021, 70, 102612. [CrossRef] [PubMed] 27. Cummins, S.; Curtis, S.; Diez-Roux, A.V.; Macintyre, S. Understanding and representing ‘place’ in health research: A relational approach. Soc. Sci. Med. 2007, 65, 1825–1838. [CrossRef] 28. Parr, H. New body-geographies: The embodied spaces of health and medical information on the Internet. Environ. Plann. Soc. Space 2002, 20, 73–95. [CrossRef] 29. Hanold, M.T. Beyond the Marathon: (De)Construction of Female Ultrarunning Bodies. Sociol. Sport J. 2010, 27, 160–177. [CrossRef] 30. Wilson, S. Digital technologies, children and young people’s relationships and self-care. Child. Geogr. 2016, 14, 282–294. [CrossRef] 31. Esmonde, K. Training, tracking, and traversing: Digital materiality and the production of bodies and/in space in runners’ fitness tracking practices. Leis. Stud. 2019, 38, 804–817. [CrossRef] 32. Lupton, D. Digital companion species and eating data: Implications for theorising digital data–human assemblages. Big Data Soc. 2016, 3, 1–5. [CrossRef] 33. Nettleton, S. Fell runners and walking walls: Towards a sociology of living landscapes and aesthetic atmospheres as an alternative to a Lakeland picturesque. Br. J. Sociol. 2015, 66, 759–778. [CrossRef] [PubMed] 34. Larsen, J. ‘Running on sandcastles’: Energising the rhythmanalyst through nonrepresentational ethnography of a running event. Mobilities 2019, 14, 561–577. [CrossRef] 35. McCormack, D. Engineering affective atmospheres on the moving geographies of the 1897 Andre Expedition. Cult. Geogr. 2008, 15, 413–430. [CrossRef] 36. Anderson, B. Encountering Affect: Capacities, Apparatuses, Conditions; Routledge: London, UK, 2017. 37. Larsen, J.; Jensen, O.B. Marathon Running in the ‘Weather’. In Weather: Spaces, Mobilities and Affects; Barry, B.E., Ed.; Routledge: London, UK, 2020. 38. Gehman, J.; Glaser, V.L.; Eisenhardt, K.M.; Gioia, D.; Langley, A.; Corley, K.G. Finding Theory-Method Fit: A Comparison of Three Qualitative Approaches to Theory Building. Soc. Sci. Electron. Publ. 2017, 27, 284–300. [CrossRef] 39. Ash, J. Technology and affect: Towards a theory of inorganically organised objects. Emot. Space Soc. 2015, 14, 84–90. [CrossRef] 40. Collins, R. Interaction Ritual Chains; Princeton University Press: Princeton, NJ, USA, 2004. 41. Urry, J.; Larsen, J. The Tourist Gaze 3.0, 3rd ed.; Sage: London, UK, 2011. 42. Ingold, T. Footprints through the weather-world: Walking, breathing, knowing. J. R. Anthropol. Inst. 2010, 16, S121–S139. [CrossRef] 43. Schusterman, R. Body Consciousness. A Philosophy of Mindfulness and Someaesthetics; Cambridge University Press: Cambridge, UK, 2008. 44. Spinney, J. Close encounters? Mobile methods, (post) phenomenology and affect. Cult. Geogr. 2015, 22, 231–246. [CrossRef]

Investigating the "Embodied Spaces of Health" in Marathon Running: The Roles of Embodiment, Wearable Technology, and Affective Atmospheres.

12-21-2021

Ouyang, Yi,Cai, Xiaomei,Li, Jie,Gao, Quan

eng

PMC7662379

International Journal of Environmental Research and Public Health Article A Comparative Study on the Performance Profile of Under-17 and Under-19 Handball Players Trained in the Sports School System Tomasz Gabrys 1 , Arkadiusz Stanula 2,* , Subir Gupta 3, Urszula Szmatlan-Gabrys 4, Daniela Benešová 1 , Łukasz Wicha 5 and Jakub Baron 2 1 Department of Physical Education and Sport Science, Faculty of Pedagogy, University of West Bohemia, 301 00 Pilsen, Czech Republic; [email protected] (T.G.); [email protected] (D.B.) 2 Institute of Sport Science, The Jerzy Kukuczka Academy of Physical Education, Mikołowska 72A, 40-065 Katowice, Poland; [email protected] 3 Faculty of Medical Sciences, University of West Indies, 11000 Cave Hill, Barbados; [email protected] 4 Faculty of Rehabilitation, Department of Anatomy, University of Physical Education, 31-571 Krakow, Poland; [email protected] 5 Polish Handball Federation, Puławska 300 A, 02-819 Warszawa, Poland; [email protected] * Correspondence: [email protected]; Tel.: +48 207-53-33 Received: 26 September 2020; Accepted: 28 October 2020; Published: 30 October 2020



 Abstract: This study evaluates the anatomical profiles, jump, sprint, power outputs, endurance, and peak blood lactate levels ([LA]peak) of handball players of two age groups—U17 (n = 77) and U19 (n = 46)—and analyses the role of training in their physical abilities. Vertical jump performance was determined by counter movement jump (CMJ) and counter movement jump with free arms (CMJFA) tests. A running-based anaerobic sprint test (RAST) determined the relative power output (watts/kg body weight) and absolute power output (watts) of the players. Sprint performance over 5 m, 10 m, and 30 m distances was evaluated. An incremental shuttle run test (40 m) was designed to determine aerobic threshold (AeT), anaerobic threshold (AnT), and [LA]peak. All parameters were measured for pivots, wingers, backs, and goalkeepers of each group. The U19 players were significantly heavier than the U17 group, but both the groups were nearly equal in height. The U19 group jumped higher than the U17 members, although the only significant difference (p = 0.032) was observed between the wingers of the groups in CMJ. Sprint performance varied marginally between the groups and only U19 pivots were found to be significantly (for distances of 5, 10, and 30 m: p = 0.047, p = 0.018, and p = 0.021, respectively) faster than U17 pivots. No difference in relative power output between the groups was noted, although the U19 players recorded higher absolute power outputs. Maximal velocity and velocities at the AeT and AnT were almost similar in the groups. Distance covered by the groups at the intensities of AeT and AnT varied only little. Higher [LA]peak was observed in the U19 players. U19 players failed to convert their superior power into speed and jump. The training pattern of the handball players needs to be revised so that U19 players may develop faster and be more enduring than the U17 group. Keywords: power output; aerobic threshold; anaerobic threshold; peak blood lactate; jump tests 1. Introduction Selection and training systems of sports schools in Poland, coordinated by various sports bodies, including the Polish Handball Federation, have merits and demerits. Besides providing sports training to their athletes, sports schools in Poland focus on other social matters as well. The assessment of Int. J. Environ. Res. Public Health 2020, 17, 7979; doi:10.3390/ijerph17217979 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2020, 17, 7979 2 of 15 a player’s talent in team sports is based on three areas: motor skills and physique, mental health, and social features [1–3]. A common training program in the sports schools has its pros and cons. A common physical training program in sports schools across the country is likely to cause similar physiological adaptation in the adolescents and young players, although individual development factor plays an important steering force in their overall development. However, a similar training pattern does not create opportunities to implement sports-specific training programs [4–6]. Handball players require specific training that allows them to perform cyclic and acyclic activities efficiently during 60 min of match play. Like many other team sports, the movement pattern of handball players during match play is intermittent, intense, and varies widely in phases of defense and attack [7,8]. Apart from physical training, the performance of a handball player is influenced by anthropometric, physiological, and kinematic factors, like many other team sports [9–11]. Match-specific fitness of a handball player can be evaluated by a number of well-designed field tests [12]. The physical and physiological characteristics of handball players of various levels have been studied extensively by researchers [13–15]. These are concerned with aerobic capacity, anaerobic endurance, and anaerobic power determined by sprint run, jump, and throw [13–15]. The differences between youth (16–19 year) handball players of various levels have been documented by researchers [16,17]. Handball matches are played at the intensity range of 65 to 85% VO2max and at a blood lactate concentration of 3 to 11 mmol/L. The VO2max of the youth (16–21 year) handball players varies from 50 to 65 mL/kg/min, which partly depends on their position of play. The blood lactate concentration in same group of handball players after a run ramp test for the exhaustion is 10–12 mmol/L [14,18–22]. The endurance capability of athletes is commonly assessed by measuring their performance or running speed at the level of anaerobic threshold (AnT), which reflects the running economy and efficiency of the player. The VO2max reflects the endurance potential of a player and is not as important a marker of economy of run as the AnT [23,24]. In spite of the dominance of aerobic metabolism, the sport of handball is interspersed by high intensity activities like jumps, throws, changes of direction, and stops that greatly tax anaerobic metabolism [18,23]. The load imposed on the players is determined mostly by the demand of the game. Much of the movement during a handball game takes place in quick succession, with brief rest or slow movement in between. Although these activities are anaerobic in nature, the need to repeat them frequently demands a high level of aerobic capacity. Endurance training is designed to delay the appearance of fatigue of the players, during both training and match-play [19,25]. Handball is a game with a large number of explosive movements such as accelerations, turns, jumps, and throws [4]. Therefore, in assessing a player’s progress, it is very important to measure the anaerobic capacity of the player. Sprint, jump, and throw are commonly measured to assess the anaerobic power of a handball player [26–28]. Studies [29,30] show that CMJ values in the groups of handball players aged 16–19 do not show significant progression similar to the mean power in the RAST test [29,31]. However, to the best of our knowledge, no studies have been conducted so far that evaluated the physical and physiological profiles of age-group handball players in the sports schools of Poland. The age of 17–19 years in the development of handball players is a period of transition from junior handball to the requirements for handball players in senior teams, as indicated by the values of motor preparation indicators recorded in other studies [14,15,29,32]. It should be expected that, during this period, there will be a significant development of motor skills such as endurance, relative power, and running speed. The sports schools of Poland still follow the guidelines for the selection of players and their training set by the concerned authority decades before. A review of the selection criteria and training programs is absolutely essential, especially in the light of recent advancement of sports science and training. The aim of the present study is to revise the selection criteria and efficacy of training of the U17 and U19 handball players of the Polish Handball Federation Sports Schools, by comparing anthropometric profiles and explosive power, sprinting ability, and selected physiological characteristics. Int. J. Environ. Res. Public Health 2020, 17, 7979 3 of 15 2. Materials and Methods 2.1. Participants A total of 133 male handball players, all of whom were students of the Polish Handball Federation Sports Schools at Gda´nsk, Kielce, Kwidzyn, and Płock, participated in this study. Players were divided into two categories: U17 (age: 15 to <17 years) and U19 (age: 17 to <19 years). In each age group, the players were divided according to the position: pivots (U17: n = 7, U19: n = 10), wingers U17: n = 8, U19: n = 22, backs (U17: n = 26, U19: n = 42), and goalkeepers (U17: n = 5, U19: n = 13). All of the U19 and some of the U17 players were selected for the Junior Polish National Team. Body weight and height of the participants and their playing positions are presented in Table 1. All subjects (in the case of 18+ years) or their parents or legal guardians (in the case of <18 years) provided their written consent to participate in this study after being informed of all procedures and risks involved in this study. They were in good health and reported no injuries and infections at the time of the study. The study was conducted during a scheduled training week, before competitive session. Table 1. Physical characteristics of handball players across their playing positions. Indicator Position U19 U17 Mean Difference (%) p-Value Effect Size Weight (kg) P 101.9 ± 11.3 92.4 ± 14.93 9.52 (9.3%) 0.176 0.70/Moderate W 80.5 ± 3.10 69.4 ± 4.89 11.13 (13.8%) <0.001 2.47/Very large B 86.5 ± 10.92 78.5 ± 8.71 7.99 (9.2%) 0.003 # 0.83/Moderate GK 92.8 ± 11.69 81.6 ± 8.07 11.15 (12.0%) 0.033 1.22/Large Height (cm) P 190.3 ± 4.89 192.3 ± 6.06 −2.01 (−1.1%) 0.478 0.36/Small W 183.4 ± 4.07 180.5 ± 7.48 2.92 (1.6%) 0.307 0.43/Small B 188.7 ± 5.60 187.0 ± 6.68 1.71 (0.9%) 0.281 0.27/Small GK 189.6 ± 5.32 188.3 ± 4.64 1.29 (0.7%) 0.554 # 0.27/Small Note: P—pivots; W—wingers; B—backs; GK—goalkeepers; # a nonparametric test was used to compare the groups. The whole experiment was divided in two sessions and conducted over two days. The two experimental sessions were separated by approximately 24 h. Subjects were instructed to refrain from all sorts of caffeine ingestion 48 h before tests. In the first session, all subjects performed a countermovement jump (CMJ) test and countermovement jump with free arms (CMJFA) test, sprint run over a distance of 30 m, and running-based anaerobic sprint test (RAST), after 20 min of break. Both the jump and the sprint tests were performed twice and only the better result was analyzed for this study. In the second session, the endurance capability of the subjects was evaluated. The endurance test and the RAST, however, were performed once only. 2.1.1. Jump Tests All the subjects performed two jump tests—(1) counter movement vertical jump without arm swing (CMJ): participants were instructed to stand upright comfortably with hands on hips. They remained in this position for 3 s before the jump was performed. Following a verbal command, the players initiated a countermovement followed by a maximal vertical jump in one continuous motion. Participants were instructed to keep their hands on their hips throughout the jump, and their legs straight while in the air [33–35]. (2) Counter movement vertical jump with arm swing (CMJFA): this differs from the CMJ in that both the arms were allowed to move freely during the vertical jump [36]. Each of the jump tests was repeated twice, with a passive recovery of 1 min between the two, and the best result was recorded and analyzed. Optojump (Microgate, Srl., Bolzano, Italy) was used for the measurement of the jump height. Int. J. Environ. Res. Public Health 2020, 17, 7979 4 of 15 2.1.2. Sprint Test This test was preceded by a non-standardized 20 min warm-up. Subjects performed two 30 m sprint runs starting from a standing position. A 4 min interval with light active recovery separated two trials. Time was recorded using SMARTSPEED PRO time gate system (Fusion Sport, Brisbane, Australia). Time was measured at the 5 m, 10 m, and 30 m marks. Time measured for the 5 m and 10 m distances indicated the ability to quick start, whereas the speed achieved for the 30 m distance reflected the speed usually obtained during the transition from defense to attack phase in typical handball match play. 2.1.3. Running-Based Anaerobic Sprint Test (RAST) Anaerobic capacity was measured by a running-based anaerobic sprint test (RAST). It has been shown that this test can replace the Wingate test to estimate anaerobic power and capacity [37]. Each subject completed six 35 m sprint runs, with 10 s passive rest between two repetitions. Time was recorded using SMARTSPEED PRO time gate system (Fusion Sport, Brisbane, Australia). The power output (in watts) for each sprint was calculated according to the following equation: Anaerobic capacity (Watt) = Weight (kg) × Distance (m) 2 ÷ Time (s) 3 (1) Fatigue Index = [Maximum power (watts) − Minimum power (watts)] ÷ Total time (s) for the 6 sprints (2) The RAST gives an estimate of the neuromuscular and energy determinants of maximal anaerobic performance and is a simple, but very useful test used in team sports like handball, where running is an important component of activities [38]. 2.1.4. Endurance Test This is a multistage fitness test that determines endurance by an incremental shuttle run. This test was conducted on a synthetic surface in an indoor hall. The test involved continuous running between two lines 40 m apart [39]. Subjects started running at the speed of 8 km/h, which is increased by 1.5 km/h after every 3 min thereafter, until exhaustion. To assure the constant running speed, subjects were instructed to adjust their pace using audio signals. The audio signal was given at the end and at the middle of 40 m distance. The end of the test was considered when the participant twice failed to reach the front line in time (objective evaluation) or he felt not able to cover another shuttle at the dictated speed (subjective evaluation) [40]. During the test, heart rate was continuously recorded at an interval of 5 s using Polar Team2 Pro chest-worn heart rate monitors (Polar Electro, Kempele, Finland). Maximal heart rate (HRmax): the highest heart rate (HR) recorded at the exhaustive stage of the endurance test was considered as the HRmax. Collection of blood samples for the measurement of lactate concentration: after the end of each stage of run, the subjects were stopped for 30 s, during which 20 mL of capillary blood was collected from earlobe by pin-prick under aseptic conditions. Blood samples were also taken at the end of the test and at the 4th and 8th minutes of the recovery period. Blood lactate concentration ([LA]) was measured using a lactate analyzer (Biosen C-Line EKF Diagnostic GmbH, Magdeburg, Germany). Peak blood lactate concentration ([LA]peak): the highest [LA] recorded following the end of the test was recorded as the [LA]peak. Determination of aerobic threshold (AeT): the running intensity at which the [LA] increased by 0.5 mmol/L was marked as the AeT [41]. Determination of anaerobic threshold (AnT): the running speed at the [LA] of 4 mmol/L was considered as the AnT of the player [42]. Int. J. Environ. Res. Public Health 2020, 17, 7979 5 of 15 The total distance of run, completed by the subject in the endurance test, was divided into three intensity zones—(a) zone 1: up to AeT, (b) zone 2: above AeT to AnT, and (c) zone 3: above the AnT level. 2.2. Statistical Analyses Mean and standard deviation were used to represent the average and the typical spread of values of all the measured variables. The normal Gaussian distribution of the data was verified by the Shapiro–Wilk’s test. If the data were normally distributed within groups, an independent samples t-test was used to test the differences between U17 and U19. If the data were not normally distributed, a Mann–Whitney U-test was used. Two separate one-way analyses of variance with a Tukey post-hoc test were used to determine whether and where differences existed in the all measured variables between the playing positions in each age group. The effect size (ES) of the intervention was calculated using Cohen’s guidelines. Threshold values for ES were >0.2 (small), >0.6 (moderate), >1.2 (large), and >2.0 (very large) [43]. Statistical significance was set at p ≤ 0.05. All calculations were performed with STATISTICA ver. 13.3 (TIBCO Software Inc., Palo Alto, CA, USA). 3. Results 3.1. Body Weight and Height Body weight and height of the U17 and U19 players are presented in Table 1. Weight and height between the U17 and U19 players for all position of play are compared. No statistically significant difference in body height between U17 and U19 players was noted. Under 19 players, however, were heavier than their U17 counterparts by 9.2 to 13.8% and the difference was significant in all cases, except in pivots. 3.2. Vertical Jump Performance Figure 1 presents the jump performance of the players. It compares the performance of the U19 and U17 players of each playing position. Superior jumping skill was demonstrated by the U19 players compared with the U17 players, for any given position of play, although the difference was non-significant in most of the cases. Wingers of the U19 group demonstrated significantly higher CMJ ability than the U17 players. As expected, CMJFA score was higher than all the respective cases of CMJ. Int. J. Environ. Res. Public Health 2020, 17, x 5 of 15 difference in body height between U17 and U19 players was noted. Under 19 players, however, were heavier than their U17 counterparts by 9.2 to 13.8% and the difference was significant in all cases, except in pivots. Table 1. Physical characteristics of handball players across their playing positions. Indicator Position U19 U17 Mean Difference (%) p-Value Effect Size Weight (kg) P 101.9 ± 11.3 92.4 ± 14.93 9.52 (9.3%) 0.176 0.70/Moderate W 80.5 ± 3.10 69.4 ± 4.89 11.13 (13.8%) <0.001 2.47/Very large B 86.5 ± 10.92 78.5 ± 8.71 7.99 (9.2%) 0.003 # 0.83/Moderate GK 92.8 ± 11.69 81.6 ± 8.07 11.15 (12.0%) 0.033 1.22/Large Height (cm) P 190.3 ± 4.89 192.3 ± 6.06 −2.01 (−1.1%) 0.478 0.36/Small W 183.4 ± 4.07 180.5 ± 7.48 2.92 (1.6%) 0.307 0.43/Small B 188.7 ± 5.60 187.0 ± 6.68 1.71 (0.9%) 0.281 0.27/Small GK 189.6 ± 5.32 188.3 ± 4.64 1.29 (0.7%) 0.554 # 0.27/Small Note: P—pivots; W—wingers; B—backs; GK—goalkeepers; # a nonparametric test was used to compare the groups. 3.2. Vertical Jump Performance Figure 1 presents the jump performance of the players. It compares the performance of the U19 and U17 players of each playing position. Superior jumping skill was demonstrated by the U19 players compared with the U17 players, for any given position of play, although the difference was non-significant in most of the cases. Wingers of the U19 group demonstrated significantly higher CMJ ability than the U17 players. As expected, CMJFA score was higher than all the respective cases of CMJ. Figure 1. Test results of counter movement jump (CMJ) and counter movement jump free arms (CMJFA) of the participants. (P—pivots; W—wingers; B—backs; GK—goalkeepers; * p < 0.05). 3.3. Sprint Performance Sprint performance (time) of the participant handball players, for various distance marks, as well as differences in performance between U17 and U19 groups, and the p-values and the ES of the difference, are presented in Table 2. The players of all positions of the U19 group outperformed the e e ti e U17 laye i both the 10 a d 30 i t u althou h the diffe e e a i ifi a t Figure 1. Test results of counter movement jump (CMJ) and counter movement jump free arms (CMJFA) of the participants. (P—pivots; W—wingers; B—backs; GK—goalkeepers; * p < 0.05). Int. J. Environ. Res. Public Health 2020, 17, 7979 6 of 15 3.3. Sprint Performance Sprint performance (time) of the participant handball players, for various distance marks, as well as differences in performance between U17 and U19 groups, and the p-values and the ES of the difference, are presented in Table 2. The players of all positions of the U19 group outperformed the respective U17 players in both the 10 m and 30 m sprint runs, although the difference was significant only in the case of pivots. On the other hand, except in the case of pivots, the U17 group members in the 5 m sprint demonstrated better performance than their U19 counterparts. Table 2. Sprint performance of the U17 and U19 participant handball players. Distance (m) Position Sprint Time (Seconds) Mean Difference (%) p-Value Effect Size U19 U17 5 m P 1.06 ± 0.04 1.10 ± 0.03 −0.03 (−3.3%) 0.045 # 1.05/Moderate W 1.08 ± 0.05 1.05 ± 0.05 0.03 (2.7%) 0.151 0.61/Moderate B 1.05 ± 0.03 1.05 ± 0.04 0.00 (0.4%) 0.717 0.11/Trivial GK 1.10 ± 0.03 1.08 ± 0.06 0.02 (1.5%) 0.574 0.29/Small 10 m P 1.80 ± 0.07 1.89 ± 0.06 −0.09 (−4.72%) 0.017 # 1.32/Large W 1.77 ± 0.07 1.78 ± 0.07 −0.01 (−0.6%) 0.721 0.15/Trivial B 1.76 ± 0.04 1.78 ± 0.07 −0.02 (−1.1%) 0.18 0.34/Small GK 1.83 ± 0.07 1.84 ± 0.08 −0.01 (−0.8%) 0.721 0.18/Trivial 30 m P 4.38 ± 0.13 4.62 ± 0.23 −0.25 (−5.6%) 0.007 # 1.27/Large W 4.21 ± 0.12 4.31 ± 0.16 −0.10 (−2.4%) 0.113 0.67/Moderate B 4.26 ± 0.13 4.32 ± 0.17 −0.06 (−1.3%) 0.142 0.37/Small GK 4.43 ± 0.24 4.48 ± 0.16 −0.05 (−1.1%) 0.625 0.26/Small Note: P—pivots, W—wingers, B—backs, GK—goalkeepers; # a nonparametric test was used to compare the groups. 3.4. Running-Based Anaerobic Sprint Test (RAST) Maximum power (Pmax), minimum power (Pmin), average power (Pav), and fatigue index (FI), recorded in RAST, are presented in Table 3. All the power outputs are expressed in watts/kg body weight in this table. All the power outputs—Pmax, Pmin, and Pav—of U19 outfield players exceeded the power outputs of U17 players of similar positions, although the difference in none of the cases was found to be significant. Only goalkeepers of the U17 group demonstrated higher Pmin and Pav than the U19 goalkeepers, although the ES was only trivial. Wingers of both the U19 and U17 groups showed higher Pmax and Pav than other players of their own group. No significant difference of FI between the groups was found and the ES varied from trivial to moderate. Table 4 shows the power output of the players determined by RAST, but the power output in this table is expressed in watts. All the power outputs (Pmax, Pmin, and Pav) of the older age group (U19) were recorded higher than those of the younger group (U17) of handball players and the difference was significant in all the cases, except Pmin between the goalkeepers. The ES varied from moderate to large. Table 3. Power output (watts/kg body weight) and fatigue index (FI) in handball players as determined by the running-based anaerobic sprint test (RAST). Indicator Position U19 U17 Mean Difference (%) p-Value Effect Size Pmax (Watts/kg body weight) P 8.05 ± 0.84 7.02 ± 1.13 1.03 (12.8%) 0.060 1.00/Moderate W 9.66 ± 0.95 9.07 ± 1.47 0.59 (6.1%) 0.301 0.44/Small B 9.17 ± 0.95 8.96 ± 1.36 0.21 (2.3%) 0.499 0.17/Trivial GK 8.30 ± 1.15 7.89 ± 0.93 0.41 (4.9%) 0.440 0.42/Small Int. J. Environ. Res. Public Health 2020, 17, 7979 7 of 15 Table 3. Cont. Indicator Position U19 U17 Mean Difference (%) p-Value Effect Size Pmin (Watts/kg body weight) P 5.70 ± 0.94 5.07 ± 0.91 0.63 (11.1%) 0.183 0.69/Moderate W 6.68 ± 0.64 6.13 ± 1.17 0.55 (8.3%) 0.218 0.52/Small B 6.57 ± 0.78 6.37 ± 0.99 0.2 (3.1%) 0.383 0.22/Small GK 5.47 ± 0.88 5.63 ± 0.90 −0.16 (−2.9%) 0.742 0.18/Trivial Pav (Watts/kg body weight) P 6.69 ± 0.77 5.88 ± 0.98 0.81 (12.1%) 0.088 0.90/Moderate W 7.98 ± 0.74 7.62 ± 1.05 0.35 (4.4%) 0.391 0.36/Small B 7.71 ± 0.81 7.57 ± 1.12 0.14 (1.9%) 0.574 0.14/Trivial GK 6.67 ± 1.05 6.75 ± 0.87 −0.08 (−1.2%) 0.874 0.08/Trivial FI P 29.24 ± 8.66 27.79 ± 7.02 1.44 (4.9%) 0.709 0.19/Trivial W 30.60 ± 5.11 32.71 ± 14.99 −2.11 (−6.9%) 0.702 0.16/Trivial B 28.27 ± 5.73 28.57 ± 7.68 −0.3 (−1.1%) 0.865 0.04/Trivial GK 34.17 ± 4.23 28.82 ± 6.36 5.35 (15.7%) 0.104 0.91/Moderate Note: P—pivots; W—wingers; B—backs; GK—goalkeepers; FI—fatigue index. Table 4. Absolute power (watts) produced in the players of various playing positions, recorded in RAST. Indicator Position U19 U17 Mean Difference (%) p-Value Effect Size Pmax (Watts) P 798.2 ± 125.4 641.8 ± 112.9 156.4 (19.6%) 0.017 1.33/Large W 807.5 ± 121.9 630.9 ± 121.5 176.6 (21.9%) <0.001 # 1.45/Large B 784.4 ± 98.2 701.2 ± 122.2 83.2 (10.6%) 0.005 0.73/Moderate GK 777.9 ± 102.0 641.1 ± 80.1 136.8 (17.6%) 0.008 1.59/Large Pmin (Watts) P 561.5 ± 88.9 459.6 ± 65.5 101.9 (18.2%) 0.015 1.35/Large W 556.8 ± 62.9 425.9 ± 89.5 130.9 (23.5%) 0.001 1.57/Large B 563.1 ± 85.3 497.0 ± 79.8 66.0 (11.7%) 0.002 0.81/Moderate GK 511.4 ± 66.2 455.7 ± 65.1 55.6 (10.9%) 0.125 0.85/Moderate Pav (Watts) P 662.6 ± 100.9 534.7 ± 80.1 127.9 (19.3%) 0.011 1.44/Large W 666.4 ± 92.2 529.8 ± 86.6 136.6 (20.5%) 0.001 # 1.55/Large B 661.0 ± 91.6 591.6 ± 96.1 69.5 (10.5%) 0.004 0.74/Moderate GK 623.8 ± 78.6 547.3 ± 64.9 76.5 (12.3%) 0.050 1.12/Moderate Note: P—pivots; W—wingers; B—backs; GK—goalkeepers; # a nonparametric test was used to compare the groups. 3.5. Endurance Performance Maximum velocity (Vmax), velocity at the aerobic threshold (VAeT), and the velocity of the players at the intensity of anaerobic threshold (VAnT), determined in endurance test, are presented in Figure 2. The U19 players of most of the playing positions showed higher Vmax, VAeT, and VAnT. When compared between the age groups (U19 and U17), the difference of the velocity for any given intensity was only marginal, no significant difference was observed in any of the cases, and the ES varied only from trivial to small. The distance covered by the players at three different intensity zones is shown in Figure 3. The total distance covered (DTOTAL) is the arithmetic sum of the distance covered up to the aerobic threshold (DAeT) and the distance covered up to the intensity of anaerobic threshold (DAnT) (but higher than the AeT level). Like velocity, the difference of distance covered for any of the three given intensities between the groups (U19 and U17) was marginal again. Only DTOTAL of the U19 backs was found to be significantly higher than that of the U17 backs. Table 5 shows HRmax% at the intensities of AeT (HRAeT) and AnT (HRAnT) of the participants. No significant difference in HRAeT and HRAnT between the age groups was found, except the HRAeT of the backs. Int. J. Environ. Res. Public Health 2020, 17, 7979 8 of 15 Maximum velocity (Vmax), velocity at the aerobic threshold (VAeT), and the velocity of the players at the intensity of anaerobic threshold (VAnT), determined in endurance test, are presented in Figure 2. The U19 players of most of the playing positions showed higher Vmax, VAeT, and VAnT. When compared between the age groups (U19 and U17), the difference of the velocity for any given intensity was only marginal, no significant difference was observed in any of the cases, and the ES varied only from trivial to small. Figure 2. Velocity of the players at three intensity zones. The distance covered by the players at three different intensity zones is shown in Figure 3. The total distance covered (DTOTAL) is the arithmetic sum of the distance covered up to the aerobic threshold (DAeT) and the distance covered up to the intensity of anaerobic threshold (DAnT) (but higher than the AeT level). Like velocity, the difference of distance covered for any of the three given intensities between the groups (U19 and U17) was marginal again. Only DTOTAL of the U19 backs was found to be significantly higher than that of the U17 backs. Figure 2. Velocity of the players at three intensity zones. Int. J. Environ. Res. Public Health 2020, 17, x 8 of 15 Figure 3. Distance covered by the players at different intensity zones in the endurance test. Table 5 shows HRmax% at the intensities of AeT (HRAeT) and AnT (HRAnT) of the participants. No significant difference in HRAeT and HRAnT between the age groups was found, except the HRAeT of the backs. Table 5. Internal load parameters of the subjects. Indicator Position U19 U17 Mean Difference (%) p-Value Effect Size HRmax [beats/min] P 195.4 ± 8.72 199.1 ± 5.93 N/A N/A N/A W 196.4 ± 5.29 200.3 ± 6.7 N/A N/A N/A B 196.0 ± 10.13 195.6 ± 5.81 N/A N/A N/A GK 193.6 ± 5.03 199.4 ± 3.66 N/A N/A N/A HRAnT [%HRmax] P 91.8 ± 2.79 89.6 ± 4.18 2.20 (2.4%) 0.244 0.60/Small W 91.6 ± 3.20 91.0 ± 4.09 0.58 (0.6%) 0.720 0.15/Trivial B 89.8 ± 4.86 91.0 ± 3.26 −1.21 (−1.4%) 0.227 0.31/Small GK 90.1 ± 3.89 91.5 ± 2.93 −1.38 (−1.5%) 0.425 0.43/Small HRAeT [%HRmax] P 83.4 ± 1.63 82.2 ± 4.90 1.20 (1.4%) 0.770 # 0.31/Small W 82.6 ± 4.09 84.5 ± 4.30 −1.88 (−2.3%) 0.292 0.44/Small B 80.8 ± 5.06 83.5 ± 3.83 −2.72 (−3.4%) 0.015 0.63/Moderate GK 82.6 ± 2.18 82.6 ± 3.99 −0.01 (−0.01%) 0.997 0/Trivial Note: P—pivots; W—wings; B—backs; GK—goalkeeper; N/A—not appropriate; # a nonparametric test was used to compare the groups. Figure 4 presents [LA]peak of the subjects recorded after the end of the endurance test. The players of all the positions of U19 demonstrated higher [LA]peak than their U17 counterparts, although the difference was statistically significant only between the backs (p < 0.001) and the pivots (p = 0.037) of the two groups. Figure 3. Distance covered by the players at different intensity zones in the endurance test. Table 5. Internal load parameters of the subjects. Indicator Position U19 U17 Mean Difference (%) p-Value Effect Size HRmax [beats/min] P 195.4 ± 8.72 199.1 ± 5.93 N/A N/A N/A W 196.4 ± 5.29 200.3 ± 6.7 N/A N/A N/A B 196.0 ± 10.13 195.6 ± 5.81 N/A N/A N/A GK 193.6 ± 5.03 199.4 ± 3.66 N/A N/A N/A HRAnT [%HRmax] P 91.8 ± 2.79 89.6 ± 4.18 2.20 (2.4%) 0.244 0.60/Small W 91.6 ± 3.20 91.0 ± 4.09 0.58 (0.6%) 0.720 0.15/Trivial B 89.8 ± 4.86 91.0 ± 3.26 −1.21 (−1.4%) 0.227 0.31/Small GK 90.1 ± 3.89 91.5 ± 2.93 −1.38 (−1.5%) 0.425 0.43/Small HRAeT [%HRmax] P 83.4 ± 1.63 82.2 ± 4.90 1.20 (1.4%) 0.770 # 0.31/Small W 82.6 ± 4.09 84.5 ± 4.30 −1.88 (−2.3%) 0.292 0.44/Small B 80.8 ± 5.06 83.5 ± 3.83 −2.72 (−3.4%) 0.015 0.63/Moderate GK 82.6 ± 2.18 82.6 ± 3.99 −0.01 (−0.01%) 0.997 0/Trivial Note: P—pivots; W—wings; B—backs; GK—goalkeeper; N/A—not appropriate; # a nonparametric test was used to compare the groups. Figure 4 presents [LA]peak of the subjects recorded after the end of the endurance test. The players of all the positions of U19 demonstrated higher [LA]peak than their U17 counterparts, although the difference was statistically significant only between the backs (p < 0.001) and the pivots (p = 0.037) of the two groups. Int. J. Environ. Res. Public Health 2020, 17, 7979 9 of 15 test was used to compare the groups. Figure 4 presents [LA]peak of the subjects recorded after the end of the endurance test. The players of all the positions of U19 demonstrated higher [LA]peak than their U17 counterparts, although the difference was statistically significant only between the backs (p < 0.001) and the pivots (p = 0.037) of the two groups. Figure 4. Peak blood lactate [LA]peak concentrations in the participants determined in the endurance test (* p < 0.05, **** p < 0.001). Figure 4. Peak blood lactate [LA]peak concentrations in the participants determined in the endurance test (* p < 0.05, **** p < 0.001). 4. Discussion Handball players need specific training that allows them to do multiple and complex physical tasks successfully. Like many other team sports, age, training, skill, and playing position serve important roles in developing efficiency in handball players [8,44]. The requirements for playing in each position are determined by the appropriate physique, absolute power, and physiological profile of the player [45]. This, in turn, sets appropriate training demands for players of each playing position, and thus can differentiate a winger from a pivot or a back [15,25,46]. The key findings of this study are that the explosive power of the U19 players was superior to that of the U17 players, which resulted in better jump performance, but failed to improve speed in the U19 group. The training program in the sports schools of Poland was unsuccessful in the improvement of endurance in U19 players. 4.1. Pivots Pivots experience more physical confrontations than players of any other positions, against the opponent team members, during a game. As a result, strength and explosive power are some of the primary requirements for pivots [6,29]. This is evident from the body weight and height of the U19 and U17 groups, where U19 pivots are nearly 10% heavier than pivots of the U17 group, but the difference in height between the two is almost absent (~1%). Another important requirement for pivots is the ability to run fast on a longer available space of the court (e.g., the 15–30 m segment of the sprint test in this study), which is especially very useful in counterattack. Fast attack and defense in quick succession, jumps, and powerful throws during game require appropriate anaerobic training of the players, especially for pivots [47]. A high training load elevates the serum growth hormone and testosterone in puberty and adolescence, which modulate muscle development and power. Training of high intensity and long duration act as appropriate stimuli that favor to improve or maintain body stature and power [48]. Raspberry and Bouchard [49] have shown that resistance training can be carried out to a maximum load of 80% without appreciable risk of injury at this age. According to Gorostiaga et al. [50], a training program including high resistance exercises with slow movements that favor muscle hypertrophy hinders the development of explosive power, and thus favors sprinting ability. There was no difference in explosive power between the U17 and U19 groups as reflected by CMJ and CMJFA performance. Pivots of the U19 group showed significantly higher sprinting performance than the U17 pivots for all the distance marks (5 m, 10 m, and 30 m). This change of direction ability in pivot players was also reported in other studies [47,51]. The ability of change of direction with maximum power in pivots largely occurs between the ages of 16 and 19 years [52]. In all power ratings, U19s dominate over U16s. This is particularly evident when the power output was expressed in watts/kg body weight [31,38]. Int. J. Environ. Res. Public Health 2020, 17, 7979 10 of 15 In case of the pivots, the anaerobic power generated was largely transferred to external loads, as noted by Krüger et al. [19]. This is partly supported by the fact that the [LA]peak of the pivots of U19s was ~23% higher than that of the younger (U17) group of players. However, the Vmax, VAeT and VAnT of the U19 pivots were only marginally higher than those of the pivots of the U17 group. 4.2. Wingers Jump, sprint; explosive strength; ability to accelerate, especially between 20 and 30 m distance; and reasonably high aerobic power are some of the key requirements for successful wingers [51,53]. There was no difference in body height between the age groups, although weight increased significantly (13.8%, p < 0.001; ES = very large) in the higher age group (U19). With a significant increase in body weight, a significant increase in anaerobic power and explosive strength is expected in U19 wingers compared with their U17 counterparts. However, an increase in muscle mass in U19, if any, and its role in the improvement of explosive strength requires further study. In the studied groups, no significant difference in speed over the distance of 5 to 10 m was found. The difference in explosive power is clearly visible in the CMJ results (p = 0.032) between the two age groups. The height of CMJ is higher by 11% and CMJFA by 9% in the U19 group. Thus, the increase in mass is accompanied by an increase in explosive power, but the increase in muscle mass did not support faster running in the U19 players. This observation is also supported by the fact that the absolute power (watts) output in the U19 players in RAST was significantly higher than that in the U17 group, but no such difference existed when the power was expressed in watts/kg body weight. While an increase in muscle mass is usually associated with an increase in power, there is no real difference in power with respect to the body mass in both age groups in this study. Improper training methods and loads probably failed to stimulate the relative increase in power in U19 players [54]. This was observed in other team sports as well [55,56]. The inclusion of agility in speed training brings much better results than repeated speed training or interval training in handball players [54,57,58]. Running economy in handball players, like many other team games, plays a crucial role in maintaining the higher intensity of work during match play without appreciable fatigue [59,60]. Running intensity above the AnT level resulted in more dependence on the anaerobic metabolism and decreased running economy of the players. The U19 players, except pivots, covered a longer distance above the AnT and produced higher [LA] than the participants of the U17 group. This suggests a decrease in the running economy in the U19 group compared with the U17 group [59]. The relationship between anthropometric parameters, including body weight and running speed, was pointed out by Kukoli et al. [61] and Young et al. [62]. The increase in absolute power generation without any change in relative power likely results from the increase in body weight of U19 players. The reason behind the lack of improvement of relative power in U19 players needs further study. 4.3. Backs One of the major requirements of backs is a high level of strength combined with muscle mass. The U19 backs demonstrated higher glycolytic capacity than the backs of U17 group, as reflected by the 23% higher [LA]peak, in spite of lower HRAeT in comparison with the U17 backs. The strength training is likely to improve the sprint, acceleration, and jumping and throwing abilities of the handball players. The longer distance covered by the backs during competitive match play needs reasonable aerobic training as well [19,53,63]. A significant difference in body weight (9%, p = 0.001; ES = moderate) of backs between the groups (U19 and U17) is noticeable, although no differences in explosive power and running speed were found between the groups in this study. No significant motor development beyond the age of 17 years is commonly found that would differentiate the U19 group from the U17 group, and this can explain why there is no significant difference in explosive power and running speed between the groups [47]. In backs, like pivots and wingers, power development in U19 players mainly results from an increase in muscle mass. A 10% increase in power is not enough to improve the speed significantly in U19 backs when compared with the backs of the U17 group. The total distance covered Int. J. Environ. Res. Public Health 2020, 17, 7979 11 of 15 (DTotal) by the backs of the U19 group exceeded the DTotal covered by the backs of the U17 group by 6%, mainly due to a much higher (>16%) DAnT, in spite of the lower DAeT. This again explains superiority of the anaerobic power in U19 backs in spite of compromised aerobic capacity. 4.4. Goalkeepers Besides game-specific skill, handball goalkeepers are trained for developing explosive power, which is a prerequisite to efficient and effective jumping, throwing the ball, and quick acceleration of movement in all possible directions [19,27,32]. The height of the goalkeepers of both the age groups, like outfield players, does not vary significantly [64]. However, U19 goalkeepers were heavier than their U17 counterparts. Probably, stronger muscles of the U19 members were responsible for their superior performance in CMJ and CMJFA when compared with the U17 group. In spite of an increase in the explosive power of leg muscles, which improved their jumping ability, the U19 players were not faster than the U17 group. Higher FI in the U19 group suggests that they were unable to maintain the desired speed in repetitive sprints in comparison with the U17 goalkeepers. Longer DTotal and DAeT by the U19 participants indicate higher aerobic capacity in U19 handball players than U17 players. More intense and frequent anaerobic training stimulated the anaerobic glycolytic system in the older group (U19) of handball players, and this was reflected by higher [LA]peak in these players than the U17 participants. The reason for the limitations of the motor development of goalkeepers of U19 may be slowing down of biological maturity after 17 years of age [65]. 5. Conclusions The aim of this cross-sectional study was to compare physical fitness and some physiological characteristics of U17 and U19 Polish handball players with special reference to their position of play. Players of both the groups were equally taller, but the U19 members were significantly heavier than the U17 participants. Players of all the U19 positions dominate over the U17 players in terms of absolute power. However, a higher body weight has eliminated these differences in relative power values. Increases in body weight and total muscle mass in U19 players were responsible for superior explosive power, which caused better performance in the jump tests (CMJ and CMJFA) when compared with U17 players. The players of the U19 group showed lower running efficiency up to AeT level and longer distance covered above AeT than the U17 group. However, the U19 players possessed higher anaerobic capacity and an efficient glycolytic system compared with the group of U17 players. Players with higher body weight (U19) worked at a significantly higher energy cost level compared with the players of lower body weight (U17) of similar playing positions. 6. Study Limitations The research has limitations on its wide use in men’s handball. The training system in a sports school has strict conditions that are different from training in sports clubs. The sports school players who participated in the research did not differ in the organization of the day, diet, and training loads. When relating the results of the presented studies to the values obtained in other training groups, the above limitations, which constitute an integral part of each sports training process, should be taken into account. 7. Practical Recommendations Training of U19 groups should be targeted to transfer power into speed. There is a clear increase in maximum power with a lack of adequate development of running speed at distances important in handball (5 and 10 m). This is because of the fact that weight gain is not accompanied by an increase in relative power, so the observed power development is only compensated for by weight gain. It is also important because of the size of players, which limits their flexibility and ability to change directions rapidly. Development of endurance and running economy of the players should not be Int. J. Environ. Res. Public Health 2020, 17, 7979 12 of 15 ignored, while focusing the training on the explosive power and sprinting ability of the players. It is recommended to increase the training impact at the age of 17–19 towards the development of running economy and speed increase on the thresholds (AeT and AnT). Author Contributions: Conceptualization, T.G., A.S., and S.G.; methodology, T.G., A.S., and S.G.; software, U.S.-G. and J.B.; validation, D.B., Ł.W., and J.B.; formal analysis, A.S., T.G., and D.B.; investigation, T.G., U.S.-G., and D.B.; resources, T.G. and Ł.W.; data curation, S.G. and J.B.; writing—original draft preparation, T.G., A.S., and S.G.; writing—review and editing, U.S.-G. and D.B.; visualization, A.S.; supervision, T.G. All authors have read and agreed to the published version of the manuscript. Funding: Financed by the project: SGS-2019-011. Conflicts of Interest: The authors declare no conflict of interest. References 1. Fraser-Thomas, J.L.; Côté, J.; Deakin, J. Youth sport programs: An avenue to foster positive youth development. Phys. Educ. Sport Pedagog. 2005, 10, 19–40. [CrossRef] 2. Nideffer, R.M.; Sagal, M.S.; Lowry, M.; Bond, J. Identifying and developing world-class performers. In The Practiceof Sport Psychology; Tenenbaum, G., Morgantown, W., Eds.; Fitness Information Technology: Morgantown, WV, USA, 2001; pp. 129–144. 3. Abbott, A.; Button, C.; Pepping, G.J.; Collins, D. Unnatural selection: Talent identification and development in sport. Nonlinear Dyn. Psychol. Life Sci. 2005, 9, 61–88. 4. Michalsik, L.B.; Aagaard, P.; Madsen, K. Locomotion characteristics and match-induced impairments in physical performance in male elite team handball players. Int. J. Sports Med. 2013, 34, 590–599. [CrossRef] 5. Pearson, D.T.; Naughton, G.A.; Torode, M. Predictability of physiological testing and the role of maturation in talent identification for adolescent team sports. J. Sci. Med. Sport 2006, 9, 277–287. [CrossRef] 6. Vaeyens, R.; Lenoir, M.; Williams, A.M.; Philippaerts, R.M. Talent identification and development programmes in sport: Current models and future directions. Sports Med. 2008, 38, 703–714. [CrossRef] 7. Marques, M.C.; van den Tilaar, R.; Vescovi, J.D.; Gonzalez-Badillo, J.J. Relationship between throwing velocity, muscle power, and bar velocity during bench press in elite handball players. Int. J. Sports Physiol. Perform. 2007, 2, 414–422. [CrossRef] 8. Pori, P.; Kovaˇciˇc, S.; Bon, M.; Dolenec, M.; Šibila, M. Various age category—Related differences in the volume and intensity of the large-scale cyclic movements of male players in team handball. Acta Univ. Palacki. Olomuc. Gymnica 2005, 35, 119–125. 9. Deutsch, M.U.; Maw, G.J.; Jenkins, D.; Reaburn, P. Heart rate, blood lactate and kinematic data of elite colts (under-19) rugby union players during competition. J. Sports Sci. 1998, 16, 561–570. [CrossRef] [PubMed] 10. Krustrup, P.; Mohr, M.; Bangsbo, J. Activity profile and physiological demands of top-class soccer assistant refereeing in relation to training status. J. Sports Sci. 2002, 20, 861–871. [CrossRef] [PubMed] 11. Reilly, T. Assessment of sports performance with particular reference to field games. Eur. J. Sport Sci. 2001, 1, 1–12. [CrossRef] 12. Wagner, H.; Orwat, M.; Hinz, M.; Pfusterschmied, J.; Bacharach, D.W.; Von Duvillard, S.P.; Müller, E. Testing game-based performance in team-handball. J. Strength Cond. Res. 2016, 30, 2794–2801. [CrossRef] [PubMed] 13. Buchheit, M.; Lepretre, P.M.; Behaegel, A.L.; Millet, G.P.; Cuvelier, G.; Ahmaidi, S. Cardiorespiratory responses during running and sport-specific exercises in handball players. J. Sci. Med. Sport 2009, 12, 399–405. [CrossRef] 14. Wagner, H.; Fuchs, P.X.; Von Duvillard, S.P. Specific physiological and biomechanical performance in elite, sub-elite and in non-elite male team handball players. J. Sports Med. Phys. Fit. 2018, 58, 73–81. [CrossRef] 15. Ziv, G.; Lidor, R. Physical characteristics, physiological attributes, and on-court performances of handball players: A review. Eur. J. Sport Sci. 2009, 9, 375–386. [CrossRef] 16. Chelly, M.S.; Hermassi, S.; Shephard, R.J. Relationships between power and strength of the upper and lower limb muscles and throwing velocity in male handball players. J. Strength Cond. Res. 2010, 24, 1480–1487. [CrossRef] [PubMed] 17. Zapartidis, I.; Vareltzis, I.; Gouvali, M.; Kororos, P. Physical Fitness and Anthropometric Characteristics in Different Levels of Young Team Handball Players. Open Sports Sci. J. 2009, 2, 22–28. [CrossRef] Int. J. Environ. Res. Public Health 2020, 17, 7979 13 of 15 18. Karcher, C.; Buchheit, M. On-Court demands of elite handball, with special reference to playing positions. Sport. Med. 2014, 44, 797–814. [CrossRef] [PubMed] 19. Krüger, K.; Pilat, C.; Ückert, K.; Frech, T.; Mooren, F.C. Physical performance profile of handball players is related to playing position and playing class. J. Strength Cond. Res. 2014, 28, 117–125. [CrossRef] [PubMed] 20. Delamarche, P.; Gratas, A.; Beillot, J.; Dassonville, J.; Rochcongar, P.; Lessard, Y. Extent of lactic anaerobic metabolism in handballers. Int. J. Sports Med. 1987, 8, 55–59. [CrossRef] 21. Michalsik, L.B.; Madsen, K.; Aagaard, P. Physiological capacity and physical testing in male elite team handball. J. Sports Med. Phys. Fit. 2015, 55, 415–429. 22. Gorostiaga, E.M.; Granados, C.; Ibáñez, J.; González-Badillo, J.J.; Izquierdo, M. Effects of an entire season on physical fitness changes in elite male handball players. Med. Sci. Sports Exerc. 2006, 38, 357–366. [CrossRef] 23. Póvoas, S.C.A.; Seabra, A.F.T.; Ascensão, A.A.M.R.; Magalhães, J.; Soares, J.M.C.; Rebelo, A.N.C. Physical and physiological demands of elite team handball. J. Strength Cond. Res. 2012, 26, 3365–3375. [CrossRef] [PubMed] 24. Tanaka, M.; Michalsik, L.; Bangsbo, J. Activity profiles during an official league game of Danish elite team handball players. Jpn. J. Phys. Fit. Sport Med. 2002, 15, 61–73. 25. Sporiš, G.; Vuleta, D.; Milanovi´c, D. Fitness profiling in handball: Physical and physiological characteristics of elite players. Coll. Antropol. 2010, 34, 1009–1014. 26. Haugen, T.A.; Tønnessen, E.; Seiler, S. Speed and countermovement-jump characteristics of elite female soccer players, 1995–2010. Int. J. Sports Physiol. Perform. 2012, 7, 340–349. [CrossRef] [PubMed] 27. Manchado, C.; Tortosa-Martínez, J.; Vila, H.; Ferragut, C.; Platen, P. Performance factors in women’s team handball: Physical and physiological aspects-a review. J. Strength Cond. Res. 2013, 27, 1708–1719. [CrossRef] 28. Kvorning, T.; Hansen, M.R.B.; Jensen, K. Strength and Conditioning Training by the Danish National Handball Team before an Olympic Tournament. J. Strength Cond. Res. 2017, 31, 1759–1765. [CrossRef] 29. Ingebrigtsen, J.; Jeffreys, I.; Rodahl, S. Physical characteristics and abilities of junior elite male and female handball players. J. Strength Cond. Res. 2013, 27, 302–309. [CrossRef] 30. Molina-López, J.; Zarzuela, I.B.; Sáez-Padilla, J.; Tornero-Quiñones, I.; Planells, E. Mediation effect of age category on the relationship between body composition and the physical fitness profile in youth handball players. Int. J. Environ. Res. Public Health 2020, 17, 2350. [CrossRef] 31. Roseguini, A.Z.; Da Silva, A.S.R.; Gobatto, C.A. Determinations and Relationships of the RAST Anaerobic Parameters, Anaerobic Threshold and Lactacidemia Response Obtained at the Beginning, Interval and the End of an Official Handball Match. Rev. Bras. Med. Esporte 2008, 14, 46–50. [CrossRef] 32. Hermassi, S.; Laudner, K.; Schwesig, R. Playing level and position differences in body characteristics and physical fitness performance among male team handball players. Front. Bioeng. Biotechnol. 2019, 7, 149. [CrossRef] [PubMed] 33. Burr, J.F.; Jamnik, V.K.; Dogra, S.; Gledhill, N. Evaluation of jump protocols to assess leg power and predict hockey playing potential. J. Strength Cond. Res. 2007, 21, 1139–1145. [CrossRef] [PubMed] 34. Petrigna, L.; Karsten, B.; Marcolin, G.; Paoli, A.; D’Antona, G.; Palma, A.; Bianco, A. A Review of Countermovement and Squat Jump Testing Methods in the Context of Public Health Examination in Adolescence: Reliability and Feasibility of Current Testing Procedures. Front. Physiol. 2019, 10, 1384. [CrossRef] [PubMed] 35. Bosco, C.; Luhtanen, P.; Komi, P.V. A simple method for measurement of mechanical power in jumping. Eur. J. Appl. Physiol. Occup. Physiol. 1983, 50, 273–282. [CrossRef] 36. Baron, J.; Bieniec, A.; Swinarew, A.S.; Gabry´s, T.; Stanula, A. Effect of 12-week functional training intervention on the speed of young footballers. Int. J. Environ. Res. Public Health 2020, 17, 160. [CrossRef] 37. Zacharogiannis, E.; Paradisis, G.; Tziortzis, S. An evaluation of tests of anaerobic power and capacity. Med. Sci. Sport Exerc. 2004, 36, 116. 38. Balˇeiunas, M.; Stonkus, S.; Abrantes, C.; Sampaio, J. Long term effects of different training modalities on power, speed, skill and anaerobic capacity in young male basketball players. J. Sport Sci. Med. 2006, 5, 163–170. 39. Beltz, N.M.; Gibson, A.L.; Janot, J.M.; Kravitz, L.; Mermier, C.M.; Dalleck, L.C. Graded Exercise Testing Protocols for the Determination of VO2max: Historical Perspectives, Progress, and Future Considerations. J. Sports Med. 2016, 2016, 1–12. [CrossRef] Int. J. Environ. Res. Public Health 2020, 17, 7979 14 of 15 40. Castagna, C.; Impellizzeri, F.M.; Belardinelli, R.; Abt, G.; Coutts, A.; Chamari, K.; D’Ottavio, S. Cardiorespiratory responses to Yo-yo Intermittent Endurance Test in nonelite youth soccer players. J. Strength Cond. Res. 2006, 20, 326–330. [CrossRef] 41. Hughson, R.L.; Green, H.J. Blood acid-base and lactate relationships studied by ramp work tests. Med. Sci. Sports Exerc. 1982, 14, 297–302. [CrossRef] 42. Mader, A.; Liesen, H.; Heck, H.; Philippi, H.; Rost, R.; Schuerch, P.; Hollmann, W. Zur Beurteilung der sportartspezifischen Ausdauerleistungsfähigkeit im Labor. Sportarzt Sportmed 1976, 27, 109–112. 43. Hopkins, W.G.; Marshall, S.W.; Batterham, A.M.; Hanin, J. Progressive statistics for studies in sports medicine and exercise science. Med. Sci. Sports Exerc. 2009, 41, 3–13. [CrossRef] [PubMed] 44. Gümü¸s, M.; Eler, S. Evaluation of Physical and Physiological Characteristics of the Olympic Champion Turkish Deaf Men’s National Handball Team. J. Educ. Train. Stud. 2018, 6, 207. [CrossRef] 45. Schwesig, R.; Hermassi, S.; Fieseler, G.; Irlenbusch, L.; Noack, F.; Delank, K.S.; Shephard, R.J.; Chelly, M.S. Anthropometric and physical performance characteristics of professional handball players: Influence of playing position. J. Sports Med. Phys. Fit. 2017, 57, 1471–1478. [CrossRef] 46. Rannou, F.; Prioux, J.; Zouhal, H.; Gratas-Delamarche, A.; Delamarche, P. Physiological profile of handball players. J. Sports Med. Phys. Fit. 2001, 41, 349–353. 47. Massuca, L.; Branco, B.; Miarka, B.; Fragoso, I. Physical fitness attributes of team-handball players are related to playing position and performance level. Asian J. Sports Med. 2015, 6, e24712. [CrossRef] 48. Zakas, A.; Mandroukas, K.; Karamouzis, G.; Panagiotopoulou, G. Physical training, growth hormone and testosterone levels and blood pressure in prepubertal, pubertal and adolescent boys. Scand. J. Med. Sci. Sports 1994, 4, 113–118. [CrossRef] 49. Malina, R.M.; Bouchard, C.; Bar-Or, O. Growth, Maturation, and Physical Activity, 2nd ed.; Human Kinetics: Champaign, IL, USA, 2004; ISBN 0880118822. 50. Gorostiaga, E.M.; Izquierdo, M.; Iturralde, P.; Ruesta, M.; Ibáñez, J. Effects of heavy resistance training on maximal and explosive force production, endurance and serum hormones in adolescent handball players. Eur. J. Appl. Physiol. Occup. Physiol. 1999, 80, 485–493. [CrossRef] 51. Michalsik, L.B. On-Court Physical Demands and Physiological Aspects in Elite Team Handball. In Handball Sports Medicine; Springer: Berlin/Heidelberg, Germany, 2018; pp. 15–33. 52. Reverter-Masía, J.; Legaz-Arrese, A.; Munguía-Izquierdo, D.; Barbany, J.R.; Serrano-Ostáriz, E. A profile of the resistance training practices of elite Spanish club teams. J. Strength Cond. Res. 2009, 23, 1537–1547. 53. Cardinale, M.; Whiteley, R.; Hosny, A.A.; Popovic, N. Activity profiles and positional differences of handball players during the world championships in Qatar 2015. Int. J. Sports Physiol. Perform. 2017, 12, 908–915. [CrossRef] 54. Buchheit, M.; Mendez-Villanueva, A.; Quod, M.; Quesnel, T.; Ahmaidi, S. Improving acceleration and repeated sprint ability in well-trained adolescent handball players: Speed versus sprint interval training. Int. J. Sports Physiol. Perform. 2010, 5, 152–164. [CrossRef] 55. Spencer, M.; Bishop, D.; Dawson, B.; Goodman, C. Physiological and metabolic responses of repeated-sprint activities:specific to field-based team sports. Sports Med. 2005, 35, 1025–1044. [CrossRef] 56. Ferrari Bravo, D.; Impellizzeri, F.M.; Rampinini, E.; Castagna, C.; Bishop, D.; Wisloff, U. Sprint vs. interval training in football. Int. J. Sports Med. 2008, 29, 668–674. [CrossRef] [PubMed] 57. Burgomaster, K.A.; Howarth, K.R.; Phillips, S.M.; Rakobowchuk, M.; Macdonald, M.J.; Mcgee, S.L.; Gibala, M.J. Similar metabolic adaptations during exercise after low volume sprint interval and traditional endurance training in humans. J. Physiol. 2008, 586, 151–160. [CrossRef] [PubMed] 58. Gibala, M.J.; Little, J.P.; van Essen, M.; Wilkin, G.P.; Burgomaster, K.A.; Safdar, A.; Raha, S.; Tarnopolsky, M.A. Short-term sprint interval versus traditional endurance training: Similar initial adaptations in human skeletal muscle and exercise performance. J. Physiol. 2006, 575, 901–911. [CrossRef] 59. Barnes, K.R.; Kilding, A.E. Running economy: Measurement, norms, and determining factors. Sports Med. Open 2015, 1, 1–15. [CrossRef] 60. Sporiš, G.; Vuˇceti´c, V.; Milanovi´c, L.; Milanovi´c, Z.; Krespi, M.; Krakan, I. A comparison anaerobic endurance capacity in elite soccer, handball and basketball players. Kinesiology 2014, 46, 52–59. 61. Kukolj, M.; Ropret, R.; Ugarkovic, D.; Jaric, S. Anthropometric, strength, and power predictors of sprinting performance. J. Sports Med. Phys. Fit. 1999, 39, 120–122. Int. J. Environ. Res. Public Health 2020, 17, 7979 15 of 15 62. Young, W.; McLean, B.; Ardagna, J. Relationship between strength qualities and sprinting performance. J. Sports Med. Phys. Fit. 1995, 35, 13–19. 63. Massuça, L.M.; Fragoso, I.; Teles, J. Attributes of top elite team-handball players. J. Strength Cond. Res. 2014, 28, 178–186. [CrossRef] 64. Hansen, C.; Sanz-Lopez, F.; Whiteley, R.; Popovic, N.; Ahmed, H.A.; Cardinale, M. Performance analysis of male handball goalkeepers at the world handball championship 2015. Biol. Sport 2017, 34, 393–400. [CrossRef] [PubMed] 65. Havolli, J. Comparison between Some Morphological Characteristics and Motor Tests of Young Handball and Football Goalkeepers. J. Phys. Fit. Med. Treat. Sport 2018, 3. [CrossRef] Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

A Comparative Study on the Performance Profile of Under-17 and Under-19 Handball Players Trained in the Sports School System.

10-30-2020

Gabrys, Tomasz,Stanula, Arkadiusz,Gupta, Subir,Szmatlan-Gabrys, Urszula,Benešová, Daniela,Wicha, Łukasz,Baron, Jakub

eng

PMC5685587

RESEARCH ARTICLE Exposure time, running and skill-related performance in international u20 rugby union players during an intensified tournament Christopher J. Carling1,2, Mathieu Lacome2*, Eamon Flanagan3, Pearse O’Doherty4, Julien Piscione2 1 Institute of Coaching and Performance, University of Central Lancashire, Preston, United Kingdom, 2 Research Department, French Rugby Union, Marcoussis, France, 3 Irish Rugby Football Union, Fitness Department, Dublin, Ireland, 4 Statsports Technologies™, Newry, Northern Ireland * [email protected] Abstract Purpose This study investigated exposure time, running and skill-related performance in two interna- tional u20 rugby union teams during an intensified tournament: the 2015 Junior World Rugby Championship. Method Both teams played 5 matches in 19 days. Analyses were conducted using global positioning system (GPS) tracking (Viper 2™, Statsports Technologies Ltd) and event coding (Opta Pro®). Results Of the 62 players monitored, 36 (57.1%) participated in 4 matches and 23 (36.5%) in all 5 matches while player availability for selection was 88%. Analyses of team running output (all players completing >60-min play) showed that the total and peak 5-minute high metabolic load distances covered were likely-to-very likely moderately higher in the final match com- pared to matches 1 and 2 in back and forward players. In individual players with the highest match-play exposure (participation in >75% of total competition playing time and >75-min in each of the final 3 matches), comparisons of performance in matches 4 and 5 versus match 3 (three most important matches) reported moderate-to-large decreases in total and high metabolic load distance in backs while similar magnitude reductions occurred in high-speed distance in forwards. In contrast, skill-related performance was unchanged, albeit with trivial and unclear changes, while there were no alterations in either total or high-speed running distance covered at the end of matches. Conclusions These findings suggest that despite high availability for selection, players were not over- exposed to match-play during an intensified u20 international tournament. They also imply PLOS ONE | https://doi.org/10.1371/journal.pone.0186874 November 14, 2017 1 / 15 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Carling CJ, Lacome M, Flanagan E, O’Doherty P, Piscione J (2017) Exposure time, running and skill-related performance in international u20 rugby union players during an intensified tournament. PLoS ONE 12(11): e0186874. https://doi.org/10.1371/journal. pone.0186874 Editor: Jaime Sampaio, Universidade de Tras-os- Montes e Alto Douro, PORTUGAL Received: April 11, 2017 Accepted: October 9, 2017 Published: November 14, 2017 Copyright: © 2017 Carling et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Funding: No specific funding was provided for this work either by the French Rugby Union Federation, Irish Rugby Football Union or Statsports Technologies Ltd. These commercial entities only provided support in the form of salaries for authors [CC, ML, EF, PO and JP], but did not have any additional role in the study design, data collection that the teams coped with the running and skill-related demands. Similarly, individual play- ers with the highest exposure to match-play were also able to maintain skill-related perfor- mance and end-match running output (despite an overall reduction in the latter). These results support the need for player rotation and monitoring of performance, recovery and intervention strategies during intensified tournaments. Introduction Rugby union is an intermittent team sport requiring players to repeatedly perform bouts of high-speed running interspersed with periods of low-speed activity [1]. Intense static exertions such as scrummaging, physical collisions and tackles also occur frequently throughout play [2]. On average, forward and back players at elite senior levels are shown to spend 14% and 8% of their match time in highly intense activities such as sprinting and tackling and in scrums, rucks and mauls [3]. Combined, these physical demands are shown to result in high levels of muscle damage [4,5], neuromuscular and perceptual fatigue [6] and compromised immunity [7] post-competition. While generally transient in nature, such disturbances typically persist for 24–48 h following match-play although muscle damage can last for several days with large variations in recovery kinetics reported across individuals [8]. At elite senior levels however, a single match is generally played per week over the course of the season [9]. Therefore, the time interval separating consecutive matches is sufficient in theory to ensure complete physical and physiological recovery [10]. In contrast to elite senior rugby union competition, congested competition schedules involving multiple matches played in a short time period occur in players in elite junior cate- gories. For example, the annual World Rugby u20 World Cup schedule requires national teams to participate in 5 matches over a 19-day period. If recovery time between successive matches is short, residual fatigue, muscle damage and reduced immunity have the potential to compromise ensuing match performance [11]. Yet to our knowledge, no data currently exist on the potential effects on match performance (e.g., running, technical actions) of participa- tion in intensified tournaments such as the u20 World Cup. Related research in junior Rugby League players has reported a progressive accumulation of fatigue represented by a reduced capacity to perform high-speed exercise during tournaments where multiple matches were played over a 5-day period [12]. An investigation more representative of the u20 World Cup schedule (cycle of 4 matches in 22 days vs. 5 matches in 19 days), albeit in professional rugby league players demonstrated fluctuations in running activity with reductions in high-speed and increases in low-speed distance covered in the latter matches [13]. No information was reported on any potential changes in technical skill-related performance in either study. Thus research investigating match-to-match running and technical skill-related performance during the u20 World Cup is warranted. Despite the aforementioned potential risk of fatigue accumulation and compromised com- petitive performance associated with insufficient recovery time during intensified tourna- ments, no information exists on the actual exposure time of players to match-play. Recent research in a professional association football club [14] has shown that despite the frequent occurrence of periods of match congestion across the season, squad rotation strategies were employed by the coaching staff to ensure that players did not endure over-exposure. Thus, in our opinion, similar data across tournaments such as the u20 World Cup are necessary to Match performance in elite u20 rugby union PLOS ONE | https://doi.org/10.1371/journal.pone.0186874 November 14, 2017 2 / 15 and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section. Competing interests: The authors have no competing interests and their commercial affiliation (French Rugby Union Federation, Irish Rugby Football Union or Statsports Technologies Ltd) does not alter their adherence to PLOS ONE policies on sharing data and materials. (as detailed online in the journal’s guide for authors http:// journals.plos.org/plosone/s/competing-interests). determine the actual extent of player exposure and therefore the aforementioned potential risk of compromised match performance. This study examined exposure time and the effects of an intensified tournament on running and skill-related match performance in international u20 players during the 2015 World Rugby u20 World Cup. Materials and methods Experimental approach to the problem The present study was conducted during the 2015 World Rugby u20 World Cup tournament. Participation time for each player was recorded to determine the extent of match exposure over this intensified schedule. Global positioning systems (GPS) and match analysis software were used to gather data related to match running and skill performance and examine the potential effects of the congested schedule on performance notably in players with high expo- sure time to match-play. Participants All players were members of the French or Irish national u20 teams. Altogether, 63 players (age: 19.8 ± 0.5 y, body mass: 99.1 ± 9.1 kg, stature: 185.4 ± 7.0 cm) participated. Prior to par- ticipation, all players received comprehensive verbal and written explanations of the study and provided voluntarily signed informed consent to wear GPS in competitive matches and to par- ticipate in the collection of performance data for the entirety of the Championship. These data arose as a condition of selection for their national team in which player performance was rou- tinely measured over the course of the competitive season [15]. Nevertheless, institutional board approval for the study was obtained from the Medical Council of the Federation Fran- c¸aise de Rugby. To ensure confidentiality, all performance data were anonymized. This study conformed to the recommendations of the Declaration of Helsinki. Competition During the competition, each team played 5 matches in 19 days. A total of 4 days (94-98h) sep- arated matches 1 and 2 and matches 2 and 3 and 5 days (118-120h) separated matches 3 and 4 and matches 4 and 5. Altogether, 226 match observations (forwards = 128 and backs = 98 matches) were collected. All participating players followed standardized recovery protocols over the course of the competition: consumption of a minimum of 40 g carbohydrates and 20 g protein in liquid or whole food form immediately after competition. Players were also requested to use cold bath, massages and compression garments. The day following the match, players performed recovery protocols (hydrotherapy session, foam rolls, compression gar- ments) and received appropriate nutritional and hydration plans. Study design In order to conduct the analyses, two categories of performance measures were employed: 1. Time-motion analyses of running performance. Each player wore a 10-Hz GPS unit (mass = 50g, size = 86x33x20mm; Viper 2™, Statsports Technologies™, Newry, Northern Ire- land) in a bespoke pocket fitted in their playing jersey which positioned the GPS unit on the upper thoracic spine between the scapulae. Independent testing has reported low typical error of measurement (range: 0.7–1.7%) and coefficient of variation (2.0–2.9%) as well as low abso- lute error (2.9–3.0%) over a range of activities including repeated 30m shuttle runs, a 132.3m circuit simulating soccer activity and 16-minute duration small-sided matches (unpublished Match performance in elite u20 rugby union PLOS ONE | https://doi.org/10.1371/journal.pone.0186874 November 14, 2017 3 / 15 data, Marathon Performance Center, 2014). All participants were familiarised with the devices as part of their daily training and practice in the season leading up to the 2015 World Rugby u20 World Cup. The GPS units were turned on at least 30 minutes prior to each match to facilitate satellite signal connection. Information on the average number of satellites to which GPS devices were connected and values for the horizontal dilution of precision were unavailable. Following the matches, GPS data were downloaded to a laptop and analysed with proprietary software (STATSport Viper Rugby v2.6.1.173, STATSports Technologies Ltd., Ireland, UK). Players whose GPS unit suffered a loss of signal for a period of time within the match were excluded (i.e., GPS fell on the ground, spikes in the data, n = 11). Each file was cropped to ensure that only data recorded when the player was on the field was included. A number of locomotor var- iables were analysed: total distance run (TD) and that covered at high-running speeds (HS) (threshold > 5.5 m.s-1). High-metabolic load distance (HI distance + distance covered while accelerating above 2 m.s-2) [16] and the total number of high-speed activities (> 5.5 m.s-1) and accelerations (> 2 m.s-2) were also recorded. Finally, the peak 5-min of HMLD (HMLD.Peak5- min) was recorded for each match and player using a 5-min rolling average with step 0.1-s. 2. Match analyses of skill-related performance. Measures of skill-related performance defined by Opta Pro1 data provider and coded by the company’s match analysts using the Sportscode software (Sportstec, Australia) included the total number of tackles, passes and car- ries along with respective completion rates in these events. Effective playing time (time the ball was in play) was also recorded. Although no data exists for elite rugby union, high levels of Opta inter-operator reliability for coding match events in elite association football have been demonstrated [17]. Data collection procedures 1. Participation patterns. Exposure time was recorded for each individual player. Basic metrics quantified from this data included total number of and percentage of the players com- pleting: (1) 3, 4 and 5 matches respectively, (2) 3, 4 and 5 matches played successively, (3) at least 60-min play [18] in 3, 4 and 5 matches played successively, (4) >240-min (equivalent to 3 complete matches) and >320-min (equivalent to 4 complete matches) total participation time over the tournament. Time loss injuries and subsequent unavailability for match selection were prospectively recorded by the team physicians respective to both teams. 2. Overall team running and skill-related performance. To investigate accumulated changes in overall team performance, running and skill-related performance measures were normalised relative to each player’s participation time and compared across matches 1 to 5. Players competing for <60-min were excluded. A total of 171 match observations were col- lected including 77 and 94 observations for backs and forwards respectively. 3. Running and skill-related performance in “high exposure” players. Players with high exposure time notably during the final three matches were assessed separately. These three matches were selected as these were considered to be the highest standard and most important matches of the competition (e.g., semi-finals, finals or matches to determine team seeding in the following year’s u20 world cup) and for which coaching staff habitually select their best performers. Hence players should have been subjected to the highest physical and technical demands in these three matches. Inclusion criteria were: (1) participation in at least 75-min in each of the final 3 matches in the series, and (2) played more than 320-min over the course of the competition (>75% of total playing time). To investigate potential accumulated changes in individual match-performance, the afore- mentioned running and skill-related measures were normalised relative to each player’s total Match performance in elite u20 rugby union PLOS ONE | https://doi.org/10.1371/journal.pone.0186874 November 14, 2017 4 / 15 playing time and compared from Match 3 to Match 5. The total distance and high metabolic load distance covered were also compared for the final 10-min period versus the mean value (minus first and last 10-min periods) for the other 10-min periods. Statistical analysis Statistical analyses were performed using R statistical software (R. 3.1.0, R Foundation for Sta- tistical Computing) using the lme4 and psychometric package. Means and standard deviations for each group or playing time were derived from a generalized linear model, with the distribu- tion and link function contingent upon the nature of the dependent variable. The overdis- persed Poisson distribution was chosen for modelling the data from the match analyses and the normal distribution was chosen for distances from the time-motion analyses. For each analysis, the match (Match 1 to Match 5) was included as a fixed effect while players and teams were included as random effects. The % differences between mean values with 90% confidence intervals (CI) are reported. A magnitude-based inferential approach was adopted [19,20]. Effect sizes (ES) were quanti- fied to indicate the practical meaningfulness of the differences in mean values. Standardisation was performed with the estimated marginal means and associated variance provided by the generalized linear model. The ES was classified as trivial (0–0.19), small (0.20–0.59), moderate (0.6–1.19), large (1.20–1.99) and very large (>2.0). If the 90% CI over-lapped small positive and negative values, the magnitude was deemed unclear. The chances that the changes in run- ning- or skill-related performance were greater for a group (i.e., greater than the smallest worthwhile change, SWC (0.2 multiplied by the between-subject standard deviation, based on Cohen’s d principle)), similar or smaller than the other group were calculated. Quantitative chances of greater or smaller changes in performance variable were assessed qualitatively with the following scale: 25−75%, possible; 75−95%, likely; 95−99%, very likely; >99%, almost cer- tain. [21] Results Match exposure The patterns of participation of players and exposure to periods of match congestion cycles are presented in Table 1. Of the 62 players, 36 (57%) played 4 matches and 23 (37) played 5 matches. Of these appearances, 39, 28 and 23 players played 3, 4 and 5 matches successively (62, 44, and 37% respectively). The proportion of backs and forwards who played 3, 4 and 5 matches successively with over 60-min of playing time was 14, 6 and 6% respectively for for- wards and 35, 19 and 8% respectively for backs. Player availability for selection overall across the competition was 88%. Overall team match performance Table 2 reports running and skill-related performance of players completing at least 60-min in the matches while Fig 1 reports standardised changes in running and skill-related perfor- mance in match 2 to 5 compared with match 1. Overall, unclear to likely small changes in HSR, HMLD, sprints and accelerations were observed for backs (ES: -0.44 ±0.44 to 0.54 ±0.54) between match 1 and the other matches. Regarding total distance covered, moderate increases were observed for match 3 and 5 compared with match 1 (ES: 0.62 ±0.31 and 0.65 ±0.44 respectively). In forwards, unclear to small changes were reported in all running-performance variables except for total distance covered. Regarding total distance covered, small to moderate Match performance in elite u20 rugby union PLOS ONE | https://doi.org/10.1371/journal.pone.0186874 November 14, 2017 5 / 15 increases were observed between match 3 and 5 compared to match 1 (ES: 0.40 ±0.42 and 0.89 ±0.80 respectively). Regarding skill-related performance, unclear to small differences in the frequency of passes and carries were observed between match 1 and the other matches. Likely moderate increases were reported between the frequencies of tackles performed by backs between match 1 and matches 2 to 4 (ES: 1.00 ±0.70; 1.30 ±0.53 and 1.10 ±0.80 for matches 2, 3 and 4 respectively). Unclear to small fluctuations in pass success rates or average gain per carries were observed in backs. Likely moderate decreases in tackles success rates occurred between match 1 and match 3 and 4 in backs (ES: 1.30 ±0.53 and 1.10 ±0.80). In forwards, there were unclear to trivial effect size differences in the frequency and success rates of skill-related performance measures between match 1 and the other matches except in tackling actions for which there was a mod- erate decrease in match 1 vs match 2 and a moderate decrease in passing success rates in match 1 vs match 4. There was no difference in effective playing time between Match 1 and 2 but possibly mod- erate to likely large increases were observed in Match 3, 4 and 5 compared with Match 1 (ES: 0.90 ±0.49; 1.75 ±0.51; 1.43 ±0.50 respectively). Match performance in “high exposure” players Table 3 reports running and skill-related performance in high exposure players. In backs, likely moderate to large decreases in total distance covered and HLMD distance covered were reported between match 3 versus match 4 and 5 (ES: -0.61 ±0.78 to -1.70 ±1.50). Regarding HSR distance covered as well as sprints and acceleration frequencies, only unclear differences were reported between match 3 versus match 4 and 5. In forwards, except for HSR distance covered (ES: 1.20 ±0.78 and 0.69 ±0.75 for Match 4 and 5 compared to Match 3 respectively), only unclear differences were reported in running related performance. Table 1. Overall participation of players in the competition and exposure to match congestion cycles. Match exposure ALL PLAYERS (62) FORWARDS (36) BACKS (26) Occurrences (n) Relative Nb (%) Occurrences (n) Relative Nb (%) Occurrences (n) Relative Nb (%) Matches played Played >320 min in total 14 22% 5 14% 9 35% Played >240 min in total 23 37% 10 28% 13 50% Participations in 3 games (nb) 47 75% 26 72% 21 81% Participations in 4 games (nb) 36 57% 19 53% 17 65% Participations in 5 games (nb) 23 37% 13 36% 10 38% Multiple match cycles Participations in 3 successive games (nb) 39 62% 23 64% 16 62% Participations in 3 successive games >60-min (nb) 14 22% 5 14% 9 35% Participations in 4 successive games (nb) 28 44% 16 44% 12 46% Participations in 4 successive games >60-min (nb) 7 11% 2 6% 5 19% Participations in 5 successive games (nb) 23 37% 13 36% 10 38% Participations in 5 successive games >60-min (nb) 4 6% 2 6% 2 8% Nb: Number. https://doi.org/10.1371/journal.pone.0186874.t001 Match performance in elite u20 rugby union PLOS ONE | https://doi.org/10.1371/journal.pone.0186874 November 14, 2017 6 / 15 In backs and forwards, unclear differences were observed in pass and tackle success rates and average gain per carries between match 3 versus match 4 and 5 although there was a large increase in tackle success rates in match 3 vs match 5 in backs (ES: 1.20 ±0.99). In backs, there were possibly moderate and likely small increases in the frequency of passes (ES: 0.77 ±0.86 and 0.41 ±0.57 respectively) along with a possibly moderate to possibly large decrease in tackle frequency in matches 4 and 5 compared with match 3 (ES: -0.87 ±0.91 and -1.58 ±1.04 respectively). In forwards, unclear differences were observed in the frequency of tackles and carries between match 3 and matches 4 and 5. Possibly small to possibly moderate increases in passing frequency were reported in match 3 compared to matches 5 and 4 (ES: 0.41 ±0.57 and 0.77 ±0.86 respectively). Fig 2 reports differences in total distance covered and HMLD distance covered between the mean 10-min versus the final 10-min period, from match 3 to match 5. Small to moderate increases in total distance covered between the final 10-min and mean 10-min period were observed in matches 4 and 5 compared to match 3 (ES: 0.33 ±0.41 and 0.95 ±1.10 respectively). Regarding HMLD, there were large increases in match 4 and 5 compared to match 3 (ES: 1.25 ±0.83 and 1.24 ±1.30 respectively). Table 2. Running and skill- performance in players competing at least 60-min from match 1 to match 5. BACKS Match 1 (13) Match 2 (14) Match 3 (13) Match 4 (12) Match 5 (12) TD (m.min-1) 66.8 ± 6.0 64.7 ± 8.3 71.3 ± 7.9 68.0 ± 5.1 70.3 ± 3.6 HSR (m.min-1) 4.4 ± 2.0 4.1 ± 1.4 4.0 ± 1.5 4.9 ± 2.3 4.4 ± 1.8 HMLD (m.min-1) 10.5 ± 2.0 10.0 ± 1.9 11.1 ± 2.8 11.3 ± 2.4 11.3 ± 2.0 Sprints (n.min-1) 0.24 ± 0.08 0.25 ± 0.07 0.24 ± 0.08 0.29 ± 0.1 0.27 ± 0.08 Accel (n.min-1) 0.31 ± 0.08 0.27 ± 0.09 0.34 ± 0.13 0.36 ± 0.11 0.35 ± 0.11 HMLD.Peak5min (m.min-1) 25.7 ± 5.2 26.9 ± 6.0 28.3 ± 6.0 30.6 ± 9.0 30.0 ± 8.3 Tackles (n) 0.05 ± 0.03 0.09 ± 0.04 0.10 ± 0.04 0.08 ± 0.03 0.06 ± 0.03 Passes (n) 0.10 ± 0.12 0.10 ± 0.11 0.07 ± 0.09 0.12 ± 0.1 0.07 ± 0.04 Carries (n) 0.08 ± 0.04 0.05 ± 0.03 0.06 ± 0.04 0.09 ± 0.03 0.10 ± 0.06 Tackles (%) 0.82 ± 0.30 0.83 ± 0.15 0.62 ± 0.23 0.63 ± 0.18 0.74 ± 0.29 Passes (%) 0.97 ± 0.06 0.98 ± 0.05 0.93 ± 0.11 0.93 ± 0.12 0.91 ± 0.14 Average Gain/Carries (m) 5.23 ± 3.00 5.10 ± 2.28 6.59 ± 8.05 5.82 ± 2.89 4.70 ± 2.29 FORWARDS Match 1 (12) Match 2 (10) Match 3 (12) Match 4 (9) Match 5 (10) TD (m.min-1) 59.8 ± 4.7 53.8 ± 6.4 62.7 ± 8.2 61.3 ± 4.6 63.6 ± 3.5 HSR (m.min-1) 1.1 ± 0.8 0.6 ± 0.5 1.0 ± 0.8 1.4 ± 0.7 1.1 ± 0.8 HMLD (m.min-1) 6.5 ± 2.2 5.2 ± 1.9 7.0 ± 2.4 6.4 ± 3.0 7.2 ± 2.0 Sprints (n.min-1) 0.09 ± 0.06 0.06 ± 0.05 0.10 ± 0.07 0.10 ± 0.06 0.09 ± 0.06 Accel (n.min-1) 0.31 ± 0.16 0.19 ± 0.11 0.31 ± 0.16 0.27 ± 0.16 0.33 ± 0.11 HMLD.Peak5min (m.min-1) 15.4 ± 3.2 16.3 ± 5.4 17.4 ± 5.1 15.7 ± 6.5 19.3 ± 4.9 Tackles (n) 0.11 ± 0.06 0.15 ± 0.01 0.13 ± 0.05 0.08 ± 0.03 0.12 ± 0.06 Passes (n) 0.03 ± 0.04 0.01 ± 0.02 0.02 ± 0.03 0.04 ± 0.04 0.04 ± 0.06 Carries (n) 0.09 ± 0.06 0.05 ± 0.05 0.10 ± 0.08 0.08 ± 0.06 0.09 ± 0.06 Tackles (%) 0.96 ± 0.06 0.89 ± 0.11 0.92 ± 0.09 0.83 ± 0.19 0.94 ± 0.08 Passes (%) 0.99 ± 0.03 1.00 ± 0.00 0.98 ± 0.06 0.91 ± 0.19 0.98 ± 0.06 Average Gain/Carries (m) 1.91 ± 1.68 1.93 ± 1.51 1.88 ± 1.34 1.56 ± 1.04 1.46 ± 1.11 Effective playing time (min) 29.3 ± 2.1 29.6 ± 3.4 35.3 ± 8.5 32.2 ± 0.2 33.1 ± 2.5 TD: Total distance; HSR: High speed running; HMLD: High metabolic load distance; ES: Effect size; % chances: % chances that the true difference is +ive/ trivial/ -ive. Number in parenthesis refers to the number of players analysed. https://doi.org/10.1371/journal.pone.0186874.t002 Match performance in elite u20 rugby union PLOS ONE | https://doi.org/10.1371/journal.pone.0186874 November 14, 2017 7 / 15 Discussion To our knowledge, this is the first study conducted in international junior rugby union players to investigate exposure time to match-play, running and skill-related match performance dur- ing an intensified tournament (5 matches in 19 days). The main findings were: (1) only <60% and <40% of players participated in 4 or 5 of all matches respectively despite a substantially higher availability rate for selection, (2) the two teams as a whole were able to maintain run- ning- and skill-related performance throughout this intensive schedule, (3) in players with the highest exposure time to play, overall running performance over the final two matches was Fig 1. Standardised differences in running (panel A)- and skill (panel B)- related performance between match 1 and match 2 to 5 in forwards and backs. Grey zone stands for trivial zone (effect size ± 0.2). TD: Total distance; HSR (High speed running); HMLD: High metabolic load distance. Accel: Accelerations; HMLD.Peak5min: Peak 5-min of high metabolic load distance. https://doi.org/10.1371/journal.pone.0186874.g001 Match performance in elite u20 rugby union PLOS ONE | https://doi.org/10.1371/journal.pone.0186874 November 14, 2017 8 / 15 affected to a certain extent although end match running output and overall skill-related perfor- mance remained stable. Match exposure In elite rugby union, the exposure time of players to competition has generally received little attention in the scientific literature [9]. No information exists for elite players in younger age categories and especially during intensified tournaments such as the u20 World Cup. In this tournament, teams are exposed to a demanding schedule of 5 matches over a 19-day period. In the present study, analysis of two international u20 teams showed that only 57% and 37% of players participated in 4 or 5 out of the 5 successive matches respectively despite player avail- ability being nearly 90% across the tournament. These findings imply that the teams’ coaching staff recognised the need to rotate and rest players over the course of the tournament. In regards to participation in successive matches, almost two-thirds of players were exposed to 3 consecutive matches although only 22% (35% of backs and 14% of forwards) played over 60-mins in all three matches. While no information is available on the actual reasons of Table 3. Running and skill-performance in “high-exposure players” from match 3 to match 5. BACKS (5) Match 3 Match 4 Match 5 Match 3 vs Match 4 Match 3 vs Match 5 ES % chances ES % chances TD (m.min-1) 73.7 ± 7.1 66.1 ± 4.6 69.9 ± 3.5 -1.20 ±0.80 0/2/97 -0.61 ±0.78 4/14/81 HSR (m.min-1) 4.9 ± 1.0 4.8 ± 1.7 3.8 ± 0.9 -0.05 ±0.79 29/33/37 -0.98 ±1.30 6/9/85 HMLD (m.min-1) 12.2 ± 0.6 11.2 ± 1.8 10.8 ± 0.9 -0.67 ±0.81 4/13/83 -1.70 ±1.50 2/3/95 Sprints (n.min-1) 0.29 ± 0.02 0.30 ± 0.06 0.26 ± 0.05 0.22 ±8.90 50/3/47 -0.57 ±0.94 9/17/75 Accel (n.min-1) 0.40 ± 0.10 0.40 ± 0.12 0.37 ± 0.11 -0.03 ±2.40 44/11/45 -0.30 ±0.96 19/24/57 HMLD.Peak5min (m.min-1) 31.8 ± 5.0 28.4 ± 6.3 26.9 ± 1.2 -0.53.± 1.04 12/15/72 -1.20.± 1.04 2/4/94 Tackles (n) 0.12 ± 0.05 0.08 ± 0.03 0.06 ± 0.02 -0.87 ±0.91 3/8/89 -1.58± 1.04 1/1/98 Passes (n) 0.03 ± 0.03 0.06 ± 0.04 0.06 ± 0.05 0.72 ±1.20 79/12/9 0.66 ±1.10 78/14/9 Carries (n) 0.08 ± 0.02 0.07 ± 0.02 0.08 ± 0.05 -0.15 ±1.60 35/18/48 -0.06 ±0.91 31/30/39 Tackles (%) 0.59 ± 0.19 0.59 ± 0.20 0.80 ± 0.12 0.01 ±1.00 37/27/36 1.20 ±0.99 95/4/2 Passes (%) 0.95 ± 0.11 0.86 ± 0.14 0.92 ± 0.14 -0.60 ±1.00 10/15/75 -0.24 ±1.00 22/25/53 Average Gain/Carries (m) 5.52 ± 7.35 5.71 ± 3.09 3.46 ± 1.51 0.03 ±0.88 36/32/32 -0.35 ±0.94 15/23/61 FORWARDS (5) Match 1 Match 2 Match 3 Match 3 vs Match 4 Match 3 vs Match 5 ES % chances ES % chances TD (m.min-1) 61.4 ± 8.8 62.5 ± 3.5 64.2 ± 2.5 0.15 ±0.61 44/39/17 0.40 ±0.66 70/24/7 HSR (m.min-1) 0.5 ± 0.3 1.4 ± 0.8 1.0 ± 0.8 1.20 ±0.78 98/1/0 0.69 ±0.75 86/11/3 HMLD (m.min-1) 5.8 ± 2.0 6.5 ± 3.6 7.1 ± 1.6 0.23 ±0.69 53/32/15 0.65 ±1.10 75/15/10 Sprints (n.min-1) 0.06 ± 0.04 0.11 ± 0.07 0.08 ± 0.05 0.91 ±0.80 93/6/1 0.43 ±0.88 67/21/11 Accel (n.min-1) 0.28 ± 0.10 0.29 ± 0.19 0.34 ± 0.1 0.07 ±0.85 40/31/30 0.53 ±0.78 76/18/6 HMLD.Peak5min (m.min-1) 17.1 ± 6.1 14.6 ± 7.6 18.4 ± 5.6 -0.33.± 1.04 20/20/60 0.19.± 1.04 51/23/27 Tackles (n) 0.13 ± 0.05 0.10 ± 0.02 0.13 ± 0.05 -0.85 ±1.10 6/11/83 0.01 ±1.1 38/25/37 Passes (n) 0.03 ± 0.03 0.06 ± 0.04 0.05 ± 0.07 0.77 ±0.86 87/9/4 0.41 ±0.57 74/22/4 Carries (n) 0.10 ± 0.08 0.09 ± 0.06 0.10 ± 0.06 -0.07 ±0.72 25/37/37 -0.06 ±0.69 25/39/36 Tackles (%) 0.87 ± 0.07 0.80 ± 0.19 0.90 ± 0.09 -0.48 ±0.80 8/19/73 0.35 ±1.40 57/18/25 Passes (%) 0.97 ± 0.07 0.85 ± 0.22 0.96 ± 0.08 -0.63 ±0.95 7/14/79 -0.06 ±2.20 41/13/45 Average Gain/Carries (m) 1.35 ± 0.72 1.14 ± 0.67 1.22 ± 1.13 -0.27 ±1.40 28/18/53 -0.13 ±0.63 18/40/42 Effective playing time (min) 35.3 ± 8.5 32.2 ± 0.2 33.1 ± 2.5 -0.5± 0.74 6/18/76 -0.34± 0.74 12/25/63 TD: Total distance; HSR: High speed running; HMLD: High metabolic load distance; ES: Effect size; % chances: % chances that the true difference is +ive/ trivial/ -ive. https://doi.org/10.1371/journal.pone.0186874.t003 Match performance in elite u20 rugby union PLOS ONE | https://doi.org/10.1371/journal.pone.0186874 November 14, 2017 9 / 15 practitioners for selection/non-selection or substitutions of players during the present con- gested competition, these results again tend to suggest that rotation strategies were employed to avoid over-exposure. Similar findings have been previously identified in an elite association football club [14]. However, before any generalisations can be made additional work is neces- sary to determine exposure time and identify the reasons for rotation strategies across all par- ticipating teams and multiple u20 World Cup competitions. Overall team performance Analyses of running and skill related performance (excluding players competing for less than 60-min) for the two teams as a whole across the 5-match schedule reported no notable changes from match to match. It is noteworthy that during the final match of the series, small to mod- erate increases in values were observed for the total distance covered, HMLD, number of accel- erations and HMLD.Peak5-min compared to those recorded in matches 1 and 2 in both backs and forwards. The frequency of passes, successful passes and tackles and average gains per Fig 2. Differences in total distance covered (panel A) and high metabolic load distance covered (panel B) between the mean 10-min versus the final 10-min period, from match 3 to match 5. Grey zone stands for trivial zone (effect size ± 0.2). Grey circles: Individual observations. Black circle and bar: Mean and standard deviation. https://doi.org/10.1371/journal.pone.0186874.g002 Match performance in elite u20 rugby union PLOS ONE | https://doi.org/10.1371/journal.pone.0186874 November 14, 2017 10 / 15 carry were lower in match 5 versus match 1 whereas the frequency of tackles and carries were higher. However, the effect sizes for these differences ranged from trivial to small. Taken together, these findings suggest that the two teams as a whole coped ‘physically’ and ‘techni- cally’ with the demands of this intensive schedule. In the absence of similar data for rugby union, comparisons can only be made with other team sports such as soccer and rugby league. In two studies in elite soccer, neither skill nor running performance declined in two teams as a whole over several successive matches played over a short time period [22,23]. Junior rugby league players in contrast [12] reported an attenuation in overall distance run and that covered in high-speeds in the final two matches during an intensified competition (5x40-minute matches played over a 5-day period). Several reasonable explanations may be forwarded for this lack of a reduction in match per- formance. First, the 4-5-day interval between matches may have been sufficient to enable full physical and/or physiological recovery and readiness for the following match [24]. Second, the systematic monitoring by the teams of recovery responses (e.g., RPE, sleep quality and quan- tity, muscle soreness) following competition combined with daily training load management enables evidence-based and informed decisions on player selection policies for the forthcom- ing match [9,25]. Third, the aforementioned standardized post-match recovery interventions possibly also aided players to maintain match performance although contrasting evidence exists for their effectiveness [26,27]. Finally, the highly developed physical qualities of players at international standards could have attenuated post-match fatigue enabling a quicker recov- ery. In rugby league, both the ability to perform high-intensity running and body strength are shown to minimise post-match fatigue and muscle damage markers [28]. Work in elite rugby union populations is necessary to verify this latter explanation. Performance in “high match exposure” players A separate analysis of the final three matches of the competition (separated by 5-days recovery intervals) was conducted as these were considered the most demanding due to the standard of the opposition and stakes: semi-finals, finals or matches to determine team seeding in the fol- lowing year’s u20 world cup. In backs who participated in a minimum 75-min play in each of these latter matches and 75% of the total team’s exposure over the entire competition, there were moderate to large decreases in total distance covered, HMLD and HMLD.Peak5min overall in games. Similar magnitude drops also occurred for HSR in forwards in matches 4 and 5 versus match 3. These findings imply that running performance overall was negatively affected in high exposure players and might be associated with a progressive accumulation of fatigue. The decline could be associated to the cumulative perceptual, physical and physiological effects of participation in several matches over a short time frame. These results also demonstrate the importance of examining performance on an individual basis notably in players with greater exposure rather than simply for the team as a whole. It is important to note however that a reduction in effective playing time in matches 4 and 5 occurred. This drop might have partly contributed to the lower distances covered. Research to identify potential reasons for such match-to-match changes in running output related to effective playing time and other contex- tual factors such as score line is necessary. Similarly, simultaneous monitoring of post-match neuromuscular, blood creatine kinase, perceptual well-being, RPE and sleep responses [9] would be pertinent to complement the present external analyses of match demands. In general, work is necessary to determine the minimal time interval necessary to ensure that elite junior players are fully recovered psychologically, physically and physiologically between consecutive matches during the present tournament. Match performance in elite u20 rugby union PLOS ONE | https://doi.org/10.1371/journal.pone.0186874 November 14, 2017 11 / 15 Interestingly, despite the decrease in overall running output in matches 4 and 5 versus match 3, no decrements in total distance covered or HMLD were observed during the final 10-minutes of play compared to the mean distance run for all other 10-min periods. Thus it seems that the high exposure players were able to maintain end-match running performance even at the latter end of the congested schedule. This result contrasts with previous research showing a general trend for reductions in running distances towards the end of matches in elite senior rugby union [29–31]. A reasonable explanation for this lack of a decline could be linked to players adopting a pacing strategy in order to maintain their ability to participate in key match actions throughout the entire course of play [32]. Recent research has shown that senior international rugby union players are able to main- tain skill-related performance over the course of match-play even when declines in running performance occur [33]. Here, a large disparity in changes in the overall frequency and success rates of technical actions was observed in backs and forwards across the three final matches rendering difficult the interpretation of findings. For example, in match 5 compared to match 4 passing frequency improved in both playing positions whereas tackle frequency dropped in backs but increased in forwards. As these patterns might only be a reflection of the present two teams and related to the opposition teams each faced (standard, style of play, tactics), we sug- gest there is a need for analysis of all participating u20 teams to provide a larger sample from which more accurate conclusions can be drawn. Limitations and research perspectives While two national teams collaborated on this research project, larger sample-size studies are necessary to determine exposure time and assess player rotation strategies across all participat- ing teams and in those that are deemed to be successful or non-successful. Monitoring of the time course of various recovery markers (perceptual, physical and physiological) is also neces- sary to allow assessment of how a congested schedule impacts post-match recovery kinetics and subsequent readiness for play. Conclusions This study shows that only <60% and <40% of players participated in 4 or 5 of all matches respectively despite high availability for selection suggesting that coaching staff operated rota- tion and rest strategies. It would seem that effective squad management strategies are necessary to aid junior international teams in sustaining work rate and skill proficiency over an intensi- fied schedule as reflected in the maintaining of running and skill-related match performance by the present teams. However, in individual players reporting the highest exposure time to play especially in the most important matches (final 3 in the 5 match series), running perfor- mance over the entire match was affected to a certain extent although overall skill-related per- formance remained stable. Similarly, running performance during the latter stages of play was also stable. These results suggest that, while overall running performance tended to decrease in high exposure players, coaches can generally be confident in their players’ ability to maintain end-match physical- and skill-related performance even during congested schedules. This pos- itive result might be linked to pacing and/or post-match recovery strategies and requires fur- ther investigation. Supporting information S1 Data. Blind Global positioning system dataset. (XLSX) Match performance in elite u20 rugby union PLOS ONE | https://doi.org/10.1371/journal.pone.0186874 November 14, 2017 12 / 15 Author Contributions Conceptualization: Christopher J. Carling, Mathieu Lacome, Eamon Flanagan, Julien Piscione. Data curation: Mathieu Lacome. Formal analysis: Mathieu Lacome, Pearse O’Doherty. Funding acquisition: Julien Piscione. Investigation: Christopher J. Carling, Mathieu Lacome. Methodology: Christopher J. Carling, Mathieu Lacome, Eamon Flanagan. Project administration: Julien Piscione. Resources: Eamon Flanagan. Software: Pearse O’Doherty. Supervision: Eamon Flanagan, Julien Piscione. Validation: Christopher J. Carling, Mathieu Lacome, Eamon Flanagan, Julien Piscione. Visualization: Pearse O’Doherty. Writing – original draft: Christopher J. Carling. Writing – review & editing: Christopher J. Carling, Mathieu Lacome. References 1. Duthie, Pyne, Hooper. Applied Physiology and Game Analysis of Rugby Union. Sports Medicine. 2003; 33(13):973–91. PMID: 14606925 2. Lindsay, Draper, Lewis, Gieseg, Gill. Positional demands of professional rugby. European Journal of Sport Science. 2015; 15(6):480–7. https://doi.org/10.1080/17461391.2015.1025858 PMID: 25830235 3. Austin, Gabbett, Jenkins. Repeated high-intensity exercise in professional rugby union. Journal of Sports Sciences. 2011; 29(10):1105–12. https://doi.org/10.1080/02640414.2011.582508 PMID: 21756130 4. Smart, Gill, Beaven, Cook, Blazevich. The relationship between changes in interstitial creatine kinase and game-related impacts in rugby union. British Journal of Sports Medicine. 2008; 42(3):198–201. https://doi.org/10.1136/bjsm.2007.040162 PMID: 18048442 5. Takarada. Evaluation of muscle damage after a rugby match with special reference to tackle plays. Brit- ish Journal of Sports Medicine. 2003; 37(5):416–9. https://doi.org/10.1136/bjsm.37.5.416 PMID: 14514532 6. West, Finn, Cunningham, Shearer, Jones, Harrington, et al. Neuromuscular Function, Hormonal, and Mood Responses to a Professional Rugby Union Match. The Journal of Strength & Conditioning Research. 2014; 28(1):194. 7. Cunniffe, Hore, Whitcombe, Jones, Baker, Davies. Time course of changes in immuneoendocrine markers following an international rugby game. European Journal of Applied Physiology. 2010; 108(1):113–22. https://doi.org/10.1007/s00421-009-1200-9 PMID: 19756700 8. Roe, Till, Darrall-Jones, Phibbs, Weakley, Read, et al. Changes in markers of fatigue following a com- petitive match in elite academy rugby union players. South African Journal of Sports Medicine. 2017; 28 (1):1–4. 9. Quarrie, Raftery, Blackie, Cook, Fuller, Gabbett, et al. Managing player load in professional rugby union: a review of current knowledge and practices. British Journal of Sports Medicine. 2016; 51 (5):421–7. https://doi.org/10.1136/bjsports-2016-096191 PMID: 27506436 10. Elloumi, Maso, Michaux, Robert, Lac. Behaviour of saliva cortisol [C], testosterone [T] and the T/C ratio during a rugby match and during the post-competition recovery days. European Journal of Applied Physiology. 2003; 90(1–2):23–8. https://doi.org/10.1007/s00421-003-0868-5 PMID: 12783234 Match performance in elite u20 rugby union PLOS ONE | https://doi.org/10.1371/journal.pone.0186874 November 14, 2017 13 / 15 11. Jones, West, Harrington, Cook, Bracken, Shearer, et al. Match play performance characteristics that predict post-match creatine kinase responses in professional rugby union players. BMC Sports Sci- ence, Medicine and Rehabilitation. 2014; 6(1):1–7. 12. Johnston, Gabbett, Jenkins. Influence of an intensified competition on fatigue and match performance in junior rugby league players. Journal of Science and Medicine in Sport. 2013; 16(5):460–5. https://doi. org/10.1016/j.jsams.2012.10.009 PMID: 23245879 13. Twist, Highton, Daniels, Mill, Close. Player Responses to Match and Training Demands During an Intensified Fixture Schedule in Professional Rugby League: A Case Study. International Journal of Sports Physiology and Performance. 2017;1–22. 14. Carling, McCall, Gall, Dupont. What is the extent of exposure to periods of match congestion in profes- sional soccer players? Journal of Sports Sciences. 2015; 33(20):2116–24. https://doi.org/10.1080/ 02640414.2015.1091492 PMID: 26460635 15. Winter, Maughan. Requirements for ethics approvals. Journal of Sports Sciences. 2009; 27(10):985– 985. https://doi.org/10.1080/02640410903178344 PMID: 19847681 16. Osgnach, Poser, Bernardini, Rinaldo, Di Prampero. Energy cost and metabolic power in elite soccer: a new match analysis approach. Medicine and science in sports and exercise. 2010; 42(1):170–8. https:// doi.org/10.1249/MSS.0b013e3181ae5cfd PMID: 20010116 17. Liu, Hopkins, Gomez, Molinuevo. Inter-operator reliability of live football match statistics from OPTA Sportsdata. 2013. (13):803–21. 18. Cunningham, Shearer, Drawer, Eager, Taylor, Cook, et al. Movement Demands of Elite U20 Interna- tional Rugby Union Players. PLOS ONE. 2016; 11(4):e0153275. https://doi.org/10.1371/journal.pone. 0153275 PMID: 27055230 19. Batterham, Hopkins. Making meaningful inferences about magnitudes. International journal of sports physiology and performance. 2006; 1(1):50–7. PMID: 19114737 20. Winter, Abt, Nevill. Metrics of meaningfulness as opposed to sleights of significance. Journal of Sports Sciences. 2014; 32(10):901–2. https://doi.org/10.1080/02640414.2014.895118 PMID: 24650348 21. Hopkins, Marshall, Batterham, Hanin. Progressive Statistics for Studies in Sports Medicine and Exer- cise Science. Medicine & Science in Sports & Exercise. 2009; 41(1):3. 22. Carling, Gall, Dupont. Are Physical Performance and Injury Risk in a Professional Soccer Team in Match-Play Affected Over a Prolonged Period of Fixture Congestion? International Journal of Sports Medicine. 2011; 33(01):36–42. 23. Dellal, Lago-Peñas, Rey, Chamari, Orhant. The effects of a congested fixture period on physical perfor- mance, technical activity and injury rate during matches in a professional soccer team. British Journal of Sports Medicine. 2015; 49(6):390–4. https://doi.org/10.1136/bjsports-2012-091290 PMID: 23422422 24. McLean, Coutts, Kelly, McGuigan, Cormack. Neuromuscular, endocrine, and perceptual fatigue responses during different length between-match microcycles in professional rugby league players. International journal of sports physiology and performance. 2010; 5(3):367–83. PMID: 20861526 25. Montgomery, Pyne, Hopkins, Dorman, Cook, Minahan. The effect of recovery strategies on physical performance and cumulative fatigue in competitive basketball. Journal of Sports Sciences. 2008; 26 (11):1135–45. https://doi.org/10.1080/02640410802104912 PMID: 18608847 26. Higgins, Cameron, Climstein. Evaluation of passive recovery, cold water immersion, and contrast baths for recovery, as measured by game performances markers, between two simulated games of rugby union. The Journal of Strength & Conditioning Research. 2015;Publish Ahead of Print:1. 27. Higgins, Climstein, Cameron. Evaluation of Hydrotherapy, Using Passive Tests and Power Tests, for Recovery Across a Cyclic Week of Competitive Rugby Union. The Journal of Strength & Conditioning Research. 2013; 27(4):954. 28. Johnston, Gabbett, Jenkins, Hulin. Influence of physical qualities on post-match fatigue in rugby league players. Journal of Science and Medicine in Sport. 2015; 18(2):209–13. https://doi.org/10.1016/j.jsams. 2014.01.009 PMID: 24594214 29. Jones, West, Crewther, Cook, Kilduff. Quantifying positional and temporal movement patterns in pro- fessional rugby union using global positioning system. European Journal of Sport Science. 2015; 15 (6):488–96. https://doi.org/10.1080/17461391.2015.1010106 PMID: 25675258 30. Lacome, Piscione, Hager, Bourdin. A new approach to quantifying physical demand in rugby union. Journal of Sports Sciences. 2013; 32(3):290–300. https://doi.org/10.1080/02640414.2013.823225 PMID: 24016296 31. Roberts, Trewartha, Higgitt, El-Abd, Stokes. The physical demands of elite English rugby union. Journal of Sports Sciences. 2008; 26(8):825–33. https://doi.org/10.1080/02640410801942122 PMID: 18569548 Match performance in elite u20 rugby union PLOS ONE | https://doi.org/10.1371/journal.pone.0186874 November 14, 2017 14 / 15 32. Waldron, Highton. Fatigue and Pacing in High-Intensity Intermittent Team Sport: An Update. Sports Medicine. 2014; 44(12):1645–58. https://doi.org/10.1007/s40279-014-0230-6 PMID: 25047854 33. Lacome, Piscione, Hager, Carling. Fluctuations in running and skill-related performance in elite rugby union match-play. European Journal of Sport Science. 2016;1–12. Match performance in elite u20 rugby union PLOS ONE | https://doi.org/10.1371/journal.pone.0186874 November 14, 2017 15 / 15

Exposure time, running and skill-related performance in international u20 rugby union players during an intensified tournament.

11-14-2017

Carling, Christopher J,Lacome, Mathieu,Flanagan, Eamon,O'Doherty, Pearse,Piscione, Julien

eng

PMC8927644

ORIGINAL RESEARCH published: 03 March 2022 doi: 10.3389/fsurg.2022.851113 Frontiers in Surgery | www.frontiersin.org 1 March 2022 | Volume 9 | Article 851113 Edited by: Songwen Tan, Central South University, China Reviewed by: Xuefeng Yang, University of South China, China Wenjun Gu, Shanghai Jiaotong University School of Medicine, China *Correspondence: Peng Huang [email protected] †These authors share first authorship Specialty section: This article was submitted to Visceral Surgery, a section of the journal Frontiers in Surgery Received: 09 January 2022 Accepted: 31 January 2022 Published: 03 March 2022 Citation: Zhao S, Liu S, Wen Y, Qi Q and Huang P (2022) Analysis of the Effect of External Counterpulsation Combined With High-Intensity Aerobic Exercise on Cardiopulmonary Function and Adverse Cardiovascular Events in Patients With Coronary Heart Disease After PCI. Front. Surg. 9:851113. doi: 10.3389/fsurg.2022.851113 Analysis of the Effect of External Counterpulsation Combined With High-Intensity Aerobic Exercise on Cardiopulmonary Function and Adverse Cardiovascular Events in Patients With Coronary Heart Disease After PCI Shiming Zhao 1†, Shaowen Liu 1†, Yuan Wen 1, Qiuhuan Qi 1 and Peng Huang 2* 1 Department of Cardiology, Wuhan Hankou Hospital, Wuhan, China, 2 Intensive Care Unit, Emergency Medical Department, Wuhan Hankou Hospital, WuHan, China Purpose: To explore the intervention effect of external counterpulsation (ECP) combined with high-intensity aerobic exercise (HIAT) on patients with coronary heart disease (CHD) after PCI. Methods: 124 patients with stable CHD after PCI admitted to our hospital from June 2018 to June 2021 were selected, and all patients were divided into control group and observation group using the random number table method. The control group received conventional treatment, The observation group received ECP combined with HIAT based on the control group. The cardiorespiratory function indexes, exercise endurance indexes, incidence of major cardiovascular adverse events (MACE), Barthel index of the two groups were observed. Results: After intervention, METs max, VO2 max, VO2 max/kg, VO2 max/HR, and PP, ED, AT, and Barthel score in both groups were significantly higher than before intervention, and patients in the observation group were significantly higher than those in the control group (P < 0.05). The incidence of MACE in the observation group (3.23%) was lower than in the control group (12.90%) (P < 0.05). Conclusion: ECP combined with HIAT can improve the cardiopulmonary function of patients with CHD after PCI, and improve exercise endurance, reduce the incidence of MACE, improve patients’ ability of daily living. Keywords: coronary heart disease, external counterpulsation, high-intensity aerobic exercise, cardiopulmonary function, adverse cardiovascular events Zhao et al. External Counterpulsation/High-Intensity Aerobic Exercise INTRODUCTION Coronary heart disease (CHD), a common disease among middle-aged and elderly people, has become the leading cause of hospitalization and death in China. The onset age of this disease is generally after 60 years old, and in recent years, the prevalence of CHD has been on a rapid rise (1). With the development of science and technology and medical treatment, percutaneous coronary intervention (PCI) is increasingly used in the treatment of CHD, which is a therapeutic method for patients with coronary artery stenosis to unblock the narrowed or occluded coronary artery lumen by transcatheter technique. It has the advantages of less trauma, quick recovery and high success rate (2, 3). However, PCI is not the end of treatment for patients with CHD. Although PCI can save patients’ lives, the incidence of major cardiovascular adverse events (MACE) after PCI is high and the recovery of cardiopulmonary function after PCI is poor (4, 5). At present, only drug or surgical treatment can not completely relieve the risk factors of patients with CHD, and it is of great clinical significance to effectively stabilize the condition of patients with CHD, reduce the incidence of coronary complications, and improve the cardiopulmonary function of patients. Research has shown that the key to improving the quality of life and prognosis of patients with CHD is not only conventional drug therapy, but also somato-psychological and other integrated rehabilitation measures are equally important (6). External counterpulsation (ECP) is a non-invasive assisted circulation device, which sequentially inflates the balloon during the diastolic phase of the heart to promote blood return to the lower extremity arteries and increase coronary artery perfusion, and is beneficial to improving myocardial blood supply and increasing oxygen-carrying capacity, thus affecting cardiopulmonary function, and has become the main non-drug treatment for various angina pectoris, heart failure and other cardiovascular diseases (7, 8). In addition, cardiac rehabilitation therapy with exercise training as the core content is gradually recognized and respected by clinical health care professionals and patients. High-intensity aerobic training (HIAT) can reduce the body’s inflammatory reaction, improve the patient’s endothelial function, promote the establishment of coronary collateral circulation and delay coronary stenosis through high-intensity effective exercise stimulation (9). HIAT not only helps to control body weight, improve patients’ blood pressure and blood glucose, but also prevents cardiovascular events, promotes mental health, and controls risk factors of cardiovascular disease as a whole, thus improves patients’ exercise function and survival quality, and has a positive impact on patients’ prognosis (10). The aim of this study was to investigate the effect of ECP combined with HIAT on cardiopulmonary function and MACE in patients with CHD after PCI. MATERIALS AND METHODS Object 124 patients with stable CHD after PCI admitted to our hospital from June 2018 to June 2021 were selected, and all patients were divided into control group and observation group using the random number table method, with 62 cases each. Inclusion Criteria Met the diagnostic criteria of coronary heart disease (11); PCI was performed successfully for the first time within 3 months; Hemodynamics was stable after PCI; Have the condition of basic movement. Exclusion Criteria Accompanied by movement restriction diseases such as bone joints and muscles; Patients with severe arrhythmia and severe heart failure that affect ECP; Severe cardiopulmonary dysfunction; Those who were unable to perform cardiopulmonary exercise test for various reasons; Accompanied by systemic serious organic diseases; Complicated infectious diseases; Mental disorder, abnormal cognitive function, Unable to cooperate with training; Increase or decrease the amount of exercise if you did not follow the instructions. Methods The control group received conventional treatment, including drug therapy, anti-blocking rehabilitation training, and daily nursing (1). The medical staff gave the patients anti-platelet aggregation, nitrates, angiotensin-converting enzyme inhibitors, and statins (2). Integrated with the guidance of the director of our rehabilitation department, the patients performed elastic band exercises with the help of researchers to ensure that the patients did not feel any discomfort on the day of training, and instructed the patient to wear a heart rate monitor. Preparatory activities and relaxation activities were performed before exercise, relaxation movements and warm-up movements include shoulder, wrist, ankle, neck, waist, hip, knee joint activities. The patients’ blood pressure and heart rate were closely monitored during exercise, and exercise was stopped immediately if symptoms such as progressive chest pain, pale complexion, ataxia, dizziness, fatigue, and shortness of breath occurred. The training forms could simply be arranged and designed according to the movement of the joint, the resistance provided by the elastic band at 100% extension was 1.7 kg. In resistance training, each isometric contraction lasted 10s, rested for 10 s, repeated 10 times as a set of training, and each training was done with 10 sets of training (3). Health education was carried out on quitting smoking and drinking, eating regularly, exercising properly, and regulating emotions. The observation group received ECP combined with HIAT based on the control group. Patients were evaluated by cardiopulmonary exercise test before the intervention. Patients were first warmed up with a power bike for 5 min with no load and rested for 3 min with an initial power of 5 W. The power was increased at a rate of 10 W/min. Patients were kept at a speed of 50–60 r/min while pedaling training. When patients had chest pain, weakness, dyspnea and other uncomfortable symptoms, or when ECG and blood pressure monitoring reached the indications for test discontinuation, the evaluation was discontinued and peak power (PP) was recorded (1). ECP: The intervention was performed with a balloon type ECP device Frontiers in Surgery | www.frontiersin.org 2 March 2022 | Volume 9 | Article 851113 Zhao et al. External Counterpulsation/High-Intensity Aerobic Exercise (P-ECP/TM, Pushkang, Chongqing). During the treatment, the patient was lying flat on the bed, and airbags were pumped on the patient’s calves and thighs as well as buttocks, which were connected to the air compressor through an air tube. Under cardiac monitoring, the balloons were inflated and deflated simultaneously with the patient’s cardiac cycle, with sequential compression of the lower limbs and buttocks during diastole and rapid deflation of the three balloons during systole, with a counterpulsation balloon inflation pressure of 260–340 mmHg and a finger pulse wave showing a diastolic/systolic wave ratio >1.2. 1 time/d, 1 month was a course of treatment (2). HIAT: After 5 min of warm-up, patients were trained with power treadmill by bicycle with aerobic exercise intensity of 80% PP, 3 min for each group, with 1 min rest between groups, 10 groups for each training, a total of 40 min. The initial training could be carried out with 60% PP as exercise load for 7 days of adaptive training. The treatment lasted for 3 months, 1 time/d, and 3 times/week. Observation Index (1) Baseline information such as patient’s age, gender, smoking history, alcohol history, combined diseases, and postoperative course of PCI were recorded. (2) Before intervention and 3 months after intervention, the K482 cardiopulmonary exercise test training system (COSME, Italy) was used to measure the patients’ cardiorespiratory function indexes. The patients’ maximal METs (METs max), maximal oxygen uptake (VO2 max), maximal oxygen uptake every kilogram (VO2 max/kg) and maximal oxygen pulse (VO2 max/HR) were recorded. (3) Before intervention and 3 months after intervention, the K482 cardiopulmonary exercise test training system (COSME, Italy) was used to measure the exercise endurance indexes of the patients. The PP, exercise duration (ED) and anaerobic threshold (AT) in the patients’ cardiopulmonary exercise test were recorded. (4) The incidence of MACE such as angina pectoris, arrhythmia and heart failure was recorded in both groups within 3 months of intervention. (5) Before intervention and 3 months after intervention, the Barthel index was used to evaluate the patients’ ability of daily living. The scale had 10 items with a total score of 100 points, >60 points: in daily life, patients could basically take care of themselves; 40–60 points: in daily life, patients needed the help from others; 20–40 points: life needs a lot of help; <20 points: in daily life, patients completely needed the help from others. The higher the score, the stronger the independence and the smaller the dependence of the patient. Statistical Methods SPSS 22.0 software was used for analysis. The measurement data was (± s), the comparison was made by t-test, the count data was (%), and the comparison was made by χ2 test. P < 0.05 was statistically significant. RESULTS Baseline Information of the Patient There was no statistical difference in age, gender, smoking history, alcohol history, combined diseases, and postoperative course of PCI between the two groups (P > 0.05). As shown in Table 1. Cardiopulmonary Function of Patients After intervention, METs max, VO2 max, VO2 max/kg, and VO2 max/HR in both groups were significantly higher than before intervention, and patients in the observation group were significantly higher than those in the control group (P < 0.05). As shown in Figure 1. Exercise Endurance of Patients After intervention, PP, ED, and AT in both groups were significantly higher than before intervention, and patients in the observation group were significantly higher than those in the control group (P < 0.05). As shown in Figure 2. Incidence of MACE in Patients The incidence of MACE in the observation group (3.23%) was lower than in the control group (12.90%) (P < 0.05). As shown in Table 2. Ability of Daily Living of Patients After intervention, the Barthel score in both groups were significantly higher than before intervention, and patients in the observation group was significantly higher than that in the control group (P < 0.05). As shown in Figure 3. DISCUSSION PCI is one of the common clinical treatment modalities for CHD, which can effectively improve myocardial blood perfusion, promote myocardial cell recovery and improve prognosis (12). However, after PCI, the myocardial blood supply of patients with CHD is insufficient, and the oxygen-carrying capacity of the body is reduced, which leads to the decline of cardiopulmonary function and exercise endurance, easily triggers MACEs such as angina pectoris, arrhythmia, heart failure, seriously affecting the physical and mental health and life safety of patients (13). At present, the clinic attaches great importance to the rehabilitation of patients with CHD, and the intervention model with the ultimate goal of improving cardiopulmonary function, improving quality of life and returning to society is gradually applied widely. ECP is a non-medical, non-invasive physiotherapy method that increases cardiac perfusion by wrapping the patient’s buttocks and lower extremities with segmental balloons. During the diastolic phase of the heart, the balloons are sequentially inflated to promote the return of blood from the arteries of the lower extremities to the aorta and then to the arteries at all levels, thereby increasing diastolic pressure, and during the systolic phase of the heart, the balloons are rapidly deflated to allow rapid flow of blood from the aorta to the lower extremities to reduce cardiac afterload (14). The Frontiers in Surgery | www.frontiersin.org 3 March 2022 | Volume 9 | Article 851113 Zhao et al. External Counterpulsation/High-Intensity Aerobic Exercise TABLE 1 | Baseline information of patients (n, %, ¯x± s). Group Number of cases Age (years) Gender Smoking history Alcohol history <60 ≥60 Male Female Control group 62 28 (45.16%) 34 (54.84%) 31 (50.00%) 31 (50.00%) 36 (58.06%) 35 (56.45%) Observation group 62 30 (48.39%) 32 (51.61%) 27 (43.55%) 35 (56.45%) 37 (59.68%) 39 (62.90%) χ2 value 0.130 0.518 0.033 0.536 P-value 0.719 0.472 0.855 i0.464 Group Number of cases Combined diseases Postoperative course of PCI (d) Diabetes Hypertension Hyperlipidemia Control group 62 19 (30.64%) 14 (22.58%) 13 (20.96%) 40.23 ± 8.13 Observation group 62 17 (27.42%) 16 (25.81%) 12 (19.35%) 38.85 ± 8.55 χ2/t value 0.273 0.920 P-value 0.872 0.359 FIGURE 1 | Cardiopulmonary function of patients. Compared with before intervention, *P < 0.05; compared with control group, #P < 0.05. principles of ECP therapy are mainly: (1) Increase aortic diastolic pressure, increase coronary blood perfusion and improve myocardial blood supply. (2) Reduce peripheral resistance, improve blood flow, and promote the formation of coronary collateral circulation. (3) Increase the shear stress of blood flow, improve the shape and function of vascular endothelial cell, repair damaged vascular endothelium, and inhibit the development of atherosclerosis. (4) Accelerate blood flow, reduce blood viscosity, improve microcirculation while increasing the oxygen uptake capacity of the body. (5) When the balloon is Frontiers in Surgery | www.frontiersin.org 4 March 2022 | Volume 9 | Article 851113 Zhao et al. External Counterpulsation/High-Intensity Aerobic Exercise FIGURE 2 | Exercise endurance of patients. Compared with before intervention, *P<0.05; compared with control group, #P < 0.05. TABLE 2 | Incidence of MACE in patients (n, %). Group Number of cases Angina pectoris Arrhythmia Heart Failure Total incidence Control group 62 5 (8.06%) 2 (3.23%) 1 (1.61%) 8 (12.90%) Observation group 62 1 (1.61%) 1 (1.61%) 0 (0.00%) 2 (3.23%) χ2 value 3.916 P-value 0.048 constantly squeezing the lower limbs, the body’s nervous system generates micro-electrical stimulation, which is conducive to relieving muscle tension and relaxing the cerebral cortex (15–17). ECP is a non-invasive, safe, effective, and inexpensive treatment device that can reduce the discomfort of patients with CHD, control the progression of the disease, and change the exercise endurance of the patient, thereby facilitating adaptation to more intense or longer exercise (18). Physical inactivity is one of the risk factors for CHD, and long-term physical inactivity may lead to a decrease in cardiorespiratory fitness, which in turn may affect the patient’s quality of life. HIAT can positively affect the cardiovascular system of patients with CHD after PCI in many ways: (1) HIAT can promote the formation of cardiac collateral circulation, improve coronary artery blood supply and intrinsic myocardial contractility, increase coronary blood flow and capillary diffusion, and improve the circulation transportation capacity of the coronary artery, thereby reducing cardiac work and improving left ventricular myocardial function. (2) HIAT can promote adaptive changes in the structure, function and regulatory capacity of the cardiovascular system and skeletal muscle system, which can increase the density of skeletal muscle capillaries, increase the number of myocardial capillaries, improve the supply of peripheral blood, increase the oxygen uptake capacity of skeletal muscle, so as to meet the body’s demand for oxygen and reducing the load on the heart. (3) Aerobic exercise can increase the shear stress of coronary blood flow, stimulate the production and release of nitric oxide synthase in vascular endothelial cells, improve the vasodilatory capacity of endothelial intact coronary arteries, improve the function of peripheral vascular endothelial cells, Frontiers in Surgery | www.frontiersin.org 5 March 2022 | Volume 9 | Article 851113 Zhao et al. External Counterpulsation/High-Intensity Aerobic Exercise FIGURE 3 | Ability of daily living of patients. Compared with before intervention, *P < 0.05; compared with control group, #P < 0.05. and thus increase myocardial perfusion. (4) HIAT enhances the oxygen utilization capacity and aerobic metabolism of muscle groups, improves mitochondrial function of cardiomyocytes, which in turn increases cardiovascular effects, improves overall patient function, and reduces the incidence of cardiovascular events. (5) HIAT reduces coronary stent lumen loss in patients with CHD after PCI, and this may be closely related to a reduction in the patient’s systemic inflammatory response (19– 21). Villelabeitia-Jaureguizar have found that compared with moderate-intensity aerobic exercise, although the patients are more laborious during HIAT, the duration of HIAT is short, and interval rest can avoid excessive fatigue and discomfort, which makes the patient’s tolerance higher (22). At the same time, HIAT brings stronger exercise stimulation to patients, and the higher the intensity of exercise, the higher the cardiorespiratory fitness of patients with CHD. METs max can reflect the level of cardiac energy metabolism and exercise capacity; VO2 max indicates the body’s maximum aerobic metabolic capacity, cardiac output and cardiac reserve function, and VO2 max is the gold standard for evaluating cardiopulmonary function; VO2 max/kg corrects the effect of body weight on oxygen uptake and was a predictor of cardiovascular events; VO2 max/HR can reflect the oxygen intake capacity of the heart’s stroke volume. PP is the maximum exercise load that the patient can tolerate in the cardiopulmonary exercise test; ED is the exercise time that the patient lasted from the beginning to the end of the cardiopulmonary exercise test evaluation; AT is the critical value of the transition from aerobic metabolism to anaerobic metabolism when the body performs increasing load exercise, which can reflect the body’s maximum aerobic exercise capacity. In this study, METs max, VO2 max, VO2 max/kg, VO2 max/HR, PP, ED, and AT of patients in the observation group were significantly higher than those in the control group, suggesting that ECP combined with HIAT can improve the cardiopulmonary function and exercise endurance of patients with CHD after PCI. In addition, we found that patients with CHD after PCI had a lower incidence of MACE and better daily living ability after interventions. The traditional single rehabilitation training model cannot provide sufficient training volume to resist the patient’s physical strength loss and cannot achieve the goal of motor learning optimization through sufficient repetitive activities, so the therapeutic effect is limited. In contrast, ECP and HIAT can improve myocardial oxygen supply, enhance the physical performance of patients, and relieve or even reduce the occurrence of angina pectoris and arrhythmias. The combined application of the two methods will eliminate obesity and bad mood and other risk factors of cardiovascular and cerebrovascular diseases, help patients gradually recover their ability to perform activities of daily living and improve the quality of survival (23, 24). It is worth mentioning that patients with contraindications to exercise can also be treated with ECP. Clinicians can give ECP to patients with CHD first, and then start HIAT when the patient’s condition is stable and there is no discomfort, which is safe and effective in the field of CHD rehabilitation. CONCLUSION In conclusion, ECP combined with HIAT can improve the cardiopulmonary function of patients with CHD after PCI, and improve exercise endurance, reduce the incidence of MACE, improve patients’ ability of daily living. This intervention brings a new model for cardiac rehabilitation. In this study, in order to ensure the uniformity of aerobic exercise intervention intensity for patients, we only used one form of exercise to train patients. In addition, when performing cardiopulmonary exercise test, due to insufficient exercise cooperation and subjective exercise effort of patients, this may affect the research results. At the same time, cardiopulmonary exercise test also require relatively high operation requirements for professional technicians. Therefore, this study needs to expand the sample size, prolong the observation time, and choose the exercise form according to the patient’s personal interests in the future, so as to further prove the long-term efficacy of ECP combined with HIAT. DATA AVAILABILITY STATEMENT The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s. ETHICS STATEMENT The studies involving human participants were reviewed and approved by the Ethics Committee of the WuHan HanKou Hospital. The patients/participants provided their written informed consent to participate in this study. AUTHOR CONTRIBUTIONS PH was the director of the entire study. All authors of this study made equal contributions, mainly including the design of the study, the inclusion of cases, the detection of results, the statistics of the data, and the writing of the paper. Frontiers in Surgery | www.frontiersin.org 6 March 2022 | Volume 9 | Article 851113 Zhao et al. External Counterpulsation/High-Intensity Aerobic Exercise REFERENCES 1. Chiarito M, Mehilli J. Left main coronary artery disease: when and how to perform PCI?. Minerva Cardioangiol. (2020) 68:405–14. doi: 10.23736/S0026-4725.20.05198-1 2. Al-Lamee RK, Nowbar AN, Francis DP. Percutaneous coronary intervention for stable coronary artery disease. Heart. (2019) 105:11–9. doi: 10.1136/heartjnl-2017-312755 3. Burzotta F, Lassen JF, Banning AP, Lefèvre T, Hildick-Smith D, Chieffo A, et al. Percutaneous coronary intervention in left main coronary artery disease: the 13th consensus document from the European bifurcation club. Euro Interven. (2018) 14:112–20. doi: 10.4244/EIJ-D-18-00357 4. Sattar S, Hussain S, Aijaz S, Khan G, Akhter Z, Malik R, et al. Major adverse cardiovascular events in patients undergoing percutaneous coronary intervention or coronary artery bypass graft with underlying chronic kidney disease. J Pak Med Assoc. (2020) 70:1901–7. doi: 10.5455/JPMA.22790 5. Berger A, Simpson A, Leeper NJ, Murphy B, Nordstrom B, Ting W, et al. Real-world predictors of major adverse cardiovascular events and major adverse limb events among patients with chronic coronary artery disease and/or peripheral arterial disease. Adv Ther. (2020) 37:240– 52. doi: 10.1007/s12325-019-01132-z 6. Salzwedel A, Jensen K, Rauch B, Doherty P, Metzendorf MI, Hackbusch M, et al. Effectiveness of comprehensive cardiac rehabilitation in coronary artery disease patients treated according to contemporary evidence based medicine: update of the cardiac rehabilitation outcome study (CROS-II). Eur J Prev Cardiol. (2020) 27:1756–74. doi: 10.1177/2047487320905719 7. Raza A, Steinberg K, Tartaglia J, Frishman WH, Gupta T. enhanced external counterpulsation therapy: past, present, and future. Cardiol Rev. (2017) 25:59– 67. doi: 10.1097/CRD.0000000000000122 8. Li B, Chen S, Qi X, Wang W, Mao B, Du J, et al. The numerical study on specialized treatment strategies of enhanced external counterpulsation for cardiovascular and cerebrovascular disease. Med Biol Eng Comput. (2018) 56:1959–71. doi: 10.1007/s11517-018-1834-z 9. Ellingsen Ø, Halle M, Conraads V, Støylen A, Dalen H, Delagardelle C, et al. High-intensity interval training in patients with heart failure with reduced ejection fraction. Circ. (2017) 135:839–49. doi: 10.1161/CIRCULATIONAHA.116.022924 10. Gomes Neto M, Durães AR, Conceição LSR, Saquetto MB, Ellingsen Ø, Carvalho VO. High intensity interval training vs. moderate intensity continuous training on exercise capacity and quality of life in patients with heart failure with reduced ejection fraction: a systematic review and meta- analysis. Int J Cardiol. (2018) 261:134–41. doi: 10.1016/j.ijcard.2018.02.076 11. Joseph J, Velasco A, Hage FG, Reyes E. Guidelines in review: comparison of ESC and ACC/AHA guidelines for the diagnosis and management of patients with stable coronary artery disease. J Nucl Cardiol. (2018) 25:509– 15. doi: 10.1007/s12350-017-1055-0 12. Fortier JH, Ferrari G, Glineur D, Gaudino M, Shaw RE, Ruel M, et al. Implications of coronary artery bypass grafting and percutaneous coronary intervention on disease progression and the resulting changes to the physiology and pathology of the native coronary arteries. Eur J Cardiothorac Surg. (2018) 54:809–16. doi: 10.1093/ejcts/ezy171 13. Ma L, Deng L, Yu H. The effects of a comprehensive rehabilitation and intensive education program on anxiety, depression, quality of life, and major adverse cardiac and cerebrovascular events in unprotected left main coronary artery disease patients who underwent coronary artery bypass grafting. Ir J Med Sci. (2020) 189:477–88. doi: 10.1007/s11845-019-02129-x 14. Kulchitskaya DB, Shovkun TV, Yarnykh EV, Konchugova TV, Knyazeva TA, Gushchina NV, et al. Impact of external counterpulsation on microcirculation in patients with coronary heart disease complicated by chronic heart failure after surgical and endovascular myocardial revascularization. Vopr Kurortol Fizioter Lech Fiz Kult. (2019) 96:5–10. doi: 10.17116/kurort20199 60515 15. Tan S, Hadinoto K, Ebrahimi A, Langrish T. Fabrication of novel casein gel with controlled release property via acidification, spray drying and tableting approach. Colloids Surf B Biointer. (2019) 177:329–37. doi: 10.1016/j.colsurfb.2019.02.019 16. Zhou ZF, Wang DJ, Li XM, Zhang CL, Wu CY. Effects of enhanced external counterpulsation on exercise capacity and quality of life in patients with chronic heart failure: a meta-analysis. Med. (2021) 100:e26536. doi: 10.1097/MD.0000000000026536 17. Sardari A, Hosseini SK, Bozorgi A, Lotfi-Tokaldany M, Sadeghian H, Nejatian M. Effects of enhanced external counterpulsation on heart rate recovery in patients with coronary artery disease. J Tehran Heart Cent. (2018) 13:13–7. 18. Xu L, Chen X, Cui M, Ren C, Yu H, Gao W, et al. The improvement of the shear stress and oscillatory shear index of coronary arteries during enhanced external counterpulsation in patients with coronary heart disease. PLoS ONE. (2020) 15:e0230144. doi: 10.1371/journal.pone.0230144 19. Villelabeitia-Jaureguizar K, Vicente-Campos D, Senen AB, Jiménez VH, Garrido-Lestache MEB, Chicharro JL. Effects of high-intensity interval vs. continuous exercise training on post-exercise heart rate recovery in coronary heart-disease patients. Int J Cardiol. (2017) 244:17–23. doi: 10.1016/j.ijcard.2017.06.067 20. Quindry JC, Franklin BA, Chapman M, Humphrey R, Mathis S. Benefits and risks of high-intensity interval training in patients with coronary artery disease. Am J Cardiol. (2019) 123:1370–7. doi: 10.1016/j.amjcard.2019.01.008 21. Hollings M, Mavros Y, Freeston J, Fiatarone Singh M. The effect of progressive resistance training on aerobic fitness and strength in adults with coronary heart disease: a systematic review and meta-analysis of randomised controlled trials. Eur J Prev Cardiol. (2017) 24:1242–59. doi: 10.1177/2047487317713329 22. Villelabeitia-Jaureguizar K, Vicente-Campos D, Berenguel Senen A, Hernández Jiménez V, Ruiz Bautista L, Barrios Garrido-Lestache ME, et al. Mechanical efficiency of high vs. moderate intensity aerobic exercise in coronary heart disease patients: a randomized clinical trial. Cardiol J. (2019) 26:130–7. doi: 10.5603/CJ.a2018.0052 23. Li B, Wang W, Mao B, Yang H, Niu H, Du J, et al. Long-term hemodynamic mechanism of enhanced external counterpulsation in the treatment of coronary heart disease: a geometric multiscale simulation. Med Biol Eng Comput. (2019) 57:2417–33. doi: 10.1007/s11517-019-02028-4 24. Dun Y, Smith JR, Liu S, Olson TP. High-Intensity Interval Training in Cardiac Rehabilitation. Clin Geriatr Med. (2019) 35:469–87. doi: 10.1016/j.cger.2019.07.011 Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher’s Note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Copyright © 2022 Zhao, Liu, Wen, Qi and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Frontiers in Surgery | www.frontiersin.org 7 March 2022 | Volume 9 | Article 851113

Analysis of the Effect of External Counterpulsation Combined With High-Intensity Aerobic Exercise on Cardiopulmonary Function and Adverse Cardiovascular Events in Patients With Coronary Heart Disease After PCI.

03-03-2022

Zhao, Shiming,Liu, Shaowen,Wen, Yuan,Qi, Qiuhuan,Huang, Peng

eng

PMC9821460

RESEARCH ARTICLE Auditory interaction between runners: Does footstep sound affect step frequency of neighboring runners? Hiroaki FurukawaID1*, Kazutoshi Kudo1,2*, Kota Kubo3☯, Jingwei Ding4☯, Atsushi Saito5 1 Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan, 2 Graduate School of Interdisciplinary Information Studies, The University of Tokyo, Tokyo, Japan, 3 Faculty of Occupational Therapy, Department of Rehabilitation, Kyushu Nutrition Welfare University, Kitakyushu, Fukuoka, Japan, 4 Graduate School of Human-Environment Studies, Kyushu University, Fukuoka, Japan, 5 Faculty of Human-Environment Studies, Kyushu University, Fukuoka, Japan ☯ These authors contributed equally to this work. * [email protected] (HF); [email protected] (KK) Abstract This study aimed to investigate the effect of footsteps of a neighboring runner (NR) on the main runner’s step frequency (SF), heart rate (HR), and rating of perceived exertion (RPE). The participants were male long-distance runners belonging to a university track and field team. Two experiments were conducted in which the main runner (participant) and NR (examiner) ran with the same running speed on two adjacent treadmills separated by a thin wall. The participants were instructed that the experimental purpose was to investigate the HR when running with others and running alone. In Experiment 1, NR performed three trials of changing the footstep tempo in 5 bpm (beat per minute) faster (+5bpmFS), 5 bpm slower (-5bpmFS), or no footsteps (NF) conditions. The results showed that the footstep condition affected the variability of the SF but not the mean SF. Next, Experiment 2 was conducted by increasing the footstep tempo condition. NR performed seven trials of changing the footstep tempo by ±3 bpm, ±5 bpm, ±10 bpm, or no footstep. The results showed that the footstep condition affected the mean SF and the SF decreased at -10bpmFS compared to NF. There were no differences in the HR and RPE between conditions. These results indicated that the footsteps of NR could influence the SF, although it was unclear whether footsteps were involved in the synchronization between runners. Overall, our findings emphasize the envi- ronmental factors that influence running behavior, including the NR’s footsteps. Introduction In running competitions, there are two types of situations: running alone and running with people. In the 60m, 1500m and 3000m time trials, studies show that the performance can be better in head-to-head than when running alone [1–3]. It is not clear why running with others improves performance in long-distance running, or how the differences in the conditions affect performance. Previous studies have reported the following factors: drafting (reduction of PLOS ONE PLOS ONE | https://doi.org/10.1371/journal.pone.0280147 January 6, 2023 1 / 19 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Furukawa H, Kudo K, Kubo K, Ding J, Saito A (2023) Auditory interaction between runners: Does footstep sound affect step frequency of neighboring runners? PLoS ONE 18(1): e0280147. https://doi.org/10.1371/journal. pone.0280147 Editor: Yury Ivanenko, Fondazione Santa Lucia Istituto di Ricovero e Cura a Carattere Scientifico, ITALY Received: September 21, 2021 Accepted: December 21, 2022 Published: January 6, 2023 Copyright: © 2023 Furukawa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Our all dataset are publicly available from https://doi.org/10.6084/m9. figshare.21779501. Funding: Japan Society for the Promotion of Science 22J15395 Hiroaki Furukawa Grants-in-Aid for Scientific Research(KAKENHI) 20H04571 Kazutoshi Kudo. Competing interests: The authors have declared that no competing interests exist. aerodynamic drag by the preceding runner) [4–6], improvement of arousal level by social facil- itation [7–9], and changes in attentional focus [10–12]. Each runner has a unique step frequency (SF) (i.e. the number of steps per minute) that optimizes their performance [13, 14]. However, the SF of two neighboring runners (NR) may leave their unique range and intermittently get close, which is considered to be a "synchroniza- tion" between runners [13]. In the 100m final of the 2009 World Championships in Athletics in Berlin, Usain Bolt set a world record, and Tyson Gay, who came in second, set the world’s second-best record. Analysis of SF of these two runners revealed that although their unique SFs were different in the semifinals, they were intermittently close in the finals, suggesting the possibility of synchronization [13]. However, it is not clear what kind of visual or auditory information causes this phenomenon. Moreover, no previous study has demonstrated that synchronization has a positive effect on running performance. As it occurs even in top athletes with optimized running movements, it is desirable to study the relationship between synchro- nization and performance and how it occurs [13]. It has been widely shown that auditory information entrains movement tempo (i.e. the number of beats or steps per minute; e.g., see Refs. 15–20), and this phenomenon is called the "entrainment" of movement tempo by auditory information [15, 16]. Auditory information with a certain tempo can also entrain SF of walking and running movements and that the tempo of SF approaches that of auditory information [16–20]. "Music” has elements of melody and harmony along with tempo and also simple beat sounds (e.g., metronome) which entrain SF of walkers and runners [19, 20]. A characteristic auditory information during running with others is the footsteps of others, which may cause SF entrainment. A meta-analysis of the effects of music on the feeling scale, heart rate (HR), oxygen con- sumption (VO2), rating of perceived exertion (RPE), and performance has shown that listen- ing to music during exercise improves the feeling scale, VO2, RPE, and performance [21]. Specifically, synchronizing the tempo of auditory information with SF reduces physiological load and produces better performance [22, 23]. Even for simple beats without melody and har- mony, SF-synchronized beats can improve performance [23]. This positive effect has been attributed to the improvement in contractile efficiency of active muscles and the reduction in metabolic cost due to synchronization with auditory stimulation [22, 24, 25]. When running with another runner, the footsteps of the other runner can be considered as external auditory information, and when SF synchronization occurs between the two runners [13], the footsteps of the other runner approach a state in which the auditory stimuli are syn- chronized with SF. Similar to a metronome or music, it may affect SF synchronization, physio- logical load, and performance. In an experiment in which two participants walked side-by-side while their visual informa- tion was cut off, their SFs synchronized even when they were not instructed to do so [26–28]. Hence, the sound of each other’s footsteps (auditory information) may affect SF, resulting in unintentional synchronization. However, it has not been clarified whether the footsteps of other runners affect SF during running, and no study shows a relationship between uninten- tional synchronization between two runners and their footsteps. Moreover, the effects of the footsteps of other runners on physiological load, RPE, and performance have not been suffi- ciently examined. Therefore, the study aims to investigate (1) whether the different footstep tempo of the NR affect the SF of the main runner, and (2) the effects of NR footsteps on HR and RPE of the main runner. In Experiment 1, we hypothesized that the footstep tempo of an NR would cause entrain- ment of the main runner’s SF, and examined its effect on SF. We set up an experimental situa- tion in which the footsteps of a NR running side-by-side were manipulated based on the SF of PLOS ONE Auditory interaction between runners PLOS ONE | https://doi.org/10.1371/journal.pone.0280147 January 6, 2023 2 / 19 the main runner to study the effect of the footsteps of one runner on the other. In Experiment 2, larger number of tempo conditions were adopted than those in Experiment 1 in order to examine the effect of the wider range of footstep tempo on the SF. Methods Participants Healthy male trained distance runners participated in Experiment 1 (N = 10) and Experiment 2 (N = 16). In Experiment 2, one participant was excluded from this analysis because he had a within-participant standard deviation (SD) of SF greater than 3 SDs from the mean across par- ticipants. Therefore, we adopted the data for 15 participants. The mean ages (±SD) were 20.7 ±1.4 years and 20.9±1.6 years, the mean height was 169.4±4.8 cm and 170.4±3.8 cm, the mean body mass was 56.5±3.6 kg and 56.1±4.2 kg, the best time for a 5000-m in the current year was 16 min 22s ± 1 min 1 s and 16 min 6 s±44 s, in Experiments 1 and 2, respectively. They had practiced distance running on a university track and field team, and had trained for at least 40 minutes per day, four days a week for the past month. Four out of ten participants in Experi- ment 1 also participated in Experiment 2. Before the experiment, we explained the outline and possible risks in writing and orally to the participants, and obtained their consent to partici- pate. This study was approved by the Ethics Committee of the Department of Health and Sports Science, Graduate School of Human and Environmental Studies, Kyushu University. Experiment 1 Experimental procedure and setup. After arriving at the laboratory, the participants’ blood pressure, resting HR, and body mass were recorded, and the experimental procedures were explained. The participants were instructed that the purpose was to conduct "an experi- ment to investigate the HR when running alone and with two people," but the original purpose was not revealed. After stretching, an HR measurement sensor (WearLink+ Coded Transmit- ter 31 XS-S, Polar) was attached to the chest, and an acceleration sensor (Stride Sensor WIND, Polar) was attached to the right shoe’s laces. Using these devices, HR and SF data were obtained every 5 s as beats per minute (bpm) and rotations per minute (rpm). A 5-min warm- up run was performed on one of the two adjacent treadmills (right side). The first trial of the experiment was started after a 5-min break. The running speed in the warm-up run and the main experiment was equivalent to approximately 70% HRmax. Based on the American Heart Association, the maximum HR estimated as “220-age” was used [29]. Stimuli. A thin wall (200 cm long, 170 cm wide, 6 cm thick, white) was placed between the two treadmills so that the runners could not see each other and only hear the footsteps (Fig 1). The running time per trial was 7 min and 30 s, and a constant speed was set, resulting in approximately 70% of HRmax (hereinafter “setting speed”). Totally, three trials were per- formed at the same setting speed. The following three conditions (1) to (3) were randomized and performed one at a time in a counterbalanced order. The rest period between trials was five minutes. Referring to the study by Dyck et al. [16], which showed that the tempo of music approached the running SF, the change rate in the footstep tempo of NR was set at ±5 bpm (equivalent to approximately ±3%). Conditions. (1) Footsteps +5 bpm condition (+5bpmFS) The participant ran at a setting speed for 7 min and 30 s, while the NS ran at the same speed for the same time, manipulating his steps. In the first 5 min, the NS listened to a metronome beeping at the same tempo as the participant’s cadence (i.e. the number of PLOS ONE Auditory interaction between runners PLOS ONE | https://doi.org/10.1371/journal.pone.0280147 January 6, 2023 3 / 19 ipsilateral steps per minute) and synchronized with the sound. Therefore, the participant listened to both his footsteps and that of the NR, which were generated at a tempo approxi- mated to their SF. The NR increased the cadence by 2.5 rpm from 5 min after the start of each trial, based on the participant’s average cadence from 4 min 40 s to 5 min ("reference time 1"). Five minutes after the start of each trial, the NR increased the cadence by 2.5 rpm. This operation increased SF (the number of steps per min) by 5 steps per minute (spm), and the participant heard footsteps 5 bpm faster. The change in the cadence of NR was performed in 5 s. For example, if the participant was running at a cadence of 90 rpm, 5 min after the start of the run, the cadence of the NR was increased to 92.5 rpm over 5 s. The participant heard footsteps with a tempo of 185 spm (92.5 rpm or 185 bpm) after 5 min and 5 s. To adjust the tempo of the metronome sound, the cadence of the participant measured by the accelerometer was displayed on a running computer (RS800CX, Polar), and the tempo of the metronome was adjusted to this value. (2) Footsteps -5 bpm condition (-5bpmFS) As in the +5bpmSF condition, the participant ran at a setting speed for 7 min and 30 s, while the NR ran at the same speed simultaneously and manipulated his steps. The NR’s cadence was set to twice that of the participant’s mean at the reference time 1, and his SF was slowed down by 5 spm from 5 min after the start of each trial. The rest of the proce- dure was the same as described in (1). (3) No footstep condition (NF). The NR walked silently from the start to the end of the run. The participant ran at a con- stant running speed for 7 min and 30 s. Measurements Cadence. The cadence was measured every 5 s using an accelerometer attached to the right shoe’s laces and recorded on a running computer (RS800CX, Polar). The cadence at ref- erence time 1 was defined as the "reference cadence," and after reference time 1 was defined as the "post-change cadence." To confirm the degree of increase or decrease in cadence due to the change in tempo of the participants’ footsteps, the ratio of the change to the mean reference Fig 1. Experimental situation. https://doi.org/10.1371/journal.pone.0280147.g001 PLOS ONE Auditory interaction between runners PLOS ONE | https://doi.org/10.1371/journal.pone.0280147 January 6, 2023 4 / 19 cadence was calculated as the "SF change rate" using the following formula: SF change rate ð%Þ ¼ PostResults of Experiment 1 SF change rate Fig 2 shows the SF change rate every 5 s in the +5bpmFS (A), -5bpmFS (B), and NF conditions. A two-way (condition × time) ANOVA showed that there was no interaction (F[5.05, 45.47] = 1.44, p = 0.23, Z2 p = 0.14). There was no significant main effect of the condition on SF change rate (F[2, 18] = 0.80, p = 0.47, Z2 p = 0.082). However, there was the marginal main effect of time on SF change rate (F[4.51, 40.62] = 2.41, p = 0.058, Z2 p = 0.21). Multiple comparison showed no differences between conditions. The summarized mean SF change rate from 5 min after the start of each trial to the end is shown in Fig 3. While the NF data were concentrated around 0%, the variation for the ±5bpmFS seemed to be larger. Fig 4 shows the mean SD of the SF change rate from 5 min after the start of each trial to the end. One-way (condition) ANOVA showed that there was a marginally significant main effect of condition on the SD of SF change rate (F[2, 18] = 3.17, p = 0.066, Z2 p = 0.26). Multiple com- parisons showed there were no differences between conditions. HR Fig 5A shows the mean HR from 0 to 5 min, and from 5 to 7 min and 30 s in each condition. To investigate the effect of the different footstep tempos of the NR, the HR was divided into two sections: 0 to 5 min, where the NR’s footsteps and the participant’s SF were synchronized; however, were different after 5 min. A two-way (condition × time) ANOVA was performed on the HR, and the results showed that there was no interaction (F[2, 18] = 0.22, p = 0.80, Z2 p = 0.024) and no main effect of condition on HR (F[2, 18] = 0.70, p = 0.51, Z2 p = 0.072). However, the main effect of time on HR was significant (F[1, 9] = 74.23, p = 0.000, Z2 p = 0.89). Fig 2. Changes in the SF change rate every 5 s in Experiment 1. The error bars represent between-participant SD. Compared to no footsteps condition (NF), the step frequency seems to decrease in the +5 bpm (A) and -5 bpm (B) footsteps condition but there were no significant differences between these conditions. https://doi.org/10.1371/journal.pone.0280147.g002 PLOS ONE Auditory interaction between runners PLOS ONE | https://doi.org/10.1371/journal.pone.0280147 January 6, 2023 6 / 19 RPE Fig 5B shows the mean RPE at 5 min and 7 min after the start of each trial. A two-way (condition × time) ANOVA for RPE showed no interaction (F[2, 18] = 1.16, p = 0.34, Z2 p = 0.11), and there was no main effect of either condition (F[2, 18] = 0.56, p = 0.58, Z2 p = 0.058) or time (F[2, 18] = 1.47, p = 0.26, Z2 p = 0.14). Discussion Effect of the footsteps of the NR on SF In this study, we examined whether the different footstep tempos of the NR affected the main runners’ SF. The participants ran at a constant speed for 7 min and 30 s, and listened to the footsteps of the NR with the same tempo for the first 5 min, and then to the footsteps of the NR whose tempo was 5 bpm faster or slower after 5 min. The SF of well-trained run- ners was reported to show small variability when they run at around comfortable speed [31]. In this study, we used a running speed that was considered to be comfortable, and when there was no perceptual information from the NR in the NF condition, the SF change rate showed little variation and the SD was small. However, when the main runner heard the footsteps of the NR and the tempo change (±5bpmFS), the SF change rate was highly variable, and the SD was large. This suggests that the footsteps of the NR may have affected the SF of the main runner. The study aimed to investigate the effect of the footsteps of the NR on the SF of a main runner’s free-running motion without instruction. For this, the original purpose was not communicated, and the participants were instructed that the experiment investigated the HR when running alone and with others. As an interview was conducted after each running condition, one of the participant said, "I felt that the SF of the person next to me became faster," and recognized the change in tempo of the footsteps of the NR. However, there was no statement that they tried to match SF to the faster footsteps, and there was no major Fig 3. Summarized mean SF change rate in Experiment 1. Each plot shows data for one participant and the data are the mean of the Step frequency (SF) change rate from 5 min after the start of each trial to the end of the trial. The data for the same participants are plotted in the same color. The means for each condition are connected by lines and the error bars represent between-participant SD. The plots were concentrated around 0% in NF, whereas the variability was larger in -5bpmFS and +5bpmFS. https://doi.org/10.1371/journal.pone.0280147.g003 PLOS ONE Auditory interaction between runners PLOS ONE | https://doi.org/10.1371/journal.pone.0280147 January 6, 2023 7 / 19 change in the SF change rate. This suggests that the SF fluctuation was not affected by the intention of this study, but occurred spontaneously. Another participant reported that he intentionally tried to match his SF to the footsteps of the NR in the -5 bpm condition. The SF decreased along with the tempo of the footsteps of the NR; however, the decrease of 5 spm did not occur in 5 s along with the change in the tempo, but gradually decreased over approximately 1 min. The participants were not instructed to synchronize their SF with the footsteps of the NR, indicating that the footsteps may encourage intentional and spontane- ous SF synchronization. The other participants did not report intentionally trying to match their SF to the tempo of the NR footsteps. SF entrainment by the footsteps of the NR Dyck et al. [16] found that a tempo change of 3% or less from the NR’s SF caused its entrain- ment in music. In the ±5bpmFS, the footstep tempo was changed by ±5 bpm, which corre- sponds to a change of approximately ±3% from the NR’s SF. Fig 4. Mean SD of SF change rate in Experiment 1. Each plot shows data for one participant, and the data are the mean SD of the step frequency (SF) change rate from 5 min after the start of each trial to the end. The data for the same participants are plotted using the same color. The means for each condition are connected by lines and the error bars represent between-participant SD. https://doi.org/10.1371/journal.pone.0280147.g004 PLOS ONE Auditory interaction between runners PLOS ONE | https://doi.org/10.1371/journal.pone.0280147 January 6, 2023 8 / 19 Similar to music, it was expected that SF would increase with a tempo change of +5 bpm and show a decrease of -5 bpm for footsteps, but no significant differences were found between the conditions. This may have been because of a large change in footstep tempo. The closer the music tempo was to the NR’s SF, the greater the entrainment effect [16]. However, it has been shown that a large frequency difference between two oscillators results in smaller entrainment effects and larger SD of the oscillation frequency [32]. In this study, the increased SD of SF under conditions with NR suggests that the tempo change was beyond the entrainment basin. We conducted Experiment 2 by adding more tempo condi- tions, with the hypothesis that the entrainment effects would occur with tempo changes closer to the main runners’ SF. Fig 5. Mean HR and RPE in Experiment 1. HR (A) was averaged from 0 to 5 min and 5 to 7 min and 30 s, and RPE (B) was at 5 and 7 min after the start of each trial in each condition. The error bars represent the between-participant SD. There was no difference in HR or RPE between conditions. https://doi.org/10.1371/journal.pone.0280147.g005 PLOS ONE Auditory interaction between runners PLOS ONE | https://doi.org/10.1371/journal.pone.0280147 January 6, 2023 9 / 19 Results of Experiment 2 SF change rate Fig 6 shows the SF change rate every 10 s in the ±3 bpm footsteps condition (±3bpmFS), the NF, the ±5 bpm footsteps condition (±5bpmFS), and the ±10 bpm footsteps condition (±10bpmFS). A two-way ANOVA (condition × time) showed that there was no interaction between condition and time (F[8.21, 114.90] = 1.11, p = 0.36, Z2 p = 0.073), with the former hav- ing a significant main effect on the SF change rate (F[6, 84] = 3.25, p = 0.006, Z2 p = 0.19). As a result of multiple comparisons using the Bonferonni test, there was a significant difference between the NF and -10bpmFS(p = 0.010). Moreover, a significant difference between -10bpmFS and +3bpmFS was found (p = 0.011). Time had a significant main effect on the SF change rate (F[3.90, 54.54] = 2.59, p = 0.048, Z2 p = 0.16). Fig 7 shows the summarized mean SF change rate from 3 min after the start of each trial to the end of the trial. Fig 8 shows the mean SD of the SF change rate from 3 min after the start of each trial to the end. One-way (condition) ANOVA showed that there was no significant main effect of condi- tion on the SD of SF change rate (F[6, 84] = 1.32, p = 0.26, Z2 p = 0.086). HR and RPE Fig 9A shows the mean HR from 0 to 3 min, and from 3 to 5 min and 30 s in each condition. To investigate the effect of the different footstep tempos of the NR, the HR was divided into two sections: 0 to 3 min, where the footsteps of the NR and the participant’s SF were synchro- nized, and after 3 min, where the footstep tempo of the NR and the participant’s SF were dif- ferent. As a result of a two-way (condition × time) ANOVA for HR, there was no interaction (F[6, 84] = 0.99, p = 0.44, Z2 p = 0.066) and no main effect of condition (F[6, 84] = 0.42, p = 0.87, Z2 p = 0.029). Time had a main effect on HR (F[1, 14] = 11.30, p = 0.005, Z2 p = 0.45). Fig 9B shows the RPE at 3 and 5 min after the start of each trial in each condition. A two- way (condition × time) ANOVA for RPE showed there was no interaction (F[6, 84] = 0.33, p = 0.92, Z2 p = 0.023) and no main effect of condition (F[6, 84] = 0.49, p = 0.82, Z2 p = 0.034). Time had a main effect on RPE (F[1, 14] = 7.98, p = 0.014, Z2 p = 0.36). Discussion The participants ran at a comfortable constant running speed for 5 min and 30 s. After hearing the footsteps of a NR whose SF and tempo were the same as theirs for the first 3 min, they ran 3, 5, or 10 bpm faster (+3bpmFS, +5bpmFS, +10bpmFS) or 3, 5, or 10 bpm slower (-3bpmFS, -5bpmFS, +10bpmFS) after 3 min. Recognition of research purpose by the participants In Experiment 2, the participants were not told the original purpose of the study but were informed that it investigated HR when running alone and with others. In the interviews assess- ing the participants’ impressions after each condition, there was no indication that they were aware that the purpose was to measure SF. This suggests that the participants’ SF fluctuations were not affected by the purpose of the study and occurred spontaneously. PLOS ONE Auditory interaction between runners PLOS ONE | https://doi.org/10.1371/journal.pone.0280147 January 6, 2023 10 / 19 Fig 6. Changes in the SF change rate every 10 s in Experiment 2. The error bars represent between-participant SD. Both +5bpmFS (C) and -5bpmFS (D) showed a tendency to decrease, reproducing the results of Experiment 1. The increase in the SF at +3bpmFS was consistent with the hypothesis. There was a significant difference between the NF and -10bpmFS as well as -10bpmFS and +3bpmFS. https://doi.org/10.1371/journal.pone.0280147.g006 PLOS ONE Auditory interaction between runners PLOS ONE | https://doi.org/10.1371/journal.pone.0280147 January 6, 2023 11 / 19 SF entrainment by the footsteps of the NR We examined whether the change in footstep tempo closer (±3 bpm) to the main runner’s SF than ±5 bpm or farther away (±10 bpm) caused the participants’ SF to be entrained. As hypothesized, there was a trend toward increased SF at +3 bpmFS, and a significant difference was detected between NF and + -10 bpm as well as +3 bpm and -10 bpm. These results partially support the hypothesis that footstep tempo changes cause SF entrainment. It has been shown that the SF of two people walking or running side-by-side approached each other [13, 26–28, 33, 34] and it has been confirmed that footsteps are a factor that causes entrainment during walking [26–28]. However, synchronization with external information is less likely to occur at higher exercise intensities [35], and it may be less likely while running, where exercise intensity is higher than in walking. Fig 7. Summarized mean SF change rate in Experiment 2. Each plot shows data for one participant and the data are the mean of the Step frequency (SF) change rate from 3 min after the start of each trial to the end of the trial. The data for the same participants are plotted using the same color. The means for each condition are connected by lines, and the error bars represent between-participant SD. The plots were concentrated around 0% in NF, whereas the variability was larger in the other conditions. The step frequency of the main runner could be close to the footstep tempo of a neighboring runner when it ranged from -10 bpm to +3 bpm. https://doi.org/10.1371/journal.pone.0280147.g007 PLOS ONE Auditory interaction between runners PLOS ONE | https://doi.org/10.1371/journal.pone.0280147 January 6, 2023 12 / 19 SF is difficult to increase and easy to decrease Overall, compared to NF, the SF seemed to decrease under all conditions except +3 bpmFS. Previous studies reported that the ratio of SF increase to SF decrease is small and the ratio of SF increase to SF decrease is large owing to the entrainment of music [16, 17, 36]. The same asymmetric trend was observed in the present study. General discussion This study aimed to investigate the effects of footsteps of a NR on the main runner’s SF, HR, and RPE. In Experiment 1, the participants ran at a comfortable constant speed for 7 min and 30 s, and after listening to the footsteps of a NR with the same tempo for the first 5 min, and then to the footsteps of a NR whose tempo was 5 bpm faster (+5bpmFS) or slower (-5bpmFS) after 5 min. In Experiment 2, the wider range of tempo conditions were adopted than those in Experiment 1; the participants heard footsteps 3 bpm, 5 bpm, and 10 bpm faster (+3bpmFS, +5bpmFS, and +10bpmFS) or 3 bpm, 5 bpm, and 10 bpm slower (-3bpmFS, -5bpmFS, and -10bpmFS). Different tempo changes of the footsteps of the NR affect the main runner’s SF In Experiment 1, the effect of NR footsteps on the SD of SF was observed. Experiment 2, wherein the footstep tempo condition was added, showed the effect of NR footsteps on SF. In both Experiments 1 and 2, there was a decreasing trend in SF at ±5 bpmFS compared to NF, although it was not statistically significant. The main effect of time on the SF was also consis- tent between Experiments 1 and 2. These indicate the reproducibility of the results. These results showed that a change in the footstep tempo of NR caused a different SF fluctuation in the main runner than in its absence. It has been shown that auditory stimuli with a certain periodicity can activate several brain structures including the basal ganglia, which is consid- ered to be a brain region that modulates locomotion, and that predicting the tempo of auditory Fig 8. Mean SD of SF change rate in Experiment 2. Each plot shows data for one participant and the data are the mean SD of the step frequency (SF) change rate from 3 min after the start of each trial to the end. The data for the same participants are plotted using the same color. The means for each condition are connected by lines, and the error bars represent between-participant SD. NF showed the lowest mean and replicated Experiment 1, but there were no significant differences among conditions. https://doi.org/10.1371/journal.pone.0280147.g008 PLOS ONE Auditory interaction between runners PLOS ONE | https://doi.org/10.1371/journal.pone.0280147 January 6, 2023 13 / 19 Fig 9. Mean HR and RPE in Experiment 2. HR (A) was averaged from 0 to 3 min and 3 to 5 min and 30 s, and RPE (B) was at 3 and 5 min after the start of each trial in each condition. The error bars represent the SD. There was no difference in HR or RPE between conditions. https://doi.org/10.1371/journal.pone.0280147.g009 PLOS ONE Auditory interaction between runners PLOS ONE | https://doi.org/10.1371/journal.pone.0280147 January 6, 2023 14 / 19 stimuli can promote the activation [37]. Several studies have shown that auditory stimuli such as music with a periodic tempo and metronomes affect the SF of runners [16, 17, 20]. These auditory stimuli can easily predict the timing of beat to beat, resulting in significant activation of the basal ganglia, which is thought to affect the tempo of the movement (SF). Auditory-motor synchronization The tempo of a movement is entrained into a specific cycle or phase in response to external sensory stimuli. This is also called sensorimotor synchronization [38]. Specifically, the syn- chronization of the motor tempo with rhythmic auditory stimuli is called auditory-motor syn- chronization. Auditory-motor synchronization studies to date have included both instructed and uninstructed synchronization experimental paradigms, although most previous studies on auditory-motor synchronization have focused on instructed synchronization tasks, such as handheld pendulum swinging [39, 40], dancing [41, 42], and tapping [15, 43]. However, auditory-motor synchronization can occur spontaneously even when the partici- pant is not instructed to match the tempo of the movement to the auditory stimulus [16, 44]. For example, synchronization between footstep sound and steps has been extensively studied in walking. Nessler et al. [26] conducted an experiment in which two participants walked on two adjacent treadmills, and their visual or auditory information were blocked, or they also walked hand in hand. In each of these conditions, synchronization between the two walkers occurred without any instruction, indicating that it can be spontaneous between two people walking side-by-side if they are provided with visual, auditory, or tactile sensory information of the other person. Although these results partially supported the hypothesis that footstep tempo changes cause SF entrainment, there were no significant differences except between NF and -10 bpm, which is not sufficient to adequately support the hypothesis. This may be due to an increased internal focus of attention caused by a higher intensity of exercise compared to walking. As exercise intensity increases, physiological sensations dominate the attention and focus on external bodily information is reduced [45, 46]. Reduced allocation of attention to the partner causes less interpersonal synchrony [47, 48]. Many participants reported their feelings about muscle status and running movements, and this increased internal focus of attention might have inter- fered with synchrony. Further empirical research is needed, including experiments with differ- ent exercise intensities. HR and RPE In Experiments 1 and 2, the effects of NR footsteps on the main runners’ HR and RPE were examined. No differences were found among the conditions. In previous studies, synchronous music and synchronous metronomes have been shown to affect HR, oxygen uptake, and per- formance [22, 23, 49, 50]. Although music has been shown to affect RPE [21], effects of beat sounds without melody or harmony (e.g., metronome) have not been observed. Since the foot- steps of other runners are a beat sound with a constant tempo without melody or harmony, they do not reduce the psychological load during running but improve the physiological load. In the present study, the RPE was not changed by listening to the footsteps of the NR, and the HR did not change. It has been shown that each runner has a unique optimal SF that mini- mizes physiological load [14]. Therefore, it is possible that the change in the tempo of the foot- steps of the NR did not lead to an improvement in the physiological load because the NR deviated from the unique optimal SF. In the present study, considering the effect of fatigue on SF, the exercise intensity was set within the range of running speed and total running time, which were reported not to cause PLOS ONE Auditory interaction between runners PLOS ONE | https://doi.org/10.1371/journal.pone.0280147 January 6, 2023 15 / 19 fatigue in previous studies [16]. It is possible that the relatively low intensity of exercise did not affect the physiological load due to footsteps. Future studies should examine the effects of other runners’ footsteps on physiological effects and performance at exercise intensities above the anaerobic work threshold and at running speeds closer to race conditions. Limitations This study has some limitations. Previous studies reported a relationship between the number of attentional resources directed toward a partner and the occurrence of interpersonal syn- chronization during side-by-side walking [48]. However, the allocation of attentional resources in a laboratory setting was different (e.g., attention tended to be directed to footsteps or vice versa), and the results may differ from those in over-ground settings obtained through over-ground running. In this study, the exercise intensity was low to moderate. Hence, it is unclear how important footsteps were for physiological and psychological load and performance in high-intensity races. Future research is required to examine and understand these issues. Conclusion We examined the effect of the footstep sounds of adjacent runners on the SF of trained run- ners. The results showed that the footstep sounds of adjacent runners can partially influence the mean and variability of step frequency, suggesting that running step characteristics can be unintentionally modulated by auditory information generated by others during running. Future research should examine the effects of multimodal information in a wide field environ- ment, such as actual long-distance running competitions. Supporting information S1 Table. Individual running speed in Experiments 1 and 2. (PDF) Author Contributions Conceptualization: Hiroaki Furukawa. Data curation: Hiroaki Furukawa. Formal analysis: Hiroaki Furukawa, Kota Kubo, Jingwei Ding, Atsushi Saito. Funding acquisition: Hiroaki Furukawa, Kazutoshi Kudo. Investigation: Hiroaki Furukawa, Kota Kubo, Jingwei Ding, Atsushi Saito. Methodology: Hiroaki Furukawa, Atsushi Saito. Project administration: Hiroaki Furukawa. Resources: Kazutoshi Kudo, Atsushi Saito. Supervision: Kazutoshi Kudo, Atsushi Saito. Visualization: Hiroaki Furukawa. Writing – original draft: Hiroaki Furukawa, Kazutoshi Kudo, Atsushi Saito. PLOS ONE Auditory interaction between runners PLOS ONE | https://doi.org/10.1371/journal.pone.0280147 January 6, 2023 16 / 19 References 1. Yamaji K, Kawai K-I, Nabekura Y. Influences of absence (solo) and presence (head-to-head competi- tion) of a competitor on psychological factors, overall running performance, and pacing during 1500-m runs. Japan Society of Sports Performance Research. 2019. 2. Tomazini F, Pasqua LA, Damasceno M V., Silva-Cavalcante MD, de Oliveira FR, Lima-Silva AE, et al. Head-to-head running race simulation alters pacing strategy, performance, and mood state. Physiol Behav. 2015; 149: 39–44. https://doi.org/10.1016/j.physbeh.2015.05.021 PMID: 26013576 3. Kakehata G, Tsukamoto H, Goto Y, Iso S, Kanosue K. Competing against another athlete side-by-side improves 60 m sprint running performance. Sci J Sport Perform. 2022; 1: 94–102. https://doi.org/10. 55860/kqyq5983 4. Hoogkamer W, Kram R, Arellano CJ. How biomechanical improvements in running economy could break the 2-hour marathon barrier. Sports Medicine. Springer International Publishing; 2017. pp. 1739– 1750. https://doi.org/10.1007/s40279-017-0708-0 PMID: 28255937 5. Hirata K, Okayama T, Teraoka T, Funaki J. Precise aerodynamics measurements of a track runner using a wind-tunnel moving-belt system. Procedia Eng. 2012; 34: 32–37. https://doi.org/10.1016/j. proeng.2012.04.007 6. Pugh LGCE. The influence of wind resistance in running and walking and the mechanical efficiency of work against horizontal or vertical forces. J Physiol. 1971; 213: 255–276. https://doi.org/10.1113/ jphysiol.1971.sp009381 PMID: 5574828 7. Triplett N. The dynamogenic factors in pacemaking and competition. Am J Psychol. 1898; 9: 507–533. 8. Zajonc RB. Social facilitation. Science. 1965. pp. 269–274. https://doi.org/10.1126/science.149.3681. 269 PMID: 14300526 9. Zajonc RB, Heingartner A, Herman EM. Social enhancement and impairment of performance in the cockroach. J Pers Soc Psychol. 1969; 13: 83–92. https://doi.org/10.1037/h0028063 10. Cioffi D. Beyond attentional strategies: A cognitive-perceptual model of somatic interpretation. Psychol Bull. 1991; 109: 25–41. https://doi.org/10.1037//0033-2909.109.1.25 11. Williams EL, Jones HS, Andy Sparks S, Marchant DC, Midgley AW, Mc Naughton LR. Competitor pres- ence reduces internal attentional focus and improves 16.1km cycling time trial performance. J Sci Med Sport. 2015; 18: 486–491. https://doi.org/10.1016/j.jsams.2014.07.003 PMID: 25085709 12. Smith AL, Gill DL, Crews DJ, Hopewell R, Morgan DW. Attentional strategy use by experienced dis- tance runners: Physiological and psychological effects. Res Q Exerc Sport. 1995; 66: 142–150. https:// doi.org/10.1080/02701367.1995.10762221 PMID: 7644834 13. Varlet M, Richardson MJ. What would be usain bolt’s 100-meter sprint world record without tyson gay? Unintentional interpersonal synchronization between the two sprinters. J Exp Psychol Hum Percept Perform. 2015; 41: 36–40. https://doi.org/10.1037/a0038640 PMID: 25559749 14. Hamill J, Derrick TR, Holt KG. Shock attenuation and stride frequency during running. Hum Mov Sci. 1995; 14: 45–60. https://doi.org/10.1016/0167-9457(95)00004-6 15. Repp BH. Sensorimotor synchronization: A review of the tapping literature. Psychon Bull Rev. 2005; 12: 969–992. https://doi.org/10.3758/bf03206433 PMID: 16615317 16. Van Dyck E, Moens B, Buhmann J, Demey M, Coorevits E, Dalla Bella S, et al. Spontaneous entrain- ment of running cadence to music tempo. Sport Med—Open. 2015;1. https://doi.org/10.1186/s40798- 015-0025-9 PMID: 26258007 17. Buhmann J, Moens B, Van Dyck E, Dotov D, Leman M. Optimizing beat synchronized running to music. PLoS One. 2018; 13: 1–21. https://doi.org/10.1371/journal.pone.0208702 PMID: 30521634 18. Leman M, Moelants D, Varewyck M, Styns F, van Noorden L, Martens JP. Activating and relaxing music entrains the speed of beat synchronized walking. PLoS One. 2013;8. https://doi.org/10.1371/ journal.pone.0067932 PMID: 23874469 19. Roerdink M, Lamoth CJC, Kwakkel G, Van Wieringen PCW, Beek PJ. Gait coordination after stroke: Benefits of acoustically paced treadmill walking. Phys Ther. 2007; 87: 1009–1022. https://doi.org/10. 2522/ptj.20050394 PMID: 17553922 20. Yefremenko A, Shesterova L, Lebediev S, Zakharina I, Apaichev A, Krajnik Y, et al. Evaluation of char- acteristics of running with an audio stimulation in prepared students from various sports. J Phys Educ Sport. 2019; 19: 696–702. https://doi.org/10.7752/jpes.2019.01100 21. Terry PC, Curran ML, Martin O V, Al TET. Effects of music in exercise and sport: A meta-analytic review. Psychol Bull. 2020; 146: 91–117. https://doi.org/10.1037/bul0000216 PMID: 31804098 22. Terry PC, Karageorghis CI, Saha AM, D’Auria S. Effects of synchronous music on treadmill running among elite triathletes. J Sci Med Sport. 2012; 15: 52–57. https://doi.org/10.1016/j.jsams.2011.06.003 PMID: 21803652 PLOS ONE Auditory interaction between runners PLOS ONE | https://doi.org/10.1371/journal.pone.0280147 January 6, 2023 17 / 19 23. Bood RJ, Nijssen M, van der Kamp J, Roerdink M. The power of auditory-motor synchronization in sports: Enhancing running performance by coupling cadence with the right beats. PLoS One. 2013;8. https://doi.org/10.1371/journal.pone.0070758 PMID: 23951000 24. Safranek MG, Koshland GF, Raymond G. Effect of auditory rhythm on muscle activity. Phys Ther. 1982; 62: 161–168. https://doi.org/10.1093/ptj/62.2.161 PMID: 7058221 25. Thaut MH, Mcintosh GC, Prassas SG, Rice RR. Effect of rhythmic auditory cuing on temporal stride parameters and EMG. Patterns in hemiparetic gait of Stroke Patients. J Neurol Rehabil. 1993; 1: 9–16. 26. Nessler JA, Gilliland SJ. Interpersonal synchronization during side by side treadmill walking is influ- enced by leg length differential and altered sensory feedback. Hum Mov Sci. 2009; 28: 772–785. https://doi.org/10.1016/j.humov.2009.04.007 PMID: 19796834 27. Zivotofsky AZ, Hausdorff JM. The sensory feedback mechanisms enabling couples to walk synchro- nously: An initial investigation. J Neuroeng Rehabil. 2007; 4: 1–5. https://doi.org/10.1186/1743-0003-4- 28 PMID: 17686150 28. Zivotofsky AZ, Gruendlinger L, Hausdorff JM. Modality-specific communication enabling gait synchroni- zation during over-ground side-by-side walking. Hum Mov Sci. 2012; 31: 1268–1285. https://doi.org/10. 1016/j.humov.2012.01.003 PMID: 22727358 29. Target heart rates chart | American heart association. [cited 18 Feb 2022]. Available: https://www.heart. org/en/healthy-living/fitness/fitness-basics/target-heart-rates 30. Onodera K, Miyashita M. A study on Japanese scale for rating of perceived exertion in Endurance Exer- cise. Japan Society of Physical Education. 1976; 21: 191–203. 31. Nakayama Y, Kudo K, Ohtsuki T. Variability and fluctuation in running gait cycle of trained runners and non-runners. Gait Posture. 2010; 31: 331–335. https://doi.org/10.1016/j.gaitpost.2009.12.003 PMID: 20056419 32. Fujii S, Kudo K, Ohtsuki T, Oda S. Intrinsic constraint of asymmetry acting as a control parameter on rapid, rhythmic bimanual coordination: A study of professional drummers and nondrummers. J Neuro- physiol. 2010; 104: 2178–2186. https://doi.org/10.1152/jn.00882.2009 PMID: 20702735 33. Chambers C, Kong G, Wei K, Kording K. Pose estimates from online videos show that side-by-side walkers synchronize movement under naturalistic conditions. PLoS One. 2019; 14: 1–17. https://doi. org/10.1371/journal.pone.0217861 PMID: 31170214 34. Ulzen NR Van, Lamoth CJC, Daffertshofer A, Semin R, Beek PJ. Characteristics of instructed and unin- structed interpersonal coordination while walking side-by-side. Neurosci Lett. 2008; 432: 88–93. https:// doi.org/10.1016/j.neulet.2007.11.070 PMID: 18242846 35. Moumdjian L, Moens B, Vanzeir E, De Klerck B, Feys P, Leman M. A model of different cognitive pro- cesses during spontaneous and intentional coupling to music in multiple sclerosis. Ann N Y Acad Sci. 2019; 1445: 27–38. https://doi.org/10.1111/nyas.14023 PMID: 30865313 36. Waterhouse J, Hudson P, Edwards B. Effects of music tempo upon submaximal cycling performance. Scand J Med Sport. 2010; 20: 662–669. https://doi.org/10.1111/j.1600-0838.2009.00948.x PMID: 19793214 37. Takakusaki K, Habaguchi T, Ohtinata-Sugimoto J, Saitoh K, Sakamoto T. Basal ganglia efferents to the brainstem centers controlling postural muscle tone and locomotion: A new concept for understanding motor disorders in basal ganglia dysfunction. Neuroscience. 2003; 119: 293–308. https://doi.org/10. 1016/s0306-4522(03)00095-2 PMID: 12763089 38. Repp BH, Su YH. Sensorimotor synchronization: A review of recent research (2006–2012). Psychon Bull Rev. 2013; 20: 403–452. https://doi.org/10.3758/s13423-012-0371-2 PMID: 23397235 39. Varlet M, Williams R, Bouvet C, Keller PE. Single (1:1) vs. double (1:2) metronomes for the spontane- ous entrainment and stabilisation of human rhythmic movements. Exp Brain Res. 2018; 236: 3341– 3350. https://doi.org/10.1007/s00221-018-5382-4 PMID: 30255198 40. Kudo K, Park H, Kay BA, Turvey MT. Environmental coupling modulates the attractors of rhythmic coor- dination. J Exp Psychol Hum Percept Perform. 2006; 32: 599–609. https://doi.org/10.1037/0096-1523. 32.3.599 PMID: 16822126 41. Miura A, Kudo K, Ohtsuki T, Kanehisa H. Coordination modes in sensorimotor synchronization of whole-body movement: A study of street dancers and non-dancers. Hum Mov Sci. 2011; 30: 1260– 1271. https://doi.org/10.1016/j.humov.2010.08.006 PMID: 21802159 42. Miura A, Kudo K, Nakazawa K. Action-perception coordination dynamics of whole-body rhythmic move- ment in stance: A comparison study of street dancers and non-dancers. Neurosci Lett. 2013; 544: 157– 162. https://doi.org/10.1016/j.neulet.2013.04.005 PMID: 23603261 43. Okano M, Kurebayashi W, Shinya M, Kudo K. Hybrid dynamics in a paired rhythmic synchronization– continuation task. Phys A Stat Mech its Appl. 2019; 524: 625–638. https://doi.org/10.1016/j.physa. 2019.04.102 PLOS ONE Auditory interaction between runners PLOS ONE | https://doi.org/10.1371/journal.pone.0280147 January 6, 2023 18 / 19 44. Van Dyck E, Buhmann J, Lorenzoni V. Instructed versus spontaneous entrainment of running cadence to music tempo. Ann N Y Acad Sci. 2021; 1489: 91–102. https://doi.org/10.1111/nyas.14528 PMID: 33210323 45. Hutchinson JC, Tenenbaum G. Attention focus during physical effort: The mediating role of task inten- sity. Psychol Sport Exerc. 2007; 8: 233–245. https://doi.org/10.1016/j.psychsport.2006.03.006 46. Tenenbaum G, Connolly CT. Attention allocation under varied workload and effort perception in rowers. Psychol Sport Exerc. 2008; 9: 704–717. https://doi.org/10.1016/j.psychsport.2007.09.002 47. Miles LK, Griffiths JL, Richardson MJ, Macrae CN. Too late to coordinate: Contextual influences on behavioral synchrony. Eur J Soc Psychol. 2010; 40: 52–60. https://doi.org/10.1002/ejsp 48. Zivotofsky AZ, Bernad-elazari H, Grossman P, Hausdor M. The effects of dual tasking on gait synchro- nization during over-ground side-by-side walking. Hum Mov Sci. 2018; 59: 20–29. https://doi.org/10. 1016/j.humov.2018.03.009 PMID: 29579621 49. Lim HBT, Karageorghis CI, Romer LM, Bishop DT. Psychophysiological effects of synchronous versus asynchronous music during cycling. Med Sci Sports Exerc. 2014; 46: 407–413. https://doi.org/10.1249/ MSS.0b013e3182a6378c PMID: 24441216 50. Simpson SD, Karageorghis CI. The effects of synchronous music on 400-m sprint performance. J Sports Sci. 2006; 24: 1095–1102. https://doi.org/10.1080/02640410500432789 PMID: 17115524 PLOS ONE Auditory interaction between runners PLOS ONE | https://doi.org/10.1371/journal.pone.0280147 January 6, 2023 19 / 19

Auditory interaction between runners: Does footstep sound affect step frequency of neighboring runners?

01-06-2023

Furukawa, Hiroaki,Kudo, Kazutoshi,Kubo, Kota,Ding, Jingwei,Saito, Atsushi

eng

PMC8910038




 Citation: Huffman, R.P.; Van Guilder, G.P. The Effect of Acetaminophen on Running Economy and Performance in Collegiate Distance Runners. Int. J. Environ. Res. Public Health 2022, 19, 2927. https://doi.org/10.3390/ ijerph19052927 Academic Editor: Jooyoung Kim Received: 25 January 2022 Accepted: 27 February 2022 Published: 2 March 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). International Journal of Environmental Research and Public Health Article The Effect of Acetaminophen on Running Economy and Performance in Collegiate Distance Runners Riley P. Huffman and Gary P. Van Guilder * Department of Recreation, Exercise & Sport Science, Western Colorado University, Gunnison, CO 81230, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-970-943-7133 Abstract: Acetaminophen (ACT) may decrease perception of pain during exercise, which could allow runners to improve running economy (RE) and performance. The aim of this study was to determine the effects of ACT on RE and 3 km time trial (TT) performance in collegiate distance runners. A randomized, double blind, crossover study was employed in which 11 track athletes (9M/2F; age: 18.8 ± 0.6 years; VO2 max: 60.6 ± 7.7 mL/kg/min) completed three intervention sessions. Participants ingested either nothing (baseline, BSL), three gelatin capsules (placebo, PLA), or three 500 mg ACT caplets (ACT). One hour after ingestion, participants completed a graded exercise test consisting of 4 × 5 min steady-state stages at ~55–75% of VO2 max followed by a 3 km TT. There was no influence of ACT on RE in any stage. Similarly, ACT did not favorably modify 3 km TT performance [mean ± SD: BSL = 613 ± 71 s; PLA = 617 ± 70 s; ACT = 618 ± 70 s; p = 0.076]. The results indicate that ACT does not improve RE or TT performance in collegiate runners at the 3 km distance. Those wanting to utilize ACT for performance must understand that ACT’s benefits have yet to be significant amongst well-trained runners. Future studies should examine the effects of ACT on well-trained runners over longer trial distances and under more controlled conditions with appropriate medical oversight. Keywords: endurance; time trial; perceived exertion; pain reliever 1. Introduction During high intensity efforts runners experience a great deal of pain [1]. This pain can be a result of muscle fatigue, tissue damage, or aggravation of previous injury [1]. In elite races, where all runners are well-trained with comparable aerobic capacities, pain management is often the primary factor for determining success [2]. Taking pain-relieving medications, which are not banned by the governing body of the sport, and which are safe to ingest for healthy individuals with no allergies to their ingredients or contraindications to the medication, has been investigated as a way to enhance exercise performance. Ac- etaminophen (ACT), also known as paracetamol, is an over-the-counter pain reliever and fever reducer. ACT can alter acute and chronic responses to exercise by increasing pain threshold and demanding a greater amount of a stimulus before pain is felt [3]. The use of analgesics is extremely prevalent among runners [4]. In a study of 806 runners conducted by Rosenbloom et al., researchers found that 87.8% of subjects had utilized analgesics within the last year [4]. Over 200 of the subjects in the study reported the use of non-steroidal anti-inflammatory drugs (NSAIDs) directly prior to a race event. The top three reasons for use before a race are: (1) to reduce inflammation/swelling (58%), (2) to increase pain tolerance (42.7%), and (3) to continue running through an injury (42.7%) [4]. As the use of NSAIDs and analgesics in running is highly prevalent for these three primary reasons, it is important for runners that wish to utilize these drugs to understand how the drug they choose to use acts on the body and the risks associated with each drug. Int. J. Environ. Res. Public Health 2022, 19, 2927. https://doi.org/10.3390/ijerph19052927 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2022, 19, 2927 2 of 14 ACT is considered to be a selective cyclooxygenase-2 (COX-2) inhibitor [5]. Being a selective COX-2 inhibitor, ACT lacks the antiplatelet and detrimental effects on gastroin- testinal mucosa of COX-1 inhibition, making it safer on the gastrointestinal tract than other drugs that are not selective inhibitors. Most NSAIDs, such as ibuprofen, are not selective inhibitors and therefore contain a risk of intestinal damage. Unlike NSAIDs, ACT is almost unanimously considered to have no anti-inflammatory activity and does not produce gastrointestinal damage or untoward cardiorenal effects [6]. Indeed, a major ad- verse effect of NSAIDs is their known tendency to cause gastrointestinal (GI) complications, such as mucosal ulceration, bleeding, perforation, and the formation of diaphragm-like strictures [7]. In a study analyzing the aggravation of exercise-induced intestinal injury by ibuprofen in athletes, Van Wijck et al. examined four scenarios around ibuprofen and exer- cise (800 mg ibuprofen before cycling, cycling without ibuprofen, 800 mg ibuprofen at rest, and rest without ibuprofen intake). They found that ibuprofen consumption and cycling resulted in increased plasma intestinal fatty acid binding protein (I-FABP) levels, reflecting small intestinal injury. These levels were higher after cycling with ibuprofen than after cycling without ibuprofen, rest with ibuprofen, or rest without ibuprofen. Additionally, small intestinal permeability increased, especially after cycling with ibuprofen, reflecting loss of gut barrier integrity. They concluded that ibuprofen aggravates exercise-induced small intestinal injury and induces gut barrier dysfunction in healthy individuals [7]. This phenomenon does not occur with ACT due to the different mechanisms of action of ACT compared to ibuprofen and other NSAIDs [5]. These studies demonstrate that those wish- ing to utilize drugs prior to races in order to increase pain tolerance are at an increased risk of adverse health effects when choosing NSAIDs, such as ibuprofen, as opposed to an analgesic drug, such as ACT [7]. They also demonstrate that ACT should not be used to treat inflammation or swelling, as ACT does not have an anti-inflammatory effect [5,6]. ACT is a safe drug at appropriate doses [6]. The amount of 7.5 g in adults is widely considered as the lowest acute dose capable of causing toxicity [6]. All studies examining ACT and its effect on performance utilize doses ranging from 0.5 g to 1.5 g [3,8–14], well under the threshold for the potential of toxicity. There have been no reports of acute toxicity in healthy adults ingesting a single dose of ACT below 125 mg/kg [6]. Unlike ibuprofen or other NSAIDs, ACT has only a small peripheral effect and acts primarily on the central nervous system [6]. Even so, the risks of ACT should be fully outlined for coaches and athletes to consider. As with many drugs, ACT can have very harmful effects, specifically to the liver, if taken above prescribed doses. Unfortunately, ACT overdose is responsible for more acute liver failure cases in the US and UK than all other etiologies combined [15]. The most common reason ACT ingestion results in death by overdose is its use in suicide attempts. Suicide attempts are a frequent cause of exposure to a single, high overdose of ACT [15]. Regrettably, unintentional overdose can occur as a result of combining multiple over-the-counter drugs, such as sleep-aids and cold medications, that may all have components of ACT [15]. Nevertheless, extensive literature reviews suggest that even susceptible people are unlikely to suffer adverse effects from therapeutic doses of ACT [15]. Additionally, there is an antidote against ACT-induced liver injury, the drug, N-acetylcysteine (NAC). NAC acts through facilitating scavenging of a reactive metabolite during the metabolism phase and is most effective when administered within 8 h of the overdose. This allows ACT-induced liver injury and liver failure to have a relatively high survival rate. As ACT is one of the most common over-the-counter drugs and because the vast majority of those who take ACT do not come close to taking over-therapeutic dosages of it, it is one of the safest over-the-counter drugs available [15]. Several studies have demonstrated significant endurance performance improvements among participants in ACT conditions compared to placebo conditions during both cy- cling and running. These improvements have been contributed to improved ability to tolerate pain as a result of prolonged exercise or a decreased perception of perceived pain or exertion during exercise. For example, the cycling studies by Delextrat et al. [8], Foster et al. [9], Mauger et al. [10], Mauger et al. [11], and Morgan et al. [12] have demon- Int. J. Environ. Res. Public Health 2022, 19, 2927 3 of 14 strated that ingestion of ACT before a cycling bout improved performance through an increased average or peak power output or a decreased time to complete a specified dis- tance [8–12]. Some researchers concluded that these improvements in performance were a result of the participants’ improved ability to tolerate pain during cycling [8–10]. Despite the many studies completed on cycling performance and ACT, there have been few studies conducted to examine the effects of ACT ingestion on running performance. In the only study examining running endurance performance and ACT ingestion, Dagli et al. [3] found that after taking ACT, recreationally active runners were able to improve 3 km time trial performance by 1.9% compared to placebo [3]. However, this study was conducted using exclusively male participants and was performed on a treadmill, which is not as specific to distance running compared to the running over ground that is characteristic of National Collegiate Athletic Association (NCAA) track and cross-country runners. Indeed, there are no well-controlled randomized studies investigating the potential exercise performance effects of ACT on well-trained, elite level male and female distance runners. Additionally, there have not been any studies examining the effects of ACT on running economy (RE). RE is determined by the steady state oxygen consumption for a standard speed [16–18]. An athlete with improved RE consumes less oxygen for a given steady state running speed [19], thus improving their performance by expending less energy throughout a race [19]. Over the course of a race, runners experience increasing amounts of fatigue, which contributes to reduced mechanical efficiency and poor economy of motion [1]. For instance, Meardon et al. [1] found that stride time became less consistent over the course of a 5 km time trial while examining stride time variability, indicating that during prolonged running there was an increased need for gate adjustments due to increasing fatigue [1]. Based on the evidence to date in a multitude of studies, ACT has been demonstrated to improve cycling performance [8–12]. Yet, the only study examining the effects of ACT ingestion on endurance running performance used recreationally active runners and did not examine its effect on RE [3]. Therefore, the purpose of this randomized, double blind, crossover experiment was to determine the effects of ACT on RE and a 3 km time trial performance in well-trained NCAA collegiate distance runners. It was hypothesized that ACT would improve RE and 3 km time trial performance through a reduction in perceived pain during running. 2. Materials and Methods 2.1. Experimental Approach In this randomized, double blind, crossover experiment, participants reported to the High Altitude Exercise Physiology Laboratory on five separate occasions (see Figure 1). The experiment was randomized by order; the research assistant randomly assigned each par- ticipant’s first condition as either a baseline condition (BSL), only water ingestion, placebo condition (PLA), water and placebo ingestion, or ACT condition (ACT), water and ACT ingestion. The random assignment was accomplished using a random number generator, which selected a number at random between one and three. Participants proceeded to complete each session based on their starting condition. For example, if a participant was randomly assigned ACT as their first condition, their condition order would be ACT then BSL then PLA. The first session for all participants consisted of completing questionnaires, informed consent, and a treadmill running familiarization. This session lasted 30–45 min and was followed by the next session two days later. In the second session, lasting about an hour, anthropomorphic measurements and determination of VO2 max were completed. In the third, fourth, and fifth sessions, each occurring one week apart and commencing on the same day of the week at the same time of day, subjects were assigned to ingest either eight ounces of water to serve as their baseline (BSL), eight ounces of water paired with three empty red gelatin capsules which served as a placebo (PLA), or eight ounces of water paired with three 500 mg capsules of ACT (1.5 g) (ACT). Following ingestion, participants waited 60 min and then completed a 20-min RE assessment on a treadmill, which also served as a warm-up for their 3 km time trial on the indoor track. Int. J. Environ. Res. Public Health 2022, 19, 2927 4 of 14 Figure 1. Experimental flowchart. (BSL = baseline) (PLA = placebo) (ACT = acetaminophen) (RE = running economy) (TT = Time Trial). 2.2. Subjects Eleven total participants (9 men and 2 women) were recruited to participate in the study. Participants were well-trained distance runners on the NCAA division II cross- country team at Western Colorado University (WCU). Participants were considered well- trained based on VO2 max and were in the 99th percentile for VO2 max in their age and sex-group based on fitness guidelines [20]. Participants completed the study during the middle of the indoor track season. They typically performed two workouts per week and four low intensity runs per week in addition to the experimental trials. One workout would consist of four to ten intervals of 400 to 1000 m with one to three minutes of jogging between repetitions. The other workout would be a continuous run of 20–35 min at 80–90% of VO2 max. Men in the study ran between 80 and 120 km/wk. Women in the study ran between 56 and 96 km/wk. Men and women runners trained at an average running velocity of 14.0 km/h and 12.5 km/h, respectively. Participants were excluded from the study if they were found to be allergic to, or had previous complications with, the drug ACT, if they were heavy alcohol users, or if they had had any liver complications in the past. Participants were also excluded from the study if they were not classified as low risk for heart disease based on the American College of Sports Medicine risk algorithm. Exclusion criteria were assessed with the physical activity readiness questionnaire (PAR-Q) [21] and a medical history questionnaire, which included questions regarding over-the-counter drug use and alcohol use. All measurements of participants were conducted in the High Altitude Performance Laboratory at WCU, except for the 3 km time trial, which was performed on the indoor track in the WCU Mountaineer Fieldhouse. All subjects provided written and verbal informed consent prior to participating in the study. This study was approved by the Institutional Review Board at WCU [HRC2020-01-01-R12]. 2.3. Procedures 2.3.1. Familiarization and Lead-In Following completion of the informed consent and other screening questionnaires, participants underwent a lead-in period to familiarize them with the VO2 max protocol and treadmill RE assessments. The familiarization session was a way for participants to gain an understanding of how to run on a treadmill with open-circuit indirect calorimetry and to get a sense of the rating of the perceived exertion (RPE) scale. In this session the participant did not run to volitional exhaustion. This allowed them to return to the lab within 48 h for the genuine VO2 max assessment without the possibility of fatigue. For this familiarization session participants were fitted with a mask attached to falconia tubing, which was attached to the metabolic cart (Parvo Medics TrueOne® 2400, Sandy City, UT, USA) to collect expired gases. Participants were also fitted with a chest strap (Polar, Lake Success, NY, USA) to monitor heart rate throughout the test. Participants ran for 10 to Int. J. Environ. Res. Public Health 2022, 19, 2927 5 of 14 12 min at increasing speeds on the treadmill (Trackmaster, Newton, KS, USA). This session was paced in a way that participants would reach an RPE of about 7 within 10–12 min, giving them an understanding of the perception of effort they would feel throughout the genuine VO2 max assessment and later RE assessments. At the conclusion of the session, participants completed a low intensity cool-down at a self-selected pace for at least five minutes and were dismissed from the lab. The entirety of the screening and familiarization session lasted 30–45 min. 2.3.2. Anthropomorphic Measurements While wearing only running attire, the participant removed their shoes and stood on a scale (Tanita, Arlington Heights, IL, USA) to be weighed in kilograms (kg). A measuring stick built into the scale was used to measure the participant’s height in centimeters (cm). Body mass index (BMI) was later calculated from these measurements using the formula BMI = weight (kg)/height (m)2. Body fat percentage was assessed using an Omron HBF-300 handheld body composition analyzer (Omron, Bannockburn, IL, USA). 2.3.3. Maximal Oxygen Consumption Following anthropomorphic measurements, participants completed a self-selected 10-min dynamic warm-up consisting of stretches and other exercises on the indoor track in the WCU Mountaineer Fieldhouse. The same warm-up routine was performed for each participant prior to the RE test and time trial. This allowed the participant to prepare to perform as they would in a typical training session or race. VO2 max was determined using open-circuit spirometry combined with indirect calorimetry (Parvo Medics TrueOne® 2400, Sandy City, UT, USA) in response to incre- mental treadmill running (Trackmaster, Newton, KS, USA). Flow and gas calibrations were performed prior to each test using standard operating procedures provided by the manufacturer. Participants were fitted with a mask attached to falconia tubing, which was attached to the metabolic cart to collect expired gases. They were also fitted with a chest strap (Polar, Lake Success, NY) to monitor heart rate throughout the test. The treadmill was set to an initial incline of one percent grade, as one percent grade most accurately reflects the energetic cost of outdoor running [22,23]. Male participants completed a 3-min warm up at 12 km/h at a 1% grade. There- after, the treadmill velocity was increased 0.8 km/h every minute until velocity reached 19.2 km/h; at this point, the velocity remained constant and grade of the treadmill was in- creased 2% every minute of the test until the participant reached volitional fatigue. Female participants followed a similar pattern. They completed a 3-min warm up at 10.5 km/h at a 1% grade. Thereafter, treadmill velocity was increased 0.8 km/h every minute until velocity reached 17.7 km/h. At this point the velocity remained constant and grade of the treadmill was increased 2% every minute of the test until the participant reached volitional fatigue. Heart rate and RPE were recorded at the end of each minute throughout the test. Participants were provided verbal encouragement throughout the test until exhaustion. VO2 data were smoothed with a 15-s moving average. VO2 max was denoted as the highest 15 s moving average obtained during the last minute of exercise with no further increase in VO2. All tests were terminated by volitional exhaustion. A true VO2 max was confirmed based on a plateau in VO2 defined by a change of <150 mL/min despite a change in workload and an RER greater than 1.10. 2.3.4. Intervention Sessions Each of the three intervention sessions took place on the same day of the week, beginning at the same time of day, exactly one week apart for each participant. Participants were instructed to avoid caffeine for four hours prior to all interventions and tests. They were also instructed to maintain similar diet and sleep habits on the days prior to testing and on the days of testing. Each session began with the participant meeting with the research assistant who would administer one of the three interventions. The participant Int. J. Environ. Res. Public Health 2022, 19, 2927 6 of 14 received either a baseline of 8 ounces of water paired with nothing, three placebo capsules paired with 8 ounces of water, or 1.5 g ACT in the form of three 500 mg capsules paired with 8 ounces of water. The order of the intervention was randomized and was blinded to the participant and the primary investigators. Following ingestion, the participant was instructed to relax and perform a non-stressful activity, such as reading or listening to music for 50 min. Thereafter, participants completed their individualized 10-min dynamic warm up routine, and then transitioned to the RE test. This timeframe was chosen because peak plasma concentration of ACT occurs approximately 45–60 min after oral administration [6]. 2.3.5. Running Economy Each participant was fitted with a heart rate monitor and a mask connected to a tube leading to the metabolic cart in the same fashion as the familiarization and VO2 max tests. The RE test consisted of four and five-minute stages at increasing running velocities. The intensity of each stage was kept relatively low as RE at lower speeds has been demonstrated to be more strongly correlated with performance [16]. The duration of five minutes for each stage was selected, as it takes approximately four to five minutes to reach steady state oxygen consumption [24]. The total duration of 20 min was selected because the participants in the study consistently warm-up for workouts and race for 20 min and the RE portion of the assessment served as a warm-up for the subsequent 3 km time trial performance measure. Heart rate, oxygen consumption, and RPE were recorded during the last minute of each stage. Male participants ran each stage at 10.5 km/h, 11.2 km/h, 12.0 km/h, and 12.9 km/h (174.4 m/min, 187.8 m/min, 201.2 m/min, and 214.6 m/min). Female participants ran each stage at 9.7 km/h, 10.5 km/h, 11.2 km/h, and 12.0 km/h (160.9 m/min, 174.4 m/min, 187.8 m/min, and 201.2 m/min). The intensity of these stages ranged from approximately 55% to 75% of VO2 max throughout the duration of the test. RE was expressed in two ways. First, as the oxygen cost required to run 1 km of horizontal distance (mL/kg/km) and second, as the caloric unit cost—the energy in kilocalories required to run 1 km of horizontal distance (kcal/kg/km). This unit has been demonstrated to be more sensitive to changes in relative velocity compared with oxygen cost [25]. Caloric unit cost was calculated by dividing the steady-state energy expenditure cost (kcal/min) obtained during the last four 15-s moving averages of each stage by body mass (kg), divided by running velocity (m/min) and multiplied by 1000 (1000 m/km), as done in a previous RE study [24]. 2.3.6. Track Time Trial Following the RE test, participants were given 15 minutes to use the restroom, change shoes if desired, and perform any additional warm up stretches or exercises as they nor- mally would prior to a race. This routine was again kept constant for each individual participant. Exactly 15 min following the conclusion of the RE test, the participants began the 3 km time trial on the indoor track at the Mountaineer Fieldhouse. Participants were instructed to complete fifteen laps on the 200 m track in the inner- most lane as quickly as possible, as they would in a race. Researchers recorded the duration of each lap split. At the completion of the time trial, the total run duration, heart rate, and oxygen saturation were recorded. The participant’s heart rate and oxygen saturation (SpO2) were measured by a fingertip pulse oximeter (American Diagnostic Corporation, Hauppauge, NY, USA). The participant then performed a low intensity cool down of their choosing and subsequently completed a four-question survey regarding their perception of effort and difficulty during the time trial. The questions on the survey were answered on a 1–10 scale with 1 being lowest and 10 highest. The questions were: (1) How would you rate your level of effort over the course of the time trial? (2) How would you rate the exercise difficulty of the entire time trial? (3) How would you rate your level of performance based on your current fitness level during the time trial? (4) How would you rate your average level of perceived exertion over the time trial? Following the completion of the survey, Int. J. Environ. Res. Public Health 2022, 19, 2927 7 of 14 the participant was dismissed for the day. Each remaining intervention was separated by exactly one week and repeated at the same time of day in the randomly determined order. 2.3.7. Statistical Analysis Descriptive characteristics of the participants are presented as means and standard deviations. With the exception of the 3 km time trial, all data met assumptions of normality. Therefore, the nonparametric Friedman’s test was used to determine treatment differences in the 3 km time trial, and data are reported with median and interquartile range as appropriate. A 3 × 4 (trial × exercise stage) analysis of variance for repeated measures with Bonferroni adjustment for pairwise multiple comparisons was used for treatment differences in submaximal exercise variables (i.e., VO2 and heart rate) and RE between the four treadmill stages. A three-way analysis of variance with repeated measures was used to determine differences in post time trial heart rate and oxygen saturation. A Friedman’s test was used to determine differences in RPE and the four-question self-evaluation form. These data were therefore displayed accordingly as median and interquartile range. All other continuous data that met assumptions of normality were reported as mean ± standard deviation. Level of statistical significance was set at p < 0.05 and SPSS version 27 (IBM-SPSS, Boston, MA, USA) was used to perform these statistical analyses. 3. Results Table 1 shows subject characteristics. Runners were normal weight based on body mass index and presented with VO2 max values at or above the 99th percentile for age and sex [20]. Each participant continued normal track training during the study: the average mileage for the men was 103 km/wk; the average mileage for the women was 76 km/wk. Table 1. Descriptive characteristics of participants. Characteristic (Units) All Participants (n = 11) Men (n = 9) Women (n = 2) Age (years) 18.8 ± 0.6 18.7 ± 0.5 19.5 ± 0.7 Height (cm) 171.1 ± 6.9 173.7 ± 4.2 159.5 ± 2.1 Weight (kg) 58.6 ± 5.3 59.7 ± 5.1 53.8 ± 2.9 BMI (kg/m2) 20.0 ± 1.1 19.8 ± 0.8 21.2 ± 1.7 BF% 9.7 ± 5.1 7.6 ± 1.0 19.5 ± 4.2 ABS VO2 max (L/min) 3.6 ± 0.6 3.8 ± 0.4 2.6 ± 0.3 REL VO2 max (mL/kg/min) 60.6 ± 7.7 63.3 ± 5.3 48.4 ± 2.2 Weekly training distance (km/wk) 98.1 ± 16.7 103.0 ± 9.9 76.0 ± 28.3 Abbreviations: cm (centimeters), kg (kilograms), kg/m2 (kilogram/meter2), BF% (body fat %) ABS (absolute), L/min (liters/minute), REL (relative), mL/kg/min (milliliters/kilogram/minute), and km/wk (kilometers per week). 3.1. Time and Splits Figure 2 shows the 3 km time trial performance for the interventions. There were no significant differences in the time to complete the time trial among the interventions. Performance times observed in the present study were within 2–5% of what the participants actually performed in a competitive 3 km race in the weeks following the study. Mean performance time between the three conditions were within five s (p = 0.076). Similarly, no significant differences were found in any of the 1 km split times between BSL, PLA, and ACT groups (p = 0.406, 0.234, and 0.811, respectively). Mean values for the first, second, and third split times from start to 1 km, 1 km to 2 km, and 2 km to 3 km were all within three seconds between BSL, PLA, and ACT conditions. Int. J. Environ. Res. Public Health 2022, 19, 2927 8 of 14 Figure 2. Box and whisker plot for 3 km time trial times in s for baseline, placebo, and ACT conditions. The box is the interquartile range, the horizontal line is the median, the cross ( within three seconds between BSL, PLA, and ACT conditions. Figure 2. Box and whisker plot for 3 km time trial times in s for baseline, placebo, and ACT condi- tions. The box is the interquartile range, the horizontal line is the median, the cross (✛) is the mean, and the error bars are the minimum and maximum. ACT; acetaminophen. 3.2. Running Economy The RE exercise intensities for stage 1, 2, 3, and 4 corresponded to approximately 60%, 66%, 71%, and 74% of V̇O2 max, respectively. All subjects achieved steady-state oxygen consumption in each stage. As shown in Figure 3, there were no significant dif- ferences between BSL, PLA, and ACT conditions in RE expressed as mL/kg/km in any of the four stages. For example, the average RE across stage 4 for BSL was 207.4 mL/kg/km, compared with 208.6 mL/kg/km for PLA and 208.5 mL/kg/km for ACT (p = 0.886; Figure 3). Likewise, when expressed as kcal/kg/km, RE was also similar among the conditions. For example, the average RE across stage 4 for BSL was 1.009 kcal/kg/km, compared with 1.015 kcal/kg/km for PLA and 1.016 kcal/kg/km for ACT (p = 0.857; Figure 4). ) is the mean, and the error bars are the minimum and maximum. ACT; acetaminophen. 3.2. Running Economy The RE exercise intensities for stage 1, 2, 3, and 4 corresponded to approximately 60%, 66%, 71%, and 74% of VO2 max, respectively. All subjects achieved steady-state oxygen consumption in each stage. As shown in Figure 3, there were no significant differences between BSL, PLA, and ACT conditions in RE expressed as mL/kg/km in any of the four stages. For example, the average RE across stage 4 for BSL was 207.4 mL/kg/km, compared with 208.6 mL/kg/km for PLA and 208.5 mL/kg/km for ACT (p = 0.886; Figure 3). Likewise, when expressed as kcal/kg/km, RE was also similar among the conditions. For example, the average RE across stage 4 for BSL was 1.009 kcal/kg/km, compared with 1.015 kcal/kg/km for PLA and 1.016 kcal/kg/km for ACT (p = 0.857; Figure 4). Figure 3. Box and whisker plots for RE expressed as mL/kg/km for baseline, placebo, and ACT conditions for (A) Stage 1: 60% of VO2 max, (B) Stage 2: 66% of VO2 max, (C) Stage 3: 71% of VO2 max, and (D) 74% of VO2 max. The box is the interquartile range, the horizontal line is the median, the cross ( 8 of 15 , kg (kilograms), kg/m2 (kilogram/meter2), BF% (body fat %) ABS ), REL (relative), mL/kg/min (milliliters/kilogram/minute), and m time trial performance for the interventions. There were no e time to complete the time trial among the interventions. in the present study were within 2–5% of what the partici- a competitive 3 km race in the weeks following the study. ween the three conditions were within five s (p = 0.076). Sim- nces were found in any of the 1 km split times between BSL, 406, 0.234, and 0.811, respectively). Mean values for the first, from start to 1 km, 1 km to 2 km, and 2 km to 3 km were all n BSL, PLA, and ACT conditions. for 3 km time trial times in s for baseline, placebo, and ACT condi- e range, the horizontal line is the median, the cross (✛) is the mean, mum and maximum. ACT; acetaminophen. ties for stage 1, 2, 3, and 4 corresponded to approximately f V̇O max respectively All subjects achieved steady state ) is the mean, and the error bars are the minimum and maximum. ACT; acetaminophen. Int. J. Environ. Res. Public Health 2022, 19, 2927 9 of 14 Figure 4. RE expressed as kcal/kg/km for baseline, placebo, and ACT conditions for (A) Stage 1: 60% of VO2 max, (B) Stage 2: 66% of VO2 max, (C) Stage 3: 71% of VO2 max, and (D) 74% of VO2 max. ACT; acetaminophen. 3.3. Heart Rate, Oxygen Consumption, and Saturation Table 2 shows the sub-maximal heart rate and oxygen consumption during the RE tests, and the post time trial oxygen saturation and heart rate. Sub-maximal oxygen consumption was similar between BSL, PLA, and ACT at each of the four RE stages (p = 0.529, 0.148, 0.234, and 0.159, respectively) as was heart rate (bpm) throughout the four RE stages (p = 0.518, 0.135, 0.071, and 0.176, respectively). Post time trial oxygen saturation (p = 0.913) and post time trial heart rate (p = 0.846) were not different between BSL, PLA, and ACT conditions. Table 2. Oxygen consumption and heart rate between baseline (BSL), placebo (PLA), and ac- etaminophen (ACT) conditions. Stage Speed (m/min) (Male, Female) BSL (M ± SD) PLA (M ± SD) ACT (M ± SD) p-Value Oxygen Consumption (mL/kg/min) 1 174.4, 160.9 36.0 ± 2.6 35.9 ± 3.6 35.0 ± 2.8 0.529 2 187.8, 174.4 38.3 ± 2.8 38.3 ± 3.7 38.9 ± 2.4 0.148 3 201.2, 187.8 41.2 ± 3.5 41.6 ± 3.5 41.8 ± 2.2 0.234 4 214.6, 201.2 44.0 ± 2.7 44.2 ± 3.9 44.2 ± 2.5 0.159 Heart Rate (bpm) 1 174.4, 160.9 134 ± 11 139 ± 8 136 ± 9 0.518 2 187.8, 174.4 143 ± 10 150 ± 9 147 ± 9 0.135 3 201.2, 187.8 149 ± 8 155 ± 8 154 ± 8 0.071 4 214.6, 201.2 158 ± 8 162 ± 9 160 ± 8 0.176 Post TT Oxygen Saturation (%) N/A (Post 3 km TT) 87.6 ± 2.8 87.6 ± 3.0 87.4 ± 3.1 0.913 Post TT Heart Rate (bpm) N/A (Post 3 km TT) 176.7 ± 6.4 174.1 ± 11.3 175.7 ± 6.2 0.846 M ± SD, (mean ± standard deviation); mL/kg/min, (milliliters/kilogram/minute); m/min, (meters/minute); bpm, (beats per minute); TT, (time trial); km, (kilometer); BSL, baseline; PLA, placebo; ACT, acetaminophen. Int. J. Environ. Res. Public Health 2022, 19, 2927 10 of 14 3.4. RPE and Questionnaire Responses As shown in Table 3, there were no significant differences in RPE during each RE stage between the three conditions. In addition, no significant differences were found in any of the post 3 km time trial questionnaire responses between the three conditions. Table 3. Rating of perceived exertion (RPE) (1 = very low RPE, 10 = very high RPE) during RE stages and post TT perception of effort and difficulty responses between BSL, PLA, and ACT trials. Stage/Question Speed (m/min) (Male, Female) BSL Med (25th, 75th) PLA Med (25th, 75th) ACT Med (25th, 75th) p-Value RPE during RE Stages 1 174.4, 160.9 1 (1, 2) 1 (1, 2) 1 (1, 2) 0.273 2 187.8, 174.4 2 (1, 2.5) 2 (2, 2.5) 2 (1, 3) 0.957 3 201.2, 187.8 2.5 (1.5, 3) 3 (2, 3) 2 (2, 4) 0.519 4 214.6, 201.2 3 (1.5, 5) 3 (2, 5) 2 (2, 5) 0.922 Responses to Questionnaire Q1 Effort level 9 (8, 9) 9 (8, 9.5) 9 (9, 9) 0.091 Q2 Exercise difficulty 8.5 (8, 9) 9 (8, 10) 9 (8, 9) 0.656 Q3 Performance level 8 (8, 9) 8 (7, 9) 8 (7.5, 9) 0.368 Q4 Perceived exertion 8 (7, 9) 8 (8, 9) 9 (8, 9) 0.140 Abbreviations: TT (time trial); BSL, baseline; PLA, placebo; ACT, acetaminophen; Med 25th, 75th (median (25th percentile, 75th percentile)); m/min (meters/minute); Q, (question). 4. Discussion In contrast to our hypothesis, the primary findings of this randomized, double-blind crossover trial indicate that supplementation with ACT does not improve RE or 3 km time trial performance in NCAA competitive cross-country athletes. Baseline RE expressed as mL/kg/km and kcal/kg/km were within 1% of the placebo and ACT conditions for all stages of the incremental exercise test. Additionally, there was no noticeable difference in the time to complete the 3 km time trial, as well as with 1 km splits with ACT. Collectively, given similar steady-state oxygen consumption, heart rate, minute ventilation, respiratory exchange ratio, and gross energy cost during incremental treadmill running between conditions, ACT supplementation does not favorably modify exercise economy or 3 km time trial performance. ACT’s primary mechanism of action for pain relief is on the serotonergic descending pain pathway [26]. ACT inhibits prostaglandin (PG) synthesis from arachidonic acid in human skeletal muscle; as a result, PGs regulate adaptations to muscular exercise [26]. An analgesic drug, such as ACT, can alter the acute and chronic responses to exercise by elevating the pain threshold and requiring a greater amount of pain before it is felt [3]. As exercise-induced pain is a contributor to volitional exhaustion or changes in pacing during exercise [2], a reduction in perceived pain should increase the athlete’s performance [2,3]. RE has been demonstrated to account for a significant amount of the variation observed in race performance amongst elite level runners [16]. RE is dependent on a variety of factors, including efficiency of form [27] and both central and peripheral fatigue [1,28,29]. Due to the influence of RE to be affected by central fatigue and its correlation with performance in elite runners, it was hypothesized that ACT, which has a direct effect on central fatigue [26,30], would improve RE in competitive athletes. The results of this study demonstrate that RE was similar across BSL, PLA, and ACT conditions for all RE stages, both as measured in mL/kg/km and in kcal/kg/km. Our findings are in contrast to those by Dagli et al. [3], who reported a 1.9% (14 s) improvement in the 3 km time trial performance. It is important to note the crucial study design and sample differences between the present study and those by Dagli and colleagues. While Dagli et al. studied recreationally active male runners with an av- erage VO2 max of 55.67 ± 5.35 mL/kg/min, measured at sea level [3], we specifically recruited in-competition NCAA elite male and female athletes with an average VO2 max of Int. J. Environ. Res. Public Health 2022, 19, 2927 11 of 14 60.6 ± 7.7 mL/kg/min, measured at 7700 feet, which had already undergone substantial endurance training. Indeed, the participants in our study completed the 3 km distance two minutes faster on average than participants in the study by Dagli et al. [3], despite completing the trials at 7700 feet as opposed to sea level. Notably, the average post exercise oxygen saturation levels in the present study are quite low, as shown in Table 2. This difference in completion time and duration of effort may be a critical reason as to why the present study did not demonstrate appreciable differences in performance with ACT. Of the previous studies examining endurance performance, the majority of studies that found statistically significant differences in performance between ACT and control conditions required exercise times of at least 20 min. For example, Mauger et al. [10] found a 30 s improvement between ACT and control in a 10-mile cycle time trial, which lasted on average 26.25 min for the ACT condition and 26.75 min for the control condition [10]. Mauger et al. [11] found a four-minute difference between ACT and control conditions in a cycle test to exhaustion in the heat in which subjects in the ACT condition were able to cycle for 22.7 min compared to only 18.8 min in the control condition [11]. Foster et al. found a 19 watt improvement in average power between ACT and control conditions in a repeated Wingate study with a total exercise time of 20 min when active recovery cycling was included [9]. Delextrat et al. found a 24-watt improvement in peak power [8] in a similar study design as Foster et al. [9]. All of these studies used a randomized double- blind crossover design [8–11]. Only two previously conducted studies investigated the effects of ACT on exercise bouts involving an exercise duration less than 20 min [3,12]. Both of these studies were also randomized double blind crossover studies with an ACT condition and a placebo condition. These two studies reported significant performance benefits of ACT compared with control conditions. However, it should be emphasized that both studies demonstrated marginal, albeit significant, improvements with ACT. First, the study by Dagli et al. [3] reported a small difference of 1.9% with ACT. Second, the study by Morgan et al. [12] also demonstrated a small, 5-watt difference in average power over a 3-min maximum cycle test, which was a 1.4% difference compared with control conditions [12]. ACT has been demonstrated to decrease participants’ RPE during running bouts of similar or increased intensities [3,13]. We did not demonstrate any significant differences in RPE between treatment conditions. Likewise, we did not demonstrate any significant differences in the perception of effort and difficulty questionnaire among trials. The median reported value for all four of the post time trial questions on the questionnaire were within 1 point of each other on the 1–10 scale across the three treatment conditions. This dis- plays that the participants did not perceive a significant difference in effort, difficulty, performance, or exertion between the three conditions. A limitation of this study was the small sample size (11) and unequal distribution of male (9) and female (2) participants. Although all statistical tests were performed while examining only one sex at a time and results were the same as when all participants were examined together, a more balanced quantity of male and female participants is recommended for future studies. Another limitation of the present study was the training schedule of participants was not controlled from week to week. Although participants completed similar weekly training volumes throughout the study, the exact workouts of the participants varied from week to week and thus may have resulted in the participants feeling more or less fatigued from each week’s workouts between the three trials. However, the variability between training loads should not have had a significant effect on the total outcome of the study, due to the randomization of the order each participant undertook. Future studies can minimize the potential for this phenomenon by scheduling data collec- tion during the off-season, where the participants are less likely to be doing high intensity training over the study duration. Furthermore, the duration between trials may not need to be an entire week, as well-trained runners typically have the ability to recover from hard efforts faster than recreationally active runners. For example, it has been suggested that “aerobic fitness enhances recovery from high intensity intermittent exercise through Int. J. Environ. Res. Public Health 2022, 19, 2927 12 of 14 enhanced aerobic contribution, increased post-exercise VO2, and possibly by increased lactate removal and increased PCr restoration, which has been linked to improved power recovery” [31,32]. Therefore, in studies involving well-trained participants, the required time between trials may only be 48–72 h as opposed to studies with recreationally active participants who require more time between trials to fully recover. Another variable that future research studies involving well-trained participants should examine is the trial distance. The present study examined the effectiveness of ACT over a relatively short time trial distance of 3 km. On average, participants completed this distance in just over ten minutes. ACT has been demonstrated to be effective in improv- ing performance amongst well-trained participants over longer durations. For example, Mauger and colleagues examined ACT’s influence on performance over a ten-mile cycle time trial in 13 trained male cyclists [10]. They found that ACT significantly improved performance by 30 s over the course of the ten-mile trial. As this study was examining a distance that took over 26 min to complete, it is possible that ACT’s effectiveness increases as the required trial duration increases, especially in studies involving well-trained par- ticipants. Future studies on well-trained runners should investigate the effectiveness of ACT over distances of at least 8 km, which would require an average duration of effort of at least 26 min, as in the study by Mauger et al. [10]. 5. Conclusions The results of the present study indicate no performance benefit of ACT on RE or a 3 km time trial performance among NCAA competitive collegiate distance runners. These results are in contrast to some [3,8–12], but not all [13,14] studies investigating the potential for ACT to improve exercise performance. To our knowledge, as this is the first study to examine the effects of ACT on well-trained collegiate distance runners, it appears that ACT has a more significant effect on performance for recreationally active runners than well-trained runners [3]. It remains to be found whether ACT is beneficial for well-trained runners at distances other than 3 km or under more controlled experimental circumstances, such as with a higher number of participants and a more favorable proportion of male and female participants. Finally, it should be emphasized that ingestion of ACT is for medical purposes only. It is not recommended for athletes to use ACT for exercise performance without a medical indication. Moreover, prior review of relevant medical history by the athletes’ healthcare team, and physician approval before an athlete uses ACT, is the best practice. Author Contributions: This work is the result of collaboration among R.P.H. and G.P.V.G. Both authors have equally contributed, reviewed, and improved the manuscript. Conceptualization, R.P.H., G.P.V.G.; Methodology, R.P.H., G.P.V.G.; Formal Analysis, R.P.H., G.P.V.G.; Investigation, R.P.H., G.P.V.G.; Data Curation, R.P.H., G.P.V.G.; Writing—Original Draft Preparation, R.P.H., G.P.V.G.; Writing—Review and Editing, R.P.H., G.P.V.G.; Visualization, R.P.H., G.P.V.G.; Supervision, G.P.V.G.; Project Administration, R.P.H., G.P.V.G. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of Western Colorado University (HRC2020-01-01-R12). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Some or all data and models that support the findings of this study are available from the corresponding author upon reasonable request. Acknowledgments: The authors would like to thank the research assistant, Jonathan Specht, who was able to begin every experimental session by administering the treatment for each participant. They also thank Coach Jennifer Michel for allowing the utilization of her athletes as participants for this project. Finally, they thank all the student athletes who volunteered their time and energy to Int. J. Environ. Res. Public Health 2022, 19, 2927 13 of 14 participate in this study. This study would not have been possible without the tremendous amount of effort they put into each session. We are very grateful for their willingness to arrive on time to each meeting, follow all instructions, and participate to their fullest capacity for the entirety of the study. Thank you to everyone who helped to make this research project possible. Conflicts of Interest: The authors declare no conflict of interest. References 1. Meardon, S.A.; Hamill, J.; Derrick, T.R. Running Injury and Stride Time Variability over a Prolonged Run. Gait Posture 2011, 33, 36–40. [CrossRef] 2. Mauger, A.R. Fatigue Is a Pain—the Use of Novel Neurophysiological Techniques to Understand the Fatigue-Pain Relationship. Front. Physiol. 2013, 4. [CrossRef] [PubMed] 3. Dagli, F.P.; Maridaki, M.; Paradisis, G.; Pilianidis, T.; Zacharogiannis, E. Effect of Acute Acetaminophen Injestion on Running Endurance Performance. Med. Sci. Sports Exerc. 2018, 49, 330–331. [CrossRef] 4. Rosenbloom, C.J.; Morley, F.L.; Ahmed, I.; Cox, A.R. Oral Non-Steroidal Anti-Inflammatory Drug Use in Recreational Runners Participating in Parkrun UK: Prevalence of Use and Awareness of Risk. Int. J. Pharm. Pract. 2020, 28, 561–568. [CrossRef] 5. Tanner, T.; Aspley, S.; Munn, A.; Thomas, T. The Pharmacokinetic Profile of a Novel Fixed-Dose Combination Tablet of Ibuprofen and Paracetamol. BMC Clin. Pharmacol. 2010, 10, 10. [CrossRef] [PubMed] 6. Bertolini, A.; Ferrari, A.; Ottani, A.; Guerzoni, S.; Tacchi, R.; Leone, S. Paracetamol: New Vistas of an Old Drug. CNS Drug Rev. 2006, 12, 250–275. [CrossRef] [PubMed] 7. Van Wijck, K.; Lenaerts, K.; Van Bijnen, A.A.; Boonen, B.; Van Loon, L.J.C.; Dejong, C.H.C.; Buurman, W.A. Aggravation of Exercise-Induced Intestinal Injury by Ibuprofen in Athletes. Med. Sci. Sports Exerc. 2012, 44, 2257–2262. [CrossRef] 8. Delextrat, A.; Ellis, M.O.; Baker, C.E.; Matthew, D.; Sum, A.; Hayes, L.D. Acetaminophen Ingestion Improves Repeated Sprint Cycling Performance in Females: A Randomized Crossover Trial. Kinesiology 2015, 47, 7. 9. Foster, J.; Taylor, L.; Chrismas, B.C.R.; Watkins, S.L.; Mauger, A.R. The Influence of Acetaminophen on Repeated Sprint Cycling Performance. Eur. J. Appl. Physiol. 2014, 114, 41–48. [CrossRef] 10. Mauger, A.R.; Jones, A.M.; Williams, C.A. Influence of Acetaminophen on Performance during Time Trial Cycling. J. Appl. Physiol. 2010, 108, 7. [CrossRef] 11. Mauger, A.R.; Taylor, L.; Harding, C.; Wright, B.; Foster, J.; Castle, P.C. Acute Acetaminophen (Paracetamol) Ingestion Improves Time to Exhaustion during Exercise in the Heat: Acetaminophen Improves Exercise Capacity in the Heat. Exp. Physiol. 2014, 99, 164–171. [CrossRef] 12. Morgan, P.T.; Vanhatalo, A.; Bowtell, J.L.; Jones, A.M.; Bailey, S.J. Acetaminophen Ingestion Improves Muscle Activation and Performance during a 3-Min All-out Cycling Test. Appl. Physiol. Nutr. Metab. 2019, 44, 434–442. [CrossRef] 13. Park, L.L.; Baker, C.E.; Sum, A.; Hayes, L.D. The Influence of Acetaminophen on Sprint Interval Treadmill Running: A Randomized Crossover Trial. Kinesiology 2016, 48, 5. [CrossRef] 14. Burtscher, M.; Gatterer, H.; Philipp, M.; Krusmann, P.; Kernbeiss, S.; Frontull, V.; Kofler, P. Effects of a Single Low-Dose Acetaminophen on Body Temperature and Running Performance in the Heat: A Pilot Project. Int. J. Physiol. Pathophysiol. Pharmacol. 2013, 5, 4. 15. Jaeschke, H. Acetaminophen: Dose-Dependent Drug Hepatotoxicity and Acute Liver Failure in Patients. Dig. Dis. 2015, 33, 464–471. [CrossRef] 16. Conley, D.L.; Krahenbuhl, G.S. Running Economy and Distance Running Performance of Highly Trained Athletes. Med. Sci. Sports Exerc. 1980, 12, 357–360. [CrossRef] 17. Barnes, K.R.; Kilding, A.E. Running Economy: Measurement, Norms, and Determining Factors. Sports Med.—Open 2015, 1, 8. [CrossRef] 18. Austin, C.; Hokanson, J.; McGinnis, P.; Patrick, S. The Relationship between Running Power and Running Economy in Well- Trained Distance Runners. Sports 2018, 6, 142. [CrossRef] 19. Abe, D.; Yanagawa, K.; Yamanobe, K.; Tamura, K. Assessment of Middle-Distance Running Performance in Sub-Elite Young Runners Using Energy Cost of Running. Eur. J. Appl. Physiol. 1998, 77, 320–325. [CrossRef] 20. Pescatello, L.S. ACSM’s Guidelines for Exercise Testing and Prescription, 9th ed.; Wolters Kluwer/Lippincott Williams & Wilkins Health: Philadelphia, PA, USA, 2014. 21. Warburton, D.E.R.; Jamnik, V.; Bredin, S.S.D.; Shephard, R.J.; Gledhill, N. The 2018 Physical Activity Readiness Questionnaire for Everyone (PAR-Q+) and electronic Physical Activity Readiness Medical Examination. Health Fit. J. Can. 2018, 11, 31–34. 22. Jones, A.; Doust, J. A 1% Treadmill Grade Most Accurately Reflects the Energetic Cost of Outdoor Running. J. Sports Sci. 1996, 14, 321–327. [CrossRef] [PubMed] 23. Waldman, H.S.; Heatherly, A.J.; Waddell, A.F.; Krings, B.M.; O’Neal, E.K. Five-Kilometer Time Trial Reliability of a Nonmotorized Treadmill and Comparison of Physiological and Perceptual Responses vs. a Motorized Treadmill. J. Strength Cond. Res. 2018, 32, 1455–1461. [CrossRef] [PubMed] 24. Kaur, G.; Binger, M.; Evans, C.; Trachte, T.; Van Guilder, G.P. No Influence of Ischemic Preconditioning on Running Economy. Eur. J. Appl. Physiol. 2017, 117, 225–235. [CrossRef] [PubMed] Int. J. Environ. Res. Public Health 2022, 19, 2927 14 of 14 25. Fletcher, J.R.; Esau, S.P.; MacIntosh, B.R. Economy of Running: Beyond the Measurement of Oxygen Uptake. J Appl Physiol 2009, 107, 5. [CrossRef] 26. Anderson, B.J. Paracetamol (Acetaminophen): Mechanisms of Action. Pediatric Anesth. 2008, 18, 915–921. [CrossRef] [PubMed] 27. Saunders, P.U.; Pyne, D.B.; Telford, R.D.; Hawley, J.A. Factors Affecting Running Economy in Trained Distance Runners. Sports Med. 2004, 34, 465–485. [CrossRef] [PubMed] 28. Gandevia, S.C. Spinal and Supraspinal Factors in Human Muscle Fatigue. Physiol. Rev. 2001, 81, 1725–1789. [CrossRef] 29. Gao, Z.; Mei, Q.; Fekete, G.; Baker, J.S.; Gu, Y. The Effect of Prolonged Running on the Symmetry of Biomechanical Variables of the Lower Limb Joints. Symmetry 2020, 12, 720. [CrossRef] 30. Pickering, G.; Loriot, M.; Libert, F.; Eschalier, A.; Beaune, P.; Dubray, C. Analgesic Effect of Acetaminophen in Humans: First Evidence of a Central Serotonergic Mechanism. Clin. Pharmacol. Ther. 2006, 79, 371–378. [CrossRef] 31. Tomlin, D.L.; Wenger, H.A. The Relationship between Aerobic Fitness and Recovery from High Intensity Intermittent Exercise. Sports Med. 2001, 31, 1–11. [CrossRef] 32. Gonzalez-Freire, M.; Scalzo, P.; D’Agostino, J.; Moore, Z.A.; Diaz-Ruiz, A.; Fabbri, E.; Zane, A.; Chen, B.; Becker, K.G.; Lehrmann, E.; et al. Skeletal Muscle Ex Vivo Mitochondrial Respiration Parallels Decline in Vivo Oxidative Capacity, Car- diorespiratory Fitness, and Muscle Strength: The Baltimore Longitudinal Study of Aging. Aging Cell 2018, 17, e12725. [CrossRef]

The Effect of Acetaminophen on Running Economy and Performance in Collegiate Distance Runners.

03-02-2022

Huffman, Riley P,Van Guilder, Gary P

eng

PMC9603534

Citation: Sanchez-Trigo, H.; Zange, J.; Sies, W.; Böcker, J.; Sañudo, B.; Rittweger, J. Effects of Aging and Fitness on Hopping Biomechanics. Int. J. Environ. Res. Public Health 2022, 19, 13696. https://doi.org/10.3390/ ijerph192013696 Academic Editors: Yufei Cui and Dariusz Mosler Received: 22 September 2022 Accepted: 18 October 2022 Published: 21 October 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). International Journal of Environmental Research and Public Health Article Effects of Aging and Fitness on Hopping Biomechanics Horacio Sanchez-Trigo 1,* , Jochen Zange 2 , Wolfram Sies 2, Jonas Böcker 2, Borja Sañudo 1 and Jörn Rittweger 2,3 1 Department of Physical Education and Sports, University of Seville, 41013 Sevilla, Spain 2 German Aerospace Center (DLR), Institute of Aerospace Medicine, 51147 Cologne, Germany 3 Department of Pediatrics and Adolescent Medicine, University Hospital Cologne, 50931 Cologne, Germany * Correspondence: [email protected] Abstract: Physical exercise promotes healthy aging and is associated with greater functionality and quality of life. Muscle strength and power are established factors in the ability to perform daily tasks and live independently. Stiffness, for mechanical reasons, is another important constituent of running performance and locomotion. This study aims to analyze the impact of age and training status on one-legged hopping biomechanics and to evaluate whether age-related power decline can be reduced with regular physical exercise. Forty-three male subjects were recruited according to their suitability for one of four groups (young athletes, senior athletes, young controls and senior controls) according to their age (young between 21 and 35, vs. older between 59 and 75) and training status (competing athletes vs. non-physically active). The impact of age and training status on one-legged hopping biomechanics were evaluated using the two-way analysis of variance (ANOVA) method. Significant differences among groups were found for hopping height (p < 0.05), ground contact time (p < 0.05), peak ground reaction force (p < 0.05) and peak power (p < 0.01). No differences among groups were found in ground-phase vertical displacement and vertical stiffness (p > 0.05). Young athletes and older non-physically active people achieved the best and worst performance, respectively. Interestingly, there were not any differences found between young non-physically active people and senior athletes, suggesting that chronic training can contribute to partly offset effects that are normally associated with aging. Keywords: physical fitness; sedentary behavior; aging; biomechanics; stiffness; muscle power 1. Introduction Current demographic data show a significant population aging in developed countries, leading to increased health care costs [1]. Preventing mobility limitations and maintaining independent functioning in the aging population is of major public health importance [2]. One of the factors associated to age-associated decline in mobility is the decline in muscle force and power, which is aggravated by a sedentary lifestyle [3]. Both sedentarism and aging cause a decline in muscle performance and functionality. Thus, it is of interest to assess separately the respective contributions of sedentarism and aging to better understand this process, and to evaluate to which degree can a physically active life compensate age- associated decline in muscle power. Master athletes are particularly interesting to study these processes. These are individuals who train to compete in athletic events at a high level beyond a typical sports retirement age [4]. Master athletes can be considered as rare examples of aging without the common confounder of increased sedentarism at older age [4]. Previous research on master athletes has shown a clear effect of age and athletic specialization on muscle power since, while aging is associated with a 40% reduction in jumping power from the 3rd to the 7th decade of life, sprint-trained master athletes have greater jumping power than endurance master athletes [4–6]. To a large extent, the age-related decline in jumping power is explicable by age-effects on body composition [7]. However, to the best of our knowledge, no previous study has yet compared age-related Int. J. Environ. Res. Public Health 2022, 19, 13696. https://doi.org/10.3390/ijerph192013696 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2022, 19, 13696 2 of 11 effects on muscle power between master athletes and a cohort matched in age, height and weight that is non-physically active. In this study, we compare the jumping performance and biomechanics of athletes and ordinary active non-athletes control subjects, both at a young age and in elder subjects typically affected by aging processes. Our aim is to quantify how far the plyometric performance of elite master athletes is superior above age matched non-athletes, and how far the jumping biomechanics of athletes with respect to non-athletes is affected by aging and fitness. To answer this research question, the effects of age and training status on jumping biomechanical parameters will be assessed. It is our hypothesis that a prolonged engagement in exercise is related to a reduced age-related decline in muscle power and performance. 2. Materials and Methods 2.1. Participants This study was conducted within the MALICoT project, which was designed to compare intramuscular connective tissue between young athletes, young non-physically active people, senior athletes and senior non-physically active people. Specifically, the study targeted power athletes (jumpers and sprinters) for the athlete groups. For the recruitment process, the study was advertised via social media and on the DLR website (www.dlr.de/ me/en/desktopdefault.aspx/tabid-15377/ accessed on 20 September 2022). An online questionnaire was filled out by interested subjects. Activity levels were quantified using the Freiburger questionnaire for physical activity [8,9] and the subjects’ energy expenditure was estimated, in terms of metabolic equivalents of task (METs). The questionnaire included questions to check criteria for inclusion and exclusion. Inclusion criteria were (a) age either between 20 and 35 for the young groups and age between 60 and 75 for the senior groups; (b) ≥4 h per week training and regular competition in sprint running or jumping events for the athletic groups, and ≤25 METs per week spent in exercise for the non-physically active group; (c) male sex; and (d) ability to provide informed consent (all groups). People were excluded when diagnosed with diabetes, when they had contraindications against magnetic resonance imaging or against muscle biopsy, or when they had experienced injuries or musculoskeletal disorders likely to interfere with the testing protocol. All participants provided written informed consent prior to participating in this study. The experimental protocol was approved by the Ethical Committee of the Ärztekammer Nordrhein in Düsseldorf, Germany (ref. no. 2018269). The study was prospectively registered on the German register of clinical trials (www.drks.de accessed on 20 September 2022) with registration number DRKS00015764. 2.2. Sample Size As mentioned above, this study is part of the MALICoT project, whose main goal is to investigate endomysium and perimysium content as a function of age and training state in humans. The lack of information on age- and training-state dependency of muscle tissue’s elastic modulus makes a statistically-motivated sample size definition difficult. However, a sample size of 12 subjects per each of the four sub-groups (young athletes, young non- physically active people, senior athletes and senior non-physically active people), and, thus, a total of 48 seemed a feasible goal, based on previous experience. On the other hand, preliminary data suggest a variation coefficient of 1.76% for reproducibility. Thus, with such a good reproducibility, this study aimed to allow the estimation of group means and their standard deviation, and to discover effects of age and training state. As stated above, there has been no previous quantitative human study on endomysium thickness. For sample size calculation, we therefore rely on previous research reporting a group difference between 6- and 18-week-old chickens of 1.03 µm with standard deviation of 1.24 µm in endomysium thickness [10]. Using a t-test to test the primary hypothesis, and setting α = 0.05 and β = 0.2, we arrive at an estimated sample size of 24 subjects per group. The study aimed, therefore, at including 12 subjects per group, and, thus, a total of 48 participants. Int. J. Environ. Res. Public Health 2022, 19, 13696 3 of 11 2.3. Testing Procedures Jumping mechanography was employed to assess motor performance. This technique relies on plyometric tests performed on a force platform to evaluate dynamic muscle function and has been found to be a reliable and sensitive measure of mobility performance in elite athletes as well as in frail patients employing both two-legged and single-legged jumps [3,11]. In the present study, a multiple one-leg hopping (M1LH) test using the dominant leg was carried out [12–14]. Subjects were instructed to start with shallow hops, increase height to maximum followed by 4 to 5 maximum hops and finally reduce hop height again. The M1LH test was performed on a force plate (Leonardo Mechanography GRFP, Novotec Medical Inc., Pforzheim, Germany) continuously measuring the vertical ground reaction forces (GRF) at a sampling rate of 800 Hz. 2.4. Data Processing The GRF signals recorded during the M1LH test were analyzed using the module ‘signal’ within the software package R (R Core Team, 2020, Vienna, Austria). Each individual hop was identified by the detecting the flying phases (absence of GRF) and the phases of ground contact (positive GRF). The following variables were calculated for each hop: • Flight time (FT): duration of the flying phase of the hop, that is, time interval in which the subject has no contact with the ground; • Ground contact time (GCT): interval of time in which the subject’s leg is contact with the ground after the FT; • Maximum GRF: peak GRF registered during the GCT after landing, and prior to the next hop; • Hopping height (HH). Calculated from flight time using the equation of uniformly accelerated motions [15]: HH = g·FT2/8 where: g—gravity acceleration constant (9.81 m/s2) FT—flight time • Vertical acceleration (AV) of the center of mass (COM) over time. Calculated from GRF and subject’s body mass [16]: AV(t) = F(t) − m·g m where: F(t)—GRF over time m—body mass g—gravity acceleration constant (9.81 m/s2) • Vertical velocity (VV) of the COM over time. Calculated from the integration in the time domain of the acceleration-time data [16]: VV(t) = Z AV(t)dt + c = Z F(t) − m·g m dt + c where: AV(t)—Vertical acceleration over time F(t)—GRF over time m—body mass g—gravity acceleration constant (9.81 m/s2) c—integration constant The integration constant (c) was based upon the assumption that vertical velocity of the COM was zero at the middle of the GCT; in other words, assuming that COM peak Int. J. Environ. Res. Public Health 2022, 19, 13696 4 of 11 downward displacement is reached at the middle of the GCT between hops [16,17]. Since GRF data is discrete, the previous integration was implemented by summation of the GRF samples. • Vertical displacement (DV) of the COM during ground contact. Calculated from numerical double integration in the time domain of the acceleration-time data, or equivalently, from the numerical integration in the time domain of the vertical velocity- time data [16,18]: DV(t) = Z VV(t)dt + c = x F(t) − m·g m dt dt + c where: VV(t)—Vertical velocity over time F(t)—GRF over time m—body mass g—gravity acceleration constant (9.81 m/s2) c—integration constant Since our goal was to determine COM displacement, the integration constant (k) was set to zero at the initial instant [19]. Given that velocity data are discrete, the previous integration was implemented by summation of the velocity samples. • Max DV(t): Maximum vertical downward displacement of the COM during ground contact (also known as countermovement depth); • Power output, normalized to subject’s body weight [20]: P(t) = F(t)·VV(t) m where: VV(t)—Vertical velocity over time F(t)—GRF over time m—body mass • Vertical stiffness (K), calculated for each hop as the ratio between the peak GRF and maximum COM displacement, according to the spring–mass model [18,21,22]. Since body size influences stiffness [23], K was normalized by body mass for each subject and expressed as kN/m/kg [19,24]: K = maxF(t) maxDV(t)·m−1 where: max F(t)—Maximum GRF; m—body mass. According to the spring–mass model, max F(t) and max DV(t) coincide in the middle of the ground-contact phase during hopping [18]. In fact, the vertical stiffness parameter, K, is only valid if the lower extremity behaves like a simple spring–mass system [19,25]. To evaluate that assumption, the linear correlation between DV(t) and F(t) was also calculated. Only those hops for which this correlation is r > 0.80 comply with the assumption of spring-like behavior [19,25]. Hops that were unable to meet this criterion were not used for data analysis. Finally, for each subject the three highest hops (the three hops with the highest HH) were selected, and the averages for all the previously described parameters were computed using these three hops. Int. J. Environ. Res. Public Health 2022, 19, 13696 5 of 11 2.5. Statistical Analysis The impact of age and training status on the M1LH test was evaluated by comparing all previously described biomechanical parameters using the two-way analysis of variance (ANOVA) with two factors (age × training status). To assess assumptions of homoscedas- ticity, Levene’s test was performed. Normality was evaluated using Shapiro–Wilk’s test. Group means were compared performing Tukey’s post-hoc test, if a significant main ef- fect was observed. Statistical significance was set at p < 0.05. Pearson correlation (r) was employed to measure linear correlation and evaluate the assumption of spring–mass-like behavior. These statistical analyses were performed using Jamovi (The Jamovi project, 2019, Version 1.0). 3. Results 3.1. Participants Characteristics Forty-three male subjects completed the study. Twenty-two young subjects (21–35 years old) and twenty-one senior subjects (59–75 years old) were recruited. Among them, ten young subjects and ten senior subjects regularly trained and competed as athletes in sprint or jumping events, while the remaining subjects (twelve young and eleven senior) were only ordinary physically active without performing intensive and specific training like the subjects in the two athletes’ groups do. Thus, four groups were established: young athletes (YA), young controls (YC), senior athletes (SA) and senior controls (SC). Their characteristics are summarized in Table 1. Table 1. Participants characteristics. Young Athletes Young Controls Senior Athletes Senior Controls N 10 12 10 11 Height [cm] 178.9 ± 7.7 180.8 ± 6.7 177.6 ± 7.6 176.9 ± 5.8 Weight [kg] 76.2 ± 13.7 75.4 ± 13.0 74.8 ± 8.4 79.8 ± 8.8 Age [years] 23.9 ± 2.3 28.9 ± 4.5 65.1 ± 4.1 66.1 ± 4.8 Activity Level [METs/week] 55.4 ± 22.8 20.4 ± 42.9 94.3 ± 39.5 23.9 ± 13.2 N: number of subjects. METs: metabolic equivalents of task. 3.2. Biomechanical Parameters Table 2 shows the descriptive statistics (average ± standard deviation) of the biome- chanical parameters calculated for the M1LH test. As described in Section 2.4, these parameters included HH, GCT, maximum GRF, maximum DV, K and maximum power. Table 2. Biomechanical parameters of the multiple one-legged hopping test. Young Athletes Young Controls Senior Athletes Senior Controls Hopping Height [cm] 16.6 ± 3.3 11.8 ± 2.5 10.7 ± 3.4 6.9 ± 2.3 Ground Contact Time [ms] 275 ± 48 320 ± 50 303 ± 53 348 ± 48 Max GRF [kN] 2.87 ± 0.52 2.32 ± 0.57 2.31 ± 0.31 2.26 ± 0.30 Max DV [%] 9.3 ± 1.8 9.8 ± 1.7 9.7 ± 2.3 9.0 ± 1.8 Vertical Stiffness [N/m/kg] 230 ± 86 165 ± 49 180 ± 59 191 ± 55 Max Power [W/kg] 32.9 ± 6.5 25.5 ± 4.8 22.7 ± 4.9 18.1 ± 3.2 Data reported as average ± standard deviation. GRF: ground reaction forces. DV: Vertical displacement of the center of mass during ground contact. 3.3. Hopping Height A two-way ANOVA was performed to analyze the effect of age and training status on hopping height revealing that there was not a statistically significant interaction between the effects of age and training status (F-value = 0.264, p = 0.610). Simple main effects analysis showed that age did have a statistically significant effect on hopping height (F- value = 34.995, p < 0.001), and that training status also had a statistically significant effect Int. J. Environ. Res. Public Health 2022, 19, 13696 6 of 11 on hopping height (F-value = 21.823, p < 0.001). Tukey’s post-hoc test results for multiple comparisons are shown in Table 3. Table 3. ANOVA results for hopping height. Comparison Mean Difference SE df t p-Value Cohen’s d 95% C.I. Lower Upper Young − Seniors 5.40 0.913 36.0 5.92 <0.001 1.88 1.09 2.66 Athletes − Controls 4.26 0.913 36.0 4.67 <0.001 1.48 0.747 2.21 Young athletes − Young controls 4.73 1.29 36.0 3.658 0.004 1.644 0.651 2.637 Young athletes − Senior athletes 5.87 1.32 36.0 4.436 <0.001 2.038 0.987 3.090 Young athletes − Senior controls 9.66 1.32 36.0 7.305 <0.001 3.356 2.127 4.586 Young controls − Senior athletes 1.14 1.26 36.0 0.903 0.803 −0.394 −1.286 0.497 Young controls − Senior controls 4.93 1.26 36.0 3.919 0.002 1.712 0.736 2.689 Senior athletes − Senior controls 3.79 1.29 36.0 2.947 0.027 1.318 0.358 2.278 3.4. Ground Contact Time A two-way ANOVA was performed to analyze the effect of age and training status on GCT revealing that there was not a statistically significant interaction between the effects of age and training status (F-value = 6.21 × 10 −7, p = 0.999). Simple main effects analysis showed that age did not have a statistically significant effect on GCT (F-value = 3.16, p = 0.084), although simple main effects analysis showed that training status did have a statistically significant effect on GCT (F-value = 8.30, p = 0.007). Tukey’s post-hoc test results for multiple comparisons are shown in Table 4. Table 4. ANOVA results for ground contact time. Comparison Mean Difference SE df t p-Value Cohen’s d 95% C.I. Lower Upper Young − Seniors −28.0 15.8 36.0 −1.78 0.084 −0.564 −1.22 0.0932 Athletes − Controls −45.4 15.8 36.0 −2.88 0.007 −0.913 −1.59 −0.234 Young athletes − Young controls −45.4 22.3 36.0 −2.032 0.195 −0.913 −1.85 0.0240 Young athletes − Senior athletes −28.0 22.8 36.0 −1.227 0.614 −0.564 −1.51 0.3776 Young athletes − Senior controls −73.4 22.8 36.0 −3.214 0.014 −1.477 −2.47 −0.4804 Young controls − Senior athletes 17.4 21.7 36.0 0.800 0.854 −0.349 −1.24 0.5407 Young controls − Senior controls −28.0 21.7 36.0 −1.290 0.575 −0.563 −1.46 0.3329 Senior athletes − Senior controls −45.4 22.2 36.0 −2.041 0.192 −0.913 −1.85 0.0200 3.5. Maximum Ground Reaction Forces A two-way ANOVA was performed to analyze the effect of age and training status on max GRF revealing that there was not a statistically significant interaction between the effects of age and training status (F-value = 3.35, p = 0.075). Simple main effects analysis showed that age did have a statistically significant effect on max GRF (F-value = 4.97, p = 0.032), and that training status also had a statistically significant effect on max GRF (F-value = 4.56, p = 0.040). Tukey’s post-hoc test results for multiple comparisons are shown in Table 5. Int. J. Environ. Res. Public Health 2022, 19, 13696 7 of 11 Table 5. ANOVA results for maximum ground reaction forces. Comparison Mean Difference SE df t p-Value Cohen’s d 95% C.I. Lower Upper Young − Seniors 0.312 0.140 36.0 2.23 0.032 0.707 0.0421 1.37 Athletes − Controls 0.299 0.140 36.0 2.14 0.040 0.677 0.0141 1.34 Young athletes − Young controls 0.5546 0.198 36.0 2.7979 0.039 1.2576 0.298 2.217 Young athletes − Senior athletes 0.5677 0.203 36.0 2.8018 0.039 1.2873 0.306 2.269 Young athletes − Senior controls 0.6103 0.203 36.0 3.0121 0.023 1.3840 0.395 2.373 Young controls − Senior athletes 0.0131 0.193 36.0 0.0681 1.000 −0.0298 −0.916 0.856 Young controls − Senior controls 0.0557 0.193 36.0 0.2893 0.991 0.1264 −0.760 1.013 Senior athletes − Senior controls 0.0426 0.197 36.0 0.2161 0.996 0.0966 −0.811 1.004 3.6. Maximum DV A two-way ANOVA was performed to analyze the effect of age and training status on maximum DV revealing that there was not a statistically significant interaction between the effects of age and training status (F-value = 0.9687, p = 0.332). Simple main effects analysis showed that age did not have a statistically significant effect on maximum DV (F-value = 0.0956, p = 0.759), and that training status did not have a statistically significant effect on maximum DV (F-value = 0.0609, p = 0.806). Tukey’s post-hoc test results for multiple comparisons are shown in Table 6. Table 6. ANOVA results for maximum DV. Comparison Mean Difference SE df t p-Value Cohen’s d 95% C.I. Lower Upper Young − Seniors 0.00186 0.00601 36.0 0.309 0.759 0.0980 −0.545 0.741 Athletes − Controls 0.00148 0.00601 36.0 0.247 0.806 0.0783 −0.565 0.721 Young athletes − Young controls −0.00443 0.00852 36.0 −0.5201 0.954 −0.2338 −1.147 0.680 Young athletes − Senior athletes −0.00406 0.00871 36.0 −0.4658 0.966 −0.2140 −1.147 0.719 Young athletes − Senior controls 0.00334 0.00871 36.0 0.3836 0.980 0.1763 −0.757 1.109 Young controls − Senior athletes 0.000375 0.00828 36.0 0.0452 1.000 −0.0198 −0.906 0.866 Young controls − Senior controls 0.00777 0.00828 36.0 0.9384 0.784 0.4100 −0.482 1.302 Senior athletes − Senior controls 0.00740 0.00848 36.0 0.8727 0.819 0.3903 −0.521 1.302 3.7. Vertical Stiffness A two-way ANOVA was performed to analyze the effect of age and training status on K revealing that there was not a statistically significant interaction between the effects of age and training status (F-value = 3.658, p = 0.064). Simple main effects analysis showed that age did not have a statistically significant effect on K (F-value = 0.385, p = 0.539), and that training status did not have a statistically significant effect on K (F-value = 1.852, p = 0.182). Tukey’s post-hoc test results for multiple comparisons are shown in Table 7. Table 7. ANOVA results for vertical stiffness. Comparison Mean Difference SE df t p-Value Cohen’s d 95% C.I. Lower Upper Young − Seniors 12.3 19.8 36.0 0.621 0.539 0.197 −0.448 0.841 Athletes − Controls 27.0 19.8 36.0 1.36 0.182 0.431 −0.220 1.08 Young athletes − Young controls 64.9 28.1 36.0 2.309 0.115 1.038 0.0931 1.982 Young athletes − Senior athletes 50.2 28.7 36.0 1.748 0.315 0.803 −0.1484 1.754 Young athletes − Senior controls 39.3 28.7 36.0 1.367 0.528 0.628 −0.3157 1.572 Young controls − Senior athletes −14.7 27.3 36.0 −0.537 0.949 0.235 −0.6532 1.123 Young controls − Senior controls −25.6 27.3 36.0 −0.937 0.785 −0.410 −1.3011 0.482 Senior athletes − Senior controls −10.9 28.0 36.0 −0.391 0.979 −0.175 −1.0828 0.733 Int. J. Environ. Res. Public Health 2022, 19, 13696 8 of 11 3.8. Maximum Power A two-way ANOVA was performed to analyze the effect of age and training status on maximum power revealing that there was not a statistically significant interaction between the effects of age and training status (F-value = 0.848, p = 0.363). Simple main effects analysis showed that age did have a statistically significant effect on maximum power (F-value = 31.105, p < 0 .001), and that training status also had a statistically significant effect on maximum power (F-value = 14.452, p < 0 .001). Tukey’s post-hoc test results for multiple comparisons are shown in Table 8. Table 8. ANOVA results for maximum power. Comparison Mean Difference SE df t p-Value Cohen’s d 95% C.I. Lower Upper Young − Seniors 0.890 0.160 36.0 5.58 <0.001 1.77 0.999 2.54 Athletes − Controls 0.607 0.160 36.0 3.80 <0.001 1.21 0.501 1.91 Young athletes − Young controls 0.754 0.226 36.0 3.33 0.010 1.497 0.5178 2.476 Young athletes − Senior athletes 1.037 0.231 36.0 4.48 <0.001 2.060 1.0061 3.114 Young athletes − Senior controls 1.497 0.231 36.0 6.47 <0.001 2.973 1.8014 4.145 Young controls − Senior athletes 0.283 0.220 36.0 1.29 0.576 −0.563 −1.4592 0.333 Young controls − Senior controls 0.743 0.220 36.0 3.38 0.009 1.476 0.5224 2.430 Senior athletes − Senior controls 0.460 0.225 36.0 2.04 0.192 0.913 −0.0196 1.846 4. Discussion The goal of this study was to assess the effects of aging and fitness on jumping performance and biomechanical parameters. To do so, four groups (i.e., YA, SA, YC and SC) were established according to their age (young, between 21 and 35, vs. older, between 59 and 75) and fitness status (competing athletes vs. non-physically active). YA and SC showed the highest (16.6 ± 3.3 cm) and lowest (6.9 ± 2.3 cm) HH, re- spectively, which differed significantly from the other two groups (YC: 11.8 ± 2.5 cm, SA: 10.7 ± 3.4 cm; all p < 0.05). GCT was significantly shorter for YA (275 ± 48 ms) compared to SC (348 ± 48 ms; p = 0.014), with no statistical differences between the other groups (YC: 320 ± 50 ms, SA: 303 ± 53 ms; all p > 0.05). Maximum GRF was significantly higher for YA (2.87 ± 0.52 kN) compared with the rest of the groups (YC: 2.32 ± 0.57 kN, SA: 2.31 ± 0.31 kN, SC: 2.26 ± 0.30 kN; all p < 0.05). Peak power was significantly higher for YA (32.9 ± 6.5 W/kg) compared with the rest of the groups (YC: 25.5 ± 4.8 W/kg, SA: 22.7 ± 4.9 W/kg, SC: 18.1 ± 3.2 W/kg; all p < 0.01), and for YC compared to SC (p < 0.01). No statistically significant differences among groups were found in maximum DV, expressed as a percentage of subject’s height (YA: 9.32 ± 1.8%, YC: 9.77 ± 1.7%, SA: 9.73 ± 2.3%, SC: 8.99 ± 1.8%; all p > 0.05). No statistically significant differences among groups were found in vertical stiffness, normalized to body mass (YA: 230 ± 86 N/m/kg, YC: 165 ± 49 N/m/kg, SA: 180 ± 59 N/m/kg, SC: 191 ± 55 N/m/kg; all p > 0.05). As expected, the best performance was observed in YA, and the worst performance was registered in SC in the described M1LH test. Interestingly, there were not any differences found between YC and SA, so these results suggest that chronic training could be associated to a counterbalance of effects that are normally associated with aging. Within young participants, YA showed significantly higher GRF and power than YC, while there were no differences in ground contact time, vertical displacement (during countermovement) and stiffness, so it could be hypothesized that higher fitness improves performance by increasing force application and muscle power, but it doesn’t affect the other biomechanical parameters. Within older participants, SA showed a significantly higher performance than SC, although there were no statistically significant differences between the analyzed biomechanical parameters, probably due to the reduced number of participants. Within trained individuals of different age, YA showed significantly higher GRF and power than SA, while there were no differences in ground contact time, vertical displacement (during Int. J. Environ. Res. Public Health 2022, 19, 13696 9 of 11 countermovement) and stiffness, suggesting, therefore, that aging negatively affects force application and muscle power, but it doesn’t affect the remaining biomechanical parameters. Age-related changes in muscle power have been previously reported in the literature [26]. Within sedentary individuals, YC had a better performance than SC probably attributable to a significantly higher muscle power, suggesting again that aging negatively affects muscle power [27]. In conclusion, both aging and sedentarism result in a decreased muscle power in the M1LH test, but lifelong training could be associated to a counterbalance of the effects of aging [28–32]. There are several limitations to the study. First, the number of participants is reduced, which could be limiting the significance of our findings. Second, there were male par- ticipants only. Including females might have unveiled other results, as there are major differences between female and male skeletal muscles, including differences in energy metabolism, fiber type composition, and contractile speed [33]. Finally, only sports with a high implication of muscle power (sprinting and jumping) were considered in the partici- pants’ selection. It would be of interest to include other athletic modalities and sports. Future research directions might include studying differences in muscle architecture and the connective tissue of the muscles to better understand the underlying causes of age- related decline in power and how to optimize physical training to counteract such processes. In the elder athletes, the superior performance may result from both, an intensive training and a genetically determined slower aging process. The number of athletes performing sprint or jumping disciplines in high age is extremely small, and much smaller compared with the more frequent elder endurance runners. The small number of cases could suggest that the conservation of plyometric performance in senior sprinters and jumpers might not only result from adaptation on training but may a have genetical component affecting aging as well. Future studies should analyze genetical characteristics of master athletes to clarify this question. More importantly, further research and action are required to propagate master athletics as a role model and therefore contribute to improve life quality in our aging society. 5. Conclusions Lifelong athletic training can contribute to partly offsetting age-related muscle power decline. Author Contributions: Conceptualization, J.R. and J.Z.; methodology, J.R. and J.Z.; formal analysis, H.S.-T.; investigation, J.Z., J.B. and W.S.; data curation, J.Z., J.B. and H.S.-T.; writing—original draft preparation, H.S.-T.; writing—review and editing, J.R. and B.S.; visualization, H.S.-T.; supervision, B.S: project administration, J.R.; funding acquisition, J.R. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethical Committee of the Ärztekammer Nordrhein in Düsseldorf, Germany (ref. no. 2018269). The study was prospectively registered on the German register of clinical trials (www.drks.de accessed on 20 September 2022) with registration number DRKS00015764. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are available on request from the corresponding author. Acknowledgments: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. Int. J. Environ. Res. Public Health 2022, 19, 13696 10 of 11 References 1. Beard, J.R.; Officer, A.; de Carvalho, I.A.; Sadana, R.; Pot, A.M.; Michel, J.P.; Lloyd-Sherlock, P.; Epping-Jordan, J.E.; Peeters, G.M.E.E.; Mahanani, W.R.; et al. The World Report on Ageing and Health: A Policy Framework for Healthy Ageing. Lancet 2016, 387, 2145–2154. [CrossRef] 2. Reid, K.F.; Fielding, R.A. Skeletal Muscle Power: A Critical Determinant of Physical Functioning In Older Adults. Exerc. Sport Sci. Rev. 2012, 40, 4. [CrossRef] 3. Runge, M.; Rittweger, J.; Russo, C.R.; Schiessl, H.; Felsenberg, D. Is Muscle Power Output a Key Factor in the Age-Related Decline in Physical Performance? A Comparison of Muscle Cross Section, Chair-Rising Test and Jumping Power. Clin. Physiol. Funct. Imaging 2004, 24, 335–340. [CrossRef] 4. Tanaka, H.; Tarumi, T.; Rittweger, J. Aging and Physiological Lessons from Master Athletes. Compr. Physiol. 2020, 10, 261–296. [CrossRef] 5. Michaelis, I.; Kwiet, A.; Gast, U.; Boshof, A.; Antvorskov, T.; Jung, T.; Rittweger, J.; Felsenberg, D. Decline of Specific Peak Jumping Power with Age in Master Runners. J. Musculoskelet. Neuronal Interact. 2008, 8, 64–70. 6. Ireland, A.; Mittag, U.; Degens, H.; Felsenberg, D.; Heinonen, A.; Koltai, E.; Korhonen, M.T.; McPhee, J.S.; Mekjavic, I.; Pisot, R.; et al. Age-Related Declines in Lower Limb Muscle Function Are Similar in Power and Endurance Athletes of Both Sexes: A Longitudinal Study of Master Athletes. Calcif. Tissue Int. 2022, 110, 196–203. [CrossRef] 7. Alvero-Cruz, J.R.; Brikis, M.; Chilibeck, P.; Frings-Meuthen, P.; Vico Guzmán, J.F.; Mittag, U.; Michely, S.; Mulder, E.; Tanaka, H.; Tank, J.; et al. Age-Related Decline in Vertical Jumping Performance in Masters Track and Field Athletes: Concomitant Influence of Body Composition. Front. Physiol. 2021, 12, 404. [CrossRef] 8. Härdi, I.; Bridenbaugh, S.A.; Cress, M.E.; Kressig, R.W. Validity of the German Version of the Continuous-Scale Physical Functional Performance 10 Test. J. Aging Res. 2017, 2017, 9575214. [CrossRef] 9. Frey, I.; Berg, A.; Grathwohl, D.K.; Keul, J. Freiburg Questionnaire of Physical Activity—Development, Evaluation and Application. Soz. Prav. 1999, 44, 55–64. [CrossRef] 10. An, J.Y.; Zheng, J.X.; Li, J.Y.; Zeng, D.; Qu, L.J.; Xu, G.Y.; Yang, N. Effect of Myofiber Characteristics and Thickness of Perimysium and Endomysium on Meat Tenderness of Chickens. Poult. Sci. 2010, 89, 1750–1754. [CrossRef] 11. Rittweger, J.; Schiessl, H.; Felsenberg, D.; Runge, M. Reproducibility of the Jumping Mechanography as a Test of Mechanical Power Output in Physically Competent Adult and Elderly Subjects. J. Am. Geriatr. Soc. 2004, 52, 128–131. [CrossRef] 12. Lang, I.; Busche, P.; Rakhimi, N.; Martin, D.D. Mechanography in Childhood: References for Grip Force, Multiple One-Leg Hopping Force and Whole Body Stiffness. J. Musculoskelet. Neuronal Interact. 2013, 13, 227–235. 13. Matheson, L.; Duffy, S.; Maroof, A.; Gibbons, R.; Duffy, C.; Roth, J. Intra-and Inter-Rater Reliability of Jumping Mechanography Muscle Function Assessments. J. Musculoskelet. Neuronal Interact. 2013, 13, 480–486. 14. Veilleux, L.; Rauch, F. Reproducibility of Jumping Mechanography in Healthy Children and Adults. J. Musculoskelet. Neuronal Interact. 2010, 10, 256–266. 15. Moir, G.L. Three Different Methods of Calculating Vertical Jump Height from Force Platform Data in Men and Women. Meas. Phys. Educ. Exerc. Sci. 2008, 12, 207–218. [CrossRef] 16. Cavagna, G.A. Force Platforms as Ergometers. J. Appl. Physiol. 1975, 39, 174–179. [CrossRef] 17. Dalleau, G.; Belli, A.; Viale, F.; Lacour, J.R.; Bourdin, M. A Simple Method for Field Measurements of Leg Stiffness in Hopping. Int. J. Sports Med. 2004, 25, 170–176. [CrossRef] 18. Hobara, H.; Kobayashi, Y.; Yoshida, E.; Mochimaru, M. Leg Stiffness of Older and Younger Individuals over a Range of Hopping Frequencies. J. Electromyogr. Kinesiol. 2015, 25, 305–309. [CrossRef] 19. Padua, D.A.; Garcia, C.R.; Arnold, B.L.; Granata, K.P. Gender Differences in Leg Stiffness and Stiffness Recruitment Strategy during Two-Legged Hopping. J. Mot. Behav. 2005, 37, 111–126. [CrossRef] 20. Harman, E.A.; Rosenstein, M.T.; Frykman, P.N.; Rosenstein, R.M.; Kraemer, W.J. Estimation of Human Power Output from Vertical Jump. J. Strength Cond. Res. 1991, 5, 116–120. [CrossRef] 21. Cavagna, G.A.; Franzetti, P.; Heglund, N.C.; Willems, P. The Determinants of the Step Frequency in Running, Trotting and Hopping in Man and Other Vertebrates. J. Physiol. 1988, 399, 81–92. [CrossRef] 22. Blickhan, R. The Spring-Mass Model for Running and Hopping. J. Biomech. 1989, 22, 1217–1227. [CrossRef] 23. Farley, C.T.; Glasheen, J.; McMahon, T.A. Running Springs: Speed and Animal Size. J. Exp. Biol. 1993, 185, 71–86. [CrossRef] 24. Hobara, H.; Kanosue, K.; Suzuki, S. Changes in Muscle Activity with Increase in Leg Stiffness during Hopping. Neurosci. Lett. 2007, 418, 55–59. 25. Farley, C.T.; Morgenroth, D.C. Leg Stiffness Primarily Depends on Ankle Stiffness during Human Hopping. J. Biomech. 1999, 32, 267–273. [CrossRef] 26. Alcazar, J.; Aagaard, P.; Haddock, B.; Kamper, R.S.; Hansen, S.K.; Prescott, E.; Alegre, L.M.; Frandsen, U.; Suetta, C.; Toledo, G. Age- and Sex-Specific Changes in Lower-Limb Muscle Power throughout the Lifespan. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 2020, 75, 1369–1378. [CrossRef] 27. Alcazar, J.; Rodriguez-Lopez, C.; Ara, I.; Alfaro-Acha, A.; Rodríguez-Gómez, I.; Navarro-Cruz, R.; Losa-Reyna, J.; García-García, F.J.; Alegre, L.M. Force-Velocity Profiling in Older Adults: An Adequate Tool for the Management of Functional Trajectories with Aging. Exp. Gerontol. 2018, 108, 1–6. [CrossRef] Int. J. Environ. Res. Public Health 2022, 19, 13696 11 of 11 28. Shur, N.F.; Creedon, L.; Skirrow, S.; Atherton, P.J.; MacDonald, I.A.; Lund, J.; Greenhaff, P.L. Age-Related Changes in Muscle Architecture and Metabolism in Humans: The Likely Contribution of Physical Inactivity to Age-Related Functional Decline. Ageing Res. Rev. 2021, 68, 101344. [CrossRef] 29. Crane, J.D.; MacNeil, L.G.; Tarnopolsky, M.A. Long-Term Aerobic Exercise Is Associated with Greater Muscle Strength throughout the Life Span. J. Gerontol. Ser. A 2013, 68, 631–638. [CrossRef] 30. Zampieri, S.; Pietrangelo, L.; Loefler, S.; Fruhmann, H.; Vogelauer, M.; Burggraf, S.; Pond, A.; Grim-Stieger, M.; Cvecka, J.; Sedliak, M.; et al. Lifelong Physical Exercise Delays Age-Associated Skeletal Muscle Decline. J. Gerontol. Ser. A 2015, 70, 163–173. [CrossRef] 31. Gudiksen, A.; Qoqaj, A.; Ringholm, S.; Wojtaszewski, J.; Plomgaard, P.; Pilegaard, H. Ameliorating Effects of Lifelong Physical Activity on Healthy Aging and Mitochondrial Function in Human White Adipose Tissue. J. Gerontol. Ser. A 2022, 77, 1101–1111. [CrossRef] 32. Molmen, H.E.; Wisloff, U.; Aamot, I.L.; Stoylen, A.; Ingul, C.B. Aerobic Interval Training Compensates Age Related Decline in Cardiac Function. Scand. Cardiovasc. J. 2012, 46, 163–171. [CrossRef] 33. Glenmark, B.; Nilsson, M.; Gao, H.; Gustafsson, J.Å.; Dahlman-Wright, K.; Westerblad, H. Difference in Skeletal Muscle Function in Males vs. Females: Role of Estrogen Receptor-β. Am. J. Physiol. Endocrinol. Metab. 2004, 287, 1125–1131. [CrossRef]

Effects of Aging and Fitness on Hopping Biomechanics.

10-21-2022

Sanchez-Trigo, Horacio,Zange, Jochen,Sies, Wolfram,Böcker, Jonas,Sañudo, Borja,Rittweger, Jörn

eng

PMC4916632

Page 1 of 2 Supplementary material Supplementary Figure 1: Distribution of estimated VO2 max Supplementary Table 1: Distribution of estimated VO2 max (ml O2/min/kg) by sex and ethnic group All children Boys Girls Ethnic group or sub-group n Mean SD N Mean SD N Mean SD All children 1625 39.4 4.5 825 40.9 4.4 800 37.8 4.0 White European 424 39.6 4.6 231 40.7 4.6 193 38.2 4.2 South Asian 407 38.5 4.4 200 39.9 4.3 207 37.1 4.1 Indian 111 37.6 4.8 64 39.1 4.9 47 35.7 4.0 Pakistani 147 38.6 3.9 77 39.9 3.7 70 37.1 3.6 Bangladeshi 121 39.1 4.4 49 41.0 4.3 72 37.8 4.0 Black African-Caribbean 413 40.1 4.4 208 41.8 4.1 205 38.3 3.9 Black African 230 40.4 4.5 113 42.4 4.1 117 38.5 4.1 Black Caribbean 148 39.7 4.1 76 41.0 4.1 72 38.3 3.8 Other 381 39.3 4.5 186 41.0 4.4 195 37.7 3.9 South Asian other and black other subgroups are not included in the table therefore the numbers in the subgroups do not add up to the main ethnic group totals for South Asians and black African-Caribbeans Page 2 of 2 Supplementary Table 2: Ethnic differences in physical activity and adiposity Mean/geometric mean* (95% CI), p-value for difference from white Europeans White European South Asian Black African-Caribbean Other Outcome (n = 324) (n =278) (n = 320) (n = 293) Counts 401,758 (392,024, 411,491) 381,981 (370,884, 393,078) 0.002 414,900 (405,040, 424,761) 0.02 394,651 (384,606, 404,696) 0.22 CPM 501 (488, 513) 459 (445, 473) <0.0001 500 (488, 513) 0.96 478 (465, 491) 0.002 Steps 10,356 (10,144, 10,567) 9,550 (9,311, 9,788) <0.0001 9,928 (9,714, 10,142) <0.001 10,000 (9,782, 10,217) 0.002 MVPA (min) 71 (68, 74) 65 (62, 68) <0.0001 72 (69, 75) 0.15 69 (66, 71) 0.05 FMI (kg/m5)* 2.10 (2.01, 2.18) 2.19 (2.09, 2.29) 0.17 1.90 (1.83, 1.98) <0.001 2.18 (2.09, 2.27) 0.20 All means are adjusted for sex, age quartiles, month and school (random effect) Abbreviations: CPM, counts per minute; FMI, fat mass index; MVPA, moderate to vigorous physical activity Supplementary Figure 1: Distribution of estimated VO2 max

Cross-sectional study of ethnic differences in physical fitness among children of South Asian, black African-Caribbean and white European origin: the Child Heart and Health Study in England (CHASE).

06-20-2016

Nightingale, C M,Donin, A S,Kerry, S R,Owen, C G,Rudnicka, A R,Brage, S,Westgate, K L,Ekelund, U,Cook, D G,Whincup, P H

eng

PMC3359364

Impact of Environmental Parameters on Marathon Running Performance Nour El Helou1,2,3*, Muriel Tafflet1,4, Geoffroy Berthelot1,2, Julien Tolaini1, Andy Marc1,2, Marion Guillaume1, Christophe Hausswirth5, Jean-Franc¸ois Toussaint1,2,6 1 IRMES (bioMedical Research Institute of Sports Epidemiology), INSEP, Paris, France, 2 Universite´ Paris Descartes, Sorbonne Paris Cite´, Paris, France, 3 Faculte´ de Pharmacie, De´partement de Nutrition, Universite´ Saint Joseph, Beirut, Lebanon, 4 INSERM, U970, Paris Cardiovascular Research Center – PARCC, Paris, France, 5 Research Department, INSEP, Paris, France, 6 Hoˆtel-Dieu Hospital, CIMS, AP-HP, Paris, France Abstract Purpose: The objectives of this study were to describe the distribution of all runners’ performances in the largest marathons worldwide and to determine which environmental parameters have the maximal impact. Methods: We analysed the results of six European (Paris, London, Berlin) and American (Boston, Chicago, New York) marathon races from 2001 to 2010 through 1,791,972 participants’ performances (all finishers per year and race). Four environmental factors were gathered for each of the 60 races: temperature (uC), humidity (%), dew point (uC), and the atmospheric pressure at sea level (hPA); as well as the concentrations of four atmospheric pollutants: NO2 – SO2 – O3 and PM10 (mg.m23). Results: All performances per year and race are normally distributed with distribution parameters (mean and standard deviation) that differ according to environmental factors. Air temperature and performance are significantly correlated through a quadratic model. The optimal temperatures for maximal mean speed of all runners vary depending on the performance level. When temperature increases above these optima, running speed decreases and withdrawal rates increase. Ozone also impacts performance but its effect might be linked to temperature. The other environmental parameters do not have any significant impact. Conclusions: The large amount of data analyzed and the model developed in this study highlight the major influence of air temperature above all other climatic parameter on human running capacity and adaptation to race conditions. Citation: El Helou N, Tafflet M, Berthelot G, Tolaini J, Marc A, et al. (2012) Impact of Environmental Parameters on Marathon Running Performance. PLoS ONE 7(5): e37407. doi:10.1371/journal.pone.0037407 Editor: Alejandro Lucia, Universidad Europea de Madrid, Spain Received February 28, 2012; Accepted April 19, 2012; Published May 23, 2012 Copyright:  2012 El Helou et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: The authors have no support or funding to report. Competing Interests: The authors have declared that no competing interests exist. * E-mail: [email protected] Introduction Like most phenotypic traits, athletic performance is multifacto- rial and influenced by genetic and environmental factors: exogenous factors contribute to the expression of the predisposing characteristics among best athletes [1,2]. The marathon is one of the most challenging endurance competitions; it is a mass participation race held under variable environmental conditions and temperatures sometimes vary widely from start to finish [3–5]. Warm weather during a marathon is detrimental for runners and is commonly referenced as limiting for thermoregulatory control [3,6]. More medical complaints of hyperthermia (internal temperature $39uC) occur in warm weather events, while hypothermia (internal temperature #35uC) sometimes occurs during cool weather events [3]. In addition, participating in an outdoor urban event exposes athletes to air pollution which raises concerns for both performance and health [7]. Runners could be at risk during competitions as they are subject to elevated ventilation rate and increased airflow velocity amplifying the dose of inhaled pollutants and carrying them deeper into the lungs [7–9]. They switch from nasal to mouth breathing, bypassing nasal filtration mechanisms for large particles. Both might increase the deleterious effects of pollutants on health and athletic performance [8,10]. Exposure to air pollution during exercise might be expected to impair an athlete’s performance in endurance events lasting one hour or more [7,10]. The relationship between marathon performance decline and warmer air temperature has been well established. Vihma [6] and Ely et al. [11,12] found a progressive and quantifiable slowing of marathon performance as WBGT (Wet Bulb Globe Temperature) increases, for men and women of wide ranging abilities. Ely et al. [13] as well as Montain et al. [14] also found that cooler weather (5–10uC) was associated with better ability to maintain running velocity through a marathon race compared to warmer conditions especially by fastest runners; weather impacted pacing and the impact was dependent on finishing position. Marr and Ely [9] found significant correlations between the increase of WBGT and PM10, and slower marathon performance of both men and women; but they did not find significant correlations with any other pollutant. PLoS ONE | www.plosone.org 1 May 2012 | Volume 7 | Issue 5 | e37407 Previous studies have mostly analysed the performances of the top 3 males and females finishers as well as the 25th-, 100th-, and 300th- place finishers [11,13–16]. Here we targeted exhaustiveness and analysed the total number of finishers in order to quantify the effect of climate on the full range of runners. The objectives of this study were 1) to analyse all levels of running performance by describing the distribution of all marathons finishers by race, year and gender; 2) to determine the impact of environmental parameters: on the distribution of all marathon runners’ performance in men and women (first and last finishers, quantiles of distribution); and on the percentage of runners withdrawals. We then modelled the relation between running speed and air temperature to determine the optimal environmental conditions for achieving the best running perfor- mances, and to help, based on known environmental parameters, to predict the distribution and inform runners on possible outcomes of running at different ambient temperatures. We tested the hypothesis that all runners’ performances distributions may be similar in all races, and may be similarly affected by temperature. Methods Data Collection Marathon race results were obtained from six marathons included in the « IAAF Gold Labeled Road Races » and « World Marathon Majors »: Berlin, Boston, Chicago, London, New York and Paris. From 2001 to 2010 (available data are limited before 2001) the arrival times in hours: minutes: seconds, of all finishers were gathered for each race. These data are available in the public domain on the official internet website of each city marathon, and on marathon archives websites [17] and complementary data when needed from official sites of each race. Written and informed consent was therefore not required from individual athletes. The total number of collected performances was 1,791,972 for the 60 races (10 years 66 marathons), including 1,791,071 performances for which the gender was known. We also gathered the total number of starters in order to calculate the number and the percentage of non-finishers (runner withdrawal) per race. Hourly weather data corresponding to the race day, time span and location of the marathon were obtained from ‘‘weather underground website’’ [5]. Four climatic data were gathered for each of the 60 races: air temperature (uC), air humidity (%), dew- point temperatures (uC), and atmospheric pressure at sea level (hPA). Each of these parameters was averaged for the first 4 hours after the start of each race. Hourly air pollution data for the day, time span and location of each race were also obtained through the concentrations of three atmospheric pollutants: NO2 – SO2 – O3 (mg.m23) from the Environmental Agency in each state (the Illinois Environmental Protection Agency for Chicago maratho’n, the Massachusetts Department of environmental Protection for Boston marathon and the New York State Department of Environmental Conservation for New York marathon), and the Environmental agency websites of the three European cities [18– 20]. All pollutants values were averaged for the first 4 hours after the start of each race. Concurrent measurements of air pollution for all ten race years (2001–2010) were only available for 3 pollutants, because air pollution monitoring sites typically measure only a subset of pollutants and may not have been operational in all years. In addition, particulate matters PM10 were collected in Paris and Berlin, but there were not enough measurements in the other four cities races days. Data Analysis and selection Men and women performances were analysed separately. For each race and each gender every year, we fitted the Normal and log-Normal distributions to the performances and tested the normality and log normality using the Kolmogorov-Smirnov D statistic. We rejected the null hypothesis that the sample is normally or log–normally distributed when p values ,0.01. The following statistics (performance levels) were determined for all runners’ performances distribution of each race, every year and for each gender: – the first percentile of the distribution (P1), representing the elite of each race. – the winner. – the last finisher. – the first quartile of the distribution (Q1), representing the 25th percentile of best performers of the studied race. – the median. – the inter quartile range (IQR), representing the statistical dispersion, being equal to the difference between the third and first quartiles. A Spearman correlation test was performed between each performance level and climate and air pollution parameters, in order to quantify the impact of weather and pollution on marathon performances. Spearman correlation tests were also performed between each environmental parameter. The year factor was not included because we previously demonstrated that for the past ten years, marathon performances were now progressing at a slower rate [21]. Temperature and running speed We modelled the relation between running speed of each performance level for each gender and air temperature, using a second degree polynomial quadratic model, which seems appro- priate to depict such physiological relations [22–24]. The second degree polynomial equation was applied to determine the optimal temperature at which maximal running speed is achieved for each level of performance for each gender, and then used to calculate the speed decrease associated with temperature increase and decrease above the optimum. We similarly modelled the relation between air temperature and the percentage of runners’ withdrawal. All analyses were performed using the MATLAB and SAS software. Results The total numbers of starters and finishers of the 6 marathons increased over the 10 studied years (Figure 1). Marathons characteristics are described in supplementary data (Table S1). The race with the least number of finishers was Boston 2001 with 13381 finishers and the highest number was seen in New York 2010 with 44763 finishers. Three marathons were held in April, the other three during fall. Air temperatures ranged from 1.7uC (Chicago 2009) to 25.2uC (Boston 2004) (Table 1). Performance distribution For all 60 studied races, the women and men’s performance distributions were a good approximation of the ‘‘log normal’’ and ‘‘normal’’ distributions (p-values of Kolmogorov-Smirnov statistics $0.01). Environmental Parameters and Marathon Running PLoS ONE | www.plosone.org 2 May 2012 | Volume 7 | Issue 5 | e37407 Figure 2 illustrates examples of 4 races’ performances distribu- tion fit: men’s performances distribution of two races in Paris (2002: Tu = 7.6uC; and 2007: Tu = 17.4uC) and Chicago (2002: Tu = 5.4uC; and 2007: Tu = 25uC). We notice a stable gap between male and female performances at all levels in all marathons, women being on average 10.3%61.6% (mean 6 standard deviation) slower than men (Table S1); mean female winners are 9.9%61.5% slower than male winners, mean female median is 9.9%61.6% than male median, and mean female Q1 are 11.1%61.5% slower that male Q1. Correlations Spearman correlations results are displayed in Table 2, detailed correlations by marathon are available in supplementary data (Table S2). The environmental parameter that had the most significant correlations with marathons performances was air temperature: it was significantly correlated with all performance levels in both male and female runners. Humidity was the second parameter with a high impact on performance; it was significantly correlated with women’s P1 and men’s all performance levels. The dew point and atmospheric pressure only had a slight influence (p,0.1) in men’s P1 and women’s P1 respectively, and did not affect the other performance levels. Concerning the atmospheric pollutants, NO2 had the most significant correlation with performance: it was significantly correlated with Q1, IQR and the median for both genders. Sulfur dioxide (SO2) was correlated with men’s P1 (p,0.01) and had a slight influence (p,0.1) on men’s Q1. Finally ozone (O3) only had a slight influence (p,0.1) on men’s Q1. In the marathon by marathon analysis, ozone (O3) had the most significant correlation with performance (Table S2): it was significantly correlated with all performance levels (P1, Q1, IQR and the median) of the Berlin and Boston (except men’s IQR) marathon for both genders. It also affected Chicago (men’s P1, Q1, and men’s median), and New York (women’s Q1) marathons. Temperature and running speed When temperature increased above an optimum, performance decreased. Figure 3 describes the relationship between marathons running speeds and air temperature, fit through a quadratic second degree polynomial curve for women’s P1 and men’s Q1 of all 60 races. For each performance level the speed decrease associated with temperature increase and decrease is presented in supplementary data (Table S3). For example the optimal temperature at which women’s P1 maximal running speed was attained was 9.9uC, and an increase of 1uC from this optimal temperature will result in a speed loss of 0.03%. The optimal temperatures to run at maximal speed for men and women, varied from 3.8uC to 9.9uC according to each level of performance (Table S3). Warmer air temperatures were associated with higher percent- ages of runners’ withdrawal during a race (Figure 4). After testing linear, quadratic, exponential and logarithmic fits, the quadratic equation was the best fit (r2 = 0.36; p,0.0001) for modelling the percentage of runners withdrawals associated with air temperature (Figure 4): %withdrawals~{0:59|t0Cz0:02|t0C2z5:75 Discussion Our study is the first to our knowledge to analyse the exhaustiveness of all marathon finishers’ performances in the three major European (Berlin, Paris and London, which were not previously analysed) and three American marathons. Previous studies have mostly analysed American marathons including Chicago, Boston and New York that are analysed in the present paper [9,11–15], but they have only included the performances of the top 3 males and females finishers as well as the 25th-, 100th-, and 300th- place finishers [11,13–15]. In the present study we 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 Finishers Non finishers Number of starters Berlin Boston Chicago London New York Paris 2001 2010 2001 2010 2001 2010 2001 2010 2001 2010 2001 2010 Figure 1. Number of starters and finishers by marathon and year (missing data points for Boston, Chicago and Paris marathons). doi:10.1371/journal.pone.0037407.g001 Environmental Parameters and Marathon Running PLoS ONE | www.plosone.org 3 May 2012 | Volume 7 | Issue 5 | e37407 Table 1. Average and range values of all weather and pollution parameters for the six marathons. Marathon Parameter N Mean Std Dev Minimum Maximum Berlin Run in September; Starts 9am Temperature (uC) 10 14.9 3.2 11.3 21.3 Dew Point (uC) 10 10.6 1.8 5.8 12.3 Humidity (%) 10 78.0 14.5 55.0 98.5 Atmospheric pressure (hPA) 10 1017.0 6.3 1003.0 1029.0 NO2 (mg.m23) 10 26.5 4.0 20.8 33.2 O3 (mg.m23) 10 41.0 17.3 21.2 81.8 PM10 (mg.m23) 8 25.1 11.4 7.6 46.5 SO2 (mg.m23) 10 5.0 3.1 1.1 10.7 Boston Run in April; Starts 10am Temperature (uC) 10 11.8 5.1 8.0 25.2 Dew Point (uC) 10 3.9 3.8 22.1 10.2 Humidity (%) 10 62.6 19.9 28.3 91.0 Atmospheric pressure (hPA) 10 1013.0 12.4 981.6 1029.0 NO2 (mg.m23) 10 29.3 10.3 14.6 50.5 O3 (mg.m23) 10 73.5 25.7 18.5 122.7 PM10 (mg.m23) 0 SO2 (mg.m23) 10 7.0 2.9 1.6 12.1 Chicago Run in October; Starts 7:30am Temperature (uC) 10 12.1 7.5 1.7 25.0 Dew Point (uC) 10 4.9 7.6 25.9 19.0 Humidity (%) 10 62.8 8.1 52.3 79.2 Atmospheric pressure (hPA) 10 1022.0 6.4 1012.0 1031.0 NO2 (mg.m23) 10 27.9 13.0 9.7 52.0 O3 (mg.m23) 10 57.1 15.1 35.9 84.0 PM10 (mg.m23) 2 26.7 11.6 15.3 38.0 SO2 (mg.m23) 9 6.5 3.1 2.1 12.4 London Run in April; Starts 9:30am Temperature (uC) 10 12.4 3.2 9.5 19.1 Dew Point (uC) 10 6.0 2.9 0.8 10.7 Humidity (%) 10 66.9 16.7 42.9 86.1 Atmospheric pressure (hPA) 10 1010.0 12.5 976.4 1020.0 NO2 (mg.m23) 10 44.8 14.5 22.8 72.2 O3 (mg.m23) 9 51.4 17.1 35.0 92.3 PM10 (mg.m23) 2 27.8 14.5 13.7 41.9 SO2 (mg.m23) 10 4.5 2.8 0.0 8.8 New York Run in November; Starts 10am Temperature (uC) 10 12.5 4.1 7.1 18.4 Dew Point (uC) 10 2.3 6.4 25.6 12.8 Humidity (%) 10 51.1 12.1 36.5 79.8 Atmospheric pressure (hPA) 10 1020.0 7.8 1009.0 1034.0 NO2 (mg.m23) 9 55.1 17.2 21.9 77.3 O3 (mg.m23) 10 32.6 12.3 11.1 53.8 PM10 (mg.m23) 10 5.0 0.0 5.0 5.0 SO2 (mg.m23) 9 19.7 12.2 4.8 42.4 Paris Run in April; Starts 8:45am Temperature (uC) 10 9.2 3.2 4.8 17.4 Dew Point (uC) 10 4.2 4.1 23.6 13.4 Humidity (%) 10 72.4 10.1 45.9 85.4 Atmospheric pressure (hPA) 10 1019.0 6.2 1005.0 1026.0 NO2 (mg.m23) 10 43.0 13.7 23.4 73.1 Environmental Parameters and Marathon Running PLoS ONE | www.plosone.org 4 May 2012 | Volume 7 | Issue 5 | e37407 analysed the total number of finishers in order to exhaustively quantify the effect of climate on runners from all performance levels. Updating and extending earlier results, this study still concludes that the main environmental factor influencing mara- thon performance remains temperature. The pattern of perfor- mance reduction with increasing temperature is analogous in men and women, suggesting no apparent gender differences. In addition the mean gap between male and female performances is the same across all marathons and all performance levels (Table 1). This is consistent with our previous work that showed that the gender gap in athletic performance has been stable for more than 25 years, whatever the environmental conditions [25]. The more the temperature increases, the larger the decreases in running speeds (Table S3). This is supported by the increased percentage of runners’ withdrawals when races were contested in very hot weather (Figure 4), and by the significant shift of the race’s results through the whole range of performance distribution (Figure 2). The significant effect of air temperature on the median values (Table 2) also suggests that all runners’ performances are similarly affected by an increase in air temperature, as seen in Figure 2 showing performances distribution of races in Paris and Chicago with different air temperatures: the significant shift of performance towards the right concerns all runners categories, from the elite to the less trained competitors. In addition the percentage of runner’s withdrawals in Chicago 2007 was the highest (30.74%) among all 60 studied races (Figure 1 and Figure 4). Roberts [26] reported that organisers tried to interrupt the race 3.5 h after the start. This was not successful as most of the finishers crossed the finish line much later (up to 7 h after the start); 66 runners were admitted to the hospital (12 intensive care cases with hydration disorders, heat shock syndromes and 1 death). During the 2004 Boston Marathon (Tu = 22.5uC) more than 300 emergency medical calls were observed, consequently the race’s start time changed from noon to 10 am in order to decrease heat stress and related casualties [26]. The 2007 London Marathon was hot by London standards (air Tu = 19.1uC vs. an average of 11.6uC for the nine other years analysed in our study), 73 hospitalisations were recorded with 6 cases of severe electrolyte imbalance and one death, the total average time (all participants’ average) was 17 min slower than usual. In contrast, the number of people treated in London 2008 in cool and rainy conditions (Tu = 9.9uC), was 20% lower [26]. Our results showed that the percentage of runners’ withdrawals from races significantly increases with increasing temperature (Figure 4). The acceptable upper limit for competition judged by the American College of Sports Medicine (ACSM) is a WBGT of 28uC, but it may not reflect the safety profile of unacclimatized, non-elite marathon runners [3,26–28]. Roberts [26] stated that marathons should not be allowed to start for non-elite racers at a WBGT of 20.5uC. Our results suggest that there is no threshold but a continuous process Table 1. Cont. Marathon Parameter N Mean Std Dev Minimum Maximum O3 (mg.m23) 10 66.9 9.8 55.2 82.1 PM10 (mg.m23) 10 37.9 32.6 16.6 132.7 SO2 (mg.m23) 10 6.4 3.7 1.5 12.2 doi:10.1371/journal.pone.0037407.t001 0 2 4 6 8 10 12 2:10 3:10 4:10 5:10 6:10 7:10 8:10 Percent 0 2 4 6 8 10 12 2:10 3:10 4:10 5:10 6:10 7:10 8:10 0 2 4 6 8 10 12 2:10 3:10 4:10 5:10 6:10 7:10 8:10 Percent Arrival time in hour:minutes 0 2 4 6 8 10 12 2:10 3:10 4:10 5:10 6:10 7:10 8:10 Arrival time in hour:minutes A - Chicago 2002 B - Paris 2002 C - Chicago 2007 D - Paris 2007 Figure 2. Distribution of performances: example of men’s performances distribution for Chicago (in 2002: T6C = 5.46C; and in 2007: T6C = 256C); and Paris (in 2002: T6C = 7.66C; and in 2007: T6C = 17.46C). doi:10.1371/journal.pone.0037407.g002 Environmental Parameters and Marathon Running PLoS ONE | www.plosone.org 5 May 2012 | Volume 7 | Issue 5 | e37407 on both side of an optimum: the larger the gap from the optimal temperature, the lower the tolerance and the higher the risk. In fact, in environments with high heat and humidity, not only is performance potentially compromised, but health is also at risk [29]; both are similarly affected. As soon as WBGT is higher than 13uC the rate of finish line medical encounters and on-course marathon dropouts begin to rise [26] as similarly seen in our study in Figure 4. Warm weather enhances the risk of exercise induced hyper- thermia; its first measurable impact is the reduction of physical performance [4,14,29–31] as it is detrimental for the cardiovas- cular, muscular and central nervous systems [32,33]. More recent work suggested that central fatigue develops before any elevation in body temperature occurs: evidence supported that subjects would subconsciously reduce their velocity earlier after the start of an exercise in hot environment, when internal temperatures are still lower than levels associated with bodily harm. Exercise is thus homeostatically regulated by the decrease of exercise intensity (decrease of running performance and heat production) in order to prevent hyperthermia and related catastrophic failures [34,35]. On the other hand, cool weather is associated with an improved ability to maintain running velocity and power output as compared to warmer conditions, but very cold conditions also tend to reduce performance [29,36,37]. Among the studied races’ winners, men’s marathon world record was beaten in Berlin in 2007 and 2008 (Haile Gebrselassie in 02:03:59), as well as women’s marathon world record, beaten in London 2003 (Paula Radcliffe in 02:15:25). The winners’ speeds couldn’t be affected in the same way than the other runners by air temperature and the other environmental parameters, because top performances can fluctuate from year to year due to numerous factors, such as prize money, race strategies, or overall competition [11]. Another explanation is that, in all of our 60 studied races, 89.5% of male winners were of African origin (57.9% from Kenya; 21.1% from Ethiopia; and 10.5% from Eritrea, Morocco and South Africa); as well as 54.5% of female winners (27.3% from Kenya and 27.3% from Ethiopia- data not shown). African runners might have an advantage over Caucasian athletes, possibly due to a unique combination of the main endurance factors such as maximal oxygen uptake, fractional utilization of VO2max and running economy [38]. They might also perform better in warm environments as they are usually thinner than Caucasian runners (smaller size and body mass index) producing less heat with lower rates of heat storage [38–40]. Psychological factors may also play a role; some hypothesis suggested that regardless of the possible existence of physiological advantages in East African runners, belief that such differences exist may create a background that can have significant positive consequences on performance [41,42]. Genetics and training influence the tolerance for hyperthermia [4,38,43]. Acclimatisation involving repeated exposures to exercise in the heat also results in large improvements in the time to fatigue. Optimal thermoregulatory responses are observed in runners who have been acclimatized to heat and who avoid thirst before and during the race. Their best performances might be less influenced by temperature as winners had been more acclimatized to it [4,29,30,44]. The avoidance of thirst sensation rather than optimum Table 2. Spearman correlations results between all marathons performance levels and environmental parameters: $ = p,0.1; * = p,0.05; ** = p,0.01; *** = p,0.001. Parameter Gender P1 Median Q1 IQR Temperature Women 0.31* 0.30* 0.35** 0.15 Men 0.48*** 0.40*** 0.44*** 0.25$ Dew Point Women 0.14 0.18 0.21 0.01 Men 0.25$ 0.19 0.20 0.10 Humidity Women 20.3* 20.16 20.19 20.21 Men 20.34** 20.28* 20.32* 20.19 Atm. Pressure Women 0.22$ 0.06 0.07 0.06 Men 0.13 0.04 0.06 0.06 NO2 Women 0.11 0.40** 0.43*** 0.33* Men 0.25$ 0.38** 0.35** 0.27* O3 Women 0.01 20.15 20.11 20.20 Men 20.05 20.21 20.24$ 20.11 PM10 Women 0.08 0.15 0.25 0.03 Men 0.10 0.10 0.09 0.16 SO2 Women 0.21 0.13 0.21 0.02 Men 0.37** 0.20 0.25$ 0.04 P1: first percentile, Q1: first quartile, IQR: Inter Quartile Range. doi:10.1371/journal.pone.0037407.t002 0 5 10 15 20 25 30 3.4 3.5 3.6 3.7 3.8 3.9 4.0 Temperature (°C) speed (m.s−1) 0 5 10 15 20 25 30 2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0 Temperature (°C) A - Women P1 B - Men Q1 Figure 3. Quadratic second degree polynomial fit for Women’s P1 running speeds vs. air temperature, r2 = 0.27; p,0.001; max = 9.96C. B) Men’s Q1 running speeds vs. air temperature, r2 = 0.24; p,0.001; max = 66C. doi:10.1371/journal.pone.0037407.g003 Environmental Parameters and Marathon Running PLoS ONE | www.plosone.org 6 May 2012 | Volume 7 | Issue 5 | e37407 hydration prevents the decline in running performance [45]; contradicting the idea that dehydration associated with a body weight loss of 2% during an exercise will impair performance, recent studies reported that Haile Gebrselassie lost 10% of his body weight when he established his world record [45–47]. Previous studies suggested that the impact of weather on speed might depend on running ability, with faster runners being less limited than slower ones [6,13,14,29]. This could be attributable to a longer time of exposition to the environmental conditions of slower runners during the race [11]. Also, slower runners tend to run in closer proximity to other runners with clustering formation [48,49], which may cause more heat stress as compared with running solo [50]. These elements, however, are not supported after analyzing the full range of finisher’s data; at a population level, temperature causes its full effect whatever the initial capacity. Differences in fitness relative to physiological potential may also contribute to differences in performance times and ability to cope with increasing heat stress [11,48,49]. There was a strong correlation of running speed with air temperature (Figure 3). The maximal average speeds were performed at an optimal temperature comprised between 3.8uC and 9.9uC depending on the performance level (Table S3); small increases in air temperatures caused marathon performances to decline in a predictable and quantifiable manner. On the other hand, large decreases in air temperatures under the optimum also reduce performances. These optimal temperatures found in the present study are comprised in the optimal temperature range of 5–10uC WBGT found in previous studies [14]; other studies stated that a weather of 10–12uC WBGT is the norm for fast field performance and reported a decrease of performance with increasing WBGT [12,27,51,52]. Best marathon times and most marathon world records were achieved in cool environmental temperatures (10–15uC) and have been run in the early morning during spring and fall [12]. Analysing Gebrselassie’s performances in Berlin reveals that they follow the same trend, with both World Records obtained at the lowest temperatures (14uC in 2007 and 13uC in 2008, vs. 18uC in 2009 and 22uC in 2006 when he also won these two races without beating the world record). The relationship between running speed and air temperature defined in our study (Figure 3) is similar to the relationship found between mortality and air temperature (asymmetrical U-like pattern) in France defined by Laaidi et al [53], where mortality rates increase with the lowest and the highest temperatures. A ‘‘thermal optimum’’ occurs in between, where mortality rates are minimal [53]. The great influence that temperature has on performance is comparable to the influence it has on mortality, suggesting that both sports performance and mortality are thermodynamically regulated. This also emphasizes the utility of prevention programs, the assessment of public health impacts and acclimatization before participating in hot marathons [53]. Similar correlations were also found between temperature and swimming performance in juvenile southern catfish [22], and between increases in summer water temperature and elevated mortality rates of adult sockeye salmon [23]; suggesting that physiological adaptations to temperature, similarly occur in various taxons, but vary within specific limits that depend on species and will modify performances. Air pollution and performance The measured levels of pollution had no impact on perfor- mance, except for ozone (Table S2) and NO2 (Table 2). Assessing the effect of any single air pollutant separately is not simple; it is not isolated in the inhaled air, but rather combined with other parameters. Therefore any possible influence might probably be due to a combination of components. In addition most marathons are held on Sunday mornings, when urban transport activity and its associated emissions are low, and photochemical reactions driven by solar radiation have not yet produced secondary pollutants such as ozone [9]. This is the most probable explanation to our results, confirming previous studies. Among the air pollutants analysed in the present study, ozone and NO2 had the greatest effect on decreasing marathon performances (Ta- ble S2). Ozone concentrations on the ground increase linearly with air temperature [7,8,10]; thus the effect of ozone in our study may be mainly associated with the temperature effect, as seen in Berlin and Chicago. However ozone and other pollutants effects are known to be detrimental to exercise performance only when exposure is sufficiently high. Many studies showed no effect of air pollutants on sports performance [9]. Some of them showed that PM2.5 and aerosol acidity were associated with acute decrements in pulmonary function, but these changes in pulmonary function were unlikely to result in clinical symptoms [54]. Others showed that chronic exposure to mixed pollutants during exercise may result in decreased lung function, or vascular dysfunction, and may compromise performance [55]. During the marathons studied here, concentrations of air pollutants never exceeded the limits set forth by national environmental agencies (US Environmental Protection Agency- EPA; AirParif; European Environmental Agency- EEA) or the levels known to alter lung function in laboratory situations [9]. Conclusions Air temperature is the most important factor influencing marathon running performance for runners of all levels. It greatly influences the entire distribution of runners’ performances as well as the percentage of withdrawals. Running speed at all levels is linked to temperature through a quadratic model. Any increase or decrease from the optimal temperature range will result in running speed decrease. Ozone also has an influence on performance but its effect might be linked to the temperature impact. The model developed in this study could be used for further predictions, in order to evaluate expected performance variations with changing weather conditions. Temperature (°C) Withdrawals (%) 0 5 10 15 20 25 30 35 0 5 10 15 20 25 30 Figure 4. Relationship between air temperature and the percentage of runners’ withdrawals, modeled with a quadratic fit (blue curve, r2 = 0.36; p,0.0001). The green curve represents the quadratic fit without the maxima (Chicago 2007: 30.74% withdrawals at a race temperature of 25uC). doi:10.1371/journal.pone.0037407.g004 Environmental Parameters and Marathon Running PLoS ONE | www.plosone.org 7 May 2012 | Volume 7 | Issue 5 | e37407 Supporting Information Table S1 Time values of different descriptive statistics and their variability by marathon and gender. 1 Value of the described statistic for all performances of all year together, hour:min:sec 2 Standard deviation of the described statistic for all performances of each year, hour:min:sec 3 IQR: Inter Quartile Range. (DOCX) Table S2 Spearman correlations results between each marathon performance levels and environmental pa- rameters: $ = p,0.1; * = p,0.05; ** = p,0.01; *** = p,0.001. P1: first percentile, Q1: first quartile, IQR: Inter Quartile Range. (DOCX) Table S3 Optimal temperatures for maximal running speeds of each level of performance, with speed losses associated with each temperature increase. (DOCX) Acknowledgments We thank the Centre National de De´veloppement du Sport and the Ministry of Health,Youthand Sport.We thankINSEPteamsfor theirfullsupport. We thank Mrs Karine Schaal for carefully reviewing the manuscript. Author Contributions Conceived and designed the experiments: JFT NEH GB AM. Analyzed the data: JT GB AM NEH MG MT. Wrote the paper: NEH GB JFT. Reviewed the paper: CH JFT. References 1. Lippi G, Favaloro EJ, Guidi GC (2008) The genetic basis of human athletic performance. Why are psychological components so often overlooked? J Physiol 586(Pt 12):3017; author reply 3019–3020. 2. Macarthur DG, North KN (2005) Genes and human elite athletic performance. Hum Genet 116(5): 331–339. 3. Cheuvront SN, Haymes EM (2001) Thermoregulation and marathon running, biological and environmental influences. Sports Med 31(10): 743–762. 4. Kenefick RW, Cheuvront SN, Sawka MN (2007) Thermoregulatory function during the marathon. Sports Med 37(4–5): 312–315. 5. Weather Underground website. Internet weather service. Weather data from each marathon race. Available: http://www.wunderground.com/history/. Accessed 2011 Mar, 30. 6. Vihma T (2010) Effects of weather on the performance of marathon runners. Int J Biometeorol 54(3): 297–306. 7. Shephard RJ (1984) Athletic performance and urban air pollution. Can Med Assoc J 131(2): 105–109. 8. Chimenti L, Morici G, Paterno A, Bonanno A, Vultaggio M, et al. (2009) Environmental conditions, air pollutants, and airway cells in runners: A longitudinal field study. J Sports Sci 27(9): 925–935. 9. Marr LC, Ely MR (2010) Effect of air pollution on marathon running performance. Med Sci Sports Exerc 42(3): 585–591. 10. Lippi G, Guidi GC, Maffulli N (2008) Air pollution and sports performance in Beijing. Int J Sports Med 29: 696–698. 11. Ely MR, Cheuvront SN, Roberts WO, Montain SJ (2007) Impact of weather on marathon-running performance. Med Sci Sports Exerc 39(3): 487–493. 12. Ely MR, Cheuvront SN, Montain SJ (2007) Neither cloud cover nor low solar loads are associated with fast marathon performance. Med Sci Sports Exerc 39(11): 2029–2035. 13. Ely MR, Martin DE, Cheuvront SN, Montain SJ (2008) Effect of ambient temperature on marathon pacing is dependent on runner ability. Med Sci Sports Exerc 40(9): 1675–1680. 14. Montain SJ, Ely MR, Cheuvront SN (2007) Marathon performance in thermally stressing conditions. Sports Med 37(4–5): 320–323. 15. Martin DE, Buoncristiani JF (1999) The effects of temperature on marathon runners’ performance. Chance 12(4): 20–24. 16. Trapasso LM, Cooper JD (1989) Record performances at the Boston Marathon: biometeorological factors. Int J Biometeorol 33(4): 233–237. 17. Online worldwide athletic results database website. Marathons races results. Available: http://www.athlinks.com. Accessed 2011 Apr, 30. 18. AirParif website. Air pollution data for Paris retrieved (March – May 2009). Available: http://www.airparif.com. 19. Station Database of the Environmental Agency. Air pollution data for Berlin retrieved (June 24, 2009). Available: http://www.env-it.de/stationen/public/language. do;jsessionid=FB278996EE26B0351076A5D974C8BD04?language=en. 20. LondonAir website. Air pollution data for London retrieved (May 26, 2009). Available: http://www.londonair.org.uk/london/asp/default.asp. 21. Berthelot G, Tafflet M, El Helou N, Len S, Escolano S, et al. (2010) Athlete atypicity on the edge of human achievement: Performances stagnate after the last peak, in 1988. PLoS ONE, 5 (1), e8800: DOI: 10.1371/journal.pone. 0008800. 22. Zeng LQ, Cao ZD, Fu SJ, Peng JL, Wang YX (2009) Effect of temperature on swimming performance in juvenile southern catfish (Silurus meridionalis). Comp Biochem Physiol A Mol Integr Physiol 153(2): 125–130. 23. Eliason EJ, Clark TD, Hague MJ, Hanson LM, Gallagher ZS, et al. (2011) Differences in thermal tolerance among sockeye salmon populations. Science 332(6025): 109–112. 24. Kirschbaum MUF, Watt MS (2011) Use of a process-based model to describe spatial variation in Pinus radiate productivity in New Zealand. Forest Ecology and Management 262: 1008–1019. 25. Thibault V, Guillaume M, Berthelot G, El Helou N, Schaal K, et al. (2010) Women and men in sport performance: the gender gap has not evolved since 1983. J Sports Sci Med 9: 214–223. 26. Roberts WO (2010) Determining a ‘‘do not start’’ temperature for a marathon on the basis of adverse outcomes. Med Sci Sports Exerc 42(2): 226–232. 27. Zhang S, Meng G, Wang Y, Li J (1992) Study of the relationships between weather conditions and the marathon race, and of meteorotropic effects on distance runners. Int J Biometeorol 36: 63–68. 28. Armstrong LE, Epstein Y, Greenleaf JE, Haymes EM, Hubbard RW, et al. (1996) American College of Sports Medicine position stand. Heat and cold illnesses during distance running. Med Sci Sports Exerc 28(12): i– x. 29. Maughan RJ, Watson P, Shirreffs SM (2007) Heat and cold, what does the environment do to the marathon runner? Sports Med 37(4–5): 396–399. 30. Hargreaves M (2008) Physiological limits to exercise performance in the heat. J Sci Med Sport 11(1): 66–71. 31. Walters TJ, Ryan KL, Tate LM, Mason PA (2000) Exercise in the heat is limited by a critical internal temperature. J Appl Physiol 89: 799–806. 32. Coyle EF (2007) Physiological regulation of marathon performance. Sports Med 37(4-5): 306–311. 33. Gonza´lez-Alonso J (2007) Hyperthermia Impairs Brain, Heart and Muscle Function in Exercising Humans. Sports Med 37(4–5): 371–373. 34. Tucker R, Rauch L, Harley YX, Noakes TD (2004) Impaired exercise performance in the heat is associated with an anticipatory reduction in skeletal muscle recruitment. Pflugers Arch 448(4): 422–430. 35. Tucker R, Marle T, Lambert EV, Noakes TD (2006) The rate of heat storage mediates an anticipatory reduction in exercise intensity during cycling at a fixed rating of perceived exertion. J Physiol 574(Pt 3): 905–915. 36. Nimmo M (2004) Exercise in the cold. J Sports Sci 22: 898–915. 37. Weller AS, Millard CE, Stroud MA, Greenhaff PL, Macdonald IA (1997) Physiological responses to a cold, wet, and windy environment during prolonged intermittent walking. Am J Physiol 272(1 Pt 2): R226–R233. 38. Larsen HB (2003) Kenyan dominance in distance running. Comp Biochem Physiol A Mol Integr Physiol 136(1): 161–170. 39. Marino FE, Lambert MI, Noakes TD (2004) Superior performance of African runners in warm humid but not in cool environmental conditions. J Appl Physiol 96: 124–130. 40. Marino FE, Mbambo Z, Kortekaas E, Wilson G, Lambert MI, et al. (2000) Advantages of smaller body mass during distance running in warm, humid environments. Pflu¨gers Arch 441(2–3): 359–367. 41. Hamilton B (2000) East African running dominance: what is behind it? Br J Sports Med 34(5): 391–394. 42. Baker J, Horton S (2003) East African running dominance revisited: a role for stereotype threat? Br J Sports Med 37(6): 553–555. 43. Sawka MN, Young A (2006) Physiological systems and their responses to conditions of heat and cold. In: Tipton CM, ed. American College of Sports Medicine’s Advanced exercise physiology. Philadelphia (PA): Lippincott Williams and Wilkins. 535–563 p. 44. Zouhal H, Groussard C, Vincent S, Jacob C, Abderrahman AB, et al. (2009) Athletic performance and weight changes during the ‘‘Marathon of Sands’’ in athletes well-trained in endurance. Int J Sports Med 30: 516–521. 45. Goulet ED (2011) Effect of exercise-induced dehydration on time-trial exercise performance: a meta-analysis. Br J Sports Med 45(14): 1149–1156. 46. Zouhal H, Groussard C, Minter G, Vincent S, Cretual A, et al. (2011) Inverse relationship between percentage body weight change and finishing time in 643 forty-two-kilometre marathon runners. Br J Sports Med 45(14): 1101–1105. 47. Beis LY, Wright-Whyte M, Fudge B, Noakes T, Pitsiladis YP (2012) Drinking Behaviors of Elite Male Runners During Marathon Competition. Clin J Sport Med. March [Epub ahead of print] doi: 10.1097/JSM.0b013e31824a55d7. 48. Alvarez-Ramirez J, Rodriguez E (2006) Scaling properties of marathon races. Physica A: Stat Mech Appl 365(2): 509–520. Environmental Parameters and Marathon Running PLoS ONE | www.plosone.org 8 May 2012 | Volume 7 | Issue 5 | e37407 49. Alvarez-Ramirez J, Rodriguez E, Dagduga L (2007) Time-correlations in marathon arrival sequences. Physica A: Stat Mech Appl 380: 447–454. 50. Dawson NJ, De Freitas CR, Mackey WJ, Young AA (1987) The stressful microclimate created by massed fun runners. Transactions of the Menzies Foundation 14: 41–44. 51. Galloway SDR, Maughan RJ (1997) Effects of ambient temperature on the capacity to perform prolonged cycle exercise in man. Med Sci Sports Exerc 29(9): 1240–2149. 52. Buoncristiani JF, Martin DE (1983) Factors affecting runners’ marathon performance. Chance 6(4): 24–30. 53. Laaidi M, Laaidi K, Besancenot JP (2006) Temperature-related mortality in France, a comparison between regions with different climates from the perspective of global warming. Int J Biometeorol 51(2): 145–153. 54. Korrick SA, Neas LM, Dockery DW, Gold DR, Allen GA, et al. (1998) Effects of ozone and other pollutants on the pulmonary function of adult hickers. Environ Health Perspect 106: 93–99. 55. Rundell KW (2012) Effect of air pollution on athlete health and performance. Br J Sports Med. Epub ahead of print. Environmental Parameters and Marathon Running PLoS ONE | www.plosone.org 9 May 2012 | Volume 7 | Issue 5 | e37407

Impact of environmental parameters on marathon running performance.

05-23-2012

El Helou, Nour,Tafflet, Muriel,Berthelot, Geoffroy,Tolaini, Julien,Marc, Andy,Guillaume, Marion,Hausswirth, Christophe,Toussaint, Jean-François

eng

PMC10415251

1 Vol.:(0123456789) Scientific Reports | (2023) 13:13020 | https://doi.org/10.1038/s41598-023-39718-x www.nature.com/scientificreports Gender differences in footwear characteristics between half and full marathons in China: a cross‑sectional survey Yuyu Xia 1,11, Siqin Shen 2,3,4,11, Sheng‑Wei Jia 1,5*, Jin Teng 6, Yaodong Gu 2, Gusztáv Fekete 4, Tamás Korim 7, Haotian Zhao 8, Qiang Wei 9 & Fan Yang 5,10* There are concerns about the risk of injuries caused by marathons in China. Since male and female runners have different injury risks, gender differences in running shoe functionality should be further complemented. A supervised questionnaire survey of 626 marathon runners was collected. The questionnaire was categorized into four sections: (1) participant profile, (2) importance of shoe properties, (3) functional evaluation of shoe properties and (4) importance ranking of shoe properties. The Mann–Whitney U test, Fisher’s exact test of cross tabulation and Chi‑square test, and two‑way ANOVA were used to analyze the results of this survey. The significance level was set at P < 0.05. The full marathon participants were older than the half marathon participants. There was no gender difference in the importance of shoe features to elite runners. In addition, women are more concerned about upper elasticity and have higher requirements for running shoes than men. Women were more focused on injury prevention, while men were more focused on running performance. Heel cushioning was identified by all participants as the most important running shoe feature. There were no gender differences between elite players’ demand for running shoes, but significant gender differences were found between genders at other running levels. In recent years, marathon running has gained tremendous popularity in China. The number of national marathon and road running events surged from 51 in 2014 to a staggering 1828 in 2019, representing an increase of over 30 times in just five years. Furthermore, the total number of participants reached an impressive 7.12 million, showing a significant 22.22% increase compared to the previous year1. This surge in participation reflects the fact that marathons are no longer limited to talented runners but have become inclusive, attracting individuals of all ages and skill levels2. However, alongside the rise in popularity and participation, the occurrence of long- distance running-related injuries has also increased3. Consequently, scholars have directed their attention towards studying the biomechanics, performance, and sports equipment related to running4–6. Among the various fac- tors that impact running, the choice of running shoes has emerged as a critical consideration for runners7. I In marathons, running shoes serve the primary purposes of protecting runners’ feet from friction and cushioning the impact force generated during ground contact. This impact force can reach levels ranging from 2 to 5 times the body weight, potentially leading to running-related injuries8–10. Research has demonstrated that altering footwear properties can influence the movement characteristics of runners, thereby affecting both their sports performance and the risk of injuries11–13, For instance, tuning the forefoot longitudinal bending stiffness of run- ning shoes can reduce energy loss in lower limb joints and improve overall running performance11; Similarly, increasing midsole thickness has been found to enhance the moment arm of the lower extremity, optimizing the running mechanism and improving running economy12,13. OPEN 1School of Social Sciences, Tsinghua University, Beijing, China. 2Faculty of Sports Science, Ningbo University, Ningbo, China. 3Faculty of Engineering, University of Pannonia, Veszprém, Hungary. 4Savaria Institute of Technology, Eötvös Loránd University, Szombathely, Hungary. 5Li Ning Sports Science Research Center, Li Ning (China) Sports Goods Company Limited, Beijing, China. 6Department of Sports Biomechanics, Beijing Sport University, Beijing, China. 7Department of Materials Engineering, Faculty of Engineering, University of Pannonia, Veszprém, Hungary. 8Department of Physical Education, Jiangnan University, Wuxi 214122, China. 9Department of Physical Education, Tangshan Normal University, Tangshan, China. 10Department of Physical Education and Research, China University of Mining and Technology-Beijing, Beijing 100083, China. 11These authors contributed equally: Yuyu Xia and Siqin Shen. *email: [email protected]; [email protected] 2 Vol:.(1234567890) Scientific Reports | (2023) 13:13020 | https://doi.org/10.1038/s41598-023-39718-x www.nature.com/scientificreports/ In addition to the increasing popularity of marathon running, there has been a notable rise in the number of female participants. In 2020, over 50 million Americans participated in running or jogging, with only 9% of the participants being male (Rizzo, N. Statistics. 120 + Running Statistics 2021/2022. Available online: https:// runre peat. com/ resea rch- marat hon- perfo rmance- across- natio ns (accessed on 7 March 2020).). Studies suggest that long-distance running strategies should be tailored based on gender, age, and the specific event a runner is training for14. Males and females exhibit differences in anatomical characteristics in long-distance running. Female runners tend to demonstrate a greater range of movement in their hip and knee joints compared to male runners, which results in lower joint stability for females compared to males15. These findings highlight the distinct needs of males and females when it comes to sports equipment. However, current footwear developers primarily produce female running shoes based on scaled-down versions of male lasts, which is an unreasonable approach for female runners. It is evident that shoe construction should consider the differences in foot shape and running characteristics between males and females, as well as the specific demands of runners16–18. It is evi- dent that such an approach is unreasonable for female runners. Shoe construction should take into account the differences in foot shape and running characteristics between males and females, as well as the specific demands of the runners8,16,19. In comparison to the large number of participants in distance running, only a select few individuals, such as footwear designers and manufacturers, have the expertise to design and determine the con- struction of running shoes20. While there is an abundance of studies and theories on the biomechanical aspects of Chinese long-distance running, such as kinetics and lower limb kinematics15,16,21,22. While previous studies have investigated the characteristics of sports shoes for various activities such as gym workouts, football, basketball, tennis, and badminton using questionnaires23–27, limited information is available regarding the specific require- ments of running shoes for marathon runners. Therefore, the self-perception of marathon runners when wearing running shoes remains an important aspect that requires further investigation and analysis. Understanding how marathon runners perceive the characteristics of their running shoes is crucial for designing footwear that meets their specific needs and enhances their overall running experience and performance. Therefore, the purpose of this cross-sectional study is to examine gender differences in the perception of running shoe requirements among participants of different performance levels in Chinese full/half marathons. By doing so, we aim to contribute to the improvement of running shoe design by taking into account gender- specific and other individual characteristic demands". Methods Study design and participants. This cross-sectional study was conducted at the Hangzhou Marathon held by the China Athletics Association (Hangzhou, China) in November 2019. The basic inclusion criteria were: above 18 years old, demonstrating regular participation in long-distance running by engaging in the activity at least four times per week for the past six months, and having participated in at least one competition of more than 5 km, including both full marathon (42.195 km) and half marathon (21.0975 km) races. The exclusion criteria were: lower limb surgery or neurological injury. Sample size calculation. The sample size for this study was determined using the online Sample Size Cal- culator (Raosoft Inc., Seattle, WA, USA, raosoft.com). Considering a 5% margin of error, 95% confidence inter- val, and 50% response distribution, a sample size of 381 was recommended. It is worth noting that approximately 36,000 runners were enrolling in the marathon’s competitions. A total of 822 runners were approached, and 626 runners returned their responses and consented to participate in the study, resulting in a response rate of 76.2%. Instruments and data collection. Data were collected through a supervised questionnaire that consisted of four sections. The questionnaire was categorized into four sections: (1) participant profile, (2) importance of shoe properties, (3) functional evaluation of shoe properties, and (4) importance ranking of shoe properties. All questionnaires were conducted after the participants finished the competition. In the first section, participant profiles were obtained, including information such as gender, age, body height, body weight, race distance (full Marathon (42.195 km) or Half Marathon (21.0975 km)), and finish time. The second section assessed the importance of various shoe properties as common requirements during running. The evaluated variables included forefoot curvature, forefoot bending stiffness, forefoot elasticity, heel curvature, heel cup, heel height, heel cushioning, midfoot anti-twist, midsole hardness, midsole thickness, out- sole grip, guidance line, insole shape, upper breathability, upper elasticity, carbon fiber plate, shoelace, and shoe mass. Participants indicated their preferences on a 5-point Likert scale, ranging from 1 (Strongly unimportant) to 5 (Extremely important). In the third section, participants were asked to evaluate whether specific shoe properties improve running performance or prevent sports injuries. The shoe properties assessed were the same as those in section two, and participants provided ratings using references A (Not important for running performance or preventing injuries), B (Important for running performance), C (Important for prevention of injuries), and D (Important for both running performance and prevention of injuries). The fourth section involved participants ranking the importance of shoe properties, and they selected the top three properties they deemed most important. This study referred to the “Chinese Athletics Association Marathon Runners Level Evaluation Standards,” and the participants were classified into the following age groups: 18–29 years, 30–34 years, 35–39 years, 40–44 years, 45–49 years, 50–54 years, 55–59 years, 60–64 years, and 65 + years. Furthermore, each participant’s finish time was divided into the following performance groups: elite-level (87 runners), first-level (191 runners), second- level (210 runners), and third-level (138 runners) (As shown in Supplementary Table S1). 3 Vol.:(0123456789) Scientific Reports | (2023) 13:13020 | https://doi.org/10.1038/s41598-023-39718-x www.nature.com/scientificreports/ Ethical considerations. The research protocol was reviewed and approved by the Li Ning Institutional Ethics Committee in accordance with the principles of the Declaration of Helsinki (approval code: LN- IRB-2019-003). Prior to participation, all participants were provided with detailed information regarding the purpose and content of the study. Informed consent was obtained from each participant. The research did not involve human clinical trials or animal testing. Data validity and collection. To ensure the consistency and reliability of the factor loadings, Cronbach’s α coefficient was employed in this study, resulting in a value of 0.874, which indicated acceptable reliability of the questionnaire. The suitability of the data for factor analysis was assessed using the Bartlett spherical test and the Kaiser–Meyer–Olkin (KMO) test. The KMO value of 0.905 indicated that the questionnaire data were suit- able for factor analysis. Furthermore, the Bartlett’s test result (X2 = 3017.032, df = 153, P = 0.000) confirmed the necessity of the analysis. The questionnaire was administered in the field, and participants completed it under the supervision of researchers who provided guidance to ensure the validity of the data. Researchers explained the definitions of footwear and foot-related terminology to avoid misunderstandings, particularly for participants with limited knowledge of footwear construction. Additionally, researchers ensured that participants did not provide ran- dom or missing answers, thus maintaining the questionnaire’s quality. All questionnaires were completed after participants finished the competition. Data analysis. Descriptive statistics were used to describe the characteristics of the participants in the first section of the study. The Kolmogorov–Smirnov test was conducted on the data from the second and third sec- tions, which revealed that the data did not conform to a normal distribution (P < 0.05). Therefore, non-paramet- ric tests were used for further analysis. The Mann–Whitney U test was employed to analyze gender differences in the “Importance of shoe properties” section, and Fisher’s Exact Test of Cross tabulation and Chi-square test were used for the analysis of the “Functional evaluation of shoe properties” section. Two-way ANOVA was used to analyze the interaction characteristics of gender and race within the context of our cross-sectional survey investigating gender differences in footwear characteristics between half and full marathons in China. The sig- nificance level was set at P < 0.05. All statistical analyses were performed using SPSS 21.0 (SPSS Inc., Chicago, IL, USA). All figures in this study were created using Origin 2021 (OriginLab Corporation, Northampton, MA, USA). Result Characteristics of the participants. A total of 626 questionnaires were collected in this study. The basic information of the participants is presented in Table 1, and all respondents gave informed consent and partici- pated voluntarily. As shown below, most runners were males (76.2%), male and female participants in the full marathon were older than the half marathon, and females had lower body mass index (BMI) values than males. Furthermore, this study used two-way ANOVA to analyze the interaction characteristics of gender and race in this survey, and found that there was no interaction between gender and race items on BMI [F(1,622) = 1.789, P = 0.182, η2 = 0.002]. The main effect analysis showed that gender (F(1,622) = 34.290, P < 0.001, η2 = 0.052) and race events [F(1,622) = 1.789, P < 0.05, η2 = 0.008)] had significant effects on BMI, respectively. For race items, the BMI values of males in both the full marathon and half marathon were significantly higher than that of females (P = 0.001, 0.000). For gender, males who participated in the half marathon had a significantly higher BMI value than the full marathon (P = 0.001), but there was no significant difference in females. Importance of shoe properties. In Table 2, females were more concerned about upper elasticity than males, and females’ demand for running shoes was generally higher than males. The Mann–Whitney U test found no gender differences in evaluating the importance of shoe properties by elite-level runners in the full marathon. Compared with first-level male runners, females rated forefoot bending stiffness and upper elastic- ity as higher importance (P = 0.044, 0.001). For second-level runners, females reported higher importance of midsole hardness and upper elasticity than males (P = 0.024, 0.007). In addition, the importance scores of upper elasticity and shoelace in the third-level female runners were significantly higher than those of the male runners (P = 0.043, 0.046). For half-marathon runners, there were no gender differences in the evaluation of shoe properties’ importance between elite-level and second-level runners, and the differences were mainly found in first- and third-level Table 1. Characteristics of participants. Gender Male (n = 478) Female (n = 148) Race Half marathon (n = 129) Full marathon (n = 349) Half marathon (n = 79) Full marathon (n = 69) Mean ± SD Mean ± SD Mean ± SD Mean ± SD Age (yr.) 35.4 ± 8.3 37.4 ± 9.6 37.3 ± 9.1 41.3 ± 9.9 Body height (cm) 173.3 ± 5.5 172.1 ± 5.5 160.1 ± 5.0 161.1 ± 5.3 Body weight (kg) 70.8 ± 11.7 66.6 ± 9.0 54.7 ± 8.8 54.0 ± 11.3 BMI (kg/m2) 23.6 ± 3.6 22.3 ± 3.3 21.1 ± 3.0 21.0 ± 4.4 4 Vol:.(1234567890) Scientific Reports | (2023) 13:13020 | https://doi.org/10.1038/s41598-023-39718-x www.nature.com/scientificreports/ participants. Table 3 showed that the importance score of forefoot elasticity for first-level female runners was significantly lower than that of males (P = 0.034). Third-level female runners rated upper elasticity as more important than males (P = 0.017), while third-level males reported higher importance of carbon fiber plate and shoe mass (P = 0.028, 0.022). Functional evaluation of shoe properties. The Fisher’s Exact Test was used to compare males’ and females’ functional evaluation of shoe properties. Table 2. Gender differences in full-marathon participants’ perceptions of the importance of shoe properties (Mean ± SD). *Indicates a significant difference, P < 0.05. Shoe function Elite-level P First-level P Second-level p Third-level P Male Female Male Female Male Female Male Female Forefoot curvature 3.83 ± 0.72 3.33 ± 1.49 0.549 3.54 ± 0.81 3.80 ± 0.63 0.104 3.46 ± 0.84 3.35 ± 0.70 0.509 3.32 ± 0.87 3.73 ± 0.77 0.103 Forefoot bending stiffness 3.97 ± 0.81 3.17 ± 1.07 0.054 3.74 ± 0.95 4.16 ± 0.61 0.044* 3.82 ± 0.89 4.00 ± 0.72 0.370 3.60 ± 0.82 3.87 ± 0.62 0.258 Forefoot elasticity 4.23 ± 0.78 3.50 ± 1.26 0.129 4.00 ± 0.92 4.16 ± 0.78 0.482 3.95 ± 0.80 4.04 ± 0.75 0.658 3.77 ± 0.82 3.73 ± 0.77 0.624 Heel curvature 3.69 ± 0.93 3.00 ± 1.00 0.170 3.37 ± 0.90 3.56 ± 0.80 0.341 3.50 ± 0.86 3.43 ± 0.71 0.572 3.43 ± 0.88 3.33 ± 0.94 0.876 Heel cup 3.60 ± 0.82 3.50 ± 1.26 0.782 3.82 ± 0.82 3.60 ± 0.75 0.204 3.88 ± 0.82 3.65 ± 0.81 0.219 3.43 ± 0.88 3.60 ± 1.02 0.675 Heel height 3.80 ± 0.86 4.17 ± 0.37 0.301 3.68 ± 0.96 3.60 ± 0.75 0.631 3.60 ± 0.86 3.78 ± 1.02 0.259 3.50 ± 0.92 3.87 ± 0.72 0.167 Heel cushioning 4.33 ± 0.89 4.17 ± 1.07 0.814 4.34 ± 0.78 4.48 ± 0.64 0.486 4.40 ± 0.73 4.39 ± 0.64 0.795 4.37 ± 0.58 4.20 ± 0.91 0.773 Midfoot anti-twist 4.00 ± 0.83 3.50 ± 1.26 0.394 3.93 ± 0.87 4.12 ± 0.86 0.242 4.08 ± 0.87 4.17 ± 0.76 0.714 3.90 ± 0.85 3.67 ± 0.94 0.317 Midsole hardness 4.01 ± 0.78 4.17 ± 0.69 0.700 3.91 ± 0.82 4.24 ± 0.81 0.052 3.89 ± 0.90 4.35 ± 0.70 0.024 3.78 ± 0.84 3.87 ± 0.88 0.700 Midsole thickness 3.86 ± 0.80 3.33 ± 1.25 0.327 3.69 ± 0.83 4.04 ± 0.77 0.053 3.60 ± 0.85 3.96 ± 0.75 0.072 3.70 ± 0.84 3.67 ± 0.87 0.771 Outsole grip 4.29 ± 0.76 3.50 ± 1.26 0.102 4.21 ± 0.81 4.36 ± 0.62 0.503 4.14 ± 0.77 4.48 ± 0.50 0.072 3.93 ± 0.93 4.07 ± 0.85 0.744 Guidance Line 3.67 ± 0.86 3.00 ± 1.29 0.190 3.66 ± 0.86 3.92 ± 0.84 0.234 3.70 ± 0.82 4.04 ± 0.81 0.082 3.45 ± 0.90 3.20 ± 0.65 0.170 Insole shape 3.84 ± 0.92 3.17 ± 0.69 0.078 3.65 ± 0.81 3.68 ± 0.88 0.902 3.54 ± 0.86 3.83 ± 0.82 0.130 3.28 ± 0.82 3.47 ± 0.62 0.477 Upper breathability 4.10 ± 0.86 3.83 ± 0.90 0.516 4.11 ± 0.73 4.04 ± 0.66 0.514 4.16 ± 0.72 4.04 ± 0.55 0.325 3.93 ± 0.70 4.00 ± 0.73 0.787 Upper elasticity 4.03 ± 0.93 3.33 ± 1.11 0.127 3.74 ± 0.91 4.36 ± 0.56 0.001* 3.63 ± 0.92 4.17 ± 0.56 0.007* 3.53 ± 0.87 4.07 ± 0.77 0.043* Carbon fiber plate 4.19 ± 0.87 3.67 ± 0.94 0.195 3.84 ± 0.97 3.84 ± 0.92 0.845 3.92 ± 0.85 3.74 ± 0.94 0.450 3.55 ± 0.94 3.73 ± 0.77 0.597 Shoelace 3.71 ± 0.93 3.67 ± 0.47 0.790 3.61 ± 0.94 3.68 ± 1.12 0.383 3.47 ± 0.77 3.65 ± 0.91 0.248 3.38 ± 0.80 3.93 ± 0.85 0.046* Shoe mass 4.46 ± 0.73 4.33 ± 0.75 0.662 4.39 ± 0.77 4.44 ± 0.50 0.863 4.33 ± 0.67 4.57 ± 0.50 0.159 4.32 ± 0.65 4.33 ± 0.70 0.866 Table 3. Gender differences in half-marathon participants’ perceptions of the importance of shoe properties (Mean ± SD). *Indicates a significant difference, P < 0.05. Shoe function Elite-level P First-level P Second-level p Third-level P Male Female Male Female Male Female Male Female Forefoot curvature 3.63 ± 0.86 3.33 ± 0.47 0.510 3.54 ± 0.78 3.54 ± 0.76 0.960 3.40 ± 0.79 3.47 ± 0.62 0.714 3.54 ± 0.80 3.59 ± 0.78 0.826 Forefoot bending stiffness 3.38 ± 0.48 4.00 ± 0.00 0.077 3.93 ± 0.75 3.75 ± 0.60 0.198 3.75 ± 0.90 3.77 ± 0.96 0.859 3.85 ± 0.84 3.82 ± 0.89 0.963 Forefoot elasticity 3.75 ± 0.43 4.00 ± 0.82 0.623 4.18 ± 0.66 3.79 ± 0.71 0.034* 3.87 ± 0.94 3.87 ± 0.99 0.923 4.12 ± 0.67 3.82 ± 1.03 0.400 Heel curvature 3.38 ± 0.70 4.00 ± 0.82 0.323 3.36 ± 0.77 3.42 ± 0.70 0.951 3.46 ± 0.84 3.17 ± 0.69 0.137 3.29 ± 0.89 3.45 ± 0.72 0.560 Heel cup 3.75 ± 0.66 3.67 ± 0.47 0.909 3.29 ± 0.96 3.79 ± 0.82 0.072 3.48 ± 1.05 3.70 ± 0.74 0.555 3.78 ± 0.84 3.45 ± 0.84 0.153 Heel height 3.50 ± 0.87 3.67 ± 0.47 0.742 3.46 ± 0.91 3.67 ± 0.80 0.616 3.44 ± 0.93 3.60 ± 0.76 0.617 3.68 ± 0.75 3.55 ± 0.72 0.367 Heel cushioning 4.50 ± 0.71 4.33 ± 0.47 0.567 4.25 ± 0.69 4.25 ± 0.60 0.850 4.27 ± 0.96 4.37 ± 0.71 0.962 4.44 ± 0.66 3.95 ± 1.19 0.145 Midfoot anti-twist 4.38 ± 0.86 4.33 ± 0.47 0.735 3.68 ± 0.93 3.92 ± 0.64 0.435 3.73 ± 1.04 3.70 ± 0.69 0.551 4.05 ± 0.82 3.86 ± 1.01 0.562 Midsole hardness 3.75 ± 1.09 4.33 ± 0.47 0.456 3.75 ± 0.83 3.96 ± 1.02 0.271 3.90 ± 0.74 3.90 ± 0.75 0.846 3.93 ± 0.64 3.86 ± 1.01 0.864 Midsole thickness 3.25 ± 0.83 3.33 ± 0.47 0.722 3.43 ± 1.02 3.75 ± 0.83 0.357 3.50 ± 0.84 3.63 ± 0.60 0.571 3.49 ± 0.74 3.77 ± 0.79 0.173 Outsole grip 3.88 ± 1.05 3.33 ± 0.47 0.394 4.21 ± 0.56 3.88 ± 0.73 0.087 4.00 ± 0.90 3.83 ± 0.90 0.380 4.24 ± 0.73 3.86 ± 1.18 0.340 Guidance Line 3.88 ± 0.78 3.67 ± 0.47 0.741 3.57 ± 0.78 3.54 ± 0.71 0.863 3.42 ± 0.84 3.40 ± 0.92 0.933 3.63 ± 0.69 3.64 ± 0.71 0.974 Insole shape 3.75 ± 0.97 4.00 ± 0.00 0.737 3.25 ± 0.87 3.54 ± 0.64 0.328 3.37 ± 1.06 3.43 ± 0.80 0.980 3.59 ± 0.91 3.59 ± 0.78 0.847 Upper breathability 3.88 ± 0.33 4.00 ± 0.00 0.540 3.82 ± 0.71 3.92 ± 0.70 0.898 3.87 ± 1.02 3.93 ± 0.89 0.903 4.02 ± 0.78 4.00 ± 1.09 0.582 Upper elasticity 4.13 ± 0.78 4.33 ± 0.47 0.741 3.82 ± 0.80 3.83 ± 0.69 0.774 3.58 ± 0.93 3.90 ± 0.79 0.123 3.61 ± 0.85 4.09 ± 0.90 0.017* Carbon fiber plate 3.50 ± 1.22 4.00 ± 0.00 0.515 3.75 ± 0.87 3.71 ± 0.61 0.797 3.62 ± 0.98 3.40 ± 0.71 0.291 3.73 ± 0.77 3.32 ± 0.55 0.028* Shoelace 3.50 ± 1.41 3.67 ± 0.47 0.981 3.61 ± 0.90 3.58 ± 0.81 0.814 3.35 ± 0.96 3.23 ± 0.76 0.609 3.49 ± 0.83 3.36 ± 0.93 0.659 Shoe mass 3.88 ± 1.27 4.67 ± 0.47 0.323 4.43 ± 0.49 4.21 ± 0.58 0.186 3.98 ± 0.99 4.00 ± 0.77 0.718 4.37 ± 0.79 3.82 ± 1.07 0.022* 5 Vol.:(0123456789) Scientific Reports | (2023) 13:13020 | https://doi.org/10.1038/s41598-023-39718-x www.nature.com/scientificreports/ This study found no gender differences in elite-level runners’ functional evaluations of shoe properties for full-marathon runners. There were significant gender differences in functional assessments of first-level runners in outsole grip, upper elasticity, shoe mass, and guidance line (P = 0.048, 0.002, 0.015, 0.001), as shown in Fig. 1. Further conducted pairwise comparisons found that for outsole grip, 57% of males believed that this property was important in improving running performance, significantly higher than 32% of females (Fig. 1). Conversely, 44% of females thought the outsole grip was important for preventing sports injuries, more than 21.1% of males. Compared with 4% of females, 35% of males believed that the upper elasticity was neither beneficial for improving running performance nor preventing sports injuries. However, 32% of women felt the upper elasticity was important for preventing sports injuries, significantly higher than 14% of males. In addition, 7% of males thought shoe mass was important for preventing sports injuries, markedly less than 24% of females. 44% of females believe that the importance of the guidance line was reflected in preventing sports injuries, and 4% of females considered that this property could prevent sports injuries and improve running performance, which was significantly higher than that of males. Second-level participants’ functional evaluation of shoe properties found significant gender differences in outsole grip and midsole hardness (P = 0.046, 0.025), as shown in Fig. 2. For the outsole grip, 21.9% of males reported that the property was not crucial for running performance and injury prevention, and only 4.3% of females agreed with this, a significant difference. In contrast, 8.7% of females rated the characteristic as neces- sary for running performance and injury prevention, significantly more than 1.9% of males. Their evaluation of the function of midsole hardness was similar, males (30.5%) who rated that midsole hardness was not crucial for both running performance and injuries prevention significantly over females (8.7%), and females who con- sidered that midsole hardness was necessary for both running performance and injuries prevention (13%) were significantly more than males (2.9%). Fisher’s Exact Test showed no gender differences in functional evaluations of shoe characteristics between elite and second-level runners for half-marathon participants. However, gender differences existed between first-level and third-level runners. Specifically, there was a significant gender difference (P = 0.012) in the functional evaluation of outsole grip for first-level runners in the half marathon. A pairwise comparison found that 45.8% of females and 14.3% of males rated this feature unimportant for running performance and injury prevention (Fig. 3). The proportion of females was significantly higher than that of males. In addition, 50% of males considered that the property of Figure 1. Gender differences in shoe properties functional perception of first-level participants in the full marathon. Note: (A) Not important for running performance and prevent injuries, (B) Important for running performance, (C) Important for prevent injuries, (D) Important for both running performance and prevent injuries. *Indicates a significant difference, P < 0.05. 6 Vol:.(1234567890) Scientific Reports | (2023) 13:13020 | https://doi.org/10.1038/s41598-023-39718-x www.nature.com/scientificreports/ outsole grip was essential to running performance, significantly more than 12.5% of women, which was statisti- cally significant, as shown in Fig. 3. In addition, there was a significant gender difference in functional evaluations of upper elasticity and forefoot bending stiffness among third-level runners (P = 0.011, 0.002). 46.3% of males and 13.6% of females thought upper elasticity was unrelated to running performance or injury prevention. However, 18.2% of females rated upper elasticity as necessary for injury prevention, significantly more than 2.4% of males. Furthermore, 61% of males and 17.3% of females considered forefoot bending stiffness unimportant for run- ning performance and injury prevention, indicating a statistically significant difference. However, 12.2% of males reported this function as important for injury prevention, significantly less than 45.5% of females. Compared to 0% of males, 9.1% of females reported that this feature was important for running performance and injury prevention, indicating a significant difference, as shown in Fig. 4. Importance ranking of shoe properties. This study used descriptive statistics to conduct frequency statistics on the importance of shoe characteristics ranked by males and females in the full marathon and half marathon, respectively. Both males and females agreed that “heel cushioning” was the most critical running shoe feature, but there were differences in the ranking of other shoe features. Specifically, the three properties that male full marathon participants rated as the most important were “heel cushioning,” “forefoot elasticity,” and “shoe mass.” The top three shoe traits for females were “heel cushioning,” “midfoot anti-twist,” and “forefoot bending stiffness,” as shown in Fig. 5. Figure 2. Gender differences in functional perception of shoe properties second-level participants in the full marathon. Note: (A) Not important for running performance and prevent injuries, (B) Important for running performance, (C) Important for prevent injuries, (D) Important for both running performance and prevent injuries. *Indicates a significant difference, P < 0.05. Figure 3. Gender differences in functional perception of shoe properties first-level participants in the half marathon. Note: (A) Not important for running performance and preventing injuries, (B) Important for running performance, (C) Important for preventing injuries, (D) Important for both running performance and preventing injuries. *Indicates a significant difference, P < 0.05. 7 Vol.:(0123456789) Scientific Reports | (2023) 13:13020 | https://doi.org/10.1038/s41598-023-39718-x www.nature.com/scientificreports/ Figure 4. Gender differences in functional perception of shoe properties of third-level participants in the half marathon. Note: (A) Not important for running performance and preventing injuries, (B) Important for running performance, (C) Important for preventing injuries, (D) Important for both running performance and preventing injuries. *Indicates a significant difference, P < 0.05. Figure 5. Ranking of the importance of shoe properties. (A)- Full-male; (B)- Full-female; (C)- Half-male; (D)- Half-male;. The red box represents the top three ranked shoe characteristics. 8 Vol:.(1234567890) Scientific Reports | (2023) 13:13020 | https://doi.org/10.1038/s41598-023-39718-x www.nature.com/scientificreports/ In addition, half-marathon participants identified "heel cushioning," "midfoot anti-twist," and "forefoot elas- ticity" as the three most important characteristics of shoes, as shown in Fig. 5. Furthermore, upon analyzing the data separately for male and female participants, we found that female participants rated "shoe mass" as one of their top three preferred characteristics, while male participants favored "forefoot elasticity" and "forefoot bend- ing stiffness" as their preferred features". Discussion In this study, we observed a significantly higher number of male participants completing marathon races com- pared to females. According to the "2019 China Marathon Big Data Report" released by the Chinese Athletics Association, the number of participants increased by 14.28% in 2019 compared to 2018. Among them, the number of male participants in China was considerably higher than females. However, in the half marathon races, the number of female participants exceeded that of males, aligning with the findings of our study but contrasting with the trend observed in the United States28. These findings highlight the gender disparities in marathon participation in China, with a higher proportion of males in the full marathon category and a higher engagement level of females in the half marathon category. To better understand the reasons behind these gender differences, it is important to consider factors such as motivation and demographics. The "2019 China Marathon Big Data Report" revealed that male full marathon runners in China were more motivated, accounting for 74.63% of all male participants. In our study, we found a similar trend, with 73% of male participants completing the full marathon. In contrast, the percentage of male participants in the half marathon was 27%, while females accounted for 53% of all female participants. These findings suggest that in Chinese marathon events, there is a significantly higher number of male participants in the full marathon category compared to females, while female participants demonstrate a higher level of engage- ment in the half marathon category. Furthermore, our study explored the age distribution of marathon participants and found that female par- ticipants were older than male participants, with an average age of over 35 years old. This result is consistent with the analysis of the age group of Chinese marathon runners from 2016 to 2019, indicating that the primary finishers of Chinese marathons are predominantly middle-aged individuals. Several factors, including physical and mental needs, social influence, and disposable time, may contribute to this age distribution29. In addition to age, we also examined the influence of gender and age on athletic performance. It was observed that regardless of gender, participants who completed the full marathon were older compared to those who com- pleted the half marathon. This finding suggests that older participants are more inclined to participate in longer endurance sports, reflecting their greater emotional control and sense of responsibility for completing tasks5,30. Another aspect we investigated was the relationship between participants’ BMI and their involvement in marathon races. We found that the BMI values of male full marathon participants were significantly lower than those of half marathon participants, and the BMI values of female participants were significantly lower than those of male participants. Previous cross-sectional studies have suggested that BMI contributes to the risk of running-related injuries in population samples31–33. More specifically, a low BMI even increases female runners’ risk of lower extremity injury31. Specifically, a low BMI increases the risk of lower extremity injury in female run- ners due to their tendency to have lower body fat percentages compared to non-marathon females32,33. Although some studies have shown no direct association between participants’ BMI and injury risk, considering BMI as a potentially modifiable risk factor becomes relevant if it is influenced by marathon activity34. Moving on to the preferences for shoe characteristics among elite runners, we found no gender differences in these preferences in both the half and full marathon categories. This observation indicates that elite runners, regardless of gender, possess a comprehensive understanding of shoes after extensive training sessions and consistently prioritize shoe properties that enhance athletic performance35,36. Their knowledge enables them to select more suitable running shoes that align with their specific running requirements35,36. Forefoot bending stiffness is a crucial factor in footwear performance development37, and it plays a significant role in maintaining both comfort and performance in running shoes38. Furthermore, it has been observed that increasing the forefoot bending stiffness in footwear can reduce the extent of metatarsophalangeal joint extension during movement39. In our study, female marathon participants consistently ranked forefoot bending stiffness as their third most important consideration, indicating a higher expectation for this characteristic compared to males. These findings highlight the significance of forefoot bending stiffness in meeting the specific needs and preferences of female runners. Previous studies on gender differences in Chinese foot shape show that Chinese females have a lower first-toe height than males19. Therefore, females wearing running shoes with the same forefoot bending stiffness at the same running interface need to generate a larger metatarsophalangeal joint moment, which is more likely to increase the risk of injury of metatarsal stress fractures39. The subjective reports of female runners also underscore this point. Additionally, female full marathon runners expressed a higher level of concern about the upper elasticity of running shoes compared to males. Previous studies have shown that upper elasticity is a critical factor affect- ing comfort and may impact shoe choice preferences40. Biomechanical studies have also shown that changes in the upper elasticity can even lead to changes in running patterns41. This preference for footwear comfort aligns with the notion that for runners, emotional value and overall experience hold significance, alongside athletic performance42. Moreover, our study analyzed the preferences of runners based on their finishing times and identified specific characteristics that different levels of runners prioritize. For instance, female three-level finishers, who took the longest to finish the race, emphasized the necessity of shoelaces. This preference aligns with the idea that shoelaces allow for a more comfortable shoe fit, enabling runners to adjust the tightness to obtain a custom fit that accommodates the shape of their foot43. Therefore, the fit design of shoelaces is vital for marathon runners, 9 Vol.:(0123456789) Scientific Reports | (2023) 13:13020 | https://doi.org/10.1038/s41598-023-39718-x www.nature.com/scientificreports/ as increased long-distance running time may lead to increased foot movement in the shoe, and ill-fitting laces can cause blisters and subungual hematomas44,45. In this study, full-marathon first-level males emphasized forefoot elasticity significantly more than females46. Studies have shown that changing the flexibility of the forefoot area of a running shoe can provide a greater range of motion in the forefoot and increase activation of the calf muscles47,48. Chen et al.’s research showed that increasing the forefoot elasticity of the soles of running shoes can reduce the activity of muscles49, thereby reducing energy consumption and improving exercise performance. In the half marathon, the first-level male participants also emphasized forefoot elasticity compared with females, which was consistent with the statistics for full-marathon participants. A study has examined the impact of shoe mass on preference, performance, and biomechanical variables50. In another study, it was found that for every 100g reduction in shoe weight, running economy improved by 1% and running performance improved by 0.7%51. In this research, third-level males reported higher importance of shoe mass. Specifically, heavier footwear reduced comfort in second and third-level runners and increased energy requirements at all running levels, potentially reducing preference52. Heavier shoes had a significant effect on ankle angle, ankle moment53 and plantar pressure (second and third-level runners)54, which is consistent with the results of this study. In the “Functional evaluation of shoe properties” part, females were more concerned about whether these properties were necessary for injury prevention, while males were more concerned about the importance of shoe properties to running performance, which may be because females’ shoe lasts usually downsized versions of males’ shoe lasts, and women rarely buy suitable shoes when purchasing running shoes, and inappropriate shoes will increase the risk of injury during running16. However, males can usually buy shoes that fit their feet and preference, which can improve sports performance. Heel cushioning was reported in this study as the most critical function for all participants, which is an essential function of running shoes. Robbins et al. suggest that the increased cushioning in running shoes can attenuate the perceived magnitude of forces acting on the foot plantar surface55. The study by Mark et al. showed that runners (rearfoot strike pattern) used the same pair of running shoes to run 480 km, and the amount of heel cushioning of the rear running shoes would be reduced by 16% to 33%56. Based on previous research results by Taunton et al., heel support and cushioning function will decrease with running shoes, and the risk of long- distance running injury will increase57. Therefore, stabilizing the heel cushioning performance of running shoes is significant for preventing injuries. In addition, male and female participants in the same schedule have different attributes of shoes ranked second and third, and the same-gender participants of different programs also have different opinions. Based on our findings and previous studies, it is important to consider specific characteristic designs in running shoes for different genders and different race distances. For example, our results had shown that female runners may benefit from shoe designs that address factors such as heel cushioning, midfoot anti- twist, and shoe mass. On the other hand, male runners in marathon races have shown a preference for shoe char- acteristics such as heel cushioning, forefoot elasticity, and forefoot bending stiffness. These examples highlight the need for gender-specific and race-specific considerations in running shoe design. Limitations Our study has several limitations that should be acknowledged when interpreting the findings and considering their generalizability. Firstly, it is important to note that participants in our study did not wear the same shoes, which may have resulted in variations in wearing experiences and shoe preferences58. This heterogeneity in footwear selection could introduce bias and potentially influence participants’ perceptions of shoe properties, thereby affecting the validity of our findings. Therefore, caution should be exercised when generalizing the results to populations where participants wear standardized shoes. Secondly, our study recruited a relatively smaller number of elite players, which limits the generalizability of the findings to the elite athlete population59,60. Elite athletes often possess unique characteristics and preferences that differ from recreational runners, and their perceptions of shoe properties may vary significantly. Hence, the applicability of our results to elite-level marathon runners should be interpreted with caution. Additionally, we acknowledge that the COVID-19 pandemic has had a significant impact on various aspects of society, including the field of sports and athletics. Unfortunately, our study did not assess data from the years 2020–2022, which coincided with the height of the pandemic. This represents a limitation in capturing the potential influence of the pandemic on Chinese marathon runners and their perceptions. Conclusion There were no gender differences between elite players’ demand for running shoes, but significant gender differ- ences were found between genders at other running levels. Both males and females agreed that “heel cushioning” was the most critical running shoe feature. Females pay more attention to the protection brought by shoes, while males pay attention to the sports performance of shoes. In conclusion, our study underscores the importance of considering gender and distance factors when design- ing running shoes. The distinct characteristics demanded by male and female runners, along with the variations related to different running distances, emphasize the need for customization and optimization in the development of running footwear. We believe that our findings contribute valuable knowledge to the field and have practical implications for the running shoe industry. Data availability All data generated or analysed during this study are included in this published article. 10 Vol:.(1234567890) Scientific Reports | (2023) 13:13020 | https://doi.org/10.1038/s41598-023-39718-x www.nature.com/scientificreports/ Received: 26 March 2023; Accepted: 29 July 2023 References 1. Sui, W. & Yang, J. Running middle-class–marathon craze in transforming period of China. Sport Soc. 0, 1–12 (2022). 2. Rygiel, V., Labrador, H., Jaworski, C. A. & Chiampas, G. Review of injury patterns of the 2018 Bank of America Chicago marathon to optimize medical planning. Curr. Sports Med. Rep. 21, 149–154 (2022). 3. Hsu, C.-L., Yang, C.-H., Wang, J.-H. & Liang, C.-C. Common running musculoskeletal injuries and associated factors among recreational gorge marathon runners: An investigation from 2013 to 2018 taroko gorge marathons. Int. J. Environ. Res. Public Health 17, 8101 (2020). 4. Chen, L. H. et al. Study of the foot morphology of recreational marathon runners. Proc. Inst. Mech. Eng. Part J. Sport Eng. Technol. 236, 43–52 (2022). 5. Yang, S.-J. et al. Gender and age differences in performance of over 70,000 Chinese finishers in the half- and full-marathon events. Int. J. Environ. Res. Public Health 19, 7802 (2022). 6. Hoogkamer, W. et al. A comparison of the energetic cost of running in marathon racing shoes. Sports Med. 48, 1009–1019 (2018). 7. Toon, D., Vinet, A., Pain, M. T. G. & Caine, M. P. A methodology to investigate the relationship between lower-limb dynamics and shoe stiffness using custom-built footwear. Proc. Inst. Mech. Eng. Part J. Sport Eng. Technol. 225, 32–37 (2011). 8. Clinghan, R., Arnold, G. P., Cochrane, L., Drew, T. S. & Abboud, R. J. The use of in-shoe pressure analysis in the evaluation of running shoes. Clin. Biomech. 23, 668–669 (2008). 9. Liu, W. & Nigg, B. M. A mechanical model to determine the influence of masses and mass distribution on the impact force during running. J. Biomech. 33, 219–224 (2000). 10. Zadpoor, A. A. & Nikooyan, A. A. Modeling muscle activity to study the effects of footwear on the impact forces and vibrations of the human body during running. J. Biomech. 43, 186–193 (2010). 11. Honert, E. C., Mohr, M., Lam, W. K. & Nigg, S. Shoe feature recommendations for different running levels: A delphi study. PLoS ONE 15, 1–17 (2020). 12. Fu, F. et al. Effect of the innovative running shoes with the special midsole structure on the female runners’ lower limb biomechan- ics. Front. Bioeng. Biotechnol. 10, 866321 (2022). 13. Hunter, I. et al. Running economy, mechanics, and marathon racing shoes. J. Sports Sci. 37, 2367–2373 (2019). 14. Cuk, I., Nikolaidis, P. T., Villiger, E. & Knechtle, B. Pacing in long-distance running: Sex and age differences in 10-km race and marathon. Medicina (Lithuania) 57, 389 (2021). 15. Isherwood, J., Wang, H. & Sterzing, T. Running biomechanics and running shoe perception of Chinese men and women. Footwear Sci. 13, 55–67 (2021). 16. Sinclair, J., Greenhalgh, A., Edmundson, C. J., Brooks, D. & Hobbs, S. J. Gender differences in the kinetics and kinematics of distance running: Implications for footwear design. Int. J. Sports Sci. Eng. 6, 118–128 (2012). 17. Krauss, I., Grau, S., Mauch, M., Maiwald, C. & Horstmann, T. Sex-related differences in foot shape. Ergonomics 51, 1693–1709 (2008). 18. Wunderlich, R. E. & Cavanagh, P. R. Gender differences in adult foot shape: Implications for shoe design. Med. Sci. Sports Exerc. 33, 605–611 (2001). 19. Hong, Y., Wang, L., Xu, D. Q. & Li, J. X. Gender differences in foot shape: A study of Chinese young adults. Sports Biomech. 10, 85–97 (2011). 20. Hoerzer, S., Trudeau, M. B., Edwards, W. B. & Nigg, B. M. Intra-rater reliability of footwear-related comfort assessments. Footwear Sci. 8, 155–163 (2016). 21. Clermont, C. A., Benson, L. C., Edwards, W. B., Hettinga, B. A. & Ferber, R. New considerations for wearable technology data: Changes in running biomechanics during a marathon. J. Appl. Biomech. 35, 401–409 (2019). 22. Gao, Z., Mei, Q., Xiang, L. & Gu, Y. Difference of walking plantar loadings in experienced and novice long-distance runners. Acta Bioeng. Biomech. 22, 127–147 (2020). 23. Apps, C., Liu, H., Pykett, J. & Sterzing, T. Gym training shoe requirements in China and England. Footwear Sci. 7, 51–62 (2015). 24. Brauner, T., Zwinzscher, M. & Sterzing, T. Basketball footwear requirements are dependent on playing position. Footwear Sci. 4, 191–198 (2012). 25. Althoff, K. & Hennig, E. M. Criteria for gender-specific soccer shoe development. Footwear Sci. 6, 89–96 (2014). 26. Sterzing, F. et al. Stereotactic body radiotherapy for liver tumors. Strahlenther. Onkol. 190, 872–881 (2014). 27. Shen, S. et al. Gender and leg-dominance differences in shoe properties and foot injuries in badminton: A cross-sectional survey. J. Foot Ankle Res. 15, 1–11 (2022). 28. Vitti, A., Nikolaidis, P. T., Villiger, E., Onywera, V. & Knechtle, B. The, “New York City Marathon”: Participation and performance trends of 1.2 M runners during half-century. Res. Sports Med. 28, 121–137 (2020). 29. Tian, H. B., Qiu, Y. J., Lin, Y. Q., Zhou, W. T. & Fan, C. Y. The role of leisure satisfaction in serious leisure and subjective well-being: Evidence from Chinese marathon runners. Front. Psychol. 11, 1–10 (2020). 30. Garcia-Naveira, A., Ruiz, R. & Pujals, C. Personality differences according to participation in or abstention from sport, competi- tion level and age category in football players based on costa and MCCRAE’S model. Revista de Psicologia del Deporte 20, 29–44 (2011). 31. Bennell, K. L. et al. Risk factors for stress fractures in female track-and-field athletes: A retrospective analysis. Clin. J. Sport Med. 5, 229–235 (1995). 32. Bennell, K. L. et al. Risk factors for stress fractures in track and field athletes: A twelve-month prospective study. Am. J. Sports Med. 24, 810–818 (1996). 33. Scott, S. A., Simon, J. E., Van Der Pol, B. & Docherty, C. L. Risk factors for sustaining a lower extremity injury in an army reserve officer training corps cadet population. Mil. Med. 180, 910–916 (2015). 34. Mohseni, M. M., Filmalter, S. E., Taylor, W. C., Vadeboncoeur, T. F. & Thomas, C. S. Factors associated with half- and full-marathon race-related injuries: A 3-year review. Clin. J. Sport Med. 31, 277–286 (2019). 35. Cohler, M. H. & Casey, E. A survey of runners’ attitudes toward and experiences with minimally shod running. PM&R 7, 831–835 (2015). 36. Rothschild, C. E. Primitive running: A survey analysis of runners’ interest, participation, and implementation. J. Strength Condi- tion. Res. 26, 2021–2026 (2012). 37. Stefanyshyn, D. & Fusco, C. Athletics: Increased shoe bending stiffness increases sprint performance. Sports Biomech. 3, 55–66 (2004). 38. Oleson, M., Adler, D. & Goldsmith, P. A comparison of forefoot stiffness in running and running shoe bending stiffness. J. Biomech. 38, 1886–1894 (2005). 39. Stefanyshyn, D. J. & Wannop, J. W. The influence of forefoot bending stiffness of footwear on athletic injury and performance. Footwear Sci. 8, 51–63 (2016). 40. Tay, C. S., Sterzing, T., Lim, C. Y., Ding, R. & Kong, P. W. Overall preference of running shoes can be predicted by suitable percep- tion factors using a multiple regression model. Hum. Factors 59, 432–441 (2017). 11 Vol.:(0123456789) Scientific Reports | (2023) 13:13020 | https://doi.org/10.1038/s41598-023-39718-x www.nature.com/scientificreports/ 41. Onodera, A. N., Neto, W. P. G., Roveri, M. I., Oliveira, W. R. & Sacco, I. C. N. Immediate effects of EVA midsole resilience and upper shoe structure on running biomechanics: A machine learning approach. PeerJ 5, e3026 (2017). 42. Furukawa, H., Matsumura, K. & Harada, S. Effect of consumption values on consumer satisfaction and brand commitment: Inves- tigating functional, emotional, social, and epistemic values in the running shoes market. Int. Rev. Manag. Mark. 9, 158 (2019). 43. Frey, C. Foot health and shoewear for women. Clin. Orthop. Relat. Res. 1976–2007(372), 32–44 (2000). 44. Mailler-Savage, E. A. & Adams, B. B. Skin manifestations of running. J. Am. Acad. Dermatol. 55, 290–301 (2006). 45. Fukano, M. & Iso, S. Changes in foot shape after long-distance running. J. Funct. Morphol. Kinesiol. 1, 30–38 (2015). 46. Arellano, C. J., Hoogkamer, W. & Kram, R. B. T.-I.-C. P. A. How biomechanical improvements in running economy could help break the 2-hour marathon barrier (2016). 47. Ishikawa, M., Komi, P. V., Grey, M. J., Lepola, V. & Bruggemann, G.-P. Muscle–tendon interaction and elastic energy usage in human walking. J. Appl. Physiol. 99, 603–608 (2005). 48. Goldmann, J.-P., Sanno, M., Willwacher, S., Heinrich, K. & Brüggemann, G.-P. The potential of toe flexor muscles to enhance performance. J. Sports Sci. 31, 424–433 (2013). 49. Chen, C.-H., Tu, K.-H., Liu, C. & Shiang, T.-Y. Effects of forefoot bending elasticity of running shoes on gait and running perfor- mance. Hum. Mov. Sci. 38, 163–172 (2014). 50. Sobhani, S. et al. Effect of rocker shoes on plantar pressure pattern in healthy female runners. Gait Posture 39, 920–925 (2014). 51. Kipp, S., Kram, R. & Hoogkamer, W. Extrapolating metabolic savings in running: Implications for performance predictions. Front. Physiol. 10, 79 (2019). 52. Sobhani, S. et al. Rocker shoe, minimalist shoe, and standard running shoe: A comparison of running economy. J. Sci. Med. Sport 17, 312–316 (2014). 53. Hollander, K., Argubi-Wollesen, A., Reer, R. & Zech, A. Comparison of minimalist footwear strategies for simulating barefoot running: A randomized crossover study. PLoS ONE 10, e0125880 (2015). 54. Sobhani, S. et al. Biomechanics of running with rocker shoes. J. Sci. Med. Sport 20, 38–44 (2017). 55. Robbins, S. E., Hanna, A. & Jones, L. A. Sensory Attenuation Induced by Modern Athletic Footwear (ASTM International, 1988). 56. Cornwall, M. W. & McPoil, T. G. Can runners perceive changes in heel cushioning as the shoe ages with increased mileage?. Int. J. Sports Phys. Ther. 12, 616 (2017). 57. Taunton, J. E. et al. A prospective study of running injuries: The Vancouver Sun Run “In Training” clinics. Br. J. Sports Med. 37, 239–244 (2003). 58. Fife, A., Ramsey, C., Esculier, J.-F. & Hébert-Losier, K. How do road runners select their shoes? A systematic review. Footwear Sci. 1–10 (2023). 59. Joubert, D. P. & Jones, G. P. A comparison of running economy across seven highly cushioned racing shoes with carbon-fibre plates. Footwear Sci. 14, 71–83 (2022). 60. Knopp, M. et al. Variability in running economy of Kenyan world-class and European amateur male runners with advanced footwear running technology: Experimental and meta-analysis results. Sports Med. 53, 1255–1271 (2023). Author contributions F.Y. and S.W.J. designed this study; J.T., Q.W. and H.Z. distributed and collected questionnaires; Y.G., G.F. and T.K. performed the statistical analyses and outcome assessments; Y.X. and S.S. wrote the original draft. All authors revised and approved the final manuscript. Competing interests The authors declare no competing interests. Additional information Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1038/ s41598- 023- 39718-x. Correspondence and requests for materials should be addressed to S.-W.J. or F.Y. Reprints and permissions information is available at www.nature.com/reprints. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. © The Author(s) 2023

Gender differences in footwear characteristics between half and full marathons in China: a cross-sectional survey.

08-10-2023

Xia, Yuyu,Shen, Siqin,Jia, Sheng-Wei,Teng, Jin,Gu, Yaodong,Fekete, Gusztáv,Korim, Tamás,Zhao, Haotian,Wei, Qiang,Yang, Fan

eng

PMC7379642

232 | Scand J Med Sci Sports. 2019;29:232–239. wileyonlinelibrary.com/journal/sms 1 | INTRODUCTION Low cardiorespiratory fitness (VO2max) is a strong indepen- dent predictor of poor metabolic health and increased risk for most non‐communicable diseases, as well as lower sustained, work productivity, and shorter life expectancy.1,2 During re- cent decades, several behavioral and environmental factors have changed which may have negatively affected population Received: 22 July 2018 | Revised: 28 September 2018 | Accepted: 12 October 2018 DOI: 10.1111/sms.13328 O R I G I N A L A R T I C L E Decline in cardiorespiratory fitness in the Swedish working force between 1995 and 2017 Elin Ekblom‐Bak1 | Örjan Ekblom1 | Gunnar Andersson2 | Peter Wallin2 | Jonas Söderling3 | Erik Hemmingsson1 | Björn Ekblom1 This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2018 The Authors. Scandinavian Journal of Medicine & Science In Sports Published by John Wiley & Sons Ltd 1Åstrand Laboratory of Work Physiology, The Swedish School of Sport and Health Sciences, Stockholm, Sweden 2Research Department, HPI Health Profile Institute, Danderyd, Sweden 3Department of Medicine, Karolinska Institutet, Karolinska University Hospital Solna, Stockholm, Sweden Correspondence Elin Ekblom‐Bak, The Åstrand Laboratory of Work Physiology, The Swedish School of Sport and Health Sciences, Stockholm, Sweden. Email: [email protected] Funding information The study was supported by the Swedish Research Council for Health, Working Life and Welfare (FORTE, Dnr 2018‐00384), and the Swedish Armed Forces grant number (AF9220915). Background: Long‐term trend analyses of cardiorespiratory fitness (VO2max) in the general population are limited. Objectives: To describe trends in VO2max from 1995 to 2017 in the Swedish work- ing force and to study developments across categories of sex, age, education, and geographic regions. Methods: A total of 354 277 participants (44% women, 18‐74 years) who partici- pated in a nationwide occupational health service screening between 1995 and 2017 were included. Changes in standardized mean values of absolute (L/min) and relative (mL/min/kg) VO2max, and the proportion with low (<32) relative VO2max are re- ported. VO2max was estimated using a submaximal cycle test. Results: Absolute VO2max decreased by −6.7% (−0.19 L/min) in the total popula- tion. Relative VO2max decreased by −10.8% (−4.2 mL/min/kg) with approximately one‐third explained by a simultaneous increase in body mass. Decreases in absolute fitness were more pronounced in men vs women (8.7% vs 5.3%), in younger vs older (6.5% vs 2.3%), in short (11.4%) vs long (4.5%) education, and in rural vs urban re- gions (6.5% vs 3.5%), all P < 0.001. The proportions with low VO2max increased from 27% to 46% (P < 0.001). Conclusion: Between 1995 and 2017, there was a steady and pronounced decline in mean cardiorespiratory fitness in Swedish adults. Male gender, young age, short edu- cation, and living in a rural area were predictive of greater reductions. The proportion with low cardiorespiratory fitness almost doubled. Given the strong associations be- tween cardiorespiratory fitness and multiple morbidities and mortality, preventing further decreases is a clear public health priority, especially for vulnerable groups. K E Y W O R D S aerobic capacity, maximal oxygen consumption, population, secular trend, VO2max EKBLOM‐BAK Et AL. | 233 EKBLOM‐BAK Et AL. levels of physical activity (PA) and thereby cardiorespiratory fitness.3 Together with an increased prevalence of overweight and obesity,4 it is plausible that the level of relative VO2max (mL/min/kg) has decreased. However, previous studies of secular trends in VO2max are limited to military conscripts5-7 or smaller samples of the general population,8-10 meaning that there is a lack of studies on secular trends in large populations of adults. Women are understudied, and with the alarming inequality in health and longevity between socioeconomic groups11 and an expected significant increase in multi‐mor- bidity among the older population over the next decades,12 subgroups analyses are highly clinically relevant. Health Profile Assessment (HPA) has been carried out in occupational health services in Sweden for almost 40 years to promote health, collecting data from approximately 40 000 annual examinations during the last years.13 The combina- tion of the large amount of HPA performed each year and the long‐term use of established and standardized methods in occupational health promotion generates a unique database, which enables analyses of level of and change in estimated VO2max in the Swedish working population over several decades. The primary aim of this paper was to describe secular trends in estimated VO2max from a submaximal cycle er- gometer test between 1995 and 2017 in a large sample of the working Swedish population, aged 18 to 74 years, and to study potential variations between women and men, different age‐groups, educational levels, and regions. 2 | MATERIALS AND METHODS This study was based on cohort data from the HPA database, managed by the HPI Health Profile Institute (Stockholm, Sweden), which also is responsible for standardization of methods used and education of the HPA coaches since in- ception. The HPA is an interdisciplinary method13,14 and includes an extensive questionnaire, measurements of anthropometrics and blood pressure, a submaximal cycle test for estimation of VO2max and a person‐centered di- alogue with an HPA coach. Participation is voluntary, is free of charge, and is offered to all employees working for a company or organization connected to occupational or other health service. From October 1982 until May 2017, 437 676 participants (18 to 74 year old) with a first‐time HPA and providing data on gender, age, and educational level were stored in the central database. The annual rate of participants was substantially lower in the first years, 1982 (n = 1) and 1994 (n = 888), compared to the following full years, 1995 (n = 1 347) vs 2016 (n = 31 529). To minimize influence of uncertainty and variations in the data collection procedure, we limited our analyses to 1995‐2017 (n = 436 126). Of these, 81.2% (n = 354 277) provided valid data of estimated VO2max and were included in the analyses. All participants provided informed consent prior to data col- lection. The study was approved by the ethics board at the Stockholm Ethics Review Board (Dnr 2015/1864‐31/2 and 2016/9‐32), and adhered to the Declaration of Helsinki. 2.1 | Estimation of VO2max Measurement of actual VO2max by a graded test to exhaus- tion in the general population is limited by numerous factors, including health risks in non‐athletic population and de- pendence on laboratory equipment and expertise. Therefore, VO2max was estimated from the standardized Astrand sub- maximal cycle ergometer test.15 Criterion validity has been tested for the Astrand test, showing no systematic bias and limited variation in mean difference between estimated and directly measured VO2max, mean difference 0.01 L O2/min (95% CI −0.10 to 0.11).8,16 All participants were requested to refrain from vigorous activity the day before the test, con- suming a heavy meal 3 hours and smoking/snuff use 1 hour before the test, and avoiding stress. The participant cycled on a calibrated ergometer at an individually adapted submaxi- mal work rate for 6 minutes to achieve a steady‐state pulse. Using the steady‐state pulse, VO2max was estimated from a sex‐specific nomogram, with corresponding age‐correction factors, expressed as absolute (L/min) and relative (mL/min/ kg) VO2max. 2.2 | Other measurements Body mass was assessed with a calibrated scale in light- weight clothing to the nearest 0.5 kg. Body height was meas- ured to the nearest 0.5 cm using a wall‐mounted stadiometer. Highest educational attainment and place of dwelling (as county in Sweden of residence) at the time for the HPA was obtained by linking the personal identity number of the par- ticipants with data from Statistics Sweden. 2.3 | Internal dropout analysis Out of the total study population with a HPA since 1995, 81 849 participants (18.8%) lacked data on estimated VO2max. Reasons for a non‐valid VO2max were medication affecting the heart rate (such as betablockers) or heart rate outside the valid range. Some participants could not perform the test because of pain complaints, illness or perceived inability. Internal participation analyses for each 2‐year time period between 1995 and 2017 revealed that included participants, compared to excluded participants, were younger (42.2 vs 46.0 years, P < 0.001), had lower body mass (78.1 vs 81.4 kg, P < 0.001) and had higher education (27.9% univer- sity degree vs 22.8%, P < 0.001); however, the differences were generally small (Table S1). EKBLOM‐BAK Et AL. 234 | EKBLOM‐BAK Et AL. 2.4 | Statistical analysis For analyses of change in VO2max between 1995 and 2017, years were grouped into 2‐year periods (except the first pe- riod where we used 3 years) for reducing variations between years and for increasing statistical power. Mean values of estimated absolute and relative VO2max per 2‐year period were standardized, using the direct method, to the popula- tion 18‐74 years old in Sweden in 2015 (n = 6,842,976) by sex, age (18‐24 years, 25‐34 years, 35‐44 years, 45‐49 years, 50‐54 years, 55‐64 years, 65‐74 years), and length of educa- tion (<9 years; 10‐12 years; ≥12 years). Standardized mean values were calculated in order to account for yearly varia- tions in important prognostic variables (age, education, gen- der, and region). Standardized mean values were stratified by sex, age (18‐34 years, 35‐49 years, 50‐74 years), educa- tion (<9 years, 10‐12 years, ≥12 years), and county (counties categorized as including the three largest cities of Sweden “Urban,” counties including a majority of rural municipali- ties defined by Swedish Association of Local Authorities and Regions “Rural,” and all other counties “All other”). Linear regression models were applied to study changes in absolute and relative VO2max over the study period within the total population and across subgroups. Absolute and relative VO2max, respectively, were introduced as depend- ent variable, and sex, age, educational level, region, and year performed as independent variables. Significant change was defined as P < 0.05 for the performed year variable. To study the interaction between subgroups in decrease of ab- solute and relative VO2max, an interaction term (performed year*sub‐group) was introduced in the above regression analyses. Significant interaction(s) were defined as P < 0.05 for the interaction term. As all changes and interaction analy- ses were significant, statement of a decrease or interaction in the manuscript refers to a significant decrease or interac- tion. To study the change in absolute and relative VO2max per year between different subgroups, the probability val- ues were computed for the difference between the B‐coef- ficients.17 Proportions of women and men with low relative VO2max (<32 mL/min/kg18) per 2‐year period were calcu- lated and standardized, using the direct method, to the same population as for the mean values (above). For sensitivity analyses, lower cutoffs, by 1 MET steps (3.5 mL/min/kg), were also analyzed; <28.5, <25, and <21.5 mL. Sex‐spe- cific odds ratios (95% CI), adjusted for age and education level, were obtained to study and compare the annual change in proportion below each cutoff. Levene’s test for equality of variances was used to study potential increased variance within subgroups between the first five and last 5 years of the study period. The statistical analyses were conducted using IBM SPSS (Statistical Package for the Social Sciences for Windows), version 24.0.0, 2016, SPSS Inc, Chicago, IL and SAS version 9.4. TABLE 1 Distribution of sex, age, and educational level as well as standardized mean (SD) of height (cm) and weight (kg) in the study population, 1995‐2017 Year Women Men N Sex Age Years of education n Height Mean (SD) Weight Mean (SD) n Height Mean (SD) Weight Mean (SD) Women Men 18‐34 y 35‐49 y 50‐74 y ≤9 y 10‐12 y >12 y 1995‐1997 4574 52% 48% 30% 48% 22% 16% 70% 14% 2395 165.4 (0.5) 66.2 (0.7) 2179 179.9 (0.4) 82.4 (0.4) 1998‐1999 6543 45% 55% 28% 44% 28% 13% 67% 19% 2964 166.4 (0.4) 66.7 (0.6) 3579 179.6 (0.4) 82.8 (0.6) 2000‐2001 12 545 49% 51% 28% 42% 31% 12% 67% 21% 6206 166.6 (0.3) 67.5 (0.6) 6339 180.0 (0.3) 84.5 (1.2) 2002‐2003 22 629 52% 48% 29% 42% 29% 11% 69% 20% 11 858 166.7 (0.5) 67.3 (0.4) 10 771 179.7 (0.2) 83.4 (0.5) 2004‐2005 37 420 52% 48% 26% 44% 31% 10% 65% 25% 19 500 166.2 (0.3) 68.4 (0.6) 17 920 179.3 (0.4) 82.6 (0.7) 2006‐2007 38 519 49% 51% 25% 44% 31% 10% 65% 25% 18 714 166.2 (0.3) 68.4 (0.4) 19 805 179.8 (0.3) 83.9 (0.6) 2008‐2009 43 479 46% 54% 26% 43% 31% 10% 65% 26% 20 068 166.2 (0.3) 68.8 (0.4) 23 411 179.7 (0.3) 84.5 (0.6) 2010‐2011 39 177 44% 56% 26% 45% 29% 9% 63% 27% 17 301 166.3 (0.2) 69.6 (0.4) 21 876 180.1 (0.3) 85.1 (0.6) 2012‐2013 57 246 41% 59% 27% 45% 28% 8% 61% 31% 23 336 166.6 (0.2) 69.5 (0.5) 33 910 180.0 (0.3) 85.0 (0.6) 2014‐2015 55 584 38% 62% 30% 43% 28% 7% 63% 30% 20 894 166.3 (0.3) 69.8 (0.5) 34 690 179.9 (0.3) 85.5 (0.6) 2016‐2017 36 561 37% 63% 33% 40% 27% 7% 64% 29% 13 464 166.1 (0.3) 69.4 (0.5) 23 097 179.9 (0.3) 85.9 (0.6) Total 354 277 44% 56% 28% 43% 29% 9% 64% 27% 156 700 166.3 (0.3) 68.3 (0.6) 197 577 179.8 (0.3) 84.1 (0.7) EKBLOM‐BAK Et AL. | 235 EKBLOM‐BAK Et AL. 3 | RESULTS Participation rates by age‐group (18‐34, 35‐49 and 50‐74 years) were similar over time, while a variation in proportion of men and women as well as participants with high education from 1995 to 2017 was more pronounced (Table 1). Standardized mean body mass was higher in both men (4.2%) and women (4.8%) in the latter compared with the early years. Absolute VO2max decreased by 6.7% (−0.19 L/min) in the total population between 1995‐1997 and 2016‐2017 (Figure 1, Table S2). Men had higher levels of absolute VO2max and experienced a greater decrease compared to women; −8.7% (−0.28 L/min) vs −5.3% (−0.13 L/min). Relative VO2max decreased even more in the total popula- tion (−10.8%, −4.2 mL/min/kg), in men (−12.4%, −4.8 mL) and women (−9.4%, −3.6 mL) (Figure 1, Table S2). The de- crease in relative VO2max was, to one‐third, explained by a simultaneous increase in body mass. Younger age‐groups had higher absolute and relative VO2max compared to middle‐aged and older age‐groups (Figure 2 A,B, Table S3). Decreases were most pronounced in the youngest age‐group (absolute VO2max −6.5%, rela- tive VO2max −9.2%), compared to the middle (−3.2% and −7.1%) and oldest age‐group (−2.3% and −6.1%). This was seen for both men and women (Table S6); however, the dif- ferences in decrease for relative VO2max were similar in all male age‐groups due to a larger increase in body mass in the middle‐aged and older age‐groups. Participants with shorter education had lower absolute and relative VO2max throughout the whole study period compared to participants with longer education (Figure 2 C,D, Tables S4 and S7). The decrease in absolute VO2max was greater in par- ticipants with short (−11.4%) compared to medium (−6.2%) and long (−4.5%) education. A simultaneous increase in body mass resulted in a greater decrease in relative VO2max (−12.8%, −11.5%, and −7.0%, respectively). Participants with ≥12 years of education experienced a levelling‐off in the de- crease over the first 10 years of the 21st century. While the re- ductions were similar across all age‐groups in participants with short and medium educational attainment, only the youngest age‐group experienced a significant decrease in VO2max in participants with high education (Table S8). There was a decrease in absolute and relative VO2max in all county‐groups (Figure 2 E,F, Table S5). Starting off with a higher value in 1995‐1997, both the rural county group (absolute VO2max −6.5%, relative VO2max −10.5%) and all other coun- ties (−9.4% and −14.0%) had a steeper decrease in VO2max compared to the group with large city‐counties (−3.5% and −7.8%). However, all county‐groups had similar values at the end of the study period. Participants with long education and in counties including the three largest cities had a lower yearly decrease in relative VO2max compared to participants in lower educational levels and other counties, respectively (Table 2). The yearly decrease over the study period was 6.8 mL/min and 0.13 mL/min/kg, respectively, with a steeper annual decrease in relative VO2max at the end of the 1990 s and 2010 to 2017 com- pared to the first decade of the 21st century (Table 2). Men experi- enced a greater decrease in relative VO2max per year compared to women, as well as younger age‐groups compared to older. The proportion with low VO2max (<32 mL/min/kg) in- creased significantly over the study period, from 27% in 1995‐1997% to 46% in 2016‐2017, with a small but sig- nificantly greater increase in men (26% to 46%) compared to women (28% to 46%), P < 0.001 (Figure 3, Table S9). Proportions below each lower cutoff (<28.5, 25, 21.5 mL) increased even further (P < 0.001) in both men and women. Potential change in variance in relative VO2max between the first five (1995‐1999) and the last five (2013‐2017) years of the study period is presented in Table S10. The variance was greater in participants with long education, among mid- dle‐aged and older at the end of the study period, while the variance was smaller in young women with short education. 4 | DISCUSSION In this large cohort with data spanning from 1995 to 2017, we found evidence of a consistent and considerable decrease in absolute cardiorespiratory fitness (VO2max) of −6.7% (−0.19 L/min) in a large sample of Swedish adults. The decrease in FIGURE 1 Change in standardized mean of absolute (L/min, left) and relative (mL/min/kg, right) VO2max from 1995 to 2017 in the total study sample and in relation to sex EKBLOM‐BAK Et AL. 236 | EKBLOM‐BAK Et AL. relative cardiorespiratory fitness was even more pronounced, −10.8% (−4.2 mL/min/kg), only partly explained by a si- multaneous increase in body weight. In sub‐group analyses, we found that reductions were more pronounced in men, in young age‐groups, in those with short education, and in rural regions. The proportions with low cardiorespiratory fitness (<32 mL) increased substantially over the study period, from 27% to 46% in the total study population, with greater, rela- tive increases using lower cutoffs. The present findings are similar to previous studies in smaller population samples and young, male military con- scripts. Craig et al reported a lower relative VO2max in 2007‐2009 compared to 1981 in Canadian children and adults.10 Repeated population‐based cross‐sectional studies in Swedish adults showed no change in absolute or relative VO2max in women between 1990, 2001 and 2013.8,9 But a decrease in relative VO2max in younger and middle‐aged men between 1990 and 2001, and in the total male group between 1990 and 2013. The decrease in relative VO2max was mainly due to an increase in body mass. In male Swedish military conscripts, no change was seen in maximal working capac- ity (absolute VO2max) assessed by cycle ergometer between 1986 and 1995, however, with mean increase in body mass of 1.9 kg over the study period.5 Moreover, relative VO2max was lower in Norwegian 18‐year‐old men in 2002 compared to 1980,6 and distance achieved in a 12‐minutes running test de- creased with almost 400 m between 1980 and 2015 in Finnish male conscripts.7 The decline in performance in the two latter cohorts was mainly explained by a simultaneous increase in body mass. The discrepancy between previous studies and the present study of change in absolute VO2max is highly inter- esting and may partly be due to the different population stud- ied. Though, from a public health point of view, the present result is alarming and may have an even greater impact on the health panorama, as a lower absolute aerobic work capacity as well as a higher body mass both have an independent asso- ciation with increased disease risk and reduced longevity.18,19 Albeit a shift in behavioral and environmental factors potentially decreasing the levels of vigorous PA in the gen- eral population, secular trend analyses of leisure‐time PA, including sports participation, show increasing levels during the past 30 years in the adult population in high‐income countries.8,20,21 The proportion of Swedish adults reporting high‐intensity exercise ≥two times/wk has increased, in all age‐groups and in all levels of education, between the late 1980 s and 2006‐2007.22 This level of exertion should be suf- ficient for at least maintaining level of VO2max in these sub- jects. However, whether self‐reported higher levels of intense activity reflect an actual increase in high‐intensity exercise can be questioned. Although an increased participation rate between 1993 and 2007 in the world’s largest cross‐country race held annually in Sweden, increased run times were seen in both top, mean, and bottom quartiles, as well as in the top and bottom 5%, irrespectively of sex and age.23 However, during the same time period, work‐related PA has de- creased significantly, with a shift from occupations requiring FIGURE 2 Change in standardized mean of absolute (L/min, left) and relative (mL/min/kg, right) VO2max from 1995 to 2017 in relation to age‐group (A and B), length of education (C and D), and region (E and F) EKBLOM‐BAK Et AL. | 237 EKBLOM‐BAK Et AL. moderate‐to‐vigorous PA to predominantly sedentary or light PA occupations.20,21,24 As sufficient amount of physical stress of the cardiorespiratory system is required to maintain or increase VO2max, it could be hypothesized that the lower work‐related levels of more intense PA may partly explain the decrease of VO2max in the studied population of Swedish employees and may be a target area for future interventions. One sub‐group that exhibited no or low decrease in both absolute and relative VO2max was middle‐aged and older participants with long education, especially during the first 10 years of the 21st century. Looking at potential time trends of participation in events requiring more strenuous physi- cal activity, there was an explosion in numbers of marathon finishers in Europe around the turn of the millennium, with approximately 200 000 finishers in year 2000 to 600 000 in 2011.25 Running is an easy accessible form of exercise, how- ever, also with a strong gradient in relation to educational level; those with highest education engages to a greater extent to endurance and strenuous exercise than those with lower education.25 Trend data from Statistics Sweden also reveal an accelerating proportion of the population that around the turn of the millennium reports high‐intensity exercise at least two times a week, with a more pronounced increase in middle‐aged and older adults but similar across educational levels.22 Although highly speculative, the increased interest and participation rates in strenuous forms of activity in some subgroups of the population may have had an impact on the lower decline of VO2max in these sub‐populations. However, the increased intra‐individual variance between the early and the latter years of the study period in the same subgroups may also indicate that a possible increase in participation in more strenuous activity may be limited to a part of, rather than the full, population of the sub‐group. The mean decrease in VO2max of 4.2 mL/min/kg, with even larger decreases in some subgroups, is highly clinically relevant. A 1 MET (3.5 mL/min/kg) increase in VO2max has been associated with 13% and 15% decreased risk of all‐ cause mortality and CVD events, respectively.26 Moreover, a 1 MET improvement in fitness between baseline and a second examination was associated with a 7%, 22%, and 12% lower risk of subsequent incidence of hypertension, metabolic syn- drome, and hypercholesterolemia, respectively, after 6‐year follow‐up in healthy adults.19 Moreover, the considerable increase in proportion of both women and men with low fitness level is notable. Low fitness level has previously been linked to a substantially higher risk of all‐cause mortality after 8‐year follow‐up.27 The popula- tion‐attributable risks assessed in the same study revealed that 9% and 15% of all deaths in men and women, respectively, with low VO2max in the studied population might have been prevented if they had become more fit. Halting the gradual reduction in cardiorespiratory fitness is a clear public health priority for a sustainable future and of high clinical relevance, mainly by providing improved opportunities for regular phys- ical activity. The greater decline in specific subgroups, with increasing gaps between subgroups, is especially alarming and may be primary targets for interventions to improve health in this population. For example, the steeper decrease in participants with short education is alarming. Lower ed- ucational level or socioeconomic status compared to higher has previously been associated with lower VO2max and/or fulfillment of recommended levels of moderate‐to‐vigorous PA.28,29 This is suggested as one important contributing fac- tor to the social inequality in health between socioeconomic groups, with low socioeconomic status associated with higher burden of disease and shorter life expectancy.11 The main strength of the present study is the large sample with yearly assessments of VO2max in the Swedish working population over a 23‐year period, with the potential to per- form highly clinically relevant analyses of variations across subgroups. Previous elucidation of change in cardiorespira- tory fitness over several decades in a large population‐based sample is, to our knowledge, non‐existing. Standardization of data in relation to the Swedish population with regard to sex, age, and length of education enabled comparison over the study period. A limitation was relatively lower number of participants during the early years compared to the latter, TABLE 2 Changes in cardiorespiratory fitness per year in the total population and across subgroups Absolute VO2max (ml·min−1) Relative VO2max (ml·min−1·kg−1) B (95% CI) B (95% CI) Total −6.8 (−7.2 to −6.4) −0.13 (−0.14 to −0.13) Women −2.4 (−3.0 to −1.9)a −0.09 (−0.09 to −0.08)a Men −10.0 (−10.6 to −9.4) −0.17 (−0.18 to −0.17) Ageb 18–34 y −14.2 (−15.1 to −13.3) −0.24 (−0.25 to −0.22) 35–49 y −5.4 (−6.0 to −4.8) −0.12 (−0.13 to −0.11) 50–74 y −0.4 (−1.1 to 0.3) −0.06 (−0.07 to −0.05) Length of education ≤9 y −8.7 (−10.0 to −7.5) −0.14 (−0.16 to −0.12) 10–12 y −7.5 (−8.0 to −7.0) −0.15 (−0.16 to −0.15) ≥12 y −3.8 (−4.7 to −2.9)c −0.08 (−0.09 to −0.07)c Region (counties) Urban −5.8 (−6.5 to −5.1)d −0.11 (−0.12 to −0.10)d Rural −7.5 (−8.4 to −6.6) −0.16 (−0.17 to −0.14) All other −7.9 (−8.6 to −7.2) −0.16 (−0.17 to −0.15) Values are adjusted for sex, age, education level and weight (only relative VO2max values). aSignificantly different women vs. men. bSignificantly different between all age‐groups. cSignificantly different from ≤9 years and 10–12 years. dSignificantly different from rural and all other counties. EKBLOM‐BAK Et AL. 238 | EKBLOM‐BAK Et AL. inducing a lower power. Another limitation is the use of a submaximal test to estimate VO2max. However, measuring actual VO2max during maximal performance would not have been feasible in this large non‐athletic population, to- gether with HPA test leaders not experts in work physiology nor with access to laboratory equipment. In addition, as- sessment of VO2max by the Astrand protocol is reported to yield a valid and reliable estimation of actual VO2max.8,16 Participants on medication that could affect heart rate re- sponse during the submaximal test were not excluded in the present study. In the total study population, 79% of the par- ticipants reported no medication, 3% reported medication, and 18% lacked data on medication use. The relative pro- portion of participants reporting medication or with miss- ing data (with the possibility that they were on medication) increased over the years from 1995 to 2017. However, most cardio‐protective treatments, including anti‐hypertensive medication, have typically heart rate‐lowering effects, which in turn would yield a higher estimated VO2max during the submaximal cycle test. So, if anything, the decline in esti- mated VO2max over the years could be somewhat underes- timated. We had only data on county of residence and not on municipality of residence. This might have yielded a too rough classification of the population when analyzing po- tential region differences. 5 | PERSPECTIVE The present study provides for the first time evidence of an over- all deterioration in cardiorespiratory fitness in a large cohort of men and women over the last three decades, which may have had a negative impact on performance and health in this popula- tion. Previous studies of secular trends in VO2max have been limited to military conscripts or smaller samples of the general population. The main driver for the decline was a deterioration in cardiorespiratory aerobic capacity, and only partly by a si- multaneous increase in body mass. The reduction was particu- larly pronounced in men, in younger ages, in participants with a low educational level, and in rural regions. Given the strong associations between cardiorespiratory fitness and multiple mor- bidities and mortality, preventing further decreases is a clear pub- lic health priority, especially for vulnerable groups. Replication of findings in other countries and populations are needed. ACKNOWLEDGEMENTS The authors gratefully acknowledge the HPI Health Profile Coaches from all over Sweden. A special thanks to the mem- bers of staff at HPI Health Profile Institute. CONFLICT OF INTEREST GA (responsible for research and method) and PW (CEO and responsible for research and method) are employed at HPI Health Profile Institute. JS reports personal fees from HPI Health Profile Institute during the conduct of the study. ORCID Elin Ekblom‐Bak http://orcid.org/0000-0002-3901-7833 Örjan Ekblom http://orcid.org/0000-0001-6058-4982 Erik Hemmingsson http://orcid.org/0000-0001-7335-3796 Björn Ekblom http://orcid.org/0000-0002-4030-5437 REFERENCES 1. Blair SN, Kampert JB, Kohl 3rd, et al. Influences of cardiorespira- tory fitness and other precursors on cardiovascular disease and all‐ cause mortality in men and women. JAMA. 1996;276(3):205‐210. 2. Astrand I. Degree of strain during building work as related to indi- vidual aerobic work capacity. Ergonomics. 1967;10(3):293‐303. 3. Pratt M, Sarmiento OL, Montes F, et al. The implications of megatrends in information and communication technology and transportation for changes in global physical activity. Lancet. 2012;380(9838):282‐293. 4. Agha M, Agha R. The rising prevalence of obesity: part A: impact on public health. Int J Surg Oncol (N Y). 2017;2(7):e17. 5. Rasmussen F, Johansson M, Hansen HO. Trends in overweight and obesity among 18‐year‐old males in Sweden between 1971 and 1995. Acta Paediatr. 1999;88(4):431‐437. FIGURE 3 Standardized proportions of women (left) and men (right) with a low VO2max using different cutoffs, from 1995 to 2017 EKBLOM‐BAK Et AL. | 239 EKBLOM‐BAK Et AL. 6. Dyrstad SM, Aandstad A, Hallen J. Aerobic fitness in young Norwegian men: a comparison between 1980 and 2002. Scand J Med Sci Sports. 2005;15(5):298‐303. 7. Santtila M, Pihlainen K, Koski H, Vasankari T, Kyrolainen H. Physical fitness in young men between 1975 and 2015 with a focus on the years 2005–2015. Med Sci Sports Exerc. 2018;50(2):292‐298. 8. Ekblom B, Engstrom LM, Ekblom O. Secular trends of physical fitness in Swedish adults. Scand J Med Sci Sports. 2007;17(3):267‐273. 9. Olsson S, Ekblom‐Bak E, Ekblom B, Kallings LV, Ekblom O, Borjesson M. Association of perceived physical health and physi- cal fitness in two Swedish national samples from 1990 and 2015. Scand J Med Sci Sports. 2018;28(2):717‐724. 10. Craig CL, Shields M, Leblanc AG, Tremblay MS. Trends in aer- obic fitness among Canadians, 1981 to 2007–2009. Appl Physiol Nutr Metab. 2012;37(3):511‐519. 11. Rosengren A, Subramanian SV, Islam S, et al. Education and risk for acute myocardial infarction in 52 high, middle and low‐ income countries: INTERHEART case‐control study. Heart. 2009;95(24):2014‐2022. 12. Kingston A, Robinson L, Booth H, Knapp M, Jagger C, MODEM project. Projections of multi‐morbidity in the older population in England to 2035: estimates from the population ageing and care simulation (PACSim) model. Age Ageing. 2018;47(3):374‐380. 13. Andersson G. The importance of exercise for sick leave and per- ceived health. Linköping: Department of Preventive and Social Medicine, Linköping University; 1987. 14. Lilliecreutz Huitema E, Andersson G, Samuelsson K. Lifestyle changes with help from Health Profile Assessment in combination with support in individual interventions for persons with acquired brain injury – A pilot study. Eur J Physiother. 2014;16:151‐158. 15. Astrand I. Aerobic work capacity in men and women with special reference to age. Acta Physiol Scand Suppl. 1960;49(169):1‐92. 16. Bjorkman F, Ekblom‐Bak E, Ekblom O, Ekblom B. Validity of the revised Ekblom Bak cycle ergometer test in adults. Eur J Appl Physiol. 2016;116(9):1627‐1638. 17. Cohen J, Cohen P, West SG, Aiken LS. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. 3rd edn. Mahwah, NJ: Lawrence Earlbaum Associates; 2003. 18. Fogelholm M. Physical activity, fitness and fatness: relations to mortality, morbidity and disease risk factors. A systematic review. Obes Rev. 2010;11(3):202‐221. 19. Lee DC, Sui X, Church TS, Lavie CJ, Jackson AS, Blair SN. Changes in fitness and fatness on the development of cardiovas- cular disease risk factors hypertension, metabolic syndrome, and hypercholesterolemia. J Am Coll Cardiol. 2012;59(7):665‐672. 20. Hallal PC, Andersen LB, Bull FC, et al. Global physical activ- ity levels: surveillance progress, pitfalls, and prospects. Lancet. 2012;380(9838):247‐257. 21. Knuth AG, Hallal PC. Temporal trends in physical activity: a sys- tematic review. J Phys Act Health. 2009;6(5):548‐559. 22. StatisticsSweden. Living Conditions, Report no 118: Leisure Activities 2006–07. Stockholm, Sweden: ISSN 1654–1707 (on- line); 0347–7193 (print).2009. 23. Aagaard P, Sahlen A, Braunschweig F. Performance trends and cardiac biomarkers in a 30‐km cross‐country race, 1993–2007. Med Sci Sports Exerc. 2012;44(5):894‐899. 24. Church TS, Thomas DM, Tudor‐Locke C et al. Trends over 5 de- cades in U.S. occupation‐related physical activity and their asso- ciations with obesity. PLoS ONE. 2011;6(5):e19657. 25. Scheerder J, Breedveld K, Borgers J. Running across Europe: The rise and size of one of the largest sport markets. Houndmills, Basingtoke, Hampshire/New York: Palgrave Macmillan UK; 2015. 26. Kodama S, Saito K, Tanaka S, et al. Cardiorespiratory fitness as a quantitative predictor of all‐cause mortality and cardiovascu- lar events in healthy men and women: a meta‐analysis. JAMA. 2009;301(19):2024‐2035. 27. Blair SN, Kohl HW 3rd, Paffenbarger RS Jr, Clark DG, Cooper KH, Gibbons LW. Physical fitness and all‐cause mor- tality. A prospective study of healthy men and women. JAMA. 1989;262(17):2395‐2401. 28. Lakka TA, Kauhanen J, Salonen JT. Conditioning leisure time physical activity and cardiorespiratory fitness in sociodemo- graphic groups of middle‐ages men in eastern Finland. Int J Epidemiol. 1996;25(1):86‐93. 29. Ekblom‐Bak E, Olsson G, Ekblom O, Ekblom B, Bergstrom G, Borjesson M. The daily movement pattern and fulfilment of phys- ical activity recommendations in swedish middle‐aged adults: the SCAPIS Pilot Study. PLoS ONE. 2015;10(5):e0126336. SUPPORTING INFORMATION Additional supporting information may be found online in the Supporting Information section at the end of the article. How to cite this article: Ekblom‐Bak E, Ekblom Ö, Andersson G, et al. Decline in cardiorespiratory fitness in the Swedish working force between 1995 and 2017. Scand J Med Sci Sports. 2019;29:232–239. https://doi. org/10.1111/sms.13328

Decline in cardiorespiratory fitness in the Swedish working force between 1995 and 2017.

11-15-2018

Ekblom-Bak, Elin,Ekblom, Örjan,Andersson, Gunnar,Wallin, Peter,Söderling, Jonas,Hemmingsson, Erik,Ekblom, Björn

eng

PMC6103506

RESEARCH ARTICLE Oxygen uptake kinetics and speed-time correlates of modified 3-minute all-out shuttle running in soccer players Mark Kramer1*, Rosa Du Randt1, Mark Watson2, Robert W. Pettitt3 1 Human Movement Science Department, Nelson Mandela University, Port Elizabeth, South Africa, 2 Psychology Department, Nelson Mandela University, Port Elizabeth, South Africa, 3 Rocky Mountain University of Health Professions, Provo, Utah, United States of America * [email protected] Abstract How parameters derived from oxygen uptake _VO2 kinetics relate to critical speed is not fully understood, and how such parameters relate to more sport-specific performances, such as shuttle running, has not been investigated. Therefore, the primary aims of the present stu- dent were to examine the _VO2 kinetics during all-out linear and shuttle running and compare physiological variables of all-out running to variables measured during a graded exercise test (GXT). Fifteen male soccer players performed a graded exercise test (GXT) and the _VO2 kinetics from a series of three different 3-min all-out tests (3MT’s) were evaluated. _VO2max achieved during the GXT did not differ from maximal _VO2 achieved during the all- out tests (F = 1.85, p = 0.13) (overall ICC = 0.65; typical error = 2.48 ml
kg-1
min-1; coefficient of variation = 4.8%). A moderate, inverse correlation (r = -0.62, p = 0.02) was observed between τ (14.7 ± 1.92 s) and CS (3.96 ± 0.52 m
s-1) despite the narrow SD for τ. No differ- ences (p > 0.05) were observed for any of the _VO2 kinetics between continuous and shuttle running bouts. The linear running 3MT (r3MT) represents a viable surrogate to the GXT and data beyond CS and D’ may be gleaned by using the bi-exponential speed-time model. Introduction Measurement of the oxygen uptake ( _VO2) responses to constant work exercise performed in various intensity domains is well researched and understood [1–3], yet research where severe- intensity exercise is performed using non-constant strategies (e.g. all-out running), has received limited attention [4,5]. Successful performance in athletic activities is dependent on the level of aerobic energy transfer, which in turn is governed by the magnitude and the time course of pulmonary _VO2 and muscle O2 consumption [3]. Measurement of _VO2 kinetics can therefore provide valuable insights pertaining to the ventilatory, cardiovascular and neuro- muscular responses to a given exercise mode, duration and intensity [2,6,7]. The speed of the increase in the _VO2 response, represented by the primary phase time con- stant τ and is reflective of muscle _VO2 kinetics [7], towards a steady state (or quasi steady- PLOS ONE | https://doi.org/10.1371/journal.pone.0201389 August 21, 2018 1 / 15 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Kramer M, Du Randt R, Watson M, Pettitt RW (2018) Oxygen uptake kinetics and speed-time correlates of modified 3-minute all-out shuttle running in soccer players. PLoS ONE 13(8): e0201389. https://doi.org/10.1371/journal. pone.0201389 Editor: Alessandro Moura Zagatto, Sao Paulo State University - UNESP, BRAZIL Received: February 20, 2018 Accepted: June 13, 2018 Published: August 21, 2018 Copyright: © 2018 Kramer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: To enhance the reproducibility of the results we have gladly deposited our data in an online repository (https:// doi.org/10.7910/DVN/3JVSOH). Funding: The authors received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. Abbreviations: _VO2, rate of pulmonary oxygen uptake; _VCO2, rate of pulmonary carbon dioxide state) therefore implicates the relative contribution of oxidative and non-oxidative metabolic processes to energy transfer [6]. Greater depletion of high energy phosphates (primarily phos- phocreatine [PCr]) and the anaerobic catabolism of glycogen to lactate is experienced when _VO2 kinetics are slower and/or the amplitude of the _VO2 kinetics are greater [2,6,7]. Faster _VO2 kinetic responses (i.e. smaller τ) are therefore indicative of healthy and/or fit individuals, whereas slower responses (i.e. larger τ) are more representative of unfit or unhealthy individu- als [7]. The extent to which the associated _VO2 kinetic parameters of male soccer players relate to critical speed is presently not well understood. Furthermore, how such parameters relate to more sport specific performances, such as all-out shuttle running, has not been investigated. Modern trends in field sports, such as soccer and rugby, have shown increases in playing intensity (i.e. time and distance spent running at speeds exceeding 21 km
hr-1), necessitating a requisite increase in the physical fitness parameters of players [8–10]. High intensity perfor- mance is characterized by the ability to sustain a high percentage of maximum oxygen uptake ( _VO2max), with the gas exchange threshold (GET) often being associated with an athlete’s maximum sustainable intensity rate [7,10–12]. However, although the GET is a good predictor of exercise performance, it is not reflective of the athlete’s competition specific intensity [13]. Alternatively, critical power (for cycling) or critical speed (for running) has emerged as a more viable substitute and has been found to be more consistent with high-intensity exercise [7]. Knowing and understanding specific speed thresholds and the physiological responses they elicit therefore has important performance implications. Exercise intensities performed at speeds below the lactate threshold (LT, or gas exchange threshold [GET]) are defined as moderate, whereby a metabolic steady state is rapidly achieved. Heavy intensity exercise is bounded by intensities above LT, but below critical power (CP or the maximal lactate steady state [MLSS]), resulting in elevated but stable blood lactate levels [2,7]. In fact, CP represents the highest _VO2 at which blood lactate and _VO2 can be stabi- lized [2]. From a _VO2 kinetics perspective, the _VO2 in the heavy intensity domain exhibits a ‘slow component’ which represents an elevated _VO2 and results in a delayed steady state of 10–15 minutes or more depending on the relative power/speed within the heavy intensity domain. Exercise in the severe domain is constrained to intensities above CP in which _VO2max can be elicited [14]. Within the severe domain, the slow component causes _VO2 to rise to maximum and blood lactate levels to rise exponentially until exercise is terminated [2,10,14]. More specifically, CP has been found to be a robust parameter representative of a fatigue threshold, placed approximately midway between GET and _VO2max, which demarcates the heavy from the severe intensity domains [12]. The CP concept was first proposed by Monod and Scherrer whereby maximal work rate and the time to exhaustion of a single muscle group exhibited a hyperbolic relationship [15]. The curvature constant of the hyperbolic relationship (termed W’; measured in kilojoules [kJ]) represents the maximum amount of work that can be completed at intensities above CP. This same relationship has since been extended to whole body exercise such as cycling [16,17], swimming [18,19], rowing [20,21], running [22,23] and even field-based sports such as soccer and rugby [24–26]. What had initially held back the broader implementation of the CP concept was the requirement of several exhaustive bouts over several days [12]. This limitation was overcome in 2006 whereby it was evidenced that a 3-min all-out exercise test (3MT) for cycling was found to accurately replicate the CP and W’ values obtained using the more cumbersome pro- tocols [27]. When running is the preferred mode of exercise, the CP term is replaced with criti- cal speed (CS; measured in m
s-1 as opposed to watts), and W’ is replaced by D’ (measured in meters, and is indicative of the maximum distance that can be covered at speeds above CS). O2 kinetics and all-out running PLOS ONE | https://doi.org/10.1371/journal.pone.0201389 August 21, 2018 2 / 15 expulsion; HRmax, maximum heart rate; BF, breathing frequency; _VE, minute ventilation; CP, critical power; RER, respiratory exchange ratio; CS, critical speed; GET, gas exchange threshold; Δ50%, midpoint between GET and _VO2max; D’, curvature constant of the speed-time relationship for high-intensity exercise (maximum distance covered at speeds above CS); r3MT, 3-minute maximal run test; TE, typical error; ICC, intra-rater correlation coefficient; CV, coefficient of variation; O2, oxygen; CO2, carbon dioxide. The same 3MT protocol has since been successfully applied to running (here referred to as r3MT to differentiate it from the cycling version) resulting in the successful derivation of CS and D’ [22,23]. The conventional r3MT requires athletes to run all-out in a straight line (or around a track), and has even been used to derive CS and D’ parameters for soccer and rugby players. Sports involving shuttle running, that incorporate multiple changes of direction, may limit the ecological validity of the r3MT, and motivates a modification of the 3MT protocol to incorporate all-out shuttle running. To our knowledge there is presently no research whether modifications of the r3MT protocol would modify the physiological loading of athletes as mea- sured by the _VO2 uptake kinetics as well as other physiological measures such as heart rate (HR), breathing frequency (BF), minute ventilation ( _V E), or the respiratory exchange ratio (RER). No studies, to the knowledge of the present authors, have measured the _VO2 kinetics during the r3MT or modified versions thereof. Similarly, given the nature of the r3MT speed-time curve, a wealth of information may be overlooked when only CS and D’ parameters are considered. Factors such as maximal speed achieved, rate of speed decay towards CS, and time to maximal speed are simply not reported in the literature. In part, this is due to a lack of mathematical modeling that accurately repre- sents the instantaneous changes in speed during the r3MT. Such modeling may lead to greater insights into physiological factors governing high intensity running performance. Given that CS and D’ are mathematically derived parameters, the mathematical model bears important consideration as variation in model selection will influence the parameter esti- mates [28,29]. Although various mathematical models exist to derive both CP and W’ (or CS and D’), none yet have attempted to model the r3MT. We introduce such a model in the pres- ent study and compare the CS and D’ parameters derived to more traditional methods pro- posed by Vanhatalo et al. [16] and Broxterman et al. [23]. The principal purpose of this study was therefore to characterize the _VO2 kinetics of linear all-out running and contrast these to the _VO2 kinetics of all-out shuttle running of varying dis- tances (i.e. 25-m and 50-m). We also distinguished the physiological parameters obtained from all-out running to those obtained from a traditional laboratory-based GXT to determine whether the physiological stresses imposed by the all-out tests were inherently different. Given that the speed-time curve of linear all-out running has not been modeled before, but that such modeling could provide important performance-related information that could be used for intervention-based analyses, a secondary purpose of the study was therefore to determine whether such a model could adequately characterize the speed-time curve. The model was compared to traditional methods of analysis for deriving CS and D’, and extended to compare the speed-time model parameters to those of the _VO2 kinetics. Materials and methods Ethics statement The Research Ethics Committee for human test subjects of the Nelson Mandela University, in accordance with the Code of Ethics of the World Medical Association (Declaration of Hel- sinki), approved all procedures. All subjects provided written informed consent after having the testing procedures explained both verbally and in written format. Experimental overview Subjects visited the testing facility on five separate occasions, with each visit separated by at least 48 hours over a two-week period. The first visit was used to familiarize subjects with the testing procedures prior to the start of experimentation. On the second visit subjects O2 kinetics and all-out running PLOS ONE | https://doi.org/10.1371/journal.pone.0201389 August 21, 2018 3 / 15 performed the incremental running test on a motorized treadmill (Woodway, USA) to deter- mine _VO2max and GET, as well as the heart rate (HR), minute ventilation ( _V E), respiratory exchange ratio (RER), breathing frequency (BF) and running velocities associated with these parameters. On the third visit, after a standardized warm-up, subjects completed the r3MT on a 400m outdoor track with a portable spirometer (Metamax 3B, Cortex Biophysik, Leipzig, Germany), global positioning system (GPS, Cortex, Germany) and Polar H7 heart rate moni- tor (Polar Electro Oy, FI-90440, Kempele, Finland) to determine peak values along with data for subsequent modeling of the _VO2 kinetics. The fourth and fifth visits utilized the same assessment set-up as that of the 3MT described previously, but the tests were modified to incorporate all-out shuttle-like turns over 25-m or 50-m distances respectively on a designated portion of the outdoor track to maintain the same surface kinetics. The sequence of the all-out testing was counterbalanced to avoid an order effect. Subjects A total of 15 male soccer players volunteered for the study. The subjects had the following characteristics (mean ± SD): age = 23.1 ± 3.1 years, height = 1.73 ± 0.06 m, and weight = 68.9 ± 8.6 kg. Subjects were recruited from the Nelson Mandela University first team soccer club, were apparently healthy, had a minimum of one-year competitive playing experience, were not taking any medications and were uninjured at the time of testing. Procedures Graded exercise test with verification protocol. The system was calibrated prior to each test using ambient air, with an assumed concentration of 20.94% O2 and 0.03% CO2, as well as a gas of known O2 and CO2 concentrations of 15% and 5% respectively as per manufacturer’s instructions. The turbine flowmeter was calibrated using a 3-L syringe (Metamax 3B, Cortex Biophysik). Prior to the GXT, subjects completed a five-minute warm-up at 6–8 km
hr-1 on a motorized treadmill (Woodway, 4Front, USA), followed by a five-minute rest period during which subjects were encouraged to complete dynamic stretches. The ramp test began at 8 km
hr-1 at an incline of 1o and increased by 1 km
hr-1 every minute until exhaustion was reached. Inspired and expired gas volume and concentrations were continuously sampled breath-by-breath using an automated open circuit spirometry device (Metamax 3B, Cortex Biophysik). Heart rate was continuously monitored throughout the test using short range telemetry (Polar H7 HR monitor, Polar, Finland). A rating of perceived exertion (RPE), using the original Borg scale [30], was used to monitor when athlete’s felt close to exhaustion. Strong verbal encouragement was given throughout the test to ensure maximal effort. Once exhaus- tion was reached, subject’s straddled the treadmill belt, upon which speed was returned to 6 km
hr-1 to allow for an active recovery period lasting 3-minutes. To determine whether a ‘true’ _VO2max was attained, a verification bout was utilized [31–34]. At the end of the 3-minute active recovery period, the treadmill speed was increased to 2-stages below the speed reached at the final stage of the primary _VO2max test, that is, if the test was initially terminated at 15 km
hr-1 using a 1 km
hr-1
min-1 protocol, then the validation bout would be initiated at 13 km
hr-1 to validate the _VO2max value. The end-stage _VO2 reached during the verification bout would need to be within 3% of the original bout to be deemed a ‘true’ _VO2max [33,34]. Gas exchange data were reduced to 10-second averages for the estimation of the GET using the following criteria: (1) the first disparate increase in _VCO2 in the _VCO2 vs. _VO2 plot using the V-slope method [35,36]; (2) an increase in _V E= _VCO2 with no increase in _V E= _VO2; and (3) the first increase in end-tidal O2 tension with no fall in end-tidal CO2 tension. _VO2max was O2 kinetics and all-out running PLOS ONE | https://doi.org/10.1371/journal.pone.0201389 August 21, 2018 4 / 15 determined using the highest _VO2 average over a 30-second period during the GXT, with vali- dation of the ‘true’ _VO2max measured with the verification bout. Oxygen uptake, HR, _V E, BF and speed at Δ50% were calculated from the initial GXT data as the midpoint between GET and _VO2max data. The speed at GET (sGET), Δ50% (sΔ50%) and at _VO2max (s _VO2max) were linearly interpolated at 1-minute preceding the sample [22]. The verification bout was used to determine whether a ‘true’ _VO2max was reached. Only two subjects failed to be within the 3% cut-off (3.9% and 4.1% respectively) and were subsequently asked to re-do the test within a one-week period. On re-testing, both subjects managed to be within the requisite cut- off and were subsequently retained for analysis. Three-minute all-out running tests. In the third session subjects completed the r3MT on an outdoor 400 m tartan sprinting track with minimal wind conditions and a clear sky. After 10–15 minutes of active warm-ups and dynamic stretching, subjects were fitted with a portable spirometer and global positioning sensor (GPS) sampling at 1 Hz (Metamax 3B, Cortex Bio- physik) along with a chest strapped wireless HR monitor (H7, Polar, Finland). The GPS system connects directly to the portable spirometer thereby allowing speed data collection that is con- gruent with the breath-by-breath data. Subjects were instructed to run all-out with maximal effort throughout the entirety of the test. Although verbal encouragement was provided throughout the test, subjects were neither informed of the elapsed time nor time remaining to discourage pacing. Subjects were instructed to stop once 3 minutes and 5 seconds had elapsed to ensure full GPS coverage. The same procedures, sprinting track, equipment and principles were applied to the modified 3MT’s (25-m and 50-m shuttle 3MT) during the fourth and fifth sessions, each test being separated by at least 48 hours. The modified 3MT’s, unlike the con- ventional r3MT, incorporated 180o turns over distances of 25-m or 50-m, and were therefore deemed more “sport specific” for activities such as soccer, rugby, and hockey given that the modified tests would require significant accelerations and decelerations for each shuttle. The number of turns required would be inversely proportional to the distance of the shuttle, in other words, 25-m shuttles would require more turns compared to 50m shuttles in the given time. The modified all-out shuttle test has been validated by comparing CS and D’ against sev- eral distance time-trials [37]. Assessment of oxygen uptake kinetics. For each subject and each 3MT test, breath-by- breath _VO2 data were linearly interpolated to give one value per second (averaging increment of 1 s), which were then time aligned to the start of the test. The oxygen uptake (O2) kinetics were modeled using a mono-exponential function [2,3,38] expressed as: _VO2ðtÞ ¼ _VO2ðBLÞ þ A1
ð1 where t is the time, S(t) is the speed at a given time, S0 is the y-asymptote (also defined as CS), Ag is the growth amplitude of the exponential, Ad is the decay amplitude of the exponential, tc is the time offset between exponential growth and decay, τg is the time constant of the expo- nential growth term and τd is the time constant of the exponential decay term. Unconstrained non-linear regression by least sum of squares (OriginPro 2017 [version 94E], OriginLab, USA) was used to determine all the coefficients. From the bi-exponential speed (S0) model, the CS is represented by the S0 term, peak speed can be determined by summing the S0 and Ad terms, and D’ can be determined by integration of the model at speeds above CS (see Fig 1). Critical speed values were obtained as the average speed of the final 30-seconds for each all- out test [22,37]. Speeds for all tests were sampled at 1Hz, allowing for comparable fidelity of Fig 1. Bi-exponential speed-time (S0) model and parameters of the r3MT for a representative subject. https://doi.org/10.1371/journal.pone.0201389.g001 O2 kinetics and all-out running PLOS ONE | https://doi.org/10.1371/journal.pone.0201389 August 21, 2018 6 / 15 speed-time data for the 25m 3MT and 50m 3MT to that of Saari, et al. [37]. It is important to note however, that greater precision of shuttle speed data should be obtained, and that the val- ues presented should interpreted with some mindfulness. Statistical analyses The Statistica (version 10.1) software package was used for statistical data analysis. Data are presented as mean ± SD unless otherwise stated. All data were assessed for normality using the Shapiro-Wilk test, and all data were found to conform to normality. A one-way analysis of var- iance (ANOVA) with repeated measures was used to compare maximum _VO2 values, _V E; HRmax RER, and BF, for each test (GXT, verification bout, r3MT, 25m 3MT and 50m 3MT), followed by a post-hoc Scheffe´ test for instances where the null-hypotheses were rejected. The parameter estimates for the _VO2 kinetics (A, δ, and τ) for all three 3MT’s were analysed using a one-way ANOVA, followed by post-hoc Scheffe´ testing for significant differences. Simple lin- ear regression was used to compare parameter estimates of the _VO2 kinetics and bi-exponen- tial speed-time models. Relative consistency between tests was assessed using the intraclass correlation coefficient (ICC α), whereas absolute consistency was evaluated using coefficient of variation (CV%) and typical error (TE) [39]. The fit of the S0-model to the raw data was evaluated using the coefficient of determination (r2) and the standard error of the estimate (SEE). Statistical significance was accepted at a level of p < 0.05. Results Graded exercise test Consistent peak _VO2 values (i.e. within 3%) in the incremental and verification bouts would provide support that a ‘true’ _VO2max was reached. Relative _VO2max values (ml
kg-1
min-1) between the GXT and the verification bout did not differ significantly (t = 1.73, p = 0.11), and were internally consistent (CV% = 1.7, TE = 0.88 ml
kg-1
min-1), thereby indicating the achievement of a true _VO2max. A summary of the physiological parameters obtained from the GXT are presented in Table 1. Comparison in terms of graded and all-out running tests Table 1 displays the physiological data from the GXT and all-out running performances. No significant differences were found between absolute or relative _VO2max; _V E, or BF for all physiological tests. Peak values for HR in the all-out tests were lower than the GXT. The RER values were higher during the all-out tests compared to the GXT. From Table 1 above it is evi- dent that CS derived from the r3MT was not significantly different from speed at Δ50% (t = 0.90, p = 0.39) and there was strong internal consistency observed between the two metrics (CV% = 6.8, TE = 0.27 m
s-1). The parameter estimates for the _VO2 uptake kinetics for each of the all-out tests are pre- sented in Table 2 (see also Fig 2). No significant differences for any of the parameter estimates could be detected, implying a potentially similar physiological response for each of the all-out tests, at least from a muscle-metabolic and cardiopulmonary perspective. The averaged maxi- mal oxygen uptake obtained for the all-out shuttle tests are presented in Fig 2A together with the 95% confidence interval (CI) for the GXT. An example of the modeled _VO2 uptake kinetics for the 25m 3MT is presented in panel B of Fig 2 (R2 = 0.97, representing the average goodness of fit for all subjects). O2 kinetics and all-out running PLOS ONE | https://doi.org/10.1371/journal.pone.0201389 August 21, 2018 7 / 15 The r3MT speed-time (S0) model In alignment with the methods described by Vanhatalo et al. [16] and Broxterman et al. [23] for deriving CS from an all-out test (i.e. the average speed of the final 30-seconds of the all-out test), the S0 parameter in the present study was compared to the average speed in the final 30-seconds of the r3MT and evaluated for absolute and relative consistency. With a TE of 0.09 m
s-1, CV of 2.3%, and ICC α of 0.97, the S0 parameter derived from the S0-model is indeed reflective of CS determined via the methods proposed by Vanhatalo et al. [16] and Broxterman et al. [23]. The same was true for the D’ parameter, which is defined as the area under the curve, but above CS during an all-out test. The D’ parameter would traditionally be Table 1. Peak values of the GXT and all-out tests. GXT Verification GET Δ50% r3MT 25m 3MT 50m 3MT ANOVA Statistics (F, p) _VO2 (L
min-1) 3.45 ± 0.29 3.42 ± 0.25 2.65 ± 0.27 3.05 ± 0.26 3.56 ± 0.35 3.71 ± 0.38 3.69 ± 0.33 F = 2.545, p = 0.065 _VO2max (ml
kg-1
min-1) 50.46 ± 3.95 49.91 ± 4.05 38.67 ± 3.89 44.57 ± 3.66 51.96 ± 4.56 53.59 ± 4.80 53.03 ± 5.17 F = 1.847, p = 0.130 _V E (L
min-1) 126.61 ± 16.92 127.19 ± 18.19 73.63 ± 15.88 100.12 ± 15.22 132.81 ± 15.03 138.34 ± 16.69 137.65 ± 16.90 F = 1.646, p = 0.173 BF (breaths
min-1) 57.87 ± 15.63 59.80 ± 13.43 44.07 ± 15.93 50.97 ± 15.44 60.93 ± 10.72 61.27 ± 8.97 60.60 ± 10.13 F = 0.192, p = 0.942 HRmax (beats
min-1) 189 ± 4 189 ± 5 165 ± 8 177 ± 5 183 ± 6c,d 179 ± 5a,b 182 ± 4a,b F = 10.260, p < 0.001 RER 1.12 ± 0.05 1.02 ± 0.04 0.94 ± 0.03 1.03 ± 0.03 1.25 ± 0.12a,c,d 1.30 ± 0.08a,b 1.30 ± 0.07a,b F = 39.255, p < 0.001 End-stage speed (m
s-1) 4.66 ± 0.36 4.10 ± 0.36 3.14 ± 0.32 3.90 ± 0.31a 3.96 ± 0.52a 3.10 ± 0.36 a,b,d 3.66 ± 0.45a F = 29.928, p < 0.001 Values are mean ± SD. GXT (graded exercise test); GET (gas exchange threshold); 3MT (3-minute all-out run test); 25-m 3MT (3-minute all-out shuttle run test over 25-m distances); 50-m 3MT (3-minute all-out shuttle run test over 50-m distances); _V_O2max (maximal rate of pulmonary oxygen uptake); _V_ E (minute ventilation); BF (breathing frequency); HR (heart rate); RER (respiratory exchange ratio). a significantly different from GXT b significantly different from r3MT c significantly different from 25m 3MT d significantly different from 50m 3MT  p < 0.05  p < 0.01  p < 0.001. Note: verification data was not included in the all-out comparison as this data was used merely to verify the GXT data. https://doi.org/10.1371/journal.pone.0201389.t001 Table 2. Parameter estimates for the _VO2 response for the all-out tests. r3MT 25m 3MT 50m 3MT ANOVA Statistics (F, df, p) _VO2ðBLÞ (ml
kg-1
min-1) 14.30 ± 5.92 16.34 ± 4.77 12.57 ± 5.63 F = 1.792, p = 0.179 A (ml
kg-1
min-1) 37.39 ± 7.59 34.69 ± 7.01 38.15 ± 8.55 F = 0.827, p = 0.444 Asymptote (ml
kg-1
min-1) 51.69 ± 4.68 51.03 ± 4.70 50.72 ± 5.19 F = 0.154, p = 0.858 τ (s) 14.67 ± 1.92 17.42 ± 4.75 14.92 ± 3.38 F = 2.765, p = 0.075 δ (s) 0.54 ± 0.31 0.56 ± 0.32 0.47 ± 0.17 F = 0.439, p = 0.647 Values are mean ± SD. _VO2ðBLÞ (baseline _VO2); τ time constant of the exponential function; δ is the time delay of the exponential function. https://doi.org/10.1371/journal.pone.0201389.t002 O2 kinetics and all-out running PLOS ONE | https://doi.org/10.1371/journal.pone.0201389 August 21, 2018 8 / 15 determined from the raw speed-time data in the absence of a mathematical model, and may therefore capture ‘noise’ inherent in raw data [16, 23]. As such, a comparison of both methods (i.e. the S0-model compared to raw data), yielded a TE of 12.31 m, CV of 7.2% and an ICC α of 0.93, again indicating strong agreement and consistency. The fit of the modeled speed data to the raw speed data showed a very strong fit (r2 = 0.91 and SEE = 0.40 m
s-1). This would imply, at the very least, that the S0-model is justifiably comparable to the ‘traditional’ methods, and may supersede these methods due to additional information gleaned from the model. The S0-model provides a total of 6 parameters (Table 3). The CS is evidenced by the S0 term, whereas the time at which peak speed (Smax) is attained is reflected by the tc term. The magnitude of the decay amplitude, which indicates the decline in speed from peak speed to CS, is indicated by the Ad parameter, and the decay time constant, reflected by τ, represents the amount of time necessary to achieve 63% of Ad. Finally, an approximation of the peak speed attained is reflected by the Smax parameter, which consists of a summation of the S0 and Ad terms (see Fig 1). Fig 2. Oxygen uptake for all 3 all-out tests. Panel A: dotted grey lines represent 95% CI of _VO2max derived from the lab-based GXT; Panel B: indicates the summarized parameter estimates of the mono-exponential equation derived for the 25m 3MT for the squad of athletes. https://doi.org/10.1371/journal.pone.0201389.g002 Table 3. Parameter estimates for the r3MT S0-model. Parameters r3MT S0 (m
s-1) 3.96 ± 0.52 tc (s) 7.67 ± 2.54 Ag (m
s-1) 19.13 ± 7.76 τg (s) 12.01 ± 8.83 Ad (m
s-1) 5.28 ± 0.78 τd (s) 36.95 ± 12.66 Smax (m
s-1) 9.24 ± 0.70 Values are mean ± SD. S0 (critical speed); tc (time delay); Ag (growth amplitude); τg (growth time constant); Ad (decay amplitude); τd (decay time constant); Smax = (S0 +Ad) (peak speed). https://doi.org/10.1371/journal.pone.0201389.t003 O2 kinetics and all-out running PLOS ONE | https://doi.org/10.1371/journal.pone.0201389 August 21, 2018 9 / 15 The link between the _VO2 uptake kinetics and the S0-model is presented in Fig 3A. We investigated a potential link between τ derived from the _VO2 uptake kinetics the CS derived from the r3MT S0 model. The regression analysis (Fig 3B) yielded a moderate, inverse correla- tion (Fig 3B). Discussion A surprising finding of the current study was the lack of a significant difference in _VO2 kinet- ics of all three all-out tests. Whether athletes performed a continuous straight-line sprint with- out directional changes (r3MT), or whether athletes sprinted all-out with 180o turns every 25-m or 50-m respectively, there were no appreciable differences in _VO2max; _V E, BF, or in parameter estimates of the _VO2 kinetic responses such as _VO2ðBLÞ, A, asymptote, τ or δ (Tables 1 and 2). Given that _VO2 kinetics are reflective of muscle metabolic processes [2,3], this would imply that the physiological, and perhaps neuromuscular, loading of linear and shuttle all-out running are similar. This is an important finding in that, for shuttle running, each change of direction requires substantial braking forces followed by propulsive forces, thereby challenging the force and endurance capacities of the leg musculature [40]. Repeated directional changes would therefore increase the aerobic demand of the legs, with a concomi- tantly greater level of muscle deoxygenation and fatigue development [40,41]. Conversely, speeds during shuttle running are typically lower compared to straight line running (due to the directional changes), which in turn would lower muscle deoxygenation and fatigue devel- opment [40]. The present study therefore lends credence to the latter body of evidence in that _VO2 kinetics were not significantly different (eluding to the muscle metabolic processes), and HRmax being lower during shuttle running compared to linear running. Also noteworthy, the _VO2max values achieved in the laboratory were consistently repro- duced in all three field-based tests. Attainment of _VO2max during the r3MT is consistent with existing literature [7,12], although much of this has focused almost exclusively on the cycling 3MT. A novel finding therefore lies in the attainment of _VO2max even during the all-out Fig 3. O2 kinetics and regression analysis. (Panel A) O2 kinetics and speed-time relationship of the r3MT; (Panel B) regression analysis of the O2 kinetics time constant. https://doi.org/10.1371/journal.pone.0201389.g003 O2 kinetics and all-out running PLOS ONE | https://doi.org/10.1371/journal.pone.0201389 August 21, 2018 10 / 15 shuttle tests, perhaps hinting at the robustness of the 3MT methodology in taxing the requisite bioenergetic pathways. Within the all-out bouts, _VO2max was achieved within 90-seconds, specifically ~74 s for the r3MT, ~87 s for the 25m 3MT, and ~75 s for the 50m 3MT, and stayed near constant for the remainder of the tests despite exponential decay in speed that would asymptote in the attainment of CS and a commensurate depletion of D’. These findings are congruent with research by Vanhatalo et al. [42], whereby _VO2max was reached within ~72 s during a 3-min all-out cycling test. The attainment, and maintenance, of _VO2max despite a commensurate exponential decrease in running speed is indicative, in part, of a progressive loss of muscle effi- ciency [42]. This progressive loss has been attributed to a higher phosphate cost of force gener- ation (i.e. mechanistic basis of fatigue) rather than a greater O2 cost of oxidative of ATP production (i.e. energy supply basis of fatigue] [42]. The rapid attainment of _VO2max during all three 3MT’s can be explained by the fact that speeds above CS lead to substantial decreases in arterial pH, as evidenced by the high RER achieved for all 3MT’s, evoking a dramatic increase in _V E primarily due to increases in BF, hence increasing the O2 cost of breathing [3,4,7]. The lack of significant differences in _VO2 uptake kinetics or physiological correlates, such as _VO2max; _V E and BF, between the three all-out tests warrants further investigation (i.e. perhaps investigating the energetics associated with all-out running and/or a muscle-blood profile). From a neurological perspective differ- ences between straight-line running and shuttle running exist [41–44]. All-out sprint exercise requires maximal recruitment of available motor-units thereby requiring increased mitochon- drial respiration; hence the increased _VO2 uptake towards maximum within 90-seconds of all- out effort [4]. Changes in muscle phosphocreatine (PCr) concentrations, which serve as an indicator of muscle metabolic perturbations, decrease exponentially during the first 30-sec- onds of all-out activity, after which the rate of utilization tends to asymptote towards a pseudo ‘steady-state’ [3,4]. The muscle metabolic perturbations therefore reach very high levels during such activities, which may in part explain the fatigue experienced during all-out running. Although this may serve as a viable explanation for the exponential speed decrements, other evidence is suggestive of a mechanistic fatigue basis, rather than an energy supply limitation [42]. The CS derived from the S0-model was comparable to the laboratory-based sΔ50%, a find- ing consistent with previous investigations using all-out cycling [16] and running [22]. When coupled with the field-based _VO2max data, the implications of the present analyses are that the physiological parameters derived from the r3MT were similar to those obtained from the laboratory-based GXT, meaning that the r3MT may serve as a potential surrogate for the GXT as a measure of aerobic fitness for athletic population groups using portable spirometry. A potential link between the parameter estimates derived from the _VO2-kinetics and the S0-model was also established, with a moderate, inverse relationship between τ and the CS attained during the r3MT (Fig 3). Such a result indicates that individuals with lower time con- stants had higher overall CS values; in other words, those who exhibited a faster time to _VO2max could sustain a higher overall CS. The τ is representative of muscle-metabolic pro- cesses, whereby intensity-dependent _VO2 closely mirrors the intramuscular [PCr] kinetics in an inverse relationship (i.e. dramatic decreases in [PCr] and other metabolites drive the respi- ratory response), with the magnitude of the response being dependent on the proximity of the intensity to CS [3,45,46]. The CS parameter attained at the end of an all-out test is reliant on primarily peripheral fatigue related factors such as reduced [PCr], elevated [Pi] and reduced pH [12,46]. It is important to note however, that at intensities above CS, additional factors O2 kinetics and all-out running PLOS ONE | https://doi.org/10.1371/journal.pone.0201389 August 21, 2018 11 / 15 may contribute to fatigue (e.g. central fatigue [impaired muscle activation, efficiency]), which could explain some of the exponential speed decay, and may account for some of the unex- plained variance between the τ and CS parameter comparisons (i.e., the τ parameter from the S0-model may be measuring a unique physiological characteristic) [42]. Future investigators may wish to evaluate experimental interventions (e.g., manipulation of inspired O2, adapta- tions to training) on the τ parameter from the S0-model. The present study presents the first link between a parameter estimate derived from _VO2 kinetics and that derived from the S0-model, tentatively hinting at the utility of the model to provide potentially useful insights to the underlying mechanics of the r3MT (Fig 3B). Future research should therefore focus on probing differences in CS and D’ between the three differ- ent versions of the r3MT, as would investigations pertaining to the energetics of turning. In other words, do turning and turning frequencies tend to tax the body to a greater extent com- pared to straight-line running? It is hypothesized at this stage that the peak speeds attained for each of the three tests would be distinctive, and that the number of turns would be vastly dif- ferent especially for the 25m 3MT compared to the 50m 3MT [47]. It is inferred therefore that differences in speed and turn quantity may be inversely proportional which may explain the overall similar physiological loads between tests obtained in the present study. It is acknowl- edged that all-out running, which is non-constant, may limit the utility of spirometry to detect the underlying physiological loads imposed on the human body. Investigating the kinetic ener- getics of turning may therefore provide insights that differentiate all-out shuttle turning from linear all-out running, whereas at this stage, no discernible differences between the various all- out modalities were apparent. Conclusions The practical findings for the study were four-fold. Firstly, all three 3MT’s yielded _VO2max values similar to laboratory-based assessments implying that the r3MT’s may provide a suit- able estimate of _VO2 uptake within a three-minute time frame, as well as providing additional parameters such as CS and D’. Secondly, no significant differences in _VO2 kinetics could be detected using present methods implying that 25-m or 50-m all-out shuttles could provide a useful alternative for determining _VO2 uptake kinetics within the severe-intensity domain. Thirdly, the introduction of a bi-exponential S0-model may provide useful insights into under- lying mechanics of the r3MT. The model may be useful in comparing the different r3MT’s based on the notion that accurate measurements of speed can be made for the all-out shuttle versions of the r3MT. Finally, the _VO2 time constant, or τ, is inversely related to CS, implying that underlying fatigue mechanisms may be similar; but, further inquiry into the all-out meth- odology is recommended. Author Contributions Conceptualization: Mark Kramer, Robert W. Pettitt. Data curation: Mark Kramer. Formal analysis: Mark Kramer, Robert W. Pettitt. Investigation: Mark Kramer. Methodology: Mark Kramer, Rosa Du Randt, Mark Watson, Robert W. Pettitt. Supervision: Robert W. Pettitt. Visualization: Mark Kramer. O2 kinetics and all-out running PLOS ONE | https://doi.org/10.1371/journal.pone.0201389 August 21, 2018 12 / 15 Writing – original draft: Mark Kramer, Rosa Du Randt, Mark Watson, Robert W. Pettitt. Writing – review & editing: Rosa Du Randt, Mark Watson. References 1. Gaesser GA, Poole DC. The slow component of oxygen uptake kinetics in humans. Exerc Sport Sci Rev. 1996; 24:35–71. PMID: 8744246 2. Jones AM, Poole DC. Oxygen uptake kinetics in sport, exercise and medicine. Taylor and Francis: New York, USA; 2005. PMCID: PMC3880088 3. Whipp BJ, Ward SA, Rossiter HB. Pulmonary O2 uptake during exercise: conflating muscular and car- diovascular responses. Med Sci Sport Exer. 2005; 37(9):1574–1585. PMCID: PMID: 16177611 4. Chidnok W, DiMenna FJ, Bailey SJ, Wilkerson DP, Vanhatalo A, Jones AM. Effects of pacing strategy on work done above critical power during high-intensity exercise. Med Sci Sport Exerc. 2013; 45 (7):1377–1385. https://doi.org/10.1249/MSS.0b013e3182860325 PMID: 23377832 5. Jones AM, Vanhatalo A. The ‘critical power’ concept: application to sports performance with focus on intermittent high-intensity exercise. Sports Med. 2017; 47(1):s65–s78. https://doi.org/10.1007/s40279- 017-0688-0 PMID: 28332113 6. Berger NJA, Jones AM. Pulmonary O2 uptake on-kinetics in sprint- and endurance-trained athletes. Appl Physiol Nutr Metab. 2007; 32:383–393. https://doi.org/10.1139/H06-109 PMID: 17510672 7. Poole DC, Jones AM. Oxygen uptake kinetics. Compr Physiol. 2012; 2(2):933–993. https://doi.org/10. 1002/cphy.c100072 PMID: 23798293 8. Bloomfield J, Polman R, O’Donoghue P. Physical demands of different positions in FA Premier League soccer. J Sport Sci Med. 2007; 6(1):63–70. PMCID: PMC3778701 9. Austin D, Gabbett T, Jenkins D. Repeated high intensity exercise in professional rugby union. J Sports Sci. 2011; 14:1105–1112. https://doi.org/10.1080/02640414.2011.582508 PMID: 21756130 10. Miñao-Espin J, Casa´is L, Lago-Peñas C, Gomez-Ruano MA. High speed running and sprinting profiles of elite soccer players. J Hum Kin. 2017; 58:169–176. https://doi.org/10.1515/hukin-2017-0086 PMID: 28828087 11. Pettitt RWE, Placek AM, Clark IE, Jamnick N, Murray SR. Sensitivity of prescribing high-intensity inter- val training using the critical power concept. Int J Exerc Sci. 2015; 8(3):202–212. https://doi.org/10. 1519/JSC.0b013e31825770d7 12. Poole DC, Burnley M, Vanhatalo A, Rossiter HB, Jones AM. Critical Power: An important fatigue thresh- old in exercise physiology. Med Sci Sport Exerc. 2016; 48(11):2320–2334. https://doi.org/10.1249/ MSS.0000000000000939 PMID: 27031742 13. McClave SA, LeBlanc M, Hawkins SA. Sustainability of critical power determined by a 3-minute all out test in cyclists. J Strength Cond Res. 2011; 25(11):3093–3098. https://doi.org/10.1519/JSC. 0b013e318212dafc PMID: 21993025 14. Hill DW, Halcomb JN, Stevens EC. Oxygen uptake kinetics during severe intensity running and cycling. Eur J Appl Physiol. 2003; 89(6):612–618. https://doi.org/10.1007/s00421-002-0779-x PMID: 12759759 15. Monod H, Scherrer J. The work capacity of a synergic muscle group. Ergonomics. 1965; 8:329–338. https://doi.org/10.1080/00140136508930810 16. Vanhatalo A, Doust JH, Burnley M. Determination of critical power using a 3-minute all-out cycling test. Med Sci Sports Exerc. 2007; 39(3):549–555. https://doi.org/10.1249/mss.0b013e31802dd3e6 PMID: 17473782 17. Carter H, Dekerle J. Metabolic stress at cycling critical power vs. running critical speed. Sci Sports. 2014; 29:51–54. https://doi.org/10.1016/j.scispo.2013.07.014 18. di Prampero P, Dekerle J, Capelli C, Zamparo P. The critical velocity in swimming. Eur J Appl Physiol. 2008; 102(2):165–171. https://doi.org/10.1007/s00421-007-0569-6 PMID: 17901978 19. Tsai MC, Thomas SG. 3-min all-out test in swimming. Int J Sports Physiol Perform. 2017; 12(1):27–35. https://doi.org/10.1123/ijspp.2015-0479 PMID: 27003111 20. Kendall KL, Smith AE, Fukuda DH, Dwyer TR, Stout JR. Critical velocity: a predictor of 2000m rowing performance in NCAA D1 female collegiate rowers. J Sports Sci. 2011; 29(9):945–950. https://doi.org/ 10.1080/02640414.2011.571274 PMID: 21574097 21. Cheng C, Yang Y, Lin H, Lee C, Wang C. Determination of critical power in trained rowers using a three-minute all-out rowing test. Eur J Appl Physiol. 2012; 112:1251–1260. https://doi.org/10.1007/ s00421-011-2081-2 PMID: 21769731 O2 kinetics and all-out running PLOS ONE | https://doi.org/10.1371/journal.pone.0201389 August 21, 2018 13 / 15 22. Pettitt RW, Jamnick N, Clark IE. 3-min all-out exercise test for running. Int J Sports Med. 2012; 33:426– 431. https://doi.org/10.1055/s-0031-1299749 PMID: 22422309 23. Broxterman RM, Ade CJ, Poole DC, Harms CA, Barstow TJ. A single test for the determination of parameters of the speed–time relationship for running. Resp Physiol Neurobiol. 2013; 185(2), 380– 385. https://doi.org/10.1016/j.resp.2012.08.024 PMID: 22981969 24. Clark IE, West BM, Reynolds SK, Murray SR, Pettitt RW. Applying the critical velocity model for an off- season interval training program. J Strength Cond Res. 2013; 27(12):3335–3341. https://doi.org/10. 1519/JSC.0b013e31828f9d87 PMID: 23478481 25. Clarke AC, Presland J, Rattray B, Pyne DB. Critical velocity as a measure of aerobic fitness in women’s rugby sevens. J Sci Med Sport. 2014; 17(1):144–148. https://doi.org/10.1016/j.jsams.2013.03.008 PMID: 23608044 26. Kramer M, Clark IE, Jamnick N, Strom C, Pettitt RW. Normative data for critical speed and D’ for high- level male rugby players. J Strength Cond Res. 2017; forthcoming. https://doi.org/10.1519/JSC. 0000000000001880 PMID: 28542091 27. Burnley M, Doust JH, Vanhatalo A. A 3-min all-out test to determine peak oxygen uptake and the maxi- mal steady state. Med Sci Sport Exerc. 2006; 38(11):1995–2003. https://doi.org/10.1249/01.mss. 0000232024.06114.a6 PMID: 17095935 28. Housh TJ, Cramer JT, Bull AJ, Johnson GO, Housh DJ. The effect of mathematical modeling on critical velocity. Eur J Appl Physiol. 2001; 84:469–475. https://doi.org/10.1007/s004210000375 PMID: 11417437 29. Bosquet L, Duchene A, Lecot F, Dupont G, Leger L. Vmax estimate from three-parameter critical veloc- ity models: validity and impact on 800m running performance prediction. Eur J Appl Physiol. 2006; 97:34–42. https://doi.org/10.1007/s00421-006-0143-7 PMID: 16468059 30. Borg G. Perceived exertion as an indicator of somatic stress. Scand J Rehab Med. 1970; 2:92–98. PMID: 5523831 31. Foster C, Kuffel E, Bradley N, Battista RA, Wright G, Porcari JP, Lucia A, deKoning JJ. _VO2max during successive maximal efforts. Eur J Appl Physiol. 2007; 102:67–72. PMCID: PMC4657417 https://doi. org/10.1007/s00421-007-0565-x PMID: 17891414 32. Midgley AW, Carroll S. The emergence of a verification phase procedure for confirming ‘true’ _VO2max. Scand J Med Sci Sports. 2009; 19:313–322. https://doi.org/10.1111/j.1600-0838.2009.00898.x PMID: 19422662 33. Kirkeberg JM, Dalleck LC, Kamphoff CS, Pettitt RW. Validity of 3 protocols for verifying _VO2max. Int J Sports Med. 2011; 32:266–270. https://doi.org/10.1055/s-0030-1269914 PMID: 21271494 34. Sawyer BJ, Tucker WJ, Bhammar DM, Gaesser GA. Using a verification test for determination of _VO2max in sedentary adults with obesity. J Strength Cond Res. 2015; 29(12):3432–3438. https://doi. org/10.1519/JSC.0000000000001199 PMID: 26382135 35. Beaver WL, Wasserman K, Whipp BJ. A new method for detecting anaerobic threshold by gas exchange. J Appl Phyiol. 1986; 60:2020–2027. https://doi.org/10.1152/jappl.1986.60.6.2020 PMID: 3087938 36. Vanhatalo A, Jones AM. Influence of prior sprint exercise on the parameters of the ‘all-out critical power test’ in men. Exp Physiol. 2009; 94(2):255–263. https://doi.org/10.1113/expphysiol.2008.045229 PMID: 18996948 37. Saari A, Dicks ND, Hartman ME, Pettitt RW. Validation of the 3-min all-out exercise test for shuttle run- ning prescription. J Strength Cond Res. 2017; forthcoming. https://doi.org/10.1519/JSC. 0000000000002120 PMID: 28723814 38. Draper SB, Wood DM. The oxygen uptake response of sprint vs endurance trained runners to severe intensity running. J Sci Med Sport. 2005; 8(2):233–243. PMID: 16075783 39. Hopkins WG. A new view of statistics. Available from: http://sportsci.org/resource/stats [Accessed 2017 Sept 23] 40. Buchheit M, Bishop D, Haydar B, Nakamura FY, Ahmaidi S. Physiological responses to shuttle repeated-sprint running. Int J Sports Med. 2010; 31:402–409. https://doi.org/10.1055/s-0030-1249620 PMID: 20422503 41. Hader K, Mendez-Villanueva A, Ahmaidi S, Williams BK, Buchheit M. Changes of direction during high- intensity intermittent runs: neuromuscular and metabolic responses. BMC Sports Sci Med Rehabil. 2014; 6(2):1–13. https://doi.org/10.1186/2052-1847-6-2 PMID: 24417863 42. Vanhatalo A, Poole DC, DiMenna FJ, Bailey SJ, Jones AM. Muscle fiber recruitment and the slow com- ponent of o2 uptake: constant work rate vs. all-out sprint exercise. Am J Physiol Regul Integr Comp Physiol. 2011; 300:R700–R707. https://doi.org/10.1152/ajpregu.00761.2010 PMID: 21160059 O2 kinetics and all-out running PLOS ONE | https://doi.org/10.1371/journal.pone.0201389 August 21, 2018 14 / 15 43. Bishop D, Girard O, Mendez-Villanueza A. Repeated sprint ability–Part II: Recommendations for train- ing. Sports Med. 2011; 41(9):741–756. https://doi.org/10.2165/11590560-000000000-00000 PMID: 21846163 44. Hader K, Mendez-Villenueva A, Palazzi D, Ahmaidi S, Buchheit M. Metabolic power requirement of change of direction speed in young soccer players: Not all is what it seems. PLoS ONE. 2016; 11(3): e0149839. https://doi.org/10.1371/journal.pone.0149839 PMID: 26930649 45. Hureau TJ, Ducrocq DP, Blain GM. Peripheral and central fatigue development during all-out repeated cycling sprints. Med Sci Sports Exerc. 2016; 48(3):391–401. https://doi.org/10.1249/MSS. 0000000000000800 PMID: 26496420 46. Burnley M, Vanhatalo A, Fulford J, Jones AM. Similar metabolic perturbations during all-out and con- stant force exhaustive exercise in humans: a 31P magnetic resonance spectroscopy study. Exp Phy- siol. 2010; 95(7):798–807. https://doi.org/10.1113/expphysiol.2010.052688 PMID: 20360422 47. Buglione A, di Prampero PE. The energy cost of shuttle running. Eur J App Physiol. 2013; 113:1535– 1543. https://doi.org/10.1007/s00421-012-2580-9 PMID: 23299795 O2 kinetics and all-out running PLOS ONE | https://doi.org/10.1371/journal.pone.0201389 August 21, 2018 15 / 15

Oxygen uptake kinetics and speed-time correlates of modified 3-minute all-out shuttle running in soccer players.

08-21-2018

Kramer, Mark,Du Randt, Rosa,Watson, Mark,Pettitt, Robert W

eng

PMC6912807

Journal of Clinical Medicine Review Pilates Method Improves Cardiorespiratory Fitness: A Systematic Review and Meta-Analysis Rubén Fernández-Rodríguez 1,2 , Celia Álvarez-Bueno 2,3,*, Asunción Ferri-Morales 4 , Ana I. Torres-Costoso 4 , Iván Cavero-Redondo 2 and Vicente Martínez-Vizcaíno 2,5 1 Movi-Fitness S.L, Universidad de Castilla La-Mancha, 16002 Cuenca, Spain; [email protected] 2 Health and Social Care Center, Universidad de Castilla La-Mancha, 16002 Cuenca, Spain; [email protected] (I.C.-R.); [email protected] (V.M.-V.) 3 Universidad Politécnica y Artística del Paraguay, Asunción 001518, Paraguay 4 Faculty of Physiotherapy and Nursing, Universidad de Castilla-La Mancha, 45071 Toledo, Spain; [email protected] (A.F.-M.); [email protected] (A.I.T.-C.) 5 Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Talca 3460000, Chile * Correspondence: [email protected] Received: 1 September 2019; Accepted: 21 October 2019; Published: 23 October 2019



 Abstract: Cardiorespiratory fitness has been postulated as an independent predictor of several chronic diseases. We aimed to estimate the effect of Pilates on improving cardiorespiratory fitness and to explore whether this effect could be modified by a participant’s health condition or by baseline VO2 max levels. We searched databases from inception to September 2019. Data were pooled using a random effects model. The Cochrane risk of bias (RoB 2.0) tool and the Quality Assessment Tool for Quantitative Studies were performed. The primary outcome was cardiorespiratory fitness measured by VO2 max. The search identified 527 potential studies of which 10 studies were included in the systematic review and 9 in the meta-analysis. The meta-analysis showed that Pilates increased VO2 max, with an effect size (ES) = 0.57 (95% CI: 0.15–1; I2 = 63.5%, p = 0.018) for the Pilates group vs. the control and ES = 0.51 (95% CI: 0.26–0.76; I2 = 67%, p = 0.002) for Pilates pre-post effect. The estimates of the pooled ES were similar in both sensitivity and subgroup analyses; however, random-effects meta-regressions based on baseline VO2 max were significant. Pilates improves cardiorespiratory fitness regardless of the population’s health status. Therefore, it may be an efficacious alternative for both the healthy population and patients suffering from specific disorders to achieve evidenced-based results from cardiorespiratory and neuromotor exercises. Keywords: aerobic capacity; cardiac rehabilitation; mind–body; Pilates; cardiorespiratory fitness; VO2 max; adults; prescription of exercise; systematic review; meta-analysis 1. Introduction Strong evidence supports that higher levels of cardiorespiratory fitness (CRF) are associated with a lower risk of cardiovascular morbidity and mortality as well as all-cause mortality [1–3]. In addition, CRF decreases the risk of developing some specific diseases [4], such as chronic obstructive pulmonary disease (COPD) and lung or colorectal cancer [5,6], most of which are associated with a large burden of disease [7]. Furthermore, several studies have shown that higher levels of CRF may attenuate the negative association between CV risk factors and sedentary behaviours independent of physical activity [8–11]. Thus, CRF emerges as an independent predictor for several chronic diseases [12] and as a remarkable overall health status measure in different populations [12]. To improve CRF, current evidence suggests that physical exercise must reach a minimum intensity [13,14] of at least 45% oxygen uptake reserve in the general population and 70%–80% in J. Clin. Med. 2019, 8, 1761; doi:10.3390/jcm8111761 www.mdpi.com/journal/jcm J. Clin. Med. 2019, 8, 1761 2 of 17 athletes [15]. Greater improvements in maximal oxygen uptake (VO2 max) are obtained with vigorous physical exercises when compared with moderate intensity exercises [3]. Moreover, it has been suggested that some types of physical exercises that are not traditionally considered as cardiorespiratory exercises [16,17], such as Pilates, could increase CRF. Pilates has become popular in recent years as a holistic exercise [16] focused on respiration, body control and accuracy of movements. Current evidence suggests positive effects of Pilates on respiratory muscle strength, balance, quality of life and overall physical performance [18–24]. These benefits are observed not only in the healthy population but also in those with specific disorders, such as chronic low back pain [16], multiple sclerosis [25], breast cancer [26] and Parkinson’s disease [27]. The neuromuscular stimulation achieved during Pilates [28] may be of sufficient intensity to improve CRF, providing benefits in VO2 max for individuals with different health conditions [29–33]. Thus, it seems that Pilates exercises include a mind–body component [34] that could have a beneficial impact in different populations. However, evidence for the comparative benefits of Pilates vs. other physical exercises in terms of VO2 max remains inconclusive [22,35], and there are no studies that have evaluated oxygen consumption during Pilates sessions. Therefore, it is difficult to assess whether Pilates exercises reach the minimum intensity needed to improve CRF. We conducted this systematic review and meta-analysis to determine the effectiveness of Pilates on CRF as measured through VO2 max. Moreover, we explored whether the effect of Pilates on CRF could be modified by the participant’s health condition or baseline VO2 max level. 2. Materials and Methods 2.1. Search Strategy and Study Selection The present review and meta-analysis were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [36] and follow the recommendations of the Cochrane Handbook for Systematic Reviews of Interventions [37]. This study was registered through PROSPERO with registration number CRD42019124054. We conducted a systematic literature search in the following databases: MEDLINE (via PubMed), Cochrane Central Register of Controlled Trials (CENTRAL), EMBASE (via Scopus), Web of Science and the Physiotherapy Evidence Database (PEDro), from each database’s inception until September 2019 for studies aimed at determining the effectiveness of the Pilates method on CRF as measured through VO2 max. The search algorithm was conducted using PICO’s strategy (type of studies, participants, interventions, comparators and outcome assessment) and combined Medical Subject Headings, free-terms and matching synonyms of the following related words: (1) population: adults, “middle aged”, “young adult”; (2) intervention: Pilates, mind–body, “exercise movement techniques”; (3) outcome: “cardiorespiratory fitness”, “aerobic fitness”, “aerobic capacity”, “heart rate”; and (4) comparator: control conditions or another physical exercise. In addition, we searched the citations included in the identified publications deemed eligible for our study. The complete search strategy for MEDLINE is presented in Table 1. Table 1. Strategy for MEDLINE. Population Intervention Outcome Adults Pilates “Cardiorespiratory fitness” OR OR OR Middle aged Mind-body “Aerobic fitness” OR OR OR Young adult Exercise Movement Techniques (Mesh) “Aerobic capacity” OR “Heart rate” OR Cardiorespiratory fitness (Mesh) J. Clin. Med. 2019, 8, 1761 3 of 17 2.2. Eligibility Criteria Two initial reviewers (RFR and CAB) independently examined the titles and abstracts of retrieved articles to identify suitable studies. Those studies in which the title and abstract were related to the aim of the present review were included for full text request. We included studies that (1) were conducted as randomised controlled trials (RCTs), non-randomised controlled trials (non-RCTs) or pre-post studies; (2) included a mean participant age ≥18 years; (3) involved participants in any health condition; and (4) were based on at least one exercise intervention described as “Pilates” (mat, machine or both). Studies were excluded if (1) outcome measurements were not reported as VO2 max values, or (2) they were not written in English, Spanish or Portuguese. A third reviewer (VMV) resolved cases of initial reviewer disagreement. Ethical Aspects The present systematic review and meta-analysis were performed by collecting and analysing data from previous studies in which informed consent had been obtained by the respective original investigators. Therefore, this study was exempt from ethics approval. 2.3. Data Extraction and Quality Assessment Two authors (RFR and CAB) independently extracted the following information from the included studies: First author’s name and year of publication; study design; characteristics of the participants included; mean age; sample size and percentage of female subjects; weekly frequency, period and modality of Pilates intervention; supervision of the intervention by a certified instructor; use of a detailed exercise protocol; the reported measurement of VO2 max; the device used to measure VO2 max; and main results. A third reviewer (VMV) resolved cases of author disagreement. The risk of bias of RCTs was assessed using the Cochrane risk-of-bias tool for randomised trials (RoB 2.0) [38], in which five domains were evaluated: Randomization process, deviations from intended interventions, missing outcome data, measurement of the outcome, and selection of the reported result. Each domain was assessed for risk of bias. Studies were graded as (1) “low risk of bias” when a low risk of bias was determined for all domains; (2) “some concerns” if at least one domain was assessed as raising some concerns, but not to be at high risk of bias for any single domain; or (3) “high risk of bias” when high risk of bias was reached for at least one domain or the study judgement included some concerns in multiple domains [38]. For pre-post studies and non-RCTs we used the Quality Assessment Tool for Quantitative Studies [39], in which seven domains were evaluated: Selection bias, study design, confounders, blinding, data collection methods, withdrawals and dropouts. Each domain was considered strong, moderate or weak. Studies were classified as “low risk of bias” if they presented no weak ratings; “moderate risk of bias” when there was at least one weak rating; or “high risk of bias” if there were two or more weak ratings [39]. Risk of bias was independently assessed by two reviewers (RFR and CAB). A third reviewer (VMV) was consulted in case of disagreement. 2.4. Data Analysis Primary data extracted from each study included mean VO2 max, standard deviation of pre-post intervention and sample size. Effect sizes (ES) and related 95% confidence intervals (CIs) were calculated for each study [40]. The Dersimonian and Laird random effects method [41] was used to compute pooled ES estimates and respective 95% CIs. We estimated the pooled ES for the effect of Pilates vs. the control group (CG). The heterogeneity of results across studies was evaluated using the I2 statistic, with I2 values of 0%–30% considered “not important” heterogeneity; >30%–50% representing moderate heterogeneity; >50%–80% representing substantial heterogeneity, and >80%–100% representing considerable heterogeneity. The corresponding p-values and 95%CI for I2 were also considered [42]. J. Clin. Med. 2019, 8, 1761 4 of 17 Finally, we conducted two additional analyses: (i) the pre-post ES of Pilates on the intervention group (Appendix A), and (ii) the mean difference of Pilates vs. CG (Appendix B). For all the analyses, when studies reported data on two intervention groups of Pilates, the effects of both groups were pooled in order to calculate the average effect size. Finally, when studies reported more than one intervention, we only considered the Pilates intervention for conducting this meta-analysis. A sensitivity analysis was conducted by removing each included study to assess the robustness of the summary estimates. Further, subgroup analysis based on participants’ health status and random-effects meta-regression by baseline VO2 max values were conducted to determine their potential effect on the pooled ES estimates. Finally, publication bias was evaluated through visual inspection of funnel plots and Egger’s regression asymmetry test for the assessment of small-study effects [43]. Statistical analyses were performed using StataSE software, version 15 (StataCorp, College Station, TX, USA). 3. Results 3.1. Systematic Review 3.1.1. Study Selection The search strategy identified 527 potential studies for inclusion. Of these, 10 studies were included in the systematic review. Only nine studies were included in the meta-analysis because one study [44] did not provide the required data to calculate ES (Figure 1). J. Clin. Med. 2019, 8, 1761 4 of 17 For all the analyses, when studies reported data on two intervention groups of Pilates, the effects of both groups were pooled in order to calculate the average effect size. Finally, when studies reported more than one intervention, we only considered the Pilates intervention for conducting this meta-analysis. A sensitivity analysis was conducted by removing each included study to assess the robustness of the summary estimates. Further, subgroup analysis based on participants’ health status and random-effects meta-regression by baseline VO2 max values were conducted to determine their potential effect on the pooled ES estimates. Finally, publication bias was evaluated through visual inspection of funnel plots and Egger’s regression asymmetry test for the assessment of small-study effects [43]. Statistical analyses were performed using StataSE software, version 15 (StataCorp, College Station, TX, USA). 3. Results 3.1. Systematic Review 3.1.1. Study Selection The search strategy identified 527 potential studies for inclusion. Of these, 10 studies were included in the systematic review. Only nine studies were included in the meta-analysis because one study [44] did not provide the required data to calculate ES (Figure 1). Figure 1. Flow of the included studies. Figure 1. Flow of the included studies. J. Clin. Med. 2019, 8, 1761 5 of 17 3.1.2. Study and Intervention Characteristics Study and intervention characteristics are summarised in Table 2. Of the 10 included studies, five were RCTs [22,29,33,35,45], two were non-RCTs [31,44] and three were pre-post studies [30,32,46]. All the studies were conducted between 2008 and 2019 and included a total of 332 participants, of which 223 were in Pilates groups (67%) and 109 in control groups (33%). The age of the participants ranged between 18 and 66 years; four studies were conducted in women only [22,31,32,46]. Furthermore, seven studies were conducted in a healthy population, including people described in the primary studies as people without health disorders or specific pathologies [22,31] (four in sedentary individuals [30,32,44,46] and one in trained runners [33]) and three studies were conducted in populations with specific health disorders, including those described in the primary studies as suffering some diseases or specific health disorders such as heart failure [35], chronic stroke [29] and overweight/obesity [45]. In control groups, participants were encouraged to continue with their routine physical activity or to obtain conventional treatment. Among control groups, two studies did not allow structured physical exercise [22,45]; one did not describe the control group activity [31]; and one performed the running conventional program [33] and two studies the conventional rehabilitation programs [29,35]. Concerning the characteristics of the Pilates interventions, the majority of studies consisted of two or three 40–60 min sessions, three times per week, over 8–16 weeks. The mean attendance at the Pilates sessions was 88.2% (80%–100%). Among the 10 studies, six described the Pilates intervention as Pilates mat [22,29,30,33,35,46], three studies combined both modalities (mat and machine) [32,44,45] and one did not report the Pilates modality [31]. Moreover, six studies were conducted by a certified instructor [22,29,30,33,35,45] or with a detailed exercise protocol [29,30,32,34,44,45]. The outcome, VO2 max, was directly measured in nine studies (two with a cycloergometer and seven with a treadmill) [29–33,35,45–47] and one study [22] used an algorithm based on heart rate to estimate VO2 max values. The studies assessed participants at the end of the Pilates intervention, an no study measured VO2 max during the Pilates session. J. Clin. Med. 2019, 8, 1761 6 of 17 Table 2. The included studies. Author Design Participants’ Characteristics Mean Age Sample Size (% Female) Frequency Period Type of Pilates Certified Instructor Detailed Protocol Outcome Measure Outcome Results Wolkodoff 2008 [44] CT Sedentary (healthy) PG = 23–64 n = 20 PG = 14 (85.7%) CG = 6 (83.3%) 40′/3.2xwk 8wks Both NA Yes -Peak VO2 mL/kg/min (Oxycon Mobile) CG change = 0.38 PG change = 6.06 17% of change in PG Guimarães et al., 2012 [35] RCPT Heart failure PG = 46 ± 12 CRG = 44 ± 11 n = 16 PG = 8 (38%) CRG = 8 (19%) 60′/2xwk 16wks Mat Yes Yes -Peak VO2 mLO2/kg/min (Vmax 229 model, SensorMedics, Yorba Linda, CA, USA) PG: improvements in peak VO2 (p = 0.01) Comparing both groups, PG showed greater improvement on peak VO2 (p = 0.02) Gildenhuys et al., 2013 [22] RCT Elderly women (healthy) PG = 66 ± 5 CG = 65 ± 5 n = 50 PG = 25 (100%) CG = 25 (100%) 60′/3xwk 8wks Mat Yes NA -VO2 max mL.kg−1 min−1 (6minWalk; indirect equation) PG did not significantly improve VO2 max (p = 0.247) Lim HS et al., 2016 [29] RCT Chronic stroke PG = 63 ± 8 CG = 62 ± 7 n = 20 PG = 10 (40%) CG = 10 (50%) 3xwk 8wks Mat Yes Yes -VO2 max mL/min -VO2 max per kg (metabolic analyzer: Quark b2, COSMED, Italy 2011) PG: VO2 max and VO2 max per kg increased significantly CG: VO2 max per kg diminished significantly Diamantoula et al., 2016 [46] Q-E Sedentary women (healthy) PG = 26 ± 5 AP = 21.3 ± 2 PG land = 20 (100%) AP = 20 (100%) 2xwk 2years Mat/aqua NA NA -VO2 max mL/min (Ergometer cycle (Amila kh803), following the Astrand-Ryhming test, based on heart rate in submaximal effort) No differences between groups, better VO2 max in total for both groups Tinoco- Fernández et al., 2016 [30] Q-E Sedentary students (healthy) PG = 18–35 n = 45 PG = 45 (78%) 60′/3xwk 10wks Mat Yes Yes -VO2 max L/kg/min -VO2 max L/min (MasterScreen CPX apparatus) Increment in peak VO2 and VO2 max Rodrigues et al., 2016 [32] Q-E Sedentary women (healthy) PG = 23 ± 2 PG = 10 (100%) 45′/2xwk 11wks Both NA Yes -VO2 max mL.kg−1 min−1 portable metabolic system (VO2000®, MedGraphics®, St. Paul, MN, USA) Peak VO2 tended to increase, but the differences were not statistically significant Mikalacki et al., 2017 [31] CT Adult women (healthy) PG = 48 ± 7 CG = 47 ± 7 n = 64 PG = 36 (100%) CG = 28 (100%) 55–60′/2xwk NA NA NA NA -Relative VO2 max -Absolute VO2 max (Medisoft, model 870c) PG: significant increase on relative VO2max, absolute VO2 max -CG: not significant changes Finatto et al., 2018 [33] RCT Trained runners (healthy) PG = 18 ± 1 CG = 18 ± 1 n = 32 PG = 15–13 NA % CG = 16–15 60′/1xwk 12wks Mat Yes NA -VO2 max mL.kg−1.min−1 (VO2000 (Medgraphics, Ann Arbor, USA) PG: significantly higher values on VO2 max (p < 0.001) Rayes et al., 2019 [45] RCT Overweight/obesePG = 55.9 ± 6.6 CG = 45.5 ± 9.3 n = 60 NA% PG = 22 CG = 25/17 60′/3xwk 8wks Both Yes Yes -VO2 max (mL/kg/min) (motorized treadmill; Inbrasport, ATL, Porto Alegre, Brazil) PG: Significant improvement on VO2 max CG: not significant changes CT: controlled trial; RCT: randomised controlled trial; RCPT: randomised controlled pilot trial; Q-E: quasi-experimental; PG: Pilates group; CG: control group; AP: Aqua-Pilates group; NA: not available; wk: week; VO2 max: maximal oxygen uptake. J. Clin. Med. 2019, 8, 1761 7 of 17 3.1.3. Quality Assessment and Risk of Bias Five RCTs were assessed according to the RoB 2.0 tool [38], of which two were assessed as “low risk of bias” and three as “some concerns” (Figure 2). The remaining five studies (non-RCTs and pre-post studies) were assessed according to the Quality Assessment Tool for Quantitative Studies [39], of which two were classified as “low risk of bias”, two as “moderate risk of bias” and one as high risk of bias (Figure 3). J. Clin. Med. 2019, 8, 1761 7 of 17 3.1.3. Quality Assessment and Risk of Bias Five RCTs were assessed according to the RoB 2.0 tool [38], of which two were assessed as “low risk of bias” and three as “some concerns” (Figure 2). The remaining five studies (non-RCTs and pre- post studies) were assessed according to the Quality Assessment Tool for Quantitative Studies [39], of which two were classified as “low risk of bias”, two as “moderate risk of bias” and one as high risk of bias (Figure 3). Figure 2. Quality assessment for RCT (RoB 2.0). Figure 3. Quality assessment for non-RCT. Figure 2. Quality assessment for RCT (RoB 2.0). J. Clin. Med. 2019, 8, 1761 7 of 17 3.1.3. Quality Assessment and Risk of Bias Five RCTs were assessed according to the RoB 2.0 tool [38], of which two were assessed as “low risk of bias” and three as “some concerns” (Figure 2). The remaining five studies (non-RCTs and pre- post studies) were assessed according to the Quality Assessment Tool for Quantitative Studies [39], of which two were classified as “low risk of bias”, two as “moderate risk of bias” and one as high risk of bias (Figure 3). Figure 2. Quality assessment for RCT (RoB 2.0). Figure 3. Quality assessment for non-RCT. Figure 3. Quality assessment for non-RCT. J. Clin. Med. 2019, 8, 1761 8 of 17 3.2. Data Synthesis 3.2.1. Meta-Analysis The pooled ES for the effect of Pilates vs. CG on CRF was 0.57 (95% CI: 0.15–1.00; I2 = 63.5%, p = 0.02) (Figure 4) and for Pilates pre-post ES was 0.51 (95% CI: 0.26–0.76; I2 = 67%, p < 0.01) (Figure A1, Appendix A). The mean difference analysis of Pilates vs. CG was 2.77 (95% CI: 1.12–4.42; I2 = 33.4%, p = 0.19) (Figure A3, Appendix B). J. Clin. Med. 2019, 8, 1761 8 of 17 3.2. Data Synthesis 3.2.1. Meta-Analysis The pooled ES for the effect of Pilates vs. CG on CRF was 0.57 (95% CI: 0.15–1.00; I2 = 63.5%, p = 0.02) (Figure 4) and for Pilates pre-post ES was 0.51 (95% CI: 0.26–0.76; I2 = 67%, p < 0.01) (Figure A1, Appendix A). The mean difference analysis of Pilates vs. CG was 2.77 (95% CI: 1.12–4.42; I2 = 33.4%, p = 0.19) (Figure B1, Appendix B). Figure 4. Meta-analysis for Pilates Method vs. control group (pooled ES analysis). 3.2.2. Sensitivity and Meta-Regression Analyses After removing studies from the analyses individually, none substantially modified the pooled ES estimate in Pilates vs. CG (Table 3), Pilates pre-post effect on intervention (Table A1, Appendix A) and mean difference of Pilates vs. CG. (Table B1, Appendix B). The subgroup analyses by participants’ health conditions modified the pooled ES estimate for Pilates vs. CG (Table 4) and mean difference of Pilates vs. CG (Table B2, Appendix B), but not for Pilates pre-post effect on intervention (Table A2, Appendix A). Table 3. Sensitivity analyses. Pilates Method vs. Control Author, Year ES LL UL I2 Guimarães et al., 2012 [35] 0.6 0.12 1.08 70.8 Gildenhuys et al., 2013 [22] 0.69 0.20 1.18 64.4 Lim HS et al., 2016 [29] 0.62 0.10 1.14 70.7 Mikalacki et al., 2017 [31] 0.62 0.03 1.22 70.8 Finatto et al., 2018 [33] 0.4 0.16 0.64 0 Rossell-Rayes et al., 2019 [45] 0.63 0.12 1.15 70.4 ES: Effect size; LL: Lower limit; UL: Upper limit. Figure 4. Meta-analysis for Pilates Method vs. control group (pooled ES analysis). 3.2.2. Sensitivity and Meta-Regression Analyses After removing studies from the analyses individually, none substantially modified the pooled ES estimate in Pilates vs. CG (Table 3), Pilates pre-post effect on intervention (Table A1, Appendix A) and mean difference of Pilates vs. CG. (Table A5, Appendix B). The subgroup analyses by participants’ health conditions modified the pooled ES estimate for Pilates vs. CG (Table 4) and mean difference of Pilates vs. CG (Table A6, Appendix B), but not for Pilates pre-post effect on intervention (Table A2, Appendix A). Table 3. Sensitivity analyses. Pilates Method vs. Control Author, Year ES LL UL I2 Guimarães et al., 2012 [35] 0.6 0.12 1.08 70.8 Gildenhuys et al., 2013 [22] 0.69 0.20 1.18 64.4 Lim HS et al., 2016 [29] 0.62 0.10 1.14 70.7 Mikalacki et al., 2017 [31] 0.62 0.03 1.22 70.8 Finatto et al., 2018 [33] 0.4 0.16 0.64 0 Rossell-Rayes et al., 2019 [45] 0.63 0.12 1.15 70.4 ES: Effect size; LL: Lower limit; UL: Upper limit. J. Clin. Med. 2019, 8, 1761 9 of 17 Table 4. Subgroup analyses by participants’ health status. Pilates Method vs. Control ES LL UL I2 Healthy 0.80 −0.05 1.65 85 Unhealthy 0.40 −0.01 0.81 0 ES: Effect size; LL: Lower limit; UL: Upper limit. The random-effects meta-regression models by VO2 max baseline levels were significant for Pilates vs. CG (p = 0.03) (Table 5) and for Pilates pre-post effect on intervention (p = 0.05) (Table A3, Appendix A) but not for mean difference of Pilates vs. CG (p = 0.08) (Table A7, Appendix B). Table 5. Meta-regression analyses by VO2 max baseline values. Coefficient p Pilates Method vs. control 0.04 0.03 * VO2 max: Maximal oxygen uptake (mL/kg/min); * Significant at p ≤ 0.05. 3.2.3. Publication Bias A significant publication bias was not found in Pilates vs. CG studies, as evidenced by both the funnel plot (Figure 5) asymmetry and an Egger’s test (p = 0.465) (Table 6), nor in the mean difference of Pilates vs. CG by funnel plot asymmetry (Figure A4, Appendix B) and an Egger’s test (p = 0.69) (Table A8, Appendix B). However, in Pilates pre-post effect studies publication bias was found (p = 0.07) (Table A4, Appendix A). J. Clin. Med. 2019, 8, 1761 9 of 17 Table 4. Subgroup analyses by participants’ health status. Pilates Method vs. Control ES LL UL I2 Healthy 0.80 −0.05 1.65 85 Unhealthy 0.40 −0.01 0.81 0 ES: Effect size; LL: Lower limit; UL: Upper limit. The random-effects meta-regression models by VO2 max baseline levels were significant for Pilates vs. CG (p = 0.03) (Table 5) and for Pilates pre-post effect on intervention (p = 0.05) (Table A3, Appendix A) but not for mean difference of Pilates vs. CG (p = 0.08) (Table B3, Appendix B). Table 5. Meta-regression analyses by VO2 max baseline values. Coefficient p Pilates Method vs. control 0.04 0.03 * VO2 max: Maximal oxygen uptake (ml/kg/min); * Significant at p ≤ 0.05. 3.2.3. Publication Bias A significant publication bias was not found in Pilates vs. CG studies, as evidenced by both the funnel plot (Figure 5) asymmetry and an Egger’s test (p = 0.465) (Table 6), nor in the mean difference of Pilates vs. CG by funnel plot asymmetry (Figure B2, Appendix B) and an Egger’s test (p = 0.69) (Table B4, Appendix B). However, in Pilates pre-post effect studies publication bias was found (p = 0.07) (Table A4, Appendix A). Table 6. Publication bias by Egger’s test. Coefficient p-Value Pilates method vs. control group 1.64 0.47 Figure 5. Funnel plot for Pilates vs control group. Figure 5. Funnel plot for Pilates vs control group. Table 6. Publication bias by Egger’s test. Coefficient p-Value Pilates method vs. control group 1.64 0.47 J. Clin. Med. 2019, 8, 1761 10 of 17 4. Discussion This systematic review and meta-analysis were performed to determine the effectiveness of Pilates interventions for improvement of CRF measured through VO2 max. Our findings highlight that Pilates is an alternative exercise to improve VO2 max values. Furthermore, our results were substantially modified by participants’ health conditions for Pilates vs. control group analyses but not for Pilates pre-post effect on intervention; otherwise, baseline VO2 max values could influence CRF improvement. Although some studies [22,35,45,46] have failed to show significant changes in CRF after Pilates intervention, no study has reported negative effects of Pilates on the CRF levels, and therefore the positive clinical implications should not be underestimated. Additionally, more significant benefits of Pilates on CRF were achieved when other activities, such as running, were included [33] and this could be explained through a synergistic relationship between these training methods. Evidence suggests that people with lower levels of CRF are more sensitive to improvement of this parameter [47]. Accordingly, in our study estimates of pooled ES were higher in those studies in which participants had lower baseline CRF levels, such as people with health disorders. Conversely, our meta-regression analyses suggested that higher levels of VO2 max at baseline are related with higher ES of the Pilates intervention. These findings should cautiously be interpreted since they may indicate that the effect of Pilates in those studies with higher VO2 max levels at baseline were distortedly overestimated. Probably these biased estimates were a consequence of reporting results in absolute terms (change in VO2 max in ml) instead of in relative terms (percentage of increase in VO2 max), but could have clinical implications suggesting that Pilates exercise is an effective rehabilitation strategy for several disorders, including some cardiac pathologies. Moreover, Pilates exercise showed high compliance levels indicating that it may be better tolerated than the aerobic exercises typically employed in rehabilitation programs. Three potential sources of improvement may explain the positive impact of Pilates intervention on CRF: Strengthening of the lumbopelvic region, increased flexibility of the ribcage and breathing exercises. First, the strengthening of lumbopelvic and core muscles induced by Pilates may produce a more efficient movement pattern in upper and lower limbs, as well as greater strength in expiratory muscles [19,33]. Second, due to the flexibility improvement, a more efficient mobility pattern of the ribcage may be achieved [30]. Finally, the breathing techniques adopted during Pilates training may increase lung capacity [29] and functionality of intercostal muscles [17]. On these bases, improved ventilation efficiency would be achieved, resulting in a higher flow of oxygenated blood into muscle tissues [35], enhanced local circulation [19,30] and muscle oxidative capacity [45], and less energy waste. Therefore, Pilates could reach the minimum intensity required to improve CRF [13,14] although no published study has verified this. Our systematic review and meta-analysis present some limitations that must be stated. First, it was not possible to blind Pilates interventions and some of the included studies did not provide details about the randomisation sequence or allocation concealment. Second, considerable levels of heterogeneity were observed in the analyses, and we cannot omit this fact. Third, the heterogeneity of participants’ health conditions and the dose and intensity of the Pilates intervention could potentially affect our estimates. Fourth, significant publication bias was evidenced by Egger’s test and unpublished results could modify the findings of the present meta-analysis. Fifth, it should be highlighted the difficulty to comply with a full training program by very busy professionals, thus, this concern should not be neglected in the implementation of our results. Sixth, rarely it is possible to measure VO2 max directly in clinical settings, thus other more applicable procedures for indirect measurement of VO2 max should be used. Seventh, although subgroup analyses by participants’ health conditions modified the ES estimates, these results should be cautiously considered due to the lack of studies in each subgroup. Finally, due to the lack of long-term assessments, we could not determine whether the benefits to CRF measured through VO2 max are preserved over time. Therefore, our results should be cautiously considered. J. Clin. Med. 2019, 8, 1761 11 of 17 5. Conclusions In summary, our results support Pilates as an effective intervention to improve CRF in both healthy people and individuals with disorders related to aerobic capacity. Despite this, further studies should be conducted, including short- and long-term measurements to determine the intensity level reached by VO2 max during Pilates intervention and whether CRF improvement is preserved over time. Author Contributions: Conceptualization, R.F.-R. and I.C.-R.; methodology, R.F.-R., C.Á.-B. and I.C.-R.; software, I.C.-R.; validation, A.I.T.-C. and A.F.-M.; formal analysis, R.F.-R.; investigation, R.F.-R. and A.I.T.-C.; resources, R.F.-R. and A.F.-M.; data curation, C.Á.-B. and V.M.-V.; writing—original draft preparation, R.F.-R. and C.Á.-B.; writing—review and editing, V.M.-V.; visualization, A.I.T.-C. and A.F.-M.; supervision, V.M.-V. and C.Á.-B. Funding: This study was funded by Apadrina la Ciencia. Acknowledgments: We are grateful to Movi-fitness, FSE and JCCM for the fellowship contract of R.F.-R. Conflicts of Interest: The authors declare no conflict of interest. Appendix A Analyses for Pilates pre-post effect on intervention (A1–A6: Meta-analysis, sensitivity analysis, subgroup analysis, meta-regression, publication bias and funnel plot). Appendix Figure A1. Meta-analysis. J. Clin. Med. 2019, 8, 1761 11 of 17 5. Conclusions In summary, our results support Pilates as an effective intervention to improve CRF in both healthy people and individuals with disorders related to aerobic capacity. Despite this, further studies should be conducted, including short- and long-term measurements to determine the intensity level reached by VO2 max during Pilates intervention and whether CRF improvement is preserved over time. Author Contributions: Conceptualization, R.F.-R. and I.C.-R.; methodology, R.F.-R., C.Á.-B. and I.C.-R.; software, I.C.-R.; validation, A.I.T.-C. and A.F.-M.; formal analysis, R.F.-R.; investigation, R.F.-R. and A.I.T.-C.; resources, R.F.-R. and A.F.-M.; data curation, C.Á.-B. and V.M.-V.; writing—original draft preparation, R.F.-R. and C.Á.-B.; writing—review and editing, V.M.-V.; visualization, A.I.T.-C. and A.F.-M.; supervision, V.M.-V. and C.Á.-B. Funding: This study was funded by Apadrina la Ciencia. Acknowledgments: We are grateful to Movi-fitness, FSE and JCCM for the fellowship contract of R.F.-R. Conflicts of Interest: The authors declare no conflict of interest. Appendix A Analyses for Pilates pre-post effect on intervention (A1–A6: Meta-analysis, sensitivity analysis, subgroup analysis, meta-regression, publication bias and funnel plot). Appendix Figure A1. Meta-analysis. Figure A1. Meta-analysis for Pilates pre-post effect on intervention. Appendix Table A1. Sensitivity analysis. Table A1. Sensitivity analyses for Pilates pre-post effect on intervention. Pilates Method Intervention Author, Year ES LL UL I2 Guimarães et al., 2012 [35] 0.52 0.25 0.79 71.1 Gildenhuys et al., 2013 [22] 0.57 0.29 0.84 69.1 Diamantoula et al., 2016 [46] 0.53 0.25 0.80 71.1 Lim HS et al., 2016 [29] 0.53 0.24 0.83 71 Figure A1. Meta-analysis for Pilates pre-post effect on intervention. Appendix Table A1. Sensitivity analysis. Table A1. Sensitivity analyses for Pilates pre-post effect on intervention. Pilates Method Intervention Author, Year ES LL UL I2 Guimarães et al., 2012 [35] 0.52 0.25 0.79 71.1 Gildenhuys et al., 2013 [22] 0.57 0.29 0.84 69.1 Diamantoula et al., 2016 [46] 0.53 0.25 0.80 71.1 Lim HS et al., 2016 [29] 0.53 0.24 0.83 71 Rodrigues et al., 2016 [32] 0.43 0.20 0.65 60.1 Tinoco-Fernández et al., 2016 [30] 0.56 0.24 0.89 71.1 Mikalacki et al., 2017 [31] 0.57 0.26 0.89 70.5 J. Clin. Med. 2019, 8, 1761 12 of 17 Table A1. Cont. Pilates Method Intervention Author, Year ES LL UL I2 Finatto et al., 2018 [33] 0.39 0.24 0.54 19.6 Rossell-Rayes et al., 2019 [45] 0.55 0.27 0.83 70.6 ES: Effect Size; LL: Lower Limit; UL: Upper limit. Appendix Table A2. Subgroup analysis. Table A2. Subgroup analyses by participants’ health status. Pilates Method Intervention ES LL UL I2 Healthy 0.64 0.26 1.02 78.9 Unhealthy 0.39 0.14 0.64 0 ES: Effect Size; LL: Lower Limit; UL: Upper limit. Appendix Table A3. Random effect meta-regression by baseline VO2 max values. Table A3. Meta-regression analysis by VO2 max baseline values. Coefficient p-Value Pilates Method intervention 0.05 0.05 * VO2 max: Maximal oxygen uptake (mL/kg/min); * Significant at p ≤ 0.05. Appendix Table A4. Publication bias (table). Table A4. Publication bias by Egger’s test. Coefficient p-Value Pilates Method intervention 2.19 0.07 * * Significant at p < 0.1. Appendix Figure A2. Funnel plot (figure). J. Clin. Med. 2019, 8, 1761 12 of 17 Rodrigues et al., 2016 [32] 0.43 0.20 0.65 60.1 Tinoco-Fernández et al., 2016 [30] 0.56 0.24 0.89 71.1 Mikalacki et al., 2017 [31] 0.57 0.26 0.89 70.5 Finatto et al., 2018 [33] 0.39 0.24 0.54 19.6 Rossell-Rayes et al., 2019 [45] 0.55 0.27 0.83 70.6 ES: Effect Size; LL: Lower Limit; UL: Upper limit. Appendix Table A2. Subgroup analysis. Table A2. Subgroup analyses by participants’ health status. Pilates Method Intervention ES LL UL I2 Healthy 0.64 0.26 1.02 78.9 Unhealthy 0.39 0.14 0.64 0 ES: Effect Size; LL: Lower Limit; UL: Upper limit. Appendix Table A3. Random effect meta-regression by baseline VO2 max values. Table A3. Meta-regression analysis by VO2 max baseline values. Coefficient p-Value Pilates Method intervention 0.05 0.05 * VO2 max: Maximal oxygen uptake (ml/kg/min); * Significant at p ≤ 0.05. Appendix Table A4. Publication bias (table). Table A4. Publication bias by Egger’s test. Coefficient p-Value Pilates Method intervention 2.19 0.07 * * Significant at p < 0.1. Appendix Figure A2. Funnel plot (figure). Figure A2. Funnel plot for Pilates pre-post effect on intervention. Figure A2. Funnel plot for Pilates pre-post effect on intervention. J. Clin. Med. 2019, 8, 1761 13 of 17 Appendix B. Mean Difference Analyses for Pilates vs. CG (B1–B6: Meta-Analysis, Sensitivity Analysis, Subgroup Analysis, Meta-Regression, Publication Bias and Funnel Plot) Appendix Figure A3. Meta-analysis. J. Clin. Med. 2019, 8, 1761 13 of 17 Appendix B. Mean difference analyses for Pilates vs. CG (B1–B6: Meta-analysis, sensitivity analysis, subgroup analysis, meta-regression, publication bias and funnel plot). Appendix Figure B1. Meta-analysis. Figure B1. Meta-analysis for Pilates vs. control group (mean difference). Appendix Table B1. Sensitivity analysis. Table B1. Sensitivity analyses for Pilates vs. control group. Pilates vs. Control Group Author, Year MD 95% CI I2 Guimarães et al., 2012 [35] 2.24 0.89, 4.58 46.7 Gildenhuys et al., 2013 [22] 3.88 2.53, 5.24 0.0 Lim HS et al., 2016 [29] 2.77 0.85, 4.69 46.5 Mikalacki et al., 2017 [31] 2.59 0.48, 4.69 46.2 Finatto et al., 2018 [33] 1.71 0.09, 3.33 0.0 Rossell-Rayes et al., 2019 [45] 2.25 0.82, 4.67 46.7 MD: Mean difference; CI: Confidence interval. Appendix Table B2. Subgroup analysis. Table B2. Subgroup analyses by participants’ health status. Pilates vs. Control Group MD 95% CI Healthy 2.77 1.12, 4.42 Unhealthy 2.67 −0.61, 5.95 MD: Mean difference; CI: Confidence interval. Appendix Table B3. Random effect meta-regression by baseline VO2 max values. Figure A3. Meta-analysis for Pilates vs. control group (mean difference). Appendix Table A5. Sensitivity analysis. Table A5. Sensitivity analyses for Pilates vs. control group. Pilates vs. Control Group Author, Year MD 95% CI I2 Guimarães et al., 2012 [35] 2.24 0.89, 4.58 46.7 Gildenhuys et al., 2013 [22] 3.88 2.53, 5.24 0.0 Lim HS et al., 2016 [29] 2.77 0.85, 4.69 46.5 Mikalacki et al., 2017 [31] 2.59 0.48, 4.69 46.2 Finatto et al., 2018 [33] 1.71 0.09, 3.33 0.0 Rossell-Rayes et al., 2019 [45] 2.25 0.82, 4.67 46.7 MD: Mean difference; CI: Confidence interval. Appendix Table A6. Subgroup analysis. Table A6. Subgroup analyses by participants’ health status. Pilates vs. Control Group MD 95% CI Healthy 2.77 1.12, 4.42 Unhealthy 2.67 −0.61, 5.95 MD: Mean difference; CI: Confidence interval. Appendix Table A7. Random effect meta-regression by baseline VO2 max values. J. Clin. Med. 2019, 8, 1761 14 of 17 Table A7. Meta-regression analysis by VO2 max baseline values. Coefficient p-Value Pilates vs. control group 0.09 0.08 VO2 max: Maximal oxygen uptake (mL/kg/min). Appendix Table A8. Publication bias (table). Table A8. Publication bias by Egger’s test. Coefficient p-Value Pilates vs. control group −0.50 0.69 Appendix Figure A4. Funnel plot (figure). J. Clin. Med. 2019, 8, 1761 14 of 17 Table B3. Meta-regression analysis by VO2 max baseline values. Coefficient p-Value Pilates vs. control group 0.09 0.08 VO2 max: Maximal oxygen uptake (mL/kg/min). Appendix Table B4. Publication bias (table). Table B4. Publication bias by Egger’s test. Coefficient p-Value Pilates vs. control group −0.50 0.69 Appendix Figure B2. Funnel plot (figure). Figure B2. Funnel plot for Pilates vs. control group (mean difference). References 1. Blair, S.N.; Kohl, H., III; Paffenbarger, R.J.; Clark, D.G.; Cooper, K.H.; Gibbons, L.W. Physical Fitness and All-Cause Mortality. Jama 1989, 262, 2395–2401, doi:10.1001/jama.262.17.2395. 2. Blair, J.B.; Kampert, H.W.; Kohl, C.E., III; Barlow, C.A.; Macera, R.S.; Paffenbarger, L.W., Jr.; Gibbons, S.N. The Influences of cardiovascular fitness and other precursers on cardiovascular disease and all-cause mortality in men and women. Aerobics center longitudinal study. J. Am. Med. Assoc. 1996, 276, 205–210. 3. Garber, C.E.; Blissmer, B.; Deschenes, M.R. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: Guidance for prescribing exercise. Med. Sci. Sports Exerc. 2011, 43, 1334–1359, doi:10.1249/MSS.0b013e318213fefb. 4. Croci, I.; Coombes, J.S.; Sandbakk, S.B. Non-alcoholic fatty liver disease: Prevalence and all-cause mortality according to sedentary behaviour and cardiorespiratory fitness. The HUNT Study. Prog. Cardiovasc. Dis. 2019, 62, 127–134, doi:10.1016/J.PCAD.2019.01.005. 5. Pozuelo-Carrascosa, D.P.; Alvarez-Bueno, C.; Cavero-Redondo, I.; Morais, S.; Lee, I.M.; Martínez-Vizcaíno, V. Cardiorespiratory fitness and site-specific risk of cancer in men: A systematic review and meta-analysis. Eur. J. Cancer 2019, 113, 58–68, doi:10.1016/j.ejca.2019.03.008. Figure A4. Funnel plot for Pilates vs. control group (mean difference). References 1. Blair, S.N.; Kohl, H., III; Paffenbarger, R.J.; Clark, D.G.; Cooper, K.H.; Gibbons, L.W. Physical Fitness and All-Cause Mortality. Jama 1989, 262, 2395–2401. [CrossRef] [PubMed] 2. Blair, J.B.; Kampert, H.W.; Kohl, C.E., III; Barlow, C.A.; Macera, R.S.; Paffenbarger, L.W., Jr.; Gibbons, S.N. The Influences of cardiovascular fitness and other precursers on cardiovascular disease and all-cause mortality in men and women. Aerobics center longitudinal study. J. Am. Med. Assoc. 1996, 276, 205–210. [CrossRef] 3. Garber, C.E.; Blissmer, B.; Deschenes, M.R. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: Guidance for prescribing exercise. Med. Sci. Sports Exerc. 2011, 43, 1334–1359. [CrossRef] [PubMed] 4. Croci, I.; Coombes, J.S.; Sandbakk, S.B. Non-alcoholic fatty liver disease: Prevalence and all-cause mortality according to sedentary behaviour and cardiorespiratory fitness. The HUNT Study. Prog. Cardiovasc. Dis. 2019, 62, 127–134. [CrossRef] J. Clin. Med. 2019, 8, 1761 15 of 17 5. Pozuelo-Carrascosa, D.P.; Alvarez-Bueno, C.; Cavero-Redondo, I.; Morais, S.; Lee, I.M.; Martínez-Vizcaíno, V. Cardiorespiratory fitness and site-specific risk of cancer in men: A systematic review and meta-analysis. Eur. J. Cancer 2019, 113, 58–68. [CrossRef] 6. Steell, L.; Ho, F.K.; Sillars, A. Dose-response associations of cardiorespiratory fitness with all-cause mortality and incidence and mortality of cancer and cardiovascular and respiratory diseases: The UK Biobank cohort study. Br. J. Sports Med. 2019, 53, 1371–1378. [CrossRef] 7. Mathers, C.D.; Loncar, D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 2006, 3, 2011–2030. [CrossRef] 8. Davidson, T.; Vainshelboim, B.; Kokkinos, P.; Myers, J.; Ross, R. Cardiorespiratory fitness versus physical activity as predictors of all-cause mortality in men. Am. Heart J. 2018, 196, 156–162. [CrossRef] 9. Lavie, C.J.; Wisløff, U.; Després, J.-P. Importance of Assessing Cardiorespiratory Fitness in Clinical Practice: A Case for Fitness as a Clinical Vital Sign: A Scientific Statement From the American Heart Association. Circulation 2016, 134, e653–e659. [CrossRef] 10. Kodama, S. Cardiorespiratory Fitness as a Quantitative Predictor of All-Cause Mortality and Cardiovascular Events. J. Am. Med. Assoc. 2009, 301, 2024–2035. [CrossRef] 11. Lee, C.D.; Blair, S.N.; Jackson, A.S. Cardiorespiratory fitness, body composition, and all-cause and cardiovascular disease mortality in men. Am. J. Clin. Nutr. 1999, 69, 373–380. [CrossRef] [PubMed] 12. Harber, M.P.; Kaminsky, L.A.; Arena, R. Impact of Cardiorespiratory Fitness on All-Cause and Disease-Specific Mortality: Advances Since 2009. Prog. Cardiovasc. Dis. 2017, 60, 11–20. [CrossRef] [PubMed] 13. American College of Sports Medicine. American College of Sports Medicine Position Stand. Med. Sci. Sport Exerc. 1998, 30, 975–991. [CrossRef] 14. Pescatello, L.; Arena, R.; Riebe, D.; Thompson, P. ACSM’s Guidelines for Exercsie Testing and Prescription, 9th ed.; Wolters Kluwer Health: Philadelphia, PA, USA, 2014; pp. 269–299. 15. Swain, D.P.; Franklin, B.A. VO(2) reserve and the minimal intensity for improving cardiorespiratory fitness. Med. Sci. Sport Exerc. 2002, 34, 152–157. [CrossRef] [PubMed] 16. Eliks, M.; Zgorzalewicz-Stachowiak, M.; Ze´nczak-Praga, K. Application of Pilates-based exercises in the treatment of chronic non-specific low back pain: State of the art. Postgrad. Med. J. 2019, 95, 41–45. [CrossRef] [PubMed] 17. Lau, C.; Yu, R.; Woo, J. Effects of a 12-Week hatha yoga intervention on cardiorespiratory endurance, muscular strength and endurance, and flexibility in Hong Kong Chinese Adults: A controlled clinical trial. Evid. Based Complement. Altern Med. 2015, 2015, 958727. [CrossRef] [PubMed] 18. De Souza, R.O.B.; de Faria Marcon, L.; de Arruda, A.S.F.; Pontes Junior, F.L.; de Melo, R.C. Effects of Mat Pilates on Physical Functional Performance of Older Adults. Am. J. Phys. Med. Rehabil. 2018, 97, 414–425. [CrossRef] 19. De Alvarenga, G.M.; Charkovski, S.A.; Dos Santos, L.K.; da Silva, M.A.B.; Tomaz, G.O.; Gamba, H.R. The influence of inspiratory muscle training combined with the Pilates method on lung function in elderly women: A randomized controlled trial. Clinics 2018, 73, e356. [CrossRef] 20. Sarmento, L.A.; Pinto, J.S.S.T.; da Silva, A.P.P.; Cabral, C.M.N.; Chiavegato, L.D. Effect of conventional physical therapy and Pilates in functionality, respiratory muscle strength and ability to exercise in hospitalized chronic renal patients: A randomized controlled trial. Clin Rehabil. 2017, 31, 508–520. [CrossRef] 21. Tozim, B.M.; Navega, M.T. Effect of pilates method on inspiratory and expiratory muscle strength in the elderly. Rev. Bras. Cineantropometria Desempenho Hum. 2018, 20, 1–9. [CrossRef] 22. Gildenhuys, G.; Fourie, M.; Shaw, I.; Shaw, B.; Toriola, A.; Witthuhn, J. Evaluation of Pilates training on agility, functional mobility and cardiorespiratory fitness in elderly women: Health and fitness. Afr. J. Phys. Act. Health Sci. 2013, 19, 505–512. 23. Irez, G.B.; Ozdemir, R.A.; Evin, R.; Irez, S.G.; Korkusuz, F. Integrating pilates exercise into an exercise program for 65+ year-old women to reduce falls. J. Sport Sci. Med. 2011, 10, 105–111. 24. De Siqueira Rodrigues, B.G.; Ali Cader, S.; Bento Torres, N.V.O.; de Oliveira, E.M.; Martin Dantas, E.H. Pilates method in personal autonomy, static balance and quality of life of elderly females. J. Bodyw. Mov. Ther. 2010, 14, 195–202. [CrossRef] [PubMed] 25. Sánchez-Lastra, M.A.; Martínez-Aldao, D.; Molina, A.J.; Ayán, C. Pilates for people with multiple sclerosis: A systematic review and meta-analysis. Mult. Scler. Relat. Disord. 2019, 28, 199–212. [CrossRef] J. Clin. Med. 2019, 8, 1761 16 of 17 26. Espíndula, R.C.; Nadas, G.B.; Rosa, M.I.D.; Foster, C.; Araújo, F.C.D.; Grande, A.J. Pilates for breast cancer: A systematic review and meta-analysis. Rev Assoc Med. Bras. 2006, 63, 1006–1011. [CrossRef] 27. Mollinedo-Cardalda, I.; Cancela-Carral, J.M.; Vila-Suárez, M.H. Effect of a Mat Pilates Program with TheraBand on Dynamic Balance in Patients with Parkinson’s Disease: Feasibility Study and Randomized Controlled Trial. Rejuvenation Res. 2017, 21, 423–430. [CrossRef] 28. Teixeira de Carvalho, F.; de Andrade Mesquita, L.S.; Pereira, R.; Neto, O.P.; Amaro Zangaro, R. Pilates and Proprioceptive Neuromuscular Facilitation Methods Induce Similar Strength Gains but Different Neuromuscular Adaptations in Elderly Women. Exp. Aging Res. 2017, 43, 440–452. [CrossRef] 29. Lim, H.S.; Kim, Y.L.; Lee, S.M. The effects of Pilates exercise training on static and dynamic balance in chronic stroke patients: A randomized controlled trial. J. Phys. Ther. Sci. 2016, 28, 1819–1824. [CrossRef] 30. Tinoco-Fernández, M.; Jiménez-Martín, M.; Sánchez-Caravaca, M.A.; Fernández-Pérez, A.M.; Ramírez-Rodrigo, J.; Villaverde-Gutiérrez, C. The Pilates method and cardiorespiratory adaptation to training. Res. Sport Med. 2016, 24, 281–286. [CrossRef] 31. Mikalaˇcki, M.; ˇCokorilo, N.; Ruiz-Montero, P.J. The effects of a pilates-aerobic program on maximum exercise capacity of adult women. Rev. Bras. Med. Esporte 2017, 23, 246–249. [CrossRef] 32. Rodrigues, A.Q.; Martins, F.M.; Barbosa, A.C.; Figueiredo, P.S.; Lima, M.O.; Vieira, E.R. Effects of an eleven-week pilates exercise program on progressive-speed walking capacity in sedentary young women: A pilot study. Hum. Mov. 2016, 17, 102–106. [CrossRef] 33. Finatto, P.; Da Silva, E.S.; Okamura, A.B.; Almada, B.P.; Oliveira, H.B.; Peyre-Tartaruga, L.A. Pilates training improves 5-km run performance by changing metabolic cost and muscle activity in trained runners. PLoS ONE 2018, 13, e0194057. [CrossRef] 34. Wells, C.; Kolt, G.S.; Bialocerkowski, A. Defining Pilates exercise: A systematic review. Complement. Ther. Med. 2012, 20, 253–262. [CrossRef] [PubMed] 35. Guimarães, G.V.; Carvalho, V.O.; Bocchi, E.A.; d’Avila, V.M. Pilates in Heart Failure Patients: A Randomized Controlled Pilot Trial. Cardiovasc. Ther. 2012, 30, 351–356. [CrossRef] [PubMed] 36. Moher, D.; Liberati, A.; Tetzlaff, J. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement (Chinese edition). J. Chinese Integr. Med. 2009, 7, 889–896. [CrossRef] 37. Higgins, J.P.T.; Green, S. Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 (updated March 2011). The Cochrane Collaboration, 2011. Available online: http://crtha.iums.ac.ir/files/crtha/files/ cochrane.pdf (accessed on 22 October 2019). 38. Sterne, J.A.C.; Savovi´c, J.; Page, M.J.; Elbers, R.G.; Blencowe, N.S.; Boutron, I.; Cates, C.J.; Cheng, H.-Y.; Corbett, M.S.; Eldridge, S.M.; et al. RoB 2: A revised tool for assessing risk of bias in randomised trials. BMJ Effect. Public Health Pract. Proj. 1998, 2–5, in press. [CrossRef] 39. Armijo-Olivo, S.; Stiles, C.R.; Hagen, N.A.; Biondo, P.D.; Cummings, G.G. Assessment of study quality for systematic reviews: A comparison of the Cochrane Collaboration Risk of Bias Tool and the Effective Public Health Practice Project Quality Assessment Tool: Methodological research. J. Eval. Clin. Pract. 2012, 18, 12–18. [CrossRef] 40. Thalheimer, W.; Cook, S. How to calculate effect sizes from published research articles: A simplified methodology. Work Res. 2002, 1, 1–9. [CrossRef] 41. Schwarzer, G. Methods for Meta-analysis in Medical Research; Sutton, A.J., Abrams, K.R., Jones, D.R., Sheldon, T.A., Song, F., Eds.; Wiley: Chichester, UK, 2000; No. of pages: Xvii+317. Statist. Med. 2003, 22, 3112–3114; ISBN 0-471-49066-0. [CrossRef] 42. Higgins, J.P.T.; Thompson, S.G. Quantifying heterogeneity in a meta-analysis. Stat. Med. 2002, 21, 1539–1558. [CrossRef] 43. Sterne, J.A.C.; Egger, M.; Smith, G.D. Investigating and dealing with publication and other biases in meta-analysis. Br. Med. J. 2001, 323, 101–105. [CrossRef] 44. Wolkodoff, N.; Peterson, S.; Miller, J. The Fitness Effects of A Combined Aerobic and Pilates Program An Eight-Week Study Using The Stamina AeroPilates Pro XP555. AeroPilates Pro XP 555 Study 2008. [CrossRef] 45. Rayes, A.B.R.; de Lira, C.A.B.; Viana, R.B. The effects of Pilates vs. aerobic training on cardiorespiratory fitness, isokinetic muscular strength, body composition, and functional tasks outcomes for individuals who are overweight/obese: A clinical trial. PeerJ 2019, 7, e6022. [CrossRef] [PubMed] J. Clin. Med. 2019, 8, 1761 17 of 17 46. Diamantoula, P.; Nikolaos, A.; Helen, S. Aqua pilates versus land pilates: Physical fitness outcomes. J. Phys. Educ. Sport 2016, 16, 573–578. [CrossRef] 47. Daussin, F.N.; Ponsot, E.; Dufour, S.P. Improvement of VO2max by cardiac output and oxygen extraction adaptation during intermittent versus continuous endurance training. Eur. J. Appl. Physiol. 2007, 101, 377–383. [CrossRef] [PubMed] © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Pilates Method Improves Cardiorespiratory Fitness: A Systematic Review and Meta-Analysis.

10-23-2019

Fernández-Rodríguez, Rubén,Álvarez-Bueno, Celia,Ferri-Morales, Asunción,Torres-Costoso, Ana I,Cavero-Redondo, Iván,Martínez-Vizcaíno, Vicente

eng

PMC10356687

Vol.:(0123456789) Sports Medicine (2023) 53:1609–1640 https://doi.org/10.1007/s40279-023-01853-w SYSTEMATIC REVIEW The Acute Demands of Repeated‑Sprint Training on Physiological, Neuromuscular, Perceptual and Performance Outcomes in Team Sport Athletes: A Systematic Review and Meta‑analysis Fraser Thurlow1,3  · Jonathon Weakley1,2,3  · Andrew D. Townshend1  · Ryan G. Timmins1,3  · Matthew Morrison1,3  · Shaun J. McLaren4,5 Accepted: 17 April 2023 / Published online: 24 May 2023 © Crown 2023 Abstract Background Repeated-sprint training (RST) involves maximal-effort, short-duration sprints (≤ 10 s) interspersed with brief recovery periods (≤ 60 s). Knowledge about the acute demands of RST and the influence of programming variables has implications for training prescription. Objectives To investigate the physiological, neuromuscular, perceptual and performance demands of RST, while also examin- ing the moderating effects of programming variables (sprint modality, number of repetitions per set, sprint repetition distance, inter-repetition rest modality and inter-repetition rest duration) on these outcomes. Methods The databases Pubmed, SPORTDiscus, MEDLINE and Scopus were searched for original research articles investi- gating overground running RST in team sport athletes ≥ 16 years. Eligible data were analysed using multi-level mixed effects meta-analysis, with meta-regression performed on outcomes with ~ 50 samples (10 per moderator) to examine the influence of programming factors. Effects were evaluated based on coverage of their confidence (compatibility) limits (CL) against elected thresholds of practical importance. Results From 908 data samples nested within 176 studies eligible for meta-analysis, the pooled effects (± 90% CL) of RST were as follows: average heart rate (HRavg) of 163 ± 9 bpm, peak heart rate (HRpeak) of 182 ± 3 bpm, average oxygen consumption of 42.4 ± 10.1 mL·kg−1·min−1, end-set blood lactate concentration (B[La]) of 10.7 ± 0.6 mmol·L−1, deciMax session ratings of perceived exertion (sRPE) of 6.5 ± 0.5 au, average sprint time (Savg) of 5.57 ± 0.26 s, best sprint time (Sbest) of 5.52 ± 0.27 s and percentage sprint decrement (Sdec) of 5.0 ± 0.3%. When compared with a reference protocol of 6 × 30 m straight-line sprints with 20 s passive inter-repetition rest, shuttle-based sprints were associated with a substantial increase in repetition time (Savg: 1.42 ± 0.11 s, Sbest: 1.55 ± 0.13 s), whereas the effect on sRPE was trivial (0.6 ± 0.9 au). Performing two more repetitions per set had a trivial effect on HRpeak (0.8 ± 1.0 bpm), B[La] (0.3 ± 0.2 mmol·L−1), sRPE (0.2 ± 0.2 au), Savg (0.01 ± 0.03) and Sdec (0.4; ± 0.2%). Sprinting 10 m further per repetition was associated with a substantial increase in B[La] (2.7; ± 0.7 mmol·L−1) and Sdec (1.7 ± 0.4%), whereas the effect on sRPE was trivial (0.7 ± 0.6). Resting for 10 s longer between repetitions was associated with a substantial reduction in B[La] (−1.1 ± 0.5 mmol·L−1), Savg (−0.09 ± 0.06 s) and Sdec (−1.4 ± 0.4%), while the effects on HRpeak (−0.7 ± 1.8 bpm) and sRPE (−0.5 ± 0.5 au) were trivial. All other moderating effects were compatible with both trivial and substantial effects [i.e. equal coverage of the confidence interval (CI) across a trivial and a substantial region in only one direction], or inconclusive (i.e. the CI spanned across substantial and trivial regions in both positive and negative directions). Conclusions The physiological, neuromuscular, perceptual and performance demands of RST are substantial, with some of these outcomes moderated by the manipulation of programming variables. To amplify physiological demands and perfor- mance decrement, longer sprint distances (> 30 m) and shorter, inter-repetition rest (≤ 20 s) are recommended. Alternatively, to mitigate fatigue and enhance acute sprint performance, shorter sprint distances (e.g. 15–25 m) with longer, passive inter- repetition rest (≥ 30 s) are recommended. Study Registration Open Science Framework. Registration https:// doi. org/ 10. 17605/ OSF. IO/ 2XQ3A Extended author information available on the last page of the article 1610 F. Thurlow et al. Key Points The most common RST set configuration is 6 × 30 m straight-line sprints with 20 s of passive inter-repetition rest. The reference estimates for HRavg (90% HRmax), VO2avg (~ 70–80% VO2max) and B[La] (10.8 mmol·L−1) dem- onstrate the substantial physiological demands of RST in team sport athletes. Associated prediction intervals for these estimates suggest that most of these demands are consistently substantial across many RST protocols, sports and athlete characteristics. Shorter inter-repetition rest periods (≤ 20 s) and longer repetition distances (> 30 m) amplify physiological demands and cause greater inter-set reductions in sprint performance (i.e. performance fatigue). Inversely, longer inter-repetition rest periods (≥ 30 s) and shorter repeti- tion distances (≤ 20 m) enhance acute sprint perfor- mance and reduce the physiological demands. Shuttle-based protocols are associated with slower rep- etition times, likely due to the added change-of-direction component, but may reduce sprint decrement. The effect of shuttle versus straight-line RST protocols on physi- ological and perceptual outcomes remains inconclusive. Performing two less repetitions per set (e.g. four as opposed to six repetitions) maintains the perceptual, performance and physiological demands of RST. The findings from our investigation provide practitioners with the expected demands of RST and can be used to help optimise training prescription through the manipu- lation of programming variables. 1 Introduction Repeated-sprint training (RST) involves maximal-effort, short-duration sprints (≤ 10 s), interspersed with brief (≤ 60 s) recovery times [1]. It appears an effective and time-efficient training modality for physical adaptations in team-sport athletes, with as few as six sessions over two weeks shown to enhance high-speed running abilities [2]. The implementation of RST can also provide athletes with exposure to maximal sprinting, acceleration and decel- eration, which are important components of team sport [3–5]. Throughout an athlete’s training program, there is a range of opportunities for RST to be used, such as during a pre-season where a progressive reduction in running volume and an increase in intensity is often implemented [6]. Alter- natively, it could be employed during the playing season to promote the maintenance of specific physical qualities (e.g. speed, aerobic fitness), used as part of late-stage rehabilita- tion or implemented at a time when a training ‘shock-cycle’ is required. However, each training program requires differ- ent outcomes, with these attained through the manipulation of programming variables. The type of stimulus is an important driver of the chronic adaptive response to training [7]. Repeated-sprint train- ing is low-volume and short in duration, typically lasting 10–20 min per session, but due to the maximal intensity at which it is performed, it can generate adaptive events that ultimately result in the capacity for enhanced performance [8, 9]. This includes an improved aerobic and metabolic capacity [10–17]. However, there is considerable variation in RST prescription, with acute programming variables (e.g. sprint distance, rest duration, number of repetitions) regu- larly manipulated in research and practice [8, 18]. These changes can influence the internal and external load experi- enced by athletes during RST (i.e. the acute demands) and subsequently have the potential to cause diverse training adaptations [12]. For instance, in a study by Iaia et al. [19], higher within-set blood lactate concentration (~ 3 mmol⋅L−1 B[La]) was recorded during RST with shorter rest times (15 s versus 30 s), which can indicate a greater anaerobic contribution to exercise [20]. Accordingly, after six-weeks of training, the 15 s rest group achieved greater improvement in 200 m sprint time and the Yo-Yo intermittent recovery test level 2 compared with the 30 s group [19], with anaerobic energy production central to performance in these events [21, 22]. Thus, it is important to understand how the manipu- lation of programming variables affects the acute demands of RST, as this evidence can be useful to help explain how and why training adaptations may manifest. There is conflicting evidence within and across studies regarding the effects of programming variables on the acute demands of RST. In a study by Alemdaroğlu et al. [23], B[La] and percentage sprint decrement (Sdec) were greater with 6 × 40 m shuttle repeated-sprints compared with the same straight-line protocol. Conversely, compared with shuttle-based sprints, straight-line sprints induced greater demands when more repetitions were performed over a shorter distance (8 × 30 m repeated-sprints) [23]. The pre- scription of active inter-repetition rest has been shown to promote higher heart rate and oxygen consumption (VO2) compared with passive rest [24]. However, Keir et al. [25] found that demands were greater when passive rest, fewer repetitions, shorter rest time and a longer sprint distance were prescribed. Ultimately, there is an infinite combina- tion of programming variables that can alter the training outcome, but the acute effects of these factors are not well 1611 Acute Demands of Repeated-Sprint Training understood. Therefore, to guide training prescription and enhance the effectiveness of RST, it is important to gain a quantitative understanding of the acute effects of each pro- gramming factor. While excessive training loads can contribute to fatigue, an appropriate training dose may allow for greater improve- ments in fitness and performance [26]. Knowledge of the acute demands of RST can help practitioners manage fatigue and target specific training outcomes. Therefore, our system- atic review and meta-analysis aims to (1) identify the most common RST set configuration, (2) evaluate and summa- rise the acute physiological, neuromuscular, perceptual and performance demands of RST, and (3) examine the meta- analytic effects of sprint modality, number of repetitions per set, sprint repetition distance, inter-repetition rest modality and inter-repetition rest duration on the acute RST demands. 2 Methods 2.1 Search Strategy This study was conducted in accordance with the ‘Preferred Reporting Items for Systematic Reviews and Meta-analyses’ (PRISMA) guidelines [27] and registered on Open Science Framework (Registration https:// doi. org/ 10. 17605/ OSF. IO/ 2XQ3A). A systematic search of the literature was con- ducted to find original research articles investigating the acute demands of RST in team sport athletes. The latest search was performed on 10 January 2022, using the elec- tronic databases Pubmed, SPORTDiscus, MEDLINE and Scopus. No restrictions were imposed on the publication date. Relevant keywords for each search term were identified through pilot searching of titles/abstracts/full-texts of previ- ously known articles. Key search terms were grouped and searched within the article title, abstract and keywords using the search phrase (‘repeat* sprint*’ OR ‘intermittent sprint*’ OR ‘multiple sprint*’) AND (‘exercise’ OR ‘ability’ OR ‘training’) AND (‘team sport’ OR ‘players’ OR ‘athletes’) AND (‘physiological’ OR ‘perceptual’ OR ‘neuromuscular’ OR ‘metabolic’ OR ‘fatigue’) NOT (‘cycling’ OR ‘swim- ming’). No medical subject headings were applied to the search phrase. Following the initial search of the literature, results were exported to EndNote library (Endnote X9, Clarivate Ana- lytics, USA) and duplicates were removed. The remaining articles were then uploaded to Covidence (http:// www. covid ence. org, Melbourne, Australia), with the titles and abstracts independently screened by two authors (F.T., M.M.). Full- texts of the remaining articles were then accessed to deter- mine their final inclusion–exclusion status. Articles selected for inclusion were agreed upon by both authors, with any disagreements resolved by discussion or a third author (J.W.). Furthermore, Google Scholar, as well as reference lists of all eligible articles and reviews [1, 8, 9, 28], were searched to retrieve any additional studies. Figure 1 dis- plays the strategy for the study selection process used in this review. 2.2 Inclusion–Exclusion Criteria The inclusion and exclusion criteria can be found in Table 1. We chose to omit any studies in which the mean athlete age was ≤ 16 years, as children may respond differently to RST [29, 30]. Studies were excluded if RST was per- formed in ≥ 30 °C because larger performance decrements may occur in hot compared with cool conditions [31]. We acknowledge that the residual effects of intense exercise may last up to 72 h [32], but acute demands measured up to 24 h following RST was selected because: (a) it is common for RST and other team sport activity to be interspersed with minimal recovery time (i.e. < 72 h), (b) pilot scoping of the literature only identified five studies [33–37] that recorded measurements on athletes > 24 h. Several studies/protocols were excluded from this investigation that implemented repeated-sprint sequences with sport skill elements [38–42] or involved a reactive component in response to an external stimulus (e.g. light sensor) [43–46]. Evidence from studies involving both single-set and multi-set repeated sprints was recorded, including the acute demands from repeated-sprint ability tests. For studies that involved pre-post testing of RST, separated by an intervention period (e.g. training, sup- plementation), only the RST baseline results were reported to ensure that the intervention period did not bias the results. Where observational time-series studies measured RST across a season, results were included for each phase (e.g. pre-season, mid-season, post-season), providing that no intervention was implemented outside of usual practice. 2.3 Classification of Study Design To provide information on study design (Supplementary Table S2), studies were categorised under four designs as follows: (1) observational – non-experimental, (2) single group pre-test post-test – experimental treatment applied to a single group of participants, with the dependent variable/s measured before and after treatment, (3) crossover – two or more experimental conditions applied to the same par- ticipants, with or without a control condition, (4) parallel groups – two or more experimental conditions applied to two groups of different participants, with or without a control condition. Additionally, single-group time-series designs were categorised under observational and denoted. 1612 F. Thurlow et al. 2.4 Selection of Outcome Measures and Programming Variables The outcome measures (Table 2) were selected on the basis of pilot scoping of the literature that identified commonly used indicators of internal responses to exercise and per- formance capacity in team sport settings [28, 47, 48]. Per- centage sprint decrement, as defined by Fitzsimons et al. [49] and Glaister et al. [50], was chosen as it is the most ecologically valid index to quantify fatigue during RST [50]. However, caution should be taken when interpreting Sdec as weak relative and absolute reliability exists between repeated-sprint ability tests [51]. Blood lactate is sensitive to changes in exercise intensity and duration and is one of the preferred methods used to assess the anaerobic glycolytic contribution to exercise [20]. Sprint force–velocity–power parameters, as defined by Samozino et al. [52], and spring- mass model parameters, as defined by Morin et al. [53], were chosen as they represent field-based methods used to assess the mechanical effectiveness of sprinting and the neuromus- cular manifestation of fatigue during over-ground running [54]. Programming variables recorded were: sprint modality (i.e. straight-line, 180° shuttle or multi-directional), number Fig. 1 Flow diagram of the study selection process 1613 Acute Demands of Repeated-Sprint Training Table 1 Study inclusion–exclusion criteria RST repeated-sprint training, U17 under 17 age group, U18 under 18 age group Criteria Inclusion Exclusion 1 Original research article Reviews, surveys, opinion pieces, books, periodicals, editorials, case studies, non-aca- demic/non-peer-reviewed text 2 Full-text available in English Cannot access the full text in English 3 Team sport athletes (field- or court-based invasion sports) of any gender Non-team sports (e.g. solo, racquet or combat sports), ice-, sand- or water-based team sports, match officials, non-athletic populations. Studies that described participants as playing intermittent sports or used a combination of team sport and non-team sport ath- letes, unless group results were separated 4 Participants mean age ≥ 16 years. Where mean age was not provided, and if an age group was listed as U17 or above, this was accepted Mean athlete age was < 16 years, or participants were described as U16 or below. Addition- ally, studies that used a combination of athletes below and above the age cut-off, unless group results were separated 5 Healthy, able-bodied, non-injured athletes Special populations (e.g. clinical, patients), athletes with a physical or mental disability, or athletes considered to be injured or returning from injury 6 RST was over-ground running on a flat surface RST was performed on a treadmill, cycle or another implement. RST was performed on a slope or sand 7 RST was performed at maximal intensity, with a mean work duration of ≤ 10 s or ≤ 80 m in distance, a recovery duration of ≤ 60 s and ≥ 2 repetitions performed in total. Single set and multi-set repeated-sprints RST was performed at submaximal intensity, with a work duration of > 10 s or > 80 m, a recovery duration of > 60 s, and only a single sprint repetition 8 RST was a fixed protocol, without any sport skill elements RST involved a reactive change of direction in response to an external stimulus (e.g. light sensor) or sport skill elements (e.g. passing, kicking, shooting) 9 Studies must have reported ≥ 1 acute outcome measure (outcome measures are presented in Sect. 2.3). Acute demands must have occurred during (within) or immediately follow- ing RST up to 24 h No relevant outcome measures were reported. RST demands occurred > 24 h 10 ≥ 1 condition or group must have performed the intervention under normal conditions (e.g. usual nutritional intake, hydrated state, normoxia, absence of ergogenic aids, ≤ 30 °C, regular warm-up protocol) RST was performed in a possibly fatigued or potentiated state (e.g. sports training, maximal fitness assessment, pre-conditioning strategies) occurring within or 24 h before RST. Placebo treatments were used before or during RST 11 Sprint times were recorded using electronic timing gates Sprint times were recorded with a hand-held stopwatch or a video-camera 1614 F. Thurlow et al. of repetitions per set, number of sets per session, sprint dis- tance or duration per repetition, inter-repetition rest dura- tion, inter-repetition rest modality, inter-set rest duration and inter-set rest modality. 2.5 Extraction of Study Information Mean and standard deviation data were extracted directly from tables and within the text of the included studies. To obtain data from studies where information was provided in figures, graph digitising software (WebPlotDigitizer, ver- sion 4.3, USA) was used. For studies where rest duration was given as an exercise to rest ratio or on a time cycle that included sprint time, an estimated ‘actual’ rest time was also established. This was determined by extracting aver- age sprint time (Savg) data from studies, where provided. For example, if Savg was 3.2 s and the recovery duration was given as 1:5 exercise to rest ratio, then the estimated recovery duration was 16 s, or if the recovery duration was given on a 30 s cycle, then the estimated recovery duration was 27 s, with recovery durations rounded to the nearest whole number. With regards to sprint modality, shuttle repeated-sprints were defined as RST where one or more 180° changes of direction were performed. Multi-directional repeated-sprints involved RST where changes of direction were performed with angles other than 180°, but due to the large variety of designs (e.g. different angles and courses), this format was excluded from the meta-analysis. For rest modality, ‘passive’ included protocols where participants were required to walk back to a two-way start line (sprints alternating from both ends) in preparation for the next sprint. Where information relating to exercise protocols (e.g. sprint distance) could not be found within the study or clarification was required, authors were contacted. If authors did not respond, samples were removed from the meta-analysis. The Participant Clas- sification Framework [55] was used to define training and performance calibre of the athletes included in our investiga- tion (Supplementary Table S2). Twenty-four estimates nested within 13 studies collected session ratings of perceived exertion (sRPE) via Borg’s 6–20 scale. For consistency with other included studies and to comply with more standard practice, 6–20 values were converted to Category–Ratio 10 (CR10®) units (deciMax) using the appropriate table conversion [56]. Standard devia- tions were converted by a factor that was proportionate to the mean value of each estimate, which ranged between 13–19 (conversion factors = 0.27–0.53). Where VO2 was expressed in absolute terms (L·min−1) [25], it was converted to relative terms (mL⋅min−1⋅kg−1) by extracting the mean body mass of the participants from the study. Where Sdec of 5% was set as the termination criteria [57], the mean number of repeti- tions was used for meta-analysis. Heart rates were inclusive of both the sprint component and inter-repetition rest peri- ods, but samples were excluded [58] which continuously Table 2 Summary of the outcome measures of interest sRPE session ratings of perceived exertion, CR10 Category-Ratio 10, CMJ counter movement jump, JH jump height, FVP force–velocity–power, V0 theoretical maximal velocity, F0 theoretical maximal force, P0 theoretical maximal power, RFpeak maximal ratio of force, DRF slope/rate of decrease in ratio of force with increasing velocity, SMM spring-mass model, Kvert vertical stiffness, Kleg leg stiffness, ΔL leg compression, Δz centre of mass vertical displacement, Fzmax maximal vertical force, HR heart rate, HRavg average heart rate, HRpeak peak heart rate, HRpost heart rate recorded immediately post exercise, % HRmax percentage of maximal heart rate, CK serum creatine kinase, CK 24h serum creatine kinase measured 24 h post exercise, B[La] blood lactate, VO2avg average oxygen consumption, % VO2peak percentage of peak oxygen consump- tion, % VO2max percentage of max oxygen consumption, Sbest best sprint time, Savg average sprint time, Stotal total sprint time, Sdec percentage sprint decrement Category Measure Metric Physiological HR HRavg, HRpeak, HRpost and/or % HRmax CK CK 24 h B[La] Post (0–10 min) VO2 VO2avg, VO2peak and/or % VO2max Neuromuscular CMJ JH Sprint FVP parameters as defined by Samozino et al. [43] V0, F0, P0, RFpeak, DRF SMM parameters as defined by Morin et al. [44] Kvert, Kleg, ΔL, Δz, Fzmax Perceptual sRPE CR10® and 6–20 sRPE scales [46] Performance Sprint times Sbest, Savg, Stotal Sdec As defined by Fitzsimons [40] and Glaister et al. [41] 1615 Acute Demands of Repeated-Sprint Training recorded heart rate during the inter-set rest periods. Due to a lack of studies reporting the effect of RST on peak heart rate (HRpeak) as a percentage of maximal heart rate (HRmax), this data was unable to be meta-analysed. However, these results [2, 59–62] are summarised in section 3.4.3. Post-exercise B[La] samples were meta-analysed together, irrespective of the exact time point that they were measured (i.e. 0–10 min). Although, for context, specific timepoints of each sample are given in Supplementary Table S3. Where studies provided multiple timepoints of B[La] collection, the highest value was used for meta-analysis. The considerable variation in measurement error between different jump systems makes it difficult to compare counter-movement jump (CMJ) height between different studies [63] and as such, CMJ height results were recorded, but not meta-analysed. For context, the type of jump measurement systems used in each study are noted alongside the results in Supplementary Table S3. 2.6 Assessment of Reporting Quality and Risk of Bias To assess the reporting quality and risk of bias within the studies included in this review, two authors (F.T. and M.M.) independently evaluated the literature using a modified ver- sion of the Downs and Black index. This scale includes 14 original items and ranks each item as 0 or 1, with higher total scores (out of 14) indicating higher quality studies. The original Downs and Black scale was reported to have accept- able test–retest (r = 0.88) and inter-rater reliability (r = 0.75) [64]. If there was an absence of clear information to assess an item on either scale, it was scored as 0. Any disagree- ments between the two authors were resolved by discussion or a third author (J.W.). 2.7 Data Analysis All analyses were performed in the statistical computing software R (Version 4.0.0; R Core Team, 2020). Studies eli- gible for meta-analysis often reported RST outcomes from several subgroups (e.g. elite versus non-elite, males versus females, etc.), from repeated measures taken on the same group of athletes (e.g. set 1 and set 2, warm-up A versus warm-up B, etc.), or a combination of both. To appropri- ately account for this hierarchical structure, in particular, the within-study correlation arising from repeated measures [65] and on the assumption that the true acute demand of RST varies between studies [66], data were analysed using multi-level mixed-effects meta-analysis via the metafor package [67]. Initial (baseline) models were run for each outcome measure with 10 or more estimates and fit using restricted maximum-likelihood. These models included only random effects, which were specified in a nested structure as studies (i.e. individual research papers; outer factor) and groups within studies (inner factor, [65]). Units of analy- sis were therefore individual estimates from groups within studies, given as the mean value of the outcome measure following RST. Both the associated standard deviation (SD) and sample size were used to calculate the variance of each estimate. When a study involved repeated measures (i.e. multiple rows of data for the same group of athletes), dependency was accounted for by replacing variance with the entire ‘V’ matrix; that is, the variance–covariance matrix of the estimates [65]. Block-diagonal covariance matrices were estimated with an assumed correlation of r = 0.5 using the clubSandwich package [68]. Since it is uncommon for studies to report the correlation coefficient between repeated measures [69], our assumption was informed by re-analysis of our previous (unpublished) work in team-sport RST. Uncertainty in meta-analysed estimates was expressed using 90% compatibility (confidence) intervals (CI), calcu- lated based on a t-distribution with denominator degrees of freedom given from the unique number of ‘group’ levels (i.e. the inner level of the random effects structure). Pooled estimates were also presented with 90% prediction intervals, which convey the likely range of the true demand of RST in similar future studies [70]. Between-study and between- group heterogeneity in each meta-analysed estimate was quantified as a SD [Sigma (σ)] [71]), with 90% CI calculated using the Q-profile method [72]. To examine the effect of programming variables on acute RST outcomes, candidate factors were added to the aforementioned baseline models as fixed effects for out- comes with sufficient estimates available (approximately 10 per moderator [73]). The five moderator variables were: sprint modality (categorical: straight-line or 180° shuttle), number of repetitions per set (continuous, linear), total dis- tance covered in each repetition (continuous, linear), inter- repetition rest modality (categorical: active or passive) and inter-repetition rest duration (continuous, linear). Factors were re-scaled so that the reference (intercept) effect repre- sented the performance or response to 6 m × 30 m straight- line sprints with 20 s passive rest between repetitions. The effects of each moderator were then estimated (along with 90% CI and 90% prediction intervals, where appropriate), with all other factors being held constant. Categorical moderators were given as the difference between levels (shuttle compared with straight-line sprints and active compared with passive inter-repetition rest). Continuous moderators were evaluated at a magnitude deemed to be practically relevant for training prescription: performing two more repetitions, sprinting 10 m further per repetition and resting for 10 s longer between repetitions. The effects of repetition distance on repetition time (average and fast- est sprint) were not shown (but were still offset to a dis- tance of 30 m), because the time taken to complete a sprint repetition is almost entirely dependent on the distance to 1616 F. Thurlow et al. be covered. The total amount of variance explained by the combination of moderators was given as a pseudo-R2 value, calculated by subtracting the total (pooled) variance from final models ( 휎2 mods ) as a fraction of baseline models ( 휎2 base ) from 1 (1 − [휎2 mods∕휎2 base]). To provide an interpretation of programming modera- tors, we (subjectively) considered the entire range of the CI representative of values compatible with our models and assumptions [74], relying mostly on the point esti- mate. To further contextualise the practical relevance of moderators, we visually scaled effects against regions of practical significance. That is, reference values for each outcome measure that have been empirically or theoreti- cally anchored to some real-world importance in the con- text of team-sport athletes and/or RST. These thresholds were: 2 bpm (~ 1%) in HRpeak [75], 1 au in CR10-scaled sRPE [76], a 1% faster or slower sprint time [77] based on the reference performance given as the intercept: 0.05 s for Savg, 0.04 s for best sprint time (Sbest) and 1% for Sdec across a set [77]. In absence of a recognised practical reference value for a change in B[La] above the anaero- bic threshold, we used the value of a small, standardized effect. Between-athlete standard deviations from included estimates (n = 120) were meta-analysed on the log scale, as previously described (SD = 1.9 mmol·L−1, 90% CI 1.7–2.22), before being multiplied by 0.2. The threshold for a moderate standardised effect (0.6 × 1.9 mmol·L−1) was also calculated and shown for visual purposes. When a CI fell entirely inside the region of practical significance or predominantly inside one region, we declared an effect as trivial. When a CI fell entirely outside the region of practical significance or predominantly outside the region, we declared an effect substantial. If there was equal cover- age of the CI across the trivial region and the substantial region in only one direction (i.e. positive or negative), the effect was deemed compatible with both trivial and substantial effects. Finally, when the CI spanned across substantial regions in both positive and negative direc- tions, including the trivial region, an effect was deemed inconclusive. 3 Results Following the screening process (Fig. 1), 215 publications were included in our investigation, with data from 908 sam- ples nested within 176 studies eligible for meta-analysis. Across all studies, there were 4818 athlete inclusions from 282 repeated-sprint protocols reported. 3.1 Study Characteristics The most common study design for investigations of acute demands of RST was single group, cross sectional observa- tional (n = 87 studies, 40%). Soccer was the most investi- gated sport (n = 104, 48%), followed by basketball (n = 33, 15%), rugby (league, union and sevens) (n = 15, 7%), futsal (n = 14, 7%), handball (n = 12, 6%), field hockey (n = 10, 5%), Australian rules football (n = 5, 2%), volleyball (n = 3, 1%), netball (n = 2, 1%) and a mixture of team sports (n = 17, 8%). Of these sports, 21 (10%) studies involved elite/inter- national level athletes, 125 (58%) studies involved highly trained/national level athletes and 58 (27%) studies involved trained/development level athletes, with 11 (5%) studies not reporting the training and performance calibre of the ath- letes. Female athletes were represented in 31 (14%) stud- ies. A summary of the participants and study characteristics of included publications are provided in Supplementary Table S2. 3.2 Outcomes for the Assessment of Reporting Quality and Risk of Bias Supplementary Table S1 summarises the outcomes of the modified Downs and Black scale for the assessment of reporting quality and risk of bias. Results ranged from 7 to 12, with a mean score of 9.6 ± 0.9. 3.3 Study Outcomes A summary of the training protocols and study outcomes of included publications are provided in Supplementary Table S3. Performance outcomes were represented in 198 (92%) of studies and the most common outcome measure was Sdec (n = 127 studies, 59%) (Fig. 2). The most common prescrip- tion of each programming variable were straight-line sprints (n = 153 protocols, 54%), performed over 30 m (n = 107, 38%), with a passive recovery (n = 186, 66%) lasting 20 s (n = 83, 29%), prescribed as one set of six repetitions (n = 122, 43%; Fig. 3). The majority of protocols (n = 263, 93%) employed one set of repeated-sprints, with two sets, three sets and four sets used in five (2%), ten (4%) and four (1%) protocols, respectively. The most common inter-set rest times for all multi-set protocols were 4 (six protocols) and 5 mins (five protocols). The number of 180° changes of direction prescribed for shuttle repeated-sprints ranged from one to two. The most prescribed mode of active recovery was a slow jog back to a one-way start line (n = 32 protocols, 33%, i.e. sprints start from one end only). There was one 1617 Acute Demands of Repeated-Sprint Training study [33] that strictly enforced a 5 m deceleration zone and one other study [78] that enforced a 6 m deceleration zone. 3.3.1 Meta‑analysed Acute Demands of Repeated‑Sprint Training The acute physiological, perceptual and performance demands of RST in team sport athletes are presented in Table 3. Also presented are the 90% CI and PI for each estimate, as well as the between sample and between study variation (σ). 3.3.2 Moderating Effects of Programming Variables on the Acute Demands of Repeated‑Sprint Training The moderating effects of programming variables on the acute physiological, perceptual and performance demands of RST are presented in Figs. 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15. All effects were evaluated as the change in each outcome measure when compared with a reference protocol of 6 m × 30 m straight-line sprints with 20 s pas- sive inter-repetition rest. Unless noted in the subsequent sections, moderating effects were deemed inconclusive [i.e. a confidence level (CL) spanning across substantial regions in both positive and negative directions, including the trivial region]. 3.3.2.1 Shuttle‑Based Sprints Shuttle-based sprints were associated with a substantial increase in Savg and Sbest (i.e. slower times; Figs. 10, 11, 12, 13), whereas the effect on sRPE was trivial (Figs.  6, 7). Performing shuttle-based sprints was compatible with both a trivial and substantial reduction in Sdec [i.e. a less pronounced decline in sprint times (faster) throughout the set; Figs. 14 and 15]. 3.3.2.2 Performing Two More Repetitions Per Set Per- forming two more repetitions per set had a trivial effect on HRpeak (Figs. 4 and 5), sRPE (Figs. 6 and 7), Savg (Figs. 12 and 13), Sdec (Figs. 14 and 15) and B[La] (Figs. 8 and 9). Additionally, performing two more repetitions per set was compatible with both a trivial and substantial increase in Sbest (i.e. slower time; Figs. 10 and 11). 3.3.2.3 Sprinting 10 m Further Per Repetition Sprinting 10 m further per repetition was associated with a substan- tial increase in B[La] (Figs. 6 and 7) and Sdec [i.e. a more pronounced decline in sprint times (slower) throughout Fig. 2 The distribution of outcome measures. Data given as the total number of studies represented (out of 215). Sbest best sprint time, Savg average sprint time, Stotal total sprint time, Sdec percentage sprint decrement, CMJ counter-movement jump, SMM spring-mass model characteristics, FVP sprint force–velocity–power profiling, sRPE rat- ings of perceived exertion, HR heart rate, B[La] blood lactate, CK serum creatine kinase, VO2 oxygen consumption 1618 F. Thurlow et al. the set; Figs. 14 and 15], whereas the effect on sRPE was trivial (Figs. 6 and 7). Additionally, sprinting 10 m further per repetition was compatible with both a trivial and sub- stantial increase in HRpeak (Figs. 4 and 5). The effects on Sbest and Savg were not evaluated. 3.3.2.4 Resting for 10 s Longer Resting for 10  s longer between repetitions was associated with a substantial reduction in B[La] (Figs. 8 and 9), Savg (Figs. 12 and 13), and Sdec (Figs.  14 and 15), while the effects on HRpeak (Figs. 4 and 5) and sRPE (Figs. 6 and 7) were trivial. Rest- ing for 10  s longer between repetitions was compatible with both a trivial and substantial reduction in Sbest (i.e. faster time; Figs. 10 and 11). 3.3.2.5 Performing Active Inter‑Repetition Rest Using an active inter-repetition rest modality was compatible with both a trivial and substantial increase in HRpeak (Figs. 4 and 5), sRPE (Figs. 6 and 7) and Sdec (Figs. 14 and 15). Fig. 3 The distribution of RST prescription across all 282 protocols. Data are given as the total number of protocols represented (percentage) [range] 1619 Acute Demands of Repeated-Sprint Training 3.3.3 Acute Demands of Repeated‑Sprint Training on Non‑Meta‑Analysed Outcomes The acute demands of straight-line and shuttle RST on non-meta-analysed outcomes are as follows: total sprint time ranged from 7.82 to 86.09 s (number of studies = 102, number of samples = 185), end-set heart rate (HRpost) ranged from 139 to 191 bpm (n = 4 and 12), HRpeak as % HRmax ranged from 85% to 97% (n = 4 and 12), average VO2 as a percentage of maximal oxygen consumption (VO2max) ranged from 73% to 83% (n = 3 and 6) and creatine kinase measured 24 h post-session ranged from 354 to 1120 µ·L−1 (n = 6 and 8). The absolute change in CMJ height ranged from 2.4 to −8.6 cm (n = 9 and 20) and the percent change ranged from 8% to −27% (n = 10 and 21). Results from studies that investigated spring-mass model parameters (n = 2 and 2) and sprint force–velocity–power parameters (n = 1 and 1) are provided in Supplementary Table S3. 3.3.4 Acute Demands of Multi‑directional Repeated‑sprint Training The acute demands of multi-directional RST are as follows: Sdec ranged from 1% to 7% (number of studies = 13, number of samples = 24), Sbest ranged from 4.36 to 8.21 s (n = 11 and 19), Savg ranged from 4.14 to 8.39 s (n = 12 and 22), total sprint time ranged from 32.22 to 83.99 s (n = 9 and 11), end-set B[La] ranged from 5.4 to 15.4 mmol·L−1 (n = 6 and 8), sRPE ranged from 5.5 to 9.1 au (n = 6 and 10) and HRpeak ranged from 178 to 195 b·min−1 (n = 6 and 10). Table 3 Meta-analysed acute physiological, perceptual and performance demands of repeated-sprint training in team sport athletes Multi-directional protocols are excluded. Heart rate results are independent of each other (HRpeak ≠ HRmax) CI confidence interval, PI prediction interval, HRavg average heart rate, % HRmax percentage of maximal heart rate, HRpeak peak heart rate, VO2avg average oxygen consumption, B[La] blood lactate, sRPE session ratings of perceived exertion, Sbest best sprint time, Savg average sprint time, Sdec percentage sprint decrement Outcome measure Number of… Pooled effect Variation (σ, 90% CI) between… Studies Samples Estimate 90% CI 90% PI Studies (σ1) Samples (σ2) HRavg bpm 12 24 163 154 to 171 131 to 194 16 (11 to 24) 6 (4 to 9) % HRmax 10 21 90 87 to 92 82 to 97 3 (2 to 6) 2 (1 to 3) HRpeak bpm 29 54 182 179 to 184 168 to 195 7 (6 to 10) 2 (1 to 3) VO2avg mL·kg−1·min−1 6 6 42.4 32.3 to 52.4 16.0 to 68.7 9.2 (0.0 to 20.6) 2.4 (0.8 to 9.4) B[La] mmol·L−1 64 120 10.7 10.1 to 11.3 5.6 to 15.8 2.6 (2.1 to 3.1) 1.7 (1.4 to 2.0) sRPE au (deciMax) 40 68 6.5 6.0 to 6.9 3.5 to 9.5 1.2 (0.7 to 1.6) 1.3 (1.1 to 1.6) Sbest s 103 191 5.52 5.26 to 5.79 2.79 to 8.25 1.57 (1.40 to 1.79) 0.45 (0.40 to 0.51) Savg s 112 200 5.57 5.31 to 5.82 2.83 to 8.3 1.54 (1.37 to 1.74) 0.57 (0.51 to 0.65) Sdec % 125 224 5.0 4.7 to 5.3 1.4 to 8.7 2.0 (1.8 to 2.3) 0.9 (0.8 to 1.1) Fig. 4 The moderating effects of programming variables on peak heart rate during repeated-sprint training with team sport athletes 1620 F. Thurlow et al. Fig. 5 The moderating effects of a sprint modality, b inter-repetition rest mode, c repetitions per set, d total repetition distance and e inter-repeti- tion rest time on peak heart rate during repeated-sprint training with team sport athletes. Larger circles, greater study size Fig. 6 The moderating effects of programming variables on session ratings of perceived exertion following repeated-sprint training with team sport athletes 1621 Acute Demands of Repeated-Sprint Training Fig. 7 The moderating effects of a sprint modality, b inter-repetition rest modality, c repetitions per set, d total repetition distance and e inter-repetition rest time on session ratings of perceived exertion fol- lowing repeated-sprint training with team sport athletes. Larger cir- cles, greater study size Fig. 8 The moderating effects of programming variables on end-set blood lactate following repeated-sprint training with team sport athletes 1622 F. Thurlow et al. Fig. 9 The moderating effects of a sprint modality, b inter-repetition rest modality, c total number of repetitions, d total repetition distance and e inter-repetition rest time on end-set blood following repeated-sprint training with team sport athletes. Larger circles, greater study size Fig. 10 The moderating effects of programming variables on best sprint time during repeated-sprint training with team sport athletes 1623 Acute Demands of Repeated-Sprint Training 4 Discussion This systematic review and meta-analysis provides the first comprehensive synthesis of the acute demands of RST in team sport athletes. It contains data from 215 studies, 282 repeated-sprint protocols and 4818 athlete inclusions. We demonstrate that physiological, neuromuscular, perceptual and performance demands incurred during RST are con- sistently substantial; a finding supported by both the meta- analysed point estimates and their 90% prediction intervals (Table 3). Moreover, the magnitude of these acute demands can be influenced by the manipulation of programming vari- ables (Table 4). Prescribing longer sprint distances (> 30 m) and/or shorter (≤ 20 s) inter-repetition rest can increase physiological demands and performance decrement. Con- versely, the most effective strategy to mitigate the acute decline in sprint performance is the prescription of longer inter-repetition rest times (≥ 30 s) and shorter sprint dis- tances (15–25 m). The effects of performing two more rep- etitions per set on our outcomes was trivial, which suggests that prescribing a lower number of successive sprints (e.g. four as opposed to six) may be a useful strategy to reduce Fig. 11 The moderating effects of a sprint modality, b inter-repetition rest modality, c repetitions per set and d inter-repetition rest time on best sprint time during repeated-sprint training with team sport athletes. Larger circles, greater study size 1624 F. Thurlow et al. sprint volume, while maintaining the desired physiologi- cal demands. The influence of shuttle-based protocols and inter-repetition rest modality remain largely inconclusive. These findings from our review and meta-analysis can be used to inform RST prescription and progression in team sport athletes. Repeated-sprint training is one method among an array of training options that practitioners can use to enhance the physical performance of team sport athletes. The meta-ana- lytic estimate of sRPE (Table 3) indicates that RST is per- ceived to be ‘very hard’ (90% PI: ‘moderate’ to ‘extremely hard’), which agrees with the intended prescription of this training modality [18, 79]. Taking into account that a typical RST session lasts for between 10–20 min, the sRPE-training load (sRPE × training duration) is a fraction of that observed during team sport practice [80–82], being approximately 65–130 au (deciMax units). However, this should be consid- ered alongside the physiological and neuromuscular stresses imposed by the RST session. The 10.1–11.3 mmol·L−1 ref- erence estimate of B[La] is well above the second lactate threshold (~ 4 mmol·L−1) and therefore indicates that there is an immediate and intensive demand placed on the anaerobic glycolytic system during RST [83]. A high rate of anaero- bic energy production, accompanied by a B[La] response exceeding 10 mmol·L−1, may be an important stimulus to elicit positive long-term changes in enzymes central for anaerobic glycolysis [28, 84]. Therefore, to potentially opti- mise the anaerobic adaptations to RST for team sport ath- letes, sessions that cause a B[La] demand of > 10 mmol·L−1 should be prescribed. Practitioners should also be conscious of the neuromuscular demands (i.e. impairment in the mus- cles ability to produce force) imposed by RST, with con- siderable decrements in CMJ height observed immediately after its implementation. However, while fatigue may be detrimental to acute performance, it also can be important for adaptation [85]. Athletes can reach VO2max during RST [86] and the aver- age VO2 demand is considerable (Table 3), corresponding to approximately 70%–80% of VO2max for the normal team sport athlete [87–90]. This also agrees with studies reporting the average VO2 demands of RST as a percentage of the ath- letes measured VO2max [24, 59, 60]. Training sessions spent with longer periods of time at a high percentage of VO2max have been suggested to be an optimal stimulus for enhanc- ing aerobic fitness, particularly in well-trained athletes [79, 91–93]. If the objective is to maximise aerobic adaptations, practitioners should therefore prescribe RST sessions that induce an average VO2 demand of > 90% max (or > 95% maximal heart rate) [79, 94], which could be achieved by manipulating certain programming variables in isolation and/or combination. Although moderator analysis of VO2 was not feasible due to a low number of samples, qualitative synthesis indicates that longer sprint distances [86], active rest periods [24] and shuttle-based RST [59, 60] can amplify the VO2 demands. While RST is a time-efficient training method that can induce small to large improvements across a range of physical parameters [8, 9], practitioners should, however, consider that RST is unlikely to be the best tool for eliciting time at or near VO2max and ultimately, for enhancing aerobic fitness [9, 79]. Pursuing utmost change in this area by implementing excessively demanding protocols could mitigate the improvement of other physical qualities (e.g. speed). Manipulating programming variables based on the goals of the training program is therefore crucial to regulate the acute demands of RST and optimise specific adaptations. 4.1 Sprint Modality There were a greater number of RST protocols that pre- scribed straight-line sprints (n = 153, 54%) compared with shuttle RST (n = 105, 37%) and multi-directional RST (n = 24, 9%). Across the 24 protocols that prescribed Fig. 12 The moderating effects of programming variables on average sprint time during repeated-sprint training with team-sport athletes 1625 Acute Demands of Repeated-Sprint Training multi-directional repeated-sprints [46, 95–111], there were a variety of different designs and angles implemented, rang- ing from 45° to 135°, for 2–5 changes of direction. Given the multitude of programming variables to consider, meta- analysis of multi-directional RST was not feasible. None- theless, we found that consistently high HRpeak (178–195 bpm and 92%–100% HRmax), sRPE (5.5–9.1 au) and post- session B[La] (5.4–15.4 mmol·L−1) were reported across all multi-directional protocols. Multi-directional sequences were designed to replicate specific movement demands of team sports, where rapid changes of direction are common [5, 112, 113]. Moreover, previous research has identified that straight-line speed and change of direction ability are differ- ent physical qualities because of their distinct biomechanical determinants [112, 113]. Greater application of multi-direc- tional and shuttle-based RST may therefore be used to help develop change of direction ability, but practitioners should be aware of the acute demands of each modality. Compared to straight-line RST, our meta-analysis shows that sprint times are clearly slower during shuttle-based RST Fig. 13 The moderating effects of a sprint modality, b inter-repetition rest modality, c repetitions per set and d inter-repetition rest time on aver- age sprint time during repeated-sprint training with team sport athletes. Larger circles, greater study size 1626 F. Thurlow et al. Fig. 14 The moderating effects of programming variables on sprint time decrement during repeated-sprint training with team sport athletes Fig. 15 The moderating effects of a sprint modality, b inter-repetition rest modality, c repetitions per set, d total repetition distance and e inter- repetition rest time on sprint time decrement during repeated-sprint training with team sport athletes. Larger circles, greater study size 1627 Acute Demands of Repeated-Sprint Training (Figs. 10 and 12), but Sdec is less (Fig. 14). Practitioners can therefore expect slower sprint velocity when changes of direction are implemented, but athletes may be able to better sustain their initial sprint performance. The effects on HRpeak and B[La] were inconclusive (Figs. 4 and 8), while the effect on sRPE was mostly trivial (Fig. 6), which may suggest that these physiological and perceptual demands of RST are independent of sprint modality. It should be noted, however, that the acute demands of RST performed with changes of direction are conditional to the number and angle of direc- tion changes, the distance between each direction change and the duration of the sequence [60, 99, 106, 114, 115]. These factors affect the absolute speeds that are attained and the muscular work performed during the sprint, propulsive and braking components. Additionally, by integrating changes of direction into RST, there is accumulation of acceleration and deceleration which can increase the neuromuscular demand [99]. This seems evident by greater reductions in CMJ height following shuttle-based RST [104, 116, 117]. Shuttle-based sprints can be applied during a RST pro- gram to emphasise change of direction, limit absolute run- ning speeds and induce a similar physiological demand to straight-line RST. There may be instances, such as towards the end of season, where practitioners want to limit the phys- iological stress on the athlete during shuttle or multi-direc- tional RST. In these cases, it has been demonstrated that decreasing the sprint duration through time-matched proto- cols is an effective strategy [99]. Therefore, when design- ing RST, practitioners need to consider the influence of the direction changes on the duration of the sprint, rather than just the overall distance, as this can have a marked effect on the internal demands [99]. Of course, straight-line sprints should be implemented if the goal is to expose athletes to higher speeds. 4.2 Number of Sprint Repetitions and Sets Repeated-sprint training is implemented in research and practice to target a broad range of outcomes, which is reflected by considerable variation in the number of sprint repetitions prescribed across studies (range 2–40 repeti- tions per set). The vast majority of protocols (n = 257, 94%) implemented just one set, with six repetitions the most prescribed number of sprints per set (n = 122 protocols, 43%). Protocols that prescribed ≥ 12-repetitions per set [19, 33–35, 61, 62, 86, 118–128] were often designed to induce a high degree of fatigue. Accordingly, high creatine kinase responses (542–1127 µ·L−1) were reported in studies prescribing high repetition protocols [33–35, 123], despite longer inter-repetition rest times (≥ 30 s). These long-series of exhaustive efforts are counterintuitive to the movement demands of team sports, where sprint efforts are more likely to occur in small clusters [129, 130]. While the moderating effects of the number of sets per session was not meta-ana- lysed due to the low number of samples, it is worth noting that with an increasing number of sets, sprint times decayed and heart rate was increased, but changes in B[La] seem negligible [58, 122, 131]. Further investigation is required to better understand the impact of the number of sets per- formed per session, as well as the overall session volume, on the acute demands of RST. A substantial physiological demand is induced with the prescription of just six sprint repetitions, as demonstrated by the estimates and PI’s for HRpeak and B[La] (Figs. 4 and 8). A large cardiac demand, inferred by the 182 bpm reference estimate of HRpeak, coupled with a B[La] response exceed- ing 10 mmol·L−1, provide a strong aerobic and anaerobic stimulus, which may underpin the improvements in high- speed running abilities observed after RST interventions [2, 8]. With the reference estimate of B[La] above 10 mmol·L−1 Table 4 Summary of the effects of programming variables on the acute demands of repeated- sprint training in team sport athletes HRpeak B[La] sRPE Sbest Savg Sdec Shuttle RST ? ? = ↑ ↑ = ↓ Two more repetitions = = = = ↑ = = 10 m longer distance = ↑ ↑ = * * ↑ Active rest = ↑ ? = ↑ = ↓ ↓ = ↑ 10 s longer rest = ↓ = ↓ ↓ ↓ Acute demands based on meta-analytic inferences and compared with the reference protocol of 6 m × 30 m straight-line sprints with 20 s passive inter-repetition rest Symbols: ‘=’ indicates ‘trivial’, ‘↑’ substantial increase’, ‘↓’ indicates a ‘substantial decrease’, ‘= ↓’ indicates ‘compatibility with both a trivial and substantial decrease’, ‘= ↑’ indicates ‘compatibility with both a trivial and substantial increase’, ‘?’ indicates ‘inconclusive’ and ‘*’ indicates that the effects were not evaluated. Note: a decrease in Sbest and Savg indicates that sprint times are faster RST repeated-sprint training, HRpeak peak heart rate, B[La] blood lactate, sRPE session ratings of per- ceived exertion, Sbest best sprint time, Savg average sprint time, Sdec percentage sprint decrement 1628 F. Thurlow et al. and HRpeak close to maximal after six repetitions, further pursuing small increases in these acute physiological out- comes by performing more repetitions does not seem worth- while. Our meta-analytic estimates show that the effects of performing two more repetitions per set was trivial on all outcome measures except Sbest, which was compatible with both trivial and substantial effects (Fig. 10). There- fore, other programming factors appear to have a greater effect on physiological, perceptual and performance out- comes. Crude estimation of the number of additional sprints required for the point estimate of each outcome measure to equal the minimum practically important difference reveals an unrealistic and impractical expectation. For example, the number of additional repetitions needed to increase sRPE by a one-unit scale change in our data is ten (i.e. 16-repeti- tions per set in total). This increase in volume and the neu- romuscular demands of high repetition sets (greater than ten repetitions) may induce excessive muscle damage [33–35, 123]. Moreover, large numbers of repetitions can result in ‘pacing’ strategies that influence the maximal nature of RST and accumulated fatigue reduces the effectiveness of later sprints [132]. This is supported by our findings that show a Sdec of 1.2% would be expected to occur in studies (groups) performing 6 more repetitions (i.e. 12-repetitions per set in total) [77]. Therefore, excessive numbers of sprint repeti- tions can exacerbate fatigue and cause sub-optimal perfor- mance during RST. Lower numbers of repetitions per set (e.g. greater than six repetitions) may be a more effective programming approach during competition periods to reduce training volume while still providing a potent physiological stimulus and allow- ing for the quality of each repetition to be maintained. In this regard, the trivial reduction expected in each outcome measure when performing four versus six repetitions may be beneficial, when viewing from a risk-reward perspective. However, a one-size-fits-all approach regarding the RST pre- scription for team sport athletes can lead to some athletes being under-stimulated, while others can be overloaded, depending on the athletes’ speed and fitness profile [133, 134]. When the number of repetitions performed is fixed, there is considerable inter-individual variation in the degree of fatigue experienced across the same group of athletes [48]. This can be incurred despite two athletes having simi- lar maximal aerobic speeds but different maximal sprinting speeds (i.e. differences in anaerobic speed reserve) [134, 135]. In our review, all studies, except one [57], prescribed a fixed number of repetitions. However, in the study by Aken- head et al. [57] the level of relative sprint decrement (5%) was prescribed with a ‘flexible’ repetition scheme, which allowed more control over the magnitude of fatigue accrued by all participants. By prescribing a level of relative sprint decrement or relative performance threshold, instead of a fixed number of repetitions, practitioners can individualise RST prescription. This could provide practitioners with the ability to autoregulate training load based on differences in physical capacities and fluctuations in prior fatigue. 4.3 Sprint Distance A sprint distance of 30 m was most implemented (n = 107 protocols, 38%), which is longer than the average sprint distance typically observed during field-based team-sports competitions (15–25 m) [136]. Additionally, 40 m was the longest sprint distance prescribed (n = 74, 26%). This dis- tance is commonly used as a proxy measure of maximal speed in team sport athletes [137, 138], as it can allow maxi- mal velocity to be reached when it is applied in a straight- line format. Furthermore, both 30 m and 40 m were often implemented as a shuttle format, with one to two changes of direction. A distance of 14 m was the shortest sprint effort prescribed, represented in two protocols [139], while 15 m was prescribed in 11 (4%) protocols. Compared with longer sprints (> 30 m), these shorter distances emphasise the acceleration phase of sprinting and were often applied with court-based athletes (i.e. basketball and handball) [122, 139–141]. Shorter distances may better reflect the competi- tive environment of court-based team sports where players are engaged in sprint efforts of 15 m and less [119, 142, 143]. Despite the prevalence of studies implementing a sprint distance of 30 m, altering the distance of each sprint effort by 10 m had the largest moderating effect on B[La] (substan- tial increase), Sdec (substantial increase [more pronounced decline in sprint times]) and HRpeak (compatible with a triv- ial and substantial increase). Longer sprints increase phos- phocreatine (PCr) depletion and glycolytic activity, while also resulting in an increased accumulation of metabolic by-products (e.g. hydrogen ions, inorganic phosphate) [1, 136]. Furthermore, longer sprints provide exposure to faster absolute running speeds and higher vertical ground reaction forces that are attained via upright running mechanics [144, 145]. This is compared with shorter distances, where the athlete spends a high proportion of time in the acceleration phase, resulting in a greater horizontal propulsive force, but smaller braking force [144, 145]. Consequently, there can be a greater strain on the musculoskeletal system during longer sprints [146–148]. This is evident through greater declines in sprint kinematics (i.e. vertical stiffness and centre of mass vertical displacement) when longer sprint distance (35 m versus 20 m) was prescribed in two studies that inves- tigated spring-mass model characteristics [54, 149]. Despite a greater physiological and neuromuscular demand imposed by longer sprints, the effect of a 10 m longer sprint on sRPE was trivial (Fig. 6). This suggests that greater distances can be prescribed without inducing a practically substantial increase in perceived exertion. 1629 Acute Demands of Repeated-Sprint Training When beginning a RST program, shorter distances (15–25 m) are a more conservative option that can be used to limit metabolic stress and neuromuscular strain. It may also be beneficial to prescribe shorter distances during maintenance/taper sessions or for athletes who may never be exposed to longer sprints during competition (e.g. court- based athletes, goalkeepers). Training progression and over- load can then be achieved by gradually increasing distance (> 30 m) with a view to expose athletes to faster absolute running speeds, greater fatigue and a high physiological demand. This could be implemented during preparation phases before commencing high-intensity training drills and match-play, or during late-stage return to play follow- ing injury. 4.4 Inter‑repetition Rest Duration There was considerable heterogeneity in the distribution of inter-repetition rest duration across the protocols, which ranged from 10 to 60 s. This was partly due to differences in the approach to rest prescription, whereby pre-determined times, time-cycles and work-to-rest ratios were all employed in different literature. A 10 s rest duration was prescribed in 11 (4%) protocols, but such short rest may make it dif- ficult for athletes to safely decelerate and make it back to the start-line in time for the next sprint. The most common rest durations were 20 s and 30 s, represented in 83 (29%) and 67 (24%) protocols, respectively. These rest durations are similar to the amount of recovery time typically afforded between sprints during team sport competition [129, 130]. A 60 s rest duration was implemented in 9 (3%) protocols. Shorter rest times (e.g. 10 s versus 20 s) are associated with slower sprint times, greater performance fatigue and an increased metabolic response. Additionally, shorter rest may lead to greater decrements in CMJ height following RST [150]. Inversely, longer inter-repetition rest times (e.g. 30 s vs 20 s) have a substantial influence on the reduction of B[La] and allow for sprint performance to be better main- tained across a set (i.e. faster Savg and lower Sdec). This is likely due to greater clearance of metabolic by-products and increased PCr resynthesis [1, 121]. An interesting finding of our study was that a 10-s longer inter-repetition rest had a trivial effect on HRpeak and sRPE. Longer inter-repetition rest may allow athletes to perform each repetition with greater speed [151] and reduce the desire for pacing. Fur- thermore, longer rest would be expected to increase set dura- tion, thereby allowing both heart rate and VO2 to increase with time [86, 106, 122]. It is possible, however, that the cardiorespiratory demand could be blunted if prolonged rest times (e.g. 60 s) are implemented. This was demonstrated in a group of well-trained university students where VO2 was 9% less when 60-s rest times were used during RST, compared with 30 s rest [151]. Collectively, our findings support the use of longer rest durations (≥ 30 s) to reduce within session fatigue and main- tain repetition quality. Longer rest times could therefore be implemented during periods of fixture congestion to reduce player fatigue during RST, or used during the intensifica- tion stage of a pre-season to maximise sprint performance [19]. Additionally, longer rest times are recommended when longer sprint distances are prescribed, which can help account for the extended work duration of these sequences. However, longer rest durations reduce the metabolic demand of RST, which could limit certain physiological adaptations (e.g. maximal accumulated oxygen deficit, changes in glyco- lytic enzymes) [28, 152] and performance in activities that require a substantial anaerobic component [19]. Therefore, shorter rest durations (≤ 20 s) can be prescribed to induce greater levels of fatigue, which could help prepare team- sport athletes for peak periods of a match, where sprint efforts can be interspersed with minimal rest [129, 130]. 4.5 Inter‑repetition Rest Modality There were a higher number of protocols that implemented passive inter-repetition rest (n = 186, 66%), as opposed to an active rest period (n = 96, 34%). Active recovery pro- tocols were commonly combined with inter-repetition rest durations of ≥ 25 s. Most protocols that prescribed an active recovery involved a slow jog at pre-defined running speeds (e.g. 2 m⋅s−1) or self-selected speeds, which were often returning to a one-way start line. Other active recovery pro- tocols implemented faster running speeds such as 8 km⋅h−1 [23, 118] and 50% of maximal aerobic speed [24, 86, 153, 154]. When these faster running speeds were prescribed, the physiological demands (i.e. heart rate, VO2, B[La]) were amplified and there was a greater Sdec compared with passive rest and active rest performed at a slow jog [24, 153–155]. Repeated jumps were performed during the inter-repetition rest period in two studies [59, 156], which increased the car- diorespiratory and muscular demands [59, 156]. However, the internal demands are likely to be more varied compared with a precise running intensity. The findings of our meta-analysis suggest that active rest may cause a substantial increase in HRpeak (Fig. 4), sRPE (Fig. 6) and Sdec (Fig. 14), although we acknowledge that these effects are also compatible with trivial values (i.e. there could be no substantial influence). Active recovery limits the oxidative potential for PCr resynthesis before each sprint, which affects the maintenance of muscle power [24, 133, 150]. This leads to greater declines in anaerobic work capacity and subsequently, repeated-sprint performance. On the contrary, passive recovery is associated with an enhanced PCr resynthesis and as our results confirm, a smaller Sdec [157, 158]. While there were no substantial differences in B[La] (Fig. 8), our meta-analysis does not consider the 1630 F. Thurlow et al. intensity of the recovery period, which ultimately deter- mines the extent of the acute demands [59, 153, 157]. The prescription of active recovery might amplify the physiological and perceptual demands to RST, as well as performance decrement, without increasing the sprint volume. This could be achieved, for example, by prescrib- ing active recovery at an intensity of ≥ 50% maximal aero- bic speed. It would be practical to implement this with a standardised recovery-run distance and rest durations of ≥ 25 s to allow the athlete to gradually decelerate from the sprint component into the recovery running speed. Yet, once again, acknowledging that the influence of active recovery on HRpeak, sRPE and Sdec were compatible with both trivial and substantial effects, we advise practitioners to place more emphasis on recovery duration for manipulating RST acute demands at present. For this reason, future research should examine the effects of specific active recovery intensities on RST physiological, perceptual, neuromuscular and perfor- mance demands. 4.6 RST Protocols with Additional Modifications The use of additional modifications to RST can be applied to augment or attenuate internal demands. Short enforced deceleration zones (< 10 m), which were prescribed in two studies [33, 78], reduce sprint performance and exacerbate the magnitude of muscle damage due to the large eccentric forces applied during rapid braking, which is further accen- tuated when higher numbers of repetitions are performed. Gradual deceleration zones (> 10 m) are therefore recom- mended to mitigate undue muscular damage. Performing repeated jumps within the inter-repetition rest period may be an effective strategy to induce a greater physiological stimu- lus during RST, while exposing athletes to sport-specific actions, without an increase in the volume of high-intensity running [59, 156]. When jumps were prescribed in studies by Buchheit et al. [59] and Padulo et al. [156], very high B[La] (10.2–13.1 m⋅mol−1), HRpeak (96%–97% heart rate max) and sRPE (7.9–8.0 au) were observed. The additional muscular work performed during the recovery period with jumps has previously been shown to increase muscle deoxy- genation of the lower limbs, but it should be noted that these sequences are also likely to reduce acute sprint performance [59, 156]. Furthermore, with only two studies investigating the effects of jumps within the inter-repetition rest period, the optimal volume and intensity of these actions are yet to be established. There is potential for other modifications to be implemented during RST, such as sport-specific skills (e.g. passing, shooting), grappling, push-ups and tackling into contact bags. These types of explosive efforts typically precede or follow high-intensity runs/sprints during match play [159–161] and may help to better simulate the physi- ological demands associated with competition. Furthermore, flying sprints that incorporate a submaximal acceleration zone may provide exposure to repeated bouts of maximal velocity sprinting, without the neuromuscular demands of rapid acceleration [162]. 4.7 Limitations There are several important issues to consider when inter- preting our findings. Depending on the outcome measure, a proportion of the variation in the meta-analysed acute demands of RST can be explained by factors other than the programming variables investigated (Supplementary Table S4). Factors directly related to individual differences in human physiology have been shown to influence the acute demands to RST, such as age [36, 100, 101, 111, 163–166], fitness level [167], playing status [46, 168–174], gender [131, 139, 175, 176] and ethnicity [177]. Furthermore, a proportion of the variation in the acute demands may also be due to the impact of programming variables not inves- tigated (e.g. number of sets), as well varied data collection methods, conditions and reporting. For example, there are inter- and intra-individual differences in B[La] accumulation depending on sampling procedures (time and site), hydration status, previous exercise and ambient temperature [18, 47, 178]. Nevertheless, the influence of the latter factors on the present review are likely to be low considering that item ten in the inclusion–exclusion criteria ensures that RST must have been performed under normal conditions (e.g. hydrated state, ≤ 30 °C) and without fatiguing exercise occurring in the previous 24 h. We also appreciate the concerns of com- paring CMJ height between different methods and devices [179], which is why CMJ outcomes were not meta-analysed. When interpreting acute heart rate and VO2 responses to training, it is important to consider the starting value at the commencement of exercise, which will influence the magni- tude of change. However, the majority of studies did not pre- sent this information, and thus, we were unable to account for this in our analyses. Additionally, there was an insuffi- cient number of samples to determine the moderating effects of programming variables on average heart rate and VO2. There was also a low number of samples for HRpeak as % HRmax, creatine kinase, spring mass-model parameters and sprint force–velocity–power parameters, which meant we were unable to meta-analyse these outcomes. Therefore, in future, researchers may wish to investigate the effects of RST on these outcomes. Finally, it should be noted that while our elected reference adjustments of 10 m and 10 s allow for comparison between sprint distance and inter-repetition rest time, respectively, this will not always represent the same relative change (i.e. an increased sprint distance from 10 m to 20 m represents a 100% change, while 30 m–40 m rep- resents a 25% change). Therefore, this information should 1631 Acute Demands of Repeated-Sprint Training be treated with caution and used within the context of the programmed session. 5 Conclusions Our systematic review and meta-analysis is the first to sum- marise the acute physiological, neuromuscular, perceptual and performance demands of RST in team sport athletes, while providing a quantitative synthesis of the effects of pro- gramming variables. RST provides a potent physiological stimulus for the physical development of team sport ath- letes, with the magnitude of the acute demands influenced by several programming variables (Table 4). Longer sprint distances and shorter inter-repetition rest periods are the most efficacious strategies to increase RST demands. When manipulated in combination, these factors are likely to have an even greater effect, from which the magnitude of within- session fatigue and acute training response can be expected to follow. Reducing the number of repetitions per set (e.g. four as opposed to six) can maintain the physiological, per- ceptual and performance demands of RST while reducing sprint volume. When combined with shorter sprint distances and increased inter-repetition rest periods, this might be a useful strategy during strenuous training and competition periods [26]. Additionally, straight-line, shuttle and multi- directional repeated-sprints can be prescribed to target movement specific outcomes, depending on the aims of the training program. While there is a large quantity of evidence relating to acute performance outcomes of RST, there is a lack of literature on cardiorespiratory (e.g. VO2) and neuro- muscular demands. The insights from our review and meta- analysis provide practitioners with the expected demands of RST and can be used to help optimise training prescription through the manipulation of programming variables. Supplementary Information The online version contains supplemen- tary material available at https:// doi. org/ 10. 1007/ s40279- 023- 01853-w. Declarations Funding Open Access funding enabled and organized by CAUL and its Member Institutions. Conflict of Interest All authors declare that they have no conflict of interests relevant to the content of this review. Data Availability All data and material reported in this systematic review and meta-analyses are from peer-reviewed publications. All extracted data is available in Supplementary Tables S2 and S3. Author Contributions Fraser Thurlow, Jonathon Weakley, Matthew Morrison and Shaun McLaren conceptualised the review and crite- ria. Fraser Thurlow, Jonathon Weakley, Matthew Morrison and Shaun McLaren completed the screening, data extraction and data analysis of all data within this manuscript. All authors created the tables and figures. All authors contributed to the writing of the manuscript. All authors reviewed, refined and approved the final manuscript. Open Access This article is licensed under a Creative Commons Attri- bution 4.0 International License, which permits use, sharing, adapta- tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. References 1. Girard O, Mendez-Villanueva A, Bishop D. Repeated-sprint ability—part I. Sports Med. 2011;41(8):673–94. 2. Taylor JM, Macpherson TW, McLaren SJ, Spears I, Weston M. Two weeks of repeated-sprint training in soccer: to turn or not to turn? Int J Sports Physiol Perform. 2016;11(8):998–1004. 3. Mendiguchia J, Conceição F, Edouard P, Fonseca M, Pereira R, Lopes H, et al. Sprint versus isolated eccentric training: Com- parative effects on hamstring architecture and performance in soccer players. PLoS One. 2020;15(2): e0228283. 4. Malone S, Roe M, Doran DA, Gabbett TJ, Collins K. High chronic training loads and exposure to bouts of maximal veloc- ity running reduce injury risk in elite Gaelic football. J Sci Med Sport. 2017;20(3):250–4. 5. Taylor JB, Wright AA, Dischiavi SL, Townsend MA, Marmon AR. Activity demands during multi-directional team sports: a systematic review. Sports Med. 2017;47(12):2533–51. 6. Bompa TO, Buzzichelli C. Periodization: theory and methodol- ogy of training. J Hum Kinet. 2019. 7. Coffey VG, Hawley JA. The molecular bases of training adapta- tion. Sports Med. 2007;37(9):737–63. 8. Taylor J, Macpherson T, Spears I, Weston M. The effects of repeated-sprint training on field-based fitness measures: a meta-analysis of controlled and non-controlled trials. Sports Med. 2015;45(6):881–91. 9. Manuel Clemente F, Ramirez-Campillo R, Nakamura FY, Sarmento H. Effects of high-intensity interval training in men soccer player’s physical fitness: a systematic review with meta- analysis of randomized-controlled and non-controlled trials. J Sports Sci. 2021;39(11):1202–22. 10. Boer P, Van Aswegen M. Effect of combined versus repeated sprint training on physical parameters in sub-elite football players in South Africa. J Phys Educ Sport. 2016;16(3):964. 11. Bravo DF, Impellizzeri FM, Rampinini E, Castagna C, Bishop D, Wisloff U. Sprint vs. interval training in football. Int J Sports Med. 2008;29(08):668–74. 12. Ross A, Leveritt M. Long-term metabolic and skeletal muscle adaptations to short-sprint training. Sports Med. 2001;31(15):1063–82. 13. Fernandez-Fernandez J, Zimek R, Wiewelhove T, Ferrauti A. High-intensity interval training vs. repeated-sprint training in tennis. J Strength Cond Res. 2012;26(1):53–62. 14. Gantois P, Batista GR, Aidar FJ, Nakamura FY, de Lima-Júnior D, Cirilo-Sousa MS, et al. Repeated sprint training improves 1632 F. Thurlow et al. both anaerobic and aerobic fitness in basketball players. Isoki- net Exerc Sci. 2019;27(2):97–105. 15. Kaynak K, Eryılmaz SK, Aydoğan S, Mihailov D. The effects of 20-m repeated sprint training on aerobic capacity in college volleyball players. Biomed Hum Kinetics. 2017;9(1):43–50. 16. Maggioni MA, Bonato M, Stahn A, La Torre A, Agnello L, Vernillo G, et al. Effects of ball drills and repeated-sprint-abil- ity training in basketball players. Int J Sports Physiol Perform. 2019;14(6):757–64. 17. Serpiello FR, McKenna MJ, Bishop DJ, Aughey RJ, Caldow MK, Cameron-Smith D, et al. Repeated sprints alter signaling related to mitochondrial biogenesis in humans. Med Sci Sports Exerc. 2012;44(5):827–34. 18. Buchheit M, Laursen PB. High-intensity interval training, solu- tions to the programming puzzle. Part 2: anaerobic energy, neuromuscular load and practical applications. Sports Med. 2013;43:927–54. 19. Iaia FM, Fiorenza M, Larghi L, Alberti G, Millet GP, Girard O. Short-or long-rest intervals during repeated-sprint training in soccer? PLoS One. 2017;12(2): e0171462. 20. Beneke R, Leithäuser RM, Ochentel O. Blood lactate diag- nostics in exercise testing and training. Int J Sports Physiol Perform. 2011;6(1):8–24. 21. Krustrup P, Mohr M, Nybo L, Jensen JM, Nielsen JJ, Bangsbo J. The Yo-Yo IR2 test: physiological response, reliabil- ity, and application to elite soccer. Med Sci Sports Exerc. 2006;38(9):1666. 22. Hautier C, Wouassi D, Arsac L, Bitanga E, Thiriet P, Lacour J. Relationships between postcompetition blood lactate concentra- tion and average running velocity over 100-m and 200-m races. Euro J Appl Physiol. 1994;68(6):508–13. 23. Alemdaroğlu U, Köklü Y, Bektaş F, Çelik G, Ünver F, Duffield R. Comparison of repeated sprint tests in young soccer players: straight versus shuttle. Kinesiol Slov. 2018;24(2):5–16. 24. Madueno MC, Dalbo VJ, Guy JH, Giamarelos KE, Spiteri T, Scanlan AT. Reduced fatigue in passive versus active recovery: an examination of repeated-change-of-direction sprints in basket- ball players. Int J Sports Physiol Perform. 2018;13(8):1034–41. 25. Keir DA, Thériault F, Serresse O. Evaluation of the running- based anaerobic sprint test as a measure of repeated sprint ability in collegiate-level soccer players. J Strength Cond Res. 2013;27(6):1671–8. 26. Laursen P, Buchheit M. Science and application of high-intensity interval training. J Hum Kinet. 2019. 27. Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-anal- yses: the PRISMA statement. PLoS Med. 2009;6(7): e1000097. 28. Bishop D, Girard O, Mendez-Villanueva A. Repeated-sprint abil- ity—part II. Sports Med. 2011;41(9):741–56. 29. Ratel S, Williams C, Oliver J, Armstrong N. Effects of age and recovery duration on performance during multiple treadmill sprints. Int J Sports Med. 2006;27(01):1–8. 30. Groslambert A, Mahon AD. Perceived exertion. Sports Med. 2006;36(11):911–28. 31. Girard O, Brocherie F, Bishop D. Sprint performance under heat stress: a review. Scand J Med Sci Sports. 2015;25:79–89. 32. Doeven SH, Brink MS, Kosse SJ, Lemmink KA. Postmatch recovery of physical performance and biochemical markers in team ball sports: a systematic review. BMJ Open Sport Exerc Med. 2018;4(1): e000264. 33. Woolley B, Jakeman J, Faulkner J. Multiple sprint exercise with a short deceleration induces muscle damage and performance impairment in young, physically active males. J Athl Enhance. 2014;3:2. 34. Clifford T, Berntzen B, Davison GW, West DJ, Howatson G, Stevenson EJ. Effects of beetroot juice on recovery of muscle function and performance between bouts of repeated sprint exer- cise. Nutrients. 2016;8(8):506. 35. Howatson G, Milak A. Exercise-induced muscle damage follow- ing a bout of sport specific repeated sprints. J Strength Cond Res. 2009;23(8):2419–24. 36. Klatt S, Kossmann M, Hottenrott L, Ferrauti A, Fasold F. Per- formance changes and recovery time in U20 and older hand- ball players after a high-intensity sprint exercise. Appl Sci. 2021;11(11):5301. 37. Eryılmaz SK, Aslankeser Z, Özdemir Ç, Özgünen K, Kurdak S. Effects of training load changes on physical performance and exercise-induced muscle damage. Rev Bras Med Esporte. 2019;25:509–14. 38. López-Segovia M, Pareja-Blanco F, Jiménez-Reyes P, González- Badillo JJ. Determinant factors of repeat sprint sequences in young soccer players. Int J Sports Med. 2015;36(02):130–6. 39. Lemmink K, Elferink-Gemser M, Visscher C. Evaluation of the reliability of two field hockey specific sprint and dribble tests in young field hockey players. Br J Sports Med. 2004;38(2):138–42. 40. Johnston RD, Gabbett TJ. Repeated-sprint and effort ability in rugby league players. J Strength Cond Res. 2011;25(10):2789–95. 41. Austin DJ, Gabbett TJ, Jenkins DG. Reliability and sen- sitivity of a repeated high-intensity exercise performance test for rugby league and rugby union. J Strength Cond Res. 2013;27(4):1128–35. 42. Iacono AD, Ardigò LP, Meckel Y, Padulo J. Effect of small- sided games and repeated shuffle sprint training on physical performance in elite handball players. J Strength Cond Res. 2016;30(3):830–40. 43. Di Mascio M, Ade J, Bradley PS. The reliability, validity and sensitivity of a novel soccer-specific reactive repeated-sprint test (RRST). Eur J Appl Physiol. 2015;115(12):2531–42. 44. Wragg C, Maxwell N, Doust J. Evaluation of the reliability and validity of a soccer-specific field test of repeated sprint ability. Eur J Appl Physiol. 2000;83(1):77–83. 45. Di Mascio M, Ade J, Musham C, Girard O, Bradley PS. Soccer- specific reactive repeated-sprint ability in elite youth soccer players: maturation trends and association with various physical performance tests. J Strength Cond Res. 2020;34(12):3538–45. 46. Brini S, Boullosa D, Calleja-González J, Delextrat A. Construct validity and reliability of a new basketball multidirectional reac- tive repeated sprint test. Int J Environ Res. 2021;18(20):10695. 47. Halson SL. Monitoring training load to understand fatigue in athletes. Sports Med. 2014;44(2):139–47. 48. Morcillo JA, Jiménez-Reyes P, Cuadrado-Peñafiel V, Lozano E, Ortega-Becerra M, Párraga J. Relationships between repeated sprint ability, mechanical parameters, and blood metabolites in professional soccer players. J Strength Cond Res. 2015;29(6):1673–82. 49. Fitzsimons M, Dawson B, Ward D, Wilkinson A. Cycling and running tests of repeated sprint ability. Aust J Sci Med Sport. 1993;25:82. 50. Glaister M, Howatson G, Pattison JR, McInnes G. The reliability and validity of fatigue measures during multi- ple-sprint work: an issue revisited. J Strength Cond Res. 2008;22(5):1597–601. 51. Lopes-Silva JP, da Silva Santos JF, Abbiss CR, Franchini E. Measurement properties and feasibility of repeated sprint ability test: a systematic review. Strength Cond J. 2019;41(6):41–61. 52. Samozino P, Rabita G, Dorel S, Slawinski J, Peyrot N, Saez de Villarreal E, et al. A simple method for measuring power, force, velocity properties, and mechanical effectiveness in sprint run- ning. Scand J Med Sci Sports. 2016;26(6):648–58. 1633 Acute Demands of Repeated-Sprint Training 53. Morin J-B, Dalleau G, Kyröläinen H, Jeannin T, Belli A. A sim- ple method for measuring stiffness during running. J Appl Bio- mech. 2005;21(2):167–80. 54. Brocherie F, Millet GP, Girard O. Neuro-mechanical and meta- bolic adjustments to the repeated anaerobic sprint test in profes- sional football players. Eur J Appl Physiol. 2015;115(5):891–903. 55. McKay AK, Stellingwerff T, Smith ES, Martin DT, Mujika I, Goosey-Tolfrey VL, et al. Defining training and performance caliber: a participant classification framework. Int J Sports Phys- iol Perform. 2022;1(aop):1–15. 56. Borg G. The Borg CR Scales Folder. Methods for measuring intensity of experience. Hasselby: Stockholm University; 2010. 57. Akenhead R, Marques JB, Paul DJ. Accelerometer load: a new way to measure fatigue during repeated sprint training? Sci Med Footb. 2017;1(2):151–6. 58. Selmi MA, Haj Sassi R, Haj Yahmed M, Moalla W, Elloumi M. Effect of between-set recovery durations on repeated sprint abil- ity in young soccer players. Biol Sport. 2016;33(2):165–72. 59. Buchheit M. Performance and physiological responses to repeated-sprint and jump sequences. Eur J Appl Physiol. 2010;110(5):1007–18. 60. Buchheit M, Bishop D, Haydar B, Nakamura FY, Ahmaidi S. Physiological responses to shuttle repeated-sprint running. Int J Sports Med. 2010;31(06):402–9. 61. Dellal A, Casamichana D, Castellano J, Haddad M, Moalla W, Chamari K. Cardiac parasympathetic reactivation in elite soccer players during different types of traditional high-intensity train- ing exercise modes and specific tests: interests and limits. Asian J Sports Med. 2015;6(4). 62. Haugen T, Tonnessen E, Leirstein S, Hem E, Seiler S. Not quite so fast: effect of training at 90% sprint speed on maxi- mal and repeated-sprint ability in soccer players. J Sports Sci. 2014;32(20):1979–86. 63. Till K, Scantlebury S, Jones B. Anthropometric and physical qualities of elite male youth rugby league players. Sports Med. 2017;47(11):2171–86. 64. Downs SH, Black N. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. J Epidemiol Community Health. 1998;52(6):377–84. 65. Cheung MW-L. A guide to conducting a meta-analysis with non- independent effect sizes. Neuropsychol Rev. 2019;29(4):387–96. 66. Borenstein M, Hedges LV, Higgins JP, Rothstein HR. A basic introduction to fixed-effect and random-effects models for meta- analysis. Res Synth. 2010;1(2):97–111. 67. Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Stat Softw. 2010;36(3):1–48. 68. Pustejovsky J. clubSandwich: Cluster-Robust (Sandwich) Vari- ance Estimators with Small-Sample Corrections. R package version 0.5.3 2021. https:// CRAN.R- proje ct. org/ packa ge= clubS andwi ch. 69. Riley RD. Multivariate meta-analysis: the effect of ignor- ing within-study correlation. J R Stat Soc A Stat Soc. 2009;172(4):789–811. 70. IntHout J, Ioannidis JP, Rovers MM, Goeman JJ. Plea for rou- tinely presenting prediction intervals in meta-analysis. BMJ Open. 2016;6(7): e010247. 71. Higgins JP. Commentary: Heterogeneity in meta-analysis should be expected and appropriately quantified. Int J Epide- miol. 2008;37(5):1158–60. 72. Viechtbauer W. Confidence intervals for the amount of hetero- geneity in meta-analysis. Stat Med. 2007;26(1):37–52. 73. Higgins JP, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al. Cochrane handbook for systematic reviews of interven- tions. New York: Wiley; 2019. 74. Greenland S. Valid p-values behave exactly as they should: some misleading criticisms of p-values and their resolution with s-values. Am Stat. 2019;73(sup1):106–14. 75. Buchheit M. Monitoring training status with HR measures: do all roads lead to Rome? Front Physiol. 2014;5:73. 76. McLaren SJ. The application of differential ratings of perceived exertion to training monitoring in team sports [Doctoral The- sis]. Middlesbrough: Teesside University; 2018. 77. Haugen T, Buchheit M. Sprint running performance monitor- ing: methodological and practical considerations. Sports Med. 2016;46(5):641–56. 78. Lakomy J, Haydon DT. The effects of enforced, rapid decelera- tion on performance in a multiple sprint test. J Strength Cond Res. 2004;18(3):579–83. 79. Buchheit M, Laursen PB. High-intensity interval train- ing, solutions to the programming puzzle. Sports Med. 2013;43(10):927–54. 80. Harrison PW, Johnston RD. Relationship between training load, fitness, and injury over an Australian rules football pre- season. J Strength Cond Res. 2017;31(10):2686–93. 81. Malone JJ, Di Michele R, Morgans R, Burgess D, Morton JP, Drust B. Seasonal training-load quantification in elite English premier league soccer players. Int J Sports Physiol Perform. 2015;10:489–97. 82. Dalton-Barron NE, McLaren SJ, Black CJ, Gray M, Jones B, Roe G. Identifying contextual influences on training load: an example in professional Rugby Union. J Strength Cond Res. 2021;35(2):503–11. 83. Gharbi Z, Dardouri W, Haj-Sassi R, Castagna C, Chamari K, Souissi N. Effect of the number of sprint repetitions on the variation of blood lactate concentration in repeated sprint ses- sions. Biol Sport. 2014;31(2):151. 84. Medbø J, Burgers S. Effect of training on the anaerobic capac- ity. Med Sci Sports Exerc. 1990;22(4):501–7. 85. Chiu LZ, Barnes JL. The fitness-fatigue model revisited: impli- cations for planning short-and long-term training. J Strength Cond Res. 2003;25(6):42–51. 86. Dupont G, Millet GP, Guinhouya C, Berthoin S. Relationship between oxygen uptake kinetics and performance in repeated running sprints. Eur J Appl Physiol. 2005;95(1):27–34. 87. Tønnessen E, Hem E, Leirstein S, Haugen T, Seiler S. Maximal aerobic power characteristics of male professional soccer play- ers, 1989–2012. Int J Sports Physiol Perform. 2013;8(3):323–9. 88. Haugen TA, Tønnessen E, Hem E, Leirstein S, Seiler S. VO2max characteristics of elite female soccer players, 1989–2007. Int J Sports Physiol Perform. 2014;9(3):515–21. 89. Gabbett TJ, Jenkins DG, Abernethy B. Relationships between physiological, anthropometric, and skill qualities and playing performance in professional rugby league players. J Sports Sci. 2011;29(15):1655–64. 90. Higham DG, Pyne DB, Anson JM, Eddy A. Physiological, anthropometric, and performance characteristics of rugby sevens players. Int J Sports Physiol Perform. 2013;8(1):19–27. 91. Midgley AW, McNaughton LR, Wilkinson M. Is there an optimal training intensity for enhancing the maximal oxygen uptake of distance runners? Sports Med. 2006;36(2):117–32. 92. Laursen PB, Jenkins DG. The scientific basis for high-intensity interval training. Sports Med. 2002;32(1):53–73. 93. Midgley A, Mc NL. Time at or near VO2max during continuous and intermittent running: a review with special reference to con- siderations for the optimisation of training protocols to elicit the longest time at or near VO^ sub 2max. J Sports Med Phys Fit. 2006;46(1):1. 94. Swain DP, Abernathy KS, Smith CS, Lee SJ, Bunn SA. Target heart rates for the development of cardiorespiratory fitness. Med Sci Sports Exerc. 1994;26(1):112–6. 1634 F. Thurlow et al. 95. Aguiar M, Abrantes C, Maçãs V, Leite N, Sampaio J, Ibáñez S. Effects of intermittent or continuous training on speed, jump and repeated-sprint ability in semi-professional soccer players. Open Sports Sci J. 2008;1(1). 96. Almansba R, Boucher VG, Parent A-A, Comtois AS. Repeated linear and quadrangular sprint as a function of anaerobic power. J Strength Cond Res. 2019;33(8):2177–84. 97. Brahim MB, Mohamed A, Shalfawi SA. The evaluation of soccer players performance on different repeated sprints tests: training and testing implications. Kinesiol Slov. 2016;22(2). 98. Brini S, Ben Abderrahman A, Boullosa D, Hackney AC, Zagatto AM, Castagna C, et al. Effects of a 12-week change-of-direction sprints training program on selected physical and physiological parameters in professional basketball male players. Int J Environ Res Public Health. 2020;17(21):8214. 99. Buchheit M, Haydar B, Ahmaidi S. Repeated sprints with directional changes: do angles matter? J Sports Sci. 2012;30(6):555–62. 100. Dellal A, Wong DP. Repeated sprint and change-of-direction abilities in soccer players: effects of age group. J Strength Cond Res. 2013;27(9):2504–8. 101. Gibson N, Currie J, Johnston R, Hill J. Relationship between measures of aerobic fitness, speed and repeated sprint ability in full and part time youth soccer players. J Sports Med Phys Fit. 2013;53(1):9–16. 102. Gonzalo-Skok O, Tous-Fajardo J, Arjol-Serrano JL, Suarez- Arrones L, Casajús JA, Mendez-Villanueva A. Improve- ment of repeated-sprint ability and horizontal-jumping per- formance in elite young basketball players with low-volume repeated-maximal-power training. Int J Sports Physiol Perform. 2016;11(4):464–73. 103. Perroni F, Corvino M, Cignitti L, Minganti C. RSA response to preseason training in semiprofessional soccer players. Sport Sci Health. 2013;9(2):59–64. 104. Ruscello B, Tozzo N, Briotti G, Padua E, Ponzetti F, D’Ottavio S. Influence of the number of trials and the exercise to rest ratio in repeated sprint ability, with changes of direction and orientation. J Strength Cond Res. 2013;27(7):1904–19. 105. Suarez-Arrones L, Tous-Fajardo J, Núñez J, Gonzalo-Skok O, Gálvez J, Mendez-Villanueva A. Concurrent repeated-sprint and resistance training with superimposed vibrations in rugby play- ers. Int J Sports Physiol Perform. 2014;9(4):667–73. 106. Zagatto AM, Ardigò LP, Barbieri FA, Milioni F, Iacono AD, Camargo BH, et al. Performance and metabolic demand of a new repeated-sprint ability test in basketball players: does the number of changes of direction matter? J Strength Cond Res. 2017;31(9):2438–46. 107. Zagatto AM, Claus GM, Dutra YM, de Poli RA, Lopes VH, Goodall S, et al. Drop jumps versus sled towing and their effects on repeated sprint ability in young basketball players. BMC Sports Sci Med Rehabil. 2022;14(1):1–11. 108. Blasco-Lafarga C, Donfrancesco G, Montoya-Vieco A, Cordel- lat A, Monteagudo P, Roldán A, et al. Quantifying performance impairment, specificity and fatigue in young soccer profession- als: UirFide Soccer test vs. Bangsbo test. Gazz Med Ital Arch Sci Med. 2020;179(7–8):437–47. 109. Kaplan T. Examination of repeated sprinting ability and fatigue index of soccer players according to their positions. J Strength Cond Res. 2010;24(6):1495–501. 110. Joo CH. The effects of short-term detraining on exercise perfor- mance in soccer players. J Exerc Rehabil. 2016;12(1):54–9. 111. Turki O, Dhahbi W, Gueid S, Hmaied S, Souaifi M, Khalifa R. Dynamic warm-up with a weighted vest: improvement of repeated change-of-direction performance in young male soccer players. Int J Sport Physiol Perform. 2020;15(2):196–203. 112. Brughelli M, Cronin J, Levin G, Chaouachi A. Under- standing change of direction ability in sport. Sports Med. 2008;38(12):1045–63. 113. Sheppard JM, Young WB. Agility literature review: classifica- tions, training and testing. J Sports Sci. 2006;24(9):919–32. 114. Padulo J, Laffaye G, Haddad M, Chaouachi A, Attene G, Migli- accio GM, et al. Repeated sprint ability in young basketball players: one vs. two changes of direction (Part 1). J Sports Sci. 2015;33(14):1480–92. 115. Attene G, Nikolaidis PT, Bragazzi NL, Dello Iacono A, Pizzolato F, Zagatto AM, et al. Repeated sprint ability in young basketball players (Part 2): the chronic effects of multidirection and of one change of direction are comparable in terms of physiological and performance responses. Front Physiol. 2016;7:262. 116. Dal Pupo J, Detanico D, Carminatti LJ, Santos SG. Physiological and neuromuscular responses in the shuttle and straight line- repeated sprint running. Apunts Med Esport. 2013;48(178):43–8. 117. Sánchez-Sánchez J, García-Unanue J, Jiménez-Reyes P, Gallardo A, Burillo P, Felipe JL, et al. Influence of the mechanical proper- ties of third-generation artificial turf systems on soccer players’ physiological and physical performance and their perceptions. PLoS One. 2014;9(10): e111368. 118. Abt G, Siegler JC, Akubat I, Castagna C. The effects of a con- stant sprint-to-rest ratio and recovery mode on repeated sprint performance. J Strength Cond Res. 2011;25(6):1695–702. 119. Figueira B, Gonçalves B, Abade E, Paulauskas R, Masiulis N, Kamarauskas P, et al. Repeated sprint ability in elite basketball players: the effects of 10× 30 m vs. 20× 15 m exercise protocols on physiological variables and sprint performance. J Hum Kinet. 2021;77(1):181–9. 120. Iaia FM, Fiorenza M, Perri E, Alberti G, Millet GP, Bangsbo J. The effect of two speed endurance training regimes on perfor- mance of soccer players. PLoS One. 2015;10(9): e0138096. 121. Little T, Williams AG. Effects of sprint duration and exercise: rest ratio on repeated sprint performance and physiological responses in professional soccer players. J Strength Cond Res. 2007;21(2):646–8. 122. Paulauskas R, Kamarauskas P, Nekriošius R, Bigwood NM. Physical and physiological response to different modes of repeated sprint exercises in basketball players. J Hum Kinet. 2020;72(1):91–9. 123. Russell M, Birch J, Love T, Cook CJ, Bracken RM, Taylor T, et al. The effects of a single whole-body cryotherapy exposure on physiological, performance, and perceptual responses of profes- sional academy soccer players after repeated sprint exercise. J Strength Cond Res. 2017;31(2):415–21. 124. Eliakim M, Meckel Y, Gotlieb R, Nemet D, Eliakim A. Motiva- tional music and repeated sprint ability in junior basketball play- ers. Acta Kinesiologiae Universitatis Tartuensis. 2012;18:29–38. 125. Ulupınar S, Hazır T, Kinİşler A. The contribution of energy sys- tems in repeated-sprint protocols: The effect of distance, rest, and repetition. Res Q Exerc Sport. 2023;94:173–9. 126. Ulupınar S, Özbay S, Gençoğlu C, Franchini E, Kishalı NF, İnce İ. Effects of sprint distance and repetition number on energy system contributions in soccer players. J Exerc Sci Fit. 2021;19(3):182–8. 127. Costello SE, O’Neill BV, Howatson G, van Someren K, Haskell-Ramsay CF. Detrimental effects on executive func- tion and mood following consecutive days of repeated high- intensity sprint interval exercise in trained male sports players. J Sports Sci. 2022;40:783–96. 128. Haugen T, Tønnessen E, Øksenholt Ø, Haugen FL, Paulsen G, Enoksen E, et al. Sprint conditioning of junior soccer play- ers: effects of training intensity and technique supervision. PLoS One. 2015;10(3): e0121827. 1635 Acute Demands of Repeated-Sprint Training 129. Spencer M, Lawrence S, Rechichi C, Bishop D, Dawson B, Goodman C. Time–motion analysis of elite field hockey, with special reference to repeated-sprint activity. J Sports Sci. 2004;22(9):843–50. 130. Dawson B. Repeated-sprint ability: where are we? Int J Sports Physiol Perform. 2012;7(3):285–9. 131. Dent JR, Edge JA, Hawke E, McMahon C, Mündel T. Sex dif- ferences in acute translational repressor 4E-BP1 activity and sprint performance in response to repeated-sprint exercise in team sport athletes. J Sci Med Sport. 2015;18(6):730–6. 132. Vollaard N, Metcalfe R, Williams S. Effect of number of sprints in a SIT session on change in VO2max: a meta-analysis. Med Sci Sports Exerc. 2017;49(6):1147–56. 133. Mendez-Villanueva A, Hamer P, Bishop D. Fatigue in repeated-sprint exercise is related to muscle power factors and reduced neuromuscular activity. Eur J Appl Physiol. 2008;103(4):411–9. 134. Sandford GN, Laursen PB, Buchheit M. Anaerobic speed/power reserve and sport performance: scientific basis, current applica- tions and future directions. Sports Med. 2021;51(10):2017–28. 135. Julio UF, Panissa VL, Paludo AC, Alves ED, Campos FA, Fran- chini E. Use of the anaerobic speed reserve to normalize the prescription of high-intensity interval exercise intensity. Eur J Sport Sci. 2020;20(2):166–73. 136. Spencer M, Bishop D, Dawson B, Goodman C. Physiological and metabolic responses of repeated-sprint activities. Sports Med. 2005;35(12):1025–44. 137. Jiménez-Reyes P, Garcia-Ramos A, Párraga-Montilla JA, Morcillo-Losa JA, Cuadrado-Peñafiel V, Castaño-Zambudio A, et al. Seasonal changes in the sprint acceleration force-veloc- ity profile of elite male soccer players. J Strength Cond Res. 2022;36(1):70–4. 138. Jiménez-Reyes P, García-Ramos A, Cuadrado-Peñafiel V, Párraga-Montilla JA, Morcillo-Losa JA, Samozino P, et al. Dif- ferences in sprint mechanical force–velocity profile between trained soccer and futsal players. Int J Sports Physiol Perform. 2019;14(4):478–85. 139. Mancha-Triguero D, García-Rubio J, Gamonales JM, Ibáñez SJ. Strength and speed profiles based on age and sex differences in young basketball players. Int J Environ Res Public Health. 2021;18(2):643. 140. Izquierdo M, Ibañez J, González-Badillo JJ, Gorostiaga EM. Effects of creatine supplementation on muscle power, endurance, and sprint performance. Med Sci Sports Exerc. 2002;34(2):332–43. 141. Broderick V, Uiga L, Driller M. Flotation-restricted environmen- tal stimulation therapy improves sleep and performance recovery in athletes. Perform Enhancement Health. 2019;7(1–2). 142. Conte D, Favero TG, Lupo C, Francioni FM, Capranica L, Tes- sitore A. Time-motion analysis of Italian elite women’s basket- ball games: individual and team analyses. J Strength Cond Res. 2015;29(1):144–50. 143. Manchado C, Pers J, Navarro F, Han A, Sung E, Platen P. Time- motion analysis in women’s team handball: importance of aero- bic performance. J Hum Sport Exerc. 2013. 144. Mero A, Komi P, Gregor R. Biomechanics of sprint running. Sports Med. 1992;13(6):376–92. 145. Higashihara A, Nagano Y, Ono T, Fukubayashi T. Differences in hamstring activation characteristics between the accelera- tion and maximum-speed phases of sprinting. J Sports Sci. 2018;36(12):1313–8. 146. Timmins RG, Opar DA, Williams MD, Schache AG, Dear NM, Shield AJ. Reduced biceps femoris myoelectrical activity influ- ences eccentric knee flexor weakness after repeat sprint run- ning. Scand J Med Sci Sports. 2014;24(4):e299–305. 147. Thelen DG, Chumanov ES, Hoerth DM, Best TM, Swanson SC, Li L, et al. Hamstring muscle kinematics during treadmill sprinting. Med Sci Sports Exerc. 2005;37(1):108–14. 148. Schache AG, Dorn TW, Blanch PD, Brown NA, Pandy MG. Mechanics of the human hamstring muscles during sprinting. Med Sci Sports Exerc. 2012;44(4):647–58. 149. Girard O, Racinais S, Kelly L, Millet GP, Brocherie F. Repeated sprinting on natural grass impairs vertical stiffness but does not alter plantar loading in soccer players. Eur J Appl Physiol. 2011;111(10):2547–55. 150. Padulo J, Tabben M, Ardigò L, Ionel M, Popa C, Gevat C, et al. Repeated sprint ability related to recovery time in young soccer players. Res Sports Med. 2015;23(4):412–23. 151. Balsom P, Seger J, Sjödin B, Ekblom B. Maximal-intensity intermittent exercise: effect of recovery duration. Int J Sports Med. 1992;13(07):528–33. 152. Tabata I, Nishimura K, Kouzaki M, Hirai Y, Ogita F, Miyachi M, et al. Effects of moderate-intensity endurance and high- intensity intermittent training on anaerobic capacity and VO2max. Med Sci Sports Exerc. 1996;28:1327–30. 153. Castagna C, Abt G, Manzi V, Annino G, Padua E, D’Ottavio S. Effect of recovery mode on repeated sprint ability in young basketball players. J Strength Cond Res. 2008;22(3):923–9. 154. Brini S, Ahmaidi S, Bouassida A. Effects of passive versus active recovery at different intensities on repeated sprint per- formance and testosterone/cortisol ratio in male senior basket- ball players. Sci Sports. 2020;35(5):e142–7. 155. Dupont G, Berthoin S. Time spent at a high percentage of max for short intermittent runs: active versus passive recovery. Can J Appl Physiol. 2004;29(S1):S3–16. 156. Padulo J, Tabben M, Attene G, Ardigò L, Dhahbi W, Chamari K. The impact of jumping during recovery on repeated sprint ability in young soccer players. Res Sports Med. 2015;23(3):240–52. 157. Buchheit M, Cormie P, Abbiss C, Ahmaidi S, Nosaka KK, Laursen P. Muscle deoxygenation during repeated sprint run- ning: effect of active vs. passive recovery. Int J Sports Med. 2009;30(06):418–25. 158. Dupont G, Moalla W, Guinhouya C, Ahmaidi S, Berthoin S. Passive versus active recovery during high-intensity intermittent exercises. Med Sci Sports Exerc. 2004;36(2):302–8. 159. Austin DJ, Gabbett TJ, Jenkins DJ. Repeated high-intensity exercise in a professional rugby league. J Strength Cond Res. 2011;25(7):1898–904. 160. Carling C, Le Gall F, Dupont G. Analysis of repeated high- intensity running performance in professional soccer. J Sports Sci. 2012;30(4):325–36. 161. Sirotic AC, Coutts AJ, Knowles H, Catterick C. A comparison of match demands between elite and semi-elite rugby league com- petition. J Sports Sci. 2009;27(3):203–11. 162. Beato M, Drust B. Acceleration intensity is an important con- tributor to the external and internal training load demands of repeated sprint exercises in soccer players. Res Sports Med. 2021;29(1):67–76. 163. Lockie RG, Davis DL, Birmingham-Babauta SA, Beiley MD, Hurley JM, Stage AA, et al. Physiological characteristics of incoming freshmen field players in a men’s Division I collegiate soccer team. Sports. 2016;4(2):34. 164. Mujika I, Spencer M, Santisteban J, Goiriena JJ, Bishop D. Age- related differences in repeated-sprint ability in highly trained youth football players. J Sports Sci. 2009;27(14):1581–90. 165. Rodríguez-Fernández A, Sánchez-Sánchez J, Ramirez-Campillo R, Rodríguez-Marroyo JA, Villa Vicente JG, Nakamura FY. Effects of short-term in-season break detraining on repeated- sprint ability and intermittent endurance according to initial per- formance of soccer player. PLoS One. 2018;13(8): e0201111. 1636 F. Thurlow et al. 166. Jorge G, Garrafoli MT, Abad CCC. Seasonal repeated sprint ability with change of direction variations in U17 and U20 elite Brazilian soccer players: a comparative study. J Strength Cond Res. 2020;34(5):1431–9. 167. Alizadeh R, Hovanloo F, Safania AM. The relationship between aerobic power and repeated sprint ability in young soccer players with different levels of VO2 max. J Phys Educ Sport. 2010;27(2). 168. Campa F, Semprini G, Júdice PB, Messina G, Toselli S. Anthro- pometry, physical and movement features, and repeated-sprint ability in soccer players. Int J Sports Med. 2019;40(02):100–9. 169. García-Unanue J, Felipe JL, Bishop D, Colino E, Ubago-Guisado E, López-Fernández J, et al. Muscular and physical response to an agility and repeated sprint tests according to the level of competition in futsal players. Front Psychol. 2020;11:3671. 170. Impellizzeri F, Rampinini E, Castagna C, Bishop D, Bravo DF, Tibaudi A, et al. Validity of a repeated-sprint test for football. Int J Sports Med. 2008;29(11):899–905. 171. Ingebrigtsen J, Bendiksen M, Randers MB, Castagna C, Krustrup P, Holtermann A. Yo-Yo IR2 testing of elite and sub-elite soc- cer players: performance, heart rate response and correlations to other interval tests. J Sports Sci. 2012;30(13):1337–45. 172. Keogh JW, Weber CL, Dalton CT. Evaluation of anthropometric, physiological, and skill-related tests for talent identification in female field hockey. Can J Appl Physiol. 2003;28(3):397–409. 173. Le Rossignol P, Gabbett TJ, Comerford D, Stanton WR. Repeated-sprint ability and team selection in Australian football league players. Int J Sports Physiol Perform. 2014;9(1):161–5. 174. Rampinini E, Sassi A, Morelli A, Mazzoni S, Fanchini M, Coutts AJ. Repeated-sprint ability in professional and amateur soccer players. Appl Physiol Nutr Metab. 2009;34(6):1048–54. 175. Delextrat A, Calleja-González J, Hippocrate A, Clarke ND. Effects of sports massage and intermittent cold-water immer- sion on recovery from matches by basketball players. J Sports Sci. 2013;31(1):11–9. 176. Tounsi M, Jaafar H, Aloui A, Tabka Z, Trabelsi Y. Effect of listening to music on repeated-sprint performance and affective load in young male and female soccer players. Sport Sci Health. 2019;15(2):337–42. 177. Galy O, Zongo P, Chamari K, Chaouachi A, Michalak E, Del- lal A, et al. Anthropometric and physiological characteristics of Melanesian futsal players: a first approach to talent identification in Oceania. Biol Sport. 2015;32(2):135. 178. Krustrup P, Mohr M, Steensberg A, Bencke J, Kjær M, Bangsbo J. Muscle and blood metabolites during a soccer game: implications for sprint performance. Med Sci Sports Exerc. 2006;38(6):1165–74. 179. McMahon JJ, Jones PA, Comfort P. Comment on: “Anthropo- metric and physical qualities of elite male youth rugby league players.” Sports Med. 2017;47(12):2667–8. 180. AbuMoh’d MF, Abubaker M. Effect of β-alanine supple- mentation on repeated sprint ability and responses of blood lactate and bicarbonate in male soccer players. Sport Mont. 2020;18(2):83–8. 181. Altimari JM, Altimari LR, Bortolotti H, Junior AF, Gabardo JM, Buzzachera CF, et al. The relative age effect on anthro- pometry, body composition, biological maturation and motor performance in young Brazilian soccer players. J Hum Kinet. 2021;77(1):147–57. 182. Archiza B, Andaku DK, Caruso FCR, Bonjorno JC Jr, Oliveira CR, Ricci PA, et al. Effects of inspiratory muscle training in pro- fessional women football players: a randomized sham-controlled trial. J Sports Sci. 2018;36(7):771–80. 183. Ayarra R, Nakamura FY, Iturricastillo A, Castillo D, Yanci J. Differences in physical performance according to the competitive level in futsal players. J Hum Kinet. 2018;64:275. 184. Aziz A, Chia M, Teh K. The relationship between maxi- mal oxygen uptake and repeated sprint performance indices in field hockey and soccer players. J Sports Med Phys Fit. 2000;40(3):195. 185. Baldi M, Da Silva JF, Buzachera C, Castagna C, Guglielmo LG. Repeated sprint ability in soccer players: associations with physiological and neuromuscular factors. J Sports Med Phys Fit. 2017;57:26–32. 186. Balsalobre-Fernández C, Tejero-González CM, del Campo- Vecino J, Bachero-Mena B, Sánchez-Martínez J. Relationships among repeated sprint ability, vertical jump performance and upper-body strength in professional basketball players. Arch Med Deporte. 2014;31(161):148–53. 187. Beato M, Coratella G, Bianchi M, Costa E, Merlini M. Short- term repeated-sprint training (straight sprint vs. changes of direc- tion) in soccer players. J Hum Kinet. 2019;70:183. 188. Beato M, Bianchi M, Coratella G, Merlini M, Drust B. A sin- gle session of straight line and change-of-direction sprinting per week does not lead to different fitness improvements in elite young soccer players. J Strength Cond Res. 2022;36(2):518–24. 189. Beaven CM, Kilduff LP, Cook CJ. Lower-limb passive heat main- tenance combined with pre-cooling improves repeated sprint ability. Front Physiol. 2018;9:1064. 190. Binnie MJ, Dawson B, Arnot MA, Pinnington H, Landers G, Peeling P. Effect of sand versus grass training surfaces during an 8-week pre-season conditioning programme in team sport athletes. J Sports Sci. 2014;32(11):1001–12. 191. Binnie MJ, Dawson B, Pinnington H, Landers G, Peeling P. Effect of training surface on acute physiological responses after interval training. J Strength Cond Res. 2013;27(4):1047–56. 192. Binnie MJ, Dawson B, Pinnington H, Landers G, Peeling P. Part 2: Effect of training surface on acute physiological responses after sport-specific training. J Strength Cond Res. 2013;27(4):1057–66. 193. Borges JH, Conceição MS, Vechin FC, Pascoal EHF, Silva RP, Borin JP. The effects of resisted sprint vs. plyometric training on sprint performance and repeated sprint ability during the final weeks of the youth soccer season. Sci Sports. 2016;31(4): e101-e5. 194. Brini S, Marzouki H, Castagna C, Bouassida A. Effects of a four-week small-sided game and repeated sprint ability training during and after Ramadan on aerobic and anaerobic capacities in senior basketball players. Ann Appl Sport Sci. 2018;6(3):7–13. 195. Brini S, Delextrat A, Bouassida A. Variation in lower limb power and three point shot performance following repeated sprints: one vs. five changes of direction in male basketball players. J Hum Kinet. 2021;77(1):169–79. 196. Brocherie F, Girard O, Forchino F, Al Haddad H, Dos Santos GA, Millet GP. Relationships between anthropometric measures and athletic performance, with special reference to repeated- sprint ability, in the Qatar national soccer team. J Sports Sci. 2014;32(13):1243–54. 197. Brocherie F, Millet GP, Hauser A, Steiner T, Rysman J, Wehr- lin JP, et al. “Live high-train low and high” hypoxic training improves team-sport performance. Med Sci Sports Exerc. 2015;47(10):2140–9. 198. Buchheit M. Repeated-sprint performance in team sport players: associations with measures of aerobic fitness, metabolic control and locomotor function. Int J Sports Med. 2012;33(03):230–9. 199. Campos FDS, Borszcz FK, Flores LJF, Barazetti LK, Teixeira AS, Hartmann Nunes RF, et al. HIIT models in addition to train- ing load and heart rate variability are related with physiological and performance adaptations after 10-weeks of training in young futsal players. Front Psychol. 2021;12: 636153. 1637 Acute Demands of Repeated-Sprint Training 200. Campos-Vazquez MA, Romero-Boza S, Toscano-Bendala FJ, Leon-Prados JA, Suarez-Arrones LJ, Gonzalez-Jurado JA. Com- parison of the effect of repeated-sprint training combined with two different methods of strength training on young soccer play- ers. J Strength Cond Res. 2015;29(3):744–51. 201. Caprino D, Clarke ND, Delextrat A. The effect of an official match on repeated sprint ability in junior basketball players. J Sports Sci. 2012;30(11):1165–73. 202. Castagna C, Manzi V, D’ottavio S, Annino G, Padua E, Bishop D. Relation between maximal aerobic power and the ability to repeat sprints in young basketball players. J Strength Cond Res. 2007;21(4):1172–6. 203. Chaouachi A, Manzi V, Wong DP, Chaalali A, Laurencelle L, Chamari K, et al. Intermittent endurance and repeated sprint abil- ity in soccer players. J Strength Cond Res. 2010;24(10):2663–9. 204. Charlot K, Zongo P, Leicht AS, Hue O, Galy O. Intensity, recov- ery kinetics and well-being indices are not altered during an offi- cial FIFA futsal tournament in Oceanian players. J Sports Sci. 2016;34(4):379–88. 205. Chen Y-S, Lu W-A, Clemente FM, Bezerra JP, Kuo C-D. Increased parasympathetic activity by foot reflexology massage after repeated sprint test in collegiate football players: a ran- domised controlled trial. Sports. 2019;7(11):228. 206. Corrêa DA, Soares DS, Gonelli PR, Cesar MC, Germano MD, Sindorf MA, et al. Effect of 29 weeks of periodized soccer train- ing on the neuromuscular performance of soccer players under 20 years of age. J Exerc Physiol Online. 2016;19(4):32–42. 207. Cuadrado-Peñafiel V, Párraga-Montilla J, Ortega-Becerra MA, Jiménez-Reyes P. Repeated sprint ability in professional soccer vs. professional futsal players. J Sports Sci. 2014;10(2):89–98. 208. da Silva JF, Guglielmo LG, Bishop D. Relationship between dif- ferent measures of aerobic fitness and repeated-sprint ability in elite soccer players. J Strength Cond Res. 2010;24(8):2115–21. 209. Dal Pupo J, Detanico D, Arins FB, Nascimento Salvador PC, Guglielmo LGA, dos Santos SG. Repeated sprint ability and muscle power levels of the futsal players of U15 and U17 cat- egories. Rev Bras Cienc Esporte. 2017;39(1):73–8. 210. Daneshfar A, Gahreman DE, Koozehchian MS, Amani Shalamzari S, Hassanzadeh Sablouei M, Rosemann T, et al. Multi directional repeated sprint is a valid and reliable test for assessment of junior handball players. Front Physiol. 2018;9:317. 211. Dardouri W, Selmi MA, Sassi RH, Gharbi Z, Rebhi A, Yahmed MH, et  al. Relationship between repeated sprint perfor- mance and both aerobic and anaerobic fitness. J Hum Kinet. 2014;40(1):139–48. 212. de Andrade VL, Palucci Vieira LH, Kalva-Filho CA, Santiago PRP. Critical points of performance in repeated sprint: a kin- ematic approach. Sci Sports. 2021. 213. Delextrat A, Hippocrate A, Leddington-Wright S, Clarke ND. Including stretches to a massage routine improves recovery from official matches in basketball players. J Strength Cond Res. 2014;28(3):716–27. 214. Delextrat A, Kraiem S. Heart-rate responses by playing position during ball drills in basketball. Int J Sports Physiol Perform. 2013;8(4):410–8. 215. Donghi F, Rampinini E, Bosio A, Fanchini M, Carlomagno D, Maffiuletti NA. Morning priming exercise strategy to enhance afternoon performance in young elite soccer players. Int J Sports Physiol Perform. 2021;16(3):407–14. 216. Doyle B, Browne D, Horan D. The relationship of aerobic endur- ance and linear speed on repeat sprint ability performance in female international footballers. Sciences. 2020;8(4):147–53. 217. Dupont G, McCall A, Prieur F, Millet GP, Berthoin S, Mil- let GP. Faster oxygen uptake kinetics during recovery is related to better repeated sprinting ability. Eur J Appl Physiol. 2010;110(3):627–34. 218. Elias GP, Varley MC, Wyckelsma VL, McKenna MJ, Minahan CL, Aughey RJ. Effects of water immersion on posttraining recovery in Australian footballers. Int J Sports Physiol Perform. 2012;7(4):357–66. 219. Elias GP, Wyckelsma VL, Varley MC, McKenna MJ, Aughey RJ. Effectiveness of water immersion on postmatch recovery in elite professional footballers. Int J Sports Physiol Perform. 2013;8(3):243–53. 220. Eniseler N, Şahan Ç, Özcan I, Dinler K. High-intensity small- sided games versus repeated sprint training in junior soccer play- ers. J Hum Kinet. 2017;60:101. 221. Eryılmaz SK, Kaynak K. Relationship between repeated sprint ability and aerobic fitness in college volleyball players. Univers J Educ Res. 2019;7(5):1198–204. 222. Essid S, Cherif M, Chtourou H, Souissi N. Time-of-day effects in physical performances and psychological responses in young elite male handball players. Biol Rhythm Res. 2021:1–12. 223. Farjallah MA, Hammouda O, Ben Mahmoud L, Graja A, Ghat- tassi K, Boudaya M, et al. Melatonin supplementation amelio- rates oxidative stress, antioxidant status and physical perfor- mances recovery during a soccer training camp. Biol Rhythm Res. 2020;51(3):441–52. 224. Fornasier-Santos C, Millet GP, Woorons X. Repeated-sprint training in hypoxia induced by voluntary hypoventilation improves running repeated-sprint ability in rugby players. Eur J Sport Sci. 2018;18(4):504–12. 225. Fort-Vanmeerhaeghe A, Montalvo A, Latinjak A, Unnithan V. Physical characteristics of elite adolescent female basketball players and their relationship to match performance. J Hum Kinet. 2016;53:167. 226. Fortin J-F, Billaut F. Blood-flow restricted warm-up alters mus- cle hemodynamics and oxygenation during repeated sprints in American football players. Sports (Basel). 2019;7(5). 227. Freitas TT, Calleja-González J, Alarcón F, Alcaraz PE. Acute effects of two different resistance circuit training protocols on performance and perceived exertion in semiprofessional basket- ball players. J Strength Cond Res. 2016;30(2):407–14. 228. Gabbett TJ. The development of a test of repeated-sprint abil- ity for elite women’s soccer players. J Strength Cond Res. 2010;24(5):1191–4. 229. Gabbett T, Jenkins D, Abernethy B. Relative importance of phys- iological, anthropometric, and skill qualities to team selection in professional rugby league. J Sports Sci. 2011;29(13):1453–61. 230. Gabbett TJ, Carius J, Mulvey M. Does improved decision- making ability reduce the physiological demands of game- based activities in field sport athletes? J Strength Cond Res. 2008;22(6):2027–35. 231. Galvin HM, Cooke K, Sumners DP, Mileva KN, Bowtell JL. Repeated sprint training in normobaric hypoxia. Br J Sports Med. 2013;47(Suppl 1):i74–9. 232. Gantois P, Aidar FJ, De Matos DG, De Souza RF, Da Silva LM, De Castro KR, et al. Repeated sprints and the relationship with anaerobic and aerobic fitness of basketball athletes. J Phys Educ Sport. 2017;17(2):910. 233. Gantois P, Aidar FJ, Dantas MP, Silva LM, Paes PP, Santana EE, et al. Aerobic fitness is associated with improved repeated sprints ability of basketball players after six weeks of training during preseason. Rev Bras Cineantropometria Desempenho Humano. 2018;20:114–24. 234. Gatterer H, Klarod K, Heinrich D, Schlemmer P, Dilitz S, Burtscher M. Effects of a 12-day maximal shuttle-run shock microcycle in hypoxia on soccer specific performance and oxi- dative stress. Appl Physiol. 2015;40(8):842–5. 235. Gharbi Z, Dardouri W, Haj-Sassi R, Chamari K, Souissi N. Aerobic and anaerobic determinants of repeated sprint ability in team sports athletes. Biol Sport. 2015;32(3):207. 1638 F. Thurlow et al. 236. Girard O, Millet GP, Thomson A, Brocherie F. Is plantar load- ing altered during repeated sprints on artificial turf in interna- tional football players? J Sports Sci Med. 2018;17(3):359. 237. González-Frutos P, Aguilar-Navarro M, Morencos E, Mallo J, Veiga S. Relationships between strength and step frequency with fatigue index in repeated sprint ability. Int J Environ Res Public Health. 2022;19(1):196. 238. Goodall S, Charlton K, Howatson G, Thomas K. Neuromuscu- lar fatigability during repeated-sprint exercise in male athletes. Med Sci Sports Exerc. 2015;47(3):528–36. 239. Hamlin MJ. The effect of contrast temperature water ther- apy on repeated sprint performance. J Sci Med Sport. 2007;10(6):398–402. 240. Hamlin MJ, Olsen PD, Marshall HC, Lizamore CA, Elliot CA. Hypoxic repeat sprint training improves rugby player’s repeated sprint but not endurance performance. Front Physiol. 2017;8:24. 241. Hammami M, Gaamouri N, Aloui G, Shephard RJ, Chelly MS. Effects of a complex strength-training program on athletic per- formance of junior female handball players. Int J Sports Physiol Perform. 2019;14(2):163–9. 242. Hermassi S, Schwesig R, Wollny R, Fieseler G, Fernandez- Fernandez J, Shephard R, et al. Shuttle versus straight repeated- sprint ability tests and their relationship to anthropometrics and explosive muscular performance in elite handball players. J Sports Med Phys Fit. 2018;58(11):1625–34. 243. Hollville E, Le Croller V, Hirasawa Y, Husson R, Rabita G, Bro- cherie F. Effect of prior fatiguing sport-specific exercise on field Hockey passing ability. Int J Sports Physiol Perform. 2018:1–7. 244. Ingebrigtsen J, Brochmann M, Castagna C, Bradley PS, Ade J, Krustrup P, et al. Relationships between field perfor- mance tests in high-level soccer players. J Strength Cond Res. 2014;28(4):942–9. 245. Jang JH, Joo CH. The effects of residential environment on the condition and fitness of soccer players in the summer. J Exerc Rehabil. 2020;16(6):522–8. 246. Jiménez-Reyes P, Cross M, Ross A, Samozino P, Brughelli M, Gill N, et al. Changes in mechanical properties of sprinting dur- ing repeated sprint in elite rugby sevens athletes. Eur J Sport Sci. 2019;19(5):585–94. 247. Jorge G, Garrafoli MT, Cal Abad CC. Seasonal repeated sprint ability with change of direction variations in U17 and U20 elite Brazilian soccer players: a comparative study. J Strength Cond Res. 2020;34(5):1431–9. 248. Kilduff LP, West DJ, Williams N, Cook CJ. The influence of pas- sive heat maintenance on lower body power output and repeated sprint performance in professional rugby league players. J Sci Med Sport. 2013;16(5):482–6. 249. Krakan I, Milanovic L, Belcic I. Effects of plyometric and repeated sprint training on physical performance. Sports. 2020;8(7):91. 250. Krueger M, Costello JT, Stenzel M, Mester J, Wahl P. The physi- ological effects of daily cold-water immersion on 5-day tourna- ment performance in international standard youth field-hockey players. Eur J Appl Physiol. 2020;120(1):295–305. 251. Lapointe J, Paradis-Deschênes P, Woorons X, Lemaître F, Billaut F. Impact of hypoventilation training on muscle oxygenation, myoelectrical changes, systemic [K+], and repeated-sprint ability in basketball players. Front Sports Act. 2020;2:29. 252. Lockie RG, Davis DL, Birmingham-Babauta SA, Beiley MD, Hurley JM, Stage AA, et al. Physiological characteristics of incoming freshmen field players in a men's division I collegiate soccer team. Sports (Basel). 2016;4(2). 253. Lockie RG, Liu TM, Stage AA, Lazar A, Giuliano DV, Hur- ley JM, et al. Assessing repeated-sprint ability in division I collegiate women soccer players. J Strength Cond Res. 2020;34(7):2015–23. 254. Lockie RG, Moreno MR, Orjalo AJ, Stage AA, Liu TM, Bir- mingham-Babauta SA, et al. Repeated-sprint ability in divi- sion I collegiate male soccer players: positional differences and relationships with performance tests. J Strength Cond Res. 2019;33(5):1362–70. 255. Lombard WP, Cai X, Lambert MI, Chen X, Mao L. Relation- ships between physiological characteristics and match demands in elite-level male field hockey players. Int J Sports Sci Coach. 2021. 256. Marcelino P, Aoki M, Arruda A, Freitas C, Mendez-Villanueva A, Moreira A. Does small-sided-games’ court area influence metabolic, perceptual, and physical performance parameters of young elite basketball players? Biol Sport. 2016;33(1):37. 257. Matzenbacher F, Pasquarelli BN, Rabelo FN, Dourado AC, Durigan JZ, Rossi HG, et al. Adaptations in the physical capac- ities of U-18 futsal athletes during a competitive season. Rev Bras Cineantropometria Desempenho Hum. 2016;18(1):50–61. 258. McGawley K, Andersson PI. The order of concurrent training does not affect soccer-related performance adaptations. Int J Sports Med. 2013;34(11):983–90. 259. Meckel Y, Doron O, Eliakim E, Eliakim A. Seasonal variations in physical fitness and performance indices of elite soccer play- ers. Sports. 2018;6(1):14. 260. Meckel Y, Gottlieb R, Eliakim A. Repeated sprint tests in young basketball players at different game stages. Eur J Appl Physiol. 2009;107(3):273. 261. Meckel Y, Machnai O, Eliakim A. Relationship among repeated sprint tests, aerobic fitness, and anaerobic fitness in elite ado- lescent soccer players. J Strength Cond Res. 2009;23(1):163–9. 262. Meckel Y, May-rom M, Ekshtien A, Eisenstein T, Nemet D, Eliakim A. Relationships among two repeated activity tests and aerobic fitness of volleyball players. J Strength Cond Res. 2015;29(8):2122–7. 263. Meckel Y, Einy A, Gottlieb R, Eliakim A. Repeated sprint abil- ity in young soccer players at different game stages. J Strength Cond Res. 2014;28(9):2578–84. 264. Michalsik LB, Madsen K, Aagaard P. Physiological capacity and physical testing in male elite team handball. J Sports Med Phys Fit. 2015;55(5):415–29. 265. Mohr M, Draganidis D, Chatzinikolaou A, Barbero-Álvarez JC, Castagna C, Douroudos I, et al. Muscle damage, inflammatory, immune and performance responses to three football games in 1 week in competitive male players. Eur J Appl Physiol. 2016;116(1):179–93. 266. Mohr M, Krustrup P. Comparison between two types of anaero- bic speed endurance training in competitive soccer players. J Hum Kinet. 2016;51(1):183–92. 267. Mohr M, Nybo L, Grantham J, Racinais S. Physiological responses and physical performance during football in the heat. PLoS One. 2012;7(6). 268. Moncef C, Said M, Olfa N, Dagbaji G. Influence of morpholog- ical characteristics on physical and physiological performances of Tunisian elite male handball players. Asian J Sports Med. 2012;3(2):74–80. 269. Moreira A, Costa EC, Coutts AJ, Nakamura FY, da Silva DA, Aoki MS. Cold water immersion did not accelerate recovery after a futsal match. Rev Bras Med Esporte. 2015. 270. Müller CB, da Veiga RS, dos Santos PE, Del Vecchio FB. Acute effects of running anaerobic-based sprint test with different loads in amateur rugby union players. Sci Sports. 2021;36(1):85–8. 271. Okuno NM, Tricoli V, Silva SB, Bertuzzi R, Moreira A, Kiss MA. Postactivation potentiation on repeated-sprint ability in elite handball players. J Strength Cond Res. 2013;27(3):662–8. 1639 Acute Demands of Repeated-Sprint Training 272. Nakamura F, Soares-Caldeira L, Laursen P, Polito M, Leme L, Buchheit M. Cardiac autonomic responses to repeated shuttle sprints. Int J Sports Med. 2009;30(11):808–13. 273. Nascimento PC, Lucas RDD, Pupo JD, Arins FB, Castagna C, Guglielmo LGA. Effects of four weeks of repeated sprint training on physiological indices in futsal players. Rev Bras Cineantropo- metria Desempenho Hum. 2015;17(1):91–103. 274. Nedrehagen ES, Saeterbakken AH. The effects of in-season repeated sprint training compared to regular soccer training. J Hum Kinet. 2015;49(1):237–44. 275. Nikolaidis P, Dellal A, Torres-Luque G, Ingebrigtsen J. Deter- minants of acceleration and maximum speed phase of repeated sprint ability in soccer players: a cross-sectional study. Sci Sports. 2015;30(1):e7–16. 276. Padulo J, Ardigo L, Attene G, Cava C, Wong D, Chamari K, et al. The effect of slope on repeated sprint ability in young soccer players. Res Sports Med. 2016;24(4):320–30. 277. Padulo J, Di Giminiani R, Ibba G, Zarrouk N, Moalla W, Attene G, et al. The acute effect of whole body vibration on repeated shuttle-running in young soccer players. Int J Sports Med. 2014;35(01):49–54. 278. Petisco C, Ramirez-Campillo R, Hernández D, Gonzalo-Skok O, Nakamura FY, Sanchez-Sanchez J. Post-activation potentia- tion: effects of different conditioning intensities on measures of physical fitness in male young professional soccer players. Front Physiol. 2019;10(JUN). 279. Purkhús E, Krustrup P, Mohr M. High-intensity training improves exercise performance in elite women volleyball players during a competitive season. J Strength Cond Res. 2016;30(11):3066–72. 280. Pyne DB, Saunders PU, Montgomery PG, Hewitt AJ, Sheehan K. Relationships between repeated sprint testing, speed, and endur- ance. J Strength Cond Res. 2008;22(5):1633–7. 281. Ramírez-Campillo R, González-Jurado JA, Martínez C, Naka- mura FY, Peñailillo L, Meylan CMP, et al. Effects of plyometric training and creatine supplementation on maximal-intensity exer- cise and endurance in female soccer players. J Sci Med Sport. 2016;19(8):682–7. 282. Rampinini E, Bishop D, Marcora SM, Ferrari Bravo D, Sassi R, Impellizzeri FM. Validity of simple field tests as indicators of match-related physical performance in top-level professional soccer players. Int J Sports Med. 2007;28(3):228–35. 283. Rey E, Padrón-Cabo A, Fernández-Penedo D. Effects of sprint training with and without weighted vest on speed and repeated sprint ability in male soccer players. J Strength Cond Res. 2017;31(10):2659–66. 284. Rodríguez-Fernández A, Rodríguez-Marroyo J, Casamichana D, Villa J. Effects of 5-week pre-season small-sided-game- based training on repeat sprint ability. J Sports Med Phys Fit. 2016;57(5):529–36. 285. Røksund OD, Kristoffersen M, Bogen BE, Wisnes A, Engeseth MS, Nilsen AK, et al. Higher drop in speed during a repeated sprint test in soccer players reporting former hamstring strain injury. Front Physiol. 2017;8. 286. Ruscello B, Esposito M, Partipilo F, Di Cicco D, Filetti C, Pantanella L, et al. Exercise-to-rest ratios in repeated sprint ability training in women’s soccer. J Sports Med Phys Fit. 2017;58(12):1790–9. 287. Salleh OM, Nadzalan AM, Mohamad NI, Rahmat A. Repeated sprint ability depending on the level of condition among university soccer players. Int J Acad Res Bus Soc Sci. 2017;7(6):506–14. 288. Sánchez-Sánchez J, García-Unanue J, Hernando E, López- Fernández J, Colino E, León-Jiménez M, et al. Repeated sprint ability and muscular responses according to the age category in elite youth soccer players. Front Physiol. 2019;10. 289. Sanchez-Sanchez J, Rodriguez A, Petisco C, Ramirez-Campillo R, Martínez C, Nakamura FY. Effects of different post-activa- tion potentiation warm-ups on repeated sprint ability in soc- cer players from different competitive levels. J Hum Kinet. 2018;61(1):189–97. 290. Sanders GJ, Turner Z, Boos B, Peacock CA, Peveler W, Lip- ping A. Aerobic capacity is related to repeated sprint abil- ity with sprint distances less than 40 meters. Int J Exerc Sci. 2017;10(2):197. 291. Scanlan AT, Madueno MC. Passive recovery promotes superior performance and reduced physiological stress across different phases of short-distance repeated sprints. J Strength Cond Res. 2016;30(9):2540–9. 292. Scanlan AT, Madueno MC, Guy JH, Giamarelos K, Spiteri T, Dalbo VJ. Measuring decrement in change-of-direction speed across repeated sprints in basketball: novel vs. traditional approaches. J Strength Cond Res. 2021;35(3):841–5. 293. Selmi W, Rebai H, Chtara M, Naceur A, Sahli S. Self-confidence and affect responses to short-term sprint interval training. Physiol Behav. 2018;188:42–7. 294. Shalfawi SAI, Enoksen E, Tønnessen E. The relationship between measures of sprinting, aerobic fitness, and lower body strength and power in well-trained female soccer players. Int J Appl Sports Sci. 2014;26(1):18–25. 295. Shalfawi SA, Ingebrigtsen J, Dillern T, Tønnessen E, Delp TK, Enoksen E. The effect of 40 m repeated sprint training on physi- cal performance in young elite male soccer players. Serb J Sports Sci. 2012;6(3):111–6. 296. Shalfawi SA, Young M, Tønnessen E, Haugen TA, Enoksen E. The effect of repeated agility training vs. repeated sprint training on elite female soccer players' physical performance. Kinesiol Slov. 2013;19(3). 297. Silva RLC, Hall E, Maior AS. Inspiratory muscle training improves performance of a repeated sprints ability test in profes- sional soccer players. J Bodywork Mov Ther. 2019;23(3):452–5. 298. Soares-Caldeira LF, de Souza EA, de Freitas VH, de Moraes SM, Leicht AS, Nakamura FY. Effects of additional repeated sprint training during preseason on performance, heart rate variability, and stress symptoms in futsal players: a randomized controlled trial. J Strength Cond Res. 2014;28(10):2815–26. 299. Spineti J, Figueiredo T, Assis M, Miranda H, Simão R. Compari- son between traditional strength training and complex contrast training on repeated sprint ability and muscle architecture in elite soccer players. J Sports Med Phys Fit. 2015;56(11):1269–78. 300. Stojanovic M, Ostojic S, Calleja-González J, Milosevic Z, Mikic M. Correlation between explosive strength, aerobic power and repeated sprint ability in elite basketball players. J Sports Med Phys Fit. 2012;52(4):375. 301. Teixeira AS, Arins FB, De Lucas RD, Carminatti LJ, Dittrich N, Nakamura FY, et al. Comparative effects of two interval shuttle- run training modes on physiological and performance adapta- tions in female professional futsal players. J Strength Cond Res. 2019;33(5):1416–28. 302. Thomassen M, Christensen PM, Gunnarsson TP, Nybo L, Bangsbo J. Effect of 2-wk intensified training and inactivity on muscle Na+–K+ pump expression, phospholemman (FXYD1) phosphorylation, and performance in soccer players. J Appl Physiol. 2010;108(4):898–905. 303. Tønnessen E, Shalfawi SA, Haugen T, Enoksen E. The effect of 40-m repeated sprint training on maximum sprinting speed, repeated sprint speed endurance, vertical jump, and aerobic capacity in young elite male soccer players. J Strength Cond Res. 2011;25(9):2364–70. 304. Torreblanca-Martinez V, Nevado-Garrosa F, Otero-Saborido FM, Gonzalez-Jurado JA. Effects of fatigue induced by 1640 F. Thurlow et al. repeated-sprint on kicking accuracy and velocity in female soc- cer players. PLoS One. 2020;15(1). 305. Trecroci A, Porcelli S, Perri E, Pedrali M, Rasica L, Alberti G, et al. Effects of different training interventions on the recovery of physical and neuromuscular performance after a soccer match. J Strength Cond Res. 2020;34(8):2189–96. 306. van den Tillaar R. Comparison of step-by-step kinematics in repeated 30-m sprints in female soccer players. J Strength Cond Res. 2018;32(7):1923–8. 307. Vasquez-Bonilla AA, Camacho-Cardeñosa A, Timón R, Mar- tínez-Guardado I, Camacho-Cardeñosa M, Olcina G. Muscle oxygen desaturation and re-saturation capacity limits in repeated sprint ability performance in women soccer players: a new physi- ological interpretation. Int J Environ Res. 2021;18(7):3484. 308. Wadley G, Le Rossignol P. The relationship between repeated sprint ability and the aerobic and anaerobic energy systems. J Sci Med Sport. 1998;1(2):100–10. 309. West DJ, Russell M, Bracken RM, Cook CJ, Giroud T, Kilduff LP. Post-warmup strategies to maintain body temperature and physical performance in professional rugby union players. J Sports Sci. 2016;34(2):110–5. 310. Yanci J, Castillo D, Iturricastillo A, Ayarra R, Nakamura FY. Effects of two different volume-equated weekly distributed short- term plyometric training programs on futsal players’ physical performance. J Strength Cond Res. 2017;31(7):1787–94. 311. Zagatto AM, Dutra YM, Claus G, de Sousa ME, de Poli RAB, Brisola GMP, et al. Drop jumps improve repeated sprint abil- ity performances in professional basketball players. Biol Sport. 2021;38(4):59–66. 312. Rampinini E, Bishop D, Marcora S, Bravo DF, Sassi R, Impel- lizzeri F. Validity of simple field tests as indicators of match- related physical performance in top-level professional soccer players. Int J Sports Med. 2007;28(03):228–35. Authors and Affiliations Fraser Thurlow1,3  · Jonathon Weakley1,2,3  · Andrew D. Townshend1  · Ryan G. Timmins1,3  · Matthew Morrison1,3  · Shaun J. McLaren4,5 * Fraser Thurlow [email protected] 1 School of Behavioural and Health Sciences, Australian Catholic University, Brisbane, Australia 2 Carnegie Applied Rugby Research (CARR) Centre, Carnegie School of Sport, Leeds Beckett University, Leeds, UK 3 Sports Performance, Recovery, Injury and New Technologies (SPRINT) Research Centre, Australian Catholic University, Brisbane, Australia 4 Newcastle Falcons Rugby Club, Newcastle Upon Tyne, UK 5 Institute of Sport, Manchester Metropolitan University, Manchester, UK

The Acute Demands of Repeated-Sprint Training on Physiological, Neuromuscular, Perceptual and Performance Outcomes in Team Sport Athletes: A Systematic Review and Meta-analysis.

05-24-2023

Thurlow, Fraser,Weakley, Jonathon,Townshend, Andrew D,Timmins, Ryan G,Morrison, Matthew,McLaren, Shaun J

eng

PMC7538888

Reviewers' Comments: Reviewer #1: Remarks to the Author: The authors are correct in identifying that an explosion of data collection from wearable exercise apps has the potential to enable new insight into exercise bioenergetics and fatigue. However, while I like the general approach taken by the authors and appreciate the labour involved in their study, I believe they have 'missed a trick' in limiting their analysis to the 'universal model of running performance' described by Mulligan et al in 2018 (and not yet validated by others). The 'critical power' model is well established in the field and has both theoretical and practical validity but is unfortunately not used by the authors; indeed, the authors are somewhat (and unjustifiably, in my opinion) dismissive of the CP concept. In summary, as presented, the study is limited to a novel and essentially unvalidated model of running performance and this calls into question at least some of the conclusions. While the approach (data mining) surely has merit, the analyses need to be less blinkered and more comprehensive in this first step. Reviewer #2: Remarks to the Author: In this study, the researchers show that a model they previously developed to predict future race performance from past race performances performs well in a large dataset of race times and distances estimated from a running watch. They extract parameters from the model related to (1) endurance and (2) running speed and VO2 Max and show that there are associations between these two parameters and training metrics extracted from the dataset. The dataset is exciting, the associations with training are interesting, and as far as I can tell, the analysis as performed was sound overall. But there are some issues with the manuscript as written and the analysis as performed that limit impact. Of most significance is that the novelty is not overwhelmingly clear. For example, the model has been previously published and shown to relate well to real-world performance data, so these aspects of the current paper are not particularly novel. The physiological parameters are shown to vary among the population, but this is not particularly novel besides the means in which the parameters were extracted. The correlations with training performance are just correlations and, as the authors acknowledge, it is not possible to determine whether the training measures associated with higher performance cause those higher performances or are merely associated with being a high-performing athlete. Given the size and, I expect, richness of the dataset, I imagine that there is much more that the investigators could have learned. I list a few questions in the following paragraphs that the authors might have explored. Do aspects of training help to explain errors in the model predictions? This could have helped us move toward a more causal link between training and race performance. There also seemed to be a systematic error in their model related to the endurance parameter. Is this related to training? Or errors in their data? Or a gap in their model? This question should have been explored more fully. Are there means to predict an athlete’s race performance from sub-maximal training performance (i.e., not races), using heart rate or any other measures the watch might provide? The current model requires subjects to performance two or more races at maximal effort to extract these parameters. While this is an improvement on physiological testing it is still a burden and does not seem to take advantage of the dataset. I presume heart rate is available, for example. Would heart rate and heart-rate variability during training help to detect some of the physiological parameters on training runs? Do the measures extracted from their model and the real-world dataset match measures extracted from gold standard lab assessments in a small (but heterogeneous) subset of the subjects? While the researchers do compare to previously published data, these tests would have provided more convincing evidence that their model is valid in a population with varying age, gender, ethnicity, and training status. Does their phenomenological model perform better than past models on this large dataset? What about a simple linear regression model? Comparing the models against additional baselines would have provided further confidence. Another major contribution could be to share the dataset with other researchers, which would be highly novel and a means to accelerate research on human performance, injury, and real-world training. I would not expect the researchers to tackle all of these problems, but I would expect more novel insights or contributions in some form. Another issue with the current submission is that I found the manuscript more challenging to read and understand than needed. Work is needed to improve the readability and clarity of the writing. As one example, the abstract as written contains very few specific details about the study that was performed. What parameters were predicted? What is performance? What are training modes? Given space, the abstract should not be exhaustive, but the key details should be described with enough specificity to give the reader a more clear understanding of what the study entailed. The Introduction, Results, and Discussion need similar improvements to more clearly and succinctly state what analysis was performed. Reviewer #3: Remarks to the Author: MAJOR In the Introduction, the authors challenge an axiom that has been characterizing exercise physiology since longer than a century, the axiom that measurement conditions should be standardized. I kindly disagree with this view. Existing models, validated experimentally in the laboratory, and applicable on the field and on large-scale numbers, come from standard experimental laboratory conditions. The theoretical models do exist indeed. They have been developed theoretically and validated by measuring V’O2max during exercise testing and the fraction of V’O2max utilization and the energy cost of the locomotion mode at stake at steady state. The basic formula id as follows: V = f * V’O2max/C (1) Where v is velocity, f the sustainable fraction of V’O2max over a given distance, and C the energy cost of the locomotion mode at stake. C has the dimension of a force and represents the metabolic energy that is to be spent to generate the force that is necessary to overcome the forces that oppose to the body movement. These are two forces: 1) air resistance (or water resistance in swimming) and 2) frictional forces. These can be expressed as follows: C = k v2 + Cf (2) This equation means that, if you plot C as a function of the square of speed, you obtain linear relationships with slope equal to constant k and y-intercept equal to the energy cost that is necessary to overcome frictional forces (high in running, low in cycling). Constant k is directly proportional to the frontal surface area of the moving body, to the aerodynamic (or hydrodynamic factor) Cx, and to the air (or water) density. With these simple relationships, it is possible to simulate and interpret all what happens in field conditions and during actual competitions. I just give the authors a few references (clearly overlooked by the authors) to make them aware of what I am saying: di Prampero PE, Int J Sports Med 7: 55-72, 1986; di Prampero PE, Eur J Appl Physiol 82: 345-360, 2000; di Prampero et al, J Appl Physiol 47: 201-206, 1979; di Prampero et al, Eur J Appl Physiol 55: 259-266, 1986; Ferretti et al, Eur J Appl Physiol 111: 391-401, 2011; Margaria et al, J Appl Physiol 18: 367-370, 1963; Minetti et al, J Appl Physiol 93: 1039-1046, 2002; Pendergast et al, J Appl Physiol 43: 475-479, 1977; Tam et al, Eur J Appl Physiol 112: 3797-3806, 2012; Zamparo et al, Eur J Appl Physiol 111: 367-378, 2011. All the elements that are necessary to set a model to be applied to large data sets and in field conditions are present in those and many other studies. What is the use of a variable like running economy, which the authors define as the steady state oxygen consumption at a given constant speed instead of C? Physically speaking, C looks more appropriate. At the end of page 2, the authors create artificially a dichotomy between laboratory tests and actual competition conditions: nevertheless, the theoretical background derived from laboratory tests is fully applicable to laboratory conditions. A sentence like “Unfortunately, these approaches predict that speeds below a critical velocity can be maintained for infinite duration which contradicts observation” has been criticized (see e.g. Ferretti, Energetics of Muscular Exercise, Springer 2015, chapter on critical power). Laboratory physiologists are perfectly aware of the fact that energy sources in the body are finite. Under Results, I read: “Main determinants of aerobic fitness and endurance in long distance runners (LDR) are maximal oxygen uptake per body weight, VO2;max , velocity dependent, sub- maximal oxygen demand, known as running economy (RE), and the lactate or ventilatory threshold (LT) that sets the limit below which a steady state blood lactate concentration is maintained”. Although a reference is given, this statement is not correct, see equation 1 in this report. Equation 1 of the article is constructed in such a way to encompass a large variety of field conditions, independent of the physiological model. Frankly, I do not see a role for such a study, unless we create an artificial opposition between classical physiological studies and a kind of “modern” approach that the authors claim. Big numbers are fascinating, but their utility depend of the context into which they are inserted and interpreted. The context exists, but the authors seem to be unaware of it. The discussion id biased by the chosen approach and does not deserve to be discussed analytically MINOR Endurance running dates back much longer than the ancient Olympic Games: it is alike that pre- historical nomadic societies used endurance running while hunting or for migrating. Page 2, line 7 : ranging instead of raging 3 Rebuttal letter “Novel insights on human exercise performance from big data mining” (NCOMMS-20-02292) Please find below our point-to-point answers to the reviewer comments (C: comment, A: answer). Answer to Reviewer #1 We thank the reviewer for her/his time spent looking over our manuscript and their comments that we address point-by-point in the following. C The authors are correct in identifying that an explosion of data collection from wearable exercise apps has the potential to enable new insight into exercise bioenergetics and fatigue. However, while I like the general approach taken by the authors and appreciate the labour involved in their study, I believe they have ’missed a trick’ in limiting their analysis to the ’universal model of running performance’ described by Mulligan et al in 2018 (and not yet validated by others). The ’critical power’ model is well established in the field and has both theoretical and practical validity but is unfortunately not used by the authors; indeed, the authors are somewhat (and unjustifiably, in my opinion) dismissive of the CP concept. A We acknowledge that the ’critical power’ model is well established. A. V. Hill described the idea behind this concept already in 1925 (The Physiological Basis of Athletic Records, Lancet, 1925). He derived the idea from running and other world records as he noted that maximum speed or power over time T follows a hyperbolic curve that can be described by the equation Pmax(T) = Pc + A/T, where Pc corresponds to critical power (although Hill did not use that term) and A represent anaerobic power reserve. Furthermore, Hill noted that this relation is limited to durations up to 12 minutes and called it short-term fatigue. He thought that short-term fatigue originates from muscles whereas other forms of fatigue that take longer to develop have more complex origins, such as neural fatigue, and are therefore much harder to describe. Thus, his idea of ’critical power’ was not meant to describe human performance over long-duration exercise. Currently, the ’critical power’ model is mainly applied to running distances up to 10km. For longer distances, the concept of a duration dependent fractional utilization of maximal aerobic power is required, as pointed out also by Reviewer #3 (see also work by di Prampero, and Peronnet & Thibault). In fact, Hill indicated in Figure 4 of his 1925 paper that the average running velocity tends to decrease logarithmically with race duration (we have attached this figure as an appendix to this rebuttal). Our universal model for running performance, as described in our previous paper in 2018 and used in the present work, builds on Hills observation. It describes running performance over a much broader exercise duration band. However, we do acknowledge that critical power has been useful in some applications. For example, it has been used in cycling, where loading of the muscles and fatigue is di↵erent from running. Below, we have attached a table in which we summarize the ’critical power’ and other models. In fact, the ’critical velocity’ vc (corresponding to critical power) indeed occurs in our model (as described in Mulligan et al in 2018) as a combination of parameters, i.e., vc = vm − D0/tc where we followed the notation of the ’critical power’ model used in A. M. Jones, A. Vanhatalo, Sports Med 47, S65 (2017). In order to highlight the di↵erence between the ’critical power’ model and our model, we have performed a detailed comparison of the models, using running world records from 1987 and 2020, and also personal records from six elite marathon runners taken from the above mentioned publication of A. M. Jones & A. Vanhatalo. Corresponding results are attached below. As the relation between the models is not directly relevant to our present data analysis of race distances between 5km and the Marathon, we have removed the reference to ’critical power’ to avoid any confusion and to not give a false impression of this concept. C In summary, as presented, the study is limited to a novel and essentially unvalidated model of running perfor- mance and this calls into question at least some of the conclusions. While the approach (data mining) surely 4 has merit, the analyses need to be less blinkered and more comprehensive in this first step. A While we agree that our model is novel, we disagree with the conclusion that it is ”essentially unvalidated”. As pointed out by Reviewer #2, ”the model has been previously published and shown to relate well to real-world performance data”. We note that other researchers have checked their models also by comparison to athletic records for a certain range of distances, and so did we for our model. To provide a constructive basis for further review, and to clear up misunderstandings, but also to defend our mathematical model used for analyzing the data, we provide below a new, rather detailed comparison of the ’critical power’ model and our model, including new graphs and tables. Our new results show that our model agrees with current athletic world and personal records with an error of less than 1%. We are not aware of any mathematical model that explains current world records from 800m to the Marathon at better accuracy. 5 Rebuttal letter “Novel insights on human exercise performance from big data mining” (NCOMMS-20-02292) Please find below our point-to-point answers to the reviewer comments (C: comment, A: answer). Answer to Reviewer #2 We thank the reviewer for her/his time spent looking over our manuscript and their comments and interesting questions that we address point-by-point in the following. C The dataset is exciting, the associations with training are interesting, and as far as I can tell, the analysis as performed was sound overall. But there are some issues with the manuscript as written and the analysis as performed that limit impact. Of most significance is that the novelty is not overwhelmingly clear. For example, the model has been previously published and shown to relate well to real-world performance data, so these aspects of the current paper are not particularly novel. The physiological parameters are shown to vary among the population, but this is not particularly novel besides the means in which the parameters were extracted. The correlations with training performance are just correlations and, as the authors acknowledge, it is not possible to determine whether the training measures associated with higher performance cause those higher performances or are merely associated with being a high-performing athlete. A Novelty of the current paper is that it can explain running performance from 5km up to the marathon in a large group of runners, with a wide range of performance levels, using two e↵ective parameters: the crossover velocity vm and the endurance parameter El. Usually, running performance is measured by VO2max alone, which is a poor indicator of performance as it ignores running economy (the energy cost of running per distance) which shows a considerable variation among athletes. In addition, running economy changes (slowly) over time, which is believed to be associated with various forms of fatigue and change of physiological parameters like, e.g., body temperature. Given the complexity of these mechanisms and their current poor understanding, it appears interesting that two parameters are rather e↵ective in describing running performances of a few hours duration. It should be stressed that our model is the first to model running over these long time scales by a logarithmic decay in fractional utilization (FU) of maximal aerobic power. Previous models considered constant or linear decrease in FU, leading to systematic errors for distances over 10km (Only the model by Peronnet & Thibault considers a combination of logarithmic and power law decays). We have added in the appendix to this rebuttal a detailed comparison of our model to other existing models that were suggested by the other reviewers. In our work, we think for the first time, one can see how training history is associated with key performance parameters on a large population level. It should be noted that endurance is impossible to measure in the laboratory as it would require multiple hours of running on a treadmill which presumably involves a number of artificial e↵ects compared to ’real world’ running. Hence, there is currently no reliable evaluation of correlations between real-world running endurance and training beyond some studies of individual athletes. It is, however, correct that our analysis does not determine if training is the cause of observed performance, and just associated with higher performance level. While this is an interesting question for future analysis, even the here detected correlations can be of practical importance: They can be useful for estimating realistic expectations for a race for less experienced runners from their training intensity and volume, and hence prevent ”hitting the wall” early in the race. In addition, our observation that endurance peaks at a given training load (in TRIMP), see Fig. 5(c), should help preventing over-training, i.e., unproductive increase in training that can cause injury and other health problems. 6 C Given the size and, I expect, richness of the dataset, I imagine that there is much more that the investigators could have learned. I list a few questions in the following paragraphs that the authors might have explored. Do aspects of training help to explain errors in the model predictions? This could have helped us move toward a more causal link between training and race performance. There also seemed to be a systematic error in their model related to the endurance parameter. Is this related to training? Or errors in their data? Or a gap in their model? This question should have been explored more fully. A We feel that we should first clarify the content of our data set since it is less rich as your comments suggest. Our model contains only the date, total distance and average velocity for all runs of the subjects. (See below for a comment on heart rate.) While more data are recorded by GPS watches, these time series with one second resolution could not be provided by our industrial partner for millions of kilometers of running. However, we agree that separate work on a smaller number of subjects with higher data detail would be very interesting and should be performed in the future. Regarding observed deviations between actual race times and model predictions, we first note that our model has been shown to outperform all existing models when applied to personal records from 800m to the marathon of elite runners (please see appendix to this rebuttal). The systematic error in the predicted marathon times in Fig.4(a) at rather small and large endurance appear to be a consequence of the difficulty to measure endurance from a few races at shorter distances when these races are not performed under prefect conditions or optimal motivation of the athlete. As Fig. 4(b) shows, this problem is most pronounced for slower runners. Fast runners demonstrated the smallest error between prediction and actual race time. This is consistent with the observation that fast runners display also highest consistency in performance over all race distances (due to higher experience and more racing attempts on a given distance), and hence their endurance parameter shows less uncertainty. This is particularly the case for elite runners (see analysis in the appendix). As there were associations between performance indicators and training background (Fig. 5), we can draw a similar conclusion: Low relative training intensity and high training volume, typical for more experienced and faster runners, is associated with smaller model error. C Are there means to predict an athletes race performance from sub-maximal training performance (i.e., not races), using heart rate or any other measures the watch might provide? The current model requires subjects to per- formance two or more races at maximal e↵ort to extract these parameters. While this is an improvement on physiological testing it is still a burden and does not seem to take advantage of the dataset. I presume heart rate is available, for example. Would heart rate and heart-rate variability during training help to detect some of the physiological parameters on training runs? A Estimating race performance from sub-maximal training performance directly is impossible without additional assumptions being made. An important quantity for endurance running performance is the decay of fractional utilization of maximal aerobic power with duration which measures for how long an runner can maintain a certain fraction of maximal aerobic power output. This quantity can be estimated from ’time to exhaustion’ experiments in the laboratory, i.e., by maximal tests. Without a precise knowledge of this quantity (measured by El in our model), a ’typical’ value can only be assumed (depending on training status). Our dataset contains for most athletes a number of races over 5km to the halfmarathon as these distances are used during training as ’test races’. Hence, for marathon runners, this information on maximal e↵ort events is usually available and provides a clear improvement over physiological testing in the laboratory where maximal e↵ort is impossible to motivate for a distance of 20km or longer. As far as heart rate is concerned, out data set does not contain heart rate data for all runs and athletes as not all runners who wore a GPS watch wear a heart rate monitor (chest strap). But even if this data would be available, there remains an important unknown: the maximal heart rate of the athlete which varies substantially among 7 individuals and cannot be determined accurately and easily from age-based formulas. Without the maximal heart rate the important relative e↵ort (the quantity p in our model) cannot be determined accurately. Because of this, our running model was built on the requirement that a priori no information about the runner’s physiology is needed. Additional challenges are that heart rate is a↵ected by external factors, such as temperature, that are often unknown. The same goes for heart rate variability as even less is understood about how di↵erent levels of heart rate variability during exercise relate to e↵ort or athletic performance. C Do the measures extracted from their model and the real-world dataset match measures extracted from gold standard lab assessments in a small (but heterogeneous) subset of the subjects? While the researchers do compare to previously published data, these tests would have provided more convincing evidence that their model is valid in a population with varying age, gender, ethnicity, and training status. A When it comes to endurance running tests conducted in the laboratory, there are several reasons why they should not be considered as benchmark for running performance: (1) Maximal tests in laboratory are difficult to repeat, possibly due to lack of motivation to go all-out without opponent or competition or even price money to win. As a result, the coefficient of variation may be as high as 25% (Billat et. al., Med Sci Sports Exerc, 1994; Wigley et al., Int J Sports Med, 2007); (2) Running mechanics varies considerably between treadmill and over ground running (Nigg et al., Med Sci Sports Exerc, 1994; Sinclair et al., Sport Biomech, 2012). One reason may difficulty to simulate wind resistance; (3) Maximal laboratory tests are short-lasting and therefore fail to account for reduction in running economy and subsequent increase in oxygen consumption at given speed that occurs over long-distance running. We note also that all existing models for running performance have been validated by comparison to athletic records, and not by laboratory testing. Demographic and other measures that rely on user input are not reliable in big data sets from tracking platforms as ours as many users never update default settings. Only location, which is given by GPS, is considered reliable. C Does their phenomenological model perform better than past models on this large dataset? What about a sim- ple linear regression model? Comparing the models against additional baselines would have provided further confidence. A The most realistic test of models is their agreement with running world records and personal records of elite athletes since those data are most consistent and obviously obtained under maximal e↵ort and controlled settings. A variety of models have been proposed in the past. Only one of them, proposed by Peronnet & Thibault [F. Peronnet, G. Thibault, Mathematical analysis of running performance and world running records, J Appl Physiol. 67, 453 (1989)] employs a logarithmically decaying fractional utilization of maximal aerobic power, based on empirical observations in athletic performances. Their model predicts world-records with an error of less than 1% but the model is complicated by the fact that it requires many physiological parameters (body weight, running economy, etc) that are unrealistically assumed to be the same for every athlete. While our model is similar to the one by Peronnet & Thibault it is di↵erent in two essential points: (1) The logarithmic decay of fractional utilization of maximal aerobic power emerges in our model from an exact solution of a self-consistency equation and (2) our model is universal in the sense that it depends only on relative (rescaled) quantities and hence can be applied to all athletes without knowing details like, e.g., body weight and size, and running economy. Our model predicts world records from 800m to the marathon with an error slightly less than the one observed for the model of Peronnet & Thibault. To provide a constructive basis for further review, and to clear up misunderstandings indicated by the other reviewers, but also to defend our mathematical model used for analyzing the data, we provide below a new, rather detailed comparison of the Peronnet & Thibault model and some other models (mentioned by the other reviewers) and our model, including new graphs and tables. The attached tables and graphs also show that a linear regression would not work since the race velocities change on a logarithmic time scale, with a marked crossover at about 2000m race distance. While we think that our new comparison can help the evaluation of our present work, it would not improve the manuscript 8 but would only make it more exhaustive to read. We note that details of our model and its validation against world records have been published earlier [M. Mulligan, G. Adam, T. Emig, A minimal power model for human running performance, PLoS ONE 13(11): e0206645 (2018)]. C Another major contribution could be to share the dataset with other researchers, which would be highly novel and a means to accelerate research on human performance, injury, and real-world training. I would not expect the researchers to tackle all of these problems, but I would expect more novel insights or contributions in some form. A Following general policy, our data set shall be made available to other researchers upon request once our work has been published. C Another issue with the current submission is that I found the manuscript more challenging to read and understand than needed. Work is needed to improve the readability and clarity of the writing. As one example, the abstract as written contains very few specific details about the study that was performed. What parameters were predicted? What is performance? What are training modes? Given space, the abstract should not be exhaustive, but the key details should be described with enough specificity to give the reader a more clear understanding of what the study entailed. The Introduction, Results, and Discussion need similar improvements to more clearly and succinctly state what analysis was performed. A We have rewritten some parts of the manuscript to improve clarity of the description of performed analysis. Specifically, the abstract contains now more details about our study. 9 Rebuttal letter “Novel insights on human exercise performance from big data mining” (NCOMMS-20-02292) Please find below our point-to-point answers to the reviewer comments (C: comment, A: answer). Answer to Reviewer #3 We thank the reviewer for her/his time spent looking over our manuscript and their comments that we address point-by-point in the following. C In the Introduction, the authors challenge an axiom that has been characterizing exercise physiology since longer than a century, the axiom that measurement conditions should be standardized. I kindly disagree with this view. Existing models, validated experimentally in the laboratory, and applicable on the field and on large-scale numbers, come from standard experimental laboratory conditions. The theoretical models do exist indeed. They have been developed theoretically and validated by measuring VO2max during exercise testing and the fraction of VO2max utilization and the energy cost of the locomotion mode at stake at steady state. The basic formula id as follows: V = F ⇤ ˙V O2max/C (.1) where v is velocity, F the sustainable fraction of VO2max over a given distance, and C the energy cost of the locomotion mode at stake. C has the dimension of a force and represents the metabolic energy that is to be spent to generate the force that is necessary to overcome the forces that oppose to the body movement. These are two forces: 1) air resistance (or water resistance in swimming) and 2) frictional forces. These can be expressed as follows: C = kv2 + Cf (.2) This equation means that, if you plot C as a function of the square of speed, you obtain linear relationships with slope equal to constant k and y-intercept equal to the energy cost that is necessary to overcome frictional forces (high in running, low in cycling). Constant k is directly proportional to the frontal surface area of the moving body, to the aerodynamic (or hydrodynamic factor) Cx, and to the air (or water) density. With these simple relationships, it is possible to simulate and interpret all what happens in field conditions and during actual competitions. I just give the authors a few references (clearly overlooked by the authors) to make them aware of what I am saying: di Prampero PE, Int J Sports Med 7: 55-72, 1986; di Prampero PE, Eur J Appl Physiol 82: 345-360, 2000; di Prampero et al, J Appl Physiol 47: 201-206, 1979; di Prampero et al, Eur J Appl Physiol 55: 259-266, 1986; Ferretti et al, Eur J Appl Physiol 111: 391-401, 2011; Margaria et al, J Appl Physiol 18: 367-370, 1963; Minetti et al, J Appl Physiol 93: 1039-1046, 2002; Pendergast et al, J Appl Physiol 43: 475-479, 1977; Tam et al, Eur J Appl Physiol 112: 3797-3806, 2012; Zamparo et al, Eur J Appl Physiol 111: 367-378, 2011. All the elements that are necessary to set a model to be applied to large data sets and in field conditions are present in those and many other studies. A We thank the Reviewer for discussing the details of the model developed by P. .E. di Prampero et al. These remarks suggest that there has been a misunderstanding which could be due to our very brief discussion of our model and in particular its relation to other models. Let us hence clarify this point, by using your notation for the model. Our model is exactly equivalent to above equations (.1) and (.2) with a particular form for the sustainable fraction F and a constant, velocity independent C which has been used previously by others [see, e.g., S. Lazzer et al., Eur J Appl Physiol, 112, 1709 (2012)] and is justified for the running velocities in our data 10 set (negligible air resistance). While the reviewer does not provide an explicit expression for F, this model has been applied to half and full marathon races, using for the sustainable fraction F(T) = f0 − f1T , (.3) i.e., a linearly decreasing function of the duration T of the race, with constants f0, f1[P. E. di Prampero et al., Eur J Appl Physiol 55, 259 (1986)]. A related model has been developed by Peronnet & Thibault [F. Peronnet, G. Thibault, J Appl Physiol, 67, 453 (1989)], using a logarithmic function for the sustainable fraction, F(T) = 1 + E MAP log(T/TMAP) , (.4) with maximal aerobic power MAP, a negative constant E and TMAP = 7min. Peronnet & Thibault motivated this choice by empirical arguments based on world record performances up to the Marathon distance. Interestingly, we have shown in our paper in 2018 [M. Mulligan, G. Adam, T. Emig, PLoS ONE 13(11): e0206645 (2018)] that the form of Eq. (.4) can be derived mathematically from a self-consistent integral equation. In the notation of our present manuscript, the sustainable fraction is given by F(T) = Pmax(T) Pm = 1 − Pl Pm log T tc (T > tc) , (.5) with Pm =MAP and tc = 6min, see Eq. (3) of our manuscript. Substituting this equation in your Eq. (.1) yields exactly our model. This and additional details of the relation between the model you described above, the so-called ’critical power’ model proposed by another Reviewer, and the model by Peronnet & Thibault are summarized in the attached table. We have also performed a new, extensive comparison of these models to current running world records and personal records of some elite marathon runners, including the model proposed in above Eqs. (.1) and (.2) with F given by Eq. (.3). All results are attached below. They show that our model has overall the smallest average error for the considered athletics records. We note that the ’critical power’ model and above model with F given by Eq. (.3) show substantial discrepancies with running records for distances longer than ⇠ 10km (see attached plots). Hence, we believe that (1) a logarithmic decay of F is essential and (2) our model is a very reasonable approach to analyze the race distances of 5km, 10km, Halfmarathon and Marathon in our data set. C What is the use of a variable like running economy, which the authors define as the steady state oxygen con- sumption at a given constant speed instead of C? Physically speaking, C looks more appropriate. A We agree that the energy cost of running, C, is the appropriate quantity. In fact, as pointed out in the previous item, our model does employ this concept. The exact relation between C in your equation and our model is v ⇤ C = Pb + Pm − Pb vm v (.6) where Pm =MAP and Pb is the resting (basal) metabolic rate (power). This relation means that we measure the energy cost of running in our model by a parameter vm which is a velocity that is close to the running speed that can be maintained for about 6min, equivalent to the time scale TMAP in the model of Peronnet & Thibault. Please note that this implies the relation Cf = (Pm − Pb)/vm and vm ⇤ C = MAP. C At the end of page 2, the authors create artificially a dichotomy between laboratory tests and actual competition conditions: nevertheless, the theoretical background derived from laboratory tests is fully applicable to laboratory conditions. A It is not our intention to suggest a general discrepancy between laboratory testing and actual race performance. Our explanations on the items above show that we indeed use the theoretical background that you suggest. The 11 crucial di↵erence between previous approaches and ours is based on our result for the duration dependence of the sustainable fraction F. And hence the point we want to rise here is the often relative short duration of laboratory testing. Incremental running test is the most common laboratory test that is conducted to determine aerobic and anaerobic thresholds as well as maximal aerobic speed and maximal heart rate. However, incremental running test is short-lasting and cannot account for the e↵ect of exercise duration on thresholds or general e↵ects of fatigue. The maximal fractional utilization F(T) can be investigated in time-to-exhaustion test such as running at certain fraction of VO2max, but the obtained results may have low test-retest repeatability as indicated by a 25% coefficient of variation [Billat et. al., Med Sci Sports Exerc, 1994; Wigley et al., Int J Sports Med, 2007]. Furthermore, running mechanics between treadmill and over ground running are di↵erent [Nigg et al., Med Sci Sports Exerc, 1994; Sinclair et al., Sport Biomech, 2012]. In conclusion, laboratory tests are most suitable for observing changes in running performance over relative short durations, but test results may not always accurately predict actual race performance due to a lack of knowledge of the function F(T) and also due to di↵erences in running mechanics that occur between treadmill and outdoor ground. Also, an important aspect when comparing laboratory testing and actual races in which world records are set is the degree of motivation of the athlete. This latter point seems particularly relevant to long lasting time-to-exhaustion tests performed to determine F. C A sentence like Unfortunately, these approaches predict that speeds below a critical velocity can be maintained for infinite duration which contradicts observation has been criticized (see e.g. Ferretti, Energetics of Muscular Exercise, Springer 2015, chapter on critical power). Laboratory physiologists are perfectly aware of the fact that energy sources in the body are finite. A With this statement on the ’critical power’ model we wanted to point out the importance of using a fractional utilization F(T) < 1 of maximal aerobic power when describing long lasting events like the marathon. C Under Results, I read: Main determinants of aerobic fitness and endurance in long distance runners (LDR) are maximal oxygen uptake per body weight, VO2max , velocity dependent, sub-maximal oxygen demand, known as running economy (RE), and the lactate or ventilatory threshold (LT) that sets the limit below which a steady state blood lactate concentration is maintained. Although a reference is given, this statement is not correct, see equation 1 in this report. A As explained before, the di↵erence between equation (1) in your report and our model consists in the function used to describe the fractional utilization F(T). The ”lactate or ventilatory threshold (LT)” is defined in our article from the duration dependence of F(T) as the fractional utilization of MAP that the runner can maintain for one hour. We have changed the name and description of this threshold in our article accordingly to avoid confusion with other concepts such as LT. As also explained before, the energy cost of running is measured in our model by the velocity vm which is directly related to C in equation (1) in this report. C Equation 1 of the article is constructed in such a way to encompass a large variety of field conditions, independent of the physiological model. Frankly, I do not see a role for such a study, unless we create an artificial opposition between classical physiological studies and a kind of modern approach that the authors claim. Big numbers are fascinating, but their utility depend of the context into which they are inserted and interpreted. The context exists, but the authors seem to be unaware of it. The discussion id biased by the chosen approach and does not deserve to be discussed analytically. A Equation 1 of our article is in fact an exact mathematical solution of the eq. (1) given in this report, with the fractional utilization of MAP given by F(T) = 1 − log(T/tc) for a race of duration T > tc with the time scale tc = 6min in this article. This form for F(T) was derived in our earlier work [M. Mulligan, G. Adam, T. Emig, PLoS ONE 13(11): e0206645 (2018)]. Hence, our chosen approach fits fully into the existing context after the importance of F(T) is understood. 12 C Endurance running dates back much longer than the ancient Olympic Games: it is alike that pre-historical nomadic societies used endurance running while hunting or for migrating. A Thank you for this interesting remark. We have modified the beginning of the introduction to give a more general presentation. C Page 2, line 7 : ranging instead of raging A Thank you. We corrected this spelling error. 13 Appendix to Rebuttal Letter: New results from a comparison of existing mathematical models A. V. Hill: The physiological basis of athletic records (1925) In his seminal work, Hill posed the question ”how long a given e↵ort can be maintained”. To answer this question he analyzed running records. In Figure 4 of his original article (reproduced in Fig. 1 below) he plotted the average running speed over the a logarithmic time scale. It can be seen that for running (an other sports) the velocity decays linearly with the logarithm of time, following two branches with di↵erent slopes. The analysis of Peronnet & Thibault [F. Peronnet, G. Thibault, Mathematical analysis of running performance and world running records, J Appl Physiol. 67, 453 (1989)] and our mathematical model, applied to current world records, confirm Hill’s observation with high accuracy, as shown in the next section. This logarithmic decay is not reproduced by the models proposed by Reviewers #1 and #3. FIG. 1 Original figure from A. V. Hill, The physiological basis of athletic records, The Lancet, September 5, 1925, showing average speed for running and other sports over a logarithmic time scale. 14 Comparison of mathematical models The mathematical models for running performance mentioned by the reviewers (reviewer #1: critical power model, reviewer #3: di Prampero’s approach) and the model by Peronnet & Thibault are summarized and compared in Table I. The last column of this table provides the relation of those models to our model. In order to assess and compare the accuracy of these models and our model we have performed detailed anal- yses of men running world records (1987 as in Peronnet & Thibault, and current as of April 2020) and personal records of six elite marathon runners (Antonio Pinto, Eliud Kipchoge, Felix Limo, Haile Gebrselassie, Mo Farah, Steve Jones; choice of athletes taken from A. M. Jones, A.Vanhatalo, Sports Med 47, S65 (2017); Data from https://www.worldathletics.org/athletes). For all models the unknown parameters were determined by minimizing the mean squared relative error between the theoretically predicted time and the actual race time, i.e., the expression Err = 1 N N X j=1 ✓Ttheory(dj) − Trace(dj) Trace(dj) ◆2 (.7) was minimized where the sum extends over N race distances dj. A numerical algorithm based on di↵erential evolution was used for this purpose. The following models were analyzed: MIT: Our model, here called the ’MIT model’ [M. Mulligan, G. Adam, T. Emig, PLoS ONE 13(11): e0206645 (2018)] CP: The ’critical power’ model [see e.g. M. Jones, A.Vanhatalo, Sports Med 47, S65 (2017)] PT: The model of Peronnet & Thibault [F. Peronnet, G. Thibault, J Appl Physiol. 67, 453 (1989)] diP: The model of di Prampero (with F(T) given by Eq. (.3) with f0 = 1) [see e.g. P. E. di Prampero et al., Eur J Appl Phys 55, 259 1986] Following the analysis in A. M. Jones, A.Vanhatalo, Sports Med 47, S65 (2017), for the CP model the race distances were restricted to dj < 15.000m for the determination of the model parameters. The analyzed race distances and times are listed along with the obtained model parameters in the attached tables, see Figs. 2 and 4. Shown are also the errors of the model predictions for each race distance and the average error (av.error) for each model. The attached plots in Figs. 3 and 5 show the race results (open circles) and the four model predictions for the average race velocity ¯v(d) as function of the race distance d as solid curves. The velocity is measured in units of vm and the distance in units of dc = vmtc which corresponds to a simple linear rescaling of time and distance. We decided to plot average velocity (in units of the velocity vm at maximal aerobic power, MAP) since this shows clearly the relative slow decay of velocity with racing distance. For example, the world records show that a marathon is raced just ⇠ 18% below the velocity at MAP. This means that a mathematical model needs to achieve a rather high precision in predicting the mean velocity in order to properly distinguish between endurance running distances. Summary of results from analyzing running records Our findings are as follows: 1. For all analyzed data sets, the average error between the model prediction and the actual race times is smallest for the MIT model, followed by the PT model. It should be noted that both models describe the fractional utilization 15 of maximal aerobic power by a logarithmic function. The typical error of both models for the marathon is well below 1%, and with the average error of our MIT model being less than half of the error of the PT model for world records. 2. The CP model shows a systematic discrepancy for distances over 10km and below 1500m. The predicted average velocity tends to a constant (”critical velocity”) with increasing distance, indicated by a dashed line in the plots. The typical error for the marathon varies around 8%, both for world and personal records. 3. The diP model also shows a systematic error in the range of long race distances. The curve for the mean velocity shows a non-monotonous curvature that bends towards too small velocities for larger distances, leading to a typical error of a few percent for the half and full marathon. 4. Interestingly, for most data sets (in particular the world records), three model predictions converge (intersect) on one particular point that is defined in the MIT model by the velocity vm and the distance dc = vmtc, corresponding approximately to the time scale TMAP ⇠ tc in the PT model over which the velocity vm ⇠ vMAP can be maintained. This observation has important consequences: It shows that all four models tend to agree with increasing accuracy when the velocity vm vMAP is approached. This implies that ’critical power’ or ’critical velocity’ can be obtained from the MIT model. This is indeed the case, and the relation is summarized in the attached Tab. I. 5. The data from world records are described by all models in general better than personal records of individual athletes since world records are a result of optimized preparation and talent of an athlete for a given distance. However, even on the level of individual athletes, the MIT model outperforms the other models, as shown by the modeling of elite marathon runners (see Tab. 4 and Fig. 5). We conclude that the models based on a constant or polynomial function F(T) for the fractional utilization of MAP give an approximate description of running records that is valid only for distances below the 5km or the 10km race or durations below 15 to 30min. This time scale is consistent with the 15min duration already observed by A. V. Hill in his 1925 paper for the ending of a rapid decrease of race velocity and the beginning of a slower, logarithmic fall. Indeed, for larger distances, a logarithmic function F(T), as used in the PT model and the our model, is essential for a consistent description of real world running records. 16 TABLE I Summary of performance models. model, reference main variables and equations relation to our model (’MIT model’) Critical Power (CP) Monod & Scherrer (1965) The model is expected to describe races from 800m up to 5km or perhaps 10km. Power P(v) and velocity v(T) sustainable over time T: [A. M. Jones, A. Vanhatalo, Sports Med 47, S65 (2017)] P(T) = Pc + W 0 T , or v(T) = vc + D0 T with critical power Pc and critical speed (CS) vc, anaerobic capacity W 0 (in W/kg) or distance D0 (in m). Fractional utilization is fixed at unity: Power P < Pc or velocity v < vc can be maintained for “infinite” time but in praxis limited by substrate. CP model close to our model around duration tc with relation vc ⇡ vm − D0 tc and D0 ⇡ Ps Pm + Ps vmtc No description of fractional utilization of MAP, corresponding to Pl = 0 in our model. di Prampero (diP) di Prampero (1986) Maximal velocity v(T) sustainable for time T [di Prampero et al., J Appl Physiol 74,2318 (1993)]: v(T) = F(T) C(v(T)) ˙Emax, ˙Emax = A T + MAP − MAP ⌧ T (1 − e−T/⌧) with work A from anaerobic sources, maximal aerobic power MAP, ⌧ = 10s, and the energy cost of running [per distance and body weight in J/(m kg)] given by C(v) = Cf + kv2 + 2v3/d (v in m/s, d in m) with k = 0.0103, Cf = 3.79. Fractional utilization F(T) of MAP over duration T is approximated by F(T) = f0 − f1T where f0 ⇡ 1, f1 ⇡ 0 for T < 20min, and f0 ⇡ 0.94, f1 ⇡ 10−3 for durations from a half to a full marathon with T in min. [di Prampero et al., Eur J Appl Phys 55, 259 1986]. MAP b= Pm Fractional utilization of MAP: F(T) = 1 − Pl Pm log T tc (T > tc) Power output required to run at velocity v: C(v)v = Pb + Pm − Pb vm v, so that Cf = (Pm − Pb)/vm and k = 0. This means C(vm)vm = Pm implying that vm is speed at MAP. Peronnet & Thibault (PT) Peronnet & Thibault (1989) Power output P(T) sustainable over time T and power Pv(v) required to at velocity v: P(T) = 8 > > > < > > > : c2(T) A T + MAP − c1(T)(MAP − BMR) (T < TMAP) c2(T) A T ⇣ 1 + f log T TMAP ⌘ + c1(T)BMR + (1 − c1(T)) ✓ MAP + E log T TMAP ◆ (T > TMAP) Pv(v) = BMR + 3.86v + C0v3 (v = d/T in m/s) " E < 0: Fractional utilization of MAP with c2(T) = 1 − e−T/k2, c1(T) = k1 T ⇣ 1 − e−T/k1⌘ . with maximal aerobic power MAP (in W/kg), anaerobic capacity A in J/kg, TMAP = 7min the maximal race duration for which the peak aerobic power is MAP, rate of peak decline E in W/kg, k1 = 30s, k2 = 20s, f = −0.233, C0 = 0.0103 + 2/d with distance d in m and basal metabolic rate BMR=1.2W/kg MAP b= Pm, BMR b= Pb, TMAP = tc A, f, C0 = 0 Our model does not include kinetics of aerobic and anaerobic metabolism at the beginning of exercise (< 30s) so that c1(T) = 0, c2(T) = 1. Fractional utilization of MAP is the same as in our model, i.e., logarithmic decrease with E = −Pl and for T < TMAP, A/T is replaced by −Ps log T tc with A ⇡ Pstc. 17 ID distance time MIT error[%] CP error[%] PT error[%] diP error[%] MIT parameters CP parameters PT parameters diP parameters WR1987men 800 01:41.73 01:42.12 +0.39 01:32.50 -9.07 01:41.67 -0.06 01:40.19 -1.51 1000 02:12.18 02:11.57 -0.46 02:05.83 -4.80 02:12.60 +0.31 02:11.69 -0.37 1500 03:29.46 03:29.09 -0.18 03:29.16 -0.14 03:30.23 +0.37 03:31.63 +1.03 1609 03:46.32 03:46.62 +0.13 03:47.33 +0.45 03:47.18 +0.38 03:49.18 +1.26 2000 04:50.81 04:51.15 +0.12 04:52.50 +0.58 04:48.07 -0.94 04:52.34 +0.53 3000 07:32.10 07:32.41 +0.07 07:39.16 +1.56 07:25.84 -1.38 07:34.92 +0.62 5000 12:58.39 12:59.38 +0.13 13:12.49 +1.81 13:04.46 +0.78 13:03.24 +0.62 10000 27:13.81 27:13.51 -0.02 27:05.82 -0.49 27:32.87 +1.17 27:00.38 -0.82 21100 1:00:55.00 1:00:35.14 -0.54 57:55.81 -4.90 1:00:49.44 -0.15 59:29.22 -2.35 42195 2:07:12.00 2:07:39.60 +0.36 1:56:31.62 -8.39 2:06:33.66 -0.50 2:08:44.79 +1.22 tc 05:28.54 vm 6.76 m/s Es=T110 %MAP/tc 0.480 El=T90 %MAP/tc 5.493 Anaerobic & aerobic metabolism A=Pstc+(Pm-Pb)25s 1820.0 J/kg D'=vmtcPs/(Ps+Pm) 255.7 m CS=vm-D'/tc 5.98 m/s VO2max 78.4 ml/(kg min) av.error 0.24 % D' 245.0 m CS 6.00 m/s av.error 3.22 % vMAP 6.36 m/s E/MAP -5.00 % A 1742.00 J/kg VO2max 82.1 ml/(kg min) av.error 0.60 % vMAP 5.96 m/s f1 1.85 ×10-5/s A 1348.00 J/kg VO2max 74.6 ml/(kg min) av.error 1.03 % WR2020men 800 01:40.91 01:41.71 +0.79 01:33.77 -7.07 01:41.32 +0.41 01:40.20 -0.70 1000 02:11.96 02:10.58 -1.04 02:05.89 -4.60 02:11.49 -0.36 02:10.77 -0.90 1500 03:26.00 03:26.09 +0.04 03:26.18 +0.09 03:27.05 +0.51 03:28.12 +1.03 1609 03:43.13 03:43.08 -0.02 03:43.68 +0.25 03:43.53 +0.18 03:45.08 +0.87 2000 04:44.79 04:45.40 +0.21 04:46.47 +0.59 04:42.74 -0.72 04:46.09 +0.46 3000 07:20.67 07:20.91 +0.05 07:27.05 +1.45 07:15.31 -1.22 07:22.99 +0.53 5000 12:37.35 12:36.66 -0.09 12:48.20 +1.43 12:41.29 +0.52 12:39.51 +0.29 10000 26:17.53 26:16.99 -0.03 26:11.10 -0.41 26:33.79 +1.03 26:05.02 -0.79 21100 58:01.00 58:05.83 +0.14 55:53.52 -3.66 58:17.54 +0.48 57:11.12 -1.43 42195 2:01:39.00 2:01:34.01 -0.07 1:52:20.93 -7.65 2:00:36.40 -0.86 2:02:35.30 +0.77 tc 05:14.60 vm 6.93 m/s Es=T110 %MAP/tc 0.435 El=T90 %MAP/tc 6.732 Anaerobic & aerobic metabolism A=Pstc+(Pm-Pb)25s 1678.0 J/kg D'=vmtcPs/(Ps+Pm) 224.4 m CS=vm-D'/tc 6.21 m/s VO2max 80.3 ml/(kg min) av.error 0.25 % D' 216.0 m CS 6.23 m/s av.error 2.72 % vMAP 6.54 m/s E/MAP -4.48 % A 1686.00 J/kg VO2max 84.8 ml/(kg min) av.error 0.63 % vMAP 6.19 m/s f1 1.77 ×10-5/s A 1247.00 J/kg VO2max 77.8 ml/(kg min) av.error 0.78 % FIG. 2 Application of four mathematical models to men running world records from 1987 and 2020: Predicted race times and model parameters (see Tab.I for models). 18 CP (fit for d≤15km): av.error=3.22% D'=245.0m, CS=6.00m/s PT: av.error=0.60% MAP=28.58W/kg, A=1742.00J/kg E=-5.00% diP: av.error=1.03% MAP=25.99W/kg, A=1348.00J/kg f=1.85×10-5/s MIT: av.error=0.24% Es=0.480, El=5.493 vm=6.76m/s, dc=2220.0m tc= 05:28.54

    



 
 

    

/


(
)/


     CP (fit for d≤15km): av.error=2.72% D'=216.0m, CS=6.23m/s PT: av.error=0.63% MAP=29.54W/kg, A=1686.00J/kg E=-4.48% diP: av.error=0.78% MAP=27.10W/kg, A=1247.00J/kg f=1.77×10-5/s MIT: av.error=0.25% Es=0.435, El=6.732 vm=6.93m/s, dc=2179.0m tc= 05:14.60

    



 
 

    

/


(
)/


    distance time CP model error [%] PT model error [%] diP model error [%] MIT model error [%] 800 01:41.73 01:32.50 -9.07 01:41.67 -0.06 01:40.19 -1.51 01:42.12 +0.39 1000 02:12.18 02:05.83 -4.80 02:12.60 +0.31 02:11.69 -0.37 02:11.57 -0.46 1500 03:29.46 03:29.16 -0.14 03:30.23 +0.37 03:31.63 +1.03 03:29.09 -0.18 1609 03:46.32 03:47.33 +0.45 03:47.18 +0.38 03:49.18 +1.26 03:46.62 +0.13 2000 04:50.81 04:52.50 +0.58 04:48.07 -0.94 04:52.34 +0.53 04:51.15 +0.12 3000 07:32.10 07:39.16 +1.56 07:25.84 -1.38 07:34.92 +0.62 07:32.41 +0.07 5000 12:58.39 13:12.49 +1.81 13:04.46 +0.78 13:03.24 +0.62 12:59.38 +0.13 10000 27:13.81 27:05.82 -0.49 27:32.87 +1.17 27:00.38 -0.82 27:13.51 -0.02 21100 1:00:55.00 57:55.81 -4.90 1:00:49.44 -0.15 59:29.22 -2.35 1:00:35.14 -0.54 42195 2:07:12.00 1:56:31.62 -8.39 2:06:33.66 -0.50 2:08:44.79 +1.22 2:07:39.60 +0.36 distance time CP model error [%] PT model error [%] diP model error [%] MIT model error [%] 800 01:40.91 01:33.77 -7.07 01:41.32 +0.41 01:40.20 -0.70 01:41.71 +0.79 1000 02:11.96 02:05.89 -4.60 02:11.49 -0.36 02:10.77 -0.90 02:10.58 -1.04 1500 03:26.00 03:26.18 +0.09 03:27.05 +0.51 03:28.12 +1.03 03:26.09 +0.04 1609 03:43.13 03:43.68 +0.25 03:43.53 +0.18 03:45.08 +0.87 03:43.08 -0.02 2000 04:44.79 04:46.47 +0.59 04:42.74 -0.72 04:46.09 +0.46 04:45.40 +0.21 3000 07:20.67 07:27.05 +1.45 07:15.31 -1.22 07:22.99 +0.53 07:20.91 +0.05 5000 12:37.35 12:48.20 +1.43 12:41.29 +0.52 12:39.51 +0.29 12:36.66 -0.09 10000 26:17.53 26:11.10 -0.41 26:33.79 +1.03 26:05.02 -0.79 26:16.99 -0.03 21100 58:01.00 55:53.52 -3.66 58:17.54 +0.48 57:11.12 -1.43 58:05.83 +0.14 42195 2:01:39.00 1:52:20.93 -7.65 2:00:36.40 -0.86 2:02:35.30 +0.77 2:01:34.01 -0.07 FIG. 3 Application of four mathematical models to men running world records from 1987 and 2020: Log-normal plot of the ’running curves’ predicted by the models (average velocity ¯v as function of race distance d, in units of vm and dc = vmtc given in the plot legend) and actual race data (red dots). The tables summarize the actual and predicted race times, along with the relative errors in percent. 19 ID distance time MIT error[%] CP error[%] PT error[%] diP error[%] MIT parameters CP parameters PT parameters diP parameters Antonio 1500 03:39.25 03:39.25 +0.00 03:31.80 -3.40 03:40.26 +0.46 03:37.58 -0.76 3000 07:41.33 07:38.67 -0.58 07:41.66 +0.07 07:34.21 -1.54 07:43.99 +0.58 5000 13:02.86 13:08.15 +0.68 13:14.80 +1.53 13:09.54 +0.85 13:16.00 +1.68 10000 27:12.47 27:25.68 +0.81 27:07.65 -0.30 27:33.80 +1.31 27:20.58 +0.50 21097 1:01:45.00 1:00:45.21 -1.61 57:56.09 -6.18 1:00:51.38 -1.45 59:54.66 -2.98 42195 2:06:36.00 2:07:26.14 +0.66 1:56:30.40 -7.97 2:07:01.20 +0.33 2:08:12.86 +1.28 tc 05:46.65 vm 6.64 m/s Es=T110 %MAP/tc 0.217 El=T90 %MAP/tc 6.224 Anaerobic & aerobic metabolism A=Pstc+(Pm-Pb)25s 1223.0 J/kg D'=vmtcPs/(Ps+Pm) 135.4 m CS=vm-D'/tc 6.25 m/s VO2max 77.1 ml/(kg min) av.error 0.72 % D' 228.5 m CS 6.00 m/s av.error 3.24 % vMAP 6.43 m/s E/MAP -5.47 % A 1318.00 J/kg VO2max 83.1 ml/(kg min) av.error 0.99 % vMAP 5.88 m/s f1 1.63 ×10-5/s A 1226.00 J/kg VO2max 73.5 ml/(kg min) av.error 1.29 % Eliud 1500 03:33.20 03:33.10 -0.05 03:23.46 -4.57 03:33.94 +0.34 03:31.20 -0.94 3000 07:27.66 07:29.73 +0.46 07:30.51 +0.64 07:26.12 -0.34 07:32.61 +1.11 3218.68 08:07.39 08:05.23 -0.44 08:06.52 -0.18 08:01.92 -1.12 08:07.99 +0.12 5000 12:46.53 12:51.56 +0.66 12:59.91 +1.75 12:55.80 +1.21 12:57.53 +1.44 10000 26:49.02 26:42.72 -0.39 26:43.41 -0.35 26:57.17 +0.51 26:41.56 -0.46 21097 59:25.00 58:48.35 -1.03 57:11.09 -3.76 58:58.62 -0.74 58:12.28 -2.04 42195 2:01:39.00 2:02:34.67 +0.76 1:55:05.94 -5.39 2:01:48.73 +0.13 2:02:48.65 +0.95 tc 08:05.23 vm 6.63 m/s Es=T110 %MAP/tc 0.260 El=T90 %MAP/tc 7.481 Anaerobic & aerobic metabolism A=Pstc+(Pm-Pb)25s 1563.0 J/kg D'=vmtcPs/(Ps+Pm) 213.2 m CS=vm-D'/tc 6.19 m/s VO2max 77.0 ml/(kg min) av.error 0.54 % D' 264.7 m CS 6.07 m/s av.error 2.37 % vMAP 6.45 m/s E/MAP -4.30 % A 1519.00 J/kg VO2max 83.5 ml/(kg min) av.error 0.63 % vMAP 6.00 m/s f1 1.36 ×10-5/s A 1324.00 J/kg VO2max 75.1 ml/(kg min) av.error 1.01 % Felix 1500 03:40.14 03:40.14 +0.00 03:30.81 -4.24 03:40.93 +0.36 03:37.48 -1.21 3000 07:40.67 07:40.67 -0.00 07:42.85 +0.47 07:36.52 -0.90 07:47.68 +1.52 5000 13:16.42 13:12.92 -0.44 13:18.90 +0.31 13:13.50 -0.37 13:24.72 +1.04 10000 27:04.54 27:34.04 +1.82 27:19.03 +0.89 27:40.64 +2.22 27:40.89 +2.24 15000 41:29.00 42:25.21 +2.26 41:19.17 -0.40 42:31.69 +2.52 42:16.77 +1.92 16093.4 46:41.00 45:43.06 -2.07 44:22.89 -4.93 45:48.98 -1.86 45:31.07 -2.50 20000 58:20.00 57:37.14 -1.22 55:19.30 -5.16 57:39.85 -1.15 57:13.80 -1.89 21097 1:02:05.00 1:00:59.48 -1.76 58:23.62 -5.94 1:01:01.00 -1.72 1:00:33.61 -2.45 42195 2:06:14.00 2:07:46.93 +1.23 1:57:28.64 -6.94 2:07:07.95 +0.71 2:08:43.21 +1.97 tc 09:59.56 vm 6.40 m/s Es=T110 %MAP/tc 0.208 El=T90 %MAP/tc 6.118 Anaerobic & aerobic metabolism A=Pstc+(Pm-Pb)25s 1563.0 J/kg D'=vmtcPs/(Ps+Pm) 220.0 m CS=vm-D'/tc 6.04 m/s VO2max 74.5 ml/(kg min) av.error 1.20 % D' 245.4 m CS 5.95 m/s av.error 3.25 % vMAP 6.39 m/s E/MAP -5.28 % A 1336.00 J/kg VO2max 82.5 ml/(kg min) av.error 1.31 % vMAP 5.78 m/s f1 1.45 ×10-5/s A 1332.00 J/kg VO2max 72.0 ml/(kg min) av.error 1.86 % Haile 800 01:49.35 01:49.20 -0.14 01:37.69 -10.66 01:49.37 +0.02 01:47.02 -2.13 1500 03:31.76 03:33.07 +0.62 03:30.09 -0.79 03:34.56 +1.32 03:35.83 +1.92 2000 04:52.86 04:49.49 -1.15 04:50.38 -0.85 04:49.56 -1.13 04:54.19 +0.45 3000 07:25.09 07:25.09 -0.00 07:30.95 +1.32 07:21.03 -0.91 07:31.83 +1.51 3218.68 08:01.08 07:59.55 -0.32 08:06.07 +1.04 07:56.24 -1.01 08:06.44 +1.12 5000 12:39.36 12:45.27 +0.78 12:52.10 +1.68 12:46.64 +0.96 12:50.23 +1.43 10000 26:22.75 26:39.32 +1.05 26:14.96 -0.49 26:46.51 +1.50 26:24.07 +0.08 16093.4 44:24.00 44:15.99 -0.30 42:33.39 -4.15 44:23.01 -0.04 43:33.24 -1.91 20000 55:48.00 55:49.62 +0.05 53:00.68 -5.00 55:54.06 +0.18 54:57.24 -1.52 21097 58:55.00 59:06.27 +0.32 55:56.83 -5.04 59:09.71 +0.42 58:13.03 -1.19 25000 1:11:37.00 1:10:51.77 -1.05 1:06:23.55 -7.29 1:10:50.90 -1.07 1:10:03.89 -2.17 42195 2:03:59.00 2:04:06.73 +0.10 1:52:24.59 -9.33 2:03:35.91 -0.31 2:07:49.12 +3.09 tc 05:18.59 vm 6.87 m/s Es=T110 %MAP/tc 0.201 El=T90 %MAP/tc 6.058 Anaerobic & aerobic metabolism A=Pstc+(Pm-Pb)25s 1189.0 J/kg D'=vmtcPs/(Ps+Pm) 123.0 m CS=vm-D'/tc 6.48 m/s VO2max 79.6 ml/(kg min) av.error 0.49 % D' 191.6 m CS 6.23 m/s av.error 3.97 % vMAP 6.62 m/s E/MAP -5.62 % A 1338.00 J/kg VO2max 86.0 ml/(kg min) av.error 0.74 % vMAP 6.19 m/s f1 2.21 ×10-5/s A 985.80 J/kg VO2max 77.7 ml/(kg min) av.error 1.54 % Mo 800 01:48.24 01:47.52 -0.67 01:35.74 -11.55 01:47.43 -0.75 01:46.05 -2.02 1500 03:28.81 03:31.18 +1.14 03:30.22 +0.67 03:33.14 +2.08 03:35.60 +3.25 3218.68 08:03.40 08:01.04 -0.49 08:11.29 +1.63 07:56.54 -1.42 08:08.16 +0.98 5000 12:53.11 12:51.26 -0.24 13:02.60 +1.23 12:49.49 -0.47 12:53.77 +0.09 10000 26:46.57 26:51.68 +0.32 26:40.29 -0.39 26:57.13 +0.66 26:31.30 -0.95 21097 59:32.00 59:33.22 +0.03 56:55.07 -4.39 59:39.84 +0.22 58:18.11 -2.07 42195 2:05:11.00 2:05:02.35 -0.12 1:54:25.40 -8.60 2:04:48.67 -0.30 2:06:34.96 +1.12 tc 10:14.01 vm 6.57 m/s Es=T110 %MAP/tc 0.269 El=T90 %MAP/tc 5.700 Anaerobic & aerobic metabolism A=Pstc+(Pm-Pb)25s 1820.0 J/kg D'=vmtcPs/(Ps+Pm) 273.2 m CS=vm-D'/tc 6.12 m/s VO2max 76.3 ml/(kg min) av.error 0.43 % D' 214.6 m CS 6.11 m/s av.error 4.07 % vMAP 6.58 m/s E/MAP -5.71 % A 1430.00 J/kg VO2max 85.3 ml/(kg min) av.error 0.84 % vMAP 6.13 m/s f1 1.99 ×10-5/s A 1048.00 J/kg VO2max 76.9 ml/(kg min) av.error 1.50 % Steve 800 01:47.43 01:47.43 +0.00 01:39.51 -7.37 01:47.79 +0.33 01:47.18 -0.24 3000 07:49.80 07:48.88 -0.20 07:51.18 +0.29 07:43.98 -1.24 07:50.84 +0.22 3218.68 08:26.71 08:24.88 -0.36 08:28.13 +0.28 08:21.43 -1.04 08:27.34 +0.12 5000 13:18.60 13:22.44 +0.48 13:29.07 +1.31 13:28.36 +1.22 13:26.01 +0.93 10000 27:39.14 27:45.63 +0.39 27:33.79 -0.32 28:04.09 +1.50 27:37.21 -0.12 21097 1:01:14.00 1:01:03.67 -0.28 58:48.55 -3.96 1:01:15.79 +0.05 1:00:18.34 -1.52 42195 2:07:13.00 2:07:10.00 -0.04 1:58:12.92 -7.08 2:06:07.20 -0.86 2:08:03.76 +0.67 tc 07:35.31 vm 6.41 m/s Es=T110 %MAP/tc 0.411 El=T90 %MAP/tc 7.838 Anaerobic & aerobic metabolism A=Pstc+(Pm-Pb)25s 1883.0 J/kg D'=vmtcPs/(Ps+Pm) 282.1 m CS=vm-D'/tc 5.79 m/s VO2max 74.5 ml/(kg min) av.error 0.25 % D' 211.0 m CS 5.92 m/s av.error 2.95 % vMAP 6.16 m/s E/MAP -3.82 % A 1584.00 J/kg VO2max 79.2 ml/(kg min) av.error 0.89 % vMAP 5.81 m/s f1 1.46 ×10-5/s A 1154.00 J/kg VO2max 72.5 ml/(kg min) av.error 0.54 % FIG. 4 Application of four mathematical models to personal records of six elite marathon runners (Antonio Pinto, Eliud Kipchoge, Felix Limo, Haile Gebrselassie, Mo Farah, Steve Jones): Predicted race times and model parameters (see Tab.I for models). 20 CP (fit for d≤15km): av.error=3.24% D'=228.5m, CS=6.00m/s PT: av.error=0.99% MAP=28.95W/kg, A=1318.00J/kg E=-5.47% diP: av.error=1.29% MAP=25.59W/kg, A=1226.00J/kg f=1.63×10-5/s MIT: av.error=0.72% Es=0.217, El=6.224 vm=6.64m/s, dc=2303.0m tc= 05:46.65

    



 
 

    

/


(
)/


    CP (fit for d≤15km): av.error=2.37% D'=264.7m, CS=6.07m/s PT: av.error=0.63% MAP=29.08W/kg, A=1519.00J/kg E=-4.30% diP: av.error=1.01% MAP=26.14W/kg, A=1324.00J/kg f=1.36×10-5/s MIT: av.error=0.54% Es=0.260, El=7.481 vm=6.63m/s, dc=3219.0m tc= 08:05.23

    



 
 

    

/


(
)/


   

 
 
(
)/

distance time CP model error [%] PT model error [%] diP model error [%] MIT model error [%] 1500 03:39.25 03:31.80 -3.40 03:40.26 +0.46 03:37.58 -0.76 03:39.25 +0.00 3000 07:41.33 07:41.66 +0.07 07:34.21 -1.54 07:43.99 +0.58 07:38.67 -0.58 5000 13:02.86 13:14.80 +1.53 13:09.54 +0.85 13:16.00 +1.68 13:08.15 +0.68 10000 27:12.47 27:07.65 -0.30 27:33.80 +1.31 27:20.58 +0.50 27:25.68 +0.81 21097 1:01:45.00 57:56.09 -6.18 1:00:51.38 -1.45 59:54.66 -2.98 1:00:45.21 -1.61 42195 2:06:36.00 1:56:30.40 -7.97 2:07:01.20 +0.33 2:08:12.86 +1.28 2:07:26.14 +0.66 distance time CP model error [%] PT model error [%] diP model error [%] MIT model error [%] 1500 03:33.20 03:23.46 -4.57 03:33.94 +0.34 03:31.20 -0.94 03:33.10 -0.05 3000 07:27.66 07:30.51 +0.64 07:26.12 -0.34 07:32.61 +1.11 07:29.73 +0.46 3218.68 08:07.39 08:06.52 -0.18 08:01.92 -1.12 08:07.99 +0.12 08:05.23 -0.44 5000 12:46.53 12:59.91 +1.75 12:55.80 +1.21 12:57.53 +1.44 12:51.56 +0.66 10000 26:49.02 26:43.41 -0.35 26:57.17 +0.51 26:41.56 -0.46 26:42.72 -0.39 21097 59:25.00 57:11.09 -3.76 58:58.62 -0.74 58:12.28 -2.04 58:48.35 -1.03 42195 2:01:39.00 1:55:05.94 -5.39 2:01:48.73 +0.13 2:02:48.65 +0.95 2:02:34.67 +0.76 distance 1500 3000 5000 10000 15000 16093.4 20000 21097 42195 FIG. 5 (a) Application of four mathematical models to personal records of elite marathon runners (Antonio Pinto, Eliud Kipchoge): Log-normal plot of the ’running curves’ predicted by the models (average velocity ¯v as function of race distance d, in units of vm and dc = vmtc given in the plot legend) and actual race data (red dots). The tables summarize the actual and predicted race times, along with the relative errors in percent. 21 % CP (fit for d≤15km): av.error=3.25% D'=245.4m, CS=5.95m/s PT: av.error=1.31% MAP=28.73W/kg, A=1336.00J/kg E=-5.28% diP: av.error=1.86% MAP=25.08W/kg, A=1332.00J/kg f=1.45×10-5/s MIT: av.error=1.20% Es=0.208, El=6.118 vm=6.40m/s, dc=3840.0m tc= 09:59.56

    



 
 

    

/


(
)/


    CP (fit for d≤15km): av.error=3.97% D'=191.6m, CS=6.23m/s PT: av.error=0.74% MAP=29.94W/kg, A=1338.00J/kg E=-5.62% diP: av.error=1.54% MAP=27.08W/kg, A=985.80J/kg f=2.21×10-5/s MIT: av.error=0.49% Es=0.201, El=6.058 vm=6.87m/s, dc=2188.0m tc= 05:18.59

    



 
 

    

/


(
)/


   

 
 
(
)/

[%] 0.05 0.46 0.44 0.66 0.39 1.03 0.76 distance time CP model error [%] PT model error [%] diP model error [%] MIT model error [%] 1500 03:40.14 03:30.81 -4.24 03:40.93 +0.36 03:37.48 -1.21 03:40.14 +0.00 3000 07:40.67 07:42.85 +0.47 07:36.52 -0.90 07:47.68 +1.52 07:40.67 -0.00 5000 13:16.42 13:18.90 +0.31 13:13.50 -0.37 13:24.72 +1.04 13:12.92 -0.44 10000 27:04.54 27:19.03 +0.89 27:40.64 +2.22 27:40.89 +2.24 27:34.04 +1.82 15000 41:29.00 41:19.17 -0.40 42:31.69 +2.52 42:16.77 +1.92 42:25.21 +2.26 16093.4 46:41.00 44:22.89 -4.93 45:48.98 -1.86 45:31.07 -2.50 45:43.06 -2.07 20000 58:20.00 55:19.30 -5.16 57:39.85 -1.15 57:13.80 -1.89 57:37.14 -1.22 21097 1:02:05.00 58:23.62 -5.94 1:01:01.00 -1.72 1:00:33.61 -2.45 1:00:59.48 -1.76 42195 2:06:14.00 1:57:28.64 -6.94 2:07:07.95 +0.71 2:08:43.21 +1.97 2:07:46.93 +1.23 distance time CP model error [%] PT model error [%] diP model error [%] MIT model error [%] 800 01:49.35 01:37.69 -10.66 01:49.37 +0.02 01:47.02 -2.13 01:49.20 -0.14 1500 03:31.76 03:30.09 -0.79 03:34.56 +1.32 03:35.83 +1.92 03:33.07 +0.62 2000 04:52.86 04:50.38 -0.85 04:49.56 -1.13 04:54.19 +0.45 04:49.49 -1.15 3000 07:25.09 07:30.95 +1.32 07:21.03 -0.91 07:31.83 +1.51 07:25.09 -0.00 3218.68 08:01.08 08:06.07 +1.04 07:56.24 -1.01 08:06.44 +1.12 07:59.55 -0.32 5000 12:39.36 12:52.10 +1.68 12:46.64 +0.96 12:50.23 +1.43 12:45.27 +0.78 10000 26:22.75 26:14.96 -0.49 26:46.51 +1.50 26:24.07 +0.08 26:39.32 +1.05 16093.4 44:24.00 42:33.39 -4.15 44:23.01 -0.04 43:33.24 -1.91 44:15.99 -0.30 20000 55:48.00 53:00.68 -5.00 55:54.06 +0.18 54:57.24 -1.52 55:49.62 +0.05 21097 58:55.00 55:56.83 -5.04 59:09.71 +0.42 58:13.03 -1.19 59:06.27 +0.32 25000 1:11:37.00 1:06:23.55 -7.29 1:10:50.90 -1.07 1:10:03.89 -2.17 1:10:51.77 -1.05 42195 2:03:59.00 1:52:24.59 -9.33 2:03:35.91 -0.31 2:07:49.12 +3.09 2:04:06.73 +0.10 distance 800 1500 3218.68 5000 10000 21097 42195 FIG. 5 (b) Application of four mathematical models to personal records of elite marathon runners (Felix Limo, Haile Gebrselassie): Log-normal plot of the ’running curves’ predicted by the models (average velocity ¯v as function of race distance d, in units of vm and dc = vmtc given in the plot legend) and actual race data (red dots). The tables summarize the actual and predicted race times, along with the relative errors in percent. 22 % CP (fit for d≤15km): av.error=4.07% D'=214.6m, CS=6.11m/s PT: av.error=0.84% MAP=29.72W/kg, A=1430.00J/kg E=-5.71% diP: av.error=1.50% MAP=26.80W/kg, A=1048.00J/kg f=1.99×10-5/s MIT: av.error=0.43% Es=0.269, El=5.700 vm=6.57m/s, dc=4033.0m tc= 10:14.01

    



 
 

    

/


(
)/


   CP (fit for d≤15km): av.error=2.95% D'=211.0m, CS=5.92m/s PT: av.error=0.89% MAP=27.59W/kg, A=1584.00J/kg E=-3.82% diP: av.error=0.54% MAP=25.26W/kg, A=1154.00J/kg f=1.46×10-5/s MIT: av.error=0.25% Es=0.411, El=7.838 vm=6.41m/s, dc=2917.0m tc= 07:35.31

    



 
 

    

/


(
)/


   [%] .14 .62 .15 .00 .32 .78 .05 .30 .05 .32 .05 .10 distance time CP model error [%] PT model error [%] diP model error [%] MIT model error [%] 800 01:48.24 01:35.74 -11.55 01:47.43 -0.75 01:46.05 -2.02 01:47.52 -0.67 1500 03:28.81 03:30.22 +0.67 03:33.14 +2.08 03:35.60 +3.25 03:31.18 +1.14 3218.68 08:03.40 08:11.29 +1.63 07:56.54 -1.42 08:08.16 +0.98 08:01.04 -0.49 5000 12:53.11 13:02.60 +1.23 12:49.49 -0.47 12:53.77 +0.09 12:51.26 -0.24 10000 26:46.57 26:40.29 -0.39 26:57.13 +0.66 26:31.30 -0.95 26:51.68 +0.32 21097 59:32.00 56:55.07 -4.39 59:39.84 +0.22 58:18.11 -2.07 59:33.22 +0.03 42195 2:05:11.00 1:54:25.40 -8.60 2:04:48.67 -0.30 2:06:34.96 +1.12 2:05:02.35 -0.12 distance time CP model error [%] PT model error [%] diP model error [%] MIT model error [%] 800 01:47.43 01:39.51 -7.37 01:47.79 +0.33 01:47.18 -0.24 01:47.43 +0.00 3000 07:49.80 07:51.18 +0.29 07:43.98 -1.24 07:50.84 +0.22 07:48.88 -0.20 3218.68 08:26.71 08:28.13 +0.28 08:21.43 -1.04 08:27.34 +0.12 08:24.88 -0.36 5000 13:18.60 13:29.07 +1.31 13:28.36 +1.22 13:26.01 +0.93 13:22.44 +0.48 10000 27:39.14 27:33.79 -0.32 28:04.09 +1.50 27:37.21 -0.12 27:45.63 +0.39 21097 1:01:14.00 58:48.55 -3.96 1:01:15.79 +0.05 1:00:18.34 -1.52 1:01:03.67 -0.28 42195 2:07:13.00 1:58:12.92 -7.08 2:06:07.20 -0.86 2:08:03.76 +0.67 2:07:10.00 -0.04 FIG. 5 (c) Application of four mathematical models to personal records of elite marathon runners (Mo Farah, Steve Jones): Log-normal plot of the ’running curves’ predicted by the models (average velocity ¯v as function of race distance d, in units of vm and dc = vmtc given in the plot legend) and actual race data (red dots). The tables summarize the actual and predicted race times, along with the relative errors in percent. Reviewers' Comments: Reviewer #1: Remarks to the Author: Given that my involvement to the review process started at a later stage, I will avoid providing detailed comments on each section as I would typically do. However, I have read the manuscript in detail. Although I appreciate the value of exploring big data sets, I have a major concern with this manuscript as I do not think that any link can be made to physiological responses to exercise, when no physiological measures have been extracted. Additionally, I would like to mention that this manuscript is quite difficult to read, and that the authors should make an effort to improve the flow and logical order of the presentation. Regardless, please find below some general comments that I would hope will help the authors reflecting further on this manuscript. I think that the authors are not fully aware of the type of testing that takes place in many laboratories. I understand that they need to highlight the relevance of “real-world” data, and that laboratory settings have limitations. However, there are many experimental studies that have produced very solid performance data that, even though they do not belong to the “real world” category, they offer information that the “real world” conditions will never provide. I fully agree that the best measure of performance is performance itself. From a performance perspective, I do not care about who has the greatest VO2max or critical intensity of exercise. I care about who runs faster. Then, from a mechanistic perspective, I bring people to the lab to try to understand why differences in performance exist, but not necessarily to make people faster. The authors stated “The undeniable fact that the best test of running performance is an actual race and not laboratory tests” is only partly true. It is the best test to measure performance. However, it is not the best test to evaluate physiological responses and to elucidate the mechanisms that control the final performance. I think that the point that I am trying to make is that, at least to a given extent, the authors seem to be misrepresenting what happens in a laboratory setting. From what I have read in this manuscript, there is nothing that connects its content to physiological responses to exercise (which are often mentioned in this document). I could accept the claim that this analysis can help establishing non-physiological outcomes that could potentially help improving performance. However, there is no physiological value that can be seriously considered in this data set. At least in my view, the model requires accepting assumptions that might make some sense, but that are not necessarily correct. The authors seem to have almost a dislike for physiological evaluations. I am fine with that. However, there is no point in discussing physiology when no physiological outcomes are presented. I do not feel comfortable with all the assumptions that need to be accepted to believe some of the key components of the analysis (e.g., MAP). Once again, the authors might have gotten it right in terms of some predictors of performance. The problem is that we will never know as no real physiological data were collected. Perhaps, performing some physiological testing in a sub-sample of participants would add validity to the project. However, the authors have already disregarded this possibility when responding to other reviewers. In relation to this, I was interested in some responses. I am presenting below just a few examples: - The authors indicated that “As far as heart rate is concerned, our data set does not contain heart rate data for all runs and athletes as not all runners who wore a GPS watch wear a heart rate monitor (chest strap). But even if this data would be available, there remains an important unknown: the maximal heart rate of the athlete which varies substantially among individuals and cannot be determined accurately and easily from age-based formulas. Without the maximal heart rate the important relative effort (the quantity p in our model) cannot be determined accurately.” I would accept that the age-based formulas are not ideal, but they can be a good approximation. Additionally, the authors have plenty of data from the participants and I am sure that there has to be some high intensity interval or sprint training, or high intensity constant speed session from which HRmax could be derived. I mean, I would be the first arguing that, even if you had the actual HRmax, there are clear limitations with this approach. However, what I find a bit surprising is that the authors are willing to accept a lot of assumptions for other parameters in their model, but then they are too concerned about not getting the HRmax 100% right. This is surprising to me. - The authors argued that “Maximal tests in laboratory are difficult to repeat, possibly due to lack of motivation to go all-out without opponent or competition or even price money to win. As a result, the coefficient of variation may be as high as 25%”. Let’s clarify that performance outcomes have large variability in both the lab and on the field, but that the variability is greatly reduced with longer durations of performance. Additionally, if the lack of motivation because of the price money is an issue, then the author should eliminate most of these data because the vast majority of the performances in the people that the authors evaluated are not worth any money. Most people are engaged for other reasons and most of them would perform as well in the lab as they do in the “real world”. I am not convinced by this line of argumentation. - Then the authors stated that “Running mechanics varies considerably between treadmill and over ground running...One reason may difficulty to simulate wind resistance”. In fact, there are portable devices to test people in the “real world”. I know, the conditions will be slightly different. However, nothing is perfect (and this includes the assumptions in the model that is presented by the authors). - Finally, the authors said, “Maximal laboratory tests are short-lasting and therefore fail to account for reduction in running economy and subsequent increase in oxygen consumption at given speed that occurs over long-distance running.” Why would this need to be the case? I just read a paper in which participants performed quite long incremental tests achieving the same VO2max as in the shorter tests (J Appl Physiol 2019; 127(6):1519-1527). Maximal tests do not need to be short. Testing protocols are adapted to what one wants to evaluate. This type of comments makes me feel that the authors might not be very familiar with laboratory testing. As a side comment, I would say that the speed and endurance relationship presented in this document are quite similar to what is typically measured in the lab. So why emphasizing so much the idea that field data are better than lab data? Also, the fact that from training data one can predict performance is pretty obvious. What one can do in a race reflects what one can do in training. I know it is nice to confirm this with data, but there is nothing novel in this finding. As a final comment, I would like to say that I do not think that the authors have a full appreciation of the relevance that exercise intensity domains and their corresponding boundaries (i.e., thresholds) have in performance. I understand that measurements of VO2 and exercise thresholds have been largely bastardized in the world of exercise testing (to which the authors contribute by arbitrarily assigning names to parameters such as MAP or LT without having any physiological way of justifying them in this study). However, when things are done properly, very precise quantification of the metabolic stress of the system can be made. Unlike what the authors insinuate, these evaluations consider economy, fatigue, substrate depletion, etc. to make predictions about performance. All I am trying to say is that the authors might have an interesting story in relation to non-physiological predictors of running performance. However, they should be very careful with not overreaching beyond of what their data can say. Reviewer #2: Remarks to the Author: I appreciate the authors’ detailed rebuttal and the appendix that they have included to compare their model to other similar models. While the paper is improved, it is still hard to follow and ascertain exactly what the novel insights are. I think that many exciting findings have resulted from the analysis, but as the paper is presently written, many of the key insights do not stand out to the reader. It is also not clear whether the focus of the paper is to provide additional evidence to validate their previously published model or to show some of the novel insights that applying their model to the dataset can generate. It might be possible to do both things, but this should be framed more explicitly at the beginning and then discussed more explicitly in the results. If the goal is to provide additional support for their previous model, then the comparisons that they include in the appendix of the rebuttal would at least be helpful to include as supplementary material. I am personally more interested in a focus on the insights gained from the application of their model to the real-world dataset. If this is the desired focus, this should be made more clear in the manuscript. Even in this case, the comparisons to other models would still provide confidence that the author’s model is reasonable and thus could still be helpful to include in supplementary material. These and other comments are discussed in more detail below. A couple general comments on the review process: Line numbers are very helpful in the manuscript review process; then the authors can note line numbers where changes have been made in the response to reviewers. As a reviewer, I can also provide specific locations relevant for my comments. An annotated version of the manuscript showing exactly where changes have been made (e.g., via highlighting) is also very helpful to me as a reviewer. Title The paper should include a more meaningful title that highlights the specific novelty of the present work. The terms “novel” and “insights” do not convey much information about the present work. The term “novel” should be removed at minimum, as I believe is policy at least for Nature. The authors were also not performing data mining by most definitions of the term, since they were using a pre-existing physiology-based model (as a side note, I think this approach is preferable, in general, to a naïve data mining one). Instead, the real-world or free-living nature of the data is relevant to highlight in the title. The size of the data is also worth noting, as the title already does. Abstract (1) “We derived two variables that explain race performance: maximal aerobic power and endurance capability. Inclusion of endurance, which describes the decline in sustainable power over duration, offers novel insights to performance analysis since a realistic estimate of this parameter is impossible in conventional laboratory testing.” The mathematical model that the authors use was presented in the authors’ previously published paper. The abstract gives the impression that the mathematical model is something newly-created for the present paper. Please revise to make the novelty of the current paper more clear (i.e., the application of the model to free-living data and interpretation of the extracted parameters). (2) The abstract is much more clear than in the previous version, but it still does not include specific results. Novel insights are mentioned. But what were these novel insights? Introduction (3) In general, the introduction (along with other parts of the paper) is unnecessarily negative about in-lab testing. Both in-lab and out-of-lab testing have strengths and weaknesses and these could be acknowledged in a more even-handed way. (4) “important insights for a variety of populations ranging from elite athletes over recreational exercisers to patients in rehabilitation” change over -> to (5) “These approach predict that the average racing velocity tends to an constant value with increasing race distance which contradicts observation” Approach ->approaches Tends to an -> tends to be a (6) “Several empirical and physiological models have been put forward for explaining running world records in terms of a few physiological parameters.” Start a new paragraph here. (7) “Our minimal and universal model characterizes a runner’s physiology by two parameters that measure endurance capability and the velocity requiring maximal aerobic power output” The authors should make more clear that the model has already been proposed and evaluated with some data from (real-world) races. The application of the model to the present dataset (and to training data?) is what makes the current paper new. The previous paper by the authors should be mentioned and cited in the introduction, for example. This should also be made more clear in the last paragraph of the introduction that lays out the goals for the paper. Results (8) “Universal Performance Model” section: The authors should more directly state that they are using the model that they present in a previous publication. Something like: (1) In previous work we developed a model that does X. To summarize, this model …. (describe the key features of the model). For more details, see XXXX. (2) Here we do XXXX with the model. If there are differences between the author’s model published previously and the one in the present model, please make these differences more clear. (9) The results section and paper in general would also benefit from a tighter focus on the key, novel findings of the paper. For example, below are some excerpts from the paper that are novel, but don’t stand out in the present draft. Focusing paragraphs in the results on each of these topics, would be helpful. Specific paragraphs could be focused around asking the associated questions and discussing the study results. The key findings could also be explicitly enumerated in the discussion. • For all RS with three and more races (N=12,309), the mean error between model prediction and actual race time was only 2.0% … As a function of physiological parameters, in the most likely parameter range the model predicted the marathon performance with an overall accuracy of better than 10%. • The ”one-hour utilization” ratio p1hU = v1hU /vm had been estimated previously from laboratory measurements and races for a smaller group of 18 male LDR to be approximately 0.82 ± 0.05 35. Strikingly, our findings from the running data for ∼ 14,000 subjects corroborate this range without any invasive measurements, as demonstrated in Fig. 2(c). • Our findings demonstrate the strong sensitivity of performance to endurance. For example, a runner with a velocity of vm = 5m/sec can improve their marathon time from 3h27min38sec to 2h53min8sec by doubling endurance from El = 3 to El = 6 (corresponding to a change in the ”one- hour utilization” from 79% to 87% of VO2max), without any change in VO2,max or RE. • We observed an initial linear increase of El with TRIMP, a plateau around El = 7.5 ± 2 for TRIMP ∼ 25,000, and a statistically significant final drop which may be due to over-training. This result suggests that there is an optimal TRIMP per TS, and the corresponding maximal endurance enables a close to optimal marathon race time for a given velocity vm (see Fig. 3(a)). (10) Minimize the use of acronyms where possible in the text to make it easier for readers to understand the paper. I suggest you remove the following: • RS (racing season) • TS (training season) • RE (running economy) • LDR (long distance runners?) If the abbreviations are needed in a figure/table they are OK to use there, as long as they are defined in the caption. (11) “by matching them with an universal, i.e., subject independent model” An universal -> a universal A comma is needed after “model” (12) “Our minimal model introduces effective parameters by measuring” It is not clear what the authors mean by “effective”. (13) “observations made by Hill in running world records” Reword to make it clear that it wasn’t Hill who was running the world records :-). (14) “Fig. 3 first shows a color coded plot of Tmarathon as function of the physiological parameters.” This type of sentence is a better fit for a caption. In the Results it is preferable to describe specific findings. There are several instances of this in the Results. (15) “To investigate the predictive power of our model in more detail, we applied our model also the RS with the marathon performance excluded” A word is missing from this sentence. (16) “Consistent and inconsistent runners can be identified from the relative difference between our model estimates and actual race times.” A better topic sentence (that covers the main focus of the paragraph) is needed to improve the logical flow of this section of the results. In general, a careful review of the entire paper to ensure each paragraph has a clear topic sentence would improve the quality of the manuscript. Discussion (17) First paragraph: this should be broken into multiple paragraphs. The discussion of the limitations would be a natural split point. (18) “This is an important advance over physiological testing in the laboratory where the required maximal effort is impossible to motivate for a distance of 20km or longer.” I don’t think the authors intend to mean that there is no use for lab-based testing. This is another place where the authors could soften their language. (e.g., important advance -> important complement). In general, the primary point that stands out from the discussion is that the real-world data is a big improvement over lab testing. I don’t think this is the most important point (as lab-based testing in a controlled environment still has great value). I would instead focus more on reviewing the specific new insights about running, training, and performance that were gleaned from the analysis. Methods (19) “Only TS with 30 or more runs were considered.” What is the rationale for this choice? Was there any requirement from the minimum chronological length of the training season? Was there any sensitivity to these or other threshold choices discussed in the paragraph? (20) Check for redundancy between material included in the Methods and Results (21) The following passage is a better fit for the results or discussion than the Methods. For our two parameter model, the quality of the fitting could be probed for all RS with more than two races. For those RS we found a rather low average error of only 2:0% between the computed and actual race times. Another applicability test of our model is the estimation of the marathon finishing time from equation(1) when the parameters vm and l are obtained from the RS without the marathon. Given all the possible uncertainties in marathon racing that are beyond the control of this study (e.g. weather, course profile, motivation of the athlete), the predictive power reflected by the results for marathon finishing time estimate in Fig. 4 is rather satisfying Reviewer #3: Remarks to the Author: The authors have provided detailed and convincing responses to most of the questions and comments that I forwarded to them. In particular, I am convinced by their response on the relationship between their model and the critical power model. However, this did not translate into a modification of the article accounting entirely for their responses to the reviewer. This is a pity. The changes in the manuscript are minor and clearly inadequate. I would like to see the reasoning that the author developed in replying to the reviewer's comments more adequately integrated in the menuscript, especially in the discussion, and I hope the suthors will show more consideration for the suggested references and comments. To respond is good, but it is not enough. 2 Rebuttal letter “Human running performance from real-world big data” (NCOMMS-20-02292) Please find below our point-to-point answers to the reviewer comments (C: comment, A: answer). All changes in the manuscript are marked by colour highlighting (deleted text in red, newly added text in blue). Also, we have included line numbers in the manuscript (colour coded version) in order to make reference to changes in the point-by-point rebuttal letter. Answer to Reviewer #1 We thank the reviewer for her/his time spent looking over our manuscript and their comments that we address point-by-point in the following. C I think that the authors are not fully aware of the type of testing that takes place in many laboratories. I under- stand that they need to highlight the relevance of real-world data, and that laboratory settings have limitations. However, there are many experimental studies that have produced very solid performance data that, even though they do not belong to the real world category, they o↵er information that the real world conditions will never provide. I fully agree that the best measure of performance is performance itself. From a performance perspec- tive, I do not care about who has the greatest VO2max or critical intensity of exercise. I care about who runs faster. Then, from a mechanistic perspective, I bring people to the lab to try to understand why di↵erences in performance exist, but not necessarily to make people faster. The authors stated The undeniable fact that the best test of running performance is an actual race and not laboratory tests is only partly true. It is the best test to measure performance. However, it is not the best test to evaluate physiological responses and to elucidate the mechanisms that control the final performance. I think that the point that I am trying to make is that, at least to a given extent, the authors seem to be misrepresenting what happens in a laboratory setting. A We agree that our presentation was not balanced between laboratory testing and our approach. This is regret- table because that is not how we think. Therefore, we now highlight how wearables can complement laboratory testing by expanding the size of population that can be tested. We also compare strengths and weaknesses of both approaches. Please see lines 46↵, 61↵, 102↵, 345↵. C From what I have read in this manuscript, there is nothing that connects its content to physiological responses to exercise (which are often mentioned in this document). I could accept the claim that this analysis can help establishing non-physiological outcomes that could potentially help improving performance. However, there is no physiological value that can be seriously considered in this data set. At least in my view, the model requires accepting assumptions that might make some sense, but that are not necessarily correct. The authors seem to have almost a dislike for physiological evaluations. I am fine with that. However, there is no point in discussing physiology when no physiological outcomes are presented. I do not feel comfortable with all the assumptions that need to be accepted to believe some of the key components of the analysis (e.g., MAP). A We have replaced throughout the manuscript ”physiological parameters” by indices of performance (aerobic power index and endurance index), extracted from running exercise data using our model. We would like to point out that our model makes assumptions that are also contained in other models proposed by exercise physiologists (e.g. Monod & Scherrer, di Prampero, Peronnet & Thibault, see our detailed comparison in the appendix to our last rebuttal letter). Hence, we believe that the key parameters of our model do have some physiological meaning. However, in order to avoid any confusion and to not make unnecessary assumptions, we now refer to our parameters as ”performance indices” and just state to which physiological variables they might be related. Please see lines 96↵. 3 C Once again, the authors might have gotten it right in terms of some predictors of performance. The problem is that we will never know as no real physiological data were collected. Perhaps, performing some physiological testing in a sub-sample of participants would add validity to the project. However, the authors have already disregarded this possibility when responding to other reviewers. A We agree that physiological testing on a smaller sample of subjects would be very useful. We plan to carry out such testing for a new group of subject in the future, to compare our model parameters to actual lab measurements. It should be noted that also previous models (e.g. Peronnet & Thibault) have been applied only to world running records to extract physiological parameters without a direct comparison to lab tests for these athletes. C The authors indicated that As far as heart rate is concerned, our data set does not contain heart rate data for all runs and athletes as not all runners who wore a GPS watch wear a heart rate monitor (chest strap). But even if this data would be available, there remains an important unknown: the maximal heart rate of the athlete which varies substantially among individuals and cannot be determined accurately and easily from age-based formulas. Without the maximal heart rate the important relative e↵ort (the quantity p in our model) cannot be determined accurately. I would accept that the age-based formulas are not ideal, but they can be a good approximation. Additionally, the authors have plenty of data from the participants and I am sure that there has to be some high intensity interval or sprint training, or high intensity constant speed session from which HRmax could be derived. I mean, I would be the first arguing that, even if you had the actual HRmax, there are clear limitations with this approach. However, what I find a bit surprising is that the authors are willing to accept a lot of assumptions for other parameters in their model, but then they are too concerned about not getting the HRmax 100% right. This is surprising to me. A Our point is that HR would not add any additional benefit to the extraction of our model parameters. When maximal and resting HR for each runner are known, the entire analysis could be based on HR instead of running velocity, yielding an expression for the maximal duration over which a given HR could be sustained, Tmax(HR). Hence the parameters vm and El could be determined from observed relations between velocity and HR, and the average HR sustained during maximal e↵ort of a given duration (races). However, an unpublished study that we performed previously on a much smaller number of subjects (20) showed that HR fluctuates more strongly than velocity, presumably due to weather conditions, non-running related stress, nutrition status, sleep status, etc. In addition, there is always a time delay between a rise (or fall) in velocity and HR which requires the exclusion of time windows with this hysteresis e↵ects. The accuracy of our model for race time predictions was on average 2%. This is definitely better than the typical error for age-based formulas for maximal HR. All these considerations led us to use velocity instead of HR in our data analysis. C The authors argued that Maximal tests in laboratory are difficult to repeat, possibly due to lack of motivation to go all-out without opponent or competition or even price money to win. As a result, the coefficient of variation may be as high as 25%. Lets clarify that performance outcomes have large variability in both the lab and on the field, but that the variability is greatly reduced with longer durations of performance. Additionally, if the lack of motivation because of the price money is an issue, then the author should eliminate most of these data because the vast majority of the performances in the people that the authors evaluated are not worth any money. Most people are engaged for other reasons and most of them would perform as well in the lab as they do in the real world. I am not convinced by this line of argumentation. A In the revised version of the manuscript we do not state that poor repeatability would compromise laboratory test results. We would like to point out that not only price money is motivation but also competing against friends, team members, or for something like age group win etc., i.e., real-world situations. 4 C Then the authors stated that Running mechanics varies considerably between treadmill and over ground run- ning...One reason may difficulty to simulate wind resistance. In fact, there are portable devices to test people in the real world. I know, the conditions will be slightly di↵erent. However, nothing is perfect (and this includes the assumptions in the model that is presented by the authors). A Our data comes from consumer-product based measurements (GPS watches) and more advanced portable devices were not available to the huge group of runners monitored. We agree on the general possibility of more advanced measurements in the ”real world”. However, these additional data are not relevant to our model as an input, and they would impose limitations on the number of available subjects. C Finally, the authors said, Maximal laboratory tests are short-lasting and therefore fail to account for reduction in running economy and subsequent increase in oxygen consumption at given speed that occurs over long-distance running. Why would this need to be the case? I just read a paper in which participants performed quite long incremental tests achieving the same VO2max as in the shorter tests (J Appl Physiol 2019; 127(6):1519-1527). Maximal tests do not need to be short. Testing protocols are adapted to what one wants to evaluate. This type of comments makes me feel that the authors might not be very familiar with laboratory testing. A We are not saying that VO2max is declining with duration. We are only saying that running economy (energy cost of running) deteriorates with duration. This is based on results in Ref. 18, 19 and 20. However, in order to provide in this context a better balance between lab testing and our approach, we have modified the paragraph with this statement, see lines 46 – 68. C As a side comment, I would say that the speed and endurance relationship presented in this document are quite similar to what is typically measured in the lab. So why emphasizing so much the idea that field data are better than lab data? Also, the fact that from training data one can predict performance is pretty obvious. What one can do in a race reflects what one can do in training. I know it is nice to confirm this with data, but there is nothing novel in this finding. A We are glad to hear that what is measured typically in the lab is quite similar to our model findings. We believe that the novel part of findings are quantitative relations between our model indexes and training volume and intensity for a very large group of runners, yielding also good statistics for typical variations in these relations. We do not think that field data are better than lab testing. We have made clear in the revised manuscript that our approach is complementary to lab testing, see lines 324 – 326. C As a final comment, I would like to say that I do not think that the authors have a full appreciation of the relevance that exercise intensity domains and their corresponding boundaries (i.e., thresholds) have in performance. I understand that measurements of VO2 and exercise thresholds have been largely bastardized in the world of exercise testing (to which the authors contribute by arbitrarily assigning names to parameters such as MAP or LT without having any physiological way of justifying them in this study). However, when things are done properly, very precise quantification of the metabolic stress of the system can be made. Unlike what the authors insinuate, these evaluations consider economy, fatigue, substrate depletion, etc. to make predictions about performance. All I am trying to say is that the authors might have an interesting story in relation to non-physiological predictors of running performance. However, they should be very careful with not overreaching beyond of what their data can say. A We accept this criticism. We have now renamed model parameters to aerobic power index and endurance index to di↵erentiate them from laboratory parameters. Overall, we have rewritten our manuscript in order to provide a more balanced presentation of what our model predicts and the concepts and measurements in the world of exercise testing in the lab. 5 Rebuttal letter “Human running performance from real-world big data” (NCOMMS-20-02292) Please find below our point-to-point answers to the reviewer comments (C: comment, A: answer). All changes in the manuscript are marked by colour highlighting (deleted text in red, newly added text in blue). Also, we have included line numbers in the manuscript (colour coded version) in order to make reference to changes in the point-by-point rebuttal letter. Answer to Reviewer #2 We thank the reviewer for her/his time spent again looking over our manuscript and their very detailed comments and suggestions that we address point-by-point in the following. C It is also not clear whether the focus of the paper is to provide additional evidence to validate their previously published model or to show some of the novel insights that applying their model to the dataset can generate. It might be possible to do both things, but this should be framed more explicitly at the beginning and then discussed more explicitly in the results. If the goal is to provide additional support for their previous model, then the comparisons that they include in the appendix of the rebuttal would at least be helpful to include as supplementary material. I am personally more interested in a focus on the insights gained from the application of their model to the real-world dataset. If this is the desired focus, this should be made more clear in the manuscript. Even in this case, the comparisons to other models would still provide confidence that the authors model is reasonable and thus could still be helpful to include in supplementary material. A The main aim of our work is ”to show some of the novel insights that applying their model to the dataset can generate”. We have made this more clear in the revised manuscript, please see lines 96↵. The model comparison of our previous rebuttal letter is not included in this work as it would go much beyond its scope. Instead, we are in the process of completing a separate publication on a detailed comparison of mathematical models for running performance which shall include the results shown in our rebuttal letter. C Title: The paper should include a more meaningful title that highlights the specific novelty of the present work. The terms novel and insights do not convey much information about the present work. The term novel should be removed at minimum, as I believe is policy at least for Nature. The authors were also not performing data mining by most definitions of the term, since they were using a pre-existing physiology-based model (as a side note, I think this approach is preferable, in general, to a naive data mining one). Instead, the real-world or free-living nature of the data is relevant to highlight in the title. The size of the data is also worth noting, as the title already does. A We have modified the title accordingly. C (1) We derived two variables that explain race performance: maximal aerobic power and endurance capability. Inclusion of endurance, which describes the decline in sustainable power over duration, o↵ers novel insights to performance analysis since a realistic estimate of this parameter is impossible in conventional laboratory testing. The mathematical model that the authors use was presented in the authors previously published paper. The abstract gives the impression that the mathematical model is something newly-created for the present paper. Please revise to make the novelty of the current paper more clear (i.e., the application of the model to free-living data and interpretation of the extracted parameters). A We have revised the abstract accordingly. 6 C (2) The abstract is much more clear than in the previous version, but it still does not include specific results. Novel insights are mentioned. But what were these novel insights? A We now described the novel insights more specifically. C (3) In general, the introduction (along with other parts of the paper) is unnecessarily negative about in-lab testing. Both in-lab and out-of-lab testing have strengths and weaknesses and these could be acknowledged in a more even-handed way. A We have given a more balanced presentation of in-lab and out-of-lab testing, please see lines 46↵, 61↵, 102↵, 345↵. C (4) important insights for a variety of populations ranging from elite athletes over recreational exercisers to patients in rehabilitation: change over ! to A done. C (5) These approach predict that the average racing velocity tends to an constant value with increasing race distance which contradicts observation: Approach ! approaches, Tends to an ! tends to be a A done. C (6) Several empirical and physiological models have been put forward for explaining running world records in terms of a few physiological parameters.: Start a new paragraph here. A done. C (7) Our minimal and universal model characterizes a runners physiology by two parameters that measure en- durance capability and the velocity requiring maximal aerobic power output. The authors should make more clear that the model has already been proposed and evaluated with some data from (real-world) races. The application of the model to the present dataset (and to training data?) is what makes the current paper new. The previous paper by the authors should be mentioned and cited in the introduction, for example. This should also be made more clear in the last paragraph of the introduction that lays out the goals for the paper. A We have modified the introduction accordingly, and referenced our previous paper. Please see lines 77 – 83. C (8) Universal Performance Model section: The authors should more directly state that they are using the model that they present in a previous publication. Something like: (1) In previous work we developed a model that does X. To summarize, this model . (describe the key features of the model). For more details, see XXXX. (2) Here we do XXXX with the model. If there are di↵erences between the authors model published previously and the one in the present model, please make these di↵erences more clear. A We modified this section accordingly (lines 108 – 124). There is no di↵erence with the model itself published previously. One of the parameters of the model (tc) was fixed at 6 minutes, as we had explained already in the previous version of the manuscript. C (9) The results section and paper in general would also benefit from a tighter focus on the key, novel findings of the paper. For example, below are some excerpts from the paper that are novel, but dont stand out in the present draft. Focusing paragraphs in the results on each of these topics, would be helpful. Specific paragraphs could be focused around asking the associated questions and discussing the study results. The key findings could also be explicitly enumerated in the discussion. 7 – For all RS with three and more races (N=12,309), the mean error between model prediction and actual race time was only 2.0% As a function of physiological parameters, in the most likely parameter range the model predicted the marathon performance with an overall accuracy of better than 10%. – The one-hour utilization ratio p1hU = v1hU/vm had been estimated previously from laboratory measurements and races for a smaller group of 18 male LDR to be approximately 0.82 ± 0.05. Strikingly, our findings from the running data for ⇠ 14,000 subjects corroborate this range without any invasive measurements, as demonstrated in Fig. 2(c). – Our findings demonstrate the strong sensitivity of performance to endurance. For example, a runner with a velocity of vm = 5m/sec can improve their marathon time from 3h27min38sec to 2h53min8sec by doubling endurance from El = 3 to El = 6 (corresponding to a change in the one-hour utilization from 79% to 87% of VO2max), without any change in VO2max or RE. – We observed an initial linear increase of El with TRIMP, a plateau around El = 7.5 ± 2 for TRIMP ⇠ 25,000, and a statistically significant final drop which may be due to over-training. This result suggests that there is an optimal TRIMP per TS, and the corresponding maximal endurance enables a close to optimal marathon race time for a given velocity vm (see Fig. 3(a)). A We have modified the results section to make our key findings stand out more clearly by adding subsections for each key finding. We could not add a itemized list of the key findings in the discussion section due to length restriction. C (10) Minimize the use of acronyms where possible in the text to make it easier for readers to understand the paper. I suggest you remove the following: – RS (racing season) – TS (training season) – RE (running economy) – LDR (long distance runners?) If the abbreviations are needed in a figure/table they are OK to use there, as long as they are defined in the caption. A We have removed these acronyms. C (11) by matching them with an universal, i.e., subject independent model: an universal ! a universal, a comma is needed after model A done. C (12) Our minimal model introduces e↵ective parameters by measuring It is not clear what the authors mean by e↵ective. A We have removed ”e↵ective”. C (13) observations made by Hill in running world records: Reword to make it clear that it wasn‘t Hill who was running the world records :-). A Thank you ;-) We have made this clear now. C (14) Fig. 3 first shows a color coded plot of Tmarathon as function of the physiological parameters. This type of sentence is a better fit for a caption. In the Results it is preferable to describe specific findings. There are several instances of this in the Results. 8 A We have moved this type of sentences to the figure captions. C (15) To investigate the predictive power of our model in more detail, we applied our model also the RS with the marathon performance excluded: A word is missing from this sentence. A We have added the word ”to” so it reads ”... also to the race season ...”. C (16) Consistent and inconsistent runners can be identified from the relative di↵erence between our model esti- mates and actual race times. A better topic sentence (that covers the main focus of the paragraph) is needed to improve the logical flow of this section of the results. In general, a careful review of the entire paper to ensure each paragraph has a clear topic sentence would improve the quality of the manuscript. A We have reviewed and modified the manuscript to ensure that each paragraph has a clear topic and we added new subsections to the result section. C (17) Discussion, first paragraph: this should be broken into multiple paragraphs. The discussion of the limitations would be a natural split point. A done. C (18) This is an important advance over physiological testing in the laboratory where the required maximal e↵ort is impossible to motivate for a distance of 20km or longer. I dont think the authors intend to mean that there is no use for lab-based testing. This is another place where the authors could soften their language. (e.g., important advance ! important complement). In general, the primary point that stands out from the discussion is that the real-world data is a big improvement over lab testing. I dont think this is the most important point (as lab-based testing in a controlled environment still has great value). I would instead focus more on reviewing the specific new insights about running, training, and performance that were gleaned from the analysis. A We agree. We have modified the manuscript in general to give a more balanced view of ”real-world” data and lab testing, and focused more on the new insights from our analysis. C (19) Methods: Only TS with 30 or more runs were considered. What is the rationale for this choice? Was there any requirement from the minimum chronological length of the training season? Was there any sensitivity to these or other threshold choices discussed in the paragraph? A This minimum run condition for training season was applied so that runner had at least trained once per week on average during the 180 day long training season. Smaller number of runs could mean an interrupted training (e.g. due to injury), and hence relation to performance would be less reliable. C (20) Check for redundancy between material included in the Methods and Results. A We believe that the Methods section should be self-contained to allow a complete account of the applied pro- cedures. However, we do have reduced some redundancy by combining some part of the Methods section with the appropriate paragraph of the Result section, please see next point. C (21) The following passage is a better fit for the results or discussion than the Methods. ”For our two parameter model, the quality of the fitting could be probed for all RS with more than two races. For those RS we found a rather low average error of only 2.0% between the computed and actual race times. Another applicability test of our model is the estimation of the marathon finishing time from equation (1) when the parameters vm and γl are obtained from the RS without the marathon. Given all the possible uncertainties in marathon racing that are beyond the control of this study (e.g. weather, course profile, motivation of the athlete), the predictive power reflected by the results for marathon finishing time estimate in Fig. 4 is rather satisfying.” 9 A We have moved part of this passage to the Results section, please see lines 198 – 209 and 439 – 443. 10 Rebuttal letter “Human running performance from real-world big data” (NCOMMS-20-02292) Please find below our point-to-point answers to the reviewer comments (C: comment, A: answer). All changes in the manuscript are marked by colour highlighting (deleted text in red, newly added text in blue). Also, we have included line numbers in the manuscript (colour coded version) in order to make reference to changes in the point-by-point rebuttal letter. Answer to Reviewer #3 We thank the reviewer for her/his time spent looking again over our manuscript and their comments that we address point-by-point in the following. C I would like to see the reasoning that the author developed in replying to the reviewer’s comments more adequately integrated in the manuscript, especially in the discussion, and I hope the authors will show more consideration for the suggested references and comments. The respond is good, but it is not enough. A As explained more clearly in the revised version, the aim of this work is neither a validation of our previously published model nor a comparison of our model to other existing models (which however are mentioned in our work). Rather, the aim of our work is to apply our model to real-world data and to extract performance parameters and relate them to racing performance and training. Due to this focus and due to length restrictions, we can not include all our reasoning from the previous reply in our manuscript. However, we have revised the manuscript overall to give a more balanced view of lab testing and our approach. To avoid confusion, we have changed the term ”physiological parameters” to ”performance indices”. In addition, we have added relevant references to previous work on theoretical concepts from exercise physiology in the Discussion section, please see lines 345 – 349. Reviewers' Comments: Reviewer #1: Remarks to the Author: I would like to thank the authors for the changes made to the this manuscript. From a conceptual perspective, I would say that I still disagree with the use of the MAP construct, as I think this is a flawed concept. However, I understand that it is commonly used and accepted by many, and that it serves the purpose of the present analysis. Aside from this comment (which is nothing but just a way of expressing my view), I am satisfied with the responses that the authors have provided and with the updated version of the manuscript. I think that focusing on performance rather than physiology makes this a much more solid and believable story. Thus, I have no further comments to make.

Human running performance from real-world big data.

10-06-2020

Emig, Thorsten,Peltonen, Jussi

eng

PMC7578824

1 Vol.:(0123456789) Scientific Reports | (2020) 10:17870 | https://doi.org/10.1038/s41598-020-73742-5 www.nature.com/scientificreports Lower leg muscle–tendon unit characteristics are related to marathon running performance Bálint Kovács1,3*, István Kóbor2,3, Zsolt Gyimes1,3, Örs Sebestyén1,3 & József Tihanyi1,3 The human ankle joint and plantar flexor muscle–tendon unit play an important role in endurance running. It has been assumed that muscle and tendon interactions and their biomechanical behaviours depend on their morphological and architectural characteristics. We aimed to study how plantar flexor muscle characteristics influence marathon running performance and to determine whether there is any difference in the role of the soleus and gastrocnemii. The right lower leg of ten male distance runners was scanned with magnetic resonance imagining. The cross-sectional areas of the Achilles tendon, soleus, and lateral and medial gastrocnemius were measured, and the muscle volumes were calculated. Additional ultrasound scanning was used to estimate the fascicle length of each muscle to calculate the physiological cross-sectional area. Correlations were found between marathon running performance and soleus volume (r = 0.55, p = 0.048), soleus cross-sectional area (r = 0.57, p = 0.04), soleus physiological cross-sectional area (PCSA-IAAF r = 0.77, p < 0.01, CI± 0.28 to 0.94), Achilles tendon thickness (r = 0.65, p < 0.01), and soleus muscle-to-tendon ratio (r = 0.68, p = 0.03). None of the gastrocnemius characteristics were associated with marathon performance. We concluded that a larger soleus muscle with a thicker Achilles tendon is associated with better marathon performance. Based on these results, it can be concluded the morphological characteristics of the lower leg muscle– tendon unit correlate with running performance. The human ankle plantar flexor muscles play a major role in producing propulsive force during endurance running1–3. The triceps surae muscle–tendon complex is equipped with a long compliant tendon and a strong and diverse muscle structure. It is a generally accepted concept that the Achilles tendon (AT) acts as a spring during running to store and return elastic energy and reduce the metabolic energy cost of the contractile element4,5. Running consists of a series of submaximal voluntary muscle contractions; thus, repetitive moderate force pro- duction is needed to propel the body forward. The magnitude of the contraction force depends on the running velocity and the motion of the lower leg. The energetic cost of contraction can be minimized if the fascicles are operating near the optimal length. During the early stance phase of running, the fascicles of the triceps surae operate under quasi-isometric conditions6–9; thus, a shorter fascicle with a low contraction velocity can result in a favourable contractile condition because shorter fascicles can maintain tension with low activation energy costs10,11. Therefore, the length change of the muscle–tendon complex mainly occurs in the tendon during the early stance phase of running6,12,13. Most elite marathon runners use rearfoot strike pattern14 where ankle joint flexion at stance is relevantly smaller15 resulting in less muscle–tendon unit lengthening compared to forefoot strike pattern. Because thicker tendon has a greater cross-sectional area (CSA), the acting force apply- ing in greater surface, thus greater amount of force needed to stretch the tendon which increase the amount of stored elastic strain energy. Therefore it can store more elastic strain energy than a thin tendon at similar tendon extension because it has greater stiffness16. According to the literature, distance runners have thicker ATs than sprinters17 and non-runners17–19. However, it should be mentioned that mechanical properties of the tendons do not depend only on the morphological characteristics of the tendon. But the material and structure of the tendon also related to the mechanical properties of the tendon, too20,21. Thus, load induced changes in tendon material also can result in changes in the mechanical properties of the tendon20,21. To stretch a thicker tendon greater muscle force is required during the first half of the stance phase during running. We can assume that this force mainly produced by the soleus (SOL) because physiological cross-sectional area (PCSA) of the SOL is significantly larger than that of the gastrocnemii (GAS)22,23, and as a consequence, SOL produces three to four times greater positive work than GAS during running12. If we assume that SOL is generating at least OPEN 1Department of Kinesiology, University of Physical Education, Alkotás u. 44, Budapest 1123, Hungary. 2Semmelweis University, MR Research Centre, Budapest, Hungary. 3These authors contributed equally: Bálint Kovács, István Kóbor, Zsolt Gyimes, Örs Sebestyén and József Tihanyi. *email: [email protected] 2 Vol:.(1234567890) Scientific Reports | (2020) 10:17870 | https://doi.org/10.1038/s41598-020-73742-5 www.nature.com/scientificreports/ twofold greater force than GAS during running then SOL contributes to elastic energy storage in the AT much more than the GAS muscles6,7,24, as well as to mechanical work6,7,25. Additionally, the SOL contains mainly slow twitch muscle fibres, and slow fibres lower the muscle volume-specific rate of energy use because slow muscles have lower rates of time-dependent cross-bridges11. Also, because GAS contains dominantly fast twitch fibers26 fatigue affects these muscles more, i.e. decreasing the mechanical output over time during running compared to SOL muscle27–29. Because muscle force generation capacity is related to CSA and the PCSA of the muscle10,30,31, greater force production could lead to morphological adaptations in the SOL. The mechanical properties of the tendons and muscles are influenced by their CSA and PCSA10; thus, the CSA and PCSA may have an impact on muscle–tendon interaction and consequently on running performance. However, this connection is not clear. Calculations with animal and cadaver muscles showed that there is an optimum PCSA/tendon cross-sectional area (tCSA) ratio10,32. Such a calculation has not been carried out on human triceps surae muscles in vivo thus far. Since the AT is the largest tendon in the human body, we may assume that the PCSA/tCSA ratio is different from the theoretical optimum and is greater for the SOL than for the GAS. If a thicker tendon is coupled with a shorter fascicle length and greater muscle stress, then the tendon stress also increases, and more elastic energy can be stored in the Achilles tendon due to the SOL force generation. To our knowledge, no previous report has investigated the correlation between triceps sure muscle morphology (i.e., CSA and PCSA) or the PCSA/tCSA ratio and running performance. Therefore, the purpose of this study was to test whether there is a link between morphological variables of triceps surae muscle tendon unit and marathon performance. Taking this information together, we hypothesized that runners who have a greater SOL PCSA, shorter fascicle length, thicker tendon and greater PCSA/tCSA ratio can complete the marathon distance in a shorter time (greater IAAF score). Addition- ally, we hypothesized that SOL morphological properties have a greater impact on running performance than GAS morphological properties. Methods Participants. Ten male marathon runners (mean and SD 29 ± 3.8 years, 177.1 ± 8.9 cm, 65.4 ± 5.8 kg) with a personal best International Amateur Athletic Federation (IAAF) score of 888.0 ± 184.0 (2 h 26 min on aver- age) volunteered for this study. IAAF score points are used to classify running race time (performance) with a numerical value, which can be used for statistical analysis33. The runners had competed on international and national levels and had an average training volume of 120–200 km per week. All participants performed their best marathon race time within 2 years before this experiment. The scans were taken during the midseason. The participants had no musculoskeletal injury or pain in the lower extremities. All participants gave written informed consent to take part in the study, which was performed in accordance with the Declaration of Helsinki and was approved by the ethics committee of University of Physical Education (TE-KEB/No07/2018). Data collection. Magnetic resonance imagining scan. MRI images were taken from the right leg to measure morphological parameters of the triceps sure muscle tendon complex. A 3T Philips scanner (Ingenia 3.0T MRI system, Amsterdam, Netherlands) was used to acquire the MRI images. The runners were positioned supine, with neutral knee (180° between shank and thigh) and ankle joint angles (90° between foot and shank). A foam pad was placed below the calcaneus that elevated the leg slightly and prevented weight-induced deformation of the muscle during the scan. The scans were performed using a T1-weighted turbo spin echo sequence (slice thickness = 5 mm, slice gap = 0 mm, slice scan order: interleaved, TR = 650 ms, TE = 20) for all measurements. Because of the limited field of view of the probe (FOV = 40 cm), the images were taken in two parts to ensure that the records contained the origin and insertion of the plantar flexor muscle–tendon complex. The overlapping images were manually removed from the analysis. The axes during the MRI image acquisition was set carefully to align as possible as it can with the muscle–tendon unit. Architectural measurement. An additional ultrasound measurement was applied to estimate the muscle archi- tecture of the SOL, medial gastrocnemius (MG) and lateral gastrocnemius (LG) (6 cm field of view, B-mode linear array probe, 13 MHz scanning frequency, Hitachi-Aloka EUB 405 plus, Japan). Participants were laid prone on a table with a neutral ankle and knee joint position. Acoustic gel was applied between the skin and the probe, which was placed at approximately 50% of the length of each muscle, but the locations were optimized for fascicle imaging34. The probe was placed manually on the skin and held carefully over the skin to avoid applying too much pressure to the tissues underneath. Data analysis. Magnetic resonance image processing. The images were analysed using ImageJ 1.44b (Na- tional Institutes of Health, USA). The CSA of each muscle and tendon was manually outlined on all of scans that the muscles and tendon were visible on and then the area was measured (Fig. 1). The images were analysed by two separate raters. All segmentations were checked by a researcher (author IK) experienced in studying and measuring MRI scans. The test–retest procedure was applied to estimate the reliability of the CSA measure- ments. Each CSA measured by the two raters was averaged and then used to calculate muscle volume and mass. The lengths of the muscles and AT were calculated by summing the number of analysed slices and multiplied by 0.5. The total volume of the plantar flexor muscles and AT was calculated by summing the volume of each slice, i.e., the product of slice area and slice thickness (0.5 cm)23,30,35. Muscle mass was calculated as muscle volume multiplied by muscle density (1.056 g/cm3)36. The PCSA was calculated by dividing muscle volume by fascicle length. Ultrasound image processing. The longest fascicle was outlined manually in each image, and then the length of the line was measured. If needed, multiple lines were drawn to follow the curvature of the fascicle37 (Fig. 2). If 3 Vol.:(0123456789) Scientific Reports | (2020) 10:17870 | https://doi.org/10.1038/s41598-020-73742-5 www.nature.com/scientificreports/ part of the fascicles was outside the field of view, fascicle length was estimated by linear extrapolation. The image analysis for muscle architecture was performed in ImageJ 1.44b (National Institutes of Health, USA). Statistics. Data are presented as the means and standard deviations. Because of the small sample size, the Sha- piro–Wilk normality test was used to test the normality of the data. To determine the relative between-rater reliability of each muscle and tendon, an intraclass correlation coefficient (ICC) was calculated using a two-way mixed-effects model (average measures), along with the upper and lower 95% confidence interval (CI±). The ICC estimate was considered good between 0.75 and 0.9 and excellent above 0.938. A Bland–Altman plot was used to determine the bias between the raters and the limits of agreement (see Supplementary material). Pearson correlations were calculated to investigate the relationship between marathon performance and the properties of muscles and tendons. The magnitude of significant correlations was quantified using the thresholds recom- mended by Hopkins 39, i.e., 0–0.1 as small, 0.1–0.3 as moderate, 0.3–0.5 as large, 0.5–0.7 as very large and 0.9–1 as extremely large correlations. Additionally, the 95% confidence intervals for each corresponding Pearson coef- ficient were calculated. In cases of non-Gaussian data distributions, a Spearman rank correlation was used. All statistical calculations were performed using SPSS (SPSS Inc., Chicago, IL, USA v. 25), and statistical significance was set at an alpha level of 0.05. Figure 1. Representative magnetic resonance image from the middle of the lower leg for the calculation cross- sectional areas (A). The triceps surae compartments were separately outlined manually. (B) A sample image of the maximal distal Achilles tendon. Each segmented area (SOL soleus, MG medial gastrocnemius, LG lateral gastrocnemius) marked with white line. Figure 2. Representative ultrasound image of soleus in sagittal plane for estimate fascicle length. The image was taken at 50% of the muscle length because that region possibly contains the longest fascicles of the muscles. Fascicle length (solid yellow line along fascicles), are drawn in the images. 4 Vol:.(1234567890) Scientific Reports | (2020) 10:17870 | https://doi.org/10.1038/s41598-020-73742-5 www.nature.com/scientificreports/ Results No architectural (fascicle length) or morphological (volume, CSA, PCSA) parameters of LG and MG correlated with marathon performance. On the other hand, marathon performance correlated with maximal CSA (r = 0.57; p = 0.041 CI± 0.08 to 0.88) and volume of the SOL (r = 0.55; p = 0.048, CI± 0.11 to 0.87). SOL muscle fascicle length negatively correlated with marathon performance (r = − 0.63, p = 0.02, CI± − 0.90 to − 0.001) (Fig. 3). PCSA of the SOL showed large positive correlation with marathon performance (r = 0.77, p < 0.01, CI± 0.28 to 0.94) (Fig. 3). The total PCSA of triceps surae also showed large positive correlation with marathon performance (r = 0.72, p < 0.009, CI± 0.16 to 0.93). The maximal CSA of AT (r = 0.65, p = 0.01, CI± 0.04 to 0.91) correlated with marathon performance (Fig. 4). The largest distal CSA of the AT also correlated with marathon performance (r = 0.65, p = 0.02). There is a positive correlation between SOL PCSA and tCSA (r = 0.61, p = 0.029) suggesting that those who have large SOL more likely to have thick AT in absolute term. The SOL PCSA/tCSA ratio also correlated with marathon performance (r = 0.68, p = 0.029, CI± 0.09–0.92) (Fig. 5). The mean and SD values of the plantar flexor muscle tendon unit properties are listed in Table 1. The calculated SOL volume was 48.89%, that of the MG was 31.66%, and that of the LG was 19.44% of the total triceps surae volume. The PCSA of the SOL was threefold greater than that of the MG and fourfold greater than that of the LG, and the SOL possessed 60.12% of the total PCSA of the triceps surae. The results of the correlation analysis are summarized in Table 2. The results of the interrater reliability test showed excellent ICC values for all muscles and the tendon (see supplementary material). Discussion The purpose of this study was to investigate if there is correlation between the morphological and architectural characteristics of triceps surae muscle–tendon unit and running performance. We hypothesized that faster marathon runners have greater PCSAs and shorter fascicle lengths in the SOL and thicker ATs than slower marathon runners. We found a positive correlation between IAAF score and the PCSA of the SOL and a negative correlation between IAAF score and the fascicle length of the SOL. Additionally, a thicker AT was linked to a better IAAF score; therefore, our results showed that the morphology of the SOL PCSA and tCSA correlate with marathon performance. This novel finding might supports the concept that the SOL plays a more important role in endurance running than the GAS muscles6,7,40. The MRI-based morphological parameters of the muscle structures (CSA, volume) are in alignment with those from previous reports23,41–43. The fascicle lengths estimated from the ultrasound images are similar to the findings of earlier studies9,37,40,42–45; thus, the calculated PCSA is also similar to that from previous reports23,42,43. As expected, we found that the SOL had a greater muscle volume, CSA, and PCSA and a greater PCSA/tCSA ratio than the GAS muscles. This can explain why the SOL muscle produces greater force and positive work than Figure 3. Correlation between IAAF and (a) soleus maximal cross-sectional are (r = 0.57, CI − 0.08 to 0.88, p = 0.041), (b) soleus fascicle length (r = − 0.63, p = 0.02, CI± − 0.90 to − 0.001), and (c) soleus physiological cross-sectional area (PCSA-IAAF r = 0.77, p < 0.01, CI± 0.28 to 0.94). Figure 4. Correlation between IAAF and (a) Achilles tendon length (r = − 0.01, p = 0.48 CI± − 0.63 to 0.62) (b) Achilles tendon maximal cross-sectional area (r = 0.65, p = 0.01, CI± 0.04 to 0.91). 5 Vol.:(0123456789) Scientific Reports | (2020) 10:17870 | https://doi.org/10.1038/s41598-020-73742-5 www.nature.com/scientificreports/ the GAS during moderate-pace running6,7,11,12 assuming that SOL and GAS are to shortening the same amounts. However, a greater force production often pairs with greater metabolic demand of the contractile elements in general, but muscle fibre composition (i.e., predominance of slow twitch fibres) can compensate for this effect26. It is known that the SOL primarily contains slow twitch muscle fibres26 and that these fibres have a lower muscle volume-specific rate of energy demand since slow muscles have lower rates of time-dependent cross-bridges46. We found that runners with greater IAAF scores had shorter SOL fascicles, possibly because muscles with shorter fascicles work more economically because they involve a smaller active volume of muscle, and therefore, a smaller amount of ATP is consumed11. The decreased muscle metabolic energy demand can lead to a decreased cost of running as well; thus, it can improve running performance and possibly running economy. The function of the AT can also decrease the metabolic energy cost of the contractile elements. It has been shown that the function of the tendon depends on the morphological characteristics of the tendon17,19,47. Since the CSA of the AT is different at each AT length, it seems important to select the appropriate CSAs that may influence running performance. Magnusson and Krajer47 reported that CSAs measured one centimetre above the calcaneal insertion showed the largest difference between runners and non-runners. In contrast, Ueno et al.17 found that the AT CSA of distance runners was significantly larger than that of sprinters and non-runners only when the Figure 5. Correlation between IAAF score (representing marathon performance) and ratio of soleus physiological cross-sectional area to Achilles tendon cross-sectional area. There is a large correlation (r = 0.68, p = 0.029 CI± 0.09 to 0.92) between these variables. Table 1. The measured and calculated (mean and SD) morphological parameters of the triceps surae muscle– tendon complex. CSA cross sectional area, AT Achilles tendon, PCSA physiological cross-sectional area. Achilles tendon Soleus Medial gastrocnemius Lateral gastrocnemius Length (cm) 22.10 ± 2.61 32.60 ± 2.89 25.95 ± 2.66 24.20 ± 2.14 Fascicle length (cm) – 3.18 ± 0.47 5.24 ± 0.72 5.37 ± 0.84 Volume (cm3) 0.53 ± 0.07 452.9 ± 88.24 293.3 ± 69.78 180.1 ± 25.81 Muscle mass (g) – 487.3 ± 93.18 309.8 ± 73.68 190.1 ± 27.25 CSA (cm2) 1.82 ± 0.13 27.16 ± 3.82 18.96 ± 3.34 13.31 ± 2.11 AT distal CSA (cm2) 1.20 ± 0.11 – – – PCSA (cm2) – 130.3 ± 33.59 60.04 ± 15.72 35.88 ± 5.73 muscle to AT volume ratio – 858.44 ± 150.47 550.70 ± 92.63 343.68 ± 59.88 Muscle PCSA to AT ratio – 78.12 ± 16.79 30.79 ± 8.0 18.39 ± 2.68 Table 2. Correlation coefficients between marathon running performance and triceps surae muscle–tendon morphological characteristics. The corresponding p value and 95% confident interval was calculated as well. Bold numbers indicate significant correlations. Variables Achilles tendon Soleus Lateral gastrocnemius Medial gastrocnemius r p 95% CI± r p 95% CI± r p 95% CI± r p 95% CI± Length − 0.01 0.48 − 0.63 to 0.62 0.34 0.16 − 0.36 to 0.79 0.12 0.36 − 0.54 to 0.69 0.21 0.27 − 0.47 to 0.74 Volume 0.32 0.18 − 0.38 to 0.79 0.55 0.048 0.11 to 0.87 0.06 0.43 − 0.59 to 0.66 0.41 0.11 − 0.28 to 0.82 CSA max 0.65 0.01 0.04 to 0.91 0.57 0.04 0.08 to 0.88 0.05 0.44 − 0.59 to 0.66 0.37 0.14 − 0.33 to 0.81 PCSA – – – 0.77 0.01 0.28 to 0.94 0.36 0.29 − 0.33 to 0.81 0.32 0.35 − 0.37 to 0.79 Fascicle length – – – − 0.63 0.02 − 0.90 to − 0.01 − 0.26 0.22 − 0.76 to 0.43 − 0.02 0.47 − 0.64 to 0.61 6 Vol:.(1234567890) Scientific Reports | (2020) 10:17870 | https://doi.org/10.1038/s41598-020-73742-5 www.nature.com/scientificreports/ CSA below the SOL-tendon junction was selected for comparison. However, in a later study, Ueno et al.19 did not find a significant correlation between distal AT CSA and running economy or running performance. In our study, we correlated both the distal and proximal AT CSA with marathon performance and found a significant association between the two variables, indicating that a thicker AT is beneficial for running the marathon dis- tance in a shorter time. It is difficult to resolve the contradiction between our results and those of Ueno et al.19. It can be assumed that AT length has a greater impact on 5000-m running performance than AT CSA since Ueno et al.19 reported a significant relationship between MG tendon length and running economy. From this point of view, we can imagine that there may be a difference in ankle kinetics and kinematics when running long or short distances. This assumption is supported by our results; namely, we did not find an association between AT length and marathon running performance. The average running speed of our runners during marathon is 5.01 ms−1 which is obviously less compared to elite 5000 m runners racing speed (14 min race time equal to 6.11 ms−1 run- ning speed). Because foot strike pattern seems to be influenced by running velocity (especially above 5 ms−1)15,48 and footwear49 i.e. track runners usually wearing (light weighted and thin) spike shoes thus, we can assume that shorter track runners possibly use forefoot strike pattern. But on the other hand, the majority of elite marathon runners are using rearfoot strike pattern14. Kinematic difference between rearfoot and forefoot strike pattern has been demonstrated15,27,28 showing that ankle joint flexion is greater during forefoot strike which lead to a greater muscle tendon unit lengthening as well. In that case a thin tendon would be better since greater elastic energy could be stored by applying smaller muscle force compared to a thick tendon. To the best of our knowledge, nobody has studied how the triceps surae muscles and tCSA ratio can be related to running performance, especially marathon running time. Even though experiments and calculations suggest that there is an optimum muscle-to-tendon area ratio that may minimize muscle–tendon mass and help deliver greater mechanical energy via the muscle–tendon system32,50. Theoretically, the optimum ratio is 3410, which reduces the incidence of tendon damage and enables the muscle–tendon complex to perform more mechanical work. We found that only the MG PCSA/tCSA ratio approached this value; the LG PCSA/tCSA ratio was con- siderably less, and the soleus PCSA/tCSA ratio was more than twice the theoretical optimum. Ker et al.10 argued that a thinner tendon requires longer fascicles to be able to shorten more. We found that fascicles in the SOL muscle are short, which contradicts this theory. However, this construction can be beneficial, especially during stretch–shortening muscle contraction. Short fascicles relative to muscle length result in large PCSAs and, as a consequence, greater force generation capacity. Because the SOL PCSA was found to be largest in our study, the SOL presumably had the capacity to exert greater force; therefore, the SOL AT was subjected to a larger stress, which assumes a larger elastic energy storage capacity in the tendon during the ankle joint flexion phase of run- ning. It has been reported that the SOL AT length is three times shorter than that of GL and GM19, but the distal tCSA presumably is the same. Since tendon stiffness depends on both the tendon length and CSA primarily, it is possible that SOL AT stiffness could potentially be greater than GM and GL tendon stiffness; in other words, the SOL has a greater contribution to tendon stiffness than the GAS. Because stiffness is related to running performance51,52, we may conclude that the SOL PCSA/tCSA ratio has a prominent role in better performance in marathon running. The correlation between SOL PCSA/tCSA ratio and IAAF score may indicate that a large SOL PCSA with thin AT correlate with better marathon race time. However, this correlation must be considered in relative term. It is unlikely that those who have large SOL muscle also would have thin tendon. We found a strong positive correlation between SOL PCSA and AT CSA suggesting that those who have large SOL more likely to have a thick AT in absolute term. This study has some limitations that must be addressed. The participants performed their personal best in the previous 2 years; thus, the current performance level was not taken into consideration. However, the athletes were regularly training during this period and reported no weight changes over this period, so we can assume that no remarkable changes occurred in their lower leg morphology. It must be noted that the limited size of the sample rases some concern about generalizing the conclusion. Extreme outlier data points could have strongly affected the magnitude and direction of our correlation analysis. The statistical method we used, prove no causal- ity only correlation between the selected variables which must be considered when interpreting the results of this study. In the present study, we did not examine the mechanical properties of the AT and plantar flexor muscles; therefore, the relationships between the mechanical characteristics and morphological properties of the plantar flexor muscle–tendon unit and remain unclear. In summary, we found that the soleus PCSA/tCSA ratio is much greater than the theoretical optimum and that a greater ratio resulted in a shorter marathon running time. From a better running performance point of view, a large PCSA of the soleus muscle and a thick Achilles tendon are beneficial because more elastic energy can presumably be stored (and recoiled) in the AT, which enables runners to run more efficiently. From our results, we can draw conclusions in accordance with our hypothesis that morphological and architectural characteristics (of the triceps surae and AT) correlate with running performance. In addition, our results allow us to conclude that in marathons running, the soleus has a more significant role than the gastrocnemius muscle. Received: 24 April 2020; Accepted: 15 September 2020 References 1. Stearne, S. M., Alderson, J. A., Green, B. A., Donnelly, C. J. & Rubenson, J. Joint kinetics in rearfoot versus forefoot running: implications of switching technique. Med. Sci. Sports Exerc. 46, 1578–1587 (2014). 2. Dorn, T. W., Schache, A. G. & Pandy, M. G. Muscular strategy shift in human running: dependence of running speed on hip and ankle muscle performance. J. Exp. Biol. 215, 1944–1956 (2012). 7 Vol.:(0123456789) Scientific Reports | (2020) 10:17870 | https://doi.org/10.1038/s41598-020-73742-5 www.nature.com/scientificreports/ 3. Hamner, S. R. & Delp, S. L. Muscle contributions to fore-aft and vertical body mass center accelerations over a range of running speeds. J. Biomech. 46, 780–787 (2013). 4. Alexander, R. M. Elastic energy stores in running vertebrates. Integr. Comp. Biol. 24, 85–94 (1984). 5. Scholz, M. N., Bobbert, M. F., van Soest, A. J., Clark, J. R. & van Heerden, J. Running biomechanics: shorter heels, better economy. J. Exp. Biol. 211, 3266–3271 (2008). 6. Lai, A., Schache, A. G., Lin, Y.-C. & Pandy, M. G. Tendon elastic strain energy in the human ankle plantar-flexors and its role with increased running speed. J. Exp. Biol. 217, 3159–3168 (2014). 7. Lai, A., Lichtwark, G. A., Schache, A. G. & Pandy, M. G. Differences in in vivo muscle fascicle and tendinous tissue behavior between the ankle plantarflexors during running. Scand. J. Med. Sci. Sport 28, 1828–1836 (2018). 8. Ishikawa, M. & Komi, P. V. Muscle fascicle and tendon behavior during human locomotion revisited. Exerc. Sport Sci. Rev. 36, 193–199 (2008). 9. Ishikawa, M., Pakaslahti, J. & Komi, P. V. Medial gastrocnemius muscle behavior during human running and walking. Gait Posture 25, 380–384 (2007). 10. Ker, R. F., Alexander, R. M. & Benett, M. B. Why are mammalian tendons so thick?. J. Zool. 216, 309–324 (1988). 11. Fletchern, J. R. & MacIntosh, B. R. Running economy from a muscle energetics perspective. Front. Physiol. 8, 433 (2017). 12. Lai, A. et al. In vivo behavior of the human soleus muscle with increasing walking and running speeds. J. Appl. Physiol. 118, 1266–1275 (2015). 13. Roberts, T. J. & Azizi, E. Flexible mechanisms: the diverse roles of biological springs in vertebrate movement. J. Exp. Biol. 214, 353–361 (2011). 14. Hanley, B., Bissas, A., Merlino, S. & Gruber, A. H. Most marathon runners at the 2017 IAAF World Championships were rearfoot strikers, and most did not change footstrike pattern. J. Biomech. 92, 54–60 (2019). 15. Novacheck, T. F. The biomechanics of running: review paper. Gait Posture 7, 77–95 (1998). 16. Geremia, J. M. et al. Effects of high loading by eccentric triceps surae training on Achilles tendon properties in humans. Eur. J. Appl. Physiol. 118, 1725–1736 (2018). 17. Ueno, H. et al. Specific adaptations of patellar and Achilles tendons in male sprinters and endurance runners. Transl. Sports Med. 1, 104–109 (2018). 18. Rosager, S. et al. Load-displacement properties of the human triceps surae aponeurosis and tendon in runners and non-runners. Scand. J. Med. Sci. Sports 12, 90–98 (2002). 19. Ueno, H. et al. Relationship between Achilles tendon length and running performance in well-trained male endurance runners. Scand. J. Med. Sci. Sports 28, 446–451 (2018). 20. Magnusson, S. P., Narici, M. V., Maganaris, C. N. & Kjaer, M. Human tendon behaviour and adaptation, in vivo. J. Physiol. 586, 71–81 (2008). 21. Fouré, A., Nordez, A. & Cornu, C. Plyometric training effects on Achilles tendon stiffness and dissipative properties. J. Appl. Physiol. 109, 849–854 (2010). 22. Albracht, K., Arampatzis, A. & Baltzopoulos, V. Assessment of muscle volume and physiological cross-sectional area of the human triceps surae muscle in vivo. J. Biomech. 41, 2211–2218 (2008). 23. Fukunaga, T. et al. Physiological cross-sectional area of human leg muscles based on magnetic resonance imaging. J. Orthop. Res. 10, 926–934 (1992). 24. Schache, A. G., Dorn, T. W., Williams, G. P., Brown, N. A. T. & Pandy, M. G. Lower-limb muscular strategies for increasing running speed. J. Orthop. Sports Phys. Ther. 44, 813–824 (2014). 25. Schache, A. G. et al. Effect of running speed on lower limb joint kinetics. Med. Sci. Sports Exerc. 43, 1260–1271 (2011). 26. Gollnick, P. D., Sjödin, B., Karlsson, J., Jansson, E. & Saltin, B. Human soleus muscle: a comparison of fiber composition and enzyme activities with other leg muscles. Pflügers Arch. Eur. J. Physiol. 348, 247–255 (1974). 27. Yong, J. R., Silder, A. & Delp, S. L. Differences in muscle activity between natural forefoot and rearfoot strikers during running. J. Biomech. 47, 3593–3597 (2014). 28. Yong, J. R. et al. Foot strike pattern during running alters muscle-tendon dynamics of the gastrocnemius and the soleus. Sci. Rep. 10, 1–11 (2020). 29. Ochs, R. M., Smith, J. L. & Edgerton, V. R. Fatigue characteristics of human gastrocnemius and soleus muscles. Electromyogr. Clin. Neurophysiol. 17, 297–306 (1977). 30. Baxter, J. R. & Piazza, S. J. Plantar flexor moment arm and muscle volume predict torque-generating capacity in young men. J. Appl. Physiol. 116, 538–544 (2014). 31. Bamman, M. M., Newcomer, B. R., Larson-Meyer, D. E., Weinsier, R. L. & Hunter, G. R. Evaluation of the strength-size relationship in vivo using various muscle size indices. Med. Sci. Sports Exerc. 32, 1307–1313 (2000). 32. Cutts, A., Alexander, R. M. & Ker, R. F. Ratios of cross-sectional areas of muscles and their tendons in a healthy human forearm. J. Anat. 176, 133–137 (1991). 33. Kunimasa, Y. et al. Specific muscle-tendon architecture in elite Kenyan distance runners. Scand. J. Med. Sci. Sports 24, 269–274 (2014). 34. Stenroth, L., Peltonen, J., Cronin, N. J., Sipilä, S. & Finni, T. Age-related differences in Achilles tendon properties and triceps surae muscle architecture in vivo. J. Appl. Physiol. 113, 1537–1544 (2012). 35. Tonson, A., Ratel, S., Fur, Y. L., Cozzone, P. & Bendahan, D. Effect of maturation on the relationship between muscle size and force production. Med. Sci. Sports Exerc. 40, 918–925 (2008). 36. Winter, D. A. Biomechanics and Motor Control of Human Movement 4th edn. (Wiley, New York, 2009). 37. Stenroth, L. et al. Triceps surae muscle-tendon properties in older endurance- and sprint-trained athletes. J. Appl. Physiol. 120, 63–69 (2015). 38. Koo, T. K. & Li, M. Y. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J. Chiropr. Med. 15, 155–163 (2016). 39. Hopkins, W. G., Marshall, S. W., Batterham, A. M. & Hanin, J. Progressive statistics for studies in sports medicine and exercise science. Med. Sci. Sports Exerc. 41, 3–13 (2009). 40. Bohm, S., Mersmann, F., Santuz, A. & Arampatzis, A. The force-length-velocity potential of the human soleus muscle is related to the energetic cost of running. Proc. R. Soc. B Biol. Sci. 286, 20192560 (2019). 41. Vescovi, J. D. & Mcguigan, M. R. Relationships between sprinting, agility, and jump ability in female athletes. J. Sports Sci. 26, 97–107 (2008). 42. Friederich, J. A. & Brand, R. A. Muscle fiber architecture in the human lower limb. J. Biomech. 23, 91–95 (1990). 43. Bolsterlee, B. et al. Three-dimensional architecture of the whole human soleus muscle in vivo. PeerJ 6, e4610 (2018). 44. Sano, K. et al. Muscle-tendon interaction and EMG profiles of world class endurance runners during hopping. Eur. J. Appl. Physiol. 113, 1395–1403 (2013). 45. Sano, K. et al. Can measures of muscle–tendon interaction improve our understanding of the superiority of Kenyan endurance runners?. Eur. J. Appl. Physiol. 115, 849–859 (2015). 46. Rall, J. A. Energetic aspects of skeletal muscle contraction: implications of fiber types. Exerc. Sports Sci. Rev. 13, 33–74 (1985). 47. Magnusson, S. P. & Kjaer, M. Region-specific differences in Achilles tendon cross-sectional area in runners and non-runners. Eur. J. Appl. Physiol. 90, 549–553 (2003). 8 Vol:.(1234567890) Scientific Reports | (2020) 10:17870 | https://doi.org/10.1038/s41598-020-73742-5 www.nature.com/scientificreports/ 48. Forrester, S. E. & Townend, J. The effect of running velocity on footstrike angle—a curve-clustering approach. Gait Posture 41, 26–32 (2015). 49. Horvais, N. & Samozino, P. Effect of midsole geometry on foot-strike pattern and running kinematics. Footwear Sci. 5, 81–89 (2013). 50. Ker, R. F., Bennett, M. B., Bibby, S. R., Kester, R. C. & Alexander, R. M. The spring in the arch of the human foot. Nature 325, 147–149 (1987). 51. Fletcher, J. R., Pfister, T. R. & Macintosh, B. R. Energy cost of running and Achilles tendon stiffness in man and woman trained runners. Physiol. Rep. 1, e00178 (2013). 52. Kubo, K., Miyazaki, D., Shimoju, S. & Tsunoda, N. Relationship between elastic properties of tendon structures and performance in long distance runners. Eur. J. Appl. Physiol. 115, 1725–1733 (2015). Acknowledgements The authors wish to thank all participants for volunteering in this study. Author contributions B.K. and I.K. conceived and conducted the scans, B.K. Z.G. Ö.S. analysed the results and contributed to the dis- cussion. J.T. supervised the entire project and contributed to the discussion. All authors reviewed the manuscript. Competing interests The authors declare no competing interests. Additional information Supplementary information is available for this paper at https ://doi.org/10.1038/s4159 8-020-73742 -5. Correspondence and requests for materials should be addressed to B.K. Reprints and permissions information is available at www.nature.com/reprints. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/. © The Author(s) 2020

Lower leg muscle-tendon unit characteristics are related to marathon running performance.

10-21-2020

Kovács, Bálint,Kóbor, István,Gyimes, Zsolt,Sebestyén, Örs,Tihanyi, József

eng

PMC5587270

RESEARCH ARTICLE Effect of water-based recovery on blood lactate removal after high-intensity exercise Francesco Lucertini1*, Marco Gervasi1, Giancarlo D’Amen1, Davide Sisti2, Marco Bruno Luigi Rocchi2, Vilberto Stocchi1, Piero Benelli1 1 Department of Biomolecular Sciences–Division of Exercise and Health Sciences, University of Urbino Carlo Bo, Urbino, Italy, 2 Department of Biomolecular Sciences–Service of Biostatistics, University of Urbino Carlo Bo, Urbino, Italy * [email protected] Abstract This study assessed the effectiveness of water immersion to the shoulders in enhancing blood lactate removal during active and passive recovery after short-duration high-intensity exercise. Seventeen cyclists underwent active water- and land-based recoveries and pas- sive water and land-based recoveries. The recovery conditions lasted 31 minutes each and started after the identification of each cyclist’s blood lactate accumulation peak, induced by a 30-second all-out sprint on a cycle ergometer. Active recoveries were performed on a cycle ergometer at 70% of the oxygen consumption corresponding to the lactate threshold (the control for the intensity was oxygen consumption), while passive recoveries were per- formed with subjects at rest and seated on the cycle ergometer. Blood lactate concentration was measured 8 times during each recovery condition and lactate clearance was modeled over a negative exponential function using non-linear regression. Actual active recovery intensity was compared to the target intensity (one sample t-test) and passive recovery intensities were compared between environments (paired sample t-tests). Non-linear re- gression parameters (coefficients of the exponential decay of lactate; predicted resting lac- tates; predicted delta decreases in lactate) were compared between environments (linear mixed model analyses for repeated measures) separately for the active and passive recov- ery modes. Active recovery intensities did not differ significantly from the target oxygen con- sumption, whereas passive recovery resulted in a slightly lower oxygen consumption when performed while immersed in water rather than on land. The exponential decay of blood lac- tate was not significantly different in water- or land-based recoveries in either active or pas- sive recovery conditions. In conclusion, water immersion at 29˚C would not appear to be an effective practice for improving post-exercise lactate removal in either the active or passive recovery modes. Introduction Short-duration high-intensity exercise leads rapidly to muscular fatigue, which can be defined as the loss of force or power in response to physical exertion resulting in reduced performance [1]. Two of the most important mechanisms involved in exercise-induced fatigue are fibers PLOS ONE | https://doi.org/10.1371/journal.pone.0184240 September 6, 2017 1 / 12 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Lucertini F, Gervasi M, D’Amen G, Sisti D, Rocchi MBL, Stocchi V, et al. (2017) Effect of water-based recovery on blood lactate removal after high-intensity exercise. PLoS ONE 12(9): e0184240. https://doi.org/10.1371/journal. pone.0184240 Editor: Alejandro Lucı´a, Universidad Europea de Madrid, SPAIN Received: December 23, 2016 Accepted: August 10, 2017 Published: September 6, 2017 Copyright: © 2017 Lucertini et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information file. Funding: The authors received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. acidosis and depletion of ATP [2], which lead to large changes in the concentration of meta- bolites, such as lactate. Lactate is no longer thought to cause fiber acidosis and is believed to provide protection against this process [3]. However its exercise induced rise in the blood coincides with fiber acidosis; hence, it remains a good indirect marker for the onset of fatigue [2, 3]. The rapid removal of lactate following intense exercise remains desirable since it is taken up by both resting muscles and fibers of the same muscle working at lower intensities and used as a carbohydrate fuel source (see [4] for a comprehensive review). In exercise and sports, blood lactate concentration ([La]b) is the most widely used marker of muscular fatigue and several studies have investigated different strategies to enhance lactate removal from the blood after intense physical activity yielding mixing results [5]. Light-to- moderate intensity active recovery has clearly been shown to be a superior lactate clearance strategy compared to passive (resting) recovery [6] after short-duration high-intensity exercise. Among such recovery strategies, water immersion is the focus of considerable attention among athletes and researchers [7]. From a physical standpoint, water immersion exerts a compressive force on the body, and there is a widely held belief among athletes and trainers that water immersion improves recovery [7]. Indeed, Wilcock et al. [8] have hypothesized that hydrostatic pressure, via extracellular fluid transfer to the intravascular compartment and the subsequent increase in cardiac output, may reduce the transport time of the metabolites, including lactate, which accumulate during exercise. However, only two investigations [9, 10] have found enhanced lactate clearance when the recovery from a lactate-accumulating exercise protocol was performed immersed in water rather than on land. Unfortunately, both of these studies were flawed due to their use of heart rate as the control for active recovery intensity since it has been shown that exercises performed at the same oxygen consumption yield signif- icantly higher heart rate responses when they are performed on land compared to when they are performed while immersed at different water depths [11–13]. Therefore, the effect of water on lactate removal from intense exercise needs further investigation. Accordingly, the aim of this study was to compare the effect of dry-land and water environ- ments on lactate clearance from the blood after short-duration high-intensity exercise. Both active and passive recovery modes were investigated separately. Materials and methods Participants Seventeen well-trained young cyclists (see Table 1 for subjects’ characteristics) were recruited. The training level of each cyclist was assessed using a questionnaire [14] (average weekly train- ing hours: 11.3±3.9). The study was approved by the Ethics Committee of the University of Urbino Carlo Bo, and the subjects signed a written informed consent form before being recruited. Experimental design A balanced randomized, crossover study design was used to test the effect of the recovery envi- ronment on blood lactate removal. The subjects were scheduled to undergo five experimental sessions at one-week intervals to allow them to fully recover between sessions. For each ses- sion, the participants reported to the laboratory well-rested, i.e. without having engaged in strenuous exercise in the previous 48 hours, and at least three hours after a light meal. In session 1 the cyclists underwent anthropometric and body composition assessments, as well as maximum oxygen consumption ( _VO2) and lactate threshold tests (see detailed Lactate clearance during water-based post-exercise recovery PLOS ONE | https://doi.org/10.1371/journal.pone.0184240 September 6, 2017 2 / 12 description of assessments and tests below). Finally, participants underwent a familiarization trial of the peak anaerobic power test (Wingate Anaerobic Test—WAnT). In sessions 2, 3, 4, and 5, [La]b was measured in four different experimental recovery condi- tions after the WAnT (see details regarding test and measurement of [La]b below). To raise [La]b rapidly we used the 30-second WAnT protocol, which is commonly used for this purpose [15]. The [La]b peak was identified as the transition from the blood lactate accumulation phase to the clearance phase. In practical terms, [La]b was measured every minute following the com- pletion of the WAnT, and the peak was considered as the [La]b value followed by a measure- ment equal or lower than that value. The identification of the [La]b peak was temporally out of synchrony with its actual attainment by two minutes because of the time it took for the mea- suring instrument to yield a result (one minute for each measurement). The actual attainment of the [La]b peak marked the end of the accumulation phase and the beginning of the clearance phase. [La]bs during the clearance phase were measured in all four of the following balanced randomly selected experimental conditions: I) passive land-based recovery (PLR)—the subject remained immobile seated on the cycle ergometer used to carry out the WAnT; II) passive water-based recovery (PWR)—the subject, immersed in water up to his shoulders, remained immobile seated on an underwater cycle ergometer; III) active land-based recovery (ALR)—the subject peddled on the cycle ergometer used to carry out the WAnT; IV) active water-based recovery (AWR)—the subject, immersed in water up to his shoulders, peddled on an underwa- ter cycle ergometer. Each of these recovery conditions lasted for 31 minutes and was always preceded by two minutes of rest, required for the preparation of the subjects and their proper positioning on the (Aqquactive Bike) underwater cycle ergometer (Aqquatix Ltd., Limena, Italy) under the water-based recovery conditions. The ambient temperature was monitored constantly during the PLR and ALR recovery conditions. The water temperature during the PWR and AWR was set at 29˚C and monitored constantly as well. The water temperature was chosen for two reasons: 1) to replicate a condi- tion that athletes can easily find in real-life settings (29˚C is the average temperature set in most swimming pools open to the public); 2) 29˚C is the closest water temperature we could get to those of the studies we planned to compare our results with (i.e. 30–31˚C in the study of Di Masi et al. [9] and 28–32˚C in the study of Ferreira et al. [10]). The intensity chosen for the ALR and AWR was calculated as a percentage of oxygen con- sumption at the blood lactate threshold ( _VO2LT), since a recovery calculated using the lactate threshold rather than the maximum _VO2 has been shown to be more suitable [16]. In runners, the intensity that maximizes the clearance of lactate is between 60% and 80% of _VO2LT [17, 18], and it has been demonstrated that the kinetic of lactate clearance is not significantly differ- ent between runners and cyclists [19–23]. Hence, the intensity of active recovery in the present Table 1. Participants’ characteristics and baseline assessments/calculations. Age (years) Height (m) Weight (kg) BMI (kg
m-2) FM (%) HRmax (bpm) V : O2peak (mL
kg-1
min-1) 70% of V : O2LT (mL
kg-1
min-1) [La]b peakP- (mmol
L-1) [La]b peakA (mmol
L-1) Mean 28.4 1.78 71.2 22.4 12.7 191.9 63.2 35.6 13.3 13.3 SD (±) 6.4 0.06 5.9 2.1 4.1 8.6 7.7 4.2 2.5 2.3 Average data calculated for seventeen subjects. Abbreviations: BMI, body mass index; FM, fat-mass; HRmax, maximal heart rate; _VO2peak, peak oxygen consumption; 70% of _VO2LT, 70% of the oxygen consumption corresponding to the lactate threshold; [La]b peak, average peak blood lactate concentration achieved before the passive (P) and the active (A) recovery conditions (average value was computed for twelve subjects for passive recoveries); SD, standard deviation. https://doi.org/10.1371/journal.pone.0184240.t001 Lactate clearance during water-based post-exercise recovery PLOS ONE | https://doi.org/10.1371/journal.pone.0184240 September 6, 2017 3 / 12 study was fixed at 70% of _VO2LT. On a practical level, in both active recovery conditions, oxy- gen consumption was constantly monitored and the subjects were instructed in real time (every two minutes) to maintain recovery intensity in the range of 65% to 75% of _VO2LT. The subjects were told to either maintain the resistance of the ergometer and the pedaling fre- quency or to increase/decrease the resistance of the ergometer (by rotating the brake knob and, if necessary, by changing the pedaling frequency as well) when the intensity fell below or rose above a level that resulted in a ±5% difference between the actual and the target intensity, respectively. Under all the conditions, [La]b was measured at 3 minutes and then every 4 min- utes (i.e. 7, 11, 15, 19, 23, 27 and 31 minutes) for a total of 8 blood draws for each recovery con- dition. [La]b was measured using the same procedure and instrument that was used in the maximum _VO2 test (see detailed description below). Assessments Anthropometry and body composition. The body mass index was calculated as the ratio between weight (kg) and height (m2), while the percentage of fat mass was calculated by the race-, age-, and sex-specific regression equation of Davidson et al. [24], using skinfolds of the biceps, triceps, subscapular and suprailiac as indicated by Durnin & Womerslay [25]. Maximum oxygen consumption and lactate threshold. The maximum _VO2 and the lac- tate threshold were assessed, using the same test, on the SRM cycle ergometer (SRM Italia, Lucca, Italy) with the same settings (height and fore-aft position of the seat, height and dis- tance of the handlebar, and length of the pedal crankarms) and the same type of bicycle pedals for each subject. The original incremental protocol to exhaustion to determine maximum _VO2 calls for a minimum of five and a maximum of nine 4-minute stages, each with resistance increments between a minimum of 20 and a maximum of 50 watts [26] and has been found to be highly reliable for the lactate threshold assessment in cyclists [27]. Although the protocol called for an intensity of 40% of maximum _VO2 for the first stage, we decided to use 30% in light of the fact that to determine the lactate threshold, the blood concentration of lactate at the end of the first stage should not be significantly higher than the resting value [26]. To calculate the resistance to apply to the cycle ergometer corresponding to 30% of maximum _VO2, the theoretical maximum oxygen consumption of each subject was estimated according to Malek et al. [28] and then converted into watts (peak) according to the regression equation of Hawley and Noakes [29]. The peak wattage was used to obtain a resistance value equal to 30%, and the difference between the two values was divided over 6 stages. Hence, we were able to determine the necessary watt increase in each stage in order to end the test hypothetically between the 5th and 9th stages (as suggested in [26]). Oxygen consumption was monitored for the duration of the trial (breath-by-breath) using the Cosmed k4b2 metabolimeter (COSMED, Rome, Italy), heart rate (HR) was recorded with the Polar RS-800 heart rate monitor (POLAR, Kempele, Finland), and blood lactate was measured (before starting the test and within 30 seconds before the end of each stage) using the Lactate Pro portable blood lactate meter (Arkray, Kioto, Japan) on micro blood samples drawn from the tip of the index finger according to the manu- facturer’s instructions. Peak oxygen consumption ( _VO2peak) was identified as the maximum value derived from the 15-breath moving average of oxygen consumption of the entire test, as suggested by Robergs et al. [30]. The lactate threshold and the corresponding _VO2LT were determined using the algorithm of Bentley et al. [31], and implemented using software expressly developed by Newell et al. [32], which requires inputting the [La]b and the steady- state oxygen consumption (as the average of the breath-by-breath measurements of the last 30s) for each stage. Lactate clearance during water-based post-exercise recovery PLOS ONE | https://doi.org/10.1371/journal.pone.0184240 September 6, 2017 4 / 12 Wingate Anaerobic Test. The test was conducted on the Peak-Bike cycle ergometer (Mon- ark Exercise AB, Vansbro, Sweden) using a standard protocol [15]. Briefly: I) 12-minute warm up during which the subjects made three short maximum accelerations (for 5 seconds) without friction load at 4, 7 and 10 minutes; II) 3 minutes of recovery seated on the ergometer; III) WAnT with friction load equal to 0.098 kp
kg-1 of body weight (i.e. about 10% of body weight). Statistical analysis The following analyses were performed using Excel (Microsoft Office, v.2010) and SPSS Statis- tics (IBM, v.20) software, with an α level of statistical significance of 0.05. Peak blood lactate comparisons. The [La]b peaks yielded before active and passive recov- eries were compared using a one-way ANOVA for reaped measures. Recovery intensity comparisons. Subjects’ compliance to the target active recovery inten- sity (i.e., 70% of _VO2LT) was evaluated. Firstly, the difference between the target recovery intensity and the pooled (ALR and AWR) values of the actual recovery intensity (average per- centage of _VO2LT) was evaluated using a 2-tailed one sample t-test (this comparison was not made for passive recovery). Subsequently, ALR vs. AWR and PLR vs. PWR average values of the actual recovery intensities were compared separately using two 2-tailed, paired sample t- tests. Individual linear regressions were performed for both the active and passive recovery conditions using the actual percentages of _VO2LT and the actual percentage of _VO2peak, respec- tively, of the whole set of breaths of each subject for the relevant condition. Average slopes and intercepts of ALR and AWR conditions were compared to the expected values of 0 and 70% of _VO2LT, respectively, by means of two 2-tailed one sample t-tests. Average slopes and intercepts were compared between PLR and PWR using two 2-tailed paired sample t-tests. The same approach as described above was used to compare the HR responses in the active recovery con- dition (the percentage of HR at the lactate threshold; HRLT) to those in the passive recovery condition (the percentage of the maximal HR; HRmax). All these comparisons were 2-tailed paired sample t-tests since no recovery intensity was planned for HR. All the t-tests were cor- rected according to Bonferroni’s criterion. Lactate clearance modeling. The lactate clearance kinetics of each recovery condition were modeled on a negative exponential function (as suggested in [33]) whose general form is: y = a0 + a1e−bx where, a0 is the predicted [La]b at rest, a1 is the difference between the predicted [La]b peak and a0 (predicted delta decrease), and b is the coefficient of the exponential decay of [La]b. Non-linear regression (NLR) fitting was optimized by using the Levenberg-Marquardt algorithm with an initial guess made on the basis of a visual inspection of [La]b over the time plots of each subject, for each recovery condition. A lower limit for predicted [La]b was fixed at a0  0.5 mmol
L−1. The coefficient of determination (R2) was used as a measure of goodness of fit: only regressions resulting in a high R2 (0.8) were considered acceptable and retained for subsequent analyses. Lactate clearance comparisons. Separate linear mixed model analyses for repeated mea- sures were performed for active and passive recovery in order to compare the values of b, a0, and a1 resulting from the lactate clearance modeling of the land- and the water-based condi- tions. Active and passive recovery were analyzed separately since it is widely accepted that lac- tate clearance varies significantly between the two conditions (e.g. see [23]). Results Table 1 shows the results of the anthropometric assessments and parameters measured during the maximum oxygen consumption and lactate threshold test, as well as other parameters that Lactate clearance during water-based post-exercise recovery PLOS ONE | https://doi.org/10.1371/journal.pone.0184240 September 6, 2017 5 / 12 were subsequently calculated. In line with Winter et al. [26], in all the tests the wattage increase of each stage always fell within the range of reference, and between 6 and 8 stages were always carried out. Peak blood lactates The [La]b peaks did not differ significantly (F(3,9) = 1.528; p = 0.273) before starting any recov- ery condition (see Table 1 and S1 File for raw data of each condition). Recovery intensities Average ambient (PLR and ALR conditions) and water temperature (PWR and AWR condi- tions) remained constant at 25.3±1.4˚C and 29±0.5˚C, respectively. Five participants did not undergo the passive recovery conditions; therefore, the number of subjects for PLR and PWR decreased to twelve. The α level of significance corrected according to the Bonferroni’s criterion resulted in a p level of statistical significance of 0.017. Pooled ALR and AWR average recovery intensity did not differ significantly from the target recovery intensity of 70% of _VO2LT (p = 0.639). Average values for passive recoveries were computed for nine subjects due to technical problems we encountered in sampling oxygen consumption in three subjects. Actual average oxygen con- sumptions during active recovery did not differ significantly between land and water conditions (p = 0.381), and the linear regression parameters intercept and slope of both environmental conditions did not differ significantly from 70 (land: p = 0.249; water: p = 0.899) and 0 (land: p = 0.147; water: p = 0.193), respectively. Regarding HR during active recovery, average values (p = 0.004) and regression intercepts (p = 0.016) differed significantly between the environ- ments, while regression slopes did not (p = 0.306). Average values of oxygen consumption dur- ing passive recovery were significantly different (p = 0.006) in the environments, whereas the regression parameters intercept (p = 0.069) and slope (p = 0.466) were not. HR average values (p = 0.007) and regression slopes (p = 0.002) of passive recovery differed significantly between the environments, while regression intercepts did not (p = 0.581). See Table 2 for details regard- ing the comparisons of the recovery intensities for both active and passive conditions. Lactate clearance modeling and comparisons NLRs were not acceptable (i.e. R2<0.5 or non-plausible predicted values) in one subject for water-based recovery and in one subject for land recovery; therefore, lactate clearance compar- isons were performed on sixteen and eleven subjects for active and passive recovery, respec- tively. The average goodness of fit for the NLRs of each condition was very high (ALR, R2: 0.98; AWR, R2: 0.99; PLR, R2: 0.97; PWR, R2: 0.96). The environment of recovery did not affect any parameter of the negative exponential equa- tions (b, a0, a1) in either active (Fig 1; F(1,16) = 0.372, p = 0.551; Cohen’s D effect size: b = 0.49; a0 = 0.64; a1 = 0.36) or passive (Fig 2; F(1,11) = 1.387, p = 0.264; Cohen’s D effect size: b = 0.24; a0 = 0.84; a1 = 0.5) recovery conditions. Discussion and conclusions The results of the present investigation clearly show that immersion to the shoulder in 29˚C water does not improve lactate clearance in active or passive recovery. These results are not in agreement with the only two studies on this specific topic in literature, which showed an over- all positive effect of the water environment in the clearance of blood lactate [9, 10]. These con- flicting results can probably be attributed to the substantial differences in the experimental Lactate clearance during water-based post-exercise recovery PLOS ONE | https://doi.org/10.1371/journal.pone.0184240 September 6, 2017 6 / 12 design of the studies, in particular, the modalities of intensity control in active recovery and the identification of the starting point of the recovery phase. In the study by Di Masi et al. [9], the subjects recovered actively in both land-based and water-based conditions at an intensity equal to 65% of the age-predicted HR (calculated with the 220-age formula), whereas in the study by Ferreira et al. [10], the subjects recovered actively in land-based and water-based conditions pedaling at 85% of the HR corresponding to the ventilatory threshold. Hence, both studies used HR to equalize the intensity levels of active recovery in the two different environmental conditions, even though it is known that during Table 2. Comparisons of the recovery intensity between land- and water-based clearance conditions for both active and passive recovery modes. Active recovery Passive recovery V : O2LT HRLT V : O2peak HRmax Actual intensities Land Mean % (±SD) 70.1 (3.9) 83.8 (8.9) * 12.6 (3.1) * 47.4 (3.6) * Water Mean % (±SD) 69 (3) 77.5 (6.4) * 13.9 (3.2) * 43.1 (3.9) * Linear regressions Land Intercept (±SD) 71.1 (3.8) 83.2 (8.5) 15.1 (4.2) 50.3 (3.9) Slope (±SD) -0.0 (0.0) 0.0 (0.0) -0.0 (0.0) -0.0 (0.0) * Water Intercept (±SD) 70.2 (5.1) 77.9 (6.7) * 16. (4.3) 49.6 (3.9) Slope (±SD) -0.0 (0.0) -0.0 (0.0) -0.0 (0.0) -0.0 (0.0) * Average data were calculated for seventeen and nine subjects for active and passive recovery, respectively. Linear regression parameters were calculated for sixteen and eleven subjects for active and passive recovery, respectively. Abbreviations: _VO2LT, oxygen consumption at the lactate threshold; HRLT, heart rate at the lactate threshold; _VO2peak, peak oxygen consumption; HRmax, maximal heart rate; SD, standard deviation; *, significantly different from the other recovery environment. https://doi.org/10.1371/journal.pone.0184240.t002 Fig 1. Blood lactate removal during active recovery. Blood lactate concentration decays during water- based (white diamonds) and land-based (black diamonds) active recovery conditions following a 30-second all-out bout of cycling. Non-linear regression curves are also shown for water-based (dashed line) and land- based (solid line) recovery. Abbreviations: [La]b, blood lactate concentration; NLR, non-linear regression. https://doi.org/10.1371/journal.pone.0184240.g001 Lactate clearance during water-based post-exercise recovery PLOS ONE | https://doi.org/10.1371/journal.pone.0184240 September 6, 2017 7 / 12 submaximal exercise, oxygen consumption being equal, HR in water is lower than it is on land by approximately 10–15 bpm [11, 13, 34–36]. Although both Di Masi et al. [9] and Ferreira et al. [10] correctly chose percentages of HR that theoretically determine workloads under the lactate threshold [16], the use of HR implies that subjects in both investigations made an active recovery that varied in terms of metabolic intensity according the conditions (land-based or water-based). Hence, the measured [La]b levels could not be compared because the intensity of the active recovery has a considerable effect on blood lactate clearance capacity [18]. On the other hand, in the present study, the intensity of the active recovery was monitored in both environmental conditions on the basis of the true oxygen consumption, which guarantees the comparability of intensity in the various experimental conditions. Moreover, we had the sub- jects recover actively at an intensity that was undoubtedly lower than the lactate threshold (70% of _VO2LT), under continuous monitoring by members of our research team, who in- formed the participants every time intensity fell below or exceeded the target level. The absence of a statistically significant difference in the intensities of oxygen consumption between the environments of recovery in the active condition points to the suitability of our experimental design. On the contrary, HR comparisons revealed a significantly lower average active recov- ery intensity (about −6%) in water compared to land conditions, which is in line with the above-mentioned studies and further supports our choice of using oxygen consumption as the control for recovery intensity. Unfortunately, passive recovery average values of oxygen con- sumption were found to be slightly (about +1.3% of _VO2peak) but significantly higher in water than on land. This result is in line with other studies (e.g. see Park et al. [34]) carried out under similar conditions of water temperature and resting duration while seated on the cycle ergom- eter. Under those water-based conditions, the average skin temperature reduces significantly [34] and oxygen consumption and metabolism increase to maintain core temperature [8]. In support of this view, five participants did not well tolerate the water environment during pas- sive recovery and after about 20 minutes started to feel too cold to conclude the 31-minute recovery and voluntarily interrupted the experiment. However, since both the regression inter- cept and slope of passive recovery oxygen consumption did not differ significantly between the environments, we are confident that comparisons of passive recovery lactate clearance between land and water-based conditions can still be drawn. That would not have been the case if HR Fig 2. Blood lactate removal during passive recovery. Blood lactate concentration decays during water- based (white diamonds) and land-based (black diamonds) passive recovery conditions following a 30-second all-out bout of cycling. Non-linear regression curves are also shown for water-based (dashed line) and land- based (solid line) recovery. Abbreviations: [La]b, blood lactate concentration; NLR, non-linear regression. https://doi.org/10.1371/journal.pone.0184240.g002 Lactate clearance during water-based post-exercise recovery PLOS ONE | https://doi.org/10.1371/journal.pone.0184240 September 6, 2017 8 / 12 had been used as the control for passive recovery intensities since average values were found to be significantly higher (about +4.3% of HRmax) on land than in water and regression slopes dif- fered significantly between the environments. The experimental protocol of this investigation called for a high number of blood samples during the recovery phase (8 blood draws in 31 minutes). This allowed us to model the [La]b decay over time and to make more accurate comparisons between the two environmental con- ditions compared to previous investigations [9, 10], hence obtaining a more complete and detailed picture of the kinetics of lactate clearance. In addition, the clear identification of the onset of the blood lactate clearance phase is indis- pensable to correctly assess its kinetics. In the studies by Di Masi et al. [9] and Ferreira et al. [10], a comparison was drawn among the [La]b values obtained from blood draws made start- ing at a time chosen arbitrarily after the completion of the trial used for [La]b accumulation. In particular, in the investigation of Di Masi et al. [9], the onset of the recovery phase was estab- lished one minute after the termination of the anaerobic exercise, whereas in the Ferreira et al. protocol [10], the onset of the recovery phase was established at five minutes after the termina- tion of the exercise. It has been shown that following a short bout of maximal physical exer- tion, the moment of peak blood lactate is rather unpredictable [33, 37]. Hence, considering the low frequency of the blood draws in these investigations, values that actually belong to the lac- tate accumulation phase immediately following the end of the maximal exercise test, may have been included among the values used to establish the average rate of blood lactate clearance identified by Di Masi et al. [9] and Ferreira et al. [10] for the two environmental conditions. This may account for the differences between the results of the above-mentioned investiga- tions and the results obtained in the present study, which only used [La]b values that clearly belonged to the clearance phase after the maximal trial because the experimental design called for the identification of the peak [La]b. In line with the interindividual variability regarding the time taken to reach [La]b peak values reported in literature [33, 37], in the present study the [La]b peak was recorded on average after 3.6±1.2 minutes from the end of the WAnT, despite the [La]b peaks were not significantly different before starting any of the recovery conditions. The main limitation of our investigation lies in the delay between the end of the WAnT and the identification of the [La]b peak and between the identification of the peak and the begin- ning of the selected recovery condition. In the first case, the limitation is due to the lactate meter used in this study, which takes one minute to yield a measurement; hence, the [La]b peak was always identified after two minutes from the onset of the clearance phase. In the sec- ond case, we elected to delay the start of all the recovery conditions by two minutes to allow for the correct positioning of the subjects on the cycle in the water-based recovery conditions. Hence, the [La]b of the first four minutes of recovery following the peak were never recorded. Nevertheless, since the kinetic of lactate clearance follows a well-known negative exponential pattern [33], it can be hypothesized that the pattern of lactate clearance from the fourth minute onwards is representative of the kinetics of the first four minutes. In any case, the aim of this investigation was to assess clearance differences attributable to the various conditions. There- fore, on a practical level, the short delay more accurately reflects the reality of recovery for ath- letes, who after a maximal exertion take a few minutes before starting their recovery immersed in water. In conclusion, our main finding suggests that, contrary to what has been postulated in liter- ature to date, the water environment does not produce different effects than the land environ- ment on the kinetics of blood lactate clearance during active recovery from intense exercise. Hence, immersion in 29˚C water would not appear to be a practice that can speed up post- exercise lactate removal in either the active or passive mode, and is therefore not advisable under the specific conditions investigated in the present study. Lactate clearance during water-based post-exercise recovery PLOS ONE | https://doi.org/10.1371/journal.pone.0184240 September 6, 2017 9 / 12 Additional research on this topic is needed and future studies should take into account pos- sible variations in aquatic environmental conditions (water temperature, exercise type and mode, depth of immersion, etc.) to further investigate the potential of the water environment in modulating blood lactate clearance after intense exercise. Supporting information S1 File. Raw data. (XLSX) Acknowledgments The authors wish to thank the Aqquatix Ltd company (Limena, Italy), which provided the Aqquactive Bike underwater cycle ergometer free of charge. We also would like to thank Sara Antonella Me for her help in supervising the experimental sessions and Eugenio Grassi for his technical assistance. Finally, we wish to thank Timothy C. Bloom for his linguistic revision of the paper. Author Contributions Conceptualization: Francesco Lucertini, Marco Gervasi, Vilberto Stocchi, Piero Benelli. Data curation: Francesco Lucertini, Marco Gervasi, Giancarlo D’Amen, Davide Sisti, Marco Bruno Luigi Rocchi. Formal analysis: Francesco Lucertini, Marco Gervasi, Davide Sisti, Marco Bruno Luigi Rocchi. Investigation: Francesco Lucertini, Marco Gervasi, Giancarlo D’Amen, Piero Benelli. Methodology: Francesco Lucertini, Marco Gervasi, Davide Sisti, Marco Bruno Luigi Rocchi, Piero Benelli. Project administration: Francesco Lucertini, Vilberto Stocchi, Piero Benelli. Resources: Vilberto Stocchi, Piero Benelli. Supervision: Francesco Lucertini, Vilberto Stocchi, Piero Benelli. Validation: Francesco Lucertini, Marco Gervasi, Giancarlo D’Amen, Davide Sisti, Marco Bruno Luigi Rocchi. Visualization: Francesco Lucertini, Marco Gervasi, Davide Sisti, Marco Bruno Luigi Rocchi. Writing – original draft: Francesco Lucertini, Marco Gervasi, Davide Sisti. Writing – review & editing: Francesco Lucertini, Marco Gervasi, Davide Sisti. References 1. Swain DP, American College of Sports Medicine, editors. ACSM’s resource manual for Guidelines for exercise testing and prescription. 7th ed. Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins; 2014. 2. Finsterer J. Biomarkers of peripheral muscle fatigue during exercise. BMC musculoskeletal disorders. 2012; 13:218. https://doi.org/10.1186/1471-2474-13-218 PMID: 23136874; PubMed Central PMCID: PMC3534479. 3. Robergs RA, Ghiasvand F, Parker D. Biochemistry of exercise-induced metabolic acidosis. American journal of physiology Regulatory, integrative and comparative physiology. 2004; 287(3):R502–16. Epub 2004/08/17. https://doi.org/10.1152/ajpregu.00114.2004 PMID: 15308499. Lactate clearance during water-based post-exercise recovery PLOS ONE | https://doi.org/10.1371/journal.pone.0184240 September 6, 2017 10 / 12 4. Adeva-Andany M, Lopez-Ojen M, Funcasta-Calderon R, Ameneiros-Rodriguez E, Donapetry-Garcia C, Vila-Altesor M, et al. Comprehensive review on lactate metabolism in human health. Mitochondrion. 2014; 17:76–100. https://doi.org/10.1016/j.mito.2014.05.007 PMID: 24929216. 5. Barnett A. Using recovery modalities between training sessions in elite athletes: does it help? Sports Med. 2006; 36(9):781–96. Epub 2006/08/30. PMID: 16937953. 6. van Hall G. Lactate kinetics in human tissues at rest and during exercise. Acta physiologica (Oxford, England). 2010; 199(4):499–508. https://doi.org/10.1111/j.1748-1716.2010.02122.x PMID: 20345411. 7. Versey NG, Halson SL, Dawson BT. Water Immersion Recovery for Athletes: Effect on Exercise Perfor- mance and Practical Recommendations. Sports Med. 2013; 43(11):1101–30. https://doi.org/10.1007/ s40279-013-0063-8 PMID: 23743793 8. Wilcock IM, Cronin JB, Hing WA. Physiological response to water immersion: a method for sport recov- ery? Sports Med. 2006; 36(9):747–65. PMID: 16937951. 9. Di Masi F, De Souza Vale RG, Dantas EHM, Barreto AC, da Silva Novaes J, Reis VM. Is blood lactate removal during water immersed cycling faster than during cycling on land? Journal of sports science & medicine. 2007; 6(2):188–92. PMID: 24149328; PubMed Central PMCID: PMC3786239. 10. Ferreira JD, Carvalho RGD, Barroso TM, Szmuchrowski LA, Śledziewski D. Effect of different types of recovery on blood lactate removal after maximum exercise. Pol J Sport Tourism. 2011; 18:105–11. 11. Barbosa TM, Garrido MF, Bragada J. Physiological adaptations to head-out aquatic exercises with dif- ferent levels of body immersion. Journal of strength and conditioning research / National Strength & Conditioning Association. 2007; 21(4):1255–9. https://doi.org/10.1519/R-20896.1 PMID: 18076241. 12. Benelli P, Colasanti F, Ditroilo M, Cuesta-Vargas A, Gatta G, Giacomini F, et al. Physiological and bio- mechanical responses to walking underwater on a non-motorised treadmill: effects of different exercise intensities and depths in middle-aged healthy women. Journal of sports sciences. 2014; 32(3):268–77. https://doi.org/10.1080/02640414.2013.824601 PMID: 24015764. 13. Benelli P, Ditroilo M, De Vito G. Physiological responses to fitness activities: a comparison between land-based and water aerobics exercise. Journal of strength and conditioning research / National Strength & Conditioning Association. 2004; 18(4):719–22. https://doi.org/10.1519/14703.1 PMID: 15574073. 14. Edwards LM, Jobson SA, George SR, Day SH, Nevill AM. Whole-body efficiency is negatively corre- lated with minimum torque per duty cycle in trained cyclists. Journal of sports sciences. 2009; 27 (4):319–25. Epub 2009/01/22. https://doi.org/10.1080/02640410802526916 PMID: 19156562. 15. Bar-Or O. The Wingate anaerobic test. An update on methodology, reliability and validity. Sports Med. 1987; 4(6):381–94. PMID: 3324256. 16. Meyer T, Gabriel HH, Kindermann W. Is determination of exercise intensities as percentages of VO2max or HRmax adequate? Medicine and science in sports and exercise. 1999; 31(9):1342–5. PMID: 10487378. 17. Devlin J, Paton B, Poole L, Sun W, Ferguson C, Wilson J, et al. Blood lactate clearance after maximal exercise depends on active recovery intensity. The Journal of sports medicine and physical fitness. 2014; 54(3):271–8. PMID: 24739289. 18. Menzies P, Menzies C, McIntyre L, Paterson P, Wilson J, Kemi OJ. Blood lactate clearance during active recovery after an intense running bout depends on the intensity of the active recovery. Journal of sports sciences. 2010; 28(9):975–82. Epub 2010/06/15. https://doi.org/10.1080/02640414.2010. 481721 PMID: 20544484. 19. Baldari C, Videira M, Madeira F, Sergio J, Guidetti L. Blood lactate removal during recovery at various intensities below the individual anaerobic threshold in triathletes. The Journal of sports medicine and physical fitness. 2005; 45(4):460–6. Epub 2006/02/01. PMID: 16446676. 20. Belcastro AN, Bonen A. Lactic acid removal rates during controlled and uncontrolled recovery exercise. Journal of applied physiology. 1975; 39(6):932–6. Epub 1975/12/01. PMID: 765313. 21. Boileau RA, Misner JE, Dykstra GL, Spitzer TA. Blood lactic acid removal during treadmill and bicycle exercise at various intensities. The Journal of sports medicine and physical fitness. 1983; 23(2):159– 67. Epub 1983/06/01. PMID: 6632854. 22. Millet GP, Vleck VE, Bentley DJ. Physiological differences between cycling and running. Lessons from triathletes. Sports Med. 2009; 39(3):179–206. ISI:000265002300002. https://doi.org/10.2165/ 00007256-200939030-00002 PMID: 19290675 23. Monedero J, Donne B. Effect of recovery interventions on lactate removal and subsequent perfor- mance. International journal of sports medicine. 2000; 21(8):593–7. Epub 2001/01/13. https://doi.org/ 10.1055/s-2000-8488 PMID: 11156281. 24. Davidson LE, Wang J, Thornton JC, Kaleem Z, Silva-Palacios F, Pierson RN, et al. Predicting fat per- cent by skinfolds in racial groups: Durnin and Womersley revisited. Medicine and science in sports and Lactate clearance during water-based post-exercise recovery PLOS ONE | https://doi.org/10.1371/journal.pone.0184240 September 6, 2017 11 / 12 exercise. 2011; 43(3):542–9. Epub 2010/08/07. https://doi.org/10.1249/MSS.0b013e3181ef3f07 PMID: 20689462; PubMed Central PMCID: PMC3308342. 25. Durnin JV, Womersley J. Body fat assessed from total body density and its estimation from skinfold thickness: measurements on 481 men and women aged from 16 to 72 years. The British journal of nutri- tion. 1974; 32(1):77–97. Epub 1974/07/01. PMID: 4843734. 26. Winter EM, Jones AM, Davison RC, Bromley PD, Mercer TH, British Association of Sport and Exercise Sciences, editors. Sport and exercise physiology testing guidelines. The British Association of Sport and Exercise Sciences guide. 1st ed. New York, NY: Routledge; 2007. 27. Hoefelmann CP, Diefenthaeler F, Costa VP, de Lucas RD, Shambrook P, Guglielmo LG. Test-retest reliability of second lactate turnpoint using two different criteria in competitive cyclists. Eur J Sport Sci. 2015; 15(4):265–70. https://doi.org/10.1080/17461391.2014.944874 PMID: 25135192. 28. Malek MH, Housh TJ, Berger DE, Coburn JW, Beck TW. A new non-exercise-based Vo2max prediction equation for aerobically trained men. Journal of strength and conditioning research / National Strength & Conditioning Association. 2005; 19(3):559–65. https://doi.org/10.1519/1533-4287(2005)19[559: ANNOPE]2.0.CO;2 PMID: 16095416. 29. Hawley JA, Noakes TD. Peak power output predicts maximal oxygen uptake and performance time in trained cyclists. European journal of applied physiology and occupational physiology. 1992; 65(1):79– 83. Epub 1992/01/01. PMID: 1505544. 30. Robergs RA, Dwyer D, Astorino T. Recommendations for improved data processing from expired gas analysis indirect calorimetry. Sports Med. 2010; 40(2):95–111. https://doi.org/10.2165/11319670- 000000000-00000 PMID: 20092364. 31. Bentley DJ, Newell J, Bishop D. Incremental exercise test design and analysis: implications for perfor- mance diagnostics in endurance athletes. Sports Med. 2007; 37(7):575–86. Epub 2007/06/28. PMID: 17595153. 32. Newell J, Higgins D, Madden N, Cruickshank J, Einbeck J, McMillan K, et al. Software for calculating blood lactate endurance markers. Journal of sports sciences. 2007; 25(12):1403–9. https://doi.org/10. 1080/02640410601128922 PMID: 17786693. 33. Beneke R, Jumah MD, Leitha¨user RM. Modelling the lactate response to short-term all out exercise. Dynamic medicine. 2007; 6(10):1–7. https://doi.org/10.1186/1476-5918-6-10 PMID: 17996077 34. Park KS, Choi JK, Park YS. Cardiovascular regulation during water immersion. Applied human science: journal of physiological anthropology. 1999; 18(6):233–41. PMID: 10675972. 35. Christie JL, Sheldahl LM, Tristani FE, Wann LS, Sagar KB, Levandoski SG, et al. Cardiovascular regu- lation during head-out water immersion exercise. Journal of applied physiology. 1990; 69(2):657–64. PMID: 2228879. 36. Connelly TP, Sheldahl LM, Tristani FE, Levandoski SG, Kalkhoff RK, Hoffman MD, et al. Effect of increased central blood volume with water immersion on plasma catecholamines during exercise. Jour- nal of applied physiology. 1990; 69(2):651–6. PMID: 2228878. 37. Weinstein Y, Bediz C, Dotan R, Falk B. Reliability of peak-lactate, heart rate, and plasma volume follow- ing the Wingate test. Medicine and science in sports and exercise. 1998; 30(9):1456–60. PMID: 9741617. Lactate clearance during water-based post-exercise recovery PLOS ONE | https://doi.org/10.1371/journal.pone.0184240 September 6, 2017 12 / 12

Effect of water-based recovery on blood lactate removal after high-intensity exercise.

09-06-2017

Lucertini, Francesco,Gervasi, Marco,D'Amen, Giancarlo,Sisti, Davide,Rocchi, Marco Bruno Luigi,Stocchi, Vilberto,Benelli, Piero

eng

PMC5551190

International Journal of Environmental Research and Public Health Article Physical and Emotional Benefits of Different Exercise Environments Designed for Treadmill Running Hsiao-Pu Yeh 1,* , Joseph A. Stone 2 , Sarah M. Churchill 2, Eric Brymer 3 and Keith Davids 1 1 Centre for Sports Engineering Research, Sheffield Hallam University, Sheffield S10 2BP, UK; [email protected] 2 Academy of Sport and Physical Activity, Sheffield Hallam University, Sheffield S10 2BP, UK; [email protected] (J.A.S.); [email protected] (S.M.C.) 3 Institute of Sport, Physical Activity and Leisure, Leeds Beckett University, Leeds LS1 3HE, UK; [email protected] * Correspondence: [email protected]; Tel.: +44-114-225-2355 Received: 24 May 2017; Accepted: 6 July 2017; Published: 11 July 2017 Abstract: (1) Background: Green physical activity promotes physical health and mental wellbeing and interesting questions concern effects of this information on designing indoor exercise environments. This study examined the physical and emotional effects of different nature-based environments designed for indoor treadmill running; (2) Methods: In a counterbalanced experimental design, 30 participants performed three, twenty-minute treadmill runs at a self-selected pace while viewing either a static nature image, a dynamic nature image or self-selected entertainment. Distance ran, heart rate (HR) and five pre-and post-exercise emotional states were measured; (3) Results: Participants ran farther, and with higher HRs, with self-selected entertainment compared to the two nature-based environment designs. Participants attained lowered anger, dejection, anxiety and increased excitement post exercise in all of the designed environments. Happiness increased during the two nature-based environment designs compared with self-selected entertainment; (4) Conclusions: Self-selected entertainment encouraged greater physical performances whereas running in nature-based exercise environments elicited greater happiness immediately after running. Keywords: green physical activity; environmental design; happiness; ecological dynamics; indoor exercise environments 1. Introduction Physical inactivity has been identified as the fourth leading risk factor for global mortality, associated with approximately 3.2 million deaths each year and implicated in the prevalence of non-communicable diseases such as cancer and cardiovascular issues [1]. As the proportion of the world’s population living in urban environments is increasing, this has become an important group to target with strategies for increasing physical activity (PA) uptake, effectiveness and adherence [2,3]. Moreover, the increasing trend for exercising requires better understanding, how considering best to design these settings to maximise benefits. This paper examines the physical and emotional effects of exercising in different indoor PA environments. Urban environments, high-density traffic, low air quality, a lack of parks, sports/recreation facilities and fear of crime in outdoor areas [4] might inhibit urban dwellers from exercising outside. Bad weather, lack of time or shorter daytime light can also act as barriers discouraging people from exercising outside, especially during winter. As a result, gyms, homes or private exercise centres can become preferred venues for PA because of the reduced concerns about safety and increased availability. In fact, the exercise gym is the most preferred PA environment [5]. Depending on the exercise activity and available facilities, exercisers physically and psychologically engage with their activity in different Int. J. Environ. Res. Public Health 2017, 14, 752; doi:10.3390/ijerph14070752 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2017, 14, 752 2 of 11 ways. For example, exercisers might prefer to exercise while watching television programmes, news or movies, or listening to music available in the external environment or on their own music devices. These varying media types offer different information sources which in turn influences exercisers’ perceptions, performance and experiences. For example, listening to music during treadmill running can influence runners’ performance compared to watching television programmes [6]. Different forms of the same entertainment might also result in different PA outcomes. For example, fast or loud music can encourage treadmill runners to run faster or for a longer time [7–9], whereas slow or quiet music has not been associated with any beneficial physical outputs for treadmill runners [9]. Hence, identifying the information in PA contexts that more effectively engages exercisers and maximises physical and mental exercise benefits is important for ensuring the effective design of PA environments. Nature has been promoted as integral to human health and wellbeing, being associated with the capacity to augment physical, cognitive and emotional wellbeing. For example, people who live in greener environments report better perceived health [10] and viewing awesome nature scenes has been associated with mood improvement [11]. When coupled with PA, the context of nature has been associated with lowered blood pressure [12,13], reduced perceived exertion [14,15], enhanced self-esteem [13,16], mood [13,14,16] and enjoyment [17–19] as well as reduced anxiety [20–23] and stress [23–25]. Furthermore, viewing static and dynamic images of nature while participating in indoor physical activities, such as running on a treadmill or cycling on an exercise bike (defined as ‘in the presence of nature’) has also demonstrated physical and mental benefits, such as lowered blood pressure [12,13], lowered perceived exertion [14,15], improved direct attention [26], mood [13–15], self-esteem [13,27], affective valence and exercise enjoyment [19]. A meta-analysis undertaken by Bowler and colleagues indicated that the most commonly reported benefit of PA in the presence of nature (indoors or outdoors) was the enhancement of emotions [28]. A number of theoretical perspectives have been proposed as useful for understanding how this might come about. Attention Restoration Theory suggests that nature environments have a restorative effect on the brain's ability to focus. Whereas, the Stress Recovery Theory [29] posits a healing power of nature that lies in an unconscious, autonomic response to natural elements that can occur without recognition and most noticeably in individuals who have been stressed before the experience [30]. Further, The Biophilia hypothesis assumes that humans have affiliations to nature [31]. Despite these explanations, theoretical perspectives have largely ignored the role of nature in PA design. Interpretations have been limited to psychological and cognitive responses, which provide a narrow perspective on the beneficial effects of nature. However, the individual, the type of PA and the environment in which the PA is performed all play influential roles in the emergence of behaviour [2]. Ecological dynamics has been proposed as an effective framework for understanding the relationship of individuals with the environment during PA [2,6]. Ecological dynamics emphasises that the realisation of affordances underpins observed effects of PA [3,6]. The notion of affordances highlights that the relationship between a perceiver’s capabilities and an environment supports opportunities (both good and bad) that facilitate a given activity [32]. To perceive an affordance is to detect an environmental property that provides an opportunity for action and it is specified in the surrounding environment available to perceivers [32]. Therefore, when performing an activity, an individual is constantly and actively detecting various types of information, such as olfactory, acoustic, haptic and visual from the environment and utilising information that is most functional during interactions. For example, when you run along a river, you might notice fish swimming in the river, but it is not functionally relevant to your running. However, a puddle on the pathway or a slippery surface near the water’s edge might be very relevant for the way that your emotions and physical actions emerge. In this way, the perception-action relationship is a reciprocal and continuous cycle that underpins human behaviour. Based on the concept of affordances, people exercising in the same physical environment, might detect or utilise different information sources which would accrue various effects on their behaviours, according to individual differences. A static scene is a frozen moment and may contain limited information for participants to utilise compared to a dynamic Int. J. Environ. Res. Public Health 2017, 14, 752 3 of 11 display which offers continuous and richer information for affordances. Hence, the functionality offered by these two types of information may differ, although such variations might not have linear effects. Previous work has examined these two types of displays and found a non-parallel relationship between static and dynamic displays on preference rating, epistemic and evaluative variables with no PA involvement [33]. When applied in a PA environment, the effects of viewing static and dynamic displays during PA remain unclear. To examine this key idea, we provided three conditions which afforded (offered) different information sources for exercisers performing PA in the same physical setting, through different environmental designs. We examined emotional and physical outcomes related to exercising when viewing two types of nature-based designs and when participants were able to choose their habitual, preferred entertainment. The two nature conditions were designed with visual-only information, whereas in the self-selected entertainment condition, participants were able to choose visual, acoustic or visual-acoustic information. In all three PA designs, participants were instructed to run at their own comfortable pace and they were allowed to change their running speed at any time during the activity. Therefore, the imposed speed of the run, e.g., the intensity of the run, was not allowed to contaminate the findings. Allowing participants to self-adjust running intensity during an experimental condition is more representative of their typical experiences during PA. It is, therefore, more likely to enhance knowledge about how to design a more appealing indoor exercising (e.g., treadmill running) environment with typical affordances of different activity contexts. We sought to investigate these PA designs to understand whether any were more likely to be beneficial for constraining experiences of physical health and mental wellbeing [34,35]. Therefore, the aim of this paper was to examine physical and emotional effects of PA in different exercise environments with and without nature-based affordances without controlling the intensity of PA. We hypothesised that participants would accrue more emotional benefits when viewing dynamic image than a static image, due to the dynamic qualities of the information present in that type of display, however, it was expected that self-selected entertainment would result in better physical performance, based on previous research findings. 2. Materials and Methods 2.1. Participants Thirty participants (mean ± SD: 18 males and 12 females; age 27.5 ± 9 years; mass 67.6 ± 11.1 kg; stature 173.7 ± 8.2 cm; BMI 22.2 ± 2.1) were recruited. All participants gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Sheffield Hallam University Research Ethics Committee of HWB-S&E-35. Twenty-four participants performed regular exercise completing more than 150 min a week, such as attending gym sessions, weightlifting, running, cycling and climbing. Six participants performed exercise irregularly or light exercise, such as power walking. 2.2. Study Design Two nature-based conditions, involving visual-only information of nature, were designed. The first involved a static image of nature and the second included a dynamic image. The dynamic image condition was a 20-minute digital video recording made at the Sheffield Botanical Gardens. The video recording was created by fixing a GoPro camera (Hero3+, GoPro, San Mateo, CA, USA) on the helmet of a person cycling along a series of paths within the gardens, capturing the trail through lawns, trees and flower beds on a sunny afternoon in spring. The video aimed to represent a first person perspective of moving through the gardens and it was filmed at 2.32 m/s to present a moderate exercise level [36]. The static image condition was composed of a single frame of the dynamic image to avoid discrepancies between images and was used throughout the twenty minutes physical activity period (see Figure 1). The third design, representative of popular gym conditions, Int. J. Environ. Res. Public Health 2017, 14, 752 4 of 11 consisted of self-selected, preferred entertainment where participants were able to choose preferences that included visual and/or auditory information. To focus on personal preferences of participants, there were no specific limitations imposed on the self-selected entertainment used for Gym exercise. Participants chose various entertainments, for example, listening to music (N = 23), watching television (e.g., BBC news/talk shows) or movies (e.g., Simpsons) with sound (N = 6) and viewing a picture (one person chose to view an image of friends). Television, movies and the static image of nature were presented on a wall-mounted monitor with a 2 × 1 m screen, situated 3 m in front of the treadmill (Figure 1). Music was presented either from wall mounted speakers or through participants’ own headphones. In each trial, participants performed a self-organised warm up for 5 min. The information panel on the treadmill was covered to ensure that participants were not able to view the distance of their run. The researcher was able to record these data by using the treadmill application program in a remote computer. There were two partitions on each side of the treadmill in order to control potential distractions by limiting participants' visible area to the forward plane. Int. J. Environ. Res. Public Health 2017, 14, 752    4 of 11  conditions,  consisted  of  self‐selected,  preferred  entertainment  where  participants  were  able  to  choose  preferences  that  included  visual  and/or  auditory  information.  To  focus  on  personal  preferences  of  participants,  there  were  no  specific  limitations  imposed  on  the  self‐selected  entertainment  used  for  Gym  exercise.  Participants  chose  various  entertainments,  for  example,  listening  to  music  (N  =  23),  watching  television  (e.g.,  BBC  news/talk  shows)  or  movies  (e.g.,  Simpsons) with sound (N = 6) and viewing a picture (one person chose to view an image of friends).  Television, movies and the static image of nature were presented on a wall‐mounted monitor with a  2 × 1 m screen, situated 3 m in front of the treadmill (Figure 1). Music was presented either from wall  mounted speakers or through participants’ own headphones. In each trial, participants performed a  self‐organised warm up for 5 min. The information panel on the treadmill was covered to ensure  that participants were not able to view the distance of their run. The researcher was able to record  these  data  by  using  the  treadmill  application  program  in  a  remote  computer.  There  were  two  partitions  on  each  side  of  the  treadmill  in  order  to  control  potential  distractions  by  limiting  participantsʹ visible area to the forward plane.    Figure 1. Left: The experimental setting, the treadmill is 3 m from the wall with projecting media and  two partitions stand next to the treadmill [6]. The projected screen is 2 × 1 m; Right: The one single  frame from the dynamic image condition used throughout the whole twenty‐minute.  2.3. Procedure  In a counterbalanced design, all participants were asked to perform a twenty‐minute treadmill  run  at  a  comfortable  self‐selected  speed  in  each  design  at  a  similar  time  of  day  (within  a  4  h  window). There was at least a seven‐day gap between conditions to ‘wash out’ condition effects and  avoid  fatigue  for  each  participant  [26].  Participants  were  informed  that  they  could  change  their  speed at any time during the run. The information displayed on the control panel of the treadmill  was covered, but participants could still change their speed by pressing a button on the treadmill  control  panel.  Before  the  first  trial,  data  on  age,  mass,  stature  and  resting  heart  rate  (HR)  were  collected. The distance run by participants in each 20‐min session was recorded and HR data were  recorded continuously (per second) for twenty minutes with a Polar HR watch (Polar RS400, Polar  Electro,  Kempele, Finland).  The  speed of  the  twenty‐minute  run  was  recorded  by  the researcher  minute‐by‐minute  (4  participants  were  excluded  because  of  an  incomplete  data  set).  The  Sport  Emotion Questionnaire (SEQ) was used to examine people’s emotional states five minutes before the  run  and  immediately  after  the  run  in  each  trial.  The  SEQ  is  a  valid  and  reliable  measure  of  sport‐specific emotions [37], and has been effectively used in different exercise groups [38]. The SEQ  is  a  22‐item  measure  for  happiness,  anxiety,  dejection,  anger  and  excitement.  The  Happiness  subscale  encompasses  a  person’s  self‐appraisal  with  regards  to  their  progress  towards  a  goal.  It  consists  of  four  items,  i.e.,  Pleased,  Joyful,  Happy  and  Cheerful.  Anxiety  is  considered  to  reflect  uncertainty regarding goal attainment and coping, and consisted of five items, i.e., Uneasy, Tense,  Nervous, Apprehensive and Anxious. Dejection is a negative emotion characterized by feelings of  Figure 1. Left: The experimental setting, the treadmill is 3 m from the wall with projecting media and two partitions stand next to the treadmill [6]. The projected screen is 2 × 1 m; Right: The one single frame from the dynamic image condition used throughout the whole twenty-minute. 2.3. Procedure In a counterbalanced design, all participants were asked to perform a twenty-minute treadmill run at a comfortable self-selected speed in each design at a similar time of day (within a 4 h window). There was at least a seven-day gap between conditions to ‘wash out’ condition effects and avoid fatigue for each participant [26]. Participants were informed that they could change their speed at any time during the run. The information displayed on the control panel of the treadmill was covered, but participants could still change their speed by pressing a button on the treadmill control panel. Before the first trial, data on age, mass, stature and resting heart rate (HR) were collected. The distance run by participants in each 20-min session was recorded and HR data were recorded continuously (per second) for twenty minutes with a Polar HR watch (Polar RS400, Polar Electro, Kempele, Finland). The speed of the twenty-minute run was recorded by the researcher minute-by-minute (4 participants were excluded because of an incomplete data set). The Sport Emotion Questionnaire (SEQ) was used to examine people’s emotional states five minutes before the run and immediately after the run in each trial. The SEQ is a valid and reliable measure of sport-specific emotions [37], and has been effectively used in different exercise groups [38]. The SEQ is a 22-item measure for happiness, anxiety, dejection, anger and excitement. The Happiness subscale encompasses a person’s self-appraisal with regards to their progress towards a goal. It consists of four items, i.e., Pleased, Joyful, Happy and Cheerful. Anxiety is considered to reflect uncertainty regarding goal attainment and coping, and consisted of five items, i.e., Uneasy, Tense, Nervous, Apprehensive and Anxious. Dejection is a negative emotion characterized by feelings of deficiency and sadness and assessed by five items, i.e., Upset, Sad, Int. J. Environ. Res. Public Health 2017, 14, 752 5 of 11 Unhappy, Disappointed and Dejected. Anger can be channeled internally to self-blame and associated with feelings of depressions or externally toward the source of the frustration. This subscale consisted of four items: Irritated, Furious, Annoyed and Angry. Excitement is proposed to occur when a person has a positive expectation of his or her ability to cope and achieve goals in a challenging situation. Exhilarated, Excited, Enthusiastic and Energetic are the four items for measuring excitement in the SEQ. The SEQ is rated with a 5-point Likert scale, i.e., not at all (0), a little (1), moderately (2), quite a bit (3) and extremely (4). Scores for each subscale are determined by calculating the mean of its assessed items. 2.4. Data Analysis Data were analysed in SPSS version 22 (IBM, Chicago, IL, USA) and an alpha level of 0.05 was used to indicate significant difference levels, with Partial eta squared used for effect size calculations. Least Significant Difference (LSD) was used for post hoc analysis. The HR data were exported from the commercial software (Polar Pro trainer 5, Polar Electro, Kempele, Finland) and mean HR for each participant for the twenty minutes of the run was used for analysis. The mean HR value for every minute of the run for all participants in the three separate conditions was calculated. Six participants were removed from the HR analysis because of technical problems. Examination of the Shapiro-Wilk test revealed distance and HR were not normally distributed. Hence, two Friedman tests were used to statistically analyse the differences in the values of the distances run and HR. Scores of the five subscales of SEQ were calculated. Five, separate, two-way repeated measures analysis of variance (ANOVAs) (time × condition) were used to examine any differences on five subscales of the sport emotion questionnaire. 3. Results 3.1. Running Distance, Heart Rate and Speed Descriptive data for running distances and heart rate (HR) for each condition are displayed in Table 1. Distance run was influenced by the designs, F (29) = 10.572, p < 0.05, pη2 = 0.2 with participants in the self-selected entertainment condition (3066.8 ± 688.5 m) running longer distances than the static image condition (2767.2 ± 662.6 m) (p < 0.05). HR was also affected by the three designs, χ2 (2) = 10.750, p < 0.05. Participants exercising with self-selected entertainment (Mdn = 149.11 bmp) achieved a higher HR than in the dynamic image condition (Mdn = 140.52 bmp, p < 0.05), and in the static image condition (Mdn = 142.03 bmp, p < 0.05). Table 1. The mean ± SD running distances and Heart Rate (HR) values in the three different conditions. Variables N Dynamic Image Static Image Self-Selected Entertainment Distance (m) 30 2891.6 ± 631.4 2767.2 ± 662.6 * 3066.8 ± 688.5 * HR (bpm) 24 141 ± 18 * 138 ± 21 * 147 ± 188 * *: indicated that p < 0.05. The mean minute-by-minute running speed in the three conditions is presented in Figure 2. The three exercise groups presented different running speeds, however with similar tendencies, i.e., gradually increasing speed throughout the twenty-minute period. Int. J. Environ. Res. Public Health 2017, 14, 752 6 of 11 Int. J. Environ. Res. Public Health 2017, 14, 752    6 of 11    Figure 2. The mean minute‐by‐minute running speed in the three different conditions.  3.2. Emotional Variables  Descriptive  data  of  pre‐and‐post  run  scores  of  five  subscales  of  SEQ  of  three  different  conditions are displayed in Table 2. The scores of each subscale range from 0 to 4.    Table 2. The pre‐and ‐post run scores of five subscales of Sport Emotion Questionnaires (SEQ) of  three different conditions (mean ± SD).  Variables  N  Dynamic Image  Static Image  Self‐Selected  Entertainment  Anxiety Pre‐test  30  0.48 ± 0.68  0.52 ± 0.74  0.40 ± 0.52  Anxiety Post‐test  30  0.17 ± 0.26  0.22 ± 0.34  0.26 ± 0.31  Dejection Pre‐test  30  0.20 ± 0.45  0.11 ± 0.15  0.17 ± 0.33  Dejection Post‐test  30  0.04 ± 0.11  0.02 ± 0.08  0.06 ± 0.16  Excitement Pre‐test  30  1.09 ± 1.01  0.95 ± 1.07  0.80 ± 1.03  Excitement Post‐test  30  1.76 ± 0.88  2.05 ± 0.93  1.89 ± 0.72  Anger Pre‐test  30  0.20 ± 0.57  0.15 ± 0.46  0.12 ± 0.26  Anger Post‐test  30  0.06 ± 0.18  0.07 ± 0.20  0.06 ± 0.18  Happiness Pre‐test  30  1.81 ± 0.86  1.78 ± 0.88  1.38 ± 0.85  Happiness Post‐test  30  2.10 ± 0.84  2.19 ± 0.86  2.04 ± 0.88  3.2.1. Happiness  Time had a main effect on reported feelings of happiness (Figure 3.). People felt happier after  running (pre‐scores 1.67 ± 0.88; post‐scores 2.11 ± 0.86; F (1, 29) = 27.185, p < 0.05, ƞp2 = 0.484). There  was also a main effect for exercise design on reported feelings of happiness, F (2, 58) = 3.656, p < 0.05,  ƞp2 =  0.112  when  the  data  of  pre‐and‐post  in  each  condition  were  pooled.  The  post  hoc  analysis  indicated that participants felt happier in the dynamic image condition (1.958 ± 0.114), p < 0.05 and in  the static image condition (1.987 ± 0.147), p < 0.05, than in the self‐selected entertainment condition  Figure 2. The mean minute-by-minute running speed in the three different conditions. 3.2. Emotional Variables Descriptive data of pre-and-post run scores of five subscales of SEQ of three different conditions are displayed in Table 2. The scores of each subscale range from 0 to 4. Table 2. The pre-and -post run scores of five subscales of Sport Emotion Questionnaires (SEQ) of three different conditions (mean ± SD). Variables N Dynamic Image Static Image Self-Selected Entertainment Anxiety Pre-test 30 0.48 ± 0.68 0.52 ± 0.74 0.40 ± 0.52 Anxiety Post-test 30 0.17 ± 0.26 0.22 ± 0.34 0.26 ± 0.31 Dejection Pre-test 30 0.20 ± 0.45 0.11 ± 0.15 0.17 ± 0.33 Dejection Post-test 30 0.04 ± 0.11 0.02 ± 0.08 0.06 ± 0.16 Excitement Pre-test 30 1.09 ± 1.01 0.95 ± 1.07 0.80 ± 1.03 Excitement Post-test 30 1.76 ± 0.88 2.05 ± 0.93 1.89 ± 0.72 Anger Pre-test 30 0.20 ± 0.57 0.15 ± 0.46 0.12 ± 0.26 Anger Post-test 30 0.06 ± 0.18 0.07 ± 0.20 0.06 ± 0.18 Happiness Pre-test 30 1.81 ± 0.86 1.78 ± 0.88 1.38 ± 0.85 Happiness Post-test 30 2.10 ± 0.84 2.19 ± 0.86 2.04 ± 0.88 3.2.1. Happiness Time had a main effect on reported feelings of happiness (Figure 3.). People felt happier after running (pre-scores 1.67 ± 0.88; post-scores 2.11 ± 0.86; F (1, 29) = 27.185, p < 0.05, ηp2 = 0.484). There was also a main effect for exercise design on reported feelings of happiness, F (2, 58) = 3.656, p < 0.05, ηp2 = 0.112 when the data of pre-and-post in each condition were pooled. The post hoc analysis indicated that participants felt happier in the dynamic image condition (1.958 ± 0.114), p < 0.05 and in the static image condition (1.987 ± 0.147), p < 0.05, than in the self-selected entertainment condition (1.713 ± 0.142; Figure 3). There were no interaction effects between time and exercise design on reported feelings of happiness, F (2, 58) = 2.337, p > 0.05, ηp2 = 0.075. Int. J. Environ. Res. Public Health 2017, 14, 752 7 of 11 Int. J. Environ. Res. Public Health 2017, 14, 752    7 of 11  (1.713  ±  0.142;  Figure  3).  There  were  no  interaction  effects  between  time  and  exercise  design  on  reported feelings of happiness, F (2, 58) = 2.337, p > 0.05, ƞp2 = 0.075.      Figure 3. Pre‐and‐post scores on happiness scale in three conditions (mean ± SD). * indicating the  time effect, p < 0.05.  3.2.2. Anxiety  Time had a main effect on reported feelings of anxiety, F (1, 29) = 16.256, p < 0.05, ƞp2 = 0.259  with participants feeling less anxious after running (pre 0.471 ± 0.066; post 0.218 ± 0.039) regardless  of PA designs. There was no main effect of designs on reported feelings of anxiety, F (2, 58) = 0.190, p  > 0.05, ƞp2 = 0.047, which showed that participants reported a similar level of anxiety across three  designs. There were no interaction effects between time and exercise design on reported feelings of  anxiety, F (2, 58) = 0.322, p > 0.05, ƞp2 = 0.016.  3.2.3. Dejection  Time had a main effect on reported feelings of dejection, F (1, 29) = 10.296, p < 0.05, ƞp2 = 0.262.  Participants  felt  less  dejected  after  running  (pre  0.162  ±  0.035;  post  0.047  ±  0.014)  regardless  of  designs. There was no main effect for condition on reported feelings of dejection, which indicated  that participants reported a similar level of dejection across three designs (F (2, 58) = 0.645, p > 0.05,  ƞp2 = 0.022). There was also no interaction between time and exercise designs on reported feelings of  dejection, F (2, 58) = 0.356, p > 0.05, ƞp2 = 0.012.  3.2.4. Anger  Time had a main effect on reported feelings of anger, F (1, 29) = 4.563, p < 0.05, ƞp2 = 0.136 with  participants  feeling  less  angry  after  running  (pre  0.161  ±  0.049;  post  0.069  ±  0.024)  regardless  of  design. There was no main effect for condition on reported feelings of anger, F (2, 58) = 0.190, p > 0.05,  ƞp2 = 0.047, which indicated that people report a similar level of anger across three designs. There  were no interactions between time and exercise design on reported feelings of anger, F (2, 58) = 0.322,  p > 0.05, ƞp2 = 0.011.  3.2.5. Excitement  Figure 3. Pre-and-post scores on happiness scale in three conditions (mean ± SD). * indicating the time effect, p < 0.05. 3.2.2. Anxiety Time had a main effect on reported feelings of anxiety, F (1, 29) = 16.256, p < 0.05, ηp2 = 0.259 with participants feeling less anxious after running (pre 0.471 ± 0.066; post 0.218 ± 0.039) regardless of PA designs. There was no main effect of designs on reported feelings of anxiety, F (2, 58) = 0.190, p > 0.05, ηp2 = 0.047, which showed that participants reported a similar level of anxiety across three designs. There were no interaction effects between time and exercise design on reported feelings of anxiety, F (2, 58) = 0.322, p > 0.05, ηp2 = 0.016. 3.2.3. Dejection Time had a main effect on reported feelings of dejection, F (1, 29) = 10.296, p < 0.05, ηp2 = 0.262. Participants felt less dejected after running (pre 0.162 ± 0.035; post 0.047 ± 0.014) regardless of designs. There was no main effect for condition on reported feelings of dejection, which indicated that participants reported a similar level of dejection across three designs (F (2, 58) = 0.645, p > 0.05, ηp2 = 0.022). There was also no interaction between time and exercise designs on reported feelings of dejection, F (2, 58) = 0.356, p > 0.05, ηp2 = 0.012. 3.2.4. Anger Time had a main effect on reported feelings of anger, F (1, 29) = 4.563, p < 0.05, ηp2 = 0.136 with participants feeling less angry after running (pre 0.161 ± 0.049; post 0.069 ± 0.024) regardless of design. There was no main effect for condition on reported feelings of anger, F (2, 58) = 0.190, p > 0.05, ηp2 = 0.047, which indicated that people report a similar level of anger across three designs. There were no interactions between time and exercise design on reported feelings of anger, F (2, 58) = 0.322, p > 0.05, ηp2 = 0.011. 3.2.5. Excitement Time had a main effect on reported feelings of excitement, F (1, 29) = 97.054, p < 0.05, ηp2 = 0.770. Participants felt more excited after running (pre 0.947 ± 0.092; post 1.906 ± 0.136) regardless of the condition. There was no main effect for condition on reported feelings of excitement, which indicated Int. J. Environ. Res. Public Health 2017, 14, 752 8 of 11 people reported similar levels of excitement across all designs (F (2, 58) = 0.459, p > 0.05, ηp2 = 0.016). There were no interactions between time and exercise condition on reported feelings of excitement, F (2, 58) = 1.318, p > 0.05, ηp2 = 0.043. 4. Discussion The aim of this paper was to examine physical and emotional effects of the design of different exercise environments, using preferred entertainment and the presence of nature (using a static and dynamic image), without imposing the same intensity levels of physical activity (PA) on all participants. For physical outcomes, the self-selected entertainment condition resulted in greater physical benefits. That is, participants ran longer distances with a higher heart rate (HR) value, compared to the nature-based exercise designs. Previous studies investigating the physical benefits of indoor exercise in the presence of nature have shown inconsistent findings, with some studies advocating enhanced physical effects, such as lowing perceived exertion [14,15] and blood pressure [12,13]. In contrast, research has shown no differences in energy expenditure [6,26], perceived exertion [26] and HR [12,26]. The varying benefits of green PA found in previous studies may be linked to the control conditions to which green PA was examined against. In the present study, by introducing a more ecological representative control conditions (i.e., self-selected entertainment) rather than imposing a less representative control condition, like asking participants to view a blank wall, we were able to examine the effects of introducing a nature-based environment compared a typical gym environment. As participants of gym-based PA would typically engage in the exercise experience using self-selected entertainment, rather than viewing urban images or a blank wall, our results suggested that, over longer running distances using self-selected entertainment could be beneficial if an individual’s main goal when exercising is to enhance physical performance. Although the findings revealed that the use of self-selected entertainment resulted in participants running farther than in the two nature designs, with a higher HR, it is worth noting that greater happiness was reported in the two nature-based exercise designs compared to the self-selected entertainment PA. All participants accrued emotional benefits with decreases in anger, dejection and anxiety and increased excitement after the run in all PA designs using indoor treadmill running. These findings suggested that nature-based exercise designs are just as effective as preferred exercise conditions with which participants were most familiar. The two nature-based designs showed stronger effects on happiness compared to self-selected entertainment conditions. The enhanced happiness scores observed in the nature-based PA designs indicate that using nature images for exercise is of some value, since if participants experience greater happiness after exercising they would be more likely to prolong exercise duration or benefit from exercise adherence [34]. A positive exercise experience is more likely to be associated with maintenance of future physical activity participation [35], which can also help in promoting physical activity. Inconsistent results in the literature might also be because of the use of different modes of PA (e.g., cycling and running), different exercise durations (e.g., 5 min, 15 min and 20 min) and different intensity levels (e.g., maintain 70–80 rpm or cycling at 50% personal peak power output). While the majority of previous studies controlled exercise intensity, based on each runner’s maximum energy output, we intentionally did not regulate the intensity of PA. Instead we designed a study which would allow us to find out how people interacted with different environmental designs by detecting information from a specific environment. An important consideration when interpreting the finding that the self-selected entertainment condition increased running distance compared to nature designs relates to the type of nature conditions presented to participants. In the static nature image condition, participants detected the same visual information with minor changes from the physical environment over twenty minutes. In this case, the same visual information from the static image might have become less functional, without providing further inspiration or encouragement for physical activity. This interpretation supports results of previous research which examined the physiological benefits of nature exposure during exercise and found the first 5 min was more efficient Int. J. Environ. Res. Public Health 2017, 14, 752 9 of 11 than the second 5 min in eliciting improvements in the recovery process following a stressor [39]. Participants might detect richer visual information from the dynamic images during running. However, the suitability of the information offered by the dynamic images might also need to be considered in further work on treadmill running [6]. The information perceived from the video might not have closely matched the physical task, i.e., treadmill running, as the recording was made while cycling in the park. This might have been a distraction for physical performance on a treadmill. Participants might have found that the richer information in the dynamic image condition lead to some dissonance between perception and action. In the self-selected entertainment condition, people chose acoustic or visual-acoustic information which constantly offered rich information acoustically and visually during the run. Further, the majority of self-selected entertainment chosen in this study was listening to music. Previous research has investigated the effects of different types of music (e.g., self-selected, motivational and simulative), demonstrating benefits, such as encouraging physical performances, enhancing enjoyment, reducing ratings of perceived exertion and improving energy efficiency [7,19,40–42]. The findings in our study, regarding better physical outcomes when running with self-selected entertainment, are aligned with previous research. Greater perceived happiness was found when people exercised in the nature-based designs compared to the self-preferred, familiar entertainment condition. This finding might be because these two nature-based exercise designs encouraged participants to engage more with the presented information, rather than focusing on physical performance and running. The exercise experience under the nature conditions might have been more dissociative, while running with music might be more associative in focusing on exercise intensity during PA [43]. Further investigations, involving interviewing participants post exercise, might be able to shed further light on this assumption. All participants experienced less anxiety, less dejection, more excitement and less anger in all three exercise designs after twenty minutes of running supports the notion that exercise has positive emotional benefits. Based on the results, the acoustic or visual-acoustic information in the self-selected entertainment condition aided runners’ physical performance outcomes while the visual nature-based information would be more beneficial to emotional wellbeing. With regards to the design of typical gym exercise conditions, there are different types of self-selected entertainment used in this study which might lead to different exercise outcomes. Future studies could consider focusing on entertainment choices in a highly specific way, without reducing the representative design of the research. Further studies could explore presentation of images from different types of nature spaces, such as beaches, oceans, and forest trails as exercise environments and different sources of information, e.g., nature sounds, could be influential and need to be examined. 5. Conclusions In conclusion, this study advances our understanding of the physical and emotional effects of different affordances in exercise designs for indoor treadmill running. However, there is much that still needs to be explored, such as different types of media or different contents of media, might accrue different effects among different age groups. Different methods, such as qualitative interviews, can also be used in future research to explore the data from a different perspective, such as the participants’ perspectives on engagement with the physical activity (PA) environment under different designs. Acknowledgments: The authors would like to thank the participants for their time, the anonymous reviewers for their comments. Author Contributions: Hsiao-Pu Yeh contributed to the concept, design, data collection, data analysis and writing of this research and manuscript. Joseph Stone, Sarah Churchill, Eric Brymer and Keith Davids contributed to the design, data analysis and writing of this research and manuscript. Conflicts of Interest: The authors declare no conflict of interest. Int. J. Environ. Res. Public Health 2017, 14, 752 10 of 11 References 1. World Health Organization. Global Recommendations on Physical Activity for Health; World Health Organization: Geneva, Switzerland, 2010. 2. Keith, D.; Duarte, A.; Brymer, E. Designing Affordances for Health-Enhancing Physical Activity and Exercise in Sedentary Individuals. Sports Med. 2016. [CrossRef] 3. Yeh, H.-P.; Stone, J.A.; Churchill, S.M.; Wheat, J.S.; Brymer, E.; Davids, K. Physical, psychological and emotional benefits of green physical activity: An ecological dynamics perspective. Sports Med. 2015. [CrossRef] [PubMed] 4. World Health Organization. Physical Activity Fact Sheet. Available online: http://www.who.int/ mediacentre/factsheets/fs385/en/ (accessed on 10 July 2017). 5. Sport England. Taking Part in Sport Once a Week—The Numbers. Available online: https://www. sportengland.org/media/10865/aps10q2summary.pdf (accessed on 6 July 2017). 6. Yeh, H.-P.; Stone, J.A.; Churchill, S.M.; Brymer, E.; Davids, K. Designing Physical Activity Environments to Enhance Physical and Psychological Effects. Procedia Eng. 2016. [CrossRef] 7. Bharani, A.; Sahu, A.; Mathew, V. Effect of passive distraction on treadmill exercise test performance in healthy males using music. Int. J. Cardiol. 2004. [CrossRef] [PubMed] 8. Tenenbaum, G.; Lidor, R.; Lavyan, N.; Morrow, K.; Tonnel, S.; Gershgoren, A.; Meis, J.; Johnson, M. The effect of music type on running perseverance and coping with effort sensations. Psychol. Sports Exerc. 2004, 5, 89–109. [CrossRef] 9. Edworthy, J.; Waring, H. The effects of music tempo and loudness level on treadmill exercise. Ergonomics 2006, 49, 1597–1610. [CrossRef] [PubMed] 10. De Vries, S.; Verheij, R.A.; Groenewegen, P.P.; Spreeuwenberg, P. Natural environments–healthy environments? An exploratory analysis of the relationship between greenspace and health. Environ. Plan. A. 2003, 35, 1717–1731. [CrossRef] 11. Joye, Y.; Bolderdijk, J. An exploratory study into the effects of extraordinary nature on emotions, mood, and prosociality. Front. Psychol. 2014. [CrossRef] [PubMed] 12. Duncan, M.J.; Clarke, N.D.; Birch, S.L.; Tallis, J.; Hankey, J.; Bryant, E.; Eyre, E.L. The effect of green exercise on blood pressure, heart rate and mood state in primary school children. Int. J. Environ. Res. PublicHealth 2014, 11, 3678–3688. [CrossRef] [PubMed] 13. Pretty, J.; Peacock, J.; Sellens, M.; Griffin, M. The mental and physical health outcomes of green exercise. Int. J. Environ. Health Res. 2005, 15, 319–337. [CrossRef] [PubMed] 14. Akers, A.; Barton, J.; Cossey, R.; Gainsford, P.; Griffin, M.; Micklewright, D. Visual Color Perception in Green Exercise: Positive Effects on Mood and Perceived Exertion. Environ. Sci. Technol. 2012, 46, 8861–8866. [CrossRef] [PubMed] 15. Wooller, J.J.; Barton, J.; Gladwell, V.F.; Micklewright, D. Occlusion of sight, sound and smell during Green Exercise influences mood, perceived exertion and heart rate. Int. J. Envoiron. Health Res. 2015, 26, 267–280. [CrossRef] [PubMed] 16. Pretty, J.; Peacock, J.; Hine, R.; Sellens, M.; South, N.; Griffin, M. Green exercise in the UK countryside: Effects on health and psychological well-being, and implications for policy and planning. J. Environ. Plan. Manag. 2007, 50, 211–231. [CrossRef] 17. Focht, B.C. Brief Walks in outdoor and laboratory environments: Effects on affective responses, enjoyment, and intentions to walk for exercise. Res. Q. Exerc. Sport 2009, 80, 611–620. [CrossRef] [PubMed] 18. Plante, T.G.; Gores, C.; Carrow, J.B.; Imbs, A.; Willemsen, E. Does Exercise Environment Enhance the Psychological Benefits of Exercise for Women? Int. J. Stress. Manag. 2007, 14, 88–98. [CrossRef] 19. Jones, L.; Karageorghis, C.I.; Ekkekakis, P. Can high-intensity exercise be more pleasant? Attentional dissociation using music and video. J. Sport Exerc. Psychol. 2014, 36, 528–541. [CrossRef] [PubMed] 20. Mackay, G.J.; Neill, J.T. The effect of “green exercise” on state anxiety and the role of exercise duration, intensity, and greenness: A quasi-experimental study. Psychol. Sport Exerc. 2010, 11, 238–245. [CrossRef] 21. Bratman, G.N.; Daily, G.C.; Levy, B.J.; Gross, J.J. The benefits of nature experience: Improved affect and cognition. Landsc. Urban Plan. 2015, 138, 41–50. [CrossRef] 22. Weng, P.Y.; Chiang, Y.C. Psychological restoration through indoor and outdoor leisure activities. J. Leis. Res. 2014, 46, 203–217. Int. J. Environ. Res. Public Health 2017, 14, 752 11 of 11 23. Marselle, M.R.; Irvine, K.N.; Warber, S.L. Walking for well-being: Are group walks in certain types of natural environments better for well-being than group walks in urban environments? Int. J. Environ. Res. Public Health 2013, 10, 5603–5628. [CrossRef] [PubMed] 24. Calogiuri, G.; Evensen, K.; Weydahl, A.; Andersson, K.; Patil, G.; Ihlebæk, C.; Raanaas, R.K. Green exercise as a workplace intervention to reduce job stress. Results from a pilot study. Work 2016, 53, 99–111. [CrossRef] [PubMed] 25. Shin, W.S. The influence of forest view through a window on job satisfaction and job stress. Scand. J. For. Res. 2007, 22, 248–253. [CrossRef] 26. Rogerson, M.; Barton, J.L. Effects of the visual exercise environments on cognitive directed attention, energy expenditure and perceived exertion. Int. J. Environ. Res. Public Health 2015, 12, 7321–7336. [CrossRef] [PubMed] 27. Wood, C.; Caroline, A.; Jules, P.; Gavin, S.; Jo, B. A randomised control trial of physical activity in a perceived environment on self-esteem and mood in UK adolescents. Int. J. Environ. Res. Public Health 2013, 23, 311–320. [CrossRef] [PubMed] 28. Bowler, D.E.; Buyung-ali, L.M.; Knight, T.M.; Pullin, A.S. A systematic review of evidence for the added benefits to health of exposure to natural environments. BMC Public Heaith 2010, 10, 456. [CrossRef] [PubMed] 29. Kaplan, R.; Kaplan, S. The Experience of Nature; Cambrige University Press: Cambridge, UK, 1989. 30. Bratman, G.N.; Hamilton, J.P.; Daily, G.C. The impacts of nature experience on human cognitive function and mental health. Ann. N. Y. Acad. Sci. 2012, 1249, 118–136. [CrossRef] [PubMed] 31. Johnson, A. The Biophilia Hypothesis; Stephen, R.K., Edward, O.W., Eds.; Island Press: Washington, DC, USA, 1994. 32. Gibson, E.J.; Pick, A.D. An Ecological Approach to Perceptual Learning and Development; Oxford University Press: Oxford, UK, 2000. 33. Heft, H.; Nasar, J.L. Evaluating environmental scenes using dynamic. Environ. Behav. 2000, 32, 301–322. [CrossRef] 34. Huberty, J.L.; Ransdell, L.B.; Sidman, C.; Flohr, J.A.; Shultz, B.; Grosshans, O.; Durrant, L. Explaining long-term exercise adherence in program explaining long-term exercise adherence in women. Res. Q. Exerc. Sport 2008, 79, 374–384. [CrossRef] [PubMed] 35. Williams, D.M.; Dunsiger, S.; Ciccolo, J.T.; Lewis, B.A.; Albrecht, A.E.; Marcus, B.H. Acute affective response to a moderate-intensity exercise stimulus predicts physical activity participation 6 and 12 months later. Psychol. Sport Exerc. 2008, 9, 231–245. [CrossRef] [PubMed] 36. Kilpatrick, M.W.; Kraemer, R.R.; Quigley, E.J.; Mears, J.L.; Powers, J.M.; Dedea, A.J.; Ferrer, N.F. Heart rate and metabolic responses to moderate-intensity aerobic exercise: A comparison of graded walking and ungraded jogging at a constant perceived exertion. J. Sports Sci. 2009, 27, 509–516. [CrossRef] [PubMed] 37. Jones, M.V.; Lane, A.M.; Bray, S.R.; Uphill, M.; Catlin, J. Development and validation of the Sport Emotion Questionnaire. J. Sport Exerc. Psychol. 2005, 27, 407–431. [CrossRef] 38. Arnold, R.; Fletcher, D. Confirmatory factor analysis of the Sport Emotion Questionnaire in organisational environments. J. Sports Sci. 2014, 414, 1–11. [CrossRef] [PubMed] 39. Brown, D.K.; Barton, J.L.; Gladwell, V.F. Viewing nature scenes positively affects recovery of autonomic function following acute-mental stress. Environ. Sci. Technol. 2013, 47, 5562–5569. [CrossRef] [PubMed] 40. Karageorghis, C.I.; Priest, D.L. Music in the exercise domain: A review and synthesis (Part I). Int. Rev. Sport Exerc. Psychol. 2012, 5, 44–66. [CrossRef] [PubMed] 41. Dyrlund, A.K.; Wininger, S.R. The effects of music preference and exercise intensity on psychological variables. J. Music Ther. 2008, 45, 114–134. [CrossRef] [PubMed] 42. Barwood, M.J.; Weston, N.J.V.; Thelwell, R.; Page, J. A motivational music and video intervention improves high-intensity exercise performance. J. Sport Sci. Med. 2009, 8, 435–442. 43. Maters, N.S.; Ogles, B.M. Associative and dissociate cognitive strategies in exercise and running: 20 years later, what do we know? Sport Psychol. 1998, 12, 253–270. [CrossRef] © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Physical and Emotional Benefits of Different Exercise Environments Designed for Treadmill Running.

07-11-2017

Yeh, Hsiao-Pu,Stone, Joseph A,Churchill, Sarah M,Brymer, Eric,Davids, Keith

eng

PMC10651037

RESEARCH ARTICLE Dose response of running on blood biomarkers of wellness in generally healthy individuals Bartek NogalID1☯, Svetlana Vinogradova1☯, Milena Jorge1, Ali Torkamani2,3, Paul Fabian1, Gil Blander1* 1 InsideTracker, Cambridge, Massachusetts, United States of America, 2 The Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, California, United States of America, 3 Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, United States of America ☯ These authors contributed equally to this work. * [email protected] Abstract Exercise is effective toward delaying or preventing chronic disease, with a large body of evi- dence supporting its effectiveness. However, less is known about the specific healthspan- promoting effects of exercise on blood biomarkers in the disease-free population. In this work, we examine 23,237 generally healthy individuals who self-report varying weekly run- ning volumes and compare them to 4,428 generally healthy sedentary individuals, as well as 82 professional endurance runners. We estimate the significance of differences among blood biomarkers for groups of increasing running levels using analysis of variance (ANOVA), adjusting for age, gender, and BMI. We attempt and add insight to our observa- tional dataset analysis via two-sample Mendelian randomization (2S-MR) using large inde- pendent datasets. We find that self-reported running volume associates with biomarker signatures of improved wellness, with some serum markers apparently being principally modified by BMI, whereas others show a dose-effect with respect to running volume. We fur- ther detect hints of sexually dimorphic serum responses in oxygen transport and hormonal traits, and we also observe a tendency toward pronounced modifications in magnesium sta- tus in professional endurance athletes. Thus, our results further characterize blood biomark- ers of exercise and metabolic health, particularly regarding dose-effect relationships, and better inform personalized advice for training and performance. Introduction Physical inactivity is one of the leading modifiable behavioral causes of death in the US [1]. Worldwide, physical inactivity is estimated to account for about 8.3% of premature mortality, an effect size that is on the same order as smoking and obesity [2]. At the same time, the potent health benefits of exercise have been proven time and time again, with results so consistent across a wide variety of chronic diseases that some posit it can be considered a medical inter- vention [3–5]. However, since most investigators report the effects of exercise in either PLOS ONE PLOS ONE | https://doi.org/10.1371/journal.pone.0293631 November 15, 2023 1 / 20 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Nogal B, Vinogradova S, Jorge M, Torkamani A, Fabian P, Blander G (2023) Dose response of running on blood biomarkers of wellness in generally healthy individuals. PLoS ONE 18(11): e0293631. https://doi.org/10.1371/journal. pone.0293631 Editor: Efrem Kentiba, Arba Minch College of Education, ETHIOPIA Received: August 9, 2023 Accepted: October 16, 2023 Published: November 15, 2023 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0293631 Copyright: © 2023 Nogal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All Mendelian Randomization data required to replicate the causal analysis can be freely accessed at: https://gwas. mrcieu.ac.uk/. No special access is required and all diseased populations or athletes [6, 7], there exists a significant gap in knowledge as to the measurable effects of exercise in the generally healthy population who exercise for the purpose of improving their healthspan, which can be projected via established measures such as blood biomarkers [8–11]. It is well established that routine laboratory biomarkers are validated proxies of the state of an individual’s overall metabolic health and other healthpan-related parameters [12]. A large body of evidence supports the effectiveness of exercise in modifying blood biomarkers toward disease mitigation in clinical cohorts as well as athletes, where the effect sizes may be larger [6, 13]. Indeed, it’s been shown that more favorable changes in response to exercise training occur usually in those with more pronounced dyslipidemia [13]. In professional athletes, the sheer volume and/or intensity of physical activity may drive large effects in various hematological, lipid, immune, and endocrine variables [6]. Our aim is to help fill the gap in understanding of the effects of exercise on blood biomarkers in the generally healthy, free-living population. Toward this end, we endeavored to explore the effects of vigorous exercise such as running in apparently healthy, mostly non-athletic cohort to better understand the landscape of blood biomarker modifications expected in the individual who partakes in recreational physical activity for the purpose of maintaining good health. For this purpose, we leveraged the InsideTracker dataset that includes information on self- reported exercise habits combined with blood biomarker and genomics data. We have previ- ously reported on the results of a longitudinal analysis on blood biomarker data from 1032 generally healthy individuals who used our automated, web-based personalized nutrition and lifestyle platform [14]. For the purpose of this investigation, we focused on running as the exer- cise of choice as it is one of the most common (purposeful) physical activity modalities prac- ticed globally by generally healthy individuals and would thus be relevant. Moreover, since this was a cross-sectional study based on self-reported exercise habits, we attempted to increase our capacity to infer intervention effects, as well as tease out potential confounders, by per- forming 2S-MR in large independent cohorts. Materials and methods Dataset We conducted an observational analysis of data from InsideTracker users. InsideTracker is a direct-to-consumer (DTC) company established in 2009 that markets and sells InsideTracker (insidetracker.com), a personalized lifestyle recommendation platform. The platform provides serum biomarker and genomics testing, and performs integrative analysis of these datasets, combined with activity/sleep tracker data toward biomarker and healthspan optimization (of note, at the time of this analysis, we did not have sufficient users with activity/sleep tracker data to include this data stream in the current study). New users were continuously added to the InsideTracker database from January 2011 to March 2022. Recruitment of participants Recruitment of participants aged between 18 and 65 and residing in North America was con- ducted through company marketing and outreach. Participants were subscribing members to the InsideTracker platform and provided informed consent to have their blood test data and self-reported information used in an anonymized fashion for research purposes. Research was conducted according to guidelines for observational research in tissue samples from human subjects. Eligible participants completed a questionnaire that included age, ethnicity, sex, die- tary preferences, physical activity, and other variables. This study employed data from 23,237 participants that met our analysis inclusion requirements, namely absence of any chronic PLOS ONE Biomarker signature of runners PLOS ONE | https://doi.org/10.1371/journal.pone.0293631 November 15, 2023 2 / 20 the datasets used can be further freely accessed via the free "TwoSampleMR" R package as described in the methods (biomarker dataset codes are shared in Supplementary tables as well). The minimal dataset required to replicate the blood biomarker results has been uploaded as Supporting information. Funding: InsideTracker was the sole funding source. The funder provided support in the form of salaries for authors B.N., S.V., P.F., M.J., and G.B., and was involved in the decision to publish, but did not have an impact on the experimental design, data analysis, and conclusions. Competing interests: B.N., S.V., P.F., and G.B. are employees of InsideTracker. This does not alter our adherence to PLOS ONE policies on sharing data and materials. disease as determined by questionnaire and metabolic blood biomarkers within normal clini- cal reference ranges. The platform is not a medical service and does not diagnose or treat med- ical conditions, so medical history and medication use were not collected. The Institutional Review Board (IRB) determine this work was not subject to a review based on category 4 exemption (“secondary research” with de-identified subjects). Biomarker collection and analysis Blood samples were collected and analyzed by Clinical Laboratory Improvement Amendments (CLIA)–approved, third-party clinical labs (primarily Quest Diagnostics and LabCorp). Partic- ipants were instructed to fast for 12 hours prior to the phlebotomy, with the exception of water consumption. Results from the blood analysis were then uploaded to the platform via elec- tronic integration with the CLIA-approved lab. Participants chose a specific blood panel from 7 possible offerings, each comprising some subset of the biomarkers available. Due to the vari- ation in blood panels offered, the participant sample size per biomarker is not uniform. Biomarker dataset preparation In our raw dataset, occasional outlier values were observed that were deemed implausible (e.g. fasting glucose < 65 mg/dL). To remove anomalous outliers in a systematic way, we used the Interquartile Range (IQR) method of identifying outliers, removing data points which fell below Q1–1.5 IQR or above Q3 + 1.5 IQR. The cohort was divided into five groups: profes- sional endurance runners (PRO), high volume amateur (>10 h/week, HVAM), medium vol- ume amateur (3–10 h/week, MVAM), low volume amateur (<3 h/week, LVAM), and sedentary (SED). Calculation of polygenic scores The variants (SNPs) comprising the polygenic risk scores were derived from publicly available GWAS summary statistics (https://www.ebi.ac.uk/gwas/). Scores were calculated across users by summing the product of effect allele doses weighted by the beta coefficient for each SNP, as reported in the GWAS summary statistics. Variant p-value thresholds were generally chosen based on optimization of respective PGS-blood biomarker correlation in the entire Inside- Tracker cohort with both blood and genomics datasets (~1000–1500 depending on the blood biomarker at the time of analysis). Genotyping data was derived from a combination of a cus- tom InsideTracker array and third party arrays such as 23andMe and Ancestry. Not all variants for any particular PGS were genotyped on every array; proxies for missing SNPs were extracted via the “LDlinkR” package using the Utah Residents (CEPH) with Northern and Western European ancestry (CEU) population (R2 > 0.8 cut-off). Only results PGSs for which there was sufficient biomarker-genotyping dataset overlap were reported (note that none of the blood biomarker PGSs met this requirement). Blood biomarker analysis with respect to running volume and polygenic scores To estimate significance of differences for blood biomarkers levels among exercise groups, we performed 3-way analysis of variance (ANOVA) analysis adjusting for age, gender, and BMI (type-II analysis-of-variance tables function ANOVA from ‘car’ R package, version 3.0–12). When estimating the effort of reported training volume on biomarkers, we assigned numerical values corresponding to 4 levels of running and performed ANOVA analysis with those levels treating it as an independent variable. P-values were adjusted using the Benjamini & Hochberg PLOS ONE Biomarker signature of runners PLOS ONE | https://doi.org/10.1371/journal.pone.0293631 November 15, 2023 3 / 20 method [15]. P-values for interaction plots were calculated with ANOVA including interaction between exercise group and polygenic scores category. When comparing runners (PRO and HVAM combined) versus sedentary individuals, we used propensity score matching method to account for existing covariates (age and gender): we identified 745 sedentary individuals with similar to runners’ age distributions among both males and females. We used ‘MatchIt’ R package (version 4.3.3) implementing nearest neighbor method for matching [16]. Mendelian randomization We attempted to add insight around the causality of exercise vs. BMI differences with respect to serum marker improvement by performing MR analyses on a subset of biomarker observa- tions where BMI featured as a strong covariate and was thus used as the IV in the 2S-MR. Thus, our hypothesis here was that BMI differences were the primary (causal) driver behind the improvement behind some biomarkers. MR uses genetic variants as modifiable exposure (risk factor) proxies to evaluate causal relationships in observational data while reducing the effects of confounders and reverse causation (S1 Fig in S1 File). These SNPs are used as instru- mental variables and must meet 3 basic assumptions: (1) they must be robustly associated with the exposure; (2) they must exert their effect on outcome via the exposure, and (3) there must be no unmeasured confounders of the associations between the genetic variants and outcome (e.g. horizontal pleiotropy) [17]. Importantly, SNPs are proper randomization instruments because they are determined at birth and thus serve as proxies of long-term exposures and can- not, in general, be modified by the environment. If the 3 above mentioned assumptions hold, MR-estimate effects of exposure on outcomes are not likely to be significantly affected by reverse causation or confounding. In the 2S-MR performed here, where GWAS summary sta- tistics are used for both exposure and outcome from independent cohorts, reverse causation and horizontal pleiotropy can readily be assessed, and weak instrument bias and the likelihood of false positive findings are minimized as a result of the much larger samples sizes [17]. Indeed, the bias in the 2S-MR using non-overlapping datasets as performed here is towards the null [17]. Furthermore, to maintain the SNP-exposure associations and linkage disequilib- rium (LD) patterns in the non-overlapping populations we used GWAS datasets from the MR-Base platform that were derived from ancestrally similar populations (“ukb”: analysis of UK Biobank phenotypes, and “ieu”: GWAS summary datasets generated by many different European consortia). To perform the analysis we used the R package “TwoSampleMR” that combines the effects sizes of instruments on exposures with those on outcomes via a meta- analysis. We used “TwoSampleMR” package functions for allele harmonization between expo- sure and outcome datasets, proxy variant substitution when SNPs from exposure were not genotyped in the outcome data (Rsq>0.8 using the 1000G EUR reference data integrated into MR-Base), and clumping to prune instrument SNPs for LD (the R script used for MR analyses is available upon request). We used 5 different MR methods that were included as part of the “TwoSampleMR” package to control for bias inherent to any one technique [18]. For example, the multiplicative random effects inverse variance-weighted (IVW) method is a weighted regression of instrument-outcome effects on instrument-exposure effects with the intercept is set to zero. This method generates a causal estimate of the exposure trait on outcome traits by regressing the, for example, SNP-BMI trait association on the SNP-biomarker measure associ- ation, weighted by the inverse of the SNP-biomarker measure association, and constraining the intercept of this regression to zero. This constraint can result in unbalanced horizontal pleiotropy whereby the instruments influence the outcome through causal pathways distinct from that through the exposure (thus violating the second above-mentioned assumption). Such unbalanced horizontal pleiotropy distorts the association between the exposure and the PLOS ONE Biomarker signature of runners PLOS ONE | https://doi.org/10.1371/journal.pone.0293631 November 15, 2023 4 / 20 outcome, and the effect estimate from the IVW method can be exaggerated or attenuated. However, unbalanced horizontal pleiotropy can be readily assessed by the MR Egger method (via the MR Egger intercept), which provides a valid MR causal estimate that is adjusted for the presence of such directional pleiotropy, albeit at the cost of statistical efficiency. Finally, to ascertain the directionality of the various causal relationships examined, we also performed each MR analysis in reverse where possible. Results Study population characteristics Table 1 shows the demographic characteristics of the study population. We observed a signifi- cant trend toward younger individuals reporting higher running volume, with more than 75% of the professional (PRO) group falling between the ages of 18 and 35 (S1 Table in S1 File). Sig- nificant differences were also observed in the distribution of males and females within study groups (Table 1). Moreover, higher running volume associated with significantly lower body mass index (BMI). Thus, moving forward, combined comparisons of blood biomarkers as they relate to running volume were adjusted for age, gender, and BMI. Endurance exercise exhibits a modest association with clusters of blood biomarker features In order to begin to understand the most important variables that may associate with endur- ance exercise in the form of running, we performed a principal component analysis (PCA), sub-dividing the male and female cohorts into two most divergent groups in terms of exercise volume: PRO/high volume amateur (HVAM) and sedentary (SED) groups. Using propensity matching, PRO and amateur athletes who reported running >10h per week were combined into the PRO-HVAM group to balance out the sample size between the exercising and non- exercising groups. This approach yielded a modest degree of separation, with hematological, inflammation, and lipid features, as well as BMI explaining some of the variance (Fig 1A through 1D). We hypothesized that there may more subtle relationships between running vol- ume and the blood biomarker features that contributed to distinguishing the endurance exer- cise and sedentary groups, thus we next performed ANOVA analyses stratified by running volume as categorized in Table 1. Significant trends in glycemic, hematological, blood lipid, and inflammatory serum traits with increasing running volumes Weighted ANOVA analyses adjusted for age, gender, and BMI showed significant differences among groups for multiple blood biomarkers (Table 2 and S2 Table in S1 File, Figs 2 and 3). Table 1. Study population demographics. Group N Female, % Age, yrs Body mass index, kg/m2 PRO 82 53.7% 33.68 20.15 ± 6.02 HVAM 1103 52.9% 39.48 22.57 ± 9.97 MVAM 6747 54.2% 41.49 23.35 ± 9.76 LVAM 10877 34.2% 41.16 24.72 ± 9.70 SED 4428 48.9% 44.25 27.83 ± 10.70 PRO = Professional, HVAM = high volume amateur (>10 h/week), MVAM = medium volume amateur (3–10 h/week), LVAM = low volume amateur (<3 h/week), SED = sedentary https://doi.org/10.1371/journal.pone.0293631.t001 PLOS ONE Biomarker signature of runners PLOS ONE | https://doi.org/10.1371/journal.pone.0293631 November 15, 2023 5 / 20 We observed a trend toward lower HbA1c, hsCRP, RDW, WBC, ferritin, gamma-glutamyl transferase (GGT), and LDL. HDL, hemoglobin (Hb), transferrin saturation (TS), alanine ami- notransferase (ALT), aspartate aminotransferase (AST), vitamin B12, folate, 25-hydroxy vita- min D, and creatine kinase (CK) tended to be higher with increasing reported training volume, particularly in PRO runners (Table 2 and S2 Table in S1 File, Figs 2 and 3 and S2 Fig in S1 File). Hct and Hb were higher only in PRO males, whereas increased running volume associated with upward trend in these biomarkers in females (Fig 3A and 3B). Increased run- ning volume was associated with markedly lower Fer in males, whereas female runners did not exhibit varying levels, and SED females showed increased levels (Fig 3C). The low ferritin observed in male and female runners was not clinically significant. ALT positively associated with running volume in females only (S2 Fig in S1 File). Serum and RBC magnesium (Mg) were both significantly lower in PRO runners relative to all other groups (Table 2 and Fig 3D Fig 1. Principal component analysis and variables plots of PRO-HVAM runners and sedentary user blood biomarkers. Females, (A) and (B); males (C) and (D). PRO-HVAM = combined professional and high-volume amateur. Alb = albumin, ALT = alanine transaminase, AST = aspartate aminotransferase, B12 = vitamin B12, Ca = calcium, Chol = total cholesterol, CK = creatine kinase, Cor = cortisol, FE = iron, EOS_PCT = eosinophil percentage, Fer = ferritin, Fol = folate, FT = free testosterone, GGT = gamma-glutamyl transferase, Glu = glucose, Hb = hemoglobin, HCT = hematocrit, HDL = high density lipoprotein, HbA1c = glycated hemoglobin, hsCRP = high-sensitivity C-reactive protein, LDL = low density lipoprotein, LYMPS_PCT = lymphocyte percentage, MCH = mean cell hemoglobin, Mg = magnesium, MONOS_PCT = monocytes percentage, MPV = mean platelet volume, Na = sodium, RBC = red blood cells, RBC_Mg = red blood cell magnesium, RDW = red blood cell distribution width, SHBG = sex hormone binding globulin, Tg = triglycerides, TIBC = total iron binding capacity, WBC = white blood cells. https://doi.org/10.1371/journal.pone.0293631.g001 PLOS ONE Biomarker signature of runners PLOS ONE | https://doi.org/10.1371/journal.pone.0293631 November 15, 2023 6 / 20 and 3E). Increasing levels of endurance exercise also appeared to be associated with higher sex-hormone binding globulin (SHBG), particularly in PRO male runners (Fig 3F). Endurance exercise correlates with lower BMI across categories of genetic risk Using publicly available GWAS summary statistics, we constructed blood biomarker polygenic risk scores (PGSs) to explore potential genetic risk-mitigating effects of endurance exercise. Since only a subset of the individuals in our cohort were genotyped, we aggregated the groups into 2 categories—PRO-HVAM and sedentary—to increase statistical power. This across- group sample size increase generally did not sufficiently power the ANOVA analysis to detect statistically significant trends, though the BMI polygenic risk was suggestively mitigated for both males and female PRO-HVAM runners across categories of genetic risk (Fig 4B). Increased running volume is associated with lower BMI which may drive biomarker changes We observed a significant downward trend in the BMI with increased running volume for both males and females, and, although some of the biomarker differences between sedentary Table 2. Blood biomarkers significantly different among sedentary individuals and those who partake in running for exercise to various degrees. BIOMARKER ANOVA P-VALUE TREND P-VALUE LOWEST MEAN HIGHEST MEAN ALB <1e-16 <0.001 MVAM PRO ALT <1e-16 <1e-16 SED PRO AST <1e-16 <0.001 SED PRO B12 <0.001 <0.001 SED PRO CHOL <0.001 0.005 PRO SED CK <1e-16 <1e-16 SED PRO COR <0.001 0.675 SED PRO FE <0.001 0.119 SED PRO FER <1e-16 <1e-16 MVAM SED FOL <1e-16 <0.001 SED PRO FT <0.001 0.013 SED PRO GGT <1e-16 <0.001 PRO SED GLU 0.087 0.184 PRO SED HB 0.002 <0.001 MVAM PRO HCT 0.053 0.055 MVAM PRO HDL <1e-16 <0.001 SED PRO HBA1C <0.001 0.010 PRO SED HSCRP <0.001 0.176 PRO SED LDL <0.001 0.006 PRO SED MG <0.001 0.276 PRO SED MPV 0.058 0.089 SED HVAM NA <1e-16 0.622 HVAM SED RBC_MG <0.001 0.773 PRO SED RDW <1e-16 0.002 PRO SED SHBG <1e-16 0.004 SED PRO TG <1e-16 <1e-16 PRO SED WBC <1e-16 <1e-16 PRO SED https://doi.org/10.1371/journal.pone.0293631.t002 PLOS ONE Biomarker signature of runners PLOS ONE | https://doi.org/10.1371/journal.pone.0293631 November 15, 2023 7 / 20 and exercising individuals remained significant after adjustment for BMI, their significance was attenuated (Fig 4A). Thus, we hypothesized that BMI may be driving a significant portion of the observed variance in some of the biomarkers across the groups. Thus, to explore causal relationships between weight and biomarker changes, we performed 2S-MR with BMI-associ- ated single-nucleotide polymorphisms (SNPs) as the instrumental variables (IVs) for a subset of the healthspan-related biomarkers where BMI explained a relatively large portion of the Fig 2. Blood biomarkers associated with running: Inflammation proxies, (A) hsCRP = high-sensitivity C-reactive protein and (B) WBC = white blood cells; blood lipids, (C) HDL = high density lipoprotein (D) LDL = low density lipoprotein, and (E) Tg = triglycerides; glycemia proxies, (F) Glu = glucose and (G) HgbA1c = glycated hemoglobin, and (H) Cor = cortisol. https://doi.org/10.1371/journal.pone.0293631.g002 PLOS ONE Biomarker signature of runners PLOS ONE | https://doi.org/10.1371/journal.pone.0293631 November 15, 2023 8 / 20 Fig 3. Blood biomarkers associated with running: (A and B) Hb (hemoglobin) and Hct (hematocrit) increase with increasing running volume, (C) Fer (ferritin) is reduced with increasing running volume, (D and E) Serum and RBC Mg (red blood cell magnesium) are reduced in professional runners, and (F) SHBG (sex hormone binding globulin) levels increase with increasing running volume in males. https://doi.org/10.1371/journal.pone.0293631.g003 Fig 4. BMI significantly varied among running groups (A) with some suggestive effects on BMI PGS modification (total number for observations (N) for T1, T2, and T3 were 87, 84, and 100, respectively) (B) T1, T2, and T3 = 1st, and 2nd and 3rd tertials of the polygenic score distribution. https://doi.org/10.1371/journal.pone.0293631.g004 PLOS ONE Biomarker signature of runners PLOS ONE | https://doi.org/10.1371/journal.pone.0293631 November 15, 2023 9 / 20 variance in our analysis. In general, these blood biomarkers associated with inflammation (hsCRP and RDW), lipid metabolism (Tg and HDL), glycemic control (HbA1c and Glu), as well as Alb and SHBG. We used GWAS summary statistics and found that most of these BMI- blood biomarker relationships examined directionally aligned with our study (except for LDL), and some were indicative of causal relationships in the BMI-biomarker direction even after considering directional pleiotropy (S3 Table in S1 File). We entertained the possibility of reverse causality and thus repeated the 2S-MR using each of the biomarker levels as the expo- sure and BMI as the outcome, and the results were generally not significant (except for WBC– see S4 Table in S1 File). Of note, to estimate the direct causal effects of running on blood parameters, we attempted to find an instrumental variable for to approximate running as the exposure from publicly available GWAS summary statistics. Toward this end, we found that increasing levels of vigorous physical activity did associate with lower hsCRP, HbA1C, higher HDL, and possibly higher SHBG (although the explained variance (R2) in this exposure was just 0.001009, the F statistic was 37.7, thus meeting the criteria of F > 10 for minimizing weak instrument bias) (Fig 5 and S3 Fig in S1 File; S5 Table in S1 File). Vigorous physical activity associates with healthier behaviors We hypothesized that those who exercise regularly may also partake in other healthful lifestyle habits that may be contributing to more optimal blood biomarker signatures of wellness. How- ever, our dataset did not allow for systematic accounting of other lifestyle habits across all run- ning groups. Thus, we again leveraged the potential of the 2S-MR approach to inform potential confounding associations between modifiable exposures and found that vigorous physical activity such as running is at least suggestively associated with several behaviors asso- ciated with improved health (S4 Fig in S1 File). Our analysis showed that those who participate in increasing levels of vigorous physical activity may be less likely to eat processed meat (IVW p = 0.0000013), sweets (IVW p = 0.32), and nap during the day (IVW p = 0.13), while increas- ing their intake of oily fish (IVW p = 0.029), salad/raw vegetable intake (IVW p = 0.00016), and fresh fruit (IVW p = 0.0027) (S6 Table in S1 File). Furthermore, following our assessment Fig 5. Two-sample Mendelian randomization shows that increasing levels of vigorous physical activity such as running is associated with improvement of (A) hsCRP = high-sensitivity C-reactive protein, (B) HDL = high density lipoprotein, and (C) HbA1c = glycated hemoglobin levels. https://doi.org/10.1371/journal.pone.0293631.g005 PLOS ONE Biomarker signature of runners PLOS ONE | https://doi.org/10.1371/journal.pone.0293631 November 15, 2023 10 / 20 of reverse causality, we found evidence for the bidirectionality in the causal relationship between vigorous activity and napping during the day and salad/raw vegetable intake, perhaps suggesting some degree of confounding due to population stratification (S7 Table in S1 File). The suggestive positive effect of fresh fruit and processed meat intake on vigorous physical activity appeared to violate MR assumption (3) (S1 Fig in S1 File) (horizontal pleiotropy p-val- ues 0.051 and 0.17, respectively–S5 Fig in S1 File). Discussion In this report, we describe the variance in wellness-related blood biomarkers among self- reported recreational runners, PRO runners, and individuals who do not report any exercise. Overall, we find that 1) recreational running as an exercise appears to be an effective interven- tion toward modifying several biomarkers indicative of improved metabolic health, 2) an apparent dose-response relationship between running volume and BMI may itself be responsi- ble for a proportion of the apparent metabolic benefits, and 3) both PRO-level status and gen- der appear to associate with heterogeneous physiological responses, particularly in iron and magnesium metabolism, as well as some hormonal traits. Self-reported running improves glycemia and lipidemia We did not observe distinct clusters corresponding to self-reported high-volume/PRO runners and the sedentary upon dimension reduction. This is, perhaps, not unexpected due, in part, to the self-selected healthspan-oriented nature of our cohort, where even the sedentary subset of individuals tends to exhibit blood biomarker levels in the normal clinical reference ranges. Furthermore, the measurement of running volume via self-report may be vulnerable to overes- timation, which may have contributed to the blending of sedentary and exercise groups with respect to the serum markers measured, resulting in only marginal separation between the groups [19, 20]. However, we did observe significant individual blood biomarker variance with respect to reported running volumes when the dataset was subjected to ANOVA, even after adjustment for age, sex, and BMI. From among glycemic control blood biomarkers, we were able to detect a relatively small exercise effect in both fasting glucose and HbA1c in this generally healthy cohort, where the average measures of glycemia were below the prediabetic thresholds in even the sedentary subset of the cohort. Larger exercise intervention effects on metabolic biomarkers may be expected in cohorts that include individuals with more clinically significant baseline values [21]. Similarly, blood lipids improved with higher self-reported running volume, and this result has been reported before in multiple controlled endurance exercise trials [22]. The literature indicates that HDL and Tg are two exercise-modifiable blood lipid biomarkers, with HDL being the most widely reported to be modified by aerobic exercise [23, 24]. Although the mechanism behind this is not entirely clear, it likely involves the modification of lecithin acyltransferase and lipoprotein lipase activities following exercise training [25]. We observed a similar trend in our blood biomarker analysis, with HDL exhibiting an upward trend with increasing reported running volume. While we also found Tg and LDL to decrease with increasing exercise volume, these trends were less pronounced. Reports generally suggest that, in order to reduce LDL more consistently, the intensity of aerobic exercise must be high enough [23]. In the case of Tg, baseline levels may have a significant impact on the exercise intervention effect, with individuals exhibiting higher baselines showing greater improve- ments [13]. PLOS ONE Biomarker signature of runners PLOS ONE | https://doi.org/10.1371/journal.pone.0293631 November 15, 2023 11 / 20 Importantly, these results suggests that exercise has a significant effect on glycemic control and blood lipids even in the self-selected, already healthy individuals who are proactive about preventing cardiometabolic disease. Self-reported running and serum proxies of systemic inflammation Chronic low-grade inflammation is one of the major risk factors for compromised cardiovas- cular health and metabolic syndrome (MetS). While there is no shortage of inflammation- reducing intervention studies on CVD patients with clinically high levels of metabolic inflam- mation, there is less emphasis on modifiable lifestyle factors that can help stave off CVD and extend healthspan in the generally healthy individual. Indeed, considering the pathological cardiovascular processes begin shortly after birth, prevention in asymptomatic individuals may be a more appropriate strategy toward decreasing the burden of CVD on the healthcare system [26]. Toward this end, increasing self-reported running volume appeared to associate with improved markers of inflammation, as shown by the lower levels of hsCRP, WBC, as well as ferritin. Of note, while the acute-phase protein, ferritin, is often used in the differential diagno- sis of iron deficiency anemia, the biomarker’s specificity appears to depend on the inflamma- tory state of the individual, as it associates with hsCRP and inflammation more than iron stores, particularly in those with higher BMI [27]. Although serum ferritin and iron is reported to be lower in male and female elite athletes [28], the observed overall negative association of ferritin with increased running volume in our cohort may be an indication of lower levels of inflammation rather than compromised iron stores, particularly since the average ferritin level across all groups was above the clinical iron deficiency thresholds. Moreover, increased levels of ferritin have been associated with insulin resistance and lower levels of adiponectin in the general population, both indicators of increased systemic inflammation [29]. Here, exercising groups with lower levels of ferritin also exhibited glycemic and blood lipid traits indicative of improved metabolic states, further supporting ferritin’s role as an inflammation proxy. Finally, Hb, TS and iron tended to be higher in those who run for exercise compared to the SED group (with the TIBC lower), again suggesting that runners, including the PRO group, were iron- sufficient in this cohort. PRO endurance runners exhibit distinct biomarker signatures PRO athletes exhibited lower serum and RBC Mg, which may be indication of the often- reported endurance athlete hypomagnesaemia [30]. While the serum Mg was still within nor- mal clinical reference range for both PRO female and male athletes, RBC Mg, a more sensitive biomarker of Mg status [31], was borderline low in female PRO athletes and might suggest suboptimal dietary intakes and/or much higher volume of running training compared to the other running groups (i.e. >>10h /week). Indeed, this group also had elevated baseline CK and AST, which suggests a much higher training intensity and/or volume. Moreover, PRO level athletes had adequate iron status and serum B12 and folate in the upper quartile of the normal reference range, suggesting that these athletes’ general nutrition status may have been adequate. These observations suggest that elite endurance runners may need to pay particular attention to their magnesium status. Further, we observed higher levels of SHBG in PRO male runners, a biomarker whose levels positively correlate with various indexes of insulin sensitivity [32]. However, since the average SHBG levels in the SED group were not clinically low in both sexes, the observed increase in SHBG levels induced by running in males may be a catabolic response, as cortisol levels in this group were also higher. Indeed, Popovic et al. have shown that endurance exercise may PLOS ONE Biomarker signature of runners PLOS ONE | https://doi.org/10.1371/journal.pone.0293631 November 15, 2023 12 / 20 increase SHBG, cortisol, and total testosterone levels at the expense of free testosterone levels [33]. This could perhaps in part be explained by higher exercise-induced adiponectin levels, which have been shown to increase SHBG via cAMP kinase (AMPK) activation [34]. However, since our data is observational, we cannot rule out overall energy balance as a significant con- tributor to SHBG levels. For example, caloric restriction (CR) has been shown to result in higher SHBG and cortisol levels [32]. Finally, regarding the abovementioned PRO group elevated AST and CK biomarkers, evi- dence suggests that normal reference ranges in both CK and AST in well-recovered athletes should be adjusted up, as training and competition have a profound, non-pathological, impact on the activity of these enzymes [35, 36]. Indeed, the recommendation appears to be not to use reference intervals derived from the general population with hard-training (particularly com- petitive) athletes [36]. Effect of BMI on blood biomarkers Since the current study is a cross-sectional analysis of self-reported running, we could not rule out the possibility that factors other than exercise were the driving force behind the observed biomarker variance among the groups examined. These factors, such as diet, sleep, and/or medications were not readily ascertained in this free-living cohort at the time of this study, but BMI was readily available to evaluate this biomarker’s potential relative contribution to the observed mean biomarker differences among self-reported runner groups. Multiple studies have attempted to uncouple the effects of exercise and BMI reduction on blood biomarker outcomes, with mixed results [37]. For example, it is relatively well-known that acute bouts of exercise improve glucose metabolism, but long-term effects are less well described [38]. Indeed, whether exercise without significant weight-loss is effective toward preventing metabolic disease (and the associated blood biomarker changes) is inconclusive [39–41]. From the literature, it appears that, for endurance exercise to have significant effect on most blood biomarkers, the volume of exercise needs to be very high, and this typically results in significant reduction in weight. Thus, in practice, it is difficult to demonstrably uncouple the effects of significant exercise and the associated weight-loss, and the results may depend on the blood biomarker in question. Indeed, there is evidence that exercise without weight-loss does improve markers of insulin sensitivity but not chronic inflamma- tion, with the latter apparently requiring a reduction in adiposity in the general population [39–41]. In our study of apparently healthy individuals, we observed a downward trend in BMI with increasing self-reported running volume, and, although this study was not longitudinal and we are thus unable to claim weight-loss, our 2S-MR analysis using BMI as the exposure nonethe- less suggests this biomarker to be responsible for a significant proportion of the modification of some blood biomarkers. Serum markers of systemic inflammation. Through our 2S-MR analyses, we show that BMI is causally associated with markers of systemic inflammation, including RDW, folate, and hsCRP [27, 42, 43]. Similar analyses have reported that genetic variants that associate with higher BMI were associated with higher CRP levels, but not the other way around [44]. The prevailing mechanism proposed to explain this relationship appears to be the pathologi- cal nature of overweight/obesity-driven adipose tissue that results in secretion of proinflam- matory cytokines such as IL-6 and TNFa, which then stimulate an acute hepatic response, resulting in increased hsCRP levels (among other effects) [45]. Thus, our 2S-MR analyses and those of others [44] would indicate that the primary factor behind the lower systemic inflammation in our cohort may be the exercise-associated lower BMI and not running PLOS ONE Biomarker signature of runners PLOS ONE | https://doi.org/10.1371/journal.pone.0293631 November 15, 2023 13 / 20 exercise per se, though the lower hsCRP in runners remained significant after adjustment for BMI in our analysis. Indeed, although a major driver behind reduced systemic inflammation may be a reduction in BMI in the general population, additive effects of other lifestyle factors such as exercise can- not be excluded. For example, a large body of cross-sectional investigations does indicate that physically active individuals exhibit CRP levels that are 19–35% lower than less active individu- als, even when adjusted for BMI as was the case in the current analysis [41]. Further, it’s been reported that physical activity at a frequency of as little as 1 day per week is associated with lower CRP in individuals who are otherwise sedentary, while more frequent exercise further reduces inflammation [41]. Significantly, our entire cohort of self-selected apparently healthy individuals did not exhibit clinically high hsCRP, with average BMI also below the overweight thresholds. Because all subjects were voluntarily participating in a personalized wellness platform intended to opti- mize blood biomarkers that included hsCRP, it is possible that some individuals from across the study groups (both running and sedentary) in our cohort partook in some form of inflam- mation-reducing dietary and/or lifestyle-based intervention. Thus, that we detected a signifi- cant difference in hsCRP between exercising and non-exercising individuals in this self- selected already generally healthy cohort may be suggestive of the potential for additional pre- ventative effect of scheduled physical activity on low-grade systemic inflammation in the gen- erally healthy individual. Blood lipids. Controlled studies that tightly track exercise and the associated adiposity reduction have reported that body fat reduction (and not improvement in fitness as measured via VO2max) is a predictor of HDL, LDL, and Tg [46]. Similarly, though BMI is an imperfect measure of adiposity, our 2S-MR analysis suggests that this biomarker is causally associated with improved levels of HDL and Tg, though not LDL. This latter finding replicates a report by Hu et al. who, using the Global Lipids Genetics Consortium GWAS summary statistics, applied a network MR approach that revealed causal associations between BMI and blood lip- ids, where Tg and HDL, but not LDL, were found to trend toward unhealthy levels with increasing adiposity [47]. On the other hand, others implemented a robust BMI genetic risk score and demonstrated a causal association of adiposity with peripheral artery disease and a multiple linear regression showed a strong association with HDL, TC, and LDL, among other metabolic parameters [48]. In our cohort, given the lack of evidence for a causal BMI-LDL association and the overall healthy levels of BMI, the observed a significant improvement in LDL may be a result of marked running intensity and/or volume, possibly combined with the aforementioned additional wellness program intervention variables. Hormonal traits. As described above, we observed a trend toward increased plasma corti- sol and SHBG in runners, particularly PRO level athletes. The effects on cortisol are consistent with a report by Houmard et al., who found male distance runners to exhibit higher levels of baseline cortisol [49]. With respect to the effects of BMI on baseline cortisol levels, this obser- vation is generally supported by our 2S-MR analyses with evidence for a consistent effect of increased cortisol with decreasing BMI. However, this association was suggestive at best, indi- cating that the higher levels of cortisol exhibited in the PRO runners with significant lower adi- posity are not likely to be solely explained by their lower BMI. Indeed, the relationship between BMI and cortisol appears to be complex, with some reports suggesting a U-shaped relationship, where the glucocorticoid’s levels associate negatively up to about a BMI of 30 kg/ m2, then exhibiting a positive correlation into obesity phenotypes [50]. MR statistical models generally do not account for such non-linearity and would require a more granular demo- graphical treatment, which is not possible using only GWAS summary statistics data in the context of 2S-MR [17, 51]. PLOS ONE Biomarker signature of runners PLOS ONE | https://doi.org/10.1371/journal.pone.0293631 November 15, 2023 14 / 20 Behavioral traits associated with increase physical activity The combination of the body of the literature that describes the effects of endurance training on blood biomarkers, and our own analysis that included markers such as CK and AST, makes us cautiously assured that most of the abovementioned blood biomarker signatures are indeed a result of the interplay between self-reported running and the associated lower BMI. How- ever, as this is a self-report-based analysis and we were unable to track other subject behaviors in this free-living cohort, we acknowledge that multiple behaviors that associate with exercise may be influencing our results. Toward this end, our exploratory 2S-MR analyses revealed potentially causal relationships between vigorous exercise and multiple dietary habits that have been shown to improve the biomarkers we examined. Indeed, diets that avoid processed meat and sweets while providing ample amounts of fresh fruits, as well as oily fish have been shown to be anti-inflammatory, and improve glycemic control and dyslipidemia [52, 53]. That physically active individuals are also more likely to make healthier dietary choices adds insight to the potential confounders in ours and others’ observational analyses, and this similar associations have previously been reported [54–56]. For example, using a calculated healthy eating motivation score, Naughton et al. showed that those who partake in more than 2 hours of vigorous physical activity are almost twice as likely to be motivated to eat healthy [56]. Indeed, upon closer examination, the genetic instruments used to approximate vigorous physical activity as the exposure in this work included variants in the genes DPY19L1, CADM2, CTBP2, EXOC4, and FOXO3 [57]. Of these, CADM2 encodes proteins that are involved in neurotransmission in brain regions well known for their involvement in executive function, including motivation, impulse regulation and self-control [58]. Moreover, variants within this locus have been associated with obesity- related traits [59]. Thus, it is likely that the improved metabolic outcomes seen here with our self-reported runners are a composite result of both these individuals exercise and dietary hab- its. Importantly, the above suggests that a holistic wellness lifestyle approach is in practice the most likely to be most effective toward preventing cardiometabolic disease. Nonetheless, the focus of this work–exercise in the form of running–is known to significantly improve cardiore- spiratory fitness (CRF), which has been shown to be an independent predictor of CVD risk and total mortality, outcomes that indeed correlate with dysregulated levels in many of the blood biomarkers examined in this work [7]. Study limitations This study is based on self-reported running and thus has several limitations. First, it is gener- ally known that subjects tend to overestimate their commitment to exercise when self-report- ing, although in our cohort is a self-selected health-oriented population that is possibly less likely to over-report their running volume. Furthermore, although the robust increasing trend in baselines for muscle damage biomarkers (CK, AST) that have been shown to be associated with participation in sports and exercise provides indirect evidence that the running groups were indeed participating in increasing volumes of strenuous physical activity, we cannot con- firm whether the reported running was performed overground or on a treadmill, which may result in some heterogeneity in physiological responses, nor can we ascertain the actual train- ing volume of PRO-level runners. We also cannot exclude the possibility that the running groups also participated in other forms of exercise (such as strength training) or partook in other wellness program interventions that may have influenced their blood biomarkers and/or BMI via lean muscle accretion. Toward this end, we have attempted to shed light on potential behavioral covariates related to vigorous physical activity via 2S-MR. Finally, while this cohort PLOS ONE Biomarker signature of runners PLOS ONE | https://doi.org/10.1371/journal.pone.0293631 November 15, 2023 15 / 20 is generally healthy, we cannot exclude the potential for unmeasured confounders such as medications, nutritional supplements, and unreported health conditions. 2S- MR enables the assessment of causal relationships between modifiable traits and is less prone to the so-called “winner’s curse” that more readily affects one-sample MR analyses [17, 51]. Because 2S-MR uses GWAS summary statistics for both exposure and outcome, it is possi- ble to increase statistical power because of the increased sample sizes. However, horizontal pleiotropy is still a concern that can skew the results. Currently, there is no gold standard MR analysis method, thus we used different techniques (IVW, MR-Egger, and median-based esti- mations–all of which are based on different assumptions and thus biases) to evaluate the con- sistency among these estimators and only reported associations as ‘causal’ if there was cross- model consistency. Nonetheless, an exposure such as BMI is a complex trait that is composed of multiple sub-phenotypes (such as years of education) that could be driving the causal associations. Conclusions Running is one of the most common forms of vigorous exercise practiced globally, thus mak- ing it a compelling target of research studies toward understanding its applicability in chronic disease prevention. Our cross-sectional study offers insight into the biomarker signatures of self-reported running in generally healthy individuals that suggest improved insulin sensitivity, blood lipid metabolism, and systemic inflammation. Furthermore, using 2S-MR in indepen- dent datasets we provide additional evidence that some biomarkers are readily modified BMI alone, while others appear to respond to the combination of varying exercise and BMI. Our additional bi-directional 2S-MR analyses toward understanding the causal relationships between partaking in vigorous physical activity and other healthy behaviors highlight the inherent challenge in disambiguating exercise intervention effects in cross sectional studies of free-living populations, where healthy behaviors such as exercising and healthy dietary habits co-occur. Overall, our analysis shows that the differences between those who run and the sed- entary in our cohort are likely a combination of the specific physiological effects of exercise, the associated changes in BMI, and lifestyle habits associated with those who exercise, such as food choices and baseline activity level. Looking ahead, the InsideTracker database is continu- ously augmented with blood chemistry, genotyping, and activity tracker data, facilitating fur- ther investigation of the effects of various exercise modalities on phenotypes related to healthspan, including longitudinal analyses and more granular dose-response dynamics. Supporting information S1 File. (PDF) S1 Dataset. (TXT) Acknowledgments We thank Michelle Cawley and Renee Deehan for their assistance with background subject matter research and insightful conversations. Author Contributions Conceptualization: Bartek Nogal. PLOS ONE Biomarker signature of runners PLOS ONE | https://doi.org/10.1371/journal.pone.0293631 November 15, 2023 16 / 20 Data curation: Svetlana Vinogradova. Formal analysis: Bartek Nogal, Svetlana Vinogradova, Paul Fabian. Investigation: Bartek Nogal, Svetlana Vinogradova. Methodology: Bartek Nogal, Svetlana Vinogradova. Project administration: Bartek Nogal. Supervision: Milena Jorge, Ali Torkamani, Gil Blander. Visualization: Svetlana Vinogradova. Writing – original draft: Bartek Nogal. Writing – review & editing: Bartek Nogal. References 1. Lavie CJ, Ozemek C, Carbone S, Katzmarzyk PT, Blair SN. Sedentary Behavior, Exercise, and Cardio- vascular Health. Circ Res. 2019; 124(5):799–815. https://doi.org/10.1161/CIRCRESAHA.118.312669 PMID: 30817262. 2. Carlson SA, Adams EK, Yang Z, Fulton JE. Percentage of Deaths Associated With Inadequate Physical Activity in the United States. Prev Chronic Dis. 2018; 15:E38. Epub 20180329. https://doi.org/10.5888/ pcd18.170354 PMID: 29602315 3. Antonicelli R, Spazzafumo L, Scalvini S, Olivieri F, Matassini MV, Parati G, et al. Exercise: a "new drug" for elderly patients with chronic heart failure. Aging (Albany NY). 2016; 8(5):860–72. https://doi.org/10. 18632/aging.100901 PMID: 26953895 4. Sgro P, Emerenziani GP, Antinozzi C, Sacchetti M, Di Luigi L. Exercise as a drug for glucose manage- ment and prevention in type 2 diabetes mellitus. Curr Opin Pharmacol. 2021; 59:95–102. Epub 20210626. https://doi.org/10.1016/j.coph.2021.05.006 PMID: 34182427. 5. Vina J, Sanchis-Gomar F, Martinez-Bello V, Gomez-Cabrera MC. Exercise acts as a drug; the pharma- cological benefits of exercise. Br J Pharmacol. 2012; 167(1):1–12. https://doi.org/10.1111/j.1476-5381. 2012.01970.x PMID: 22486393 6. Lee EC, Fragala MS, Kavouras SA, Queen RM, Pryor JL, Casa DJ. Biomarkers in Sports and Exercise: Tracking Health, Performance, and Recovery in Athletes. J Strength Cond Res. 2017; 31(10):2920–37. https://doi.org/10.1519/JSC.0000000000002122 PMID: 28737585 7. Lin X, Zhang X, Guo J, Roberts CK, McKenzie S, Wu WC, et al. Effects of Exercise Training on Cardio- respiratory Fitness and Biomarkers of Cardiometabolic Health: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. J Am Heart Assoc. 2015; 4(7). Epub 20150626. https://doi.org/10. 1161/JAHA.115.002014 PMID: 26116691 8. Li X, Ploner A, Wang Y, Zhan Y, Pedersen NL, Magnusson PK, et al. Clinical biomarkers and associa- tions with healthspan and lifespan: Evidence from observational and genetic data. EBioMedicine. 2021; 66:103318. Epub 2021/04/05. https://doi.org/10.1016/j.ebiom.2021.103318 PMID: 33813140 9. Mailliez A, Guilbaud A, Puisieux F, Dauchet L, Boulanger E. Circulating biomarkers characterizing phys- ical frailty: CRP, hemoglobin, albumin, 25OHD and free testosterone as best biomarkers. Results of a meta-analysis. Exp Gerontol. 2020; 139:111014. Epub 20200626. https://doi.org/10.1016/j.exger.2020. 111014 PMID: 32599147. 10. Hirata T, Arai Y, Yuasa S, Abe Y, Takayama M, Sasaki T, et al. Associations of cardiovascular biomark- ers and plasma albumin with exceptional survival to the highest ages. Nat Commun. 2020; 11(1):3820. Epub 20200730. https://doi.org/10.1038/s41467-020-17636-0 PMID: 32732919 11. Erema VV, Yakovchik AY, Kashtanova DA, Bochkaeva ZV, Ivanov MV, Sosin DV, et al. Biological Age Predictors: The Status Quo and Future Trends. Int J Mol Sci. 2022; 23(23). Epub 20221201. https://doi. org/10.3390/ijms232315103 PMID: 36499430 12. Hartmann A, Hartmann C, Secci R, Hermann A, Fuellen G, Walter M. Ranking Biomarkers of Aging by Citation Profiling and Effort Scoring. Front Genet. 2021; 12:686320. Epub 20210521. https://doi.org/10. 3389/fgene.2021.686320 PMID: 34093670 13. Trejo-Gutierrez JF, Fletcher G. Impact of exercise on blood lipids and lipoproteins. J Clin Lipidol. 2007; 1(3):175–81. Epub 20070607. https://doi.org/10.1016/j.jacl.2007.05.006 PMID: 21291678. PLOS ONE Biomarker signature of runners PLOS ONE | https://doi.org/10.1371/journal.pone.0293631 November 15, 2023 17 / 20 14. Westerman K, Reaver A, Roy C, Ploch M, Sharoni E, Nogal B, et al. Longitudinal analysis of biomarker data from a personalized nutrition platform in healthy subjects. Sci Rep. 2018; 8(1):14685. Epub 2018/ 10/04. https://doi.org/10.1038/s41598-018-33008-7 PMID: 30279436 15. Fox JWS. An R Companion to Applied Regression. Third ed: Sage, Thousand Oaks CA; 2019. 16. Ho D, Imai K, King G, Stuart EA. MatchIt: Nonparametric Preprocessing for Parametric Causal Infer- ence. Journal of Statistical Software. 2011; 42(8):1–28. https://doi.org/10.18637/jss.v042.i08 17. Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, et al. Guidelines for perform- ing Mendelian randomization investigations. Wellcome Open Res. 2019; 4:186. Epub 20200428. https://doi.org/10.12688/wellcomeopenres.15555.2 PMID: 32760811 18. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018; 7. Epub 20180530. https://doi. org/10.7554/eLife.34408 PMID: 29846171 19. Bogl LH, Pietilainen KH, Rissanen A, Kaprio J. Improving the accuracy of self-reports on diet and physi- cal exercise: the co-twin control method. Twin Res Hum Genet. 2009; 12(6):531–40. https://doi.org/10. 1375/twin.12.6.531 PMID: 19943715. 20. Yuen HK, Wang E, Holthaus K, Vogtle LK, Sword D, Breland HL, et al. Self-reported versus objectively assessed exercise adherence. Am J Occup Ther. 2013; 67(4):484–9. https://doi.org/10.5014/ajot.2013. 007575 PMID: 23791324 21. Ostman C, Smart NA, Morcos D, Duller A, Ridley W, Jewiss D. The effect of exercise training on clinical outcomes in patients with the metabolic syndrome: a systematic review and meta-analysis. Cardiovasc Diabetol. 2017; 16(1):110. Epub 20170830. https://doi.org/10.1186/s12933-017-0590-y PMID: 28854979 22. Fikenzer K, Fikenzer S, Laufs U, Werner C. Effects of endurance training on serum lipids. Vascul Pharmacol. 2018; 101:9–20. Epub 20171201. https://doi.org/10.1016/j.vph.2017.11.005 PMID: 29203287. 23. Mann S, Beedie C, Jimenez A. Differential effects of aerobic exercise, resistance training and combined exercise modalities on cholesterol and the lipid profile: review, synthesis and recommendations. Sports Med. 2014; 44(2):211–21. https://doi.org/10.1007/s40279-013-0110-5 PMID: 24174305 24. Tambalis K, Panagiotakos DB, Kavouras SA, Sidossis LS. Responses of blood lipids to aerobic, resis- tance, and combined aerobic with resistance exercise training: a systematic review of current evidence. Angiology. 2009; 60(5):614–32. Epub 20081030. https://doi.org/10.1177/0003319708324927 PMID: 18974201. 25. Calabresi L, Franceschini G. Lecithin: cholesterol acyltransferase, high-density lipoproteins, and ather- oprotection in humans. Trends Cardiovasc Med. 2010; 20(2):50–3. https://doi.org/10.1016/j.tcm.2010. 03.007 PMID: 20656215. 26. Kemper HC, Snel J, Verschuur R, Storm-van Essen L. Tracking of health and risk indicators of cardio- vascular diseases from teenager to adult: Amsterdam Growth and Health Study. Prev Med. 1990; 19 (6):642–55. https://doi.org/10.1016/0091-7435(90)90061-n PMID: 2263575. 27. Khan A, Khan WM, Ayub M, Humayun M, Haroon M. Ferritin Is a Marker of Inflammation rather than Iron Deficiency in Overweight and Obese People. J Obes. 2016; 2016:1937320. Epub 20161227. https://doi.org/10.1155/2016/1937320 PMID: 28116148 28. Nabhan D, Bielko S, Sinex JA, Surhoff K, Moreau WJ, Schumacher YO, et al. Serum ferritin distribution in elite athletes. J Sci Med Sport. 2020; 23(6):554–8. Epub 20191227. https://doi.org/10.1016/j.jsams. 2019.12.027 PMID: 31901316. 29. Ku BJ, Kim SY, Lee TY, Park KS. Serum ferritin is inversely correlated with serum adiponectin level: population-based cross-sectional study. Dis Markers. 2009; 27(6):303–10. https://doi.org/10.3233/ DMA-2009-0676 PMID: 20075513 30. Pollock N, Chakraverty R, Taylor I, Killer SC. An 8-year Analysis of Magnesium Status in Elite Interna- tional Track & Field Athletes. J Am Coll Nutr. 2020; 39(5):443–9. Epub 20191212. https://doi.org/10. 1080/07315724.2019.1691953 PMID: 31829845. 31. Arnaud MJ. Update on the assessment of magnesium status. Br J Nutr. 2008; 99 Suppl 3:S24–36. https://doi.org/10.1017/S000711450800682X PMID: 18598586. 32. Simo R, Saez-Lopez C, Barbosa-Desongles A, Hernandez C, Selva DM. Novel insights in SHBG regu- lation and clinical implications. Trends Endocrinol Metab. 2015; 26(7):376–83. Epub 20150601. https:// doi.org/10.1016/j.tem.2015.05.001 PMID: 26044465. 33. Popovic B, Popovic D, Macut D, Antic IB, Isailovic T, Ognjanovic S, et al. Acute Response to Endurance Exercise Stress: Focus on Catabolic/anabolic Interplay Between Cortisol, Testosterone, and Sex Hor- mone Binding Globulin in Professional Athletes. J Med Biochem. 2019; 38(1):6–12. Epub 20190301. https://doi.org/10.2478/jomb-2018-0016 PMID: 30820178 PLOS ONE Biomarker signature of runners PLOS ONE | https://doi.org/10.1371/journal.pone.0293631 November 15, 2023 18 / 20 34. Simo R, Saez-Lopez C, Lecube A, Hernandez C, Fort JM, Selva DM. Adiponectin upregulates SHBG production: molecular mechanisms and potential implications. Endocrinology. 2014; 155(8):2820–30. Epub 20140514. https://doi.org/10.1210/en.2014-1072 PMID: 24828613. 35. Mougios V. Reference intervals for serum creatine kinase in athletes. Br J Sports Med. 2007; 41 (10):674–8. Epub 20070525. https://doi.org/10.1136/bjsm.2006.034041 PMID: 17526622 36. Banfi G, Morelli P. Relation between body mass index and serum aminotransferases concentrations in professional athletes. J Sports Med Phys Fitness. 2008; 48(2):197–200. PMID: 18427415. 37. Ross R, Janiszewski PM. Is weight loss the optimal target for obesity-related cardiovascular disease risk reduction? Can J Cardiol. 2008; 24 Suppl D:25D–31D. https://doi.org/10.1016/s0828-282x(08) 71046-8 PMID: 18787733 38. Ross R. Does exercise without weight loss improve insulin sensitivity? Diabetes Care. 2003; 26(3):944– 5. https://doi.org/10.2337/diacare.26.3.944 PMID: 12610063. 39. Cerqueira E, Marinho DA, Neiva HP, Lourenco O. Inflammatory Effects of High and Moderate Intensity Exercise-A Systematic Review. Front Physiol. 2019; 10:1550. Epub 20200109. https://doi.org/10.3389/ fphys.2019.01550 PMID: 31992987 40. Church TS, Earnest CP, Thompson AM, Priest EL, Rodarte RQ, Saunders T, et al. Exercise without weight loss does not reduce C-reactive protein: the INFLAME study. Med Sci Sports Exerc. 2010; 42 (4):708–16. https://doi.org/10.1249/MSS.0b013e3181c03a43 PMID: 19952828 41. Plaisance EP, Grandjean PW. Physical activity and high-sensitivity C-reactive protein. Sports Med. 2006; 36(5):443–58. https://doi.org/10.2165/00007256-200636050-00006 PMID: 16646631. 42. Salvagno GL, Sanchis-Gomar F, Picanza A, Lippi G. Red blood cell distribution width: A simple parame- ter with multiple clinical applications. Crit Rev Clin Lab Sci. 2015; 52(2):86–105. Epub 20141223. https://doi.org/10.3109/10408363.2014.992064 PMID: 25535770. 43. Carmel R, Green R, Rosenblatt DS, Watkins D. Update on cobalamin, folate, and homocysteine. Hema- tology Am Soc Hematol Educ Program. 2003:62–81. https://doi.org/10.1182/asheducation-2003.1.62 PMID: 14633777. 44. Welsh P, Polisecki E, Robertson M, Jahn S, Buckley BM, de Craen AJ, et al. Unraveling the directional link between adiposity and inflammation: a bidirectional Mendelian randomization approach. J Clin Endocrinol Metab. 2010; 95(1):93–9. Epub 20091111. https://doi.org/10.1210/jc.2009-1064 PMID: 19906786 45. Maachi M, Pieroni L, Bruckert E, Jardel C, Fellahi S, Hainque B, et al. Systemic low-grade inflammation is related to both circulating and adipose tissue TNFalpha, leptin and IL-6 levels in obese women. Int J Obes Relat Metab Disord. 2004; 28(8):993–7. https://doi.org/10.1038/sj.ijo.0802718 PMID: 15211360. 46. Katzmarzyk PT, Leon AS, Rankinen T, Gagnon J, Skinner JS, Wilmore JH, et al. Changes in blood lip- ids consequent to aerobic exercise training related to changes in body fatness and aerobic fitness. Metabolism. 2001; 50(7):841–8. https://doi.org/10.1053/meta.2001.24190 PMID: 11436192. 47. Hu X, Zhuang XD, Mei WY, Liu G, Du ZM, Liao XX, et al. Exploring the causal pathway from body mass index to coronary heart disease: a network Mendelian randomization study. Ther Adv Chronic Dis. 2020; 11:2040622320909040. Epub 20200527. https://doi.org/10.1177/2040622320909040 PMID: 32523662 48. Huang Y, Xu M, Xie L, Wang T, Huang X, Lv X, et al. Obesity and peripheral arterial disease: A Mende- lian Randomization analysis. Atherosclerosis. 2016; 247:218–24. Epub 20151229. https://doi.org/10. 1016/j.atherosclerosis.2015.12.034 PMID: 26945778. 49. Houmard JA, Costill DL, Mitchell JB, Park SH, Fink WJ, Burns JM. Testosterone, cortisol, and creatine kinase levels in male distance runners during reduced training. Int J Sports Med. 1990; 11(1):41–5. https://doi.org/10.1055/s-2007-1024760 PMID: 2180832. 50. Schorr M, Lawson EA, Dichtel LE, Klibanski A, Miller KK. Cortisol Measures Across the Weight Spec- trum. J Clin Endocrinol Metab. 2015; 100(9):3313–21. Epub 20150714. https://doi.org/10.1210/JC. 2015-2078 PMID: 26171799 51. O’Donnell CJ, Sabatine MS. Opportunities and Challenges in Mendelian Randomization Studies to Guide Trial Design. JAMA Cardiol. 2018; 3(10):967. https://doi.org/10.1001/jamacardio.2018.2863 PMID: 30326490. 52. Hosseini B, Berthon BS, Saedisomeolia A, Starkey MR, Collison A, Wark PAB, et al. Effects of fruit and vegetable consumption on inflammatory biomarkers and immune cell populations: a systematic litera- ture review and meta-analysis. Am J Clin Nutr. 2018; 108(1):136–55. https://doi.org/10.1093/ajcn/ nqy082 PMID: 29931038. 53. Djousse L, Arnett DK, Coon H, Province MA, Moore LL, Ellison RC. Fruit and vegetable consumption and LDL cholesterol: the National Heart, Lung, and Blood Institute Family Heart Study. Am J Clin Nutr. 2004; 79(2):213–7. https://doi.org/10.1093/ajcn/79.2.213 PMID: 14749225. PLOS ONE Biomarker signature of runners PLOS ONE | https://doi.org/10.1371/journal.pone.0293631 November 15, 2023 19 / 20 54. L D. Physical Activity and Dietary Habits of College Students. The Journal of Nurse Practitioners. 2015; 11(2):192–8.e2. 55. Shi X, Tubb L, Fingers ST, Chen S, Caffrey JL. Associations of physical activity and dietary behaviors with children’s health and academic problems. J Sch Health. 2013; 83(1):1–7. https://doi.org/10.1111/j. 1746-1561.2012.00740.x PMID: 23253284. 56. Naughton P, McCarthy SN, McCarthy MB. The creation of a healthy eating motivation score and its association with food choice and physical activity in a cross sectional sample of Irish adults. Int J Behav Nutr Phys Act. 2015; 12:74. Epub 20150606. https://doi.org/10.1186/s12966-015-0234-0 PMID: 26048166 57. Klimentidis YC, Raichlen DA, Bea J, Garcia DO, Wineinger NE, Mandarino LJ, et al. Genome-wide association study of habitual physical activity in over 377,000 UK Biobank participants identifies multiple variants including CADM2 and APOE. Int J Obes (Lond). 2018; 42(6):1161–76. Epub 20180613. https://doi.org/10.1038/s41366-018-0120-3 PMID: 29899525 58. Arends RM, Pasman JA, Verweij KJH, Derks EM, Gordon SD, Hickie I, et al. Associations between the CADM2 gene, substance use, risky sexual behavior, and self-control: A phenome-wide association study. Addict Biol. 2021; 26(6):e13015. Epub 20210218. https://doi.org/10.1111/adb.13015 PMID: 33604983 59. Morris J, Bailey MES, Baldassarre D, Cullen B, de Faire U, Ferguson A, et al. Genetic variation in CADM2 as a link between psychological traits and obesity. Scientific Reports. 2019; 9(1):7339. https:// doi.org/10.1038/s41598-019-43861-9 PMID: 31089183 PLOS ONE Biomarker signature of runners PLOS ONE | https://doi.org/10.1371/journal.pone.0293631 November 15, 2023 20 / 20

Dose response of running on blood biomarkers of wellness in generally healthy individuals.

11-15-2023

Nogal, Bartek,Vinogradova, Svetlana,Jorge, Milena,Torkamani, Ali,Fabian, Paul,Blander, Gil

eng

PMC6856151

1 Scientific RepoRtS | (2019) 9:16858 | https://doi.org/10.1038/s41598-019-53329-5 www.nature.com/scientificreports Small vertebrates running on uneven terrain: a biomechanical study of two differently specialised lacertid lizards françois Druelle 1*, Jana Goyens 1, Menelia Vasilopoulou-Kampitsi1 & peter Aerts1,2 While running, small animals frequently encounter large terrain variations relative to their body size, therefore, terrain variations impose important functional demands on small animals. Nonetheless, we have previously observed in lizards that running specialists can maintain a surprisingly good running performance on very uneven terrains. The relatively large terrain variations are offset by their capacity for leg adjustability that ensures a ‘smooth ride’ of the centre of mass (CoM). The question as to how the effect of an uneven terrain on running performance and locomotor costs differs between species exhibiting diverse body build and locomotor specializations remains. We hypothesise that specialized runners with long hind limbs can cross uneven terrain more efficiently than specialized climbers with a dorso-ventrally flattened body and equally short fore and hind limbs. This study reports 3D kinematics using high-speed videos (325 Hz) to investigate leg adjustability and CoM movements in two lacertid lizards (Acanthodactylus boskianus, running specialist; Podarcis muralis, climbing specialist). We investigated these parameters while the animals were running on a level surface and over a custom- made uneven terrain. We analysed the CoM dynamics, we evaluated the fluctuations of the positive and negative mechanical energy, and we estimated the overall cost of transport. Firstly, the results reveal that the climbers ran at lower speeds on flat level terrain but had the same cost of transport as the runners. Secondly, contrary to the running specialists, the speed was lower and the energy expenditure higher in the climbing specialists while running on uneven terrain. While leg movements adjust to the substrates’ variations and enhance the stability of the CoM in the running specialist, this is not the case in the climbing specialist. Although their legs are kept more extended, the amplitude of movement does not change, resulting in an increase of the movement of the CoM and a decrease in locomotor efficiency. These results are discussed in light of the respective (micro-)habitat of these species and suggest that energy economy can also be an important factor for small vertebrates. Locomotion requires mechanical work to counter inertia (and gravity when moving upwards) and to overcome resistive forces from the environment. Issues relating to substrate structure and organisation alter the locomotion of animals, and adaptations for ecologically-relevant ways of moving can be found in various aspects of the animal biological system1–3. The design of the limbs4,5, the type of gait6 and the posture7 can, therefore, influence loco- motor performance and efficiency. Relative to their body size, small animals are more prone to encounter large terrain variations than larger animals do. Apart from the fact that their locomotor cost (J/kg/m) is already high compared to large animals8–10, terrain structure and organisation at the scale relevant to the animal may impose important additional energetic challenges in small animals. For instance, uneven terrain requires manoeuvring and intermittent running to bypass obstacles or, can require moving up and down along the running trajectory to cross obstacles. Therefore, running over uneven terrains will unavoidably result in both perturbations of the overall goal directed movement as well as higher costs. Encountering large terrain variation relative to body size is a very common scenario for small lizards. Investigating the impact of an uneven substrate on the kinematics of running lizards is, therefore, essential to gain insight into the relationship between fitness and performance in an appropriate ecological context. Although 1Laboratory for Functional Morphology, University of Antwerp, Antwerp, Belgium. 2Department of Sport Sciences, University of Ghent, Ghent, Belgium. *email: [email protected] open 2 Scientific RepoRtS | (2019) 9:16858 | https://doi.org/10.1038/s41598-019-53329-5 www.nature.com/scientificreports www.nature.com/scientificreports/ previous studies have explored obstacle negotiation in lizards11–14, hardly anything is known about crossing exten- sive uneven terrains. In our previous study15, we made the observation that Acanthodactylus boskianus, a running specialist, is not only specifically adapted for high-speed running on an even, level surface, but is also able to maintain its high performance on structurally uneven terrain. The relatively large terrain variations were offset by important capacities in leg adjustability that ensured a ‘smooth ride’ for the centre of mass. Despite being a desert species, A. boskianus is also adapted to deal with large terrain variations at a scale relevant to its size. The question as to how the effect of an uneven terrain on running performance and locomotor costs differs between species exhibiting different locomotor specializations remains. In this context, and assuming that the functional demands imposed by natural environments (in terms of structure and organisation) are reflected in the locomo- tor system2,3, lizards with different morphologies should exhibit different locomotor performance. According to the physiological and biomechanical theory, the sprinters would benefit from a laterally compressed body, long hind limbs (with primarily long and slender zeugopods and autopods) and a more parasagittal running limb posture. This enables these lizards to take large strides and to reach high speeds. In contrast, the climbers would benefit from dorso-ventrally flattened bodies and strong, short equal fore and hind limbs with a sprawling posture to enable them to keep a close and firm contact with the substrate1,16–21. Here, we compare locomotor performance and kinematics in a running specialist belonging to the Lacertidae family, A. boskianus, to a climbing specialist, the lacertid Podarcis muralis, when negotiating an uneven terrain. Both species are described as active foragers, i.e. species for which the locomotion accounts for a large portion of the energy budget (mean number of moves per minute: 2.01 and 3.05, respectively, and percentage of time moving: 28.80% and 20.54%22), but they are representative of different locomotor specializations. P. muralis is described as a specialized climber primarily seen as a rock-dwelling lizard, thus commonly encountering both highly uneven and vertical structures as well as flat terrains23–25. A. boskianus is considered a specialist in fast running and acceleration in an open desert environment26–29. According to our previous results15, we presently hypothesise that the running costs (J/kg/m) should hardly be affected by the imposed terrain variations in A. boskianus. The anticipated good performance of the running specialist on the uneven terrain may be related to naturally occurring sand ridges in its (micro-)habitat. In the present study we also compare the centre of mass dynamics, limb behaviour and locomotor costs of A. boskianus with results for P. muralis when tested on the same terrain. Although the climbing specialist commonly lives in rocky and uneven environments, our experimental terrain should strongly perturbate the running performance of this species because its habitat commonly offers many hiding places that do not require running any great dis- tance. Furthermore, their anatomy, i.e. a flattened body with short limbs (see above), allows it to maintain close and firm contact with the substrate, thus P. muralis are expected to follow the uneven substrate topology closely, leading to perturbations in their running mechanics. In this context, we hypothesise that, on the flat terrain, A. boskianus will show better locomotor performance and lower costs to those of P. muralis17,30,31. Furthermore, the latter should be more perturbated by the uneven terrain than the running specialist. The respective limb and CoM dynamics should result from the differences in limb length and design4,32 and from the respective ecologically-relevant escaping strategies of these species, i.e. running a great distance in A. boskianus and hiding as fast as possible in P. muralis. Methods Subject details. Seven adult male A. boskianus were obtained from a commercial dealer (Amfibia, Antwerpen, Belgium) and seven adult male P. muralis were collected using hand foraging techniques in the wild (Mechelen, Belgium; the P. muralis individuals were released in their natural environment after the experiments). All animal care and experimental procedures were carried out in accordance with the regulations and guidelines of the University of Antwerp. The present protocol was approved by the ethical committee of the University of Antwerp (ECD-dossier 2013-76). Experimental protocol and acquisition of data. We constructed an adjustable racetrack including a central part that could remain flat (control) or be covered with hemi-spheres (uneven terrain). The hemi-spheres were 25 mm high, i.e. equal to ≈0.4 times snout vent length of our animal sample (63.95 ± 3.18 mm in A. boski- anus and 61.26 ± 3.19 mm in P. muralis). We painted the flat and uneven terrains with adhesive paint and sand was additionally spread and glued to the surface. This significantly increased the roughness of the substrates to enable the animals to run at top speed. The experiments took place in the morning in November 2017 for A. boskianus and in April 2018 for P. mura- lis. All the animals were first kept in an incubator set at 37 °C for A. boskianus and 30 °C for P. muralis to optimise their respective locomotor performance26,28. For each individual, 15 anatomical parts were marked with white using water based paint: top of the snout, back of the head, side of the head, shoulder, mid-trunk, hip, mid-tail, knees, proximal part of the feet, elbows and proximal part of the hands. During a 3-week period, we tested each lizard randomly on each substrate every day. The lizards were encouraged to run along the racetrack by means of hand chasing and one or two consecutive trials were performed per substrate [a minimum of 30 minutes rest time (in the incubator) between the different per-substrate trials was ensured]. We recorded the running animals with four synchronized high-speed digital video cameras operating at 325 frames.s−1 and 1/800 shutter speed (© 2018 NorPix Inc., system 10 GigE Vision, 1920 × 1080). The cameras were positioned perpendicular to the runway, at the top and in diagonal for increasing the accuracy of the 3D reconstruction (see Figure A in Supplementary Material). Calibration was performed using a custom-made calibrated construction (477 × 143 × 96 mm) on which 40 dots were digitized. After the recording, the digitization of the body markers was performed manu- ally frame-by-frame using Matlab (R2019a) and the DLTdv5 application developed by the T. Hedrick lab33. A strong selection criterion was applied on the selection of the sequences to be digitized. Sequences were considered appropriate when the running individuals were crossing the substrate in a straight line and at a constant speed. 3 Scientific RepoRtS | (2019) 9:16858 | https://doi.org/10.1038/s41598-019-53329-5 www.nature.com/scientificreports www.nature.com/scientificreports/ This resulted in 61 strides analyzed for A. boskianus and 51 for P. muralis. Further information about the present experimental protocol can be found in our previous paper15. Locomotion analysis. On the raw data (digitized markers), we first applied a fourth order low-pass Butterworth filter with a cut-off frequency of 60 Hz. This is well above the mean stride frequency in our study (mean frequency = 11.74 Hz in A. boskianus and 11.69 Hz in P. muralis). Second, a general filter using a piecewise cubic spline interpolation method was applied for missing data. For instance, the very fast movement of the limbs during the swing phase sometimes made few dots impossible to digitize correctly. In these occasional cases, we kept the running sequence with the few missing dots (usually 1–5 frames). If more than one third of the frames was missing, we removed the complete stride from the dataset. We then estimated the position of the body cen- tre of mass (CoM) based on the dissections of three A. boskianus cadavers15 and one P. muralis cadaver. After freezing and segmenting the body, the body parts were subsequently weighed on a micro balance (MT5 Mettler Toledo, Greifensee, Switzerland; precision: 0.01 mg), and each marker was provided with a percentage of the total body mass (the limb CoMs are estimated at the knees and elbows). The weighted arithmetic mean of all markers enabled us to calculate the instantaneous position of the CoM in all digitised frames. We corrected the height of the CoM for substrate height by substracting 25 mm from CoM height on the uneven terrain (i.e. the radius of the hemi-spheres). In our sample, the average position of the CoM was estimated to be 23.9 ± 1.9% of the trunk from the hip joint for A. boskianus and 23.7 ± 4.9% for P. muralis. The estimated trajectory of the CoM from the slope of the regression line in the XY-plane allowed us to recalculate the global frame of reference using a rotation matrix, with an X-axis aligned with the direction of motion, and the Y-axis perpendicular to the X-axis in lateral direction; the Z-axis is aligned with the gravity vector. Morphometrics and body movements were used to determine the instantaneous mechanical energy of each body segment (head, proximal trunk, mid-trunk, distal trunk and tail) over a stride period: E mgZ m Z X Y I ya pi ( ) 2 ( ) 2 si 2 2 2 2 2 = + + + + +      Where m is the mass of the segment si, g is the gravitational acceleration (9.81 m.s−2), Z is the instantaneous height of the CoM of the segment considered (the segment CoM is estimated from the different markers), Z, X and Y are the linear velocity of the segment CoM, ya and pi are the angular velocity of the segment si in the fron- tal and sagittal plane, respectively; note that the roll rotation is not included in the calculations as it is expected to be minimal comparing to the yaw and pitch. I is the inertia of the segment si and is estimated using the moment of inertia calculation for a uniform rod, as follows: I mL 12 si 2 = Where L is the length of the segment si. Each limb was considered as a point mass at the level of the elbow or knee and the instantaneous mechanical energy was calculated as follows:    E mgZ m Z X Y ( ) 2 pi 2 2 2 = + + + The total instantaneous minimal energy is calculated as the sum of all Esi and Epi and the time differential of the total energy yields the instantaneous power during the stride. The integral of the positive power allows us to calculate the average positive work and the integral of the negative power allows us to calculate the average neg- ative work over a stride. The overall efficiency of the muscles depends on their contractile properties as well as their elastic compo- nents. Although the elastic components stretched during the preceding phase of negative work may increase the efficiency of the muscles, the maximal efficiency of the conversion of chemical energy into the positive mechani- cal work is approximately 25% in animals34–36. It has also been shown that large animals should benefit more from elastic energy savings than smaller animals5,35. Therefore, in the present study, we are assuming that muscles can perform positive work with a maximal efficiency of 25%. We therefore estimated the energy cost of transport from the sum of the positive work times 4 and the negative work times 1. Statistical analysis. Assessing morphological differences between species. Comparisons in morphometrics (body mass and segment lengths) were conducted between both species using exact Permutation tests for inde- pendent samples. In this context, the statistical unit is the individual and permutations are an appropriate test for the small sample size (n = 7). Assessing kinematic differences among and within species. In the present protocol, a strong selection criterion had been previously performed on the running sequences (see previous). Each selected stride comes from a different running sequence, thus ensuring stride independence. In addition, using dimensionless quantities is a way to control for potential random effects related to individuals because we expect individual differences in running kinematics to be related to size. Hence, we have considered the strides as our experimental units and the strides are compared, on the one hand, across speed and species on the control substrate (a), and on the other hand, across speed and substrates within species (b). All kinematic data were log10-transformed before analysis in order to ensure normality and homoscedascity assumptions. 4 Scientific RepoRtS | (2019) 9:16858 | https://doi.org/10.1038/s41598-019-53329-5 www.nature.com/scientificreports www.nature.com/scientificreports/ a) Between species on the control substrate Using analyses of variance (ANOVA), we first tested for differences between species in mean speed and dimensionless speed [assessed using the Froude Number × v l g 2 ; where v is the stride average speed, l is the length of the tibia of the individual considered15,37, and g is the gravita- tional acceleration (9.81 m.s−2)]. Second, a set of covariance analyses (ANCOVAs) were performed on different response variables including species as factor and dimensionless speed as a covariate. The response variables tested are: the dimensionless spatio-temporal parameters [dimensionless stride length (stride length divided by tibia length), dimensionless stride frequency (squared frequency divided by tibia length times g) and duty factor (proportion of stance phase relative to stride duration)], the amplitude of CoM and foot displacements on the Y-axis (lateral) and Z-axis (vertical), the relative average position of the foot in the 3 planes, and the relative height at which the CoM is maintained. b) Within species between substrates The same statistical tests were performed but the substrate was included as a factor instead of the species in the ANCOVAs. In addition, we compared the average cost of transport between species and substrates using ANOVAs. We also compared the slopes of the linear models between the cost of transport and absolute speed using the “lsmeans” package in R. All the statistical analyses were performed using R (version 3.3.2), but the permutation tests were performed using StatXact (version 3.1). The significance level was set at P < 0.05. Results and Discussion Morphological features associated to running and climbing skills in Lacertidae. Figure 1 shows the morphological differences between A. boskianus and P. muralis. These differences can be related to their respective running and climbing skills. While the snout-vent length is not different between both species, A. boski- anus has longer hind limbs (femur + tibia) than P. muralis (independent Permutation test = 3.062; P = 0.0006; Table A in Supplementary material). The mass of the hind limbs is more than 2 times larger than the mass of the forelimbs in A. boskianus (5.5 g vs 2.2 g), while fore- and hind limbs masses are almost equal in P. muralis (1.8 g vs 2.3 g). Both species exhibit longer hind limbs than forelimbs (A. boskianus: paired Permutation test = 2.551; P = 0.0156; P. muralis: paired Permutation test = 2.514; P = 0.0156), but the difference between fore- and hind limb lengths is significantly larger in A. boskianus than in P. muralis (independent Permutation test = 2.806; P = 0.0012). The long hind limbs of A. boskianus relative to the forelimbs may enhance their running capacities, while the small difference in fore- and hind limb lengths in P. muralis certainly enhances their climbing skills21. Kinematic differences between runners and climbers when running on level surface. According to the trade-off hypothesis, being a specialist in one locomotor mode should impair performance in other modes17,21,30,31. Table 1 shows the average spatio-temporal parameters for a running specialist (A. boskianus) and Figure 1. Comparisons of the measured morphological features between the running and climbing specialists. A. boskianus are in orange and P. muralis are in green. Symbol significance: *P < 0.05, **P < 0.01, ***P < 0.001. Lizard drawings are from Menelia Vasilopoulou-Kampitsi. 5 Scientific RepoRtS | (2019) 9:16858 | https://doi.org/10.1038/s41598-019-53329-5 www.nature.com/scientificreports www.nature.com/scientificreports/ a climbing specialist (P. muralis) when running on a flat/even substrate. A. boskianus run significantly faster than P. muralis (ANOVA F = 12.89, P = 0.0008). After correcting for size effects, there is no significant difference in the average speed between A. boskianus and P. muralis (ANOVA F = 2.395, P = 0.128; Fig. 2A). Correcting for size and speed effects, A. boskianus exhibits a higher stride frequency (ANCOVA F = 4.548, P = 0.0382; Fig. 2B) and a lower duty factor (ANCOVA F = 68.84, P < 0.0001; Fig. 2D) than P. muralis. The amplitude of the upward displacements of the foot (i.e. the foot clearance) is larger in A. boskianus (ANCOVA F = 15.53, P = 0.0003), while the lateral dis- placements of the foot are larger in P. muralis (ANCOVA F = 16.44, P = 0.0002; Fig. 3). On average, P. muralis places its feet further, laterally, from the hip (ANCOVA F = 12.65, P = 0.0009), i.e. the posture is relatively more sprawled, and the same happens in the fore-aft direction (ANCOVA F = 13.01, P = 0.0007), i.e. the foot is more retracted in P. muralis (Fig. 4). There is no difference in the amplitude of CoM translation in the lateral and upward directions on the flat terrain. However, the CoM is maintained at a significantly lower height in P. muralis compared to A. boski- anus (9.21 ± 3.03 mm and 17.15 ± 3.66 mm, respectively; ANCOVA F = 43.74, P < 0.0001). To sum up, A. boskianus use more parasagittal hind limb postures with a larger foot clearance, exhibit lower duty factor, higher stride fre- quency and keep the CoM relatively higher than P. muralis. These specificities thus emerge in A. boskianus which is a fast runner in general and a better sprinter than P. muralis on level surface. P. muralis run with a CoM very close to the surface, which is advantageous for balance in lizards that climb vertical surfaces21, while A. boskianus keep the CoM higher, avoiding touching the substrate and providing space for parasagittal limb displacements. In this way, A. boskianus run much faster, as observed in lizards living in open habitat38. A. boskianus P. muralis Flat (control) Uneven Flat (control) Uneven Mean SD Mean SD Mean SD Mean SD Speed (m.s−1) 1.72 0.48 1.56 0.45 1.16 0.27 0.92 0.15 Stride frequency (Hz) 11.74 2.52 12.24 2.89 11.69 2.29 11.19 2.46 Duty factor (%) 26.67 5.49 33.43 7.28 53.25 11.66 55.97 6.22 Stride length (mm) 141.18 20.6 122.47 23.21 77.09 16.13 67.34 11.97 Table 1. Mean ± SD for spatio-temporal parameters. Figure 2. Average dimensionless speed (A) and spatio-temporal parameters calculated for each species and for each substrate: Dimensionless stride frequency (B), dimensionless stride length (C), duty factor (D). The brown colour is for A. boskianus, the green colour is for P. muralis. Within each species, darker bars represent the control (flat surface), lighter bars represent the uneven terrain (i.e. hemi-spheres). Error bars show standard deviations. Symbol significance: *P < 0.05, **P < 0.01, ***P < 0.001. 6 Scientific RepoRtS | (2019) 9:16858 | https://doi.org/10.1038/s41598-019-53329-5 www.nature.com/scientificreports www.nature.com/scientificreports/ Impact of running over an uneven terrain for a running specialist. According to previous research15, the small fast running specialist A. boskianus, is able to cope with complex substrates without there being any impact on sprint speed (Fig. 2). Although the stride length decreases (141 mm vs 122 mm; ANCOVA F = 4.075, P = 0.0482) and the duty factor increases (27% vs 33%; ANCOVA F = 9.134, P = 0.004), the average speed is not significantly impaired in this context. The orbit characterizing the movement of the foot relative to the hip also remains similar across the substrates in the sagittal plane (the XZ-plane) and in the frontal plane (the XY-plane; Fig. 5). We observed a significant increase in the amplitude of the foot clearance (ANCOVA F = 10.655, P = 0.0018; see Fig. 3) and the height at which the CoM is maintained decreases significantly (17.15 ± 3.66 mm vs 12.44 ± 3.28 mm; ANCOVA F = 16.565, P = 0.0001). In general, the complex terrain impacts few kinematic aspects of A. boskianus (Figs 3 and 4). The changes occur mainly at the level of the legs that adjust instantaneously to the substrate variations through larger amplitudes of the foot clearance. This enables A. boskianus to keep the trajectory, as well as the movement amplitude, of the CoM stable. Impact of running over an uneven terrain for a climbing specialist. Contrary to A. boskianus, the average speed when negotiating the uneven terrain decreases significantly in P. muralis (1.16 ± 0.27 m.s−1 vs 0.92 ± 0.15 m.s−1; ANOVA F = 5.346, P = 0.025; Fig. 2). The stride frequency also decreases significantly (11.69 ± 2 Hz vs 11.19 ± 2 Hz; ANCOVA F = 9.148, P = 0.004; Fig. 2). Although the amplitude of the foot move- ments does not change on the uneven terrain, the centre of the orbit of the foot movement shifts downwards on the sagittal plane (ANCOVA F = 7.67, P = 0.008; Figs 4 and 5); it does not change in the frontal and trans- versal planes. The CoM translation in the Z-direction increases (ANCOVA F = 5.09, P = 0.029) and the relative height at which the CoM is maintained decreases significantly (9.21 ± 3 mm vs 6.22 ± 2 mm; ANCOVA F = 7.732, P = 0.0078). Costs of transport in running and climbing specialists. The cost of transport does not differ between the two species when running on a flat substrate (ANOVA F = 0.029, P = 0.87), however A. boskianus still run on average 50% faster than P. muralis. The substrate type (flat or uneven) does not impact the cost of transport in A. boskianus which supports the hypothesis that the morphology of A. boskianus is strongly adapted for fast running27,29. When these animals encounter large terrain variations relative to leg length, they can continue to minimise the energy costs related to running. In A. boskianus, leg movements adjust to the substrates’ variations, enhancing the stability of their CoM15. On the contrary, the complex terrain provokes a significant increase in the cost of transport in P. muralis (ANOVA F = 4.445, P = 0.041; Fig. 6) but the relationship between the cost of transport and speed is not impacted, i.e. there is no significant difference between the slopes of the regression lines among and within species. The general increase in the cost of transport in the climbing specialist P. muralis Figure 3. Average amplitudes of the CoM and foot displacements in the Y- and Z-directions. See Fig. 1 for symbol significance. 7 Scientific RepoRtS | (2019) 9:16858 | https://doi.org/10.1038/s41598-019-53329-5 www.nature.com/scientificreports www.nature.com/scientificreports/ is mainly related to an increase of the positive external energy (Fig. 7). Although it can keep the energy costs associated with running at a low level on an even terrain, the costs increase significantly when the demands of the terrain become too high. In the context of the present uneven terrain, the running performance of P. muralis is strongly affected and the locomotor economy is lost. Overall, our results show that runners and climbers have the same cost of transport on level terrain, although climbers run at a slower speed. Contrary to our hypothesis, the cost of transport is not higher in the climbers Figure 4. Average position of the foot relative to the hip per stride and corrected for size in the three planes of movement. See Fig. 1 for colour and symbol significance. Figure 5. Mean trajectory of the foot movement relative to the hip on the sagittal (Z) and frontal (Y) planes (note that the orbits are not corrected for size). The hip is represented by a white circle. The orange colour is for A. boskianus (adapted from15), the green colour is for P. muralis. Within each species, darker orbits represent the control (flat surface), lighter orbits represent the uneven terrain (i.e. hemi-spheres). 8 Scientific RepoRtS | (2019) 9:16858 | https://doi.org/10.1038/s41598-019-53329-5 www.nature.com/scientificreports www.nature.com/scientificreports/ running on a level surface. Nevertheless, it is possible that the lower running speed of climbers allows them to maintain low energy costs (the slope between speed and cost of transport does not differ between the two spe- cies, but if P. muralis was able to run faster, Fig. 6 suggests even higher costs relative to A. boskianus). Thus, the morphological features associated with climbing impair sprint speed, as there is no difference in dimensionless speed. As anticipated, on the one hand, the uneven terrain has no influence on the average speed and the cost of transport in the running specialist, A. boskianus. On the other hand, the climber, P. muralis, encounters many more difficulties when negotiating uneven terrain. Indeed, speed and energy expenditure are impaired in P. mura- lis running on uneven terrain. In general, their legs are kept more extended, but the amplitude of movement does not change. Hence, leg movements do not adjust to the terrain as observed in A. boskianus, resulting in an increase of the movement of the CoM and a decrease in locomotor efficiency. Movement is obviously related to muscle effort, and it can be expected that the maximal locomotor power output will be limited by the force that can be generated by the muscles. Some authors have argued that small animals (mammals and reptiles) do not rely on elastic energy mechanisms for locomotion, thus they exhibit important metabolic costs27,35. Although sprawled leg postures should increase the required muscle forces32,39,40, Figure 6. Relationship between speed and cost of transport (estimated from the fluctuations of the minimal mechanical energy) in P. muralis (in green) and A. boskianus (in orange). Squares indicate the strides performed on the flat (control) substrate and the solid lines represent the respective linear models, circles indicate the strides performed on the uneven substrates and the dashed lines represent the linear models. Figure 7. Positive and negative minimal mechanical energy in A. boskianus and P. muralis on the control and complex terrain. The solid colour represents the positive energy (+) and the diagonal lines represent the negative energy (−). 9 Scientific RepoRtS | (2019) 9:16858 | https://doi.org/10.1038/s41598-019-53329-5 www.nature.com/scientificreports www.nature.com/scientificreports/ our results suggest that, overall, the costs of transport associated with running are similar in two lizard species adopting either more parasagittal leg postures (A. boskianus) or more sprawled postures (P. muralis). However, we have observed that the costs of transport are larger in P. muralis than in A. boskianus when crossing uneven terrain. The necessity to limit metabolic costs may be less important in fast climbers than in fast runners because climbers from rocky environments primarily rely on explosive power generation in order to find shelter rapidly within close proximity. This is indeed a typical behavioural strategy of P. muralis25. On the other hand, small run- ning specialists such as A. boskianus definitely need endurance too, in order to escape to the nearest hiding place, which can be located at a distance, certainly for a desert species such as A. boskianus. As a result, they need to be able to keep the energy costs associated with fast locomotion at a low level; when targeted by a predator, making a stop in the middle of the pathway is not an option. A. boskianus can minimize the energetic challenges imposed by uneven terrain by limiting the movement amplitude of the CoM. Our study, therefore, supports the assump- tion that locomotor economy is optimized in accordance to the ecological relevance35. conclusion The capacity to negotiate uneven terrain at the scale of the animal size is not a common capacity shared by liz- ards in general. The climbing specialist tested in this study displays the lowest performance on uneven terrain. Saxicolous habitats are the primary niche of P. muralis, and it certainly poses many opportunities for hiding and escaping. In this way the obstacles and vertical substrates that have to be dealt with are commonly much larger than the size of these lizards. Our finding of the lower velocity and a higher energy cost on the uneven terrain for P. muralis compared to A. boskianus, support the theory that the former uses a behavioural strategy of swiftly escaping to a close hiding place when confronted with danger. For them, short running burst can be very effective. The running specialist A. boskianus, on the other hand, presumably runs away rapidly over long(er) distances under similar circumstances. This can, again, be linked to its specific structural microhabitat. A. boskianus lizards live in open environments such as deserts, where hiding spots can be located a long distance away. The specific structural microhabitat found in the desert may resemble most closely the uneven terrain in our experiments because on sandy substrates, sand ridges are often present, as a result of complex interactions between flowing sand masses and wind. This microstructure of a substrate that is very flat on a larger scale, may challenge small lizards such as A. boskianus in a very similar way as the uneven terrain in our experiments. This could explain why they perform so well on this substrate, both in terms of velocity and energy expenditure. Our study, therefore, supports the hypothesis that microhabitats impose functional demands that species are adapted for, rather than large ecological niches41. Furthermore, locomotor costs can also be important factors in small vertebrates. Given their ecological niche, locomotor economy may represent a significant constraint for the evolution of lizards. Data availability All the data used in the statistical tests can be found in Supplementary Material (Dataset 2). Received: 31 May 2019; Accepted: 29 October 2019; Published: xx xx xxxx References 1. Preuschoft, H., Witte, H., Christian, A. & Fischer, M. Size Influences on Primate Locomotion and Body Shape, with Special Emphasis on the Locomotion of ‘Small Mammals’. Folia Primatologica 66(1-4), 93–112 (1996). 2. Arnold, S. J. Morphology, performance and fitness. American Zoologist 23(2), 347–361 (1983). 3. Aerts, P., Van Damme, R., Vanhooydonck, B., Zaaf, A. & Herrel, A. Lizard locomotion: how morphology meets ecology. Netherlands Journal of Zoology 50(2), 261–278 (2000). 4. Steudel, K. The work and energetic cost of locomotion. I. The effects of limb mass distribution in quadrupeds. Journal of Experimental Biology 154(1), 273–285 (1990). 5. Biewener, A., Alexander, R. M. & Heglund, N. Elastic energy storage in the hopping of kangaroo rats (Dipodomys spectabilis). Journal of Zoology 195(3), 369–383 (1981). 6. Kilbourne, B. M. & Hoffman, L. C. Scale effects between body size and limb design in quadrupedal mammals. PloS one 8(11), e78392 (2013). 7. Minetti, A. E., Ardigò, L. P., Reinach, E. & Saibene, F. The relationship between mechanical work and energy expenditure of locomotion in horses. Anglais 202(17), 2329–2338 (1999). 8. Taylor, C. R., Heglund, N. C. & Maloiy, G. Energetics and mechanics of terrestrial locomotion. I. Metabolic energy consumption as a function of speed and body size in birds and mammals. Journal of Experimental Biology 97(1), 1–21 (1982). 9. Full, R., Mechanics and energetics of terrestrial locomotion: bipeds to polypeds in Energy Transformations in Cells and Animals, edited by et al. Stuttgart, W. E. 1989), pp. 175–182. 10. Schmidt-Nielsen, K., Scaling: why is animal size so important? (Cambridge University Press, 1984). 11. Kohlsdorf, T. & Biewener, A. Negotiating obstacles: running kinematics of the lizard Sceloporus malachiticus. Journal of Zoology 270(2), 359–371 (2006). 12. Olberding, J. P., McBrayer, L. D. & Higham, T. E. Performance and three-dimensional kinematics of bipedal lizards during obstacle negotiation. Journal of Experimental Biology 215(2), 247–255 (2012). 13. Tucker, D. B. & McBrayer, L. D. Overcoming obstacles: the effect of obstacles on locomotor performance and behaviour. Biological Journal of the Linnean Society 107(4), 813–823 (2012). 14. Parker, S. E. & McBrayer, L. D. The effects of multiple obstacles on the locomotor behavior and performance of a terrestrial lizard. Journal of Experimental Biology 219(7), 1004–1013 (2016). 15. Druelle, F., Goyens, J., Vasilopoulou-Kampitsi, M. & Aerts, P. Compliant legs enable lizards to maintain high running speeds on complex terrains. Journal of Experimental Biology 222(6), jeb195511 (2019). 16. Goodman, B. A., Miles, D. B. & Schwarzkopf, L. Life on the rocks: habitat use drives morphological and performance evolution in lizards. Ecology 89(12), 3462–3471 (2008). 17. Losos, J., Walton, B., & Bennett, A., Trade-offs between sprinting and clinging ability in Kenyan chameleons. Functional Ecology, 281–286 (1993). 18. Preuschoft, H. & Günther, M., Biomechanics and body shape in primates compared with horses. Zeitschrift für Morphologie und Anthropologie, 149–165 (1994). 10 Scientific RepoRtS | (2019) 9:16858 | https://doi.org/10.1038/s41598-019-53329-5 www.nature.com/scientificreports www.nature.com/scientificreports/ 19. Van Damme, R., Aerts, P. & Vanhooydonck, B. No trade-off between sprinting and climbing in two populations of the Lizard Podarcis hispanica (Reptilia: Lacertidae). Biological Journal of the Linnean Society 60(4), 493–503 (1997). 20. Clemente, C. J., Withers, P. C., Thompson, G. G. & Lloyd, D. Lizard tricks: overcoming conflicting requirements of speed versus climbing ability by altering biomechanics of the lizard stride. Journal of Experimental Biology 216(20), 3854–3862 (2013). 21. Herrel, A., Meyers, J. J. & Vanhooydonck, B. Relations between microhabitat use and limb shape in phrynosomatid lizards. Biological Journal of the Linnean Society 77(1), 149–163 (2002). 22. Verwaijen, D. & Van Damme, R. Wide home ranges for widely foraging lizards. Zoology 111(1), 37–47 (2008). 23. Brana, F. Morphological correlates of burst speed and field movement patterns: the behavioural adjustment of locomotion in wall lizards (Podarcis muralis). Biological Journal of the Linnean Society 80(1), 135–146 (2003). 24. Brown, R.M., Taylor, D.H. & Gist, D.H., Effect of caudal autotomy on locomotor performance of wall lizards (Podarcis muralis). Journal of Herpetology, 98–105 (1995). 25. Vanhooydonck, B. & Van Damme, R. Relationships between locomotor performance, microhabitat use and antipredator behaviour in lacertid lizards. Functional Ecology 17(2), 160–169 (2003). 26. Duvdevani, I. & Borut, A., Mean body temperature and heat absorption in four species of Acanthodactylus lizards (Lacertidae). Herpetologica, 176–181 (1974). 27. Curtin Nancy, A., Woledge Roger, C. & Aerts, P. Muscle directly meets the vast power demands in agile lizards. Proceedings of the Royal Society B: Biological Sciences 272(1563), 581–584 (2005). 28. Pérez-Mellado, V. Ecology of lacertid lizards in a desert area of eastern Morocco. Journal of Zoology 226(3), 369–386 (1992). 29. Goyens, J. & Aerts, P., Head stabilisation in fast running lizards. Zoology (2017). 30. Vanhooydonck, B. & Van Damme, R. Evolutionary trade-offs in locomotor capacities in lacertid lizards: are splendid sprinters clumsy climbers? Journal of evolutionary biology 14(1), 46–54 (2001). 31. Sathe, E. A. & Husak, J. F. Sprint sensitivity and locomotor trade-offs in green anole (Anolis carolinensis) lizards. Journal of Experimental Biology 218(14), 2174–2179 (2015). 32. Biewener, A. A. Scaling body support in mammals: limb posture and muscle mechanics. Science 245(4913), 45–48 (1989). 33. Hedrick, T. L. Software techniques for two-and three-dimensional kinematic measurements of biological and biomimetic systems. Bioinspiration & biomimetics 3(3), 034001 (2008). 34. Cavagna, G. & Kaneko, M. Mechanical work and efficiency in level walking and running. The Journal of Physiology 268(2), 467–481 (1977). 35. Reilly, S. M., McElroy, E. J. & Biknevicius, A. R. Posture, gait and the ecological relevance of locomotor costs and energy-saving mechanisms in tetrapods. Zoology 110(4), 271–289 (2007). 36. Hill, A. V. The heat of shortening and the dynamic constants of muscle. Proceedings of the Royal Society of London. Series B-Biological Sciences 126(843), 136–195 (1938). 37. Aerts, P., Van Damme, R., Van Elsacker, L. & Duchene, V. Spatio-temporal gait characteristics of the hind-limb cycles during voluntary bipedal and quadrupedal walking in bonobos (Pan paniscus). American journal of physical anthropology 111, 503–517 (2000). 38. Zaaf, A., Van Damme, R., Herrel, A. & Aerts, P. Spatio-temporal gait characteristics of level and vertical locomotion in a ground- dwelling and a climbing gecko. Journal of Experimental Biology 204(7), 1233–1246 (2001). 39. Daley, M. A. & Biewener, A. A. Leg muscles that mediate stability: mechanics and control of two distal extensor muscles during obstacle negotiation in the guinea fowl. Philosophical Transactions of the Royal Society of London B: Biological Sciences 366(1570), 1580–1591 (2011). 40. Bishop, K. L., Pai, A. K. & Schmitt, D. Whole Body Mechanics of Stealthy Walking in Cats. PloS one 3(11), e3808 (2008). 41. Higham, T. E. Lizard Locomotion in Behavior of Lizards: Evolutionary and Mechanistic Perspectives, edited by V Bels & A Russel, pp. 47 (2019). Acknowledgements We are very grateful to Gregory Desor and Jan Scholliers for their valuable help for designing and building the complex terrain. We thank Lise Eerdekens, Nick Van Hul and Ilse Goyens for their valuable help in digitising a large number of running sequences. We thank the referees for their constructive and detailed comments on the first version of the manuscript. We are very grateful to Josie Meaney-Ward who revised and improved the English of the manuscript. This work was supported by Fonds Wetenschappelijk Onderzoek (FWO project G0E02.14N). J.G. was funded by an FWO postdoctoral fellowship (12R5118N). M.V.-K. was funded by the Department of Biology, University of Antwerp. Author contributions Conceptualization and methodology: F.D., J.G., P.A.; Data collection and investigation: F.D., M.V.-K.; Analyses: F.D., J.G., P.A.; Data curation: F.D.; Writing - original draft: F.D.; Writing - review & editing: F.D., J.G., M.V.-K., P.A.; Project administration: P.A.; Funding acquisition: P.A., J.G. Competing interests The authors declare no competing interests. Additional information Supplementary information is available for this paper at https://doi.org/10.1038/s41598-019-53329-5. Correspondence and requests for materials should be addressed to F.D. Reprints and permissions information is available at www.nature.com/reprints. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 11 Scientific RepoRtS | (2019) 9:16858 | https://doi.org/10.1038/s41598-019-53329-5 www.nature.com/scientificreports www.nature.com/scientificreports/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre- ative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not per- mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2019

Small vertebrates running on uneven terrain: a biomechanical study of two differently specialised lacertid lizards.

11-14-2019

Druelle, François,Goyens, Jana,Vasilopoulou-Kampitsi, Menelia,Aerts, Peter

eng

PMC8295593

Physiological Reports. 2021;9:e14956. | 1 of 9 https://doi.org/10.14814/phy2.14956 wileyonlinelibrary.com/journal/phy2 1 | INTRODUCTION The classification of endurance exercise fatigue encom- passes diverse models and theories (Abbiss & Laursen, 2005), components (Carriker, 2017), and various aspects of muscular function (Wan et al., 2017), biochemical bal- ance (Jastrzębski et al., 2015) as well as both the central and peripheral nervous systems (Davis & Walsh, 2010; Received: 3 April 2021 | Revised: 11 June 2021 | Accepted: 17 June 2021 DOI: 10.14814/phy2.14956 O R I G I N A L A R T I C L E Fractal correlation properties of heart rate variability as a biomarker of endurance exercise fatigue in ultramarathon runners Bruce Rogers1 | Laurent Mourot2,3 | Gregory Doucende4 | Thomas Gronwald5 This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2021 The Authors. Physiological Reports published by Wiley Periodicals LLC on behalf of The Physiological Society and the American Physiological Society 1College of Medicine, University of Central Florida, Orlando, FL, USA 2EA3920 Prognostic Factors and Regulatory Factors of Cardiac and Vascular Pathologies, Exercise Performance Health Innovation (EPHI) platform, University of Bourgogne Franche- Comté, Besançon, France 3National Research Tomsk Polytechnic University, Tomsk Oblast, Russia 4Université de Perpignan Via Domitia, Laboratoire Européen Performance Santé Altitude (LEPSA), Besançon, France 5Faculty of Health Sciences, Department of Performance, Neuroscience, Therapy and Health, MSH Medical School Hamburg, University of Applied Sciences and Medical University, Hamburg, Germany Correspondence Bruce Rogers, College of Medicine, University of Central Florida, 6850 Lake Nona Boulevard, Orlando, FL 32827- 7408, USA. Email: [email protected] Funding information This research received no external funding. Abstract Although heart rate variability (HRV) indexes have been helpful for monitoring the fatigued state while resting, little data indicate that there is comparable potential dur- ing exercise. Since an index of HRV based on fractal correlation properties, alpha 1 of detrended fluctuation analysis (DFA a1) displays overall organismic demands, alteration during exertion may provide insight into physiologic changes accompany- ing fatigue. Two weeks after collecting baseline demographic and gas exchange data, 11 experienced ultramarathon runners were divided into two groups. Seven runners performed a simulated ultramarathon for 6 h (Fatigue group, FG) and four runners performed daily activity over a similar period (Control group, CG). Before (Pre) and after (Post) the ultramarathon or daily activity, DFA a1, heart rate (HR), running economy (RE) and countermovement jumps (CMJ) were measured while running on a treadmill at 3 m/s. In Pre versus Post comparisons, data showed a decline with large effect size in DFA a1 post intervention only for FG (Pre: 0.71, Post: 0.32; d = 1.34), with minor differences and small effect sizes in HR (d = 0.02) and RE (d = 0.21). CG showed only minor differences with small effect sizes in DFA a1 (d = 0.19), HR (d = 0.15), and RE (d = 0.31). CMJ vertical peak force showed fatigue- induced decreases with large effect size in FG (d = 0.82) compared to CG (d = 0.02). At the completion of an ultramarathon, DFA a1 decreased with large effect size while run- ning at low intensity compared to pre- race values. DFA a1 may offer an opportunity for real- time tracking of physiologic status in terms of monitoring for fatigue and possibly as an early warning signal of systemic perturbation. K E Y W O R D S DFA a1, endurance exercise, fatigue, marathon, running 2 of 9 | ROGERS Et al. McMorris et al., 2018; Martínez- Navarro et al., 2019; Martin et al., 2018; for an overview see Ament & Verkerke, 2009). Objective means to quantify fatigue related to endurance exercise may include various modalities including salivary hormone markers (Deneen & Jones, 2017), muscle enzyme elevation (Martínez- Navarro et al., 2019), blood lactate con- centration (Jastrzębski et al., 2015), markers of substrate availability (Schader et al., 2020), cortical activity (Ludyga et al., 2016), functional testing such as the counter movement jump (Wu et al., 2019) and measures of running economy (Scheer et al., 2018). Fatigue can be also measured subjec- tively through “rating of perceived effort” (RPE, Halperin & Emanuel, 2020) such as the well- known Borg scale (Borg, 1982). Although well established, none of these tools are eas- ily implemented for practical usage in the vast majority of endurance athletes. Since exercise- related fatigue is an in- evitable consequence of a long duration endurance session, an easily available objective biomarker using a low- cost con- sumer wearable device would be ideal. While resting heart rate (HR) variability (HRV) may provide information on functional overreaching, and post exercise HRV may indicate autonomic recovery status (Manresa- Rocamora et al., 2021; Stanley et al., 2013), neither modality can answer the ques- tion of whether a specific exercise endeavor is leading to a fatigued state as the activity occurs. Recently, a nonlinear index of HRV based on fractal correla- tion properties termed alpha 1 (short- term scaling exponent) of detrended fluctuation analysis (DFA a1) has been shown to change with increasing exercise intensity (Gronwald & Hoos, 2020). This index represents the fractal, self- similar nature of cardiac beat- to- beat intervals. At low exercise intensity, DFA a1 values usually are near 1 or slightly above, signifying a well correlated, fractal pattern. As intensity rises, the index will drop past 0.75 near the aerobic threshold (AT) then approach uncorrelated, random patterns represented by values near 0.5 at higher work rates (Rogers, Giles, Draper, Hoos et al., 2021). The underlying mechanism for this behavior is felt to be due to alterations in autonomic nervous system balance, primarily withdrawal of the parasympathetic branch and enhancement of the sympathetic branch as well as other potential factors (Gronwald et al., 2020). As opposed to other HRV indexes that reach a nadir value at the aerobic threshold (SDNN: the total variability as the standard deviation of all normal RR in- tervals; SD1: standard deviation of the distances of the points from the minor axis in the Poincaré plot), DFA a1 has a wide dynamic range sufficient to differentiate mild versus moder- ate versus severe intensity domains. For example, at the AT, a DFA a1 near 0.75 is usually present (Rogers, Giles, Draper, Hoos et al., 2021), whereas SDNN and SD1 are already at their lowest values (Gronwald et al., 2020). One advantageous prop- erty of DFA a1 revolves around its dimensionless nature, as values appear to apply to an individual regardless of fitness status. For example, a value of 0.5 corresponds to an exercise intensity well above the AT in most individuals without hav- ing prior knowledge of the current HR or power (Gronwald et al., 2020). In addition to its recent usage to delineate the AT during exercise testing, DFA a1 has an extensive literature as a final common pathway of assessing total body “organismic de- mand” (Gronwald & Hoos, 2020). This concept refers to DFA a1 status as an index of overall systemic internal load rather than being purely related to isolated single factor measures of external load such as cycling power, or metrics of subsystem internal loads such as HR, respiratory rate, or VO2. Therefore, the dimensionless index DFA a1 shows great potential as a descriptor of the Network Physiology of Exercise (NPE), re- cently introduced by Balagué et al., (2020). In particular, this index is well suited for the demarcation of the complex dynam- ics of internal load development over the course of prolonged endurance exercise as well as for the assessment of athletes' fatigued state while still in the process of exercising. Although various endurance exercise modalities can lead to fatigue, the ultramarathon represents one of the most ex- treme examples. As defined by a run distance of over 42 km with a variety of surface/terrain/elevation characteristics (Scheer et al., 2020), it has been associated with electrolyte imbalance, severe muscle damage, end organ dysfunction, altered oxygen cost of running, and hormonal dysregula- tion (Knechtle & Nikolaidis, 2018; Ramos- Campo et al., 2016). At the same time, the pace is generally considered moderate, with only slight lactate elevations above baseline noted (Jastrzębski et al., 2015; Ramos- Campo et al., 2016). Therefore, it represents an extreme setting of prolonged but moderate level exercise intensity that can lead to major sys- temic perturbation. Since DFA a1 has been shown to be a marker of overall organismic demand, it would be of interest to explore its behavior after such an endeavor. In addition, since it has also been noted to be a proxy for the aerobic threshold, alteration of this relationship may indicate the need for pace adjustment for the purpose of intensity distri- bution. Although relatively short durations of exercise below the AT do not seem to lead to major alterations in DFA a1 behavior (Rogers, 2020), physiologic disruption produced by an ultramarathon certainly could do so. Hence, the aim of this report is to evaluate the change in exercise associated DFA a1 dynamics toward the end of a simulated ultramarathon and compare this to changes in HR and running economy while still performing dynamic exercise. 2 | MATERIALS AND METHODS 2.1 | Participants Eleven experienced (nine male, two female) ultramarathon runners without major past medical history, medications, or  | 3 of 9 ROGERS Et al. recent illness were recruited for the study. All had purpose- fully trained for an ultramarathon and were experienced in performing a race of greater than 50 km or longer than 6 h in total duration. 2.2 | Baseline assessment As part of the baseline assessment, participants performed a familiarization of countermovement jumps (CMJ) prac- tice with an emphasis on the speed of jump. An incremental treadmill test to exhaustion was done to determine peak oxy- gen uptake (VO2MAX), the first and second ventilatory thresh- olds 2 weeks prior to the ultramarathon run. After a warm- up of about 10 min at 3 m/s, the initial running speed was set at 3.6 m/s with the first stage lasting 2 min. The speed was then progressively increased by 0.28 m/s every 2 min until exhaustion. Breath- by- breath gas exchange was continu- ously measured via metabolic cart (Metalyzer 3B- R3system; Cortex Biophysics, Leipzig, Germany). Ventilatory thresh- olds were determined visually with the first threshold defined by the V slope method and second threshold by the change in VCO2/ventilation ratio (Beaver et al., 1986). VO2MAX was defined as the average VO2 over the last 60 s of the test. Peak effort was confirmed by failure of VO2 and/or HR to increase with further increases in work rate. Pertinent demographic data are shown in Table 1 including age, height, weight, years of training, weekly training volume, and results of the gas exchange testing. Participants did not consume caffeine, alcohol, or any stimulant for the 24  h before testing. The experimental design of the study was approved by the local Human Research Ethic Committee (2016- A00511- 50), con- ducted in conformity with the latest version of the Declaration of Helsinki and written informed consent for all participants was obtained. 2.3 | Study protocol Initially, all participants underwent a CMJ testing sessions with 3 CMJ trials and 30 s rest between to assess fatigue- induced changes in the neuromuscular function (Claudino et al., 2017). The maximum jump height and the vertical peak force normalized per the participants’ body mass(N/ kg) were measured using a portable force platform (Quattro- Jump, Kistler, Winterthur, Switzerland) at a sampling rate of 500 Hz. The average values of the 3 CMJ trials were used in the subsequent statistical analysis. All participants then per- formed a treadmill run (Pre) at a fixed velocity of 3 m/s for a duration of 5 min the day before the simulated ultramara- thon for measurements of oxygen uptake (VO2). Breath- by- breath gas exchange was continuously measured by the same metabolic cart as in the initial assessment (Metalyzer 3B- R3 system; Cortex Biophysics, Leipzig, Germany). VO2 was averaged over the last 1 min to estimate the running econ- omy (Bontemps et al., 2020). The following day, seven par- ticipants ran a simulated ultramarathon for approximately 6 h (Fatigue group, FG, see Table 2), while the remaining four TABLE 1 Demographic data and data from the baseline assessment of all participants (n = 11) Group Age Sex BW [Kg] Ht [cm] Yrs training Hrs/wk training VO2MAX [ml/ kg/min] VT1 [ml/ kg/min] VT2 [ml/ kg/min] FG 1 20 M 70 190 6 13 80 52 68 FG 2 24 M 65 175 10 12 75 48 65 FG 3 22 M 81 186 10 11 74 47 63 FG 4 44 F 54 162 6 11 63 39 52 FG 5 45 M 64 170 5 5 55 36 45 FG 6 43 M 72 176 30 5 53 35 43 FG 7 49 M 71 170 12 8 52 34 42 Mean±SD 35 (±12) – 68 (±8) 176 (±9) 11 (±8) 9 (±3) 64 (±11) 42 (±7) 54 (±10) CG 1 24 M 67 162 8 15 75 46 62 CG 2 32 M 68 178 6 9 75 47 65 CG 3 40 M 68 177 20 9 70 45 60 CG 4 42 F 60 168 3 4 49 30 41 Mean ± SD 35 (±7) – 66 (±3) 171 (±7) 9 (±6) 9 (±4) 67 (±11) 42 (±7) 57 (±9) d 0.07 – 0.33 0.48 0.25 0.01 0.22 0.06 0.27 Group: Fatigue group with number of the participant (FG) and Control group with number of the participant (CG), Age, current age, Sex; BW, Body weight; Ht, Height; Yrs training, total years of marathon training; Hrs/wk training, approximate hours per week of marathon- related training; VO2MAX, peak oxygen uptake reached on baseline ramp test; VT1, first ventilatory threshold; VT2, second ventilatory threshold. Mean (± standard deviation, SD) and Cohen's d for group comparisons in last row. 4 of 9 | ROGERS Et al. TABLE 2 Pre and Post intervention data for both groups and all participants (n=11) Group Pre Post Ultramarathon HR [bpm] DFA a1 RE [ml/ kg/min] CMJ vertical peak force [N/ kg] CMJ jump height [cm] VO2 run/ VT1 [%] HR [bpm] DFA a1 RE [ml/ kg/min] CMJ vertical peak force [N/ kg] CMJ jump height [cm] VO2 run/ VT1 [%] Time [h:min] Distance [Km] Speed [m/s] FG 1 158 1.286 39 – – 75% 170 0.353 41 – – 78% 5:50 42 2.0 FG 2 125 1.192 37 21.7 32.2 77% 133 0.396 36 21.7 30.1 75% 6:35 48 2.0 FG 3 134 0.776 29 18.9 34.6 61% 134 0.356 33 17.1 29.0 70% 6:35 48 2.0 FG 4 132 0.269 36 21.6 21.7 92% 131 0.358 35 20.0 19.9 90% 5:52 44 2.1 FG 5 149 0.706 37 21.9 23.1 102% 141 0.314 37 18.4 20.5 102% 5:54 39 1.8 FG 6 141 0.313 41 20.7 26.9 117% 135 0.124 35 18.8 24.5 100% 6:15 43 1.9 FG 7 148 0.436 35 16.7 15.0 102% 143 0.317 32 16.5 13.8 93% 6:10 45 2.0 Mean±SD 141 (±11) 0.71 (±0.41) 36 (±4) 20.2 (±1.9) 25.6 (±6.6) 89 (±19) 141 (±13) 0.32 (±0.09) 36 (±3) 18.8 (±1.7) 23.0 (±5.6) 87 (±12) 6:10 (±0:19) 44 (±3) 2.0 (±0.1) CG 1 129 1.201 34 25.7 33.7 74% 127 1.301 33 26.1 34.8 72% CG 2 140 0.853 36 20.2 24.5 76% 136 0.806 35 19.4 24.4 74% CG 3 110 1.063 32 22.4 23.7 71% 103 1.157 32 22.5 24.0 71% CG 4 163 0.559 38 17.9 12.3 125% 158 0.598 37 18.5 13.9 122% Mean±SD 135 (±22) 0.92 (±0.28) 35 (±3) 21.6 (±3.3) 23.6 (±8.8) 87 (±25) 131 (±22) 0.97 (±0.32) 34 (±2) 21.6 (±3.4) 24.3 (±8.5) 85 (±21) d 0.34 0.56 0.38 0.53 0.28 0.12 0.58 3.25 0.49 1.17 0.19 0.12 Group, Fatigue group with number of the participant (FG) and Control group with number of the participant (CG); HR, average heart rate; DFA a1, short- term scaling exponent alpha1 of detrended fluctuation analysis; RE, running economy via oxygen uptake; CMJ, counter movement jump assessment (please consider that there is one data pair missing in FG due to technical issues) ; VO2 run/VT1, ratio of the oxygen uptake measured during the Pre or Post 3 m/s treadmill run to that of the oxygen uptake of the first ventilatory threshold from baseline assessment; Time, time spent performing the simulated ultramarathon; Distance, distance performed in the simulated ultramarathon; Speed, calculated average run speed of the ultramarathon based on time and distance. Mean (± standard deviation; SD) and Cohen's d for group comparisons in last row.  | 5 of 9 ROGERS Et al. participants (Control group, CG) did normal nonstrenuous daily activity for 6 h. Participants ran on an 11.5- km off road trail loop at a freely chosen pace (with an elevation change of 550 m) without rest periods and were allowed to ingest food and water freely. Immediately following the completion of the 6 h run or 6 h nonstrenuous activity, an identical CMJ assessment and treadmill test (Post) was performed on each individual for the same measurement parameters. No change in protocol occurred between pre and post intervention test- ing. Estimated running speed was calculated based on total covered distance and elapsed time. 2.4 | RR measurements and calculation of DFA a1 A Polar H10 (Polar Electro Oy, Kempele, Finland) HR moni- toring (HRM) device with a sampling rate of 1000 Hz was used to detect RR intervals in all individuals during the Pre and Post treadmill run over 5 min. All RR data were recorded with a Suunto Memory Belt (Suunto, Vantaa, Finland), downloaded as text files, and then imported into Kubios HRV Software Version 3.4.3 (Biosignal Analysis and Medical Imaging Group, Department of Physics, University of Kuopio, Kuopio, Finland; Tarvainen et al., 2014). Kubios preprocessing settings were set to the default values includ- ing the RR detrending method which was kept at “Smoothn priors” (Lambda = 500). DFA a1 window width was set to 4 ≤ n ≤ 16 beats. The RR series was then corrected by the Kubios “automatic method” (Lipponen & Tarvainen, 2019) and relevant parameters exported as text files for further anal- ysis. DFA a1 and average HR were calculated from the RR data series of the 2 min time window consisting of the start of minute 4 to the end of minute 5 of the treadmill exercise in both Pre and Post conditions. Two min time windowing was chosen based on previous calculations as to the mini- mal required beat count (Chen et al., 2002). Artifact levels measured by Kubios HRV were below 5%. This limit was previously shown to have minimal effect on DFA a1 during exercise (Rogers, Giles, Draper, Mourot et al., 2021). 2.5 | Statistics Statistical analyses of means and standard deviations were performed for demographic data, Pre and Post treadmill run DFA a1, average HR and VO2 in Microsoft Excel 365. Additional statistical analysis was performed using SPSS 23.0 (IBM Statistics, United States) for Windows (Microsoft, USA). The Shapiro– Wilk test was applied to verify the Gaussian distribution of the data. The degree of variance homogeneity was verified by the Levene's test. To account for the unbalanced and small participant numbers of the elite ultramarathon runners group comparison of demographic data, data of baseline assessment, pre intervention data and to analyze the effects of the intervention (Pre vs. Post) on dependent variables (DFA a1, HR, RE, and CMJ) were em- ployed via effect size calculation (Coe, 2002) (the mean difference between scores divided by the pooled standard de- viation of group comparison and Pre versus Post comparison of each variable). The interpretation of effect sizes is based on Cohen's thresholds for small effects (d < 0.5), moderate effects (d ≥ 0.5), and large effects (d > 0.8) (Cohen, 1988). 3 | RESULTS Mean and standard deviations for measured parameters are listed in Table 2 for each group (FG vs. CG). There were only small effect sizes in group comparison in demographic data and data from baseline assessment (Table 1). Pre intervention data showed small to medium effect sizes in comparison of both groups in dependent variables HR, DFA a1, RE, and CMJ (Table 2). In Pre versus Post comparisons, data showed a decline with large effect size in DFA a1 (d = 1.38) and CMJ vertical peak force (d = 0.82) post intervention only for FG, with minor differences and small effect sizes in HR (d = 0.02), RE (d = 0.21) or CMJ jump height (d = 0.43). CG showed only minor differences with small effect sizes in DFA a1 (d = 0.19), HR (d = 0.15), RE (d = 0.31) and CMJ vertical peak force (d = 0.02), and jump height (d = 0.09) (Figure 1). 4 | DISCUSSION The aim of this study was to determine if a simulated ultra- marathon run- induced changes in a nonlinear HRV index of fractal correlation properties, DFA a1, during dynamic exer- cise. Since the ultramarathon has been shown to cause major perturbation of many metabolic, systemic, and neuromuscu- lar systems (Knechtle & Nikolaidis, 2018; Ramos- Campo et al., 2016), it is ideal for investigating whether a HRV index representing overall organismic demand also exhib- its analogous alterations while still performing the exercise. This particular index is especially well suited for the assess- ment of overall physiologic status during activity by virtue of its excellent dynamic range over mild, moderate, and severe exercise intensity domains (Gronwald et al., 2020). A major finding of this report is that after a 6 h ultramarathon, DFA a1 was markedly suppressed while running at a pace close to the aerobic threshold. Vertical peak force decreases from CMJ assessment confirmed fatigue- induced changes in the neuro- muscular function of the lower- limbs. Despite the expected systemic effects, neither HR nor running economy appeared to be altered after the ultramarathon. Past analyses have 6 of 9 | ROGERS Et al. shown variable effects on measures of running economy post ultramarathon with both higher and neutral oxygen usage at a fixed running speed (Scheer et al., 2018; Vernillo et al., 2019). In regard to HR over the course of a marathon, it ap- pears that this metric is not very helpful in monitoring ongo- ing fatigue. HR can remain stable without much upward drift over the course of a marathon, at the cost of a slight decrease in speed (Billat et al., 2012). Therefore, if one were attempt- ing to track signs of metabolic distress by observing HR, VO2, or DFA a1 in this particular study, only DFA a1 would have revealed changes while activity was ongoing. As compared with Pre measurements, DFA a1 was markedly suppressed in all athletes during the exercise at a fixed low intensity pace after the ultramarathon, comprising values well past uncorre- lated patterns and falling into the anticorrelated range. These values are generally associated with the highest exercise in- tensity domain and should not occur during low to moderate work rates (Gronwald & Hoos, 2020). In accordance with this observation, prior studies of prolonged cycling exercise (60 min or until voluntary exhaustion) with constant power at 90% to 100% of the second lactate threshold, showed DFA a1 exhibiting a clear decrease comparing the beginning and end of the exercise bout, potentially showing an effect of fatigue (Gronwald et al., 2018, 2019). In the present study, all but one of the FG individuals had suppression of DFA a1 from their Pre- values. Although the CG did not have similar DFA a1 values compared to the FG before the ultramarathon they did not have a meaningful decline, when tested again after normal daily activity. In terms of running pace, the ultramar- athon speed was well below that of the treadmill test of 3 m/s and below the AT as demonstrated by baseline VO2 measure- ments. Despite this point, it appears that blood lactate does accumulate above baseline but still remains at a steady state during an ultramarathon run (Jastrzębski et al., 2015; Ramos- Campo et al., 2016). Therefore, it seems that blood lactate could underestimate the severity of this type of long duration exercise in terms of whole body systemic effects. The mechanism of DFA a1 decline during both increas- ing exercise intensity and high organismic demand revolves around autonomic nervous system balance as well as other potential factors (Sandercock & Brodie, 2006; Papaioannou et al., 2013; White & Raven, 2014; Michael et al., 2017). As overall demand rises there is a withdrawal of the parasympa- thetic and stimulation of the sympathetic system (White & Raven, 2014) affecting the sinoatrial node leading to a loss of fractal correlation properties of the HR times series. This can also be described in terms of a “networking” process (Balagué et al., 2020), related to integration of many meta- bolic, neuromuscular and hormonal inputs. With increasing exercise intensity and/or fatigue it seems that organismic reg- ulation starts to disengage subsystems (e.g., dissociation of cardiac and respiratory systems) in terms of a disintegration, decoupling, and segregation process (Gronwald et al., 2020). This behavior could be interpreted as a protective feedback mechanism where interactions of subsystems fail before the whole system fails. Interestingly, studies have indicated that DFA a1 rises in the immediate post ultramarathon recov- ery period during supine resting conditions, showing highly correlated patterns with increased correlation properties of HR time series (Martínez- Navarro et al., 2019). This activ- ity could be explained as a systematic reorganization of the organism with increased correlation properties in cardiac au- tonomic regulation with a predominance of parasympathetic activity during passive or active recovery with very low ex- ercise intensity (parasympathetic reactivation) (Casties et al., 2006; Kannankeril & Goldberger, 2002; Stanley et al., 2013). FIGURE 1 (a) Mean, 95% confidence interval and individual responses while running on a treadmill at 3 m/s for DFA a1 Pre and Post ultramarathon run (FG) in seven participants, (b) Mean, 95% confidence interval and individual responses while running on a treadmill at 3 m/s for DFA a1 Pre and Post daily activity (CG) in four participants  | 7 of 9 ROGERS Et al. It may also be related to a counter regulation (overcompensa- tion) of the organism to the prior load (Hautala et al., 2001). The organism responds with a highly correlated behavior signifying more order in recovery (Balagué et al., 2020; Gronwald et al., 2019). 4.1 | Limitations and future directions A limitation of this study is a lack of time related de- tail of speed, HR, and DFA a1 during the ultramarathon. Additional study looking at a comprehensive analysis of DFA a1 and related metrics throughout the entire run would certainly be of interest, especially at what point does its be- havior begin to deviate from normal. Periodic blood lactate determinations would also have been of interest, but dif- ficult on a practical basis. Although a derived running pace can be inferred from the overall session distance/time, it is possible that some heterogeneity was present. The over- all derived pace of 2 m/s was consistent with an intensity below the AT since VO2 measurements at 3 m/s were usu- ally slightly above or below the AT. Two female partici- pants were included but just one was in the FG. Given the limited data on female participants further evaluation of DFA a1 behavior during long duration endurance exercise is needed. An important potential issue in measuring DFA a1 during running may entail an artifactual suppression of correlation properties due to device bias, present in some in- dividuals more than others (Rogers, Giles, Draper, Mourot et al., 2021). Despite possessing low artifact data, in two of the FG participants, DFA a1 was already markedly sup- pressed at a running speed corresponding to their VT1. For this reason, DFA a1 Pre- values were different (with mod- erate effect size) in FG versus CG. Further study regarding the issue of inappropriate DFA a1 suppression at moderate running speed is needed. Sample size was relatively small but consistent with the difficulty in recruiting appropriate participants. On a practical note, the required measurement equipment consists of only a consumer grade HRM device which most athletes can easily obtain. Although this study employed a retrospective analysis to determine DFA a1, as mobile technology improves, it is conceivable that real- time DFA a1 monitoring during endurance exercise could be used to inform an individual about current physiologic (fatigue) status and potential metabolic destabilization (Rogers and Gronwald, 2021; Gronwald et al., 2021). It is also possible that altered DFA a1 kinetics such as a delay of its decline over a given pace/distance following a train- ing intervention could signify an improving performance status. Finally, although during race conditions, pace ad- justment to mitigate DFA a1 decline is of unclear value, it certainly merits potential study during training for inten- sity distribution and as a safety precaution. 5 | CONCLUSION At the completion of an ultramarathon, DFA a1 decreased with large effect size while running at low intensity com- pared to pre- race values. Despite running at a relatively easy pace, these values were consistent with those only seen at the highest levels of internal load and organismic demand. DFA a1 may offer an opportunity for real- time tracking of physi- ologic status in terms of monitoring for fatigue and possibly as an early warning signal of systemic perturbation. ACKNOWLEDGMENTS This research was supported by the Université of Franche Comté and TPU development program. CONFLICTS OF INTEREST The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. AUTHOR CONTRIBUTIONS B.R. and T.G. conceived the study. G.D. and L.M. performed the physiologic testing. B.R. wrote the first draft of the arti- cle. B.R. and T.G. performed the data analysis. All authors (B.R., G.D., L.M., and T.G.) revised it critically for impor- tant intellectual content, final approval of the version to be published, and accountability for all aspects of the work. INFORMED CONSENT STATEMENT Informed consent was obtained from all subjects involved in the study. DATA AVAILABILITY STATEMENT The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ORCID Bruce Rogers  https://orcid.org/0000-0001-8458-4709 Thomas Gronwald  https://orcid.org/0000-0001-5610-6013 REFERENCES Abbiss, C. R., & Laursen, P. B. (2005). Models to explain fatigue during prolonged endurance cycling. Sports Medicine (Auckland, N. Z.), 35(10), 865– 898. https://doi.org/10.2165/00007 256- 20053 5100- 00004. Ament, W., & Verkerke, G. J. (2009). Exercise and fatigue. Sports Medicine (Auckland, N. Z.), 39(5), 389– 422. https://doi. org/10.2165/00007 256- 20093 9050- 00005. Balagué, N., Hristovski, R., Almarcha, M., Garcia- Retortillo, S., & Ivanov, P. C. (2020). Network physiology of exercise: Vision and perspectives. Frontiers in Physiology, 11, 611550. https://doi. org/10.3389/fphys.2020.611550. Beaver, W. L., Wasserman, K., & Whipp, B. J. (1986). A new method for detecting anaerobic threshold by gas exchange. Journal of 8 of 9 | ROGERS Et al. Applied Physiology, 60(6), 2020– 2027. https://doi.org/10.1152/ jappl.1986.60.6.2020. Billat, V. L., Petot, H., Landrain, M., Meilland, R., Koralsztein, J. P., & Mille- Hamard, L. (2012). Cardiac output and perfor- mance during a marathon race in middle- aged recreational runners. ScientificWorldJournal., 2012, 1– 9. https://doi. org/10.1100/2012/810859. Bontemps, B., Vercruyssen, F., Gruet, M., & Louis, J. (2020). Downhill running: What are the effects and how can we adapt? A Narrative Review. Sports Medicine, 50(12), 2083– 2110. https://doi. org/10.1007/s4027 9- 020- 01355 - z. Borg, G. A. (1982). Psychophysical bases of perceived exertion. Medicine and Science in Sports and Exercise, 14(5), 377– 381. https://doi.org/10.1249/00005 768- 19820 5000- 00012. Carriker, C. R. (2017). Components of fatigue: Mind and body. Journal of Strength and Conditioning Research, 31(11), 3170– 3176. https://doi.org/10.1519/JSC.00000 00000 002088. Casties, J. F., Mottet, D., & Le Gallais, D. (2006). Non- linear analyses of heart rate variability during heavy exercise and recovery in cy- clists. International Journal of Sports Medicine, 27(10), 780– 785. https://doi.org/10.1055/s- 2005- 872968. Chen, Z., Ivanov, P. C., Hu, K., & Stanley, H. E. (2002). Effect of non- stationarities on detrended. Claudino, J. G., Cronin, J., Mezêncio, B., McMaster, D. T., McGuigan, M., Tricoli, V., Amadio, A. C., & Serrão, J. C. (2017). The coun- termovement jump to monitor neuromuscular status: A meta- analysis. Journal of Science and Medicine in Sport, 20(4), 397– 402. https://doi.org/10.1016/j.jsams.2016.08.011. Coe, R. (2002) It’s the effect size, stupid: what effect size is and why it is important. Annual Conference of the British Educational Research Association. September 12– 14, 2002. England.University of Exeter. fluctuation analysis. Phys. Rev. E 65:041107. Cohen, J. (1988). Statistical power analysis for the behavioural sci- ences; Erlbaum. Davis, M. P., & Walsh, D. (2010). Mechanisms of fatigue. The Journal of Supportive Oncology, 8(4), 164– 174. Deneen, W. P., & Jones, A. B. (2017). Cortisol and alpha- amylase changes during an ultra- running event. International Journal of Exercise Science, 10(4), 531– 540. Gronwald, T., Berk, S., Altini, M., Mourot, L., Hoos, O., & Rogers, B. (2021). Real- time estimation of aerobic threshold and exercise intensity distribution using fractal correlation properties of heart rate variability: A single- case field application in a former olympic triathlete. Frontiers in Sports and Active Living, 3, 668812. Gronwald, T., & Hoos, O. (2020). Correlation properties of heart rate variability during endurance exercise: A systematic review. Annals of Noninvasive Electrocardiology, 25(1), e12697. https://doi. org/10.1111/anec.12697. Gronwald, T., Hoos, O., & Hottenrott, K. (2019). Effects of acute nor- mobaric hypoxia on non- linear dynamics of cardiac autonomic activity during constant workload cycling exercise. Frontiers in Physiology - Exercise Physiology, 10, 999. https://doi.org/10.3389/ fphys.2019.00999. Gronwald, T., Hoos, O., & Hottenrott, K. (2019). Effects of a short- term cycling interval session and active recovery on non- linear dy- namics of cardiac autonomic activity in endurance trained cyclists. Journal of Clinical Medicine, 8(2), 194. https://doi.org/10.3390/ jcm80 20194. Gronwald, T., Hoos, O., Ludyga, S., & Hottenrott, K. (2018). Non- linear dynamics of cardiac autonomic activity during cycling cadence exercise with varied cadence. Human Movement Science, 60, 225– 233. Gronwald, T., Rogers, B., & Hoos, O. (2020). Fractal correlation properties of heart rate variability: A new biomarker for inten- sity distribution in endurance exercise and training prescription? Frontiers in Physiology, 11, 550572. https://doi.org/10.3389/ fphys.2020.550572. Halperin, I., & Emanuel, A. (2020). Rating of perceived effort: Methodological concerns and future directions. Sports Medicine (Auckland, N. Z.), 50(4), 679– 687. https://doi.org/10.1007/s4027 9- 019- 01229 - z. Hautala, A., Tulppo, M. P., Mäkikallio, T. H., Laukkanen, R., Nissilä, S., & Huikuri, H. V. (2001). Changes in cardiac autonomic regula- tion after prolonged maximal exercise. Clinical Physiology, 21(2), 238– 245. https://doi.org/10.1046/j.1365- 2281.2001.00309.x. Jastrzębski, Z., Żychowska, M., Konieczna, A., Ratkowski, W., & Radzimiński, Ł. (2015). Changes in the acid- base balance and lac- tate concentration in the blood in amateur ultramarathon runners during a 100- km run. Biology of Sport, 32(3), 261– 265. https://doi. org/10.5604/20831 862.1163372. Kannankeril, P. J., & Goldberger, J. J. (2002). Parasympathetic effects on cardiac electrophysiology during exercise and recovery. American Journal of Physiology. Heart and Circulatory Physiology, 282(6), H2091– H2098. https://doi.org/10.1152/ajphe art.00825.2001. Knechtle, B., & Nikolaidis, P. T. (2018). Physiology and pathophysi- ology in ultra- marathon running. Frontiers in Physiology, 9, 634. https://doi.org/10.3389/fphys.2018.00634. Lipponen, J. A., & Tarvainen, M. P. (2019). A robust algorithm for heart rate variability time series artefact correction using novel beat classification. Journal of Medical Engineering & Technology, 43(3), 173– 181. https://doi.org/10.1080/03091 902.2019.1640306. Ludyga, S., Gronwald, T., & Hottenrott, K. (2016). Effects of high vs. low cadence training on cyclists’ brain cortical activity during exercise. Journal of Science and Medicine in Sport, 19(4), 342– 347. Manresa- Rocamora, A., Flatt, A. A., Casanova- Lizón, A., Ballester- Ferrer, J. A., Sarabia, J. M., Vera- Garcia, F. J., & Moya- Ramón, M. (2021). Heart rate- based indices to detect parasympathetic hy- peractivity in functionally overreached athletes. A meta- analysis. Scandinavian Journal of Medicine and Science in Sports, 31(6), 1164– 1182. https://doi.org/10.1111/sms.13932. Martin, K., Meeusen, R., Thompson, K. G., Keegan, R., & Rattray, B. (2018). Mental fatigue impairs endurance performance: A phys- iological explanation. Sports Medicine (Auckland, N. Z.), 48(9), 2041– 2051. https://doi.org/10.1007/s4027 9- 018- 0946- 9. Martínez- Navarro, I., Sánchez- Gómez, J. M., Collado- Boira, E. J., Hernando, B., Panizo, N., & Hernando, C. (2019). Cardiac damage biomarkers and heart rate variability following a 118- Km moun- tain race: Relationship with performance and recovery. Journal of Sports Science and Medicine, 18(4), 615– 622. McMorris, T., Barwood, M., & Corbett, J. (2018). Central fatigue theory and endurance exercise: Toward an interoceptive model. Neuroscience and Biobehavioral Reviews, 93, 93– 107. https://doi. org/10.1016/j.neubi orev.2018.03.024. Michael, S., Graham, K. S., & Oam, D. G. M. (2017). Cardiac auto- nomic responses during exercise and post- exercise recovery using heart rate variability and systolic time intervals- a review. Frontiers in Physiology, 8, 301. https://doi.org/10.3389/fphys.2017.00301. Papaioannou, V. E., Verkerk, A. O., Amin, A. S., & de Bakker, J. M. (2013). Intracardiac origin of heart rate variability, pacemaker  | 9 of 9 ROGERS Et al. funny current and their possible association with critical illness. Current Cardiology Reviews, 9(1), 82– 96. Ramos- Campo, D. J., Ávila- Gandía, V., Alacid, F., Soto- Méndez, F., Alcaraz, P. E., López- Román, F. J., & Rubio- Arias, J. Á. (2016). Muscle damage, physiological changes, and energy balance in ultra- endurance mountain- event athletes. Applied Physiology, Nutrition and Metabolism, 41(8), 872– 878. https://doi.org/10.1139/ apnm- 2016- 0093. Rogers, B. (2020). A personalized low intensity exercise prescription based on an index of non linear heart rate variability: a case report. The Journal of Athletic Enhancement, 9(1), 327. Rogers, B., Giles, D., Draper, N., Hoos, O., & Gronwald, T. (2021). A new detection method defining the aerobic threshold for endur- ance exercise and training prescription based on fractal correlation properties of heart rate variability. Frontiers in Physiology, 11, 596567. https://doi.org/10.3389/fphys.2020.596567. Rogers, B., Giles, D., Draper, N., Mourot, L., & Gronwald, T. (2021). Influence of artefact correction and recording device type on the practical application of a non- linear heart rate variability biomarker for aerobic threshold determination. Sensors (Basel), 21(3), 821. https://doi.org/10.3390/s2103 0821. Rogers, B., & Gronwald, T. (2021) From laboratory to roadside: Real- time assessment and monitoring of the aerobic threshold in endurance- typed sports. BJSM Blog: https://blogs.bmj.com/ bjsm/2021/02/13/from- labor atory - to- roads ide- real- time- asses sment - and- monit oring - of- the- aerob ic- thres hold- in- endur ance- typed - sports. Published Feb 13, 2021 Sandercock, G. R., & Brodie, D. A. (2006). The use of heart rate vari- ability measures to assess autonomic control during exercise. Scandinavian Journal of Medicine and Science in Sports, 16(5), 302– 313. https://doi.org/10.1111/j.1600- 0838.2006.00556.x. Schader, J. F., Haid, M., Cecil, A., Schoenfeld, J., Halle, M., Pfeufer, A., Prehn, C., Adamski, J., Nieman, D. C., & Scherr, J. (2020). Metabolite shifts induced by marathon race competition differ be- tween athletes based on level of fitness and performance: A sub- study of the Enzy- MagIC Study. Metabolites, 10(3), 87. https://doi. org/10.3390/metab o1003 0087. Scheer, V., Basset, P., Giovanelli, N., Vernillo, G., Millet, G. P., & Costa, R. J. S. (2020). Defining off- road running: A position statement from the ultra sports science foundation. International Journal of Sports Medicine, 41(5), 275– 284. Scheer, V., Vieluf, S., Cramer, L., Jakobsmeyer, R., & Heitkamp, H. C. (2018). Changes in running economy during a 65- km ultramar- athon. Frontiers in Physiology, 9, 1809. https://doi.org/10.3389/ fphys.2018.01809. Stanley, J., Peake, J. M., & Buchheit, M. (2013). Cardiac parasympa- thetic reactivation following exercise: Implications for training prescription. Sports Medicine (Auckland, N. Z.), 43(12), 1259– 1277. https://doi.org/10.1007/s4027 9- 013- 0083- 4. Tarvainen, M. P., Niskanen, J. P., Lipponen, J. A., Ranta- Aho, P. O., & Karjalainen, P. A. (2014). Kubios HRV– heart rate vari- ability analysis software. Computer Methods and Programs in Biomedicine, 113(1), 210– 220. https://doi.org/10.1016/j. cmpb.2013.07.024. Vernillo, G., Doucende, G., Cassirame, J., & Mourot, L. (2019). Energetically optimal stride frequency is maintained with fa- tigue in trained ultramarathon runners. Journal of Science and Medicine in Sport, 22(9), 1054– 1058. https://doi.org/10.1016/j. jsams.2019.04.003. Wan, J. J., Qin, Z., Wang, P. Y., Sun, Y., & Liu, X. (2017). Muscle fatigue: general understanding and treatment. Experimental & Molecular Medicine, 49(10), e384. https://doi.org/10.1038/ emm.2017.194. White, D. W., & Raven, P. B. (2014). Autonomic neural control of heart rate during dynamic exercise: revisited. Journal of Physiology, 592(12), 2491– 2500. https://doi.org/10.1113/jphys iol.2014.271858. Wu, P. P., Sterkenburg, N., Everett, K., Chapman, D. W., White, N., & Mengersen, K. (2019). Predicting fatigue using countermovement jump force- time signatures: PCA can distinguish neuromuscular versus metabolic fatigue. PLoS One, 14(7), e0219295. https://doi. org/10.1371/journ al.pone.0219295. How to cite this article: Rogers, B., Mourot, L., Doucende, G., & Gronwald, T. (2021). Fractal correlation properties of heart rate variability as a biomarker of endurance exercise fatigue in ultramarathon runners. Physiological Reports, 9, e14956. https://doi.org/10.14814/ phy2.14956

Fractal correlation properties of heart rate variability as a biomarker of endurance exercise fatigue in ultramarathon runners.

[]

Rogers, Bruce,Mourot, Laurent,Doucende, Gregory,Gronwald, Thomas

eng

PMC4239061

Running for Exercise Mitigates Age-Related Deterioration of Walking Economy Justus D. Ortega1*, Owen N. Beck1,2, Jaclyn M. Roby2, Aria L. Turney1, Rodger Kram2 1 Department of Kinesiology & Recreation Administration, Humboldt State University, Arcata, California, United States of America, 2 Department of Integrative Physiology, University of Colorado, Boulder, Colorado, United States of America Abstract Introduction: Impaired walking performance is a key predictor of morbidity among older adults. A distinctive characteristic of impaired walking performance among older adults is a greater metabolic cost (worse economy) compared to young adults. However, older adults who consistently run have been shown to retain a similar running economy as young runners. Unfortunately, those running studies did not measure the metabolic cost of walking. Thus, it is unclear if running exercise can prevent the deterioration of walking economy. Purpose: To determine if and how regular walking vs. running exercise affects the economy of locomotion in older adults. Methods: 15 older adults (6963 years) who walk $30 min, 3x/week for exercise, ‘‘walkers’’ and 15 older adults (6965 years) who run $30 min, 3x/week, ‘‘runners’’ walked on a force-instrumented treadmill at three speeds (0.75, 1.25, and 1.75 m/s). We determined walking economy using expired gas analysis and walking mechanics via ground reaction forces during the last 2 minutes of each 5 minute trial. We compared walking economy between the two groups and to non-aerobically trained young and older adults from a prior study. Results: Older runners had a 7–10% better walking economy than older walkers over the range of speeds tested (p = .016) and had walking economy similar to young sedentary adults over a similar range of speeds (p = .237). We found no substantial biomechanical differences between older walkers and runners. In contrast to older runners, older walkers had similar walking economy as older sedentary adults (p = .461) and ,26% worse walking economy than young adults (p,.0001). Conclusion: Running mitigates the age-related deterioration of walking economy whereas walking for exercise appears to have minimal effect on the age-related deterioration in walking economy. Citation: Ortega JD, Beck ON, Roby JM, Turney AL, Kram R (2014) Running for Exercise Mitigates Age-Related Deterioration of Walking Economy. PLoS ONE 9(11): e113471. doi:10.1371/journal.pone.0113471 Editor: Yuri P. Ivanenko, Scientific Institute Foundation Santa Lucia, Italy Received September 8, 2014; Accepted October 23, 2014; Published November 20, 2014 Copyright:  2014 Ortega et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper. Funding: Support was provided by the California State University Program for Education and Research in Biotechnology New Investigator [Grant #: HM531] (http://www.calstate.edu/csuperb/grants/). This funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Support was also provided by the National Institutes of Health Clinical and Translational Science Award [Grant #: UL1 TR000154] (http://www.ncats. nih.gov/research/cts/ctsa/funding/funding.html). This funder provided the facility where the data was collected. Competing Interests: The authors have declared that no competing interests exist. * Email: [email protected] Introduction Walking performance typically deteriorates with advanced age [1], and impaired walking performance is a key predictor of morbidity among older adults [2]. A distinctive characteristic of impaired walking performance among older adults is a 15–20% greater metabolic cost for walking (worse economy) compared to young adults [3–5]. Several factors are known to determine the metabolic cost of walking in humans across all ages. These major biomechanical factors include the costs associated with: supporting body weight, performing mechanical work, leg swing and balance [5–8]. Studies investigating age-related biomechanical determi- nants of walking cost have found that older adults have a similar cost of balance and perform a similar amount, or even less, external mechanical work during walking as young adults [5,9,10]. Despite these similarities, other studies suggest that a decrease in muscular efficiency and an increase in antagonist leg muscle co- activation, contribute to the greater cost of walking in both healthy sedentary and active older adults [3,5,7,10,11]. Yet, no study has found a sole mechanical determinant that accounts for the 15– 20% greater metabolic cost of walking in older adults. Therefore, interventions for improving walking economy in older age have been elusive. Recent studies by Thomas et al. [12] and Malatesta et al. [13] show that vigorous walking interval training effectively reduces the metabolic cost of walking in older adults by as much as 20%. Yet, the mechanisms for the decreases were not elucidated. Conversely, a generalized year-long training program that included resistance, aerobic and balance exercises had no effect on post-training walking economy in older adults [14]. The different effects of these PLOS ONE | www.plosone.org 1 November 2014 | Volume 9 | Issue 11 | e113471 exercise interventions, high intensity aerobic versus generalized exercise with only a moderate aerobic component, suggest higher intensity aerobic activities may mitigate the typical age-related decrease in walking economy, and consequently, preserve mobility into older age. In contrast, running economy does not exhibit the same age- related trend as walking economy. Two studies have reported that adults (45–61 years) who consistently participated in running exercise retain a similar metabolic economy of running as young runners (23–27 years) [15,16]. Although these results seem to support the hypothesis that vigorous aerobic exercise mitigates the decline in locomotion economy, i.e. metabolic cost of running and walking, it is also possible that a decline in running economy does not occur until late into the 6th decade of life, as observed with walking economy [17]. Perhaps the subjects in these studies [15,16] were not ‘‘old’’ enough to exhibit declines in locomotion economy. Another possible explanation is that running economy, unlike walking economy, is simply not affected by age. However, since these running studies did not measure walking economy, it remains unclear if regular participation in running exercise mitigates the typical age-related deterioration of walking economy. Our purpose was to determine if and how regular participation in walking or running exercise affects the metabolic cost and biomechanics of walking in older adults. We hypothesized that older runners would consume less metabolic energy for walking than older walkers. Further, we also investigated whether the two groups demonstrate different walking biomechanics. We measured metabolic rates, ground reaction forces and spatio-temporal stride variables of two groups, older walkers and older runners, while they walked on a dual-belt, force-sensing treadmill at three speeds. Methods Subjects Thirty healthy older adults (15 males and 15 female) who either walk (4 Male, 11 Female) or run (10 Male, 5 Female) regularly for exercise volunteered. Table 1 summarizes the anthropometric characteristics of the subjects. We recruited subjects with a minimum age of 65 years, which is in accordance with prior studies reporting age-related impairments of walking performance become most apparent at this age [3,18–20]. All subjects were free of neurological, orthopedic and cardiovascular disorders. Walkers self-reported walking for exercise three or more times per week for at least 30 minutes per bout and for at least six months prior to the study. Runners self-reported running for exercise three or more times per week for at least 30 minutes per bout and for at least six months prior to the study. The experiment was performed in accordance with the ethical standards of the 1964 Declaration of Helsinki and was approved by the Humboldt State University and University of Colorado Institutional Review Boards. All subjects gave written informed consent prior to participation in the study. Protocol Subjects completed three sessions. In the first session, subjects underwent a physician’s examination to determine neurological, orthopedic and cardiovascular health, a body composition test (DXA) to determine percent body fat and lean tissue mass and a VO2 max treadmill test to determine maximal aerobic capacity. In the second session, at least five days following the first session, we measured standing metabolic rate and familiarized the subjects to treadmill walking. For the treadmill familiarization, subjects walked on a dual-belt, force-instrumented treadmill (FIT, Bertec Corporation, Columbus, OH, USA) at three speeds (0.75, 1.25 and 1.75 m/s) for at least 7 minutes at each speed. These speeds correspond to 1.67, 2.80, 3.91 MPH. Thus, subjects completed a minimum of 21 minutes total of walking familiarization. This familiarization period is over double the recommended minimum treadmill habituation time of 10 minutes [21,22]. In the third session, at least two days following familiarization, we measured each subject’s metabolic rate during quiet standing and while walking on the treadmill at three speeds (0.75, 1.25 and 1.75 m/s) in random order. All trials were five minutes in duration with at least five minutes of rest between trials. Throughout each trial, we measured the rates of oxygen consumption (VO2) and carbon dioxide production (VCO2) in order to determine metabolic rate. We calculated the average VO2 and VCO2 for the last two minutes of each trial. We also measured ground reaction forces (GRFs) from the force-instrumented treadmill for 1 minute during the last 2.5 minutes of each trial to determine kinetics and spatio- temporal stride variables. Metabolic Power Consumption We measured VO2 and VCO2 using an open-circuit expired gas analysis system (TrueOne 2400, ParvoMedic, Sandy, UT, USA). We calculated average gross metabolic power per kilogram body mass (W/kg) [23] using the average VO2 (mlO2/min) and VCO2 (mlCO2/min) for the last two minutes of each trial, when VO2 and respiratory exchange ratio reached steady state ensuring that each subject was working sub-maximally and oxidative metabolism was the main metabolic pathway. We then divided gross metabolic power by speed to calculate gross metabolic cost of transport (CoT) (J/kg/m) for walking. Ground Reaction Forces and Spatio-temporal Stride Variables For each walking trial, we collected the ground reaction forces (vertical and horizontal components) of each leg from the force- sensing treadmill at 2000 Hz for a 1 minute period during the last 2.5 minutes of each trial. A custom MATLAB script (Math Work Inc., Natick, Mass) was then used to process all force data. The Figure 1. Mean (SE) gross metabolic power as a function of walking speed in older walkers (m) and older runners (X) walkers (m). Lines represent least square regression for older walkers (y = 2.709x2–3.539x+4.523, r2 = 0.86) and older runners (y = 2.382x2– 3.189x+4.233, r2 = 0.89). Symbols shown on vertical axis represent standing metabolic rate of both groups. Asterisks (*) indicate significant differences between older runners and walkers (p,0.05). doi:10.1371/journal.pone.0113471.g001 Running Mitigates Age-Related Decline of Walking Economy PLOS ONE | www.plosone.org 2 November 2014 | Volume 9 | Issue 11 | e113471 GRF data were filtered with a 4th order zero-lag low pass Butterworth filter with a cutoff frequency of 30 Hz. For each trial, we calculated vertical and horizontal peak GRFs across all 10 strides. Using the filtered GRF data, we determined gait cycle events and spatio-temporal stride variables (stride frequency, stance time, and duty factor as percent of the gait cycle) for 10 strides of each trial (10 steps per each leg). Statistical Analyses We used a repeated-measures ANOVA (p,.05) to determine statistical differences due to exercise group (walkers vs. runners) and walking speed, as well as, the exercise group-walking speed interaction. When a significant main effect of exercise group was found, we performed independent-samples t-tests with Bonferroni correction to determine at which speed(s) the differences occurred. To determine if difference in metabolic cost and GRF was related to sex differences in our runner and walker groups, we examined differences in metabolic cost, ground reaction forces and spatio- temporal stride variables due to sex among each group and analyzed difference in metabolic cost, GRFs and spatio-temproal stride variables using sex as a covariate. We found no effect of sex on any dependent variable and differences between runners and walkers were not affected by sex. We performed all statistical analyses using SPSS 21.0 (SPSS, Inc.) software. In addition to our comparison between older walkers and runners, we used a mixed- model repeated-measures ANOVA (p,.05) to make further post- hoc comparisons of gross metabolic cost in walkers and runners collected in the present study to data for young and older sedentary adults previously collected in our lab at similar speeds [5]. To make these comparison between exercise/age group (old walkers, old runners, old sedentary and young sedentary) using a linear mixed model, walking speed squared (m/s)2 was used as the repeated measure. Results In support of our hypothesis, older runners consumed 7–10% less metabolic energy for walking than older walkers across the range of speeds tested (Fig. 1; p = .016). Gross metabolic power consumption increased significantly across the range of walking speeds tested in both older runners and walkers, (p,.0001). Compared to walking at the slowest speed of 0.75 m/s, gross metabolic power increased by 95% to walk at 1.75 m/s in older walkers but only 86% in older runners (speed X group interaction, p = .009). Mass-specific standing metabolic rates were similar between older runners and walkers (p = .250; Table 1). Following from the metabolic rate data, the older runners had an average of 7–10% lower gross metabolic cost of transport compared to the older walkers. Older walkers and runners exhibited similar U-shape relations between gross CoT and walking speed (Fig. 2). Between the three speeds, gross CoT was significantly lower at the intermediate speed of 1.25 m/s as compared to the faster and slower walking speeds in both the older walkers (3.4960.09 J/kg/m, p,.0001) and older runners (3.1860.08 J/kg/m, p,.0001). Although there were a greater number of male runners in the study, our statistical analysis showed that the difference in metabolic cost between runners and walkers was not due to sex or any other anthropometric variable. Despite the substantial differences in walking economy, older walkers and runners exhibited nearly identical spatio-temporal stride variables and kinetics across the range of speeds (Table 2). Among spatio-temporal gait characteristics, we found no signifi- cant differences between older walkers and older runners in Table 1. Subject characteristics (Mean 6SD) with statistics for older walkers and older runners. Older Walkers (n = 15; 4M, 11 F) Older Runners (n = 15; 10M, 5 F) Age, years 68.963.0 68.964.7 Height, m 1.6160.09 1.7060.09* Leg length, m 0.8360.06 0.8860.06 Body mass, kg 61.7611.0 66.56 13.0 Lean tissue mass, kg 39.267.1 48.669.2* Body fat, % body mass 31.569.6 23.466.0* VO2 Max, mlO2/kg/min 27.763.6 37.365.3* Standing metabolic rate, W/kg 1.3460.21 1.2660.14 0.75 m/s, gross metabolic power, W/kg 3.3960.33 3.1860.31* 1.25 m/s, gross metabolic power, W/kg 4.3360.56 3.9760.40* 1.75 m/s, gross metabolic power, W/kg 6.3360.71 5.9560.52* Asterisk indicates the only significant group difference (p,.05). doi:10.1371/journal.pone.0113471.t001 Figure 2. Mean (SE) gross metabolic cost of transport as a function of speed in older walkers (m) and older runners (X). Asterisks (*) indicate significant differences between older walkers and runners (p,.05). doi:10.1371/journal.pone.0113471.g002 Running Mitigates Age-Related Decline of Walking Economy PLOS ONE | www.plosone.org 3 November 2014 | Volume 9 | Issue 11 | e113471 regards to stride time, stride frequency (p = .879), single leg stance time (p = .126) or duty factor (p = .126). However, older runners walked with slightly (6%) shorter strides in relation to their leg length compared to older walkers (p = .033). This difference remained nearly constant across the range of speeds. With regards to ground reaction forces, older walkers and runners exhibited similar first (p = .838) and second (p = .282) peak vertical ground reaction force (Figure 3). Additionally, peak anterior-posterior braking (p = .182) and propulsive (p = .056) ground reaction forces were similar for both exercise groups. We also compared gross metabolic cost of walking for older walkers and older runners to data from young and older sedentary adults collected in our lab from a prior study over a similar range of speeds [5]. The speeds used in these two studies were slightly different. Thus, in order to statistically make this comparison using a linear mixed model repeated measures ANOVA, we determined gross metabolic power as a function of speed squared (Fig. 4). The results of this analysis showed that across the range of speeds, older walkers consume metabolic energy at a similar rate as sedentary older adults (p = .461) and 14–22% faster than young sedentary adults (p,.0001). In contrast, older runners consume metabolic energy at a slower rate compared to older sedentary adults (p = .016). However, our most striking finding was that older runners consumed metabolic energy at a similar rate as young sedentary adults across the range of walking speeds (p = .237). Discussion and Conclusions In this study, we distinguished the effects of regular walking vs. running exercise on the metabolic cost and biomechanics of walking in older adults. In support of our hypothesis, older runners consumed less metabolic energy for walking than older walkers. Although the older runners consumed less metabolic energy for walking than the older walkers, the two groups had almost identical walking biomechanics. Given that there were virtually no differences in walking biomechanics between the older walkers and runners, other factors Table 2. Spatio-temporal stride variables and ground reaction force data (Mean 6SD) with statistics for older walkers and older runners. Older Walkers (n = 15) Older Runners (n = 15) Speed 0.75 m/s Stride Time, sec 1.2660.11 1.1960.08 Stance Time, % of stride 6562 6661 Swing Time, % of stride 3562 3461 Stride Frequency, Hz 0.8060.07 0.8460.06 Stride Length, Leg Length 1.1460.08 1.0260.10* First Peak VGRF, BW% 10463 10463 Second Peak VGRF, BW% 10163 10062 Braking HGRF, BW% 2861 2861 Propulsive HGRF, BW% 1162 1061 Speed 1.25 m/s Stride Time, sec 1.0460.07 1.0560.06 Stance Time, % of stride 6362 6462 Swing Time, % of stride 3762 3762 Stride Frequency, Hz 0.9760.07 0.9560.06 Stride Length, Leg Length 1.5760.08 1.4960.10* First Peak VGRF, BW% 11065 10864 Second Peak VGRF, BW% 10665 10563 Braking HGRF, BW% 21762 21662 Propulsive HGRF, BW% 1960.02 1762 Speed 1.75 m/s Stride Time, sec 0.9260.05 0.9360.04 Stance Time, % of stride 616 1 6362 Swing Time, % of stride 3961 3862 Stride Frequency, Hz 1.0960.06 1.0860.05 Stride Length, Leg Length 1.8860.10 1.8360.10 First Peak VGRF, BW% 134612 12964 Second Peak VGRF, BW% 11069 11967 Braking HGRF, BW% 22862 22665 Propulsive HGRF, BW% 2663 2563 Peak vertical ground reaction forces (VGRF) and horizontal ground reaction forces (HGRF) are represented as % body weight (BW). Asterisk indicates significant group difference (p,.05). doi:10.1371/journal.pone.0113471.t002 Running Mitigates Age-Related Decline of Walking Economy PLOS ONE | www.plosone.org 4 November 2014 | Volume 9 | Issue 11 | e113471 must underlie the lower cost of walking observed for the older runners. One factor may be muscle co-activation. Older adults, both sedentary and active walkers, use 30–50% greater co- activation of antagonist leg muscles compared to young adults [6,10,24]. It has been suggested that older adults may use greater co-activation to increase joint stiffness and the stabilization of the body, thus reducing the risk of walking related falls [25]. Yet, increased co-activation has been associated with increased metabolic cost of walking in older adults [6,10]. It is possible that older runners are able to maintain a lower metabolic cost of walking compared to older walkers because they use less antagonist leg muscle activation. Some research shows that older adults who participated in a lower limb strength training program reduce leg muscle co-activation by 5–10% [26]. Perhaps, by regularly running three or more times per week for 30 minutes per bout, older runners are able to maintain or even increase leg muscle strength and reduce co-activation. However, a decrease in co-activation associated with running that is similar in magnitude to the decrease observed after strength training is likely not sufficient to explain the 7–10% difference in metabolic cost of walking. It is also possible that other neuromuscular factors such as widening of EMG/motoneuronal bursts [27] may also help to explain the difference in metabolic cost between older runners and walkers. Better muscular efficiency may also help explain why older runners have a lower metabolic cost of walking than older walkers. Aging has been associated with reduced muscular efficiency [10,28]. More specifically, mitochondrial dysfunction associated with the uncoupling of oxidative phosphorylation (reduced ATP synthesis per O2 uptake) effectively reduces muscular efficiency and increased the metabolic cost of muscle activation [28]. Interestingly, recent evidence suggests that aerobic exercise training may ameliorate mitochondrial uncoupling and improve muscular efficiency in older adults [29]. Perhaps studies of cycling efficiency in older adults can provide insight. In contrast to the effects of running we have observed, the muscular efficiency of cycling declines with age despite regular cycling exercise [30]. More recently, Brisswalter et al. [31] measured the cycling efficiency of active triathletes (who regularly swim, bike, and run for exercise) across age-groups and found a decline in cycling efficiency past the 5th decade. These data suggest that older cyclist and triathletes are unable to maintain muscular efficiency with age. However, Peiffer et al. [32] found no difference in cycling efficiency between their youngest age group (3963 years) and their oldest (6564 years). Intriguingly, their oldest training group cycled 58 km more per week (359 km per week) than the youngest group. Possibly the greater quantity of aerobic cycling exercise mitigated the decrease in muscular efficiency with age. Alternatively, the intensity of exercise may hold the key to maintaining or improving muscular efficiency. Two prior studies have found that 6–7 weeks of vigorous aerobic exercise (fast walking) that elicits a heart rate close to the ventilatory threshold can improve walking economy by 8–20% [12,13]. More vigorous aerobic exercise such as walking uphill, fast walking or running may be required to elicit improvement in walking economy. Clinicians and others who work with older adults to improve their fitness may need to prescribe more vigorous, more prolonged and/ or more frequent aerobic exercise to prevent the decline in walking performance. To test this hypothesis and help guide clinicians, a future study should investigate the effects of different intensity aerobic exercises on muscular efficiency and more specifically, the economy of walking. Limitations One limitation of the current study is the cross-sectional design. It is possible that older runners may not be economical walkers because of the effect of running exercise but rather they run because they are more economical in their locomotion. To better address this issue, a future study might quantify the longitudinal effects of a running training program. One such study conducted by Trappe et al. [16] on the longitudinal effect of running exercise on running economy spanned 22 years. In that study, Trappe et al. [16] showed that running economy did not decline in older adults who maintained their health and fitness over the 22 year period, whereas runners who became unfit had worse running economy. Although these results suggest that running may help to prevent a decline in running economy, Trappe et al. [16] did not measure walking economy. Figure 3. Average individual leg vertical (A) and horizontal (B) ground reaction force for older walkers (dashed lines) and older runners (solid lines) at the intermediate walking speed of 1.25 m/s. doi:10.1371/journal.pone.0113471.g003 Running Mitigates Age-Related Decline of Walking Economy PLOS ONE | www.plosone.org 5 November 2014 | Volume 9 | Issue 11 | e113471 Another potential limitation of the current study is the different numbers of male and female participants in each group. Although the sex difference may have influenced the difference in anthropometrics between runners and walker, our results showed no main effect of sex on walking economy (p = .211) and no sex difference in walking economy among older runners (p = .131) or older walkers (p = .331). Based on post-hoc power analysis, it is clear that we did not have sufficient statistical power to detect sex differences that might exist but that would require ,300 subjects. However, when treated as covariates, sex and anthropometrics did not statistically account for the difference in walking economy between runners and walkers. Thus, while it would have been preferable to have a larger sample size with more similar sex and anthropometric matched cohorts, it would not have changed our overall conclusion. Future Studies Based on the results of this study and others, future studies of the effect of age and exercise on walking economy are warranted. Although the average age of our runners and walkers was 69 years, a future study might look to see if running exercise continues to prevent or slow the decline in walking economy in even older runners (over the age of 80 years). It seems plausible that at some age that exercise may not be able to sufficiently offset the normal decline in muscular efficiency and walking economy associated with aging. It is also not known whether there is an intensity threshold of aerobic exercise that is needed to prevent the decline in walking economy. Thus, it would be beneficial for future studies to investigate the relative effect of exercises with different levels of aerobic intensity on walking economy. Conclusions In conclusion, older runners mitigate the age-related deteriora- tion of walking economy. However, older walkers are unable to forestall the decline of walking economy as they require the same metabolic consumption as sedentary older adults. The difference in walking economy between older runners and older walkers remains unexplained due to no substantial differences found in either the kinetic or spatio-temporal data between the groups. Other factors such as decreased muscle co-activation and/or increased muscular efficiency may contribute to the superior walking economy exhibited by the older runners. Author Contributions Conceived and designed the experiments: JO OB JR RK. Performed the experiments: JO OB JR AT RK. Analyzed the data: JO OB JR AT RK. Contributed reagents/materials/analysis tools: JO RK. Wrote the paper: JO OB JR AT RK. References 1. Himann JE, Cunningham DA, Rechnitzer PA, Paterson DH (1988) Age- Related-Changes in Speed of Walking. Med Sci Sports Exerc 20: 161–166. 2. Studenski S, Perera S, Patel K, Rosano C, Faulkner K, et al. (2011) Gait speed and survival in older adults. JAMA 305: 50–58. 3. Martin PE, Rothstein DE, Larish DD (1992) Effects of age and physical activity status on the speed-aerobic demand relationship of walking. J Appl Physiol 73: 200–206. 4. Waters RL, Lunsford BR, Perry J, Byrd R (1988) Energy-speed relationship of walking: standard tables. J Orthop Res 6: 215–222. 5. Ortega JD, Farley CT (2007) Individual limb work does not explain the greater metabolic cost of walking in elderly adults. J Appl Physiol 102: 2266–2273. 6. Ortega JD, Farley CT (2014) Effects of aging on mechanical efficiency and muscle activation during level and uphill walking. J Electromyogr Kinesiol 0. 7. Ortega JD, Fehlman LA, Farley CT (2008) Effects of aging and arm swing on the metabolic cost of stability in human walking. J Biomech 41: 3303–3308. Epub 2008 Sep 3323. 8. Gottschall JS, Kram R (2005) Energy cost and muscular activity required for leg swing during walking. J Appl Physiol 99: 23–30. 9. Franz JR, Lyddon NE, Kram R (2012) Mechanical work performed by the individual legs during uphill and downhill walking. J Biomech 45: 257–262. 10. Mian OS, Thom JM, Ardigo LP, Narici MV, Minetti AE (2006) Metabolic cost, mechanical work, and efficiency during walking in young and older men. Acta Physiol Scand 186: 127–139. 11. Malatesta D, Simar D, Dauvilliers Y, Candau R, Borrani F, et al. (2003) Energy cost of walking and gait instability in healthy 65- and 80-yr-olds. J Appl Physiol 95: 2248–2256. 12. Thomas EE, Vito GD, Macaluso A (2007) Speed training with body weight unloading improves walking energy cost and maximal speed in 75- to 85-year- old healthy women. J Appl Physiol 103: 1598–1603. 13. Malatesta D, Simar D, Ben Saad H, Prefaut C, Caillaud C (2010) Effect of an overground walking training on gait performance in healthy 65- to 80-year-olds. Exp Gerontol 45: 427–434. 14. Mian OS, Thom JM, Ardigo LP, Morse CI, Narici MV, et al. (2007) Effect of a 12-month physical conditioning programme on the metabolic cost of walking in healthy older adults. Eur J Appl Physiol 100: 499–505. 15. Quinn TJ, Manley MJ, Aziz J, Padham JL, MacKenzie AM (2011) Aging and factors related to running economy. Journal of strength and conditioning research/National Strength & Conditioning Association 25: 2971–2979. 16. Trappe SW, Costill DL, Vukovich MD, Jones J, Melham T (1996) Aging among elite distance runners: a 22-yr longitudinal study. J Appl Physiol 80: 285–290. 17. Prince F, Corriveau H, Hebert R, Winter DA (1997) Gait in the elderly. Gait Posture 5: 128-135. 18. Studenski S, Perera S, Patel K, Rosano C, Faulkner K, et al. (2011) Gait speed and survival in older adults. JAMA: the journal of the American Medical Association 305: 50–58. 19. Himann JE, Cunningham DA, Rechnitzer PA, Paterson DH (1988) Age-related changes in speed of walking. Medicine and science in sports and exercise 20: 161–166. 20. Murray MP, Kory RC, Clarkson BH (1969) Walking patterns in healthy old men. J Gerontol 24: 169–178. 21. Wall JC, Charteris J (1981) A kinematic study of long-term habituation to treadmill walking. Ergonomics 24: 531–542. 22. Van de Putte M, Hagemeister N, St-Onge N, Parent G, de Guise JA (2006) Habituation to treadmill walking. Biomed Mater Eng 16: 43–52. 23. Brockway JM (1987) Derivation of formulae used to calculate energy expenditure in man. Hum Nutr Clin Nutr 41: 463–471. 24. Franz JR, Kram R (2012) How does age affect leg muscle activity/coactivity during uphill and downhill walking? Gait Posture 37: 378–384. Figure 4. Gross metabolic power as a function of speed2 in older sedentary adults (N), older walkers (m), older runners (X), and young sedentary adults (#). Lines denote least square regression within each group (older sedentary: y = 1.46x+2.30, r2 = 0.91; older walkers: y = 1.31x+2.52, r2 = 0.86; older runners: y = 1.12x+2.42, r2 = 0.88; young sedentary: y = 1.01x+2.27, r2 = 0.87). Symbols on vertical axis represent standing metabolic rate of each group. doi:10.1371/journal.pone.0113471.g004 Running Mitigates Age-Related Decline of Walking Economy PLOS ONE | www.plosone.org 6 November 2014 | Volume 9 | Issue 11 | e113471 25. Finley JM, Dhaher YY, Perreault EJ (2012) Contributions of feed-forward and feedback strategies at the human ankle during control of unstable loads. Exp Brain Res 217: 53–66. 26. Hakkinen K, Kallinen M, Izquierdo M, Jokelainen K, Lassila H, et al. (1998) Changes in agonist-antagonist EMG, muscle CSA, and force during strength training in middle-aged and older people. J Appl Physiol 84: 1341–1349. 27. Monaco V, Ghionzoli A, Micera S (2010) Age-related modifications of muscle synergies and spinal cord activity during locomotion. J Neurophysiol 104: 2092– 2102. 28. Amara CE, Shankland EG, Jubrias SA, Marcinek DJ, Kushmerick MJ, et al. (2007) Mild mitochondrial uncoupling impacts cellular aging in human muscles in vivo. Proc Natl Acad Sci U S A 104: 1057–1062. 29. Conley KE, Jubrias SA, Amara CE, Marcinek DJ (2007) Mitochondrial dysfunction: impact on exercise performance and cellular aging. Exerc Sport Sci Rev 35: 43–49. 30. Sacchetti M, Lenti M, Di Palumbo AS, De Vito G (2010) Different effect of cadence on cycling efficiency between young and older cyclists. Med Sci Sports Exerc 42: 2128–2133. 31. Brisswalter J, Wu SX, Sultana F, Bernard T, Abbiss C (2014) Age difference in efficiency of locomotion and maximal power output in well-trained triathletes. Eur J Appl Physiol: 1–8. 32. Peiffer JJ, Abbiss CR, Chapman D, Laursen PB, Parker DL (2008) Physiological characteristics of masters-level cyclists. J Strength Cond Res 22: 1434–1440. Running Mitigates Age-Related Decline of Walking Economy PLOS ONE | www.plosone.org 7 November 2014 | Volume 9 | Issue 11 | e113471

Running for exercise mitigates age-related deterioration of walking economy.

11-20-2014

Ortega, Justus D,Beck, Owen N,Roby, Jaclyn M,Turney, Aria L,Kram, Rodger

eng

PMC9794057

1 S5 Table. Results of round 1. Table A. Factors rated as ‘relevant’ in round 1 (level of agreement 70-100%), n=99. Factor Level of agreement (%) Training Endurance capacity 96,3 Maximal oxygen consumption 100,0 Economy of movement (=energy utilization) 96,3 Strength capacity 70,4 Power capacity 85,2 Lactate threshold 96,3 Lung volume 77,8 Heart volume 85,2 Recovery speed 88,9 Metabolism Glycolysis capacity (=break down of glucose) 100,0 Mitochondrial biogenesis (=growth of pre-existing mitochondria) 100,0 Myoglobin storage capacity (=iron/ oxygen-binding protein) 88,9 Thermogenesis (=production of heat in the body) 70,4 Angiogenesis (=formation of new blood vessels) 85,2 Fat metabolism (break down of fat for energy) 88,9 Lactate dehydrogenase metabolism 85,2 Lactate buffering system (=regulation of lactate level) 96,3 Body Weight / BMI 88,9 Total fat mass 88,9 Subcutaneous adipose tissue (=fat under the skin) 70,4 Lean mass (=mass of all organs except body fat including bones, muscles, blood, skin) 88,9 Tendon stiffness 88,9 Number of red blood cells (=erythrocytes) 100,0 Muscle fibres - hypertrophy capacity (=muscle growth) 70,4 Muscle fibres - type 1 vs. type 2a/b (=slow vs. fast twitch fibres) 100,0 Muscle fibres - transformation capacity (type 1 vs. type 2) 92,6 Muscle fibres - contraction velocity capacity 74,1 Hormones Erythropoietin (EPO) level 92,6 Insulin-like growth factor-1 (IGF-1) level 92,6  2 Growth hormone level 92,6 Cortisol level 96,3 Epinephrine level 77,8 Norepinephrine level 77,8 Testosterone level 100,0 Dihydrotestosterone level 88,9 Oestradiol level 85,2 Dehydroepiandrosterone level 70,4 Ghrelin level 74,1 Progesterone level 77,8 Follicle-stimulating hormone level 70,4 Gonadocorticoids level 77,8 Human chorionic gonadotropin level 70,4 Gonadotropin-releasing hormone level 77,8 Thyroid hormones level 81,5 Androstenedione level 77,8 Nutrition Valine level 70,4 Leucine level 85,2 L-carnitine level 81,5 Carnosine level 77,8 Creatine level 81,5 Carbohydrate metabolism 100,0 Saturated fat metabolism 77,8 Unsaturated fat metabolism 74,1 Cholesterol level 74,1 Omega 3 level 74,1 Omega 6 level 70,4 Vitamin A deficiency 74,1 Beta carotene deficiency 77,8 Vitamin B complex vitamins (B1-12) deficiency 88,9 Vitamin C deficiency 77,8 Vitamin D deficiency 92,6 Vitamin E deficiency 74,1 Folic acid deficiency 77,8 Iron deficiency 100,0 Zinc deficiency 85,2 Magnesium deficiency 85,2 Selenium deficiency 74,1 Caffeine metabolism 81,5  3 Antioxidant level 81,5 Bicarbonate level 77,8 Cell hydration status 88,9 Electrolyte balance/ hydration status 96,3 Steroid metabolism 92,6 Immune system Detoxification process 81,5 Cytokine responses 85,2 Healing function of skeletal tissue 88,9 Healing function of soft tissue 81,5 Blood pressure regulation 85,2 Injuries Risk of left ventricular hypertrophy 74,1 Risk of metabolic myopathy 70,4 Risk of stress fractures 85,2 Risk of upper respiratory tract infections 85,2 Risk of non-functional overreaching 88,9 Risk of joint injuries 88,9 Psychological Stress resistance 100,0 Motivation capacity 100,0 Resilience capacity 92,6 Concentration capacity 92,6 Emotion regulation 96,3 Pain sensitivity 96,3 Self-control 96,3 Self-confidence 100,0 Risk of eating disorders 85,2 Environment Smoking behaviour 74,1 Alcohol usage 85,2 Sleep quality 96,3 Level of fatigue 96,3 Heat resistance capacity 85,2 Altitude training sensitivity 85,2  4 Table B. Factors rated as ‘moderate’ in round 1 (level of agreement 40-69%), n=19. Factor Level of agreement (%) Training Speed capacity 51,9 Coordination capacity 63,0 Flexibility capacity 59,3 Metabolism Basal metabolism rate (=calories required to keep the body functioning at rest) 59,3 Creatine kinase metabolism 55,6 Body Regional fat mass 66,7 Visceral adipose tissue (=fat around internal organs) 55,6 Bone mineral density 51,9 Hormones Anti-Müllerian hormone level 55,6 Nutrition Vitamin K deficiency 66,7 Gluten intolerance 55,6 Lactose intolerance 59,3 Alcohol metabolism 48,1 Injuries Injuries Risk of lumbar disk degeneration 59,3 Injuries Risk of inguinal hernia 55,6 Psychological Aggression regulation 66,7 Risk of addiction 66,7 Intro vs. extroverted personality 59,3 Ability to differentiate 66,7  5 Table C. Factors rated as ‘not relevant’ in round 1 (level of agreement 0-39%), n=2. Factor Level of agreement (%) Training Agility capacity 25,9 Reaction time 14,8 Table D. Proposed factors from round 1. (Sedentary) lifestyle in amateur athletes Table E. Free text comments from round 1. “For my studies, human muscle fibers are classified in 1, 2a and 2x. 2b fibers are present only in some animals, but not in human. Feel free to accept or not my suggestion, just a thought.” “List is complete” “Since I'm not an expert in hormonal function, my opinion of these factors might not be very accurate.” “Without incorporating a number of people, it would have taken some time to come to this list. On the opposite end, the list is very comprehensive, or is it just very long.”

Factors associated with high-level endurance performance: An expert consensus derived via the Delphi technique.

12-27-2022

Konopka, Magdalena J,Zeegers, Maurice P,Solberg, Paul A,Delhaije, Louis,Meeusen, Romain,Ruigrok, Geert,Rietjens, Gerard,Sperlich, Billy

eng

PMC9728914

RESEARCH ARTICLE Recovery of performance and persistent symptoms in athletes after COVID-19 Shirin VollrathID1*, Daniel Alexander Bizjak1, Jule Zorn1, Lynn Matits1,2, Achim Jerg1, Moritz Munk1, Sebastian Viktor Waldemar Schulz1, Johannes Kirsten1, Jana SchellenbergID1, Ju¨rgen Michael SteinackerID1 1 Division of Sports and Rehabilitation Medicine, Department of Medicine, Ulm University Hospital, Ulm, Germany, 2 Clinical & Biological Psychology, Institute of Psychology and Education, Ulm University, Ulm, Germany * [email protected] Abstract Introduction After the acute Sars-CoV-2-infection, some athletes suffer from persistent, performance- impairing symptoms, although the course of the disease is often mild to moderate. The rela- tion between cardiopulmonary performance and persistent symptoms after the acute period is still unclear. In addition, information about the development of this relationship is lacking. Objective To assess the prevalence of persistent symptoms over time and their association with the performance capability of athletes. Methods We conducted two cardiopulmonary exercise tests (CPET) in a three months interval with 60 athletes (age: 35.2±12.1 years, 56.7% male) after infection with Sars-CoV-2 (t0: study inclusion; t1: three months post t0). At each examination, athletes were asked about their persistent symptoms. To evaluate the change of Peak VO2/BM (Body Mass) between the time before infection and the first examination, the VO2/BM (predVO2) before infection was predicted based on anthropometric data and exercise history of the athletes. For data analy- sis, athletes were grouped according to their symptom status (symptom-free, SF; persistent symptoms, PS) and its progression from the first to the second examination 1) SF-SF, 2) PS-SF and 3) PS-PS. Results Comparing the SF and PS groups at t0, significant differences for Max Power/BM, Max Power/lbm (lean body mass), Peak VO2, Peak VO2/BM, Peak VO2/lbm, Peak VO2/HR, Peak VE, Peak Vt and VE/VCO2-Slope were observed. Regarding the progression over three months, an increase in Max Power/BM was shown in SF-SF and PS-SF (tendency). PLOS ONE PLOS ONE | https://doi.org/10.1371/journal.pone.0277984 December 7, 2022 1 / 16 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Vollrath S, Bizjak DA, Zorn J, Matits L, Jerg A, Munk M, et al. (2022) Recovery of performance and persistent symptoms in athletes after COVID-19. PLoS ONE 17(12): e0277984. https://doi.org/10.1371/journal.pone.0277984 Editor: Emiliano Cè, Universita degli Studi di Milano, ITALY Received: August 5, 2022 Accepted: November 7, 2022 Published: December 7, 2022 Copyright: © 2022 Vollrath et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the article and its Supporting information files. Funding: The Study was funded by the Federal Institut of Sport Science of Germany. Project Number: 070106/20-23 https://www.bisp.de/ SharedDocs/Kurzmeldungen/DE/Nachrichten/ 2021/COVID19KohortenstudiePodcast.html Funder did not play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Max Power/lbm increased in SF-SF and PS-PS (tendency). A decrease of VE/VCO2-Slope in PS-PS was found. Conclusion COVID-19 led to a decline in performance that was greater in PS than in SF. Additionally, PS had decreased ventilatory parameters compared to SF. Furthermore, an improvement over time was observed in some CPET parameters and a partial recovery was observed judging by the decrease in various symptoms. Introduction The long-term sequelae of COVID-19 are manifold and patients suffer from the symptoms for up to 12 weeks after infection (Long-COVID) or even longer (Post-COVID) [1–3]. Not only people who were hospitalized in the acute period, even people with a mild or moderate disease course can suffer from Long-COVID [4]. Furthermore, athletes, who mostly do not have comorbidities, can be seriously affected by COVID-19 [5, 6]. In addition, symptoms can also occur for the first time after recovery from the infection [1], resulting in a lower performance capability of patients and athletes [7–10]. The limitations reported by patients vary in severity and symptomatic expression [11, 12]. To exclude organic restrictions and / or to evaluate the performance capability of the cardio- pulmonary and respiratory system, cardiopulmonary exercise testing (CPET) can be con- ducted [13]. Previous research focused on various CPET variables like breathing reserve, Respiratory Exchange Ratio, Peak VO2, Peak Heart Rate or PETCO2 [14–16]. CPET is already recommended and used in Long-COVID and Post-COVID studies to assess the limitations in the cardiopulmonary and respiratory system after COVID-19 [17–21]. To gain insights how persistent symptoms develop over time, it is important to monitor patients who have previ- ously been infected with Sars-CoV-2 over at least several months up to several years by mea- suring objective performance parameters, like Peak VO2 or VE/VCO2-Slope over time [22]. Although the majority of athletes represent a healthy part of the general population, they have an increased need for health monitoring because they expose their bodies to increased loads during heavy exercise, training and competition. However, there is only limited knowl- edge about the development of the performance and cardiopulmonary function of athletes after a Sars-CoV-2 infection, especially when athletes suffer from persistent symptoms. Therefore, this study aims to evaluate the athletes’ symptom state, cardiopulmonary func- tion, and performance capacity after infection and three months later. Thus, the predicted performance capacity before infection was compared with the performance capacity post- infection. Furthermore, it was of interest whether athletes with persistent symptoms have decreased cardiopulmonary function and performance. In addition, the relationship between the predicted aerobic capacity and decreased infection-related performance was analyzed. Finally, the development of the recovery process of athletes with and without persistent symp- toms over three months was studied. Material and methods The study was conducted at the Division for Sports and Rehabilitation Medicine, Center of Internal Medicine of the University Hospital in Ulm, Germany. All athletes were participants PLOS ONE Recovery of performance and persistent symptoms in athletes after COVID-19 PLOS ONE | https://doi.org/10.1371/journal.pone.0277984 December 7, 2022 2 / 16 Competing interests: The authors have declared that no competing interests exist. Abbreviations: Bf, Breathing Frequency; BM, Body Mass; CFS, Chronic Fatigue Syndrome; CoSmo-S, COVID-19 in elite sports – A multicenter cohort study; COVID-19, Corona Virus Disease 2019; CPET, Cardiopulmonary Exercise Test; IC, Inspiratory Capacity; lbm, Lean Body Mass; MVV, Maximal Voluntary Volume; PETCO2, Partial End tidal Carbon dioxide; predVO2, Prediction of Volume Oxygen before infection; PS, Persistent Symptoms Group; RER, Respiratory Exchange Ratio; SF, Symptom-free Group; t0, Study inclusion, first examination; t1, Three months post t0, second examination; VE/VCO2-Slope, Ventilation / Volume Carbon dioxide Slope; VE, Ventilation; VO2, Volume Oxygen; Vt/VC, Tidal Volume / Vital capacity; Vt, Volume Tidal; VT1, Ventilatory Threshold 1. of the CoSmo-S study (COVID-19 in German Competitive Sports) [23]. The inclusion criteria were 1) Age  18 years, 2) Sport at least three times per week (20 metabolic equivalents (METs) / week), 3) confirmed Sars-CoV-2 infection but at least > 2 weeks after a positive PCR-test. Further details of the inclusion / exclusion criteria and the study design can be found in the study protocol by Niess et al. [23]. Ethical approval All participating athletes took part voluntarily and gave informed consent prior to inclusion. The study was performed in accordance with the Declaration of Helsinki. The study was approved by the ethics committee of Ulm University (EK 408/20). Investigation period The period of investigation was between June 2020 and January 2022, but the examination of study participants is still ongoing at the time of submission of this manuscript. All athletes who had at least two examinations, in a three months interval, with CPET until January 2022 were included in this pilot evaluation. Study population In total, 60 persons were included (56.7% male). There were two time points of investigation: The first one (t0) (4.1 ± 3.8 months after infection) was at the day of study inclusion; the sec- ond one (t1) three months later (3.3 ± 0.5 months). Examination of symptoms At both examination dates, the athletes were asked about the presence of persistent symptoms based on the international consensus criteria for myalgic encephalomyelitis / chronic fatigue syndrome and medical history evaluation [24]. The symptoms were differentiated into eight symptom categories (Fatigue and performance decrease, Sleeping disorders, Neurocognitive dis- orders, Respiratory disorders, Autonomic disorders, Pain, Psychological-related items, Immuno- logical disorders). The severity of the symptoms is not rated in this questionnaire. All symptoms assigned to the different categories appeared for the first time after or during COVID-19. Once a symptom was mentioned, it was documented as "present" in the respective category and the participant was grouped into the category Persistent Symptoms (Group PS). Athletes who reported no symptoms related to COVID-19 at t0 were assigned to the symptom- free group (Group SF). The athletes could report multiple persistent symptoms. Thus, an ath- lete could be listed in several symptom categories. Examination of body composition For measuring weight and body fat, a bio-impedance-scale (InBody 770, InBody Europe B.V., Eschborn, Germany) was used. Lean Body Mass (lbm) was calculated as follows: lbm (kg) = weight (kg)–body fat (kg). Lean body mass was used to diminish differences by gender. Prediction of peak oxygen consumption before infection The VO2 Peak before infection (predVO2) was predicted by three different experts blinded to all information except the kind of sport, training and performance data before infection from an athletes-questionnaire, medical history and anthropometric data. They estimated the VO2 Peak (ml/min/kg/BM) in intervals with a width of 5 ml in the range from “ 15 ml” to “> 65 ml”. PLOS ONE Recovery of performance and persistent symptoms in athletes after COVID-19 PLOS ONE | https://doi.org/10.1371/journal.pone.0277984 December 7, 2022 3 / 16 CPET The physical performance was tested by a CPET, conducted with the breath-by-breath (Ergos- tik, Geratherm Respiratory, Bad Kissingen, Germany). All examinations were conducted on a cycling ergometer (Excalibur Sport, LODE B.V., Groningen, Netherlands). The ramp protocol was chosen according to the estimated fitness level, age, sex and weight of the athletes so that total exhaustion was reached in the desired time (8–12 min). The same protocol was chosen at the second measuring point. All CPETs were evaluated by the same examiner. To set individ- ual reference values like calculated MVV (maximal voluntary volume) and IC (inspiratory capacity) for the athletes, a spirometry was conducted before CPET using the same device. The following variables were measured: Max Power/BM (W/kg BM), Max Power/lbm (W/ kg lean body mass), Peak VO2 (l/min), Peak VO2/BM (ml/min/kg BM), Peak VO2/lbm (ml/ min/kg lean body mass), Peak Heart Rate (HR) (1/min), Peak VO2/HR (ml/beat), Peak Venti- lation (VE) (l/min), Peak Tidal Volume (Vt) (l/breath), Peak Breathing frequency (Bf) (1/ min), Peak Tidal Volume / Vital capacity (Vt/VC) (%), Ventilation/Volume of CO2 –Slope (VE/VCO2-Slope). Change of performance over three months To determine whether the CPET variables changed over three months, three subgroups, depending on their symptom status were formed in terms of progression: At t0 persistent symptoms and at t1 symptom-free ! PS-SF, both at t0 and t1 persistent symptoms ! PS-PS, both at t0 and t1 symptom-free ! SF-SF. Statistics The statistical analysis was performed using IBM SPSS Statistics, version 28.0.0.0 (IBM Deutschland GmbH, Ehningen, Germany). Graphs were created with R version 4.1.1 (R Core Team, 2020). To evaluate the correlation of the different experts who estimated the predVO2, a Spearman rho test was conducted. PredVO2 was calculated from the mean of the estimated intervals of each rater. To calculate the difference between predVO2 and Peak VO2/BM in SF and PS, a sign-test was conducted. To evaluate whether a difference in predVO2 between SF and PS exists a Mann-Whitney-U test was conducted. To examine the differences in CPET parameters between the different symptom groups (PS: atheltes with persistent symptoms, SF: symptom-free athletes), t-tests and Mann-Whit- ney-U tests were calculated. To control for possible confounding variables (time since infec- tion and age), robust linear regression models were conducted. To evaluate whether there is a change of the CPET variables in each group over three months, a paired two-tailed t-test as well as a paired Wilcoxon-test were used. Cohen’s d was calculated as effect size of the differences between the variables. Missing values were always excluded in pairs in the analyses. The significance level for all tests was set at (p<0.05). Results Progression of symptoms In total, 60 athletes were included. However, not all parameters could be used for the analysis due to implausible values during measurement or missing values. These values have been excluded from the analysis presented in this study. At the first examination (t0), 16 of the 60 athletes were symptom-free. At t1, 23 athletes were symptom-free, of which nine athletes PLOS ONE Recovery of performance and persistent symptoms in athletes after COVID-19 PLOS ONE | https://doi.org/10.1371/journal.pone.0277984 December 7, 2022 4 / 16 previously had symptoms at t0. While 44 athletes reported suffering from at least one symptom at t0, this number decreased to 37 athletes at t1. Fig 1 shows the number of athletes with or without symptoms at the two examinations. Symptom categories Fig 2 shows the number of athletes at both measurement points who reported at least one symptom in the corresponding symptom category. With the medical history and the Interna- tional Consensus CFS questionnaire [24], athletes were asked about persistent symptoms after COVID-19. It was possible to report symptoms for more than one category. The number of athletes with symptoms in the categories Fatigue and performance decrease, Neurocognitive disorders, Respiratory disorders, Autonomic disorders and Pain decreased over time. Contrary to that, the number of athletes with symptoms in the categories sleeping disorders, psychologi- cal-related items and immunological disorders increased over time. Number of symptom categories Among the athletes who had persistent symptoms at t0, the highest number of athletes (n = 11) had symptoms that belonged to two symptom categories, closely followed by both four symp- tom categories (n = 10) and one symptom category (n = 10). At t1, 11 athletes had symptoms from one symptom category, and nine athletes had symptoms from three symptom categories. The highest numbers of symptom categories were seven at t0 and eight at t1. Prediction of peak oxygen consumption before infection The results of predVO2 of the three different expert raters correlated significantly (p<0.001). The correlation factor shows how well the raters correspond with each other: rsp = 0.800, rsp = 0.628 rsp = 0.537. Fig 3 shows the means of the intervals of predVO2 and Peak VO2/BM at t0 for symptom-free athletes and athletes with persistent symptoms. The prediction of predVO2 differed significantly between SF and PS (p = 0.015). In both groups, there were differences between predVO2 and Peak VO2/BM (SF: p = 0.004, PS: p<0.001). In SF, the means of pre- dVO2 and Peak VO2/BM were in the intervals “> 45ml  50ml” and “> 40ml  45ml”, respectively. In PS, the means of predVO2 and Peak VO2/BM were in the intervals “> 40  45ml” and “> 30ml  35ml”, respectively. In 45 athletes, Peak VO2/BM was decreased at least one interval (~5 ml/min/kg BM) compared to the value predicted for the time before the infection. Of these, 19 athletes had a peak VO2/BM lower by more than 10 ml/min/kg BM Fig 1. Development of symptom status. Symptom status of all 60 athletes at t0 (first examination date) and t1 (3.3 ± 0.5 months post first examination). 35 of 44 athletes, who had persistent symptoms at t0 still stated persistent symptoms at t1 (progression group PS-PS). Nine athletes became symptom- free over three months (progression group PS-SF). 14 of the 16 athletes remained symptom-free over the observation period (SF-SF). Two athletes developed symptoms over time. Data were collected with medical history and the International Consensus CFS questionnaire [24]. https://doi.org/10.1371/journal.pone.0277984.g001 PLOS ONE Recovery of performance and persistent symptoms in athletes after COVID-19 PLOS ONE | https://doi.org/10.1371/journal.pone.0277984 December 7, 2022 5 / 16 compared to predVO2, and two athletes had a deficit of more than 25 ml/min/kg BM com- pared to predVO2. Athletes who were symptom-free at t0 had a lower decrease of Peak VO2/ BM than athletes with persistent symptoms. In eleven athletes, predVO2 was one interval lower or at the same interval as measured at t0. CPET Table 1 shows the anthropometric and CPET data of all athletes at t0. The 35.15 (±12.14) years old population had a mean of 36.80ml (±10.53ml) maximal oxygen consumption and was able to perform 3.66W (±1.12W) per kilogram body mass. The descriptive data tables for PS and SF (S1 and S2 Tables) and for the progression groups (S3 Table) can be found in the appendix. The mean exercising time of the CPET was 09:27 min (±01:50 min). In 80.3% of the conducted CPETs, a RER  1.15 (Respiratory Exchange Ratio) was achieved (t0: 76.6%; t1: 85.0%). Differences between symptom-free athletes and athletes with persistent symptoms Table 2 shows the anthropometric data for SF and PS at t0. Age (p = 0.044) and time since infection (p = 0.004) were possible factors influencing the results of the Mann-Whitney-U test. Fig 2. Course of symptom categories over investigation period. Number of athletes who stated at least one symptom in the eight symptom categories at t0 (first examination date) and t1 (3.3 ± 0.5 months post first examination). Data of symptom categories were collected with medical history and the International Consensus CFS questionnaire [24]. In total 44 athletes stated at least one symptom at t0 and 37 athletes at t1 (3.3 ± 0.5 months post first examination). Athletes could state symptoms in multiple categories. https://doi.org/10.1371/journal.pone.0277984.g002 PLOS ONE Recovery of performance and persistent symptoms in athletes after COVID-19 PLOS ONE | https://doi.org/10.1371/journal.pone.0277984 December 7, 2022 6 / 16 Therefore, age and time since infection were considered as possible confounders in further analyses. The detailed test statistic can be found in the appendix (S4 Table). Fig 4 shows significant differences between PS and SF at t0. Significant differences were found between Max Power/BM (dif = 25.17%, p = 0.001, d = 0.419), Max Power/lbm Table 1. Anthropometric and CPET data of study population (N = 60) at t0 (first examination date). Values are given as mean and standard deviation (SD). Anthropometrics & CPET variables N Mean (±SD) Age (years) 60 35.15 (±12.14) Body Mass (kg) 60 74.78 (±15.11) Height (cm) 60 175.75 (±9.05) Body Mass Index (kg/m2) 60 24.03 (±3.61) Lean Body Mass (kg) 58 59.12 (±11.59) Max Power/BM (W/kg BM) 60 3.66 (±1.12) Max Power/lbm (W/kg lbm) 58 4.59 (±12.14) Peak VO2 (l/min) 56 2.74 (±0.87) Peak VO2/BM (ml/min/kg BM) 56 36.80 (±10.53) Peak VO2 /lbm (ml/min/ kg lbm) 55 46.04 (±9.77) Peak HR (1/min) 52 171.69 (±14.87) Peak VO2/HR (ml/beat) 50 15.58 (±4.64) Peak VE (l/min) 60 106.48 (±35.16) Peak Bf (1/min) 60 39.77 (±8.00) Peak Vt (l/breath) 60 2.66 (±0.66) Peak Vt/VC (%) 60 56.98 (±8.67) VE/VCO2-Slope 60 25.83 (±4.45) Abbreviations: Bf: Breathing frequency; lbm: Lean Body Mass; VE: Ventilation; VE/VCO2-Slope: Ventilation / Volume Carbon dioxide Slope; VO2: Volume Oxygen; Vt: Volume Tidal; Vt/VC: Tidal Volume / Vital capacity https://doi.org/10.1371/journal.pone.0277984.t001 Fig 3. Differences between predVO2 and Peak VO2/BM. Significant differences of means between the calculated Peak VO2 (predVO2) and in PS and SF at t0 (first examination). In both groups the measured values were significantly below the values predicted for the time before infection. p<0.01, p<0.001. https://doi.org/10.1371/journal.pone.0277984.g003 PLOS ONE Recovery of performance and persistent symptoms in athletes after COVID-19 PLOS ONE | https://doi.org/10.1371/journal.pone.0277984 December 7, 2022 7 / 16 (dif = 16.86%, p = 0.008, d = 0.347), Peak VO2 (dif = 23.17%, p = 0.004, d = 0.385), Peak VO2/ BM (dif = 24.63%, p<0.001, d = 0.476), Peak VO2 /lbm (dif = 16.32%, p = 0.005, d = 0.375) (Peak VO2/HR (dif = 21.53%, p = 0.009, d = 0.371), Peak VE (dif = 20.43%, p = 0.021, d = 0.299) and VE/VCO2-Slope (dif = -13.77%, p = 0.008, d = 0.340). When considering time since infection as a confounder, a tendency was observed for the variable Peak Vt (p = 0.082). Without consideration of any confounder (dif = 15.67%, p = 0.010, d = 0.331) or with consid- eration of age (p = 0.012) there was a significant difference, respectively. Even with consider- ation of time since infection or age, no differences were found for Peak HR, Peak Bf, and Peak Vt/VC. Table 2. Anthropometric data and test statistic for being a confounder in SF (symptom-free) and PS (persistent symptoms) at t0 (first examination date). Group & Examination Time Point SF at t0 PS at t0 Mean (±SD) Mean (±SD) U p-value Age (years) 30.06 (±9.21) 37.00 (±12.63) 2.016 0.044 Body Mass (kg) 73.49 (±11.38) 75.24 (±16.34) 0.117 0.907 Height (cm) 177.19 (±8.50) 175.23 (±9.28) -0.494 0.621 Body Mass Index (kg/m2) 23.20 (±2.73) 24.33 (±3.87) 0.828 0.408 Lean Body Mass (kg) (N = 58) 63.12 (±10.3) 57.58 (±11.94) -1.583 0.113 Time since infection (months) 2.44 (±3.08) 4.73 (±3.96) 2.880 0.004 U = test statistic of Mann-Whitney-U-Test, significance level p<0.05. https://doi.org/10.1371/journal.pone.0277984.t002 Fig 4. Differences in CPET between SF and PS at study inclusion. SF (symptom-free) had significantly higher mean values for (A) Max Power/BM, (B) Max Power/lbm, (C) Peak VO2, Peak VO2/BM, (E) Peak VO2/lbm, (F) Peak VO2/ HR, and (G) Peak VE at t0 (first examination date). PS (persistent symptoms) had higher mean value for (I) VE/VCO2- Slope compared to SF at t0. (H) Without confounder, a significantly higher mean value of Peak Vt in SF could be observed. p<0.05, p<0.01, p<0.001. https://doi.org/10.1371/journal.pone.0277984.g004 PLOS ONE Recovery of performance and persistent symptoms in athletes after COVID-19 PLOS ONE | https://doi.org/10.1371/journal.pone.0277984 December 7, 2022 8 / 16 Change of CPET parameters over three months per progression group The variable Max Power/BM (Fig 5, Panel A) changed between both examinations in SF-SF (dif = 4.05%, t(13) = -3.239, p = 0.006, d = -0.866). In PS-SF, a tendency (p<0.1) for the vari- able Max Power/BM could be observed (dif = 4.39%, t(8) = -1.999, p = 0.081, d = -0.666). These athletes were able to generate more power per kg BM at t1 than at t0. In SF-SF, a significant difference between the two examination dates was shown for Max Power/lbm (dif = 3.67%, t(13) = -2.847, p = 0.014, d = -0.761) (Fig 5, Panel B). For Max Power/ lbm in PS-PS a tendency was observed (dif = 1.94%, t(34) = 1.872, p = 0.061) (Fig 5, Panel B). In PS-SF, this variable did not change. No other variables changed in SF-SF. In PS-PS a difference for the variable VE/VCO2-Slope (dif = -5.98%, t(34) = 2.827, p = 0.008, d = -0.478) was observed. The mean of this variable decreased over time (Fig 5, Panel C). Variable VE/VCO2-Slope did not change in SF-SF and PS-SF. Discussion We hypothesized that athletes have a decreased performance after COVID-19 and the perfor- mance decrease is related to persistent symptoms. In the follow-up examination, three months later, we observed a decrease in symptoms and a partial recovery of performance. Symptoms Our study showed that at t0, 73.3% of athletes who were previously infected with Sars-CoV-2 still suffered from COVID-19-related symptoms, collected with a questionnaire based on the international consensus criteria for myalgic encephalomyelitis / chronic fatigue syndrome. Three categories were most frequently reported: fatigue and performance decrease, neurocog- nitive disorders and sleeping disorders. Komici et al. [6] conducted a study with competitive athletes in which they found that anosmia was the most common persistent symptom, whereas results similar to ours were shown by Carfi et al. [25]. They observed that 12.6% of the patients who had been hospitalized due to COVID-19 showed ongoing symptoms after 60.3 days since onset of the first COVID-19 symptom. The most reported long-term symptom in their study was fatigue, followed by dyspnea. Goe¨rtz et al. [26] also found persistent symptoms in hospital- ized persons and in people with mild or moderate courses. The symptoms most frequently observed by them are equivalent to those in the study by Carfi et al. [25]. Fatigue is also the most reported symptom category in our study, but the second one is sleeping disorders or neu- rocognitive disorders. These are functional impairments that may get more present over a Fig 5. Change of CPET variables over three months. (A) Variable Max Power/BM had in SF-SF (symptom-free– symptom free) significantly higher mean values at t1 (three months post first examination) than at t0 (first examination date). (B) Variable Max Power/lbm had in SF-SF significantly higher mean values at t1 than at t0. (C) Variable VE/ VCO2-Slope had in PS-PS significantly lower mean values at t1 than at t0. p<0.05, p<0.01. https://doi.org/10.1371/journal.pone.0277984.g005 PLOS ONE Recovery of performance and persistent symptoms in athletes after COVID-19 PLOS ONE | https://doi.org/10.1371/journal.pone.0277984 December 7, 2022 9 / 16 longer period, for example, sleeping disorders increased in our study over time. A further rea- son could be the different study population. Goe¨rtz et al. [26] observed a decline in symptoms for hospitalized and non-hospitalized patients over time. Their cohort was asked about symptoms during the acute infection and about existing symptoms approximately 80 days after the onset of initial symptoms. These patients reported a reduction in fatigue (95% vs. 87%) and dyspnea (90% vs. 71%). This is in line with our results at the follow-up after three months where fewer athletes reported persis- tent symptoms. In addition, in our study, the number of persistent symptoms per person also declined in most of the cases. However, the symptoms in the categories of sleeping disorders, psychological-related items and immunological disorders increased. The origins of mental ill- ness after a Sars-CoV-2 infection can be manifold, e.g. neurotrophic factors or impaired learn- ing and memory [27]. Raveendran et al. [28] showed that psychological-related items are common for Long-COVID, which is in accordance with our results. This could be indicative of a development of psychological items due to an ongoing inflammation in the brain or the persistent low physical capability, which can negatively affect mental health [29]. A recent review by Haller et al. [30] showed that persistent fatigue is a risk factor for a decreased life quality and work capacity. Furthermore, they observed that pre-existing psychological disor- ders also increase the risk for the Post-COVID syndrome [30]. Therefore, our results that psy- chological disorders increased over time, could indicate that the life quality decreases with persistent fatigue. Therefore, it seems necessary to monitor not only the performance capabil- ity but also the mental well-being, for example with the EQ-5D questionnaire [31], and for symptom collection, the International consensus CFS questionnaire could be used [24]. Prediction of peak oxygen consumption before infection The results of the comparison between predVO2 and the measured VO2/BM show that COVID-19 leads to a decrease in performance, regardless of whether or not persistent symp- toms exist. However, the performance decline is smaller in symptom-free athletes than in ath- letes with persistent symptoms. Furthermore, athletes with a higher estimated predVO2 are less likely to suffer from persistent symptoms. This is consistent with the findings by Massey et al. [32] who also found a lower prevalence of Long-Covid in athletes compared with the gen- eral population. Performance in CPET 19.7% of our conducted CPETs ended without objective total exhaustion (RER<1.15), although all of our athletes reported subjective exhaustion at the end of the test. This suggests that some individuals were unable to exhaust themselves due to unknown mechanisms. Additionally, we found decreased Max Power/lbm, Peak VO2/lbm, Peak VO2, and Peak VO2/HR, in athletes with symptoms compared to athletes without symptoms. This declined performance capability stands in contrast to the results by Anastasio et al. [33], who could not find differences in oxygen consumption at the maximum load between elite cross-country ath- letes with a mild-moderate disease course and healthy peers. However, they found differences at the ventilatory threshold 1 (VT1), which is an indicator of the aerobic capacity and thus it has an impact on the general performance capability. This result is in line with the result of a reduced Peak VO2/HR in our study because a reduced Peak VO2/HR can be, among others, an indicator of a limited aerobic capacity. A difference to our study is the shorter average period between infection and examination, the training status (all of their study participants com- peted in national and international competitions) as well as the gender distribution (77% male). As mentioned above, they only included mild-moderate COVID-19 courses and thus PLOS ONE Recovery of performance and persistent symptoms in athletes after COVID-19 PLOS ONE | https://doi.org/10.1371/journal.pone.0277984 December 7, 2022 10 / 16 they excluded all patients who suffered from dyspnea. This could explain why no differences were found for other ventilatory variables. Our results are in accordance with the results by Skjørten et al. [34]. They also observed decreased performance capability parameters, like peak oxygen uptake and oxygen pulse, three months after the Sars-CoV-2 infection in hospitalized patients. In addition, reduced Peak VO2 was observed by Debeaumont et al. [22] in a CPET six months after infection, while Barbage- lata et al. [35] focused on patients with and without Post-COVID-19 syndrome and also found a lower Peak VO2 in patients with Post-COVID-19 syndrome. This result is in accordance with our result with an above-average fit cohort: Athletes with persistent symptoms have a lower Peak VO2/BM than athletes who are symptom-free. In contrast to this, Komici et al. [6] could not find any significant differences in CPET between competitive athletes who were either post-acute Sars-CoV-2 infection or who had not been infected. However, they did not cluster in accordance to their persistent symptoms. Ventilatory parameters of CPET In our study, the ventilatory parameters Peak VE, Peak Vt and VE/VCO2-Slope differed signif- icantly between PS and SF at t0. It was already shown that besides a reduced VO2 peak con- sumption, patients with persistent dyspnea have a higher VE/VCO2-Slope and a higher PETCO2 than symptom-free patients [14]. While Ladlow et al. [36] did not find an association between reported symptoms by the patients and the perceived functional limitation and dysau- tonomia, they found an association between dysautonomia and reduced work rate, VO2 peak and VE/VCO2-Slope. Further studies also showed an increased VE/VCO2-Slope [37, 38]. A high VE/VCO2-Slope could be a result of hyperventilation, a lung obstruction or reduced lung perfusion [39]. However, the breathing frequency did not differ significantly between PS and SF, but Peak Vt was lower in PS compared to SF. This could indicate that these athletes are not able to inhale the same volume compared to athletes from SF. Decreased Peak Vt can be caused by prolonged sedentary behavior due to persistent symptoms which limit activity dur- ing daily life. Due to this circumstance, the auxiliary respiratory muscles can degenerate and this could result in a less efficient and less powerful breathing technique [39]. However, the ratio Peak Vt/VC was not significantly different between both groups. Therefore, further research regarding a potential auxiliary respiratory muscle degeneration may be useful. CPET variables of the three months follow-up To be able to provide information on how performance develops over time, long-term moni- toring is necessary. Recent published studies and recommendations also state the need for long time monitoring [21, 40, 41]. Studies with long-term monitoring and repeated examina- tions of patients and athletes are rare. In our monitoring study, we showed that there is an improvement of the VE/VCO2-Slope and a tendency of improvement for Max Power/lbm for athletes with persistent symptoms (PS-PS). A tendency of enhancement of Max Power/BM was shown in PS-SF, and in SF-SF an improvement of Max Power/BM and Max Power/lbm. The results for SF-SF could indicate that a larger amount of training, after isolation and protec- tion of the body due to the infection, led to an improvement of the performance over time that had been decreased due to acute illness. It can be assumed that athletes are more likely to return to sport after an acute infection than people with a more sedentary lifestyle. The observed decrease of VE/VCO2-Slope in PS-PS indicates that there is a slow regenera- tion of the ventilatory efficiency, despite persistent symptoms. It is possible that there are still persistent symptoms while the intensity of the symptoms declines. Although we did not assess the intensity, this would be in accordance with the results by Rooney et al. [42], who showed PLOS ONE Recovery of performance and persistent symptoms in athletes after COVID-19 PLOS ONE | https://doi.org/10.1371/journal.pone.0277984 December 7, 2022 11 / 16 that there is an ongoing but still incomplete regeneration occurring in a proportion of previ- ously infected individuals. A change in the VE/VCO2-Slope can result from a change in venti- lation. However, we did not see any significant differences in Peak VE, Peak Vt, or Peak Bf. Therefore, we assume that the improved VE/VCO2-Slope is caused by a better lung perfusion. Furthermore, the tendency of improvement in Max Power/lbm and the descriptive data showed an improvement in the general performance capacity (Peak VO2, Peak VO2/BM, VO2/HR), which may indicate a general recovery. In PS-SF, a tendency of improvement for Max Power/BM was found. This is in accordance with the results of PS-PS. The performance capacity increased slightly over time. Furthermore, descriptive data showed slightly improved ventilatory parameters. Regarding the ventilatory parameters, it is possible that the athletes declared being symptom-free at t1 because they potentially felt better due to less restriction during ventilation. Limitations Nine of 60 athletes in the study met the criteria of the PS-SF group. This relatively low number is a probable reason why no significant results could be shown here. However, a tendency could be shown that might indicate that significant differences could be shown with a higher number of athletes. One further limitation was that the intensity of the symptoms was not asked. Furthermore, the partly unknown medical history and the athletes’ unknown behavior between the examinations reduce the explanatory power of this study’s results. However, the different training behavior between the two examination dates is difficult to control due to the different duration of symptoms. Although the participants were selected according to the inclusion criteria, a certain heterogeneity of the group could not be avoided. Conclusion The study showed that after COVID-19, 70.3% of athletes stated having symptoms in the ques- tionnaire at the first examination and 61.7% had persistent symptoms at the second examination. In both groups, the maximal oxygen uptake was decreased compared to predicted maximal oxy- gen uptake before infection. Moreover, the reduction in VO2/BM in symptom-free athletes was smaller than in athletes with persistent symptoms. Athletes with a higher maximal oxygen uptake before infection were less likely to report persistent symptoms after a Sars-CoV-2 infection. The study showed differences in ventilatory (VE, VE/VCO2-Slope) as well as in general per- formance parameters (e.g. Max Power/BM, PeakVO2/BM and Peak HR/VO2) between symp- tom-free athletes and athletes with persistent symptoms. Which area (respiratory, cardiac and/ or muscular) is restricted and how long the restrictions last seems to be individual. Neverthe- less, the decrease of the respiratory equivalent for athletes with long-term symptoms indicate a slow recovery of the respiratory tract. To explain the mechanism of this fact further studies are needed. In further studies, possible correlations of symptom categories, and CPET parameters, for example, Max Power/lbm, Peak VO2/kg or VE/VCO2-Slope should be investigated. Fur- thermore, there is a need to investigate the reason for performance decline and long-lasting symptoms and the reasons why a number of people suffer from persistent symptoms for such a long period and other do not. To gain further insights into athletes’ recovery and the progres- sion of persistent symptoms, a longer period of monitoring as well as a still higher number of patients are needed. Supporting information S1 Table. Descriptive data of the CPET variables for SF (symptom-free) and PS (persistent symptoms) at t0 (first examination date). Abbreviations: Bf: Breathing frequency; lbm: Lean PLOS ONE Recovery of performance and persistent symptoms in athletes after COVID-19 PLOS ONE | https://doi.org/10.1371/journal.pone.0277984 December 7, 2022 12 / 16 Body Mass; VE: Ventilation; VE/VCO2-Slope: Ventilation / Volume Carbon dioxide Slope; VO2: Volume Oxygen; Vt: Volume Tidal; Vt/VC: Tidal Volume / Vital capacity. (DOCX) S2 Table. Descriptive data of the CPET variables for SF (symptom-free) and PS (persistent symptoms) at t1 (three months post first examination). Abbreviations: Bf: Breathing fre- quency; lbm: Lean Body Mass; VE: Ventilation; VE/VCO2-Slope: Ventilation / Volume Car- bon dioxide Slope; VO2: Volume Oxygen; Vt: Volume Tidal; Vt/VC: Tidal Volume / Vital capacity. (DOCX) S3 Table. Descriptive data of the CPET variables for SF-SF (symptom-free—symptom free), PS-PS (persistent symptoms—persistent symptoms) and PS-SF (persistent symp- toms–symptom-free) at t0 (first examination date) and t1 (three months post first exami- nation). Abbreviations: Bf: Breathing frequency; lbm: Lean Body Mass; VE: Ventilation; VE/ VCO2-Slope: Ventilation / Volume Carbon dioxide Slope; VO2: Volume Oxygen; Vt: Volume Tidal; Vt/VC: Tidal Volume / Vital capacity. (DOCX) S4 Table. Test statistic for the Mann-Whitney-U test without confounder, considering time since infection or considering age. for each test: df = 1Abbreviations: β: beta weight; Bf: Breathing frequency; lbm: Lean Body Mass; tsi: time since infection; VE: Ventilation; VE/ VCO2-Slope: Ventilation / Volume Carbon dioxide Slope; VO2: Volume Oxygen; Vt: Volume Tidal; Vt/VC: Tidal Volume / Vital capacity. (DOCX) Acknowledgments The authors thank all medical assistance for the study support and contribution to the develop- ment and achievement of this research and all patients who participated in this study. All authors have seen and approved the proposed article. Author Contributions Conceptualization: Shirin Vollrath, Daniel Alexander Bizjak, Achim Jerg, Moritz Munk, Johannes Kirsten. Data curation: Shirin Vollrath, Lynn Matits. Formal analysis: Shirin Vollrath, Johannes Kirsten. Funding acquisition: Ju¨rgen Michael Steinacker. Investigation: Shirin Vollrath, Jule Zorn. Methodology: Shirin Vollrath. Project administration: Achim Jerg. Supervision: Ju¨rgen Michael Steinacker. Validation: Ju¨rgen Michael Steinacker. Visualization: Lynn Matits. Writing – original draft: Shirin Vollrath. PLOS ONE Recovery of performance and persistent symptoms in athletes after COVID-19 PLOS ONE | https://doi.org/10.1371/journal.pone.0277984 December 7, 2022 13 / 16 Writing – review & editing: Shirin Vollrath, Daniel Alexander Bizjak, Jule Zorn, Moritz Munk, Sebastian Viktor Waldemar Schulz, Johannes Kirsten, Jana Schellenberg, Ju¨rgen Michael Steinacker. References 1. World Health Organization. A clinical case definition of post COVID-19 condition by a Delphi consensus: World Health Organization; 2021 [cited 2022 Feb 17]. https://www.who.int/publications/i/item/WHO- 2019-nCoV-Post_COVID-19_condition-Clinical_case_definition-2021.1. 2. Venkatesan P. NICE guideline on long COVID. The Lancet Respiratory Medicine 2021; 9(2):129. https://doi.org/10.1016/S2213-2600(21)00031-X PMID: 33453162 3. Nalbandian A, Sehgal K, Gupta A, Madhavan MV, McGroder C, Stevens JS et al. Post-acute COVID- 19 syndrome. Nat Med 2021; 27(4):601–15. https://doi.org/10.1038/s41591-021-01283-z PMID: 33753937 4. Bell ML, Catalfamo CJ, Farland LV, Ernst KC, Jacobs ET, Klimentidis YC et al. Post-acute sequelae of COVID-19 in a non-hospitalized cohort: Results from the Arizona CoVHORT. PLoS One 2021; 16(8): e0254347. https://doi.org/10.1371/journal.pone.0254347 PMID: 34347785 5. Martinez MW, Tucker AM, Bloom OJ, Green G, DiFiori JP, Solomon G et al. Prevalence of Inflammatory Heart Disease Among Professional Athletes With Prior COVID-19 Infection Who Received Systematic Return-to-Play Cardiac Screening. JAMA Cardiol 2021; 6(7):745–52. https://doi.org/10.1001/ jamacardio.2021.0565 PMID: 33662103 6. Komici K, Bianco A, Perrotta F, Dello Iacono A, Bencivenga L, D’Agnano V et al. Clinical Characteris- tics, Exercise Capacity and Pulmonary Function in Post-COVID-19 Competitive Athletes. J Clin Med 2021; 10(14). 7. Naeije R, Caravita S. Phenotyping long COVID. Eur Respir J 2021; 58(2). https://doi.org/10.1183/ 13993003.01763-2021 PMID: 34244323 8. Lopez-Leon S, Wegman-Ostrosky T, Perelman C, Sepulveda R, Rebolledo PA, Cuapio A et al. More than 50 long-term effects of COVID-19: a systematic review and meta-analysis. Sci Rep 2021; 11 (1):16144. https://doi.org/10.1038/s41598-021-95565-8 PMID: 34373540 9. Stavem K, Ghanima W, Olsen MK, Gilboe HM, Einvik G. Persistent symptoms 1.5–6 months after COVID-19 in non-hospitalised subjects: a population-based cohort study. Thorax 2021; 76(4):405–7. https://doi.org/10.1136/thoraxjnl-2020-216377 PMID: 33273028 10. de Marzo V, Clavario P, Lotti R, Barbara C, Porcile A, Guglielmi G et al. Cardiopulmonary exercise test- ing in COVID-19 patients at 3 months follow-up. European Heart Journal 2021; 42(Supplement_1). https://doi.org/10.1016/j.ijcard.2021.07.033 PMID: 34311011 11. Nutini A, Zignin C, Cosentino A. Kinesiology of Regeneration in Post COVID-19 Syndrome. RSS 2021; 11(1):19–25. 12. Peter Raphael S.; Nieters Alexandra; Kra¨usslich Hans-Georg; Brockmann Stefan O.; Go¨pel Siri; Kindle Gerhard et al. (2022): Post-acute sequelae of covid-19 six to 12 months after infection: population based study. In: BMJ (Clinical research ed.) 379, e071050. https://doi.org/10.1136/bmj-2022-071050 PMID: 36229057 13. Alba GA, Ziehr DR, Rouvina JN, Hariri LP, Knipe RS, Medoff BD et al. Exercise performance in patients with post-acute sequelae of SARS-CoV-2 infection compared to patients with unexplained dyspnea. EClinicalMedicine 2021; 39:101066. https://doi.org/10.1016/j.eclinm.2021.101066 PMID: 34476393 14. Aparisi A´ , Ybarra-Falco´n C, Garcı´a-Go´mez M, Tobar J, Iglesias-Echeverrı´a C, Jaurrieta-Largo S et al. Exercise Ventilatory Inefficiency in Post-COVID-19 Syndrome: Insights from a Prospective Evaluation. J Clin Med 2021; 10(12). https://doi.org/10.3390/jcm10122591 PMID: 34208271 15. Crisafulli E, Dorelli G, Sartori G, Dalle Carbonare L. Exercise ventilatory inefficiency may be a relevant CPET-feature in COVID-19 survivors. Int J Cardiol 2021; 343:200. https://doi.org/10.1016/j.ijcard. 2021.09.026 PMID: 34537303 16. Ferreira EVM, Oliveira RKF. Mechanisms of exercise intolerance after COVID-19: new perspectives beyond physical deconditioning. J Bras Pneumol 2021; 47(5):e20210406. https://doi.org/10.36416/ 1806-3756/e20210406 PMID: 34878057 17. Vonbank K, Lehmann A, Bernitzky D, Gysan MR, Simon S, Schrott A et al. Predictors of Prolonged Car- diopulmonary Exercise Impairment After COVID-19 Infection: A Prospective Observational Study. Front Med (Lausanne) 2021; 8:773788. https://doi.org/10.3389/fmed.2021.773788 PMID: 35004742 18. Clavario P, de Marzo V, Lotti R, Barbara C, Porcile A, Russo C et al. Cardiopulmonary exercise testing in COVID-19 patients at 3 months follow-up. Int J Cardiol 2021; 340:113–8. https://doi.org/10.1016/j. ijcard.2021.07.033 PMID: 34311011 PLOS ONE Recovery of performance and persistent symptoms in athletes after COVID-19 PLOS ONE | https://doi.org/10.1371/journal.pone.0277984 December 7, 2022 14 / 16 19. Mohr A, Dannerbeck L, Lange TJ, Pfeifer M, Blaas S, Salzberger B et al. Cardiopulmonary exercise pat- tern in patients with persistent dyspnoea after recovery from COVID-19. Multidiscip Respir Med 2021; 16(1):732. https://doi.org/10.4081/mrm.2021.732 PMID: 33623700 20. Halle M, Bloch W, Niess AM, Predel H-G, Reinsberger C, Scharhag J et al. Exercise and sports after COVID-19-Guidance from a clinical perspective. Transl Sports Med 2021; 4(3):310–8. https://doi.org/ 10.1002/tsm2.247 PMID: 34230908 21. Steinacker JM, Schellenberg J, Bloch W, Deibert P, Friedmann-Bette B, Grim C et al. Recommendations for Return-to-Sport after COVID-19: Expert Consensus. German Journal of Sports Medicine 2022; 73(4). 22. Debeaumont D, Boujibar F, Ferrand-Devouge E, Artaud-Macari E, Tamion F, Gravier F-E et al. Cardio- pulmonary Exercise Testing to Assess Persistent Symptoms at 6 Months in People With COVID-19 Who Survived Hospitalization: A Pilot Study. Phys Ther 2021; 101(6). https://doi.org/10.1093/ptj/ pzab099 PMID: 33735374 23. Niess AM, Widmann M, Gaidai R, Go¨lz C, Schubert I, Castillo K et al. COVID-19 in German Competitive Sports: Protocol for a Prospective Multicenter Cohort Study (CoSmo-S). Int J Public Health 2022; 67:1604414. https://doi.org/10.3389/ijph.2022.1604414 PMID: 35197815 24. Carruthers BM, Jain AK, de Meirleir KL, Peterson DL, Klimas NG, Lerner AM et al. Myalgic Encephalo- myelitis/Chronic Fatigue Syndrome. Journal of Chronic Fatigue Syndrome 2003; 11(1):7–115. 25. Carfı` A, Bernabei R, Landi F. Persistent Symptoms in Patients After Acute COVID-19. JAMA 2020; 324 (6):603–5. https://doi.org/10.1001/jama.2020.12603 PMID: 32644129 26. Goe¨rtz YMJ, van Herck M, Delbressine JM, Vaes AW, Meys R, Machado FVC et al. Persistent symp- toms 3 months after a SARS-CoV-2 infection: the post-COVID-19 syndrome? ERJ Open Res 2020; 6 (4). https://doi.org/10.1183/23120541.00542-2020 PMID: 33257910 27. Woods JA, Hutchinson NT, Powers SK, Roberts WO, Gomez-Cabrera MC, Radak Z et al. The COVID- 19 pandemic and physical activity. Sports Medicine and Health Science 2020; 2(2):55–64. https://doi. org/10.1016/j.smhs.2020.05.006 PMID: 34189484 28. Raveendran AV, Jayadevan R, Sashidharan S. Long COVID: An overview. Diabetes Metab Syndr 2021; 15(3):869–75. https://doi.org/10.1016/j.dsx.2021.04.007 PMID: 33892403 29. Troyer EA, Kohn JN, Hong S. Are we facing a crashing wave of neuropsychiatric sequelae of COVID- 19? Neuropsychiatric symptoms and potential immunologic mechanisms. Brain Behav Immun 2020; 87:34–9. https://doi.org/10.1016/j.bbi.2020.04.027 PMID: 32298803 30. Haller J, Kocalevent R-D, Nienhaus A, Peters C, Bergelt C, Koch-Gromus U. Anhaltende Fatigue als Folge einer COVID-19-Infektion bei Bescha¨ftigten im Gesundheitswesen: Risikofaktoren und Auswir- kungen auf die Lebensqualita¨t. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2022; 65(4):471–80. https://doi.org/10.1007/s00103-022-03511-4 PMID: 35298664 31. Devlin N, Roudijk B, Ludwig K, editors. Value Sets for EQ-5D-5L: A Compendium, Comparative Review & User Guide. 1st ed. 2022. Cham: Springer International Publishing; Imprint Springer; 2022. (Springer eBook Collection). 32. Massey D, Saydah S, Adamson B, Lincoln A, Aukerman DF, Berke EM et al. Prevalence of COVID-19 and Long COVID in Collegiate Student Athletes from Spring 2020 to Fall 2021: A Retrospective Survey; 2022. 33. Anastasio F, La Macchia T, Rossi G, D’Abbondanza M, Curcio R, Vaudo G et al. Mid-term impact of mild-moderate COVID-19 on cardiorespiratory fitness in e´lite athletes. J Sports Med Phys Fitness 2021. https://doi.org/10.23736/S0022-4707.21.13226-8 PMID: 34758007 34. Skjørten I, Ankerstjerne OAW, Trebinjac D, Brønstad E, Rasch-Halvorsen Ø, Einvik G et al. Cardiopul- monary exercise capacity and limitations 3 months after COVID-19 hospitalisation. Eur Respir J 2021; 58(2). https://doi.org/10.1183/13993003.00996-2021 PMID: 34210791 35. Barbagelata L, Masson W, Iglesias D, Lillo E, Migone JF, Orazi ML et al. Cardiopulmonary Exercise Testing in Patients with Post-COVID-19 Syndrome. Med Clin (Barc) 2021. https://doi.org/10.1016/j. medcli.2021.07.007 PMID: 34417020 36. Ladlow P, O’Sullivan O, Houston A, Barker-Davies R, May S, Mills D et al. Dysautonomia following COVID-19 is not associated with subjective limitations or symptoms but is associated with objective functional limitations. Heart Rhythm 2022; 19(4):613–20. https://doi.org/10.1016/j.hrthm.2021.12.005 PMID: 34896622 37. Motiejunaite J, Balagny P, Arnoult F, Mangin L, Bancal C, Vidal-Petiot E et al. Hyperventilation as one of the mechanisms of persistent dyspnoea in SARS-CoV-2 survivors. Eur Respir J 2021; 58(2). https:// doi.org/10.1183/13993003.01578-2021 PMID: 34385265 38. Rinaldo RF, Mondoni M, Parazzini EM, Pitari F, Brambilla E, Luraschi S et al. Deconditioning as main mechanism of impaired exercise response in COVID-19 survivors. Eur Respir J 2021; 58(2). https:// doi.org/10.1183/13993003.00870-2021 PMID: 33926969 PLOS ONE Recovery of performance and persistent symptoms in athletes after COVID-19 PLOS ONE | https://doi.org/10.1371/journal.pone.0277984 December 7, 2022 15 / 16 39. Kroidl R, Schwarz S, Lehnigk B, Fritsch J, editors. Kursbuch Spiroergometrie: Technik und Befundung versta¨ndlich gemacht. 3. vollsta¨ndig u¨berarbeitete und erweiterte Auflage. Stuttgart: Thieme; 2014. http://nbn-resolving.org/urn:nbn:de:bsz:24-epflicht-1917605. 40. Balachandar V, Mahalaxmi I, Subramaniam M, Kaavya J, Senthil Kumar N, Laldinmawii G et al. Follow- up studies in COVID-19 recovered patients—is it mandatory? Sci Total Environ 2020; 729:139021. https://doi.org/10.1016/j.scitotenv.2020.139021 PMID: 32360909 41. Lo¨llgen H, Bachl N, Papadopoulou T, Shafik A, Holloway G, Vonbank K et al. Recommendations for return to sport during the SARS-CoV-2 pandemic. BMJ Open Sport Exerc Med 2020; 6(1):e000858. https://doi.org/10.1136/bmjsem-2020-000858 PMID: 34192007 42. Rooney S, Webster A, Paul L. Systematic Review of Changes and Recovery in Physical Function and Fitness After Severe Acute Respiratory Syndrome-Related Coronavirus Infection: Implications for COVID-19 Rehabilitation. Phys Ther 2020; 100(10):1717–29. PLOS ONE Recovery of performance and persistent symptoms in athletes after COVID-19 PLOS ONE | https://doi.org/10.1371/journal.pone.0277984 December 7, 2022 16 / 16

Recovery of performance and persistent symptoms in athletes after COVID-19.

12-07-2022

Vollrath, Shirin,Bizjak, Daniel Alexander,Zorn, Jule,Matits, Lynn,Jerg, Achim,Munk, Moritz,Schulz, Sebastian Viktor Waldemar,Kirsten, Johannes,Schellenberg, Jana,Steinacker, Jürgen Michael

eng

PMC8523042

Sprinters 0.5 1.0 1.5 2.0 2.5 2 3 4 5 1 0.5 1.0 1.5 2.0 2.5 2 3 4 5 2 0.5 1.0 1.5 2.0 2.5 2 3 4 5 3 0.5 1.0 1.5 2.0 2.5 2 3 4 5 4 0.5 1.0 1.5 2.0 2.5 2 3 4 5 5 0.5 1.0 1.5 2.0 2.5 2 3 4 5 6 0.5 1.0 1.5 2.0 2.5 2 3 4 5 7 0.5 1.0 1.5 2.0 2.5 2 3 4 5 8 0.5 1.0 1.5 2.0 2.5 2 3 4 5 9 0.5 1.0 1.5 2.0 2.5 2 3 4 5 10 0.5 1.0 1.5 2.0 2.5 2 3 4 5 11 0.5 1.0 1.5 2.0 2.5 2 3 4 5 12 0.5 1.0 1.5 2.0 2.5 2 3 4 5 13 0.5 1.0 1.5 2.0 2.5 2 3 4 5 14 0.5 1.0 1.5 2.0 2.5 2 3 4 5 15 0.5 1.0 1.5 2.0 2.5 2 3 4 5 16 0.5 1.0 1.5 2.0 2.5 2 3 4 5 17 0.5 1.0 1.5 2.0 2.5 2 3 4 5 18 0.5 1.0 1.5 2.0 2.5 2 3 4 5 19 0.5 1.0 1.5 2.0 2.5 2 3 4 5 20 Cadence (steps/s) S1 Fig Step length (m)

Spatiotemporal inflection points in human running: Effects of training level and athletic modality.

10-18-2021

Goto, Yuta,Ogawa, Tetsuya,Kakehata, Gaku,Sazuka, Naoya,Okubo, Atsushi,Wakita, Yoshihiro,Iso, Shigeo,Kanosue, Kazuyuki

eng

PMC6192093

Research Article Modelling of Running Performances: Comparisons of Power-Law, Hyperbolic, Logarithmic, and Exponential Models in Elite Endurance Runners H. Vandewalle UFR de Sant´e, M´edecine et Biologie Humaine, Universit´e Paris XIII, Bobigny, France Correspondence should be addressed to H. Vandewalle; [email protected] Received 1 April 2018; Revised 2 August 2018; Accepted 2 September 2018; Published 3 October 2018 Academic Editor: Ronald E. Baynes Copyright © 2018 H. Vandewalle. Thisis an open accessarticle distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Many empirical and descriptive models have been proposed since the beginning of the 20th century. In the present study, the power- law(Kennelly)andlogarithmic (P´eronnet-Thibault)modelswere comparedwithasymptotic modelssuchas2-parameter hyperbolic models (Hill and Scherrer), 3-parameter hyperbolic model (Morton), and exponential model (Hopkins). These empirical models were compared from the performance of 6 elite endurance runners (P. Nurmi, E. Zatopek, J. V¨a¨at¨ainen, L. Vir´en, S. Aouita, and H. Gebrselassie) who were world-record holders and/or Olympic winners and/or world or European champions. These elite runners were chosen because they participated several times in international competitions over a large range of distances (1500, 3000, 5000, and 10000 m) and three also participated in a marathon. The parameters of these models were compared and correlated. The less accurate models were the asymptotic 2-parameter hyperbolic models but the most accurate model was the asymptotic 3-parameter hyperbolic model proposed by Morton. The predictions of long-distance performances (maximal running speeds for 30 and 60 min and marathon) by extrapolation of the logarithmic and power-law models were more accuratethan the predictions by extrapolation in all the asymptotic models. The overestimations of these long-distance performances by Morton’s model were less important than the overestimations by the other asymptotic models. 1. Introduction Many models [1–11] of running performances based on biomechanics and physiology have been proposed. These models are generally complex. For example, the physiological model proposed by P´eronnet and Thibault [7] included the inertia, power, and capacity of the anaerobic and aerobic metabolisms. Empirical and descriptive models have also been pro- posed since the beginning of the 20th century and presented in many reviews [12–21]. Empirical models are derived by observation and experimentation rather than by theoretical considerations [14]. The empirical models are less complex than the biomechanical and physiological models but are also less explicative. The most famous empirical models corre- sponded to a power-law model (Kennelly, 1906), asymptotic hyperbolic models (Hill, 1927; Scherrer, 1954), and, more recently, a logarithmic model (P´eronnet and Thibault, 1987) and 3-parameter asymptotic models (Hopkins, 1989; Morton, 1996). The asymptotic models correspond to horizontal asymptote equations: the functions approach a horizontal line when tlim tends to infinity. In these models, it is assumed that the speeds lower than these asymptotes can be maintained infinitely. The empirical models of running exercises are often used to estimate (i) the improvement in performance [22] (ii) the effects of age [23, 24] and sex [25, 26] on running performance (iii) the future performances and running speeds over given distances (iv) the endurance capability [7, 8], that is, “the ability to sustain a high fractional utilization of maximal oxygen uptake for a prolonged period of time” Hindawi BioMed Research International Volume 2018, Article ID 8203062, 23 pages https://doi.org/10.1155/2018/8203062 2 BioMed Research International Table 1: Individual performances (in seconds) of elite endurance runners. 1500 3000 5000 10000 Marathon Nurmi 233 500 868 1806 Zatopek 233 488 837 1734 8583 V¨a¨at¨ainen 224 473 808 1672 Vir´en 222 463 796 1658 7991 Aouita 209 449 778 1646 Gebrselassie 214 445 759 1583 7439 (v) the speed of training sessions [27] (vi) the maximal aerobic speed [7, 8] The maximal aerobic speed, otherwise known as MAS, is the lowest running speed at which maximum oxygen uptake (V02 max) occurs, and is also referred to as the velocity at V02 max (vV02 max). MAS is useful for training prescrip- tion and monitoring training loads. P´eronnet and Thibault suggested estimating MAS by computing the maximal speed corresponding to 7 min [8]. The maximal lactate steady state, defined as the highest constant power output that can be maintained without a progressive increase in blood lactate concentration, is usually sustainable for 30 to 60 min. [28– 30]. The first studies on the modelling of running perfor- mances were based on the world records because these records measured under standard external conditions repre- sent the most reliable index of human performance [31, 32]. The running times of the slower runners are more variable than those of the faster runners [33]. The best performances of world elite runners are probably very close to their maximal performances because they generally correspond to the results of many competitions against other elite runners and the motivation is probably optimal during these races. Now, the best performances of elite endurance runners who ran on different distances and were the best of their times can be found on the Internet (Wikipedia, etc.). Therefore, it is possible to study the characteristics of the different models which have been proposed for endurance exercises with the best performances of elite endurance runners. The performances of different runners were used in each study on the modelling of world and Olympic records [7, 22, 31, 32, 34, 35]. In contrast, in the present investigation, each model was computed only from the performances of a single runner. The computations of each model were repeated for different world elite endurance runners (P. Nurmi, E. Zatopek, J. V¨a¨at¨ainen, L. Vir´en, S. Aouita, and H. Gebrselassie) who were world-record holders and/or Olympic winners and/or world or European champions. They participated several times in international competitions over the same distances (1500, 3000, 5000, and 10000 m) that corresponded to a large range of distances. Their best individual performances are presented in Table 1. Moreover, if a model is not perfect for a large range of performances, the values of its parameters computed from different ranges of distances will be significantly different. In the present study, the parameters of the different models were computed with 3 ranges of distances: (i) 1500-3000-5000-10000 m for the largest range (ii) 1500-3000-5000 m, which is equivalent to the range of tlim generally used in the studies on critical speed or critical power (from 3 to 15 min) (iii) 3000-5000-10000 m, which corresponds to exercises slower than maximal aerobic speed Several previous investigations studied the evolution of the parameters in the models of running performances at different times [22, 34]. Similarly, the six elite endurance athletes of the present study ran at different times and their performances were performed in different conditions (cinder tracks versus synthetic tracks, nutrition, etc.) and were the results of different running exercises (for example, an equivalent of fartleck for Nurmi, an equivalent of interval- training for Zatopek, and altitude training for Gebrselassi´e), which could partly explain the evolution of the performances in these world elite runners and could also change the best model of individual running performances. The present study (1) applied the power-law and logarith- mic models and four asymptotic models (two 2-parameter hyperbolic models, a 3-parameter hyperbolic model, and a 3-parameter exponential model) to the individual perfor- mances of the elite runners, (2) compared the accuracy of these models and the effects of the range of performances on their parameters to assess which is the best model, and (3) compared the predictions of MAS by interpolation and the prediction of maximal running speeds for long distances (30, 60 min and also marathon in 3 runners) by extrapolation. 2. History of the Power-Law, Hyperbolic, Logarithmic, and Exponential Models 2.1. Power-Law Model (Kennelly). In 1906, Kennelly [12] studied the relationship between running speed (S) and the time of the world records (tlim) and proposed a power law: Dlim = ktlim g (1) where k is a constant and g an exponent. This power law between distance and time corresponds to a power law between time and speed (S): S = Dlim tlim = ktlim g tlim = ktlim g - 1 (2) Exponent g is probably an expression of endurance capability. Indeed, the tlim-Dlim relationship would be perfectly linear if BioMed Research International 3 g is equal to 1. It is likely that the curvatures of the tlim-S and tlim-Dlim relationships depend on the decrease in the fraction of maximal aerobic metabolism that can be sustained during long lasting exercises. The value of exponent g is independent of scaling as it is independent of the expression of tlim, S, and Dlim. In theory, parameter k should be correlated to maximal running speed because k is equal to the maximal running speed corresponding to one second. Indeed, when tlim is equal to 1s S = ktlim g – 1 = k ∗ 1g - 1 = k ∗ 1 = k (3) In 1981, a similar power-law model was proposed by Riegel [36]: tlim = aDlim b (4) S = Dlim tlim = Dlim aDlim b = (Dlim 1 - b) a (5) As Dlim = ktlim g Dlim 1/g = (ktlim g)1/g = k1/gtlim tlim = Dlim 1/g k1/g = (Dlim 1 - b) a a = k1/g (6) and Dlim 1/g = (Dlim 1 - b) 1 g = 1 - b b = 1 - 1 g = (g – 1) g (7) These equations of Riegel have recently been applied to a large study on 2303 recreational endurance runners [37]. 2.2. Hyperbolic Model (Hill, Scherrer). In 1927, Hill [1] pro- posed a hyperbolic model to describe the world-record curve in running and swimming. Hill observed that the “running curve,” or the relationship between a runner’s power output (P) and the total duration of a race (T), can be described by a hyperbolic function: P = (A T ) + R (8) where A and R represent the capacity of anaerobic metabolism and the rate of energy release from aerobic metabolism, respectively. In 1954, Scherrer et al. proposed a linear relationship [38] between the exhaustion time (tlim) of a local exercise (flexions or extensions of the elbow or the knee) performed at different constant power outputs (P) and the total amount of work performed at exhaustion (Wlim) for tlim ranging between 3 and 30 minutes: Wlim = a + btlim (9) Consequently, the relationship between P and tlim is hyper- bolic: Wlim = Ptlim = a + btlim tlim = a (P – b) (10) After the publication of an article in English (1965) by Monod and Scherrer [39], Ettema (1966) applied the critical-power concept to world records in running, swimming, cycling, and skating exercises [40] and proposed a linear relationship between Dlim and tlim for world records from 1500 to 10000 m: Dlim = a + btlim (11) where tlim corresponded to the world record for a given distance (Dlim). It was assumed that the energy cost of running, i.e., the energy expenditure per unit of distance, was almost independent of speed under 20 km.h−1. Consequently, Dlim and parameter a were equivalent to amounts of energy. Therefore, parameter a has been interpreted as equivalent to an energy store and an estimation of maximal Anaerobic Distance Capacity (ADC expressed in metres) for running exercises whereas slope b was considered as a critical velocity (SCrit). Dlim = ADC + SCrit1tlim (12) tlim = ADC (S – SCrit1) (13) However, the linear Wlim-tlim was an approximation as indicated by Scherrer and Monod (1960): “The relationship W = f(t) is not perfectly linear as shown on Figure 2(a), where the curves tend towards abscissa beyond 30 minutes” [41]. In the study by Ettema in 1966, SCrit and ADC depended on the range of tlim, which was confirmed by more recent studies [42, 43]. In 1981, the linear Wlim-tlim relationship was adapted to exercises on a stationary cycle ergometer and it was demon- strated that slope b of the Wlim-tlim relationship was highly correlated with the ventilatory threshold [44]. Therefore, slope b was proposed as an indicator of general endurance and the concept of critical power or critical velocity was again studied. Different equations were proposed for the estimation of SCrit (or CP). For example, SCrit on a treadmill [45] was computed from the linear relationship between Dlim and the inverse of tlim (1/tlim): S = a ( 1 tlim ) + b = ADC2 ( 1 tlim ) + SCrit2 (14) More recently, Morton [15] proposed a fourth model for the critical power, a nonlinear model including a third parameter corresponding to maximal instantaneous power (Pmax). This model has been adapted to running exercises with an instantaneous maximal running speed (SMax): tlim = ADC3 (S – SCrit3) – ADC3 (SMax - SCrit3) (15) Actually, the different asymptotic hyperbolic models are the most used and studied [46]. 4 BioMed Research International 2.3. Logarithmic Model (P´eronnet-Thibault). The metabolic model proposed by P´eronnet and Thibault [7, 8] included factors that took into account the contributions of aerobic and anaerobic metabolism to total energy output according to the duration of the race. The inertia of the aerobic metabolism at the beginning of the exercise was also included in the model. In addition, the use of anaerobic store SA was assumed to decrease beyond TMAP (exhaustion time corresponding to maximal aerobic power): SA = A for T ≤ TMAP SA = A – 0.233A ln ( T TMAP ) for T > TMAP (16) A runner is only capable of sustaining his maximal aerobic power for a finite period of time. The performances in long distance events depend on the ability to utilize a large percentage of VO2max over a prolonged period of time (endurance capability). P´eronnet and Thibault [7, 8] assumed that tlim corresponding to maximal aerobic speed (tMAS) is equal to 7 min. They proposed the slope (E) of the relationship between the fractional utilization of MAS and the logarithm of tlim/7min (420 s) as an index of endurance capability: S = MAS – E7min ln ( tlim 420) 100 S MAS = 100 – E ln ( tlim 420) (17) where MAS is the maximal running speed corresponding to 7 min and E is the endurance index corresponding to MAS (E =100 E7min/MAS). There was a significant correla- tion between the ventilatory threshold and E in marathon runners [47], which suggested that E was an index of aerobic endurance. The values of E and MAS7min can be estimated from two running performances with a nomogram [48]. 2.4. Exponential Model. Hopkins et al. [13] have presented an asymptotic exponential model for short-duration (10 s - 3 min) running exercises on a treadmill with 5 different slopes (9 to 31%). This model was It = I∞ + (I0 – I∞) exp (–tlim 𝜏 ) (18) where I∞ is the slope corresponding to infinite time, I0 the slope corresponding to a time equal to zero, It the slope corresponding to tlim, and 𝜏 is a time constant. This model can be adapted to running exercises on a track: S = S∞ + (S0 – S∞) exp (–tlim 𝜏 ) (19) This asymptotic exponential model derived from Hopkins’ model has been used and compared to the different asymp- totic hyperbolic models in several studies [49–52]. 3. Methods The logarithmic, power-law, and hyperbolic models which are 2-parameter models were computed by linear least-square regressions between time data and speed data (or distance data). Time data correspond to tlim or the logarithm of tlim. Speed data correspond to speed or the logarithm of speed. The models by Morton and Hopkins are 3-parameter models whose individual regressions were computed by an iterative least square method. 3.1. Computation of the Empirical Models 3.1.1. Computation of the Power-Law Model. If Y = A∗X, the logarithm of Y is equal to ln (Y) = ln (A) + ln (X) (20) If Y = X-B, the logarithm of Y is equal to ln (Y) = -B ln (X) (21) If Y = A∗X- B, the logarithm of Y is equal to ln (Y) = ln (A) - B ln (X) = C - B ln (X) (22) where C = ln(A) and exp(C) = exp[ln(A)] = A. Therefore, the power laws between tlim and Dlim or S can be determined by computing the regression between the natural logarithms of Dlim and tlim: ln (Dlim) = 𝛼 + 𝛾 ln (tlim) = ln (k) + g ln (tlim) k = eln(k) = e𝛼 (23) 3.1.2. Computation of the Hyperbolic Models. In the present study, three estimations of critical velocity (SCrit1, SCrit2, and SCrit3) were computed: Dlim = ADC1 + SCrit1tlim Y = 𝛼1 + 𝛽1X (12 bis) where Y = Dlim; X = tlim; 𝛼1 = ADC1; 𝛽1 = SCrit1 S = a + b ( 1 tlim ) = SCrit2 + ADC2 ( 1 tlim ) Y = 𝛼2 + 𝛽2X (14 bis) where Y = S; X = 1/tlim; 𝛼2 = SCrit2; 𝛽2 = ADC2 In the 3-parameter model by Morton tlim = ADC3 (S – SCrit3) – ADC3 (SMax - SCrit3) (15) Let C = ADC3/(SMax - SCrit3) tlim = ADC3 (S – SCrit3) – C SMax = SCrit3 + ADC3 C (24) BioMed Research International 5 First, this equation was computed by an iterative least square method for a hyperbolic decay formula with 3 parameters (Y0, a, and b): Y = Y0 + ab (x + b) (25) where Y0 = - C, b = - SCrit3, and ab = ADC3 Unfortunately, there was no convergence of the iteration. Therefore, an iteration was tested for another equation: tlim + C = ADC3 (S – SCrit3) S – SCrit3 = ADC3 (tlim + C) S = SCrit3 + ADC3 (tlim + C) (24 bis) This equation was computed with an iterative least square method for a similar hyperbolic decay formula with 3 parameters (Y0, a, and b): Y = Y0 + ab (x + b) (26) where Y = S, Y0 = SCrit3, ab = ADC3, and b = C. As the value of Smax = SCrit3 + ADC/C Smax = Y0 + ab b = Y0 + a (27) Fortunately, there was a convergence in the iteration for this equation. 3.1.3. Computation of the Logarithmic Model. The value of E was estimated by computing the regression between S and the logarithm of tlim/420 for the different distances: S = 𝛼 – 𝛽 ln ( tlim 420) (28) When tlim = 420, S is equal to MAS and ln(tlim/420) is equal to 0. Therefore S = MAS = 𝛼 + 0 E = 100𝛽 MAS = 100𝛽 𝛼 (29) 3.1.4. Computation of the Exponential Model. At least three distances are necessary to compute Hopkins’ model (see (19)) which is a three-parameter model (S∞, a1, and b1) like Morton’s model. S = S∞ + (S0 – S∞) exp (−tlim 𝜏 ) = S∞ + a exp (–btlim) (30) The regressions were computed by an iterative least square method for a single exponential decay formula with 3 param- eters (Y0, a, and b): Y = Y0 + 𝛼 exp (−𝛽X) (31) where X = tlim, Y0 = S∞, 𝛼 = a, and 𝛽 = b 3.2. Estimations of Maximal Running Speeds corresponding to 7, 30, and 60 Minutes. The estimations of the individual maximal running speeds corresponding to 7 minutes (esti- mation of maximal aerobic speed, MAS) were performed by interpolation from the 1500-3000-5000m performances. The estimations of the maximal running speed during 30 min were done by extrapolation from the 1500-3000-5000m performances. The 30-min running times were compared with the 10000 m performances (S10000). The estimations of the maximal running speed during 60 min were done by extrapolation from the 1500-3000-5000- 10000 m performances. 3.3. Accuracy of the Estimations of Running Speed. The individual running speeds corresponding to the different distances (1500, 3000, 5000, and 10000 m) were estimated from the individual regressions of the different models and compared with the actual speeds for the same distances. First, for each model, the individual running speeds corre- sponding to tlim between 1 and 1900 s were computed from the individual regressions with an increment equal to 1 s. Secondly, the individual relationships between distance and the estimated value of tlim were computed by multiplying tlim and the corresponding estimated speed (distance = speed x time). Then, the individual estimated values of running speed corresponding to 1500, 3000, 5000, and 10000 m were registered and compared with the actual values of running speeds. Thereafter, the ratios of estimated speed to actual speed were computed for each distance and each runner. 3.4. Statistics. All the computations of the model and the statistics were performed with the SigmaPlot software (Systat, Chicago, USA). 3.4.1. Comparisons of the Parameters. The comparisons of the parameters, computed from different ranges of distances or from different running models (SCrit1, SCrit2, SCrit3, S∞, SMax, and S0), were studied with a nonparametric paired test (Wilcoxon signed rank test) since the sample sizes were low (6 runners). Significance was accepted at critical P<0.05. The probability was equal to 0.031 in Wilcoxon signed rank test when all the individual values of a parameter are either lower or higher than all the corresponding individual values of a parameter in another model (or another performance range). 3.4.2. Comparison of the Accuracy in the Different Models. In statistics, the sum of the squares of residuals (deviations predicted from actual empirical values of data) is a measure of the discrepancy between the data and an estimation model. A small sum of the squares of residuals indicates a tight fit of the model to the data. However, in the present study, the comparisons of the accuracy in the different models cannot be based on the differences in the sums of the squares of residuals because the residuals in the power-law model corresponded to the logarithm of the residuals and because the individual regres- sion of the first hyperbolic model (SCrit1) did not correspond to regressions between tlim and running speeds (S) but 6 BioMed Research International regressions between tlim and distances (Dlim). Moreover, it would be assumed that there was a homoscedasticity in the residuals of the running speeds, which could not be tested with only 4 datasets in an individual regression. In addition, the residuals of computed running speeds could be more important in the faster runners. In the present study, the residuals were computed as equal to the differences between 1 and the ratios of estimated speed to actual speed for each distance and each runner. For a given running model, the squares of these residuals were computed for each distance and each runner, which corresponded to 24 squares (4 distances x 6 runners). The values of the squares of a model were compared with the values of squares for the same distances and same runners in another model. The statistical significance values of the 24 paired differences between two running models were tested with paired Student’s t-tests after normality tests (Kolmogorov-Smirnov tests). When the normality tests failed, the paired Student’s t-tests were replaced with the Wilcoxon signed rank tests. In addition, for each runner, the sum of squared errors for the four distances was computed for each model. The square root of the mean of this sum (root mean square error, RMSE) was computed for each runner and each model. A large error has a disproportionately large effect on RMSE which is, consequently, sensitive to outliers. 4. Results 4.1. Power-Law Model Applied to Elite Runners. The effects of the distance range were not significant for exponent g (0.063 < P < 0.125) as well as parameter k (0.063 < P < 0.094). The estimations of the logarithm of running speeds (S) were close to the logarithm of actual speeds (Figure 1(a)). The correlation coefficients of the individual linear relationships (see (5)) between ln(S) and ln(tlim) or ln(Dlim) and ln(tlim) were higher than 0.999 in all the runners for 1500-10000m. Similarly, the ratios of estimated to actual speeds (Table 3) for the four distances were accurate: the errors were lower than 1%, except the 10000 m performance by Nurmi (error equal to 1.1%). Marathon performances were under the extrapolation of the lines of regression computed from the 1500-10000m track performances (Figure 1(b)). 4.2. Hyperbolic Model Applied to Elite Endurance Runners 4.2.1. SCrit1 Model. The linear relationships between time (tlim) and distance (Dlim) are presented in Figure 2. For all the runners, the correlation coefficients of the linear regression between tlim and Dlim were higher than 0.999 for the different ranges of Dlim. Parameters SCrit1 and ADC1 are presented in Table 4. As in previous studies on critical power [42, 43], the values of SCrit1 depended of the range of tlim. All the differences in SCrit1 and ADC1 were significant (P = 0.031 in the Wilcoxon signed rank test): the values SCrit1 computed from 1500 to 5000m were significantly higher than SCrit1 computed from 3000 to 10000m. The ratios of the estimated running speeds to the actual speed estimated from SCrit1 model are presented in Table 5. The errors are moderate (< 2%) except for 1500 m. The values of ADC1 largely depended on the range of performances as shown in Figure 3. When the individual critical speeds decreased because of a change in the range of performances, the corresponding ADC1 increased. These increases in ADC1 were much more important than the decrease in SCrit1. For example, SCrit1 computed from 3000- 10000 m was 3.8% lower than SCrit1 computed from 1500-5000 m (Table 3) whereas the corresponding increase in ADC1 was equal to 79% (319 ± 53 m versus 178 ± 39 m, Figure 3). 4.2.2. SCrit2 Model. The individual S-1/tlim relationships were not linear (Figure 4(a)) when long distances (10 km) were included. The correlation coefficients of the linear regressions between 1/tlim and Dlim were equal to 0.976 ± 0.0126. Parameters SCrit2 and ADC2 depended on the range of distances (Table 6). All the differences in SCrit2 and ADC2 in function of the distance ranges were significant (P = 0.031). When SCrit2 decreased because of a change in the range of performances, the corresponding ADC2 increased. These variations in ADC2 were much more important than the variation in SCrit2 (Table 6). 4.2.3. Comparison of the SCrit1 and SCrit2 Models. As in previ- ous studies [49–52], the estimates of SCrit differed according to the mathematical model used to describe the speed-tlim relationships. The values of SCrit2 (Table 6) were significantly higher (P = 0.031) than SCrit1 (Table 4). Indeed, the values of SCrit1 were slightly lower in all the elite endurance runners than the value of SCrit2 when they were computed with three (3-5-10km) or four (1.5-3-5-10km) distances (Figure 5(a)). When short distances (1500 m) were included, the differences between SCrit1 and SCrit2 increased as demonstrated in Fig- ure 5(a). However, SCrit1 and SCrit2 computed from the same range of performance were highly correlated (P ≥ 0.996). The values of ADC2 (Table 6) were significantly lower (P = 0.031) than ADC1 (Table 4) but were significantly correlated (0.940 < r < 0.992; P <0.001). Interestingly, as shown in Figure 5(b), the values of SCrit1 were equal to SCrit2 when both were computed from the same two distances, only (for example, 1.5 and 10 or 3 and 10 km). Similarly, ADC1 and ADC2 were equal when both were only computed from the same two distances. For all the runners, the correlation coefficients for the linear regressions between 1/tlim and Dlim in SCrit2 model were lower than for the tlim-Dlim regressions in SCrit1 model. In contrast, the ratios of estimated to actual speeds (Table 7) were more accurate in the SCrit2 model: the errors on 1500 m and RMSE were lower (P = 0.031) than in the SCrit1 model. On the other hand, the errors on 10000 m were higher (P = 0.031) in the SCrit2 model. 4.2.4. Morton’s Model Applied to Elite Runners. In all the runners, the performances estimated from Morton’s model were very close to their actual performances (Figure 6). When the 3-parameter model by Morton was computed with 4 distances (from 1500 m to 10000 m), the correlation coefficient was very high (0.999 ± 0.000752) in all the BioMed Research International 7 7.5 7.0 6.5 6.0 5.5 S (m.s-1) 200 300 400 500 700 1000 2000 tlim (s) Gebrselassie Aouita V ̈a ̈at ̈ainen Zatopek Nurmi Vir ́en (a) 7.5 7.0 6.5 6.0 5.5 5.0 4.5 S (m.s-1) 200 500 1000 2000 5000 10000 tlim (s) (b) Figure 1: (a) Individual linear relationships (power-law model) with logarithmic scales for running speed and tlim. The performances by Nurmi and Zatopek were the same for the 1500 m distance. (b) Extrapolation of the linear relationships (dashed lines) to marathon performances. 10000 8000 6000 4000 2000 0 10000 8000 6000 4000 2000 0 Dlim (m) 200 400 600 800 1000 1200 1400 1600 1800 200 400 600 800 1000 1200 1400 1600 1800 tlim (s) Aouita V ̈a ̈at ̈ainen Nurmi Gebrselassie Zatopek Vir ́en Figure 2: Linear relationships between exhaustion time (tlim) and distance (Dlim). runners. When this model was computed with 3 distances (1500-3000-5000 m or 3000-5000-10000 m), the correlation coefficients were equal to 1 in all the runners. The differences in SCrit, SMax, and ADC between the ranges of distances (Table 8) were all significant (P = 0.031). 450 400 350 300 250 200 150 100 ADC1(m) 5.25 5.50 5.75 6.00 6.25 6.50 SCrit1 (m.s-1) N Z Va V A G Figure 3: Relation between critical speed and Anaerobic Distance Capacity (ADC1) for different ranges of distances: 1500 to 5000 m (black dots), 3000 to 10000 m (empty circles), and 1500 to 10000 m (grey dots). The ratios of estimated to actual speeds are presented in Table 9. In all the runners, the errors were very low (< 0.5%) for all the distances, from 1500 to 10000 m. However, the val- ues of S corresponding to a marathon were overestimated in the three runners who participated in this road competition (Figure 6(b)). 4.3. Logarithmic Model Applied to Elite Runners. The values of parameters E and MAS in the logarithmic model depended on the range of running distance (Table 10) but these differences were not significant for MAS between 1500-10000 and 1500-5000 ranges and for E between 1500-5000 range and the two other distance ranges (P = 0.063). The correlation coefficients were high, 0.995 ± 0.005, for the logarithmic model including the four distances from 1500 8 BioMed Research International 7.4 7.2 7.0 6.8 6.6 6.4 6.2 6.0 5.8 5.6 5.4 0.000 0.001 0.002 0.003 0.004 0.005 1/tlim 20 km 10 km 5 km 3 km 1.5 km S (m.s-1) Gebrselassie Aouita V ̈a ̈at ̈ainen Zatopek Nurmi Vir ́en (a) 0 500 1000 1500 2000 7.5 7.0 6.5 6.0 5.5 S (m.s - 1) tlim (s) (b) 0 500 1000 1500 2000 7.5 7.0 6.5 6.0 5.5 tlim (s) S (m.s - 1) (c) Figure 4: (a) Individual S-1/tlim relationships in elite endurance runners. ((b) and (c)) Individual hyperbolic curves corresponding to SCrit1 model (dashed curves) and SCrit2 model (solid curves). 6.4 6.2 6.0 5.8 5.6 5.4 5.2 1.5, 3, 5, 10 km (All) 3, 5, 10 km 6.4 6.2 6.0 5.8 5.6 5.4 5.2 SCrit1 (m.s-1) SCrit2 (m.s-1) SCrit computed from (a) 1.5 & 10 km 3 & 10 km 6.4 6.2 6.0 5.8 5.6 5.4 5.2 6.4 6.2 6.0 5.8 5.6 5.4 5.2 SCrit1 (m.s-1) SCrit2 (m.s-1) SCrit computed from (b) Figure 5: (a) Relationships between the individual values of SCrit1 and SCrit2 computed from 3 distances (black dots) or 4 distances (empty circles). (b) Relationships between SCrit1 and SCrit2 computed from 2 distances, only. BioMed Research International 9 Aouita V ̈a ̈at ̈ainen Nurmi 8.0 7.5 7.0 6.5 6.0 5.5 0 500 1000 1500 2000 tlim (s) S (m.s- 1) (a) Gebrselassie Zatopek Vir ́en 7.0 6.5 6.0 5.5 5.0 0 2000 4000 6000 8000 Marathon tlim (s) S (m.s- 1) (b) Figure 6: Relationship between running speed (S) and time (tlim) in Morton’s model computed from 1500 to 10000 m. (b) The same model in the three subjects who ran the marathon. 7.2 7.0 6.8 6.6 6.4 6.2 6.0 5.8 5.6 5.4 200 400 600 800 1000 2000 S (m.s-1) tlim (s) Gebrselassie Aouita V ̈a ̈at ̈ainen Zatopek Nurmi Vir ́en (a) 7.0 6.6 6.2 5.8 5.4 5.0 4.6 500 1000 5000 10000 S (m.s-1) tlim (s) (b) Figure 7: (a) Individual linear regressions between the logarithms of tlim and running speeds. The data corresponding to 1.5 km were not included in the computation of the regressions. The performances by Nurmi and Zatopek were the same for the 1500 m distance. (b) Extrapolation of the speed-ln(tlim) relationships of the 3000-10000 m performances to tlim corresponding to a marathon (dashed lines). The scale of tlim is a logarithmic scale. to 10000 m. The ratios of estimated to actual speeds for the four distances were accurate (Table 11): all the errors were lower than 1%. When the 1500 m distance was not included as suggested by P´eronnet and Thibault [7, 8], the correlation coefficient was higher (0.999 ± 0.002). The individual running perfor- mances between 3000 and 10000 m were well described by the logarithmic model as shown by the linear regressions between speed and the logarithm of tlim (Figure 6(a)). All the individual 1500m performances were above the individual regression lines computed from 3000 to 10000 m (Figure 6(a)) as in the logarithmic model including the 1500 m performances (Table 10). On the other hand, marathon performances were under the extrapolation of the lines of regression computed from the 3000-10000 m track performances (Figures 7(a) and 7(b)). 4.4. Exponential Models Applied to Elite Runners. The rela- tionships between tlim and S in the exponential model are presented in Figure 8. As for the other models, the values of parameters S∞, S0, and 1/𝜏 depended on the range of tlim-Dlim (Table 12). When computed from 4 distances (Figure 8), the individ- ual regressions were accurate (r = 0.998 ± 0.0014). Similarly, the ratios of estimated to actual speeds for the four distances were highly accurate (Table 13): all the errors were lower 10 BioMed Research International Table 2: Parameters k and g according to the ranges of distances used in the computation of the power-law model. 1500-10000 m 1500-5000 m 3000-10000 m k g k g k g Nurmi 9.55 0.926 10.2 0.915 8.80 0.938 Zatopek 8.65 0.945 8.86 0.941 8.39 0.950 V¨a¨at¨ainen 8.99 0.944 9.36 0.938 8.45 0.953 Vir´en 9.17 0.943 9.20 0.943 9.14 0.944 Aouita 11.0 0.920 11.3 0.915 10.5 0.927 Gebrselassie 9.24 0.949 9.12 0.951 9.25 0.948 Mean 9.43 0.938 9.67 0.934 9.08 0.943 SD 0.81 0.012 0.90 0.015 0.76 0.010 8.2 7.8 7.4 7.0 6.6 6.2 5.8 5.4 8.2 7.8 7.4 7.0 6.6 6.2 5.8 5.4 0 300 600 900 1200 1500 1800 0 300 600 900 1200 1500 1800 1.5 km 1.5 km 3 km 3 km 5 km 5 km 10 km 10 km Aouita V ̈a ̈at ̈ainen Nurmi Gebrselassie Zatopek Vir ́en S (m.s-1) tlim (s) Figure 8: Individual relationships between running speed and tlim in the Hopkins model computed with 4 distances (1500-10000 m). than 0.75%. As expected, the 3-parameter model was more accurate (r = 1) for the description of the elite runner performances when it was computed from 3 distances (1.5- 3-5 km or 3-5-10 km), only. 4.5. Prediction of Running Speeds 4.5.1. Prediction of Maximal Aerobic Speed. Maximal aerobic speed (MAS) can be estimated by computing the maximal speed corresponding to 7 min [7, 8] from the different models. These estimations (Table 14) were performed by interpolation from the 1500-5000m performances. The effect sizes were small for all the differences (0.037 < Cohen’s d < 0.218). The estimations of MAS were almost equal for SCrit1 and SCrit2 models that were significantly lower than the estimations of all the other models. The differences between all the other models were not significant (P ≥ 0.063). The correlations between the different estimations were highly significant (r > 0.998; P < 0.001). 4.5.2. Prediction of Maximal Speed during 30 Min. The estimations of the maximal running speed during 30 min done by extrapolation from the 1500-5000m performances are compared with the 10000 m performances (S10000) in Table 15. The correlations between the different estimations were highly significant (r ≥ 0.860; P < 0.0025). All the different estimations were significantly correlated with S10000 (r ≥ 0.989; P < 0.001). The effect sizes were small for the power-law and logarithmic models (Cohen’s d = 0.131) or for the hyperbolic and exponential models (Cohen’s d = 0.033) but large for the difference between power-law and exponential models (Cohen’s d = 0.742). The 30-minute running speed estimated from asymptotic models was sig- nificantly higher than those estimated from power-law and logarithmic models (P = 0.031). The 30-min running speed was overestimated by the hyperbolic and exponential models because these estimations were approximately 2.5% higher than S10000 (P = 0.031) although the individual values of tlim corresponding to 10000 m (Table 2) were lower than 1800 s (from 1583 to 1734 s) except for Nurmi (1806 s). On the contrary, the 30-minute estimated speeds computed with the logarithmic and power-law models were probably close to the actual 30-minute performances since they were slightly lower (0.7 and 1.4%) than S10000. 4.5.3. Prediction of Maximal Speed during 60 Min. The esti- mations of maximal running speed during 60 min (Table 16) were done by extrapolation from the 1500-10000 m perfor- mances. The effect size between power-law and logarithmic models was small (Cohen’s d = 0.073). All the predictions of the 60-min speeds from the different models were signif- icantly correlated (r ≥ 0.964; P < 0.002). However, the 60- minute running speed predicted from the asymptotic models was significantly higher (P = 0.031) than those estimated from BioMed Research International 11 Table 3: Ratios of estimated to actual speeds for the different distances in the power-law model. RMSE = root mean square of the errors between estimated running speed and actual speed. 1500 3000 5000 10000 RMSE Nurmi 0.9909 1.0058 1.0057 0.9899 0.00794 Zatopek 0.9958 1.0016 1.0005 0.9954 0.00323 V¨a¨at¨ainen 0.9919 1.0051 0.9994 0.9924 0.00612 Viren 0.9974 0.9974 0.9975 0.9963 0.00290 Aouita 0.9965 1.0077 1.0022 0.9981 0.00448 Gebrselassi´e 1.0022 1.0039 0.9995 1.0042 0.00309 Mean 0.996 1.004 1.001 0.996 0.00463 SD 0.0041 0.0037 0.0029 0.00495 0.00203 Table 4: Values of SCrit1 and ADC of the SCrit1 model according to the range of distances. ∗: P = 0.031 for all the differences between the different ranges. 1500-10000 m 1500-5000 m 3000-10000 m SCrit1 ADC1 SCrit1 ADC1 SCrit1 ADC1 Nurmi 5.39 284 5.51 228 5.35 339 Zatopek 5.65 226 5.79 160 5.61 282 V¨a¨at¨ainen 5.86 220 5.99 161 5.83 262 Vir´en 5.90 245 6.09 160 5.85 314 Aouita 5.89 332 6.14 225 5.83 413 Gebrselassie 6.19 230 6.42 133 6.13 301 Mean 5.81∗ 256∗ 5.99∗ 178∗ 5.77∗ 319∗ SD 0.27 44 0.31 39 0.26 53 Table 5: Ratios of estimated to actual speeds for the different distances in the SCrit1 model. RMSE = root mean square of the errors between estimated running speeds and actual speeds. 1500 3000 5000 10000 RMSE Nurmi 1.032 0.992 0.992 1.002 0.0173 Zatopek 1.033 0.994 0.990 1.002 0.0175 V¨a¨at¨ainen 1.026 0.997 0.991 1.002 0.0138 Vir10n 1.044 0.992 0.988 1.003 0.0231 Aouita 1.056 0.993 0.983 1.004 0.0296 Gebrselassi´e 1.043 0.995 0.985 1.003 0.0231 Mean 1.039 0.994 0.988 1.003 0.021 SD 0.011 0.002 0.004 0.001 0.006 Table 6: Values of SCrit2 and ADC2 according to the range of distances. ∗: P = 0.031 for all the differences between the different ranges. 1500-10000 m 1500-5000 m 3000-10000 m SCrit2 ADC2 SCrit2 ADC2 SCrit2 ADC2 Nurmi 5.47 233 5.54 211 5.37 318 Zatopek 5.74 171 5.82 146 5.64 257 V¨a¨at¨ainen 5.93 176 6.00 156 5.86 235 Vir´en 6.02 174 6.14 141 5.88 283 Aouita 6.04 248 6.18 210 5.89 368 Gebrselassie 6.32 157 6.44 123 6.19 257 Mean 5.92∗ 193∗ 6.02∗ 165∗ 5.81∗ 286∗ SD 0.29 38 0.31 37 0.28 49 12 BioMed Research International Table 7: Ratios of estimated to actual speeds for the different distances in the SCrit2 model. RMSE = root mean square of the errors between estimated running speed and actual speed. 1500 3000 5000 10000 RMSE Nurmi 1.005 0.989 0.996 1.011 0.00834 Zatopek 1.005 0.990 0.994 1.012 0.00855 V¨a¨at¨ainen 1.003 0.994 0.994 1.009 0.00659 Viren 1.006 0.987 0.993 1.015 0.0112 Aouita 1.007 0.986 0.989 1.019 0.0132 Gebrselassi´e 1.006 0.989 0.990 1.016 0.0110 Mean 1.005 0.989 0.993 1.014 0.00981 SD 0.0013 0.0027 0.0025 0.0035 0.00241 Table 8: Values of SCrit3, SMax and ADC of Morton’s model according to the range of distances. ∗: P = 0.031 for all the differences between the different ranges. 1500-10000 m 1500-5000 m 3000-10000 m SCrit3 SMax ADC SCrit3 SMax ADC SCrit3 SMax ADC Nurmi 5.29 7.74 504 5.31 7.85 470 5.27 7.45 549 Zatopek 5.51 7.05 539 5.61 7.25 388 5.44 6.74 760 Vaatainen 5.78 7.69 393 5.94 9.70 211 5.59 6.77 982 Viren 5.67 7.23 793 5.77 7.31 605 5.60 7.08 995 Aouita 5.69 8.16 772 5.98 9.07 410 5.41 7.34 1666 Gebrselassi´e 5.92 7.40 868 6.28 7.85 292 5.46 7.05 2961 Mean 5.64∗ 7.55∗ 645∗ 5.82∗ 8.17∗ 396∗ 5.46∗ 7.07∗ 1319∗ SD 0.22 0.40 193 0.33 0.99 137 0.12 0.29 888 Table 9: Ratios of estimated to actual speeds for the different distances in Morton’s model. RMSE = root mean square of the errors between estimated running speed and actual speed. 1500 3000 5000 10000 RMSE Nurmi 1.0000 1.0004 0.9995 1.0003 0.00036 Zatopek 0.9997 1.0012 0.9985 1.0005 0.00100 V¨a¨at¨ainen 0,9996 1.0027 0.9965 1.0014 0.00236 Viren 0.9997 1.0008 0.9990 1.0003 0.00069 Aouita 0.9993 1.0038 0.9953 1.0017 0.00315 Gebrselassi´e 0.9992 1.0033 0.9968 1.0010 0.00241 Mean 0.9996 1.0020 0.9976 1.0009 0.0017 SD 0.0003 0.0014 0.0016 0.0006 0.0011 Table 10: Values of MAS and E in the logarithmic model according to the range of distances. a: P = 0.031 between 1500-10000 and 3000-10000 m; b: P = 0.031 between 1500-5000 and 3000-10000 m. 1500-10000 m 1500-5000 m 3000-10000 m MAS E MAS E MAS E Nurmi 6.13 7.18 6.12 8.48 6.05 5.90 Zatopek 6.22 5.35 6.22 5.87 6.19 4.83 V¨a¨at¨ainen 6.44 5.47 6.43 6.24 6.38 4.49 Vir´en 6.52 5.54 6.52 5.72 6.51 5.39 Aouita 6.77 7.82 6.76 8.52 6.71 6.96 Gebrselassie 6.78 5.05 6.78 4.94 6.77 4,98 Mean 6.48 6.07 6.47 6.63 6.43a,b 5.42a SD 0.27 1.14 0.27 1.51 0.29 0.90 BioMed Research International 13 Table 11: Ratios of estimated to actual speeds for the different distances in the logarithmic model. RMSE = root mean square of the errors between estimated running speeds and actual speeds. 1500 3000 5000 10000 RMSE Nurmi 0.992 1.009 1.009 0.990 0.00916 Zatopek 0.997 1.004 1.003 0.996 0.00352 V¨a¨at¨ainen 0.993 1.008 1.002 0.994 0.00622 Viren 0.999 1.000 1.001 0.998 0.00135 Aouita 0.994 1.008 1.003 0.995 0.00594 Gebrselassi´e 0.999 1.002 0.998 1.001 0.00167 Mean 0.996 1.005 1.003 0.996 0.00460 SD 0.0031 0.0037 0.0038 0.0037 0.00302 Table 12: Values of S∞, S0 and 1/𝜏 of the exponential model according to the range of distances. ∗: P = 0.031 for all the differences between the different ranges. 1500-10000 m 1500-5000 m 3000-10000 m S∞ S0 1/𝜏 S∞ S0 1/𝜏 S∞ S0 1/𝜏 Nurmi 5.52 7.06 0.00224 5.64 7.24 0.00298 5.48 6.68 0.00167 Zatopek 5.73 6.81 0.00187 5.87 6.96 0.00280 5.68 6.57 0.00132 Vaatainen 5.97 7.17 0.00228 6.13 7.50 0.00397 5.86 6.69 0.00115 Vir´en 5.96 7.10 0.00163 6.11 7.20 0.00234 5.91 6.94 0.00127 Aouita 6.03 7.76 0.00202 6.31 8.13 0.00354 5.87 7.23 0.00114 Gebreselassie 6.23 7.29 0.00151 6.50 7.52 0.00323 5.99 7.03 0.00073 Means 5.91∗ 7.20∗ 0.00193∗ 6.09∗ 7.43∗ 0.00314∗ 5.80∗ 6.86∗ 0.00121∗ SD 0.25 0.32 0.00032 0.31 0.40 0.00057 0.19 0.25 0.00031 Table 13: Ratios of the estimated to actual speeds in the different distances for the exponential model. RMSE = root mean square of the errors between estimated running speeds and actual speeds. 1500 3000 5000 10000 RMSE Nurmi 0.999 1.004 0.996 1.002 0.00293 Zatopek 0.999 1.003 0.997 1.001 0.00227 V¨a¨at¨ainen 0.998 1.005 0.994 1.002 0.00405 Viren 0.999 1.002 0.998 1.001 0.00162 Aouita 0.998 1.007 0.993 1.002 0.00530 Gebrselassi´e 0.998 1.005 0.996 1.001 0.00309 Mean 0.999 1.004 0.996 1.001 0.00321 SD 0.0005 0.0018 0.0018 0.0006 0.00131 Table 14: Estimation of maximal running speed (m.s−1) corresponding to 420 s computed from the different models. ∗: P = 0.031 for the differences with Morton’s model, exponential, logarithmic and power-law models. SCrit1 SCrit2 Morton Exponential Log Power Nurmi 6.0491 6.0454 6.09 6.10 6.12 6.11 Zatopek 6.1714 6.1669 6.20 6.21 6.22 6.20 V¨a¨at¨ainen 6.3716 6.3737 6.39 6.39 6.43 6.41 Vir´en 6.4693 6.4690 6.52 6.52 6.52 6.53 Aouita 6.6776 6.6812 6.72 6.72 6.76 6.75 Gebrselassie 6.7348 6.7349 6.76 6.76 6.78 6.79 Means 6.412∗ 6.412∗ 6.45 6.45 6.47 6.47 SD 0.272 0.274 0.27 0.27 0.27 0.28 14 BioMed Research International Table 15: Maximal running speed (m.s−1) during 30 min, computed from the different models. S10000: running speed over 10000 m; ∗: P = 0.031 for the differences with logarithmic and power-law models. 1: P = 0.031 for the differences with SCrit1 model. 3: P = 0.031 for the differences with Morton’s model. S: P = 0.031 for the differences with S10000. Log Power Morton SCrit1 Exp SCrit2 S10000 Nurmi 5.36 5.40 5.55 5.63 5.65 5.66 5.54 Zatopek 5.69 5.69 5.80 5.88 5.88 5.90 5.77 V¨a¨at¨ainen 5.84 5.85 6.06 6.08 6.13 6.09 5.98 Vir´en 5.98 6.01 6.05 6.18 6.13 6.21 6.03 Aouita 5.92 5.96 6.19 6.27 6.31 6.30 6.07 Gebrselassie 6.29 6.32 6.43 6.49 6.50 6.52 6.32 Mean 5.85S 5.87 6.01∗,S 6.09∗, 3, S 6.10∗,3, S 6.11∗,1,3,S 5.95 SD 0.31 0.31 0.30 0.30 0.31 0.30 0.27 Table 16: Maximal runningspeed (m.s−1) during 60 min computed from the different models. ∗: P = 0.031 for the differences with logarithmic model. P: P = 0.031 for the differences with power-law model. 1: P = 0.031 for the differences with SCrit1 model. 3: P = 0.031 for the differences with Morton’s model. E: P = 0.031 for the differences with exponential model. Log Power Morton SCrit1 Exp SCrit2 Nurmi 5.18 5.21 5.42 5.47 5.52 5.53 Zatopek 5.50 5.52 5.65 5.71 5.73 5.78 V¨a¨at¨ainen 5.68 5.69 5.88 5.92 5.96 5.98 Vir´en 5.74 5.75 5.86 5.97 5.95 6.07 Aouita 5.63 5.69 5.89 5.99 6.02 6.11 Gebrselassie 6.04 6.08 6.13 6.25 6.22 6.36 Mean 5.63 5.66∗ 5.81∗,P 5.88∗,P,3 5.91∗,P,3 5.97∗,P,1,3,E SD 0.28 0.29 0.24 0.27 0.25 0.29 power-law and logarithmic models. Moreover, the prediction of the 60-minute running speed from the power-law model was higher than that from the logarithmic model (P = 0.031). It is possible that the 60-minute running speeds estimated from power-law and logarithmic models were slightly overestimated because the world record on one hour by Gebrselassie was about 2.5% slower (5.913 m.s−1 instead of 6.04 m.s−1 for the logarithmic model and 6.08 m.s−1 for the power-law model). On the other hand, the record by Zatopek on 20 km (3591 s; 5.57 m.s−1) was slightly faster than the 60- minute running speeds S estimated from the power-law (5.52 m.s−1) and logarithmic (5.50 m.s−1) models. 4.5.4. Prediction of Marathon Performances. The overestima- tions of the marathon running speed (Figure 9) by the dif- ferent models were similar in the 3 runners. The predictions of marathon running speeds from the logarithmic model (red curves in Figure 9) were 5.216 m.s−1 for Zatopek, 5.457 m.s−1 for Viren, and 5.792 m.s−1 for Gebrselassi´e, which corresponded to overestimations equal to 6.1%, 3.4%, and 2.1%, respectively. The overestimations by the power-law model (blue curves in Figure 9) were slightly higher than those of the logarithmic model in the 3 runners. On the other hand, the overestimations were more impor- tant with the four asymptotic models (hyperbolic models and exponential model). These overestimations by the asymptotic models were similar for the 3 runners who ran the marathon distance. The large overestimations were similar for the Table 17: Average values of the 6 runners Roots Mean Square Errors (RMSE) for the different models. RMSE Morton’s model 0.00166 ± 0.00113 Exponential model 0.00321 ± 0.00131 Power-law model 0.00463 ± 0.00203 Logarithmic model 0.00464 ± 0.00302 SCrit2 model 0.00981 ± 0.00241 SCrit1 model 0.0207 ± 0.00566 SCrit1 and SCrit2 models (orange curves) and exponential model (black curve). In the 3 marathon runners, the lowest overestimations by an asymptotic model corresponded to Morton’s model (green curves). 4.6. Comparison of the Accuracies of the Different Models. For the modelling of the four distances (from 1500 to 10000 m), the lowest mean values of the RMSE of the six runners corresponded to Morton’s model (Table 17). The statistical significance values of the differences of the squared errors between the different models for the four distances and six runners (n = 24) are presented in Table 18. The accuracy of Morton’s model was significantly better than those of all the other models. The accuracies of the power- law and logarithmic models were not statistically different. The accuracies of SCrit1 and SCrit2 models were not statistically BioMed Research International 15 Table 18: Values of paired Student’t test (underlined) or Wilcoxon signed rank test for the difference in squared errors between the running models. Power law SCrit1 SCrit2 Morton Logarithmic Exponential Power law X SCrit1 0.003 X SCrit2 < 0.001 0.484 X Morton 0.001 0.001 < 0.001 X Logarithmic 0.830 < 0.001 < 0.001 0.005 X Exponential 0.061 < 0.001 < 0.001 < 0.001 0.017 X Table 19: Correlation coefficients of the linear regressions between the different endurance indices. ∗: P = 0.05; ∗∗∗: P <0.001. SCrit1 SCrit3 g E SCrit1/S420 SCrit3/S420 SCrit1 X SCrit3 0.965∗∗ X g 0.551 0.513 X E 0.538 0.499 0.999∗∗∗ X S∞ 0.985∗∗∗ 0.984∗∗∗ 0.435 0.422 SCrit1/S420 0.976∗∗∗ 0.973∗∗∗ X SCrit3/S420 0.683 0.676 0.775 X S∞/S420 0.720 0.711 0.824∗ 0.991∗∗∗ different but were significantly lower than those of all the other models. 4.7. Correlations between the Parameters of the Different Models 4.7.1. Correlations of the Endurance Indices. In Table 19, the comparisons of the endurance indices concern the indices computed with the running performances from 1500 to 5000 m that corresponded to the usual range of tlim (3.5 to 15 min) in the studies on the modelling of the individual performances in nonelite runners. The correlations between the dimensionless indices (E and g) and either SCrit1 or SCrit3 or S∞ were not significant. In contrast, SCrit1, SCrit3, and S∞ were significantly correlated. When SCrit1 was normalised to an estimate of maxi- mal aerobic speed (S420) computed from the same model (Table 14), its correlations with the dimensionless indices g and E became significant (Table 19). After normalisation to S420 computed from the same model (Table 14), the correla- tion coefficients between SCrit3 or S∞ and the dimensionless indices (E and g) increased but were not significant. 4.7.2. Correlations between S𝑀𝑎𝑥, S0, and k. When k, SMax, and S0 were computed from the performances in the 4 distances (from 1500 to 10000 m, Tables 2, 8, and 12), these parameters were significantly correlated (P ≤ 0.044): Smax = 0.0617 + 1.040S0 r = 0.824 k = −3.923 + 1.770Smax r = 0.862 k = −7.55 + 2.357S0 r = 0.910 (32) Parameter SMax was significantly higher than S0 (P = 0.031). Parameter k was significantly higher than SMax and S0 (P = 0.031). When SMax, S0, and k were computed from 3 distance per- formances (1500-3000-5000) their values were significantly higher (P = 0.031) for SMax and S0 but there was no significant correlation between SMax, S0, and k (r ≤ 0.788; P ≥ 0.063). 5. Discussion Interestingly, for a given distance and a given model, the ratios of estimated to actual speeds were similar for the six runners (Tables 3, 5, 7, 9, 11, and 13). Indeed, for a given distance and a given model, the ratios of estimated to actual speed were not spread around 1 but either all the ratios were higher than 1 or all were lower (except several runners in the power-law model and one in the logarithmic model). Therefore, the modelling of the running performances was probably similar for the six elite runners although they ran in different conditions and they were probably trained according to different programmes. However, it cannot be excluded that there were submaximal performances in some runners. Indeed, the models would be similar if the ratios of submaximal speeds to maximal speeds are the same for each distance in a runner. 5.1. Effects of the Range of t𝑙𝑖𝑚. In the present study, there were significant differences in the parameters computed from the 3 different ranges of distances for the 3 hyperbolic models and the exponential model. The effect of the range of tlim on a parameter is the most important for parameter ADC computed from the 3 16 BioMed Research International 7.0 6.5 6.0 5.5 5.0 2000 4000 6000 8000 SCrit2 SCrit1 Exponential Morton Power-law Logarithmic S (m.s-1) tlim (s) 7.0 6.5 6.0 5.5 5.0 2000 4000 6000 8000 S (m.s-1) tlim (s) 7.0 6.5 6.0 5.5 5.0 2000 0 0 0 4000 6000 8000 S (m.s-1) tlim (s) Gebrselassie Zatopek Viren Figure 9: Comparisons of the relationship between (tlim) and running speed (S) of the logarithmic model, power-law model, SCrit1 and SCrit2 models, Morton’s model, and exponential model computed from 4 distance performances (1500, 3000, 5000, and 10000 m; empty circles) in the three runners who participated in marathon (black dots). different hyperbolic models (Figure 3 and Tables 4, 6, and 8). When the individual critical speeds decreased because of a change in the range of performances, the corresponding ADC increased. These increases in ADC1 (79%) were much larger than the decreases in Scrit1 (3.8%) in the present study. The dependence of ADC on the range of performances can be verified (Figure 10) with the data of 19 elite endurance runners 530 490 450 410 370 330 290 250 210 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6.0 6.1 6.2 N C H Ku I K Z R A Va W V J Wa B M G O E ADC1 (m) SCrit1 (m.s-1) Figure 10: Relation between SCrit1 and ADC1 in the 19 elite runners whose ranges of performances were different: 1500-10000 m (black dots), 5000-10000 m (empty circles), and 3000-5000 m (grey dots). who were world-record holders and/or Olympic winners and/or world champions: Aouita (A), Bekele (B), Coe (C), El Gerrouj (E), Gebreselassie (G), Halberg (H), Ifter (I), Jazy (J), Keino (K), Kuts (Ku), Mo Farah (M), Nurmi (N), Ovett (O), Ryun (R), V¨a¨at¨ainen (Va), Viren (V), Wadoux (W), Walker (WA), and Zatopek (Z). The values of ADC1 were high (448 ± 67 m) in elite runners whose data included 5000 and 10000 m, only (empty circles). The values of ADC1 were lower (254 ± 38 m) in elite runners whose data included all the distances from 1500 to 10000 m (black dots). In elite runners whose data did not include the 10000 m performances, ADC1 were intermediate (263 ± 43 m). Moreover, the values of ADC are much higher in Morton’s model (Table 8) than in SCrit1 and SCrit2 models (Tables 4 and 6). Therefore, the anaerobic capacity cannot be estimated from the hyperbolic models. 5.2. Endurance Indices. Parameter E of the logarithmic model by P´eronnet and Thibault is an estimation of endurance capability [7, 8]. However, the validity of parameter E as an endurance index is questionable because MAS is computed assuming that the value of tlim corresponding to MAS (tMAS) is equal to 7 min (420s) [7], which is contested. Indeed, in a review on the exhaustion time at VO2max [53], the value of tMAS was 6 min. In another study on the energetics of the best performances in middle distance running [9] the value of tMAS was estimated as equal to 14 min. Therefore, the interest of parameter E as an endurance index can be questioned because it depends on tMAS. The effect of tMAS on the endurance index by P´eronnet- Thibault can be calculated [54]: S = 𝛼1 - 𝛽1 ln ( tlim 420) MAS420 = 𝛼1 E420 = 100𝛽1 𝛼1 (33) BioMed Research International 17 If T = tMAS S = 𝛼2 - 𝛽2 ln (tlim T ) MAST = 𝛼2 ET = 100𝛽2 𝛼2 S = (𝛼1 + 𝛽1 ln (420)) - 𝛽1 ln (tlim) S = (𝛼2 + 𝛽2 ln (T)) - 𝛽2 ln (tlim) (34) The slopes between S and tlim are the same. Therefore 𝛽1 = 𝛽2 S = (𝛼1 + 𝛽1 ln (420)) - 𝛽1 ln (tlim) = (𝛼2 + 𝛽1 ln (T)) - 𝛽1 ln (tlim) 𝛼1 + 𝛽1 ln (420) = 𝛼2 + 𝛽1 ln (T) 𝛼2 = 𝛼1 + 𝛽1 ln (420) - 𝛽1 ln (T) = 𝛼1 - 𝛽1 ln ( T 420) ET = 100𝛽2 𝛼2 = 100𝛽1 (𝛼1 - 𝛽1 ln (T/420) ET E420 = [100𝛽1/ (𝛼1 -𝛽1 ln (T/420)] [100𝛽1/𝛼1] = 𝛼1 (𝛼1 - 𝛽1 ln (T/420)) ET E420 = 1 (1 - (𝛽1/𝛼1) ln (T/420)) = 1 (1 - E420 ln (T/420) /100) (35) In Figure 11, this relationship between ratio ET/E420 and T (see (35)) is computed for 3 theoretical runners: an elite endurance runner (E420 = 4), a medium level endurance runner (E420 = 8), and a low level endurance runner (E420 = 16). The effect of tMAS is much more important in the low level endurance runner than in the elite endurance runner (Figure 10). Large variations in tMAS have small effects on the classi- fication of runners because the differences in E420 between elite and medium or low level runners are very large (from 4 to 16). For example, if tMAS is equal to 14 min instead of 7 min, the medium level endurance runner would still be considered as a medium level endurance runner in spite of the increase of E (8.47 instead of 8). Similarly, the elite endurance runner would still be considered as an elite runner in spite of the increase in E (4.11 instead of 4) if tMAS is also equal to 14 min instead of 7 min. On the other hand, if tMAS is equal to 4 min instead of 7 min, the medium level endurance runner would still be considered as a medium level endurance runner in spite of the decrease in E (7.66 instead of 8.00). Similarly, 1.14 1.10 1.06 1.02 0.98 0.94 0.90 240 360 420 480 540 600 660 720 780 840 300 ET/E420 E420 = 16 E420 = 8 E420 = 4 T = tMAS (s) Figure 11: Effect of tMAS (T) on the ratio ET/E7min for an elite endurance runner (E7min = 4), a medium level endurance runner (E7min = 8), and a low-level endurance runner (E7min = 16). the low level endurance runner would still be considered as a low level endurance runner in spite of the decrease in E (14.7 instead of 16) if tMAS is also equal to 4 min instead of 7 min. The endurance capability can also be estimated by the asymptotic models if parameters SCrit1, SCrit2, SCrit3, and S∞ are normalised to maximal aerobic speed (MAS). However, the values of MAS computed from the asymptotic models also depend on tMAS. Therefore, the validity of these endurance indices is questionable. Parameter g of the power-law model by Kennelly has a high interest because it can be demonstrated that exponent g is a dimensionless index of endurance that does not depend on tMAS unlike parameter E in the logarithmic model. The curvature of the Dlim-tlim equation depends on exponent g. In the elite endurance runners the Dlim-tlim equation is almost perfectly linear (Figure 2) whereas this equation is more curved in runners who are not endurance athletes. For example, exponent g was close to 1 in elite endurance runners and lower than 0.9 in physical education students [55]. It can be demonstrated that exponent g is equal to the ratio of the slope of the Dlim-tlim equation to MAS when tlim is equal to tMAS. Indeed, the slope of Dlim-tlim is equal to the first derivative of the power-law equation. Therefore, the slope of the Dlim–tlim equation is equal to dDlim dtlim = d (ktlim g) dtlim = kgtlim g – 1 (36) For tlim equal to tMAS, the running speed corresponds to MAS: S = MAS = ktMAS g - 1 k = MAS (tMAS g – 1) = MAStMAS 1 – g (37) Therefore dDlim dtlim = (MAStMAS 1 – g) (gtlim g – 1) (38) 18 BioMed Research International 2.0 0.85 1.0 0.0 0.0 1.0 2.0 tlim/tMAS Slope = g = 0.85 Dlim = 15.8 tlim Dlim/Dlim at MAS (a) 5000 4000 3000 2000 1000 0 Distance (m) 0 200 400 600 800 time (s) tlim 1 = 0.40 tMAS tlim2 = 4.23 tlim1 tlim 2 tMAS tlim 1 Dlim = 15.8 tlim 0.85 tangent tMAS SCrit(tlim 1 tlim 2) (b) Figure 12: (a) Slope of the tangent at tMAS of the curve corresponding to the power-law model with tlim normalised to tMAS and Dlim normalised to Dlim at maximal aerobic speed (MAS). (b) Comparison of a critical speed computed from two values of tlim with the tangent at tMAS (420s). When tlim = tMAS, dDlim dtMAS = (MAStMAS 1 – g) (gtMAS g – 1) = gMAS (dDlim/dtMAS) MAS = g (39) Consequently, the ratio of the Dlim-tlim slope to MAS corre- sponding to tMAS is equal to exponent g and is independent of tMAS unlike the endurance indices computed from the other models. In Figure 12(a), Dlim and tlim are normalised to DMAS (Dlim at MAS) and tMAS, respectively. Dlim DMAS = Dlim (tMASMAS) = ktlim g (tMASMAS) = (MAS/ (tMAS g – 1)) tlim g (tMASMAS) = ( tlim tMAS ) g (40) The slope of the line joining two points corresponding to tlim1 and tlim2 of the Dlim-tlim curve in Figure 12(b) is equal to exponent g when it is parallel to the tangent of the curve at tMAS. In Figure 12(b), ratio tlim1/tmas is equal to 0.4 and ratio tlim2/tlim1 is equal to 4.23. In many studies on SCrit (or PCrit) the range of tlim is from 3 to 15 min, which corresponds to tlim1 equal to about 0.4-0.5 tMAS (if tMAS corresponds to 7 or 6 min) and ratio tlim2/tlim1 about 4-5. This range of tlim also corresponds to the performances on 1500 and 5000 m in endurance runners. In the present study, when SCrit1 is computed from 1500-3000-5000m and is normalised to S420 (Table 14), the value of SCrit1/S420 is equal to 0.934 ± 0.016 and is significantly correlated (r = 0.976; P < 0.001) to g (0.934 ± 0.16). The product of exponent g and MAS is the equivalent of a critical speed computed from a 3-15-minute tlim range. For example, the product of exponent g and S420 estimated from power-law model (Table 14) is equal to 6.04 ± 0.30 m.s−1 and is significantly correlated (r = 0.998; P < 0.001) with SCrit1 that is slightly but significantly (P = 0.031) lower (5.99 ± 0.31 m.s−1). The similar values of SCrit/S420 and g and the close values of SCrit1 and product g∗S420 and their significant correlation confirm the hypothesis that exponent g is an endurance index. 5.3. Correlations between the Parameters of the Different Mod- els. The correlation between g and E was highly significant (r = 0.999, Table 19), which confirms the hypothesis that exponent g is an endurance index. Parameters SCrit1, SCrit2, SCrit3, and S∞ were highly correlated (P ≥ 0.965). These parameters that depend not only on endurance capability but also on maximal aerobic speed were not correlated with dimensionless parameters g and E (r ≤ 0.551). When SCrit1, SCrit3, and S∞ were normalised to an estimate of maximal aerobic speed (S420) computed from their model (Table 14), these parameters became dimensionless. The value of SCrit1/S420 was significantly correlated with the dimension- less indices g, and E (Table 19). After normalisation to S420, the correlation coefficients between SCrit3/S420 or S∞/S420 and E or g increased (r ≥ 0.676) but were not significant perhaps because of the small number of runners. Indeed, a correlation coefficient equal to 0.6664 would have been significant if there were 9 runners. A study [56] compared the critical speeds from different mathematical models in 12 middle- or long-distance male runners on a track in order to determine which model provides the most accurate prediction of performance in 1 hour. In this latter study, the parameters SCrit1, SCrit2, SCrit3, and S∞ were also significantly correlated (0.85 < r < 0.99, p < 0.01) and the differences between these different critical speeds were the same as in the present study for the 1500-5000 m range: SCrit3 < SCrit1 < SCrit2 < S∞. The meaning of parameters SMax (Morton’s Model) and S0 (exponential model) is identical and corresponds, in theory, to maximum running speed. When SMax and S0 BioMed Research International 19 were computed from the 4 distance performances (from 1500 to10000 m, Tables 8 and 12), these parameters were significantly correlated (r = 0.824; P = 0.044). However, SMax was significantly higher than S0 (P = 0.31). When SMax and S0 were computed from the 3 distance performances (from 1500 to 5000 m) their values were higher. A previous study [57] compared which parameter (SMax or S0) is closest to maximum speed by measuring maximal velocity during a sprint. The values of SMax and S0 were well correlated (r = 0.93, P<0.001) but they were significantly different. As in the present study, SMax (7.80 ± 0.93 m.s−1) was higher than S0 (7.49 0.90 m.s−1) but lower than the actual maximum speed (8.43 ± 0.33 m.s−1) on a track. However, SMax and S0 were computed from the performances on a treadmill whereas the actual maximum running speed was measured on a track during short sprints with photocells placed at 30 and 40 m. It is likely that it would be better to measure actual maximum speed during a 60 m sprint on a track with a laser apparatus and to compare it with SMax and S0 from Morton’s model and exponential models computed from performances on a track instead of a treadmill. In the present study, parameter k of the power-law model was 25% higher than SMax and 31% higher than S0. However, k was significantly correlated with SMax and S0. These results confirm the hypothesis that parameter k should be correlated with the maximal running speed because it is equal to the running speed corresponding to one second. However, the value of k depends on the time unit. If the running performances are evaluated in minutes, parameter k would be equal to the maximal speed corresponding to 1 minute whereas SMax and S0 would still correspond to maximal running speed but expressed in m.min−1. 5.4. Prediction of Long Distances. The asymptotes of hyper- bolic and exponential model correspond to SCrit1, SCrit2, SCrit3, and S∞, respectively. In these models, the speeds lower than these asymptotes can be maintained infinitely. Therefore, the extrapolations of the asymptotic hyperbolic and exponential models overestimate the running speeds on very long distances (Figure 9). In fact, power-law and logarithmic models are also asymptotic models but these asymptotes are equal to zero. The overestimations of marathon performances from the extrapolations of power-law and logarithmic models (Figures 1(b), 6(b), and 9) are much smaller. Similarly, the computations of 30-minute and 60-minute running speeds by extrapolation of the asymptotic models (Table 7) were probably overestimations whereas the extrapolations of the power-law and logarithmic models were probably close to the actual running speeds. The overestimations of marathon performances by the logarithmic and power-law models (Figures 1, 6, and 9) are probably due not only to the causes of fatigue in long distances [58] but, perhaps, also to the effects of ground (track versus road, slopes, etc.), wind, shoes, and age. 5.5. Which Is the Optimal Empirical Model? The optimal running model is an accurate, useful, and practical model. 5.5.1. Which Is the Most Accurate Model? When computed from 4 distances, the individual correlation coefficients of all the models were high in all the elite runners. The correlation coefficients were the highest for the 3-parameter models by Morton and Hopkins and they were equal to 1 when they were computed from 3 distances only. These correlation coefficients equal to 1 were expected. Similarly, the regression coefficients of all the 2-parameter running models would have been equal to 1, if they were computed with only two distances. The values of RMSE were the lowest for the 3-parameter models (Table 17). Morton’s model was the most accurate as demonstrated by the ratios of estimated to actual running speeds which were very close to 1 for each distance (Table 9). Indeed, the differences between the estimated to actual running speeds were lower than 0.5% in each distance for all the runners. This model was significantly more accurate than all the other models as shown in Table 18. However, if a running model is perfect, there should be no significant difference between its parameters computed from different ranges of distances. Morton’s model was probably not perfect because its parameters were significantly different (P = 0.031) when they were computed from different ranges of distances. In the present study, the empirical models consist of single equations and are less complex than the physiological and biomechanical models, which probably explained that the parameters of all these empirical models depended on the range of tlim. Indeed, the causes of fatigue differ for short, medium, and long distances [58]. The SCrit1 and SCrit2 models and the concepts of critical speed (or critical power) are by far the most used and taught [21, 46]. Nonetheless, SCrit1 and SCrit2 models were the less accurate models for the relationship between running speed and tlim. The curves derived from (12) and (14) did not describe accurately the relationships between speed and tlim (Figures 4(b) and 4(c)). The only points corresponding to 10000 m performances were close to the curves derived from (12) whereas the only points corresponding to 1500 m performances were close to the curves derived from (14). Consequently, the speed-tlim relationship would be better described by the mean values of ADC and SCrit: ADC = (ADC1 + ADC2) 2 = (𝛼1 + 𝛽2) 2 SCrit = (SCrit1 + SCrit2) 2 = (𝛼2 + 𝛽1) 2 (41) Even if the description of the individual speed-tlim relation- ships was better with the curves computed from the mean values of ADC and SCrit in (12) and (14) (Figure 13), this new hyperbolic model is not optimal when it is compared with the figures of the other models. 5.5.2. Which Is the Most Useful Model? The empirical models of running exercises are often used to estimate the running speeds over given distances, the endurance capability, and MAS. The race performance calculation requires 2 or 3 parameters depending on the model used. On the other hand, for each running model in the present study, there is only one 20 BioMed Research International 0 500 1000 1500 2000 7.5 7.0 6.5 6.0 5.5 tlim (s) S (m.s - 1) Figure 13: Individual relationships between speed and tlim com- puted from the mean values of ADC and SCrit in (12) and (14). parameter that is an expression of the long-distance running capability. Indeed, parameter ADC in the hyperbolic models is not reliable and parameters k, SMax, and S0 that are maximal speed indices are probably not useful for endurance runners. Similarly the parameter corresponding to the time constant (𝜏) in Hopkins’ model is not useful. The useful parameters of the asymptotic model cor- respond to SCrit1, SCrit2, SCrit3, and S∞. In theory, these parameters represent the fastest speed that can be maintained for a very long time. However, when SCrit1 was computed from exercises shorter than 20 min, the subjects were generally only able to maintain SCrit1 for less than 30 min and the running velocities that could be maintained for 60 minutes on a treadmill were largely overestimated by SCrit1 [59]. In another study on the relationship between critical velocity and marathon performance [60], SCrit1 (4.43 m.s−1) was 44% faster than the marathon running speed (3.07 m.s−1). Nonetheless, the correlation between marathon performance and SCrit1 was more significant than the correlations with the other physiological parameters. In this latter study, it was possible to calculate an approximation of the marathon performance from SCrit1 (r = 0.87 and SEE = 14 min). Approximations of long-distance performances (> 10000 m) are probably also possible with SCrit2, SCrit3, and S∞ since they are highly correlated with SCrit1 (P ≥ 0.965). For example, in the study on 12 trained middle- and long-distance male runners [56], the correlation coefficients of SCrit1, SCrit2, and S∞ with the maximal running speed during 60 min were equal to 0.90, 0.91, and 0.93, respectively. Amazingly, the correlation coefficient with the 60-min running speed was the lowest (0.80) for SCrit3 in these middle- and long-distance runners but the overestimation was the smallest (0.13 ± 0.21 m.s−1) as in the present study. It is likely that the logarithmic and power-law models that are not asymptotic are the best empirical models for the predictions of very long distances by extrapolation as suggested in Table 15 and Figure 9. The predictions of the running speeds corresponding to 30 min, 60 min, and marathon by extrapolation of Morton’s model were higher than the same predictions from the logarithmic and power- law models. But the overestimations of the running speeds corresponding to 30 min, 60 min, and marathon by Morton’s model were lower than the overestimations by the other asymptotic models (Tables 15 and 16 and Figure 9). On the other hand, the predictions of competition performances between 1500 and 10000 m (for example, one or two miles or 2000 m) by interpolation should be better with the 3- parameter models by Morton or Hopkins whose accuracies were the best. Similarly, the running speed corresponding to 6 or 7 min (an estimation of MAS) should be more accurate when computed with these 3-parameter models. The endurance index of the power-law model (exponent g) should be the most useful since it is the only endurance index that does not depend on tMAS (Section 5.2). 5.5.3. Which Is the Most Practical? The most practical model should be the less sensitive to a slightly submaximal perfor- mance and the easiest to compute. Unfortunately, no study compares the sensitivity of the different models to submaximal performances. However, in a previous study [61], some results were assumed to be the effect of submaximal performances on SCrit1 model whose sensitivity was discussed in a review on the critical power concept [16]. Similarly, the values of parameter k that is an index of maximal running speed were overestimated in several physical education students in a previous study [55], which was probably the effect of submaximal running performances. Indeed, in 4 physical education students, parameters k were largely overestimated since they were higher than 20 m.s−1, whereas the maximal running speed is about 12.2 m.s−1 for the best world sprinter U. Bolt [62]. The comparison of parameters k of Ovett and Coe [63] is also a demonstration of the effects of submaximal performances on the modelling of running performances with the power-law model. Indeed, the differences between Ovett and Coe for the performances over 800, 1500, and 2000 m are around 1 second but the inclusion of longer distances (3000 m and 5000 m) causes large differences in the values of k and g. The value of k was largely higher than 12 m.s−1 for Coe but not for Ovett. The best performance for a given distance is probably maximal if the elite runner has run this distance many times, which was not the case for Coe in the 3000 m and 5000 m distances. In the present study, the sensitivity of Morton’s model to submaximal performances could be not negligible. Indeed, the parameters of this model were significantly different when they were computed from different distance ranges although the differences between the estimated and the actual speeds were very low (< 0.5%). The sensitivity of Morton’s model to submaximal performances could also explain why the correlation coefficient of SCrit3 with the 60 min speed was the lowest in the study on the twelve middle- and long-distance runners [56]. Many runners compete over two distances, only (either 800 and 1500 m or 5000 and 10000 m or half-marathon and marathon). Their performances on the other distances could be slightly submaximal and, consequently, the 3-parameter BioMed Research International 21 models by Morton or Hopkins could be not optimal for these runners. The 3-parameter models need a software that can com- pute the parameters by iteration. The 2-parameter models are easier to compute either by a nomogram [48] or by the current database software (Microsoft Excel, LibreOffice Calc, etc.). The calculation of SCrit1 is much easier than the parameters of the other models. Particularly, it is very easy to calculate SCrit1 from two running performances: SCrit1=(Dlim2 – Dlim1) (tlim2 – tlim1) (42) In addition, the SCrit1 model is the only model that can directly predict the performance corresponding to a distance from its parameters (ADC1 and SCrit1): Dlim = ADC1 + SCrit1 ∗ tlim tlim = (Dlim - ADC1) SCrit1 (43) In the present study, the other models can only predict performances corresponding to a value of tlim. In these models, the protocol presented in Section 3.3 is necessary for the prediction of a performance corresponding to a distance. 6. Conclusion The comparison of the accuracies of the different models in the six elite endurance runners suggests that the most accurate model is the asymptotic 3-parameter hyperbolic model proposed by Morton and that the less accurate models are SCrit1 and SCrit2 models which are the most often used. However, it is likely that logarithmic and power-law models are the most accurate models for the predictions of long- distance performances (maximal running speeds for 30 and 60 min or marathon) by extrapolation. In addition, exponent g of the power-law model is an interesting endurance index that does not depend on tMAS. The comparison of the sen- sitivity of the different models to submaximal performances should be studied to select the most practical model. Data Availability All the “experimental” data are presented in Table 1. All the results of the computations according to the different models are presented in the next 15 tables (from Table 2 to Table 16). Conflicts of Interest The author declares that they have no conflicts of interest. References [1] A. V. Hill, Muscular Movement in Man: The Factors Governing Speed and Recovery from Fatigue, McGraw-Hill, New York, NY, USA, London, UK, 1927. [2] F. M. Henry, “Time-velocity equations and oxygen require- ments of “all-out” and “steady-pace” running,” Research Quar- terly for Exercise and Sport, vol. 25, no. 2, pp. 164–177, 1954. [3] D. L. Costill, “The relationship between selected physiological variables and distance running performance,” The Journal of Sports Medicine and Physical Fitness, vol. 7, no. 2, pp. 61–66, 1967. [4] J. B. Keller, “A theory of competitive running,” Physics Today, vol. 26, no. 9, pp. 43–47, 1973. [5] R. Margaria, P. Aghemo, and F. Pineral Limas, “A simple relation between performance in running and maximal aerobic power,” Journal of Applied Physiology, vol. 38, no. 2, pp. 351-352, 1975. [6] A. J. Ward-Smith, “A mathematical theory of running, based on the first law of thermodynamics, and its application to the performance of world-class athletes,” Journal of Biomechanics, vol. 18, no. 5, pp. 337–349, 1985. [7] F. P´eronnet and G. Thibault, “Analyse physiologique de la per- formance en course `a pied : r´evision du mod`ele hyperbolique,” Journal de Physiologie Paris, vol. 82, pp. 52–60, 1987. [8] F. Peronnet and G. Thibault, “Mathematical analysis of running performance and world running records,” Journal of Applied Physiology, vol. 67, no. 1, pp. 453–465, 1989. [9] P. E. di Prampero, “Factors limiting maximal performance in humans,” European Journal of Applied Physiology, vol. 90, no. 3- 4, pp. 420–429, 2003. [10] P. E. Di Prampero, C. Capelli, P. Pagliaro et al., “Energetics of best performances in middle-distance running,” Journal of Applied Physiology, vol. 74, no. 5, pp. 2318–2324, 1993. [11] A. J. Ward-Smith, “The bioenergetics of optimal performances in middle-distance and long- distance track running,” Journal of Biomechanics, vol. 32, no. 5, pp. 461–465, 1999. [12] A. E. Kennelly, “An approximate law of fatigue in the speeds of racing animals,” Proceedings of the American Academy of Arts and Sciences, vol. 42, no. 15, pp. 275–331, 1906. [13] W. G. Hopkins, I. M. Edmond, B. H. Hamilton, D. J. Mac Far- lane, and B. H. Ross, “Relation between power and endurance for treadmill running of short duration,” Ergonomics, vol. 32, no. 12, pp. 1565–1571, 1989. [14] R. H. Morton and D. J. Hodgson, “The relationship between power output and endurance: A brief review,” European Journal of Applied Physiology, vol. 73, no. 6, pp. 491–502, 1996. [15] R. Hugh Morton, “A 3-parameter critical power model,” Ergonomics, vol. 39, no. 4, pp. 611–619, 1996. [16] H. Vandewalle, J. F. Vautier, M. Kachouri, J.-M. Lechevalier, and H. Monod, “Work-exhaustion time relationships and the critical power concept,” The Journal of Sports Medicine and Physical Fitness, vol. 37, no. 2, pp. 89–102, 1997. [17] R. H. Morton, “The critical power and related whole-body bioenergetic models,” European Journal of Applied Physiology, vol. 96, no. 4, pp. 339–354, 2006. [18] L. V. Billat, J. P. Koralsztein, and R. H. Morton, “Time in human endurance models: From empirical models to physiological models,” Sports Medicine, vol. 27, no. 6, pp. 359–379, 1999. [19] V. Bocquet and V. Billat, “Mod`eles math´ematiques et physi- ologiques de la performance humaine,” Science & Sports, vol. 14, no. 6, pp. 278–291, 1999. [20] L. Bosquet, L. L´eger, and P. Legros, “Methods to determine aerobic endurance,”Sports Medicine, vol. 32, no. 11, pp. 675–700, 2002. [21] D. C. Clarke and P. F. Skiba, “Rationale and resources for teaching the mathematical modeling of athletic training and performance,” American Journal of Physiology - Advances in Physiology Education, vol. 37, no. 2, pp. 134–152, 2013. [22] J. Dekerle, X. Nesi, and H. Carter, “The distance - Time rela- tionship over a century of running Olympic performances: A 22 BioMed Research International limit on the critical speed concept,” Journal of Sports Sciences, vol. 24, no. 11, pp. 1213–1221, 2006. [23] P. T. Nikolaidis, M. A. Zingg, and B. Knechtle, “Performance trends in age-group runners from 100 m to marathon—The World Championships from 1975 to 2015,” Scandinavian Journal of Medicine & Science in Sports, vol. 27, no. 12, pp. 1588–1596, 2017. [24] R. Lepers and P. J. Stapley, “Master athletes are extending the limits of human endurance,” Frontiers in Physiology, vol. 7, 2016. [25] S. N. Cheuvront, R. Carter III, K. C. Deruisseau, and R. J. Moffatt, “Running performance differences between men and women: An update,” Sports Medicine, vol. 35, no. 12, pp. 1017– 1024, 2005. [26] M. J. Joyner, “Physiological limits to endurance exercise perfor- mance: influence of sex,” The Journal of Physiology, vol. 595, no. 9, pp. 2949–2954, 2017. [27] F. P´eronnet, Le Marathon. Deuxi`eme ´edition, Vigot Edition, Paris, 1991. [28] B. Baron, T. D. Noakes, J. Dekerle et al., “Why does exercise terminate at the maximal lactate steady state intensity?” British Journal of Sports Medicine, vol. 42, no. 10, pp. 828–833, 2008. [29] P. Fontana, U. Boutellier, and C. Kn¨opfli-Lenzin, “Time to exhaustion at maximal lactate steady state is similar for cycling and running in moderately trained subjects,” European Journal of Applied Physiology, vol. 107, no. 2, pp. 187–192, 2009. [30] R. Beneke, R. M. Leith¨auser, and O. Ochentel, “Blood lactate diagnostics in exercise testing and training,” International Jour- nal of Sports Physiology and Performance, vol. 6, no. 1, pp. 8–24, 2011. [31] S. Savaglio and V. Carbone, “Scaling in athletic world records,” Nature, vol. 404, no. 6775, p. 244, 2000. [32] V. Carbone and S. Savaglio, “Scaling laws and forecasting in athletic world records,” Journal of Sports Sciences, vol. 19, no. 7, pp. 477–484, 2001. [33] W. G. Hopkins and D. J. Hewson, “Variability of competitive performance of distance runners,” Medicine & Science in Sports & Exercise, vol. 33, no. 9, pp. 1588–1592, 2001. [34] J. S. Katz and L. Katz, “Power laws and athletic performance,” Journal of Sports Sciences, vol. 17, no. 6, pp. 467–476, 1999. [35] J. M. Garc´ıa-Manso and J. M. Mart´ın-Gonz´alez, “Laws of Potency or scale: its application to the sportive phenomenon,” Fitness and Performance Journal, vol. 7, no. 3, pp. 195–202, 2008. [36] P. S. Riegel, “Athletic records and human endurance,” American Scientist, vol. 69, no. 3, pp. 285–299, 1981. [37] A. J. Vickers and E. A. Vertosick, “An empirical study of race times in recreational endurance runners,” BMC Sports Science, Medicine and Rehabilitation, vol. 8, no. 1, 2016. [38] J. Scherrer, M. Samson, and A. Pal´eologue, “Etude du travail musculaire et de la fatigue. Donn´ees ergom´etriques obtenues chez l’homme,” Journal de Physiologie, vol. 46, pp. 887–916, 1954. [39] H. Monod and J. Scherrer, “The work capacity of a synergic muscular group,” Ergonomics, vol. 8, no. 3, pp. 329–338, 1965. [40] J. H. Ettema, “Limits of human performance and energy- production,” Internationale Zeitschrift f¨ur Angewandte Physiolo- gie Einschließlich Arbeitsphysiologie, vol. 22, no. 1, pp. 45–54, 1966. [41] J. Scherrer and H. Monod, “Le travail musculaire local et la fatigue chez l’Homme,” Journal de Physiologie, Paris, vol. 52, pp. 419–501, 1960. [42] J. F. Vautier, H. Vandewalle, H. Arabi, and H. Monod, “Critical power as an endurance index,” Applied Ergonomics, vol. 26, no. 2, pp. 117–121, 1995. [43] D. Bishop, D. G. Jenkins, and A. Howard, “The critical power function is dependent on the duration of the predictive exercise tests chosen,” International Journal of Sports Medicine, vol. 19, no. 2, pp. 125–129, 1998. [44] T. Moritani, A. N. Ata, H. A. Devries, and M. Muro, “Critical power as a measure of physical work capacity and anaerobic threshold,” Ergonomics, vol. 24, no. 5, pp. 339–350, 1981. [45] R. L. Hughson, C. J. Orok, and L. E. Staudt, “A high velocity treadmill running test to assess endurance running potential,” International Journal of Sports Medicine, vol. 5, no. 1, pp. 23–25, 1984. [46] D. C. Poole, M. Burnley, A. Vanhatalo, H. B. Rossiter, and A. M. Jones, “Critical power: An important fatigue threshold in exercise physiology,” Medicine & Science in Sports & Exercise, vol. 48, no. 11, pp. 2320–2334, 2016. [47] F. P´eronnet, G. Thibault, E. C. Rhodes, and D. C. McKenzie, “Correlation between ventilatory threshold and endurance capability in marathon runners,” Medicine & Science in Sports & Exercise, vol. 19, no. 6, pp. 610–615, 1987. [48] H. Vandewalle, “A nomogram of performances in endurance running based on logarithmic model of p´eronnet-thibault,” American Journal of Engineering Research (AJER), vol. 6, pp. 78– 85, 2017. [49] G. A. Gaesser, T. J. Carnevale, A. Garfinkel, D. O. Walter, and C. J. Womack, “Estimation of critical power with nonlinear and linear models,” Medicine & Science in Sports & Exercise, vol. 27, no. 10, pp. 1430–1438, 1995. [50] T. J. Housh, J. T. Cramer, A. J. Bull, G. O. Johnson, and D. J. Housh, “The effect of mathematical modelling on critical velocity,” European Journal of Applied Physiology, vol. 84, no. 5, pp. 469–475, 2001. [51] A. J. Bull, T. J. Housh, G. O. Johnson, and S. R. Rana, “Physiological responses at five estimates of critical velocity,” European Journal of Applied Physiology, vol. 102, no. 6, pp. 711– 720, 2008. [52] H. C. Bergstrom, T. J. Housh, J. M. Zuniga et al., “Differences among estimates of critical power and anaerobic work capacity derived from five mathematical models and the three-minute all-out test,” The Journal of Strength and Conditioning Research, vol. 28, no. 3, pp. 592–600, 2014. [53] L. V. Billat and J. P. Koralsztein, “Significance of the velocity at VO2max and time to exhaustion at this velocity,” Sports Medicine, vol. 22, no. 2, pp. 90–108, 1996. [54] H. Vandewalle, “Mathematical modeling of the running per- formances in endurance exercises: comparison of the models of Kennelly and P´eronnet-Thibaut for World records and elite endurance runners,” American Journal of Engineering Research (AJER), vol. 6, no. 9, pp. 317–323, 2017. [55] B. Zinoubi, H. Vandewalle, and T. Driss, “Modeling of running performances in humans: comparison of power laws and critical speed,” The Journal of Strength and Conditioning Research, vol. 31, no. 7, pp. 1859–1867, 2017. [56] F. X. Gamelin, J. Coquart, N. Ferrari et al., “Prediction of one- hour running performance using constant duration tests,” The Journal of Strength and Conditioning Research, vol. 20, no. 4, pp. 735–739, 2006. [57] L. Bosquet, A. Duchene, F. Lecot, G. Dupont, and L. Leger, “Vmax estimate from three-parameter critical velocity models: BioMed Research International 23 Validityandimpacton800mrunningperformance prediction,” European Journal of Applied Physiology, vol. 97, no. 1, pp. 34–42, 2006. [58] M. Burnley and A. M. Jones, “Power–duration relationship: Physiology, fatigue, and the limits of human performance,” European Journal of Sport Science, vol. 18, no. 1, pp. 1–12, 2018. [59] M. L. Pepper, T. J. Housh, and G. O. Johnson, “The accuracy of the critical velocity test for predicting time to exhaustion during treadmill running,” International Journal of Sports Medicine, vol. 13, no. 2, pp. 121–124, 1992. [60] S.-I. Florence and J. P. Weir, “Relationship of critical velocity to marathon running performance,” European Journal of Applied Physiology, vol. 75, no. 3, pp. 274–278, 1997. [61] M. Kachouri, H. Vandewalle, V. L. Billat et al., “Critical velocity of continuous and intermittent running exercise. An example of the limits of the critical power concept,” European Journal of Applied Physiology and Occupational Physiology, vol. 73, no. 5, pp. 484–487, 1996. [62] R. Graubner and E. Nixdorf, “Biomechanical analysis of the sprint and hurdles events at the iaaf world championships in athletics,” New Studies in Athletics, vol. 26, no. 1-2, pp. 19–53, 2009. [63] H. Vandewalle, “Application of kennellymodel of running performances to elite endurance runners,” Research Inventy: International Journal of Engineering and Science, vol. 7, pp. 12– 16, 2017.

Modelling of Running Performances: Comparisons of Power-Law, Hyperbolic, Logarithmic, and Exponential Models in Elite Endurance Runners.

10-03-2018

Vandewalle, H

eng

PMC7029455

REVIEW Open Access The exoskeleton expansion: improving walking and running economy Gregory S. Sawicki1,2,3*, Owen N. Beck1,2, Inseung Kang1 and Aaron J. Young1,3* Abstract Since the early 2000s, researchers have been trying to develop lower-limb exoskeletons that augment human mobility by reducing the metabolic cost of walking and running versus without a device. In 2013, researchers finally broke this ‘metabolic cost barrier’. We analyzed the literature through December 2019, and identified 23 studies that demonstrate exoskeleton designs that improved human walking and running economy beyond capable without a device. Here, we reviewed these studies and highlighted key innovations and techniques that enabled these devices to surpass the metabolic cost barrier and steadily improve user walking and running economy from 2013 to nearly 2020. These studies include, physiologically-informed targeting of lower-limb joints; use of off-board actuators to rapidly prototype exoskeleton controllers; mechatronic designs of both active and passive systems; and a renewed focus on human-exoskeleton interface design. Lastly, we highlight emerging trends that we anticipate will further augment wearable-device performance and pose the next grand challenges facing exoskeleton technology for augmenting human mobility. Keywords: Wearable robotics, Assistive devices, Metabolic cost, Walk, Run, Energetic, Economy, Augmentation Background Exoskeletons to augment human walking and running economy: previous predictions and recent milestones The day that people move about their communities with the assistance of wearable exoskeletons is fast ap- proaching. A decade ago, Ferris predicted that this day would happen by 2024 [1] and Herr foresaw a future where people using exoskeletons to move on natural ter- rain would be more common than them driving auto- mobiles on concrete roads [2]. Impressively, Ferris and Herr put forth these visions prior to the field achieving the sought-after goal of developing an exoskeleton that breaks the ‘metabolic cost barrier’. That is, a wearable assistive device that alters user limb-joint dynamics, often with the intention of reducing user metabolic cost during natural level-ground walking and running com- pared to not using a device. When the goal is to reduce effort, metabolic cost is the gold-standard for assessing lower-limb exoskeleton performance since it is an easily attainable, objective measure of effort, and relates closely to overall performance within a given gait mode [3, 4]. For example, reducing ‘exoskeleton’ mass improves user running economy, and in turn running performance [4]. Further, enhanced walking performance is often related to improved walking economy [3] and quality of life [5, 6]. To augment human walking and running perform- ance, researchers seriously began attempting to break the metabolic cost barrier using exoskeletons in the first decade of this century, shortly after the launch of DAR- PA’s Exoskeletons for Human Performance Augmenta- tion program [7–10]. It was not until 2013 that an exoskeleton broke the metabolic cost barrier [11]. In that year, Malcolm and col- leagues [11] were the first to break the barrier when they developed a tethered active ankle exoskeleton that re- duced their participants’ metabolic cost during walking (improved walking economy) by 6% (Fig. 1). In the follow- ing 2 years, both autonomous active [12] and passive [13] ankle exoskeletons emerged that also improved human walking economy (Fig. 1). Shortly after those milestones, Lee and colleagues [14] broke running’s metabolic cost barrier using a tethered active hip exoskeleton that im- proved participants’ running economy by 5% (Fig. 1). Since then, researchers have also developed autonomous © The Author(s). 2020 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. * Correspondence: [email protected]; [email protected] 1The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA Full list of author information is available at the end of the article Sawicki et al. Journal of NeuroEngineering and Rehabilitation (2020) 17:25 https://doi.org/10.1186/s12984-020-00663-9 active [15, 16] and passive [17, 18] exoskeletons that im- prove human running economy (Fig. 1). In seven short years, our world went from having zero exoskeletons that could reduce a person’s meta- bolic cost during walking or running to boasting many such devices (Fig. 2). Continued progress to convert laboratory-constrained exoskeletons to au- tonomous systems hints at the possibility that exo- skeletons may soon expand their reach beyond college campuses and clinics, and improve walking and running economy across more real-world venues. If research and development continues its trajectory, lower-limb exoskeletons will soon augment human walking and running during everyday life – hopefully, fulfilling Ferris’s and Herr’s predictions. “What a time to be alive” – Aubrey Drake Graham. Exoskeleton user performance: insights and trends To highlight the recent growth of exoskeleton technol- ogy, we compiled peer-reviewed publications that re- ported that an exoskeleton improved user walking or running economy versus without using a device through December 2019. We indexed Web of Science for articles in the English language that included the following topic: Fig. 1 Milestones illustrating the advancement of exoskeleton technology. Tethered (blue) and autonomous (red) exoskeletons assisting at the ankle (circle), knee (triangle), and hip (square) joint to improve healthy, natural walking (left) and running (right) economy versus using no device are shown Sawicki et al. Journal of NeuroEngineering and Rehabilitation (2020) 17:25 Page 2 of 9 (exoskeleton or exosuit or exotendon or assist robot) and (metabolic or energetic or economy) and (walking or running or walk or run). Of the 235 indexed articles, we only included publications that reported that an exo- skeleton statistically improved their cohort’s walking and/or running economy versus an experimental no exo- skeleton condition. We excluded studies that did not ex- perimentally compare exoskeleton assisted walking or running to a no device condition, choosing to focus on devices that have been shown to break the metabolic cost barrier in the strictest sense. In total, 23 publica- tions satisfied our criteria, and six of these articles im- proved walking economy during “special” conditions: load carriage [19–21], inclined slope [21, 22], stair ascent [23], and with enforced long steps [24] (Fig. 2 and Table 1). We categorized exoskeletons into a special cat- egory, when researchers increased their participant’s metabolic cost above natural level-ground locomotion (e.g. by adding mass to the user’s body), and subse- quently used an exoskeleton to reduce the penalized metabolic cost. Seventeen publications presented improved human walking and/or running economy using an exoskeleton versus without using a device during preferred level- ground conditions: twelve exoskeletons improved walk- ing economy [11–13, 25–33], four improved running economy [14, 15, 17, 18], and one improved both walk- ing and running economy [16] versus using no device (Fig. 2). These studies demonstrate that exoskeletons improved net metabolic cost during walking by 3.3 to 19.8% versus using no device. For context, improving walking economy by 19.8% is equivalent to the change in metabolic cost due to a person shedding a ~ 25 kg rucksack while walking [34]. Moreover, four exoskele- tons improved net metabolic cost during running by 3.9 to 8.0% versus the no device condition (Table 1). Theor- etically, improving running economy by 8% would en- able the world’s fastest marathoner to break the current marathon world record by over 6 min [35] – How about a 1:50 marathon challenge? We labeled six studies as “special” due to an added metabolic penalty placed on the user such as load car- riage [19–21], enforced unnaturally long steps [24], in- clined ground slope [21, 22], and/or stair ascent [23] (Fig. 1). Each of these exoskeletons mitigated the nega- tive penalty by reducing metabolic cost. Yet, in some cases [21, 24], the authors also performed a comparison at level ground walking without an added “special” pen- alty. In these cases, the exoskeleton did not significantly mitigate (and may have increased) metabolic cost. For other “special” cases [19, 22, 23], exoskeletons have achieved a metabolic cost benefit in other relevant stud- ies using the same device [12, 26]. However, in such cases, there were differences in the experimental setup such as the utilized controller, recruited cohort, and test- ing conditions. Despite the popular notion that devices with greater power density (e.g., tethered exoskeletons with powerful Fig. 2 The year that each exoskeleton study was published versus the change in net metabolic cost versus walking or running without using the respective device. Red indicates autonomous and blue indicates a tethered exoskeletons. Different symbols indicate the leg joint(s) that each device directly targets. Asterisk indicates special case and cross indicates a passive exoskeleton Sawicki et al. Journal of NeuroEngineering and Rehabilitation (2020) 17:25 Page 3 of 9 Table 1 Detailed device specifications for exoskeletons that improved healthy, natural walking, and/or running economy versus using no device Number LeadAuthor Year Metabolic Reduction (%) Sample Size Target Joint(s) Auto /Tethered Active /Passive Walk /Run Speed (m/s) Mode Device Mass (kg) Note 1 G Sawicki 2009 14 9 Ankle Tethered Active Walk 1.25 Level Ground 2.36 Long Step Lengths 2 P Malcolm 2013 6 8 Ankle Tethered Active Walk 1.38 Level Ground 1.52 3 L Mooney 2014a 8 7 Ankle Autonomous Active Walk 1.5 Level Ground 4 Load Carry (23 kg) 4 L Mooney 2014b 10 7 Ankle Autonomous Active Walk 1.4 Level Ground 3.6 5 S Collins 2015 7.2 9 Ankle Autonomous Passive Walk 1.25 Level Ground 0.91 6 L Mooney 2016 11 6 Ankle Autonomous Active Walk 1.4 Level Ground 3.6 7 K Seo 2016 13.2 5 Hip Autonomous Active Walk 1.17 Level Ground 2.8 8 G Lee 2017 5.4 8 Hip Tethered Active Run 2.5 Level Ground 0.81 9 S Galle 2017 12 10 Ankle Tethered Active Walk 1.25 Level Ground 1.78 10 Y Lee 2017 13.2 5 Hip Autonomous Active Walk 1.14 Level Ground 2.6 11 K Seo 2017 15.5 5 Hip Autonomous Active Walk 1.17 Inclined Slope 2.4 5% grade 12 H Lee 2017 7 30 Hip Autonomous Active Walk 1.1 Level Ground 2.8 Elderly 13 R Nasiri 2018 8 10 Hip Autonomous Passive Run 2.5 Level Ground 1.8 14 S Lee 2018 14.9 7 Hip, Ankle Autonomous Active Walk 1.5 Level Ground 9.3 Load Carry (6.8 kg) 15 Y Ding 2018 17.4 8 Hip Tethered Active Walk 1.25 Level Ground 1.37 16 J Kim 2018 3.9 8 Hip Autonomous Active Run 2.5 Level Ground 4.7 Hybrid System 17 D Kim 2018 10.16 15 Hip Autonomous Active Walk N/A Stair Ascent 2.8 Elderly/128 Steps 18 F Panizzolo 2019 3.3 9 Hip Autonomous Passive Walk 1.1 Level Ground 0.65 Elderly 19 M MacLean 2019 4.2 4 Knee Autonomous Active Walk 0.5 Inclined Slope 8.4 Load Carry (18.1 kg) / 15 deg incline 20 C Simpson 2019 6.4 12 Hip Autonomous Passive Run 2.67 Level Ground N/A Ankle Attachment 21 J Kim 2019 9.3 9 Hip Autonomous Active Walk 1.5 Level Ground 5 Hybrid System 22 J Kim 2019 4 9 Hip Autonomous Active Run 2.5 Level Ground 5 Hybrid System 23 B Lim 2019 19.8 6 Hip Autonomous Active Walk 1.11 Level Ground 2.1 24 C Khazoom 2019 5.6 8 Ankle Tethered Active Walk 1.4 Level Ground 6.2 Sawicki et al. Journal of NeuroEngineering and Rehabilitation (2020) 17:25 Page 4 of 9 off-board motors and lightweight interfaces) would re- duce user metabolic cost beyond that capable by autono- mous devices, to date tethered systems have not improved user walking/running economy beyond that of autonomous systems (t-test: p = 0.90) (Fig. 2). Namely, tethered exoskeletons have improved user net metabolic cost during walking by 5.4 to 17.4% and autonomous exoskeletons have improved net metabolic cost during walking by 3.3 to 19.8%. These data are from a variety of devices (Table 1), walking speeds, and control systems, and thus more rigorous comparisons between autono- mous and tethered systems may reveal a more stark per- formance benefit of tethered systems due to their inherently smaller added mass penalty. Even though distal leg muscles are thought to be more economical/efficient than proximal leg muscles [36, 37], ankle exoskeletons broke the metabolic cost barrier before hip exoskeletons. Perhaps that is because researchers ini- tially targeted the ankles because they yield the greatest positive mechanical power output of any joint [37]. Not- ably, only one knee exoskeleton has improved walking economy [21] (Fig. 2). Finally, hip exoskeletons (17.4% metabolic reduction for a tethered device and 19.8% for an autonomous device) have numerically improved metabolic cost by more than ankle exoskeletons (12% metabolic re- duction for a tethered case and 11% for an autonomous device), perhaps due to the physiological differences be- tween ankle and hip morphology [37, 38] and/or due to the location of the device’s added mass [39]. A closer examination of the subset of exoskeletons that have yielded the greatest metabolic benefit provides insight into the factors that may maximize users’ benefits with future devices. One emerging factor is the exoskel- eton controller. There are numerous methods to com- mand [40] and control exoskeleton torque profiles. For example, myoelectric controllers depend on the user’s muscle activity [41, 42] and impedance controllers de- pend on the user’s joint kinematics [43]. Time-based controllers do not take the state of the user as direct in- put, and only depend on the resolution offered by the chosen torque versus time parameterization [27, 30, 44]. Recent exoskeleton studies indicate that both magnitude [45, 46] and perhaps more importantly, timing of assist- ance [11, 47, 48], affect user metabolism. Additionally, time-based controllers have the flexibility to generate a generalized set of assistive torque patterns that can be optimized on the fly and considerably improve walking and running economy over zero-torque conditions [30, 44]. Interestingly, the optimal exoskeleton torque pat- terns that emerge do not correspond to physiological torques in either their timing or magnitude [14, 44]. But, at least at the ankle, getting the timing right seems para- mount, as data from optimized exoskeleton torque pat- terns show lower variability in the timing versus magnitude of the peak torque across many users [44]. Fi- nally, regarding the magnitude of exoskeleton torque and the net mechanical energy transfer from the device to the user, more is not always better with respect to im- proving user locomotion economy [13, 27, 44, 46]. Leading approaches and technologies for advancing exoskeletons Exoskeleton testbeds enable systematic, high throughput studies on human physiological response Tethered exoskeleton testbeds have accelerated device de- velopment. In the first decade of the twenty-first century, most exoskeletons were portable, but also cumbersome and limited natural human movement. In addition, these devices were typically designed for one-off, proof of con- cept demonstrations; not systematic, high-throughput re- search [49–52]. As researchers began focusing on studies that aimed to understand the user’s physiological response to exoskeleton assistance, a key innovation emerged - the laboratory-based exoskeleton testbed. Rather than placing actuators on the exoskeleton’s end-effector, researchers began placing them off-board and attached them through tethers (e.g., air hoses and Bowden cables) to streamlined exoskeleton end-effectors [45, 53, 54]. This approach en- abled researchers to conduct high throughput, systematic studies during treadmill walking and running to determine optimal exoskeleton assistance parameters (e.g., timing and magnitude of mechanical power delivery [27, 55]) for improving walking and running economy. Furthermore, the high-performance motors on recent tethered exoskel- eton testbeds have relatively high torque control band- width that can be leveraged to render the dynamics of existing or novel design concepts [43, 56]. Testing mul- tiple concepts prior to the final device development could enable researchers to quickly diagnose the independent ef- fects of design parameters on current products and test novel ideas [57]. Thus, we reason that exoskeleton test- beds have progressed exoskeleton technology by enabling researchers to optimize a high number of device parame- ters [58], test new ideas, and then iterate designs without having to build one-off prototypes. Embedding ‘smart mechanics’ into passive exoskeletons provides an alternative to fully powered designs Laboratory-based exoskeletons are moving into the real- world through the use of small, transportable energy supplies [59] and/or by harvesting mechanical energy to power the device [60]. Despite these improvements, an- other way to circumnavigate the burden of lugging around bulky energy sources is by developing passive exoskeletons [13, 17, 18, 31]. Passive exoskeletons have been able to assist the user by storing and subsequently returning mechanical energy to the user without inject- ing net positive mechanical work. Passive exoskeletons Sawicki et al. Journal of NeuroEngineering and Rehabilitation (2020) 17:25 Page 5 of 9 are typically cheaper and lighter than active devices (e.g., Collins et al.’s ankle exoskeleton is 400 g [13]) and, like active devices, are hypothesized to primarily improve walking and running economy by reducing active muscle volume [61]. However, due to their simplified designs, passive exoskeletons are in some ways less adaptable than powered devices. Passive devices can only offer fixed mechanical properties that are at best only switch- able between locomotion bouts. Thus, while passive sys- tems may be adequate for providing assistance during stereotyped locomotion tasks such as running on a track or hiking downhill at fixed speed, they may not be able to handle variable conditions. On the other hand, active devices offer the opportunity to apply any generic torque-time profile, but require bulky motors and/or gears that need a significant source of power to do so. Thus, combining features from active and passive exo- skeletons to create a new class of pseudo-passive (or semi-active) devices may yield a promising future direc- tion for exoskeleton technology [59]. For example, rather than continuously modulating the assistance torque pro- file, a pseudo-passive device might inject small amounts of power to change the mechanical properties of an underlying passive structure during periods when it is unloaded [62]. The pseudo-passive approach likely bene- fits from the streamlined structural design (e.g., small motors) and adaptability that requires only small amounts of energy input (e.g., small batteries). Providing comfort at the human-exoskeleton interface Regardless of active or passive exoskeleton design, re- searchers struggle to effectively and comfortably inter- face exoskeletons to the human body [63]. That is primarily due to the human body having multiple de- grees of freedom, deforming tissues, and sensitive points of pressure. Accordingly, many researchers utilize cus- tom orthotic fabrication techniques [46, 64, 65], and/or malleable textiles (commonly referred to as exo-suits) [16, 66–68] to tackle this challenge. Textile-based exo- skeletons may be superior to traditional rigid exoskeletons due to their lower mass, improved comfort, fewer kine- matic restrictions, and better translation to practical-use [16, 67, 68]. Reaffirming soft technology, the tethered exo- skeleton that best improves walking economy versus not using a device is currently an exoskeleton with a soft, mal- leable user-device interface [67] (Fig. 2). Exoskeleton controllers using artificial intelligence and on- line optimization to adapt to both user and environment may facilitate the transition to ‘real-world’ functionality Researchers are also developing smart controllers that constantly update exoskeleton characteristics to optimize user walking and running economy. This is exemplified by Zhang and colleagues [44], who developed a controller that rapidly estimates metabolic profiles and adjusts ankle exoskeleton torque profiles to optimize human walking and running economy. We foresee smart controllers enab- ling exoskeletons to move beyond conventional fixed as- sistance parameters, and steering user physiology in-a- closed-loop with the device to maintain optimal exoskel- eton assistance across conditions [30, 69]. Since measuring metabolic cost throughout everyday life is unrealistic, fu- ture exoskeletons may incorporate embedded wearable sensors (e.g., electromyography surface electrodes, pulse oximetry units, and/or low-profile ultrasonography probes) that inform the controller of the user’s current physiological state [70, 71] and thereby enable continuous optimizing of device assistance [20, 72, 73] to minimize the user’s estimated metabolic cost. At a high level of control, researchers are using tech- niques to detect user intent, environmental parameters, and optimize exoskeleton assistance across multiple tasks [15, 16, 68, 74, 75]. An early version of this tech- niques paradigm was implementing proportional myo- electric control into exoskeletons [76–78]. This strategy directly modulates exoskeleton torque based on the tim- ing and magnitude of a targeted muscle’s activity, which can adapt the device to the users changing biomechan- ics. However, this strategy has yielded mixed results [42, 79, 80] and is challenging to effectively use due to quick adaptations that occur to accommodate various tasks as well as slower changes that occur due to learning the de- vice [41]. Scientists have made exciting advances using machine learning and artificial intelligence techniques to fuse information from both sensors on the user and de- vice to better merge the user and exoskeleton [81, 82], but these techniques have not yet been commercially translated to exoskeleton technology to the authors’ knowledge. These strategies have the potential to enable exoskeletons to discern user locomotion states (such as running, walking, descending ramps, and ascending stairs) and alter device parameters to meet the respective task demands. Conclusion Closing remarks and vision for the future of exoskeleton technology In the near-term, we predict that the exoskeleton expan- sion will break researchers out of laboratory confine- ment. Doing so will enable studies that directly address how exoskeleton-assistance affects real-world walking and running performance without relying on extrapo- lated laboratory-based findings. By escaping the labora- tory, we expect that exoskeleton technology will expand beyond improving human walking and running economy over the next decade and begin optimizing other aspects of locomotor performance that influence day-to-day mo- bility in natural environments. To list a few grand- Sawicki et al. Journal of NeuroEngineering and Rehabilitation (2020) 17:25 Page 6 of 9 challenges, exoskeletons may begin to augment user sta- bility, agility, and robustness of gait. For example, exo- skeletons may make users, · More stable by modulating the sensorimotor response of their neuromuscular system to perturbations [83–85]. · More agile and faster by increasing the relative force capacity of their muscles [86]. · More robust by dissipating mechanical energy to prevent injury during high impact activities like rapid cutting maneuvers or falling from extreme heights [87]. To make these leaps, engineers will need to continue to improve exoskeleton technology, physiologists will need to refine the evaluation of human performance, clinicians will need to consider how exoskeletons can further re- habilitation interventions, psychologists will need to better understand how user’s interact with and embody exoskel- etons, designers will need to account for exoskeletons in space planning, and healthcare professionals may need to update their exercise recommendations to account for the use of exoskeletons. Combined, these efforts will help es- tablish a ‘map’ that can be continuously updated to help navigate the interaction between human, machine, and environment. Such guidelines will set the stage for exo- skeletons that operate in symbiosis with the user to blur lines between human and machine. Closing the loop be- tween exoskeleton hardware, software, and the user’s bio- logical systems (e.g., both musculoskeletal and neural tissues) will enable a new class of devices capable of steer- ing human neuromechanical structure and function over both short and long timescales during walking and run- ning. On the shortest of time scales, exoskeletons that have access to body state information have the potential to modify sensory feedback from mechanoreceptors and augment dynamic balance. On the longest of timescales, exoskeletons that have access to biomarkers indicating tis- sue degradation [88] could modify external loads to shape the material properties of connective tissues and maintain homeostasis. Until then, we focus our attention on the ability of exo- skeletons to improve human walking and running econ- omy. So far, 17 studies have reported that exoskeletons improve natural human walking and running economy (Fig. 2). As these devices evolve and become more avail- able for public use, they will not only continue to improve walking and running economy of young adults, but they will also augment elite athlete performance, allow older adults to keep up with their kinfolk, enable people with disability to outpace their peers, and take explorers deeper into the wilderness. Acknowledgements None. Authors’ contributions All authors contributed to writing the manuscript. G. Sawicki and A. Young jointly conceived of the review paper idea, extracted the trends, and determined the leading approaches and technologies. O. Beck and I. Kang performed a literature search to benchmark progress in exoskeletons vs. time to improve the economy of human locomotion. They logged all the studies and categorized them by joint and gait mode. All authors drafted, edited, and approved the final manuscript. Funding This work was funded in part by NSF National Robotics Initiative (award # 1830215) to A.J.Y., U.S. Army Natick Soldier Research, Development and Engineering Center (W911QY18C0140) to G.S.S, and an NIH National Institute on Aging fellowship award (F32AG063460) to O.N.B. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies listed. Availability of data and materials Not applicable. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Author details 1The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA. 2School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA. 3Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, USA. Received: 5 August 2019 Accepted: 13 February 2020 References 1. Ferris DP. The exoskeletons are here. J Neuroeng Rehabil. 2009;6:17. 2. Herr H. Exoskeletons and orthoses: classification, design challenges and future directions. J Neuroeng Rehabil. 2009;6:21. 3. Beneke R, Meyer K. Walking performance and economy in chronic heart failure patients pre and post exercise training. Eur J Appl Physiol Occup Physiol. 1997;75(3):246–51. 4. Hoogkamer W, et al. Altered running economy directly translates to altered distance-running performance. Med Sci Sports Exerc. 2016;48(11):2175–80. 5. Newman AB, et al. Association of long-distance corridor walk performance with mortality, cardiovascular disease, mobility limitation, and disability. JAMA. 2006;295(17):2018–26. 6. Gerardi D, et al. Variables related to increased mortality following out- patient pulmonary rehabilitation. Eur Respir J. 1996;9(3):431–5. 7. Norris JA, et al. Effect of augmented plantarflexion power on preferred walking speed and economy in young and older adults. Gait Posture. 2007; 25(4):620–7. 8. Walsh CJ, Endo K, Herr H. A quasi-passive leg exoskeleton for load-carrying augmentation. Int J Humanoid Robot. 2007;4(3):487–506. 9. Sawicki GS, Ferris DP. Mechanics and energetics of level walking with powered ankle exoskeletons. J Exp Biol. 2008;211:1402–13. 10. Gregorczyk KN, et al. Effects of a lower-body exoskeleton device on metabolic cost and gait biomechanics during load carriage. Ergonomics. 2010;53:1263–75. 11. Malcolm P, et al. A simple exoskeleton that assists plantarflexion can reduce the metabolic cost of human walking. PLoS One. 2013;8:e56137. 12. Mooney LM, Rouse EJ, Herr HM. Autonomous exoskeleton reduces metabolic cost of human walking. J Neuroeng Rehabil. 2014;11(1):151. 13. Collins SH, Wiggin MB, Sawicki GS. Reducing the energy cost of human walking using an unpowered exoskeleton. Nature. 2015;522:212–5 advance online publication. 14. Lee G, et al. Reducing the metabolic cost of running with a tethered soft exosuit. Sci Robot. 2017;2(6):eaan6708. Sawicki et al. Journal of NeuroEngineering and Rehabilitation (2020) 17:25 Page 7 of 9 15. Kim J, et al. Autonomous and portable soft exosuit for hip extension assistance with online walking and running detection algorithm. In: 2018 IEEE International Conference on Robotics and Automation (ICRA); 2018. 16. Kim J, et al. Reducing the metabolic rate of walking and running with a versatile, portable exosuit. Science. 2019;365(6454):668. 17. Nasiri R, Ahmadi A, Ahmadabadi MN. Reducing the energy cost of human running using an unpowered exoskeleton. IEEE Trans Neural Syst Rehabil Eng. 2018;26(10):2026–32. 18. Simpson CS, et al. Connecting the legs with a spring improves human running economy. J Exp Biol. 2019;222(17):jeb202895. 19. Mooney LM, Rouse EJ, Herr HM. Autonomous exoskeleton reduces metabolic cost of human walking during load carriage. J Neuroeng Rehabil. 2014;11(1):80. 20. Lee S, et al. Autonomous multi-joint soft exosuit with augmentation-power- based control parameter tuning reduces energy cost of loaded walking. J Neuroeng Rehabil. 2018;15(1):66. 21. MacLean MK, Ferris DP. Energetics of walking with a robotic knee exoskeleton. J Appl Biomech. 2019;35(5):320. 22. Seo K, Lee J, Park YJ. Autonomous hip exoskeleton saves metabolic cost of walking uphill. In: 2017 IEEE International Conference on Rehabilitation Robotics (ICORR); 2017. 23. Kim D-S, et al. A wearable hip-assist robot reduces the cardiopulmonary metabolic energy expenditure during stair ascent in elderly adults: a pilot cross-sectional study. BMC Geriatr. 2018;18(1):230. 24. Sawicki GS, Ferris DP. Powered ankle exoskeletons reveal the metabolic cost of plantar flexor mechanical work during walking with longer steps at constant step frequency. J Exp Biol. 2009;212:21–31. 25. Mooney LM, Herr HM. Biomechanical walking mechanisms underlying the metabolic reduction caused by an autonomous exoskeleton. J Neuroeng Rehabil. 2016;13:4. 26. Seo K, et al. Fully autonomous hip exoskeleton saves metabolic cost of walking. In: 2016 IEEE International Conference on Robotics and Automation (ICRA); 2016. 27. Galle S, et al. Reducing the metabolic cost of walking with an ankle exoskeleton: interaction between actuation timing and power. J Neuroeng Rehabil. 2017;14(1):35. 28. Lee Y, et al. A flexible exoskeleton for hip assistance. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 2017. 29. Lee H, et al. A wearable hip assist robot can improve gait function and cardiopulmonary metabolic efficiency in elderly adults. IEEE Trans Neural Syst Rehabil Eng. 2017;25(9):1549–57. 30. Ding Y, et al. Human-in-the-loop optimization of hip assistance with a soft exosuit during walking. Science Robotics. 2018;3(15):eaar5438. 31. Panizzolo FA, et al. Reducing the energy cost of walking in older adults using a passive hip flexion device. J Neuroeng Rehabil. 2019;16(1):117. 32. Lim B, et al. Delayed outputf feedback control for gait assistance with a robotic hip exoskeleton. IEEE Trans Robot. 2019;35(4):1055–62. 33. Khazoom C, et al. Design and control of a multifunctional ankle exoskeleton powered by magnetorheological actuators to assist walking, jumping, and landing. IEEE Robot Automation Lett. 2019;4(3):3083–90. 34. Farley CT, McMahon TA. Energetics of walking and running: insights from simulated reduced-gravity experiments. J Appl Physiol. 1992;73(6):2709–12. 35. Kipp S, Kram R, Hoogkamer W. Extrapolating metabolic savings in running: implications for performance predictions. Front Physiol. 2019;10:79. 36. Umberger BR, Rubenson J. Understanding muscle energetics in locomotion: new modeling and experimental approaches. Exerc Sport Sci Rev. 2011; 39(2):59–67. 37. Sawicki GS, Lewis CL, Ferris DP. It pays to have a spring in your step. Exerc Sport Sci Rev. 2009;37(3):130. 38. Chen W, et al. On the biological mechanics and energetics of the hip joint muscle–tendon system assisted by passive hip exoskeleton. Bioinspir Biomim. 2018;14(1):016012. 39. Browning RC, et al. The effects of adding mass to the legs on the energetics and biomechanics of walking. Med Sci Sports Exerc. 2007;39(3):515–25. 40. Yan T, et al. Review of assistive strategies in powered lower-limb orthoses and exoskeletons. Robot Auton Syst. 2015;64:120–36. 41. Koller JR, et al. Learning to walk with an adaptive gain proportional myoelectric controller for a robotic ankle exoskeleton. J Neuroeng Rehabil. 2015;12(1):1. 42. Young AJ, Gannon H, Ferris DP. A biomechanical comparison of proportional electromyography control to biological torque control using a powered hip exoskeleton. Front Bioeng Biotechnol. 2017;5:37. 43. Zhang J, Cheah CC, Collins SH. Torque Control in Legged Locomotion. In: Sharbafi MA, Seyfarth A, editors. Bioinspired Legged Locomotion. Amsterdam: Elsevier; 2017. p. 347–400. 44. Zhang J, et al. Human-in-the-loop optimization of exoskeleton assistance during walking. Science. 2017;356(6344):1280. 45. Quinlivan BT, et al. Assistance magnitude versus metabolic cost reductions for a tethered multiarticular soft exosuit. Sci Robot. 2017;2(2):1–10. 46. Kang I, Hsu H, Young A. The effect of hip assistance levels on human energetic cost using robotic hip exoskeletons. IEEE Robot Automation Lett. 2019;4(2):430–7. 47. Jackson RW, Collins SH. An experimental comparison of the relative benefits of work and torque assistance in ankle exoskeletons. J Appl Physiol. 2015; 119(5):541–57. 48. Ding Y, et al. Effect of timing of hip extension assistance during loaded walking with a soft exosuit. J Neuroeng Rehabil. 2016;13(1):87. 49. Guizzo E, Goldstein H. The rise of the body bots [robotic exoskeletons]. IEEE Spectr. 2005;42(10):50–6. 50. Zoss AB, Kazerooni H, Chu A. Biomechanical design of the Berkeley lower extremity exoskeleton (BLEEX). IEEE/ASME Trans Mechatronics. 2006;11(2):128–38. 51. Walsh CJ, Pasch K, Herr H. An autonomous, underactuated exoskeleton for load-carrying augmentation. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 2006. 52. Raytheon XOS 2 exoskeleton, second-generation robotics suit. 2010; Available from: http://www.army-technology.com/projects/raytheon-xos-2-exoskeleton-us/. 53. Caputo JM, Collins SH. An experimental robotic testbed for accelerated development of ankle prostheses. In: 2013 IEEE International Conference on Robotics and Automation; 2013. 54. Ding Y, et al. Multi-joint actuation platform for lower extremity soft exosuits. In: 2014 IEEE International Conference on Robotics and Automation (ICRA); 2014. 55. Young A, et al. Influence of power delivery timing on the energetics and biomechanics of humans wearing a hip exoskeleton. Front Bioeng Biotechnol. 2017;5:4. 56. Witte KA, Collins SH. Design of Lower-Limb Exoskeletons and Emulator Systems. In: Rosen J, Ferguson PW, editors. Wearable Robotics. Amsterdam: Elsevier; 2020. p. 251–74. 57. Caputo JM, Collins SH, Adamczyk PG. Emulating prosthetic feet during the prescription process to improve outcomes and justifications. In: 2014 IEEE International Workshop on Advanced Robotics and its Social Impacts; 2014. 58. Kim M, et al. Human-in-the-loop bayesian optimization of wearable device parameters. PLoS One. 2017;12(9):e0184054. 59. Diller S, Majidi C, Collins SH. A lightweight, low-power electroadhesive clutch and spring for exoskeleton actuation. In: 2016 IEEE International Conference on Robotics and Automation (ICRA); 2016. 60. Donelan JM, et al. Biomechanical energy harvesting: generating electricity during walking with minimal user effort. Science. 2008;319(5864):807–10. 61. Beck ON, et al. Exoskeletons improve locomotion economy by reducing active muscle volume. Exerc Sport Sci Rev. 2019;47(4):237–45. 62. Braun DJ, et al. Variable stiffness spring actuators for low-energy-cost human augmentation. IEEE Trans Robot. 2019;35(6):1435–49. 63. Yandell MB, et al. Physical interface dynamics alter how robotic exosuits augment human movement: implications for optimizing wearable assistive devices. J Neuroeng Rehabil. 2017;14(1):40. 64. Giovacchini F, et al. A light-weight active orthosis for hip movement assistance. Robot Auton Syst. 2015;73:123–34. 65. Lv G, Zhu H, Gregg RD. On the design and control of highly backdrivable lower-limb exoskeletons: a discussion of past and ongoing work. IEEE Control Syst Mag. 2018;38(6):88–113. 66. Asbeck AT, et al. Biologically-inspired soft exosuit. In: IEEE International Conference on Rehabilitation Robotics; 2013. 67. Panizzolo FA, et al. A biologically-inspired multi-joint soft exosuit that can reduce the energy cost of loaded walking. J Neuroeng Rehabil. 2016;13(1):43. 68. Lee S, et al. Autonomous Multi-Joint Soft Exosuit for Assistance with Walking Overground. In: 2018 IEEE International Conference on Robotics and Automation (ICRA); 2018. 69. Felt W, et al. “Body-in-the-loop”: optimizing device parameters using measures of instantaneous energetic cost. PLoS One. 2015;10:e0135342. 70. Ingraham KA, Ferris DP, Remy CD. Evaluating physiological signal salience for estimating metabolic energy cost from wearable sensors. J Appl Physiol. 2019;126(3):717–29. 71. Slade P, et al. Rapid energy expenditure estimation for ankle assisted and inclined loaded walking. J Neuroeng Rehabil. 2019;16(1):67. Sawicki et al. Journal of NeuroEngineering and Rehabilitation (2020) 17:25 Page 8 of 9 72. Huang H, et al. A cyber expert system for auto-tuning powered prosthesis impedance control parameters. Ann Biomed Eng. 2016;44(5):1613–24. 73. Kumar S, et al. Extremum seeking control for model-free auto-tuning of powered prosthetic legs. In: IEEE Transactions on Control Systems Technology; 2019. p. 1–16. 74. Huang H, et al. Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion. IEEE Trans Biomed Eng. 2011;58(10):2867–75. 75. Young AJ, Hargrove LJ. A classification method for user-independent intent recognition for transfemoral amputees using powered lower limb prostheses. IEEE Trans Neural Syst Rehabil Eng. 2016;24(2):217–25. 76. Ferris DP, et al. An improved powered ankle-foot orthosis using proportional myoelectric control. Gait Posture. 2006;23(4):425–8. 77. Sawicki GS, Ferris DP. A pneumatically powered knee-ankle-foot orthosis (KAFO) with myoelectric activation and inhibition. J Neuroeng Rehabil. 2009;6:23. 78. Ferris DP, Lewis CL. Robotic lower limb exoskeletons using proportional myoelectric control. In: 2009 Annual international conference of the Ieee engineering in medicine and biology society, vol. 1-20; 2009. p. 2119–24. 79. Koller JR, Remy CD, Ferris DP. Biomechanics and energetics of walking in powered ankle exoskeletons using myoelectric control versus mechanically intrinsic control. J Neuroeng Rehabil. 2018;15(1):42. 80. Grazi L, et al. Gastrocnemius myoelectric control of a robotic hip exoskeleton can reduce the user's lower-limb muscle activities at push off. Front Neurosci. 2018;12:71. 81. Young A, Kuiken T, Hargrove L. Analysis of using EMG and mechanical sensors to enhance intent recognition in powered lower limb prostheses. J Neural Eng. 2014;11(5):056021. 82. Kang I, et al. Electromyography (EMG) signal contributions in speed and slope estimation using robotic exoskeletons. In: 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR); 2019. 83. Steele KM, et al. Muscle recruitment and coordination with an ankle exoskeleton. J Biomech. 2017;59:50–8. 84. Kao P-C, Lewis CL, Ferris DP. Short-term locomotor adaptation to a robotic ankle exoskeleton does not alter soleus Hoffmann reflex amplitude. J Neuroeng Rehabil. 2010;7:33. 85. Kao P-C, Lewis CL, Ferris DP. Joint kinetic response during unexpectedly reduced plantar flexor torque provided by a robotic ankle exoskeleton during walking. J Biomech. 2010;43(7):1401–7. 86. Weyand PG, et al. Faster top running speeds are achieved with greater ground forces not more rapid leg movements. J Appl Physiol. 2000;89(5):1991–9. 87. Sutrisno A, Braun DJ. Enhancing mobility with quasi-passive variable stiffness exoskeletons. IEEE Trans Neural Syst Rehabil Eng. 2019;27(3):487–96. 88. Lane AR, et al. Body mass index and type 2 collagen turnover in individuals after anterior cruciate ligament reconstruction. J Athl Train. 2019;54(3):270–5. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Sawicki et al. Journal of NeuroEngineering and Rehabilitation (2020) 17:25 Page 9 of 9

The exoskeleton expansion: improving walking and running economy.

02-19-2020

Sawicki, Gregory S,Beck, Owen N,Kang, Inseung,Young, Aaron J

eng

PMC7310409

REVIEW Open Access A review of the ketogenic diet for endurance athletes: performance enhancer or placebo effect? Caitlin P. Bailey* and Erin Hennessy Abstract Background: The ketogenic diet has become popular among endurance athletes as a performance enhancer. This paper systematically reviews the evidence regarding the effect of the endurance athlete’s ketogenic diet (EAKD) on maximal oxygen consumption (VO2 max) and secondary performance outcomes. Methods: PubMed and Web of Science searches were conducted through November 2019. Inclusion criteria were documentation of EAKD (< 50 g daily carbohydrate consumed by endurance athletes), ketosis achieved (measured via serum biomarker), VO2 max and/or secondary outcomes, English language, and peer reviewed-publication status. Articles were excluded if they were not a primary source or hypotheses were not tested with endurance athletes (i.e., individuals that compete at submaximal intensity for extended time periods). Study design, diet composition, adherence assessment, serum biomarkers, training protocols, and VO2 max/secondary outcomes were extracted and summarized. Results: Searches identified seven articles reporting on VO2 max and/or secondary outcomes; these comprised six intervention trials and one case study. VO2 max outcomes (n = 5 trials, n = 1 case study) were mixed. Two of five trials reported significant increases in VO2 max across all diets; while three trials and one case study reported no significant VO2 max findings. Secondary outcomes (n = 5 trials, n = 1 case study) were Time to Exhaustion (TTE; n = 3 articles), Race Time (n = 3 articles), Rating of Perceived Exertion (RPE; n = 3 articles), and Peak Power (n = 2 articles). Of these, significant findings for EAKD athletes included decreased TTE (n = 1 article), higher RPE (n = 1 article), and increased Peak Power (n = 1 article). Conclusion: Limited and heterogeneous findings prohibit definitive conclusions regarding efficacy of the EAKD for performance benefit. When compared to a high carbohydrate diet, there are mixed findings for the effect of EAKD consumption on VO2 max and other performance outcomes. More randomized trials are needed to better understand the potentially nuanced effects of EAKD consumption on endurance performance. Researchers may also consider exploring the impact of genetics, recovery, sport type, and sex in moderating the influence of EAKD consumption on performance outcomes. Keywords: Ketogenic diet, High fat diet, Ketosis, Endurance athlete, VO2 max Background The ketogenic diet prescribes a significant reduction in carbohydrate intake, which facilitates physiological changes that promote the utilization of ketones [1]. Recently this diet has received attention from the endurance community as a potential ergogenic aid because it minimizes the body’s reliance on carbohydrates. Despite evidence-based guidance for athletes to consume adequate carbohydrates [2], it has been proposed that the biological constraints of carbohy- drate storage may limit athletes who compete over ex- tended time periods [3, 4]. Carbohydrates are stored in the body predominately as glycogen in muscle tissue (300 g) and liver tissue (90 g), in addition to glucose in the blood © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. * Correspondence: [email protected] The Gerald J. and Dorothy R. Friedman School of Nutrition Science and Policy at Tufts University, 150 Harrison Avenue, Boston, MA 02111, USA Bailey and Hennessy Journal of the International Society of Sports Nutrition (2020) 17:33 https://doi.org/10.1186/s12970-020-00362-9 stream (30 g) [5]. This amounts to roughly 1680 kcal of available energy from carbohydrate at any one time. As a result, endurance athletes must replenish their glycogen stores every one to three hours during activity [5]. This continual consumption redirects nutrients from exercising muscles to the gut to aide digestion, potentially leading to reduced exercise economy and digestive disturbances, which compromise the athlete’s ability to maximize training and competition outcomes [3]. Additionally, research indi- cates that training with low muscle glycogen availability promotes molecular changes that enhance training-derived endurance adaptations [6]. Furthermore, ketogenic diets have been shown to reduce lactate accumulation after exer- cise, contributing to enhanced recovery [7, 8]. Taken together, this evidence suggests that reduced reliance on carbohydrates via ketosis can produce beneficial results for endurance athletes. In contrast to the limitations of carbohydrate storage, the body can reserve large amounts of energy in the form of fat. One pound of fat yields approximately 3500 kcal, mak- ing fat a vast source of energy, even among relatively lean endurance athletes. In theory, if endurance athletes tolerate the ketogenic diet, they could achieve longer training pe- riods with sustained energy levels and reduced need for re- fueling, allowing them to maximize the aerobic benefits from training and competing. In fact, there is some evi- dence that, among highly trained individuals, benefits of the diet include a steady supply of energy for the body and brain during prolonged exercise and accelerated recovery time post-exercise [4]. While scientists continue to explore potential benefits and drawbacks of the endurance athlete’s ketogenic diet (EAKD), several public figures in the athletic community have already embraced the diet as ergogenic [9, 10]. However, to the authors’ knowledge, there have been no systematic reviews of EAKD consumption and endur- ance outcomes (e.g., VO2 max, TTE, Race Time, RPE, Peak Power) from which such conclusions may be drawn. To fill this gap, the present review characterizes the na- ture and extent of available scientific evidence regarding the claim that EAKD consumption results in improved endurance performance, as measured by maximal oxygen uptake (VO2 max). VO2 max is considered the gold stand- ard for measuring aerobic fitness. It is measured via a graded exercise test on a treadmill or a cycle ergometer, and quantified as the body’s maximum oxygen use in mil- liliters per kilogram of body weight per minute [11]. Higher levels of VO2 max indicate greater endurance cap- acity. It is important to note that while VO2 max is an established measure of endurance capacity, relative VO2 max is confounded by changes in body weight and thus not without limitations. For this reason, secondary per- formance outcomes (i.e., time to exhaustion [TTE], race time, rating of perceived exertion [RPE], peak power) were also collected for analysis. This manuscript is intended to enhance the athletic and scientific communities’ knowledge of the potential benefits and consequences of adopting the EAKD, and to identify gaps in the current literature that may create opportun- ities for future study. Specifically, this review focuses on peer-reviewed articles examining endurance athletes (e.g., cyclists, runners, race walkers, triathletes) participating in three or more weeks of EAKD consumption. The included studies looked at a variety of outcomes; however, the pri- mary outcome of interest to this review is VO2 max. Main text Methods Articles were identified for inclusion via electronic data- base literature searches. An initial search was conducted using Web of Science and PubMed, on February 1, 2018. Subsequent searches of Web of Science and PubMed were conducted, using identical search criteria, in order to capture the most recent publications avail- able. The final search was conducted on November 17, 2019. The following key terms were used to search the databases for articles by topic: ketogenic, race, walker, cyclist, runner, marathon, endurance, and athlete. The full search strategy used for both da- tabases is as follows: ((ketogenic) AND (race[Title] OR walker*[Title] OR cyclist*[Title] OR runner*[Title] OR marathon*[Title] OR endurance[Title] OR athlet*[Ti- tle])). Asterisks denote truncation. Additional inclu- sion criteria were English language, peer reviewed- publication status, ketosis achieved (as measured via serum biomarkers), and documentation of VO2 max and/or secondary outcomes. The following exclusions were applied to the searches in order to narrow the scope of the article lists generated: NOT (epilepsy or child or mice or mouse or diabet* or rat* or seizure). Articles were included for review if the title, abstract, or key words indicated that the study focused on the ke- togenic diet in the context of endurance sport training and/or racing (i.e., the EAKD). Articles that met inclu- sion criteria from each database were compiled using Endnote software. Duplicates were removed, and ab- stracts were pre-screened for source type. Articles were excluded if they were not a primary source. After identifying all eligible records, a data matrix was developed and data were extracted on the following vari- ables: study design, athlete type (i.e., sport, training level, age range), diet type (i.e., EAKD, high carbohydrate, periodised carbohydrate) and composition, recruitment numbers, study length, dietary adherence assessment method, serum biomarkers for ketosis, training proto- cols, and VO2 max/secondary outcomes. Data from the matrix are presented in Tables 1 and 2. Results were synthesized qualitatively. Bailey and Hennessy Journal of the International Society of Sports Nutrition (2020) 17:33 Page 2 of 11 Table 1 Descriptive results Reference Sample size Population, age range Study design Study length Methods Diet composition Diet provision & assessment Ketosis biomarker Training protocol VO2 Max protocol Prospective trials Burke et al. 2017 [12] N = 29 Professional male race walkers with international race experience, 21–32 years Self-selected diet (non-random assignment) 3 weeks Diets: EAKD (< 50 g CHO, 75–80% FAT, 15–20% PRO [n = 10])a; HCD (60– 65% CHO, 20% FAT, 15– 20% PRO [n = 9])a; PCHO (60–65% CHO, 20% FAT, 15–20% PRO [n = 10])a Personalized menus developed by professional chef and RDs. All foods provided/recorded by research team. Beta-hydroxybutyrate levels post-EAKD: 0.8–2.0 mmol/liter Olympic-level training camp. Included daily race walking, resistance training, and/or cross training. Treadmill test Carr et al. 2018 [7] N = 24 Male (n = 17) and female (n = 7) elite race walkers Self-selected diet (non-random assignment) 3 weeks Diets: EAKD (< 50 g CHO, 75–80% FAT, 15–20% PRO [n = 9]); HCD (60– 65% CHO, 20% FAT, 15– 20% PRO [n = 8]); PCHO (60–65% CHO, 20% FAT, 15–20% PRO [n = 7]) Menus developed by professional chef and RDs. All foods provided/recorded by research team. Elevated serum ketone bodies post- EAKD: 1 mmol/liter Supervised, sport- specific, 3-week training protocol. Treadmill test Heatherly et al. 2018 [13] N = 8 Middle-age, recreationally competitive male runners, 39.5 ± 9.9 years Pre-posttest 3 weeks Diets: EAKD (< 50 g CHO, target 70% FAT [ad libitum]); HCD (habitual pre-study diet, reported as “moderate to high CHO”) Participants provided with daily macronutrient targets and instructed to self-track diet using diet software. Elevated serum ketone bodies post- dietary intervention compared to pre- EAKD levels: 0.7 ± 0.52 mmol/liter (EAKD) vs. 0.25 ± 0.09 mmol/liter (CHO) Participants continued normal recreational athletic activity for study duration. Treadmill test (pre-EAKD only). % baseline VO2 max at various race paces post- EAKD reported. McSwiney et al. 2018 [14] N = 20 Male endurance trained athletes (e.g., triathlon, cycling, marathon, ultra- marathon), 18– 40 years Self-selected diet (non-random assignment) 12 weeks Diets: EAKD (< 50 g CHO, > 75% FAT, 10–15% PRO [n = 9]); HCD (65% CHO, 20% FAT, 14% PRO [n = 11]) Participants received detailed handouts (e.g., example meal plans, shopping lists), nutrition counseling, and weekly check-ins. Weekly weighed food diary submitted. Beta-hydroxybutyrate levels post-EAKD: 0.5 mmol/liter ≥ 7h hours of endurance exercise and 2 strength training sessions per week. Cycle ergometer test Phinney et al. 1983 [15] N = 5 Elite male cyclists, 20–30 years Pre-posttest 4 weeks Diets: EAKD (< 20 g CHO, 85% FAT, 15% PRO); HCD (1.75 g PRO/kg/day, remainder as 66% CHO and 33% FAT)a Participants received three meals per day. Portions were weighed and intake monitored. Beta-hydroxybutyrate levels post-EAKD: 1.28 ± 0.35 mmol/liter Participants were asked to continue normal training, monitored via daily diary. Cycle ergometer test Shaw et al. 2019 [16] N = 8 Male endurance trained athletes (n = 2 marathoners, n = 4 ultra- marathoners, n = 2 triathletes), 29.6 ± 5.1 years Randomized repeated measures crossover study 31-days (4.5 weeks) per condition with a 14- to 21-day washout period Diets: EAKD (< 50 g CHO, 75–80% FAT, 15–20% PRO); HCD (43% CHO, 38% FAT, 19% PRO)a Participants received education session with RD, info booklet, personalized menu plan, meal/snack examples, and lifestyle advice. All had daily contact with a registered dietitian for monitoring. Beta-hydroxybutyrate levels post-EAKD: ≥0.3 mmol/liter by day 3 and ≥ 0.5 mmol/liter by day 7 Participants designed their own 28-day training plan (running and cyc- ling) and were asked to replicate this during each dietary period. Treadmill test Bailey and Hennessy Journal of the International Society of Sports Nutrition (2020) 17:33 Page 3 of 11 Table 1 Descriptive results (Continued) Reference Sample size Population, age range Study design Study length Methods Diet composition Diet provision & assessment Ketosis biomarker Training protocol VO2 Max protocol Case studies Zinn et al. 2017 [17] N = 5 Recreational athletes involved in competitive endurance sport for 5+ years, 49– 55 years Pilot case study, mixed methods research 10 weeks Diet: EAKD (< 50 g CHO, ad libitum FAT, 1.5 g/kg PRO [n = 5]) Participants provided with daily macronutrient prescription and instructed to self-track diet using diet software. Beta-hydroxybutyrate levels: 0.5–4.2 mmol/ liter Participants continued normal recreational athletic activity for study duration. Cycle ergometer test EAKD Endurance Athlete Ketogenic Diet, HCD High Carbohydrate Diet, PCHO Periodised carbohydrate diet: percentages based on weekly rather than daily diet, CHO Carbohydrate, PRO Protein, RD Registered dietitian aIsocaloric diets Bailey and Hennessy Journal of the International Society of Sports Nutrition (2020) 17:33 Page 4 of 11 Table 2 Study outcomes: VO2 max and secondary outcomes. Dashes indicate that studies did not assess the specified variable(s) Reference VO2 max outcomes (mL/kg/min) Time to exhaustion (TTE) Race time/Time trial Rating of perceived exertion (RPE) Peak power Prospective Trials Burke et al. 2017 [12] Significant increase in VO2 max from baseline (p < 0.001) in all three groups. VO2 Max of the HCD group was significantly lower than for the other groups both pre- and post-diet (p ≤ 0.02). Pre- vs. post-intervention EAKD: 66.3 vs. 71.1 HCD: 61.6 vs. 66.2 PCHO: 64.9 vs. 67.0 _ _ EAKD group: Non- significant increase in 10 km race time from baseline. HCD and PCHO groups: Significant decrease in race time (p < 0.01). Pre- vs. post- intervention EAKD: 23 s slower HCD: 190 s faster PCHO: 124 s faster EAKD group: Significantly higher RPE values for post-intervention graded economy test compared with pre-intervention RPE values (p ≤ 0.01). Non- significant trend for higher RPE values during 25 km long walk for both pre- and post-testing. _ _ Carr et al. 2018 [7] Significant increase in VO2 max from baseline (p < 0.05) in all three groups. Between groups analysis not reported. Pre- v. post-intervention (M ± SD) EAKD: 61.1 ± 5.3 vs. 63.4 ± 4.1 HCD: 57.6 ± 4.6 vs. 58.3 ± 4.1 PCHO: 58.1 ± 3.3 vs. 60.2 ± 3.8 _ _ _ _ _ _ _ _ Heatherly et al. 2018 [13] Post-EAKD VO2 max not measured. Study reported % baseline VO2 max at various race paces. At 10 km, 21 km, 42 km and sub-42 km (but not 5 km) race paces, % relative VO2 max was significantly greater post-EAKD. Example (10 km pace; p < 0.05): EAKD: 98.7 ± 11.3 HCD: 92.8 ± 5.3 _ _ 5 km time trial time was not significantly different pre- vs. post- EAKD (p > 0.10). Pre- vs. post- intervention EAKD: 23.45 ± 2.25 min. HCD: 23.92 ± 2.57 min. Overall RPE did not differ significantly pre- vs. post- EAKD during 5 km time trial (P > 0.10). Pre- vs. post-intervention EAKD: 8.4 ± 1.2 HCD: 8.0 ± 1.0 _ _ McSwiney et al. 2018 [14] Increase in both groups post-diet. Non-significant dif- ference between groups (p = 0.968). Pre- vs. post-intervention EAKD: 53.6 ± 6.8 vs. 57.3 ± 6.7 HCD: 52.6 ± 6.4 vs. 57.2 ± 6.1 _ _ 100 km time trial time was not significantly different between groups (p = 0.057). Pre- vs. post- intervention EAKD: 4.07 min.sec faster HCD: 1.13 min.sec faster _ _ Post-intervention peak power was significantly different between groups (p = 0.047). Pre- vs. post-intervention EAKD: 8.3 ± 2.2 vs. 9.7 ± 2.3; 1.4 watts/kg increase HCD: 9.1 ± 2.6 vs. 8.4 ± 2.2; 0.7 watts/kg decrease Phinney et al. 1983 [15] Non-significant decrease from baseline (HCD; p > 0.01). Pre- vs. post-intervention EAKD: 5.00 ± 0.20 HCD: 5.10 ± 0.18 Non-significant increase in mean exercise times from baseline (HCD). Pre- vs. post- intervention EAKD: 151 ± 25 _ _ _ _ _ _ Bailey and Hennessy Journal of the International Society of Sports Nutrition (2020) 17:33 Page 5 of 11 Table 2 Study outcomes: VO2 max and secondary outcomes. Dashes indicate that studies did not assess the specified variable(s) (Continued) Reference VO2 max outcomes (mL/kg/min) Time to exhaustion (TTE) Race time/Time trial Rating of perceived exertion (RPE) Peak power min. HCD: 147 ± 13 min. Shaw et al. 2019 [16] No significant change from pre-intervention levels for either dietary exposure (p > 0.05). Pre-intervention (all athletes) 59.4 ± 5.2 No significant difference between dietary interventions (p = 0.56). Pre- vs. post- intervention EAKD: 239 ± 27 vs. 219 ± 53 min. (p = 0.36) HCD: 237 ± 44 vs. 231 ± 35 min. (p = 0.44) _ _ RPE values were similar for each dietary intervention during run-to-exhaustion trials. 1-h, 2-h, at exhaustion EAKD: 11.4 ± 0.9, 12.1 ± 1.4, 19.38 ± 0.52 HCD: 11.7 ± 0.8, 12.8 ± 0.9, 19.38 ± 0.52 _ _ Case studies Zinn et al. 2017 [17] Non-significant change from baseline (M ± SD): − 1.69 ± 3.4 (p = 0.63). (with a decrease in four of the five athletes) Significant decrease in TTE for all participants (p = 0.004). Mean change from baseline EAKD: − 2 ± 0.7 min. _ _ _ _ Four out of five athletes experienced a decrease in peak power from baseline (p = 0.07). Mean change from baseline EAKD: − 18 ± 16.4 watts EAKD Endurance Athlete Ketogenic Diet, HCD High Carbohydrate Diet, PCHO Periodised carbohydrate diet Bailey and Hennessy Journal of the International Society of Sports Nutrition (2020) 17:33 Page 6 of 11 Results Search results Figure 1 illustrates the screening process and articles in- cluded in this review. In brief, searches from Web of Sci- ence and PubMed generated n = 60 articles (n = 33 and 27, respectively). After removing duplicates and pre-screening, 28 articles remained. After further review, 21 additional re- cords were excluded (see Fig. 1 for reasons for exclusion). All exclusions were conducted to emphasize the effect of ketogenic diet consumption on sport-specific performance in endurance athletes. The screening process produced seven eligible articles: six prospective trials (n = 1 random- ized crossover study, n = 3 non-randomized trials, n = 2 pre-posttest), and one case study. See Fig. 1 for a flow chart of the screening process. Descriptive results Among the seven studies included in this review, sex and athlete type were inextricable variables. Five of seven stud- ies examined VO2 max outcomes in only male athletes Fig. 1 Flow chart depicting the literature search and review process to arrive at the final analytic sample (n = 7). Arrows pointing right indicate the number of articles excluded and for what reason Bailey and Hennessy Journal of the International Society of Sports Nutrition (2020) 17:33 Page 7 of 11 [12–16]. However, among those studies, athlete type var- ied: one study recruited male runners [13], one recruited male race walkers [12], one recruited male cyclists [15], and two recruited a mixed sample of male endurance ath- letes [14, 16]. Two of the seven studies recruited both male and female athletes; one recruited a sample of race walkers [7] and the other recruited a sample of mixed en- durance athletes [17]. Ages for study participants ranged from 18 to 55 years. All seven studies included an EAKD (< 50 g daily carbohydrate). Of the six trial studies, all in- cluded a standard, high carbohydrate comparison diet [7, 12–16], while the case study provided no comparison diet [17]. Studies either provided participants with meals [7, 12, 15] or with dietary guidance, including sample meal plans [13, 14, 16, 17]. Adherence to diet was tracked via objective researcher observation and measurement [7, 12, 15] or participant self-report (e.g., weighed food diaries, dietary analysis software) [13, 14, 16, 17]. All studies expli- citly reported tracking serum ketone levels as a biomarker for ketosis. All studies lasted between three and 12 weeks. Performance outcome results VO2 max outcomes (mL/kg/min; n = 6 studies) were mixed: two studies reported significant increases in VO2 max across all diets [7, 12], and four reported no significant VO2 max outcomes [14–17]. In a three-week nonrando- mized trial, Carr et al. reported significant increases in VO2 max from baseline for all diet types (EAKD: 61.1 ± 5.3 vs. 63.4 ± 4.1; HCD: 57.6 ± 4.6 vs. 58.3 ± 4.1; PCHO: 58.1 ± 3.3 vs. 60.2 ± 3.8; p < 0.05) [7]. Using a similar design, Burke et al. found a significant increase in VO2 max for all ath- letes (EAKD: 66.3 vs. 71.1; HCD: 61.6 vs. 66.2; PCHO: 64.9 vs. 67.0; p < 0.001) [12]. McSwiney et al. showed a 3.7-unit increase in relative VO2 max among the EAKD group after 12 weeks (53.6 ± 6.8 vs. 57.3 ± 6.7) [14]. This was a smaller increase than the 4.6-unit increase observed in the com- parison diet group (52.6 ± 6.4 vs. 57.2 ± 6.1); furthermore, the increase in relative VO2 max during EAKD consump- tion was inflated by a 6-kg mean reduction in body mass among the participants. The difference in increase between the two groups was not significant (p = 0.968) [14]. Shaw et al., a randomized crossover study, found no significant changes in VO2 max from baseline (59.4 ± 5.2) after either 31 days of EAKD or high carbohydrate comparison diet (p > 0.05) [16]. Using a pre-posttest design, Phinney et al. found no difference in VO2 max between a high carbohy- drate comparison diet and EAKD (pre-intervention HCD: 5.10 ± 0.18; EAKD: 5.00 ± 0.20; p > 0.01) [15]. Heatherly et al., also a pre-posttest design, measured VO2 max pre- but not post-EAKD consumption [13]. Instead, this study reported on the percent of baseline (pre-dietary interven- tion) VO2 max achieved at various race paces tested post- EAKD consumption. Researchers found that the percent of baseline relative VO2 max achieved was significantly greater post-EAKD at 10 km, 21 km, 42 km, and sub-42 km (but not 5 km) race paces (see Table 2; p < 0.05) [13]. Fi- nally, Zinn et al. showed a non-significant decrease from baseline VO2 max in athletes consuming the EAKD after 10 weeks (− 1.69 ± 3.4; p = 0.63) [17]. Zinn et al. was a case study with no reference comparison diet. Secondary outcomes (n = 6 studies) were also mixed. Of three studies that reported TTE, Shaw et al. and Phinney et al. each found no significant difference in TTE by diet type [15, 16], while Zinn et al. reported a significant decrease from baseline (pre-dietary intervention) for all five case study participants consuming the EAKD (− 2 ± 0.7 min.; p = 0.004) [17]. Differences in race times by dietary intervention were reported by three studies [12–14] and found to be significant in one [12]. Specifically, Burke et al. reported a significant decrease in race time among high carbohydrate and periodized carbohydrate groups (HCD: − 190 s; PCHO: − 124 s; p < 0.01), while the EAKD group had a non-significant increase in race time (EAKD: + 23 s; p > 0.01) [12]. RPE was measured in three studies [12, 13, 16] and found to be significantly different from baseline in one [12]. Burke et al. reported higher RPE values among the EAKD group post-intervention compared with pre- intervention (p ≤ 0.01) [12]. Finally, peak power was mea- sured in two studies [14, 17]. McSwinney et al. reported that post-intervention peak power was significantly differ- ent between diets, with EAKD athletes improving their peak power and comparison diet athletes decreasing their peak power (EAKD: 8.3 ± 2.2 vs. 9.7 ± 2.3 watts/kilogram; HCD: 9.1 ± 2.6 vs. 8.4 ± 2.2 watts/kilogram; p = 0.047). Zinn et al. found a mean decrease in peak power from baseline (− 18 ± 16.4 watts; p = 0.07) with a decrease in four out of five athletes [17]. See Table 2 for a full list of results. Discussion It has been hypothesized that consuming a ketogenic diet may enhance performance among endurance athletes by promoting a shift in substrate utilization that enhances physiological training benefits [3, 18]. The present review explores this hypothesis by examining associations between EAKD consumption and VO2 max, a biomarker for endurance capacity [11]. Two of the seven studies in- cluded in this review found a significant increase in VO2 max post-EAKD consumption [7, 12]. However, both arti- cles reported significant VO2 max increases across all di- ets, and that outcomes were independent of dietary intervention. Interestingly, both studies were conducted among elite race walkers that self-selected their dietary intervention, and the athletes that self-selected into the EAKD had slightly higher average baseline and post- treatment VO2 max values [7, 12]. Furthermore, Burke et al., reported that VO2 max values for the high carbohy- drate comparison group were significantly lower than EAKD or periodised carbohydrate groups at baseline and Bailey and Hennessy Journal of the International Society of Sports Nutrition (2020) 17:33 Page 8 of 11 follow-up (p ≤ 0.02) [12]. This suggests that other factors may influence athletes’ choice of diet and aerobic capacity concomitantly, such as genetic variation in trainability and/or chronic substrate utilization [19, 20]. A review conducted by Williams et al. revealed the potential for 97 genes to predict VO2 max trainability, suggesting that gen- etics may account for differing training outcomes among athletes [20]. Certain dietary preferences, which both acutely and chronically influence substrate utilization, have also been linked to gene variations, highlighting the possibility for both dietary choices and training outcomes to be mediated by genetics [19, 21]. Randomized con- trolled trials and genome-wide association studies can be leveraged to control for, and explore the impact of, such factors in future studies of the EAKD. Four of the seven studies reviewed reported non- significant VO2 max outcomes [14–17]. In a non- randomized trial, McSwiney et al. reported a VO2 max increase in both groups of male endurance athletes post- EAKD (EAKD: 53.6 ± 6.8 vs. 57.3 ± 6.7; HCD: 52.6 ± 6.4 vs. 57.2 ± 6.1) with a non-significant difference between groups (p = 0.968) [14]. In a pre-posttest design, Phinney et al. reported a non-significant decrease in VO2 max from baseline among five elite male cyclists (pre- vs. post-EAKD: 5.10 ± 0.18 vs. 5.00 ± 0.20; p > 0.01) [15]. In a case study, Zinn et al. reported a non-significant de- crease among five recreational endurance athletes con- suming the EAKD (− 1.69 ± 3.4; p = 0.63) [17]. Finally, in a randomized crossover study, Shaw et al. reported no significant changes from baseline (59.4 ± 5.2) among male endurance athletes during either dietary interven- tion (p > 0.05) [16]. Heatherly et al. did not report VO2 max outcomes, in- stead providing the percentage of baseline VO2 max achieved at various race paces (i.e., 5 km, 10 km, 21 km, 42 km, sub-42 km) [13]. The significantly greater percentages of baseline VO2 max achieved post-EAKD consumption at 10 km, 21 km, 42 km, and sub-42 km race paces demon- strate that the EAKD was negatively correlated with the athletes’ aerobic efficiency at these paces. This is corrobo- rated by some of the secondary outcomes reported in Table 2, including reports of EAKD being associated with signifi- cantly higher RPE [12], and decreased TTE [17]. Only one study reported significant positive secondary findings: a higher peak power in athletes post-EAKD compared to the standard, high carbohydrate diet [14]. The authors of the study hypothesized that this outcome was likely due to an improved power to weight ratio among the EAKD athletes, who lost an average of 6 kg of body mass. Despite the popularity of the diet as an ergogenic aid, this review provides evidence that EAKD consumption produces mixed results, in terms of endurance perform- ance, when compared to a high carbohydrate diet. Several biological mechanisms may help to explain the potential for mixed and/or detrimental effects, including changes in fuel economy, production of certain metabolic byproducts, and reduced energy intake. For example, the EAKD sig- nificantly increases fat oxidation, requiring greater oxygen consumption due to the increased oxygen demands dur- ing fatty acid metabolism versus carbohydrate metabolism [12, 22]. This increased demand for oxygen reduces the beneficial impact of an increased VO2 max because a greater percentage of maximal oxygen uptake is now re- quired to maintain any given race pace [13]. Second, EAKD metabolites such as tryptophan and ammonia may promote fatigue by influencing the central nervous system [23, 24]. Finally, it has been shown that the EAKD leads to increased satiety and reduced energy intake [25]. Reduced energy intake, and the accompanying weight loss, may be beneficial for some individuals but could also present a sustainability issue for highly active athletes. Substantial reductions in body weight may negatively impact mental, hormonal, and bone health, as well as recovery time and general exercise performance [26, 27]. Illustrating these mechanisms, Heatherly et al. reported that athletes exhib- ited greater oxygen consumption at race pace on the EAKD versus a high carbohydrate diet and that ad libitum EAKD consumption resulted in decreased intake of roughly 1000 kcal per day, leading to a 3 % loss of body mass over the study period [13]. In multiple studies, participant self-reports (e.g., inter- view data, training logs) suggested that the EAKD may have promoted perceived fatigue and decreased ability to train for certain athletes [17], particularly those training in summer months [13]. This could be a combined re- sult of the alterations in fuel economy, metabolism, and energy intake described above, though not all athletes reported experiencing negative side effects. Based on focus group results, one study reported that athletes had more positive than negative perceptions of the diet [17], suggesting that there may be additional unknown vari- ables influencing EAKD outcomes across individuals and/or settings (e.g., temperature, humidity [13]). One hypothesis for the variation in performance out- comes among studies might stem from the heterogeneity across the training/recovery protocols and fitness levels of the athletes [28]. Both studies exhibiting a statistically significant increase in VO2 max examined the effects of EAKD consumption in professional race walkers with high base levels of aerobic capacity, a factor that has been associated with faster recovery times and greater positive adaptations to training [29–31]. Both studies also explicitly included a recovery protocol in their train- ing prescription, which could impact the athletes’ train- ing outcomes [28]. Due to limited information on training/recovery protocols in many of these studies, strong conclusions cannot be generated regarding the impact of training versus diet on performance outcomes. Bailey and Hennessy Journal of the International Society of Sports Nutrition (2020) 17:33 Page 9 of 11 However, based on previous evidence, it is reasonable to hypothesize that these protocol differences may have con- tributed to the diverse outcomes reported [6, 28, 32]. In examining the results, it is important to bear in mind that this review consists of just seven studies, only one of which was randomized [16]. Carr et al., Burke et al., and McSwiney et al. were all prospective trials, however they allowed participants to choose their dietary intervention [7, 12, 14]. Although this self-selection method generally improves rates of adherence to the diets, it also introduces risk of bias in that those athletes who chose the EAKD may have other lifestyle or dietary tendencies that could affect their biological response to the diet. Heatherly et al. and Phinney et al. were pre-posttest studies, which are subject to threats to internal validity, such as the fact that passage of time results in natural decreases in VO2 max [13, 15]. Finally, Zinn et al. was a case study [17]. Al- though the article provides a wealth of hypothesis generat- ing observations, without a comparison group we cannot conclude whether the EAKD was more or less effective than the standard, high carbohydrate diet for athletes. All studies had relatively small sample sizes, which re- duced the statistical power of the analyses. It is possible that, with a larger sample size, the seven studies might have exhibited corroboratory results. The small sample sizes also exacerbated the problem of drop-out rates, which were considerable in one of the five studies. McSwiney et al. lost 18 participants in the EAKD group and nine in the comparison group, resulting in a partici- pation rate of 33 and 55%, respectively [14]. At the review level, heterogeneity in dietary interven- tions, adherence measurements, VO2 max testing proce- dures, training protocols, and athlete types all introduced variation that made comparisons across studies difficult. For example, four studies measured VO2 max using a treadmill test [7, 12, 13, 16], while the other three studies used a cycle ergometer [14, 15, 17]. Previous reviews sug- gest that these two testing procedures produce inconsist- ent results, with higher VO2 max outcomes reported for treadmill as compared to cycle ergometer tests [33]. Therefore, inter-article comparisons of the change in VO2 max by diet from baseline may be more reliable than inter-article comparisons of the absolute outcome values reported. Furthermore, research suggests that VO2 max may be an inaccurate predictor of endurance performance in runners, specifically due to variations in running econ- omy and fatigue [34, 35]. Therefore, VO2 max may not be a strong indicator of endurance capacity in some sports, further complicating this measure as a comparison across heterogeneous groups of athletes. In addition to VO2 max outcomes, Table 2 provides a matrix of secondary outcomes (i.e., TTE, race time, RPE, peak power), which can be used to complement the VO2 max findings from this review. For example, although all three diet groups in the study by Burke et al. experi- enced a significant increase in VO2 max from baseline, only the comparison groups (i.e., high carbohydrate, per- iodised carbohydrate) experienced faster 10 km race walk times. Furthermore, the EAKD group reported sig- nificantly higher RPE values compared to baseline dur- ing a graded economy test. Future research in this field can benefit from utilizing a variety of performance met- rics, such as the ones discussed in this review, to tri- angulate overall effects of diet on athletic performance, limiting biases introduced from relying on one marker alone. Additionally, as this research area develops, it may be prudent to conduct reviews among athletes of a single type (e.g., runners only, cyclists only) to limit the hetero- geneity among studies. Because only two databases were used to identify arti- cles for review, it is possible that other studies of EAKD and endurance performance do exist in the literature. However, exploratory investigations of other databases retrieved no additional articles that met inclusion cri- teria. It is noteworthy that six of seven studies included in this review were published within the last 5 years, suggesting that scientific attention to this topic is fairly recent. Due to the contemporary nature of the research question, it is also possible that yet-to-be-published re- search exists on this topic. Therefore, future reviews may eventually produce more conclusive evidence. Fi- nally, the potential risk of reporting bias is always present. Unfortunately, it is difficult to assess publication bias because we cannot know the extent of the evidence that has gone unpublished. However, due to the contro- versial nature of this topic among scientists and lay people alike, it seems likely that both significant and null findings would be publishable. Conclusions Despite popular interest in the ketogenic diet as an ergo- genic aid in endurance sport, there are few published studies examining the effect of EAKD consumption on VO2 max and other outcomes (i.e., TTE, race time, RPE, peak power). When compared to a high carbohydrate diet, there are mixed findings for the effect of EAKD consumption on endurance performance. This may be partially due to the heterogeneity across studies and/or variability in athletes’ individual genetic factors, espe- cially those that directly influence metabolism. The limited number of published studies point to a need for more research in this field. Specifically, randomized studies performed in mixed sex samples are needed. Re- searchers might also consider examining EAKD-like diets that do not induce ketosis. Such research will expand our understanding of the diet’s effects in diverse athlete popu- lations, all of whom serve to benefit from further know- ledge, be the findings supportive of the diet or not. Bailey and Hennessy Journal of the International Society of Sports Nutrition (2020) 17:33 Page 10 of 11 Abbreviations EAKD: Endurance athlete’s ketogenic diet; VO2 max: Maximal oxygen uptake; TTE: Time to exhaustion; RPE: Rating of perceived exertion Acknowledgments The authors would like to thank Amy LaVertue, Research & Instruction Librarian at the Tufts University Hirsch Health Sciences Library, for her time and enthusiasm in the planning stages of this review. Authors’ contributions CB performed all background research, database searches, and wrote and edited the final manuscript. EH provided guidance throughout the research, writing, and submission processes, as well as editing of the final manuscript. Author’s information CB is a research scientist and recreational endurance athlete with a master’s degree in Nutrition Interventions, Communication, and Behavior Change from The Gerald J. and Dorothy R. Friedman School of Nutrition Science and Policy at Tufts University. EH is a Research Assistant Professor at The Gerald J. and Dorothy R. Friedman School of Nutrition Science and Policy at Tufts University. Funding Not applicable. Availability of data and materials All data analyzed in this review are included in the following published articles. Burke, L.M., et al., Low carbohydrate, high fat diet impairs exercise economy and negates the performance benefit from intensified training in elite race walkers. J Physiol, 2017. 595(9): p. 2785-2807.Carr, A.J., et al., Chronic Ketogenic Low Carbohydrate High Fat Diet Has Minimal Effects on Acid-Base Status in Elite Athletes. Nutrients, 2018. 10(2). Heatherly, A.J., et al., Effects of Ad libitum Low-Carbohydrate High-Fat Dieting in Middle-Age Male Runners. Med Sci Sports Exerc, 2018. 50(3): p. 570–579. McSwiney, F.T., et al., Keto-adaptation enhances exercise performance and body composition responses to training in endurance athletes. Metabolism, 2018. 81: p. 25–34. Phinney, S.D., et al., The human metabolic response to chronic ketosis without caloric restriction: Preservation of submaximal exercise capability with reduced carbohydrate oxidation. Metabolism, 1983. 32(8): p. 769–776. Shaw, D.M., et al., Effect of a Ketogenic Diet on Submaximal Exercise Capacity and Efficiency in Runners. Med Sci Sports Exerc, 2019. 51(10): p. 2135–2146. Zinn, C., et al., Ketogenic diet benefits body composition and well-being but not performance in a pilot case study of New Zealand endurance athletes. J Int Soc Sports Nutr, 2017. 14: p. 22. Ethics approval and consent to participate The studies examined in this review were approved by appropriate governing bodies for ethical research. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Received: 12 February 2019 Accepted: 4 June 2020 References 1. Hartman AL, Vining EP. Clinical aspects of the ketogenic diet. Epilepsia. 2007;48(1):31–42. 2. Nutrition Guide. 2020, United States Anti-Doping Agency: https://www. usada.org/. 3. Costa RJS, Hoffman MD, Stellingwerff T. Considerations for ultra-endurance activities: part 1- nutrition. Res Sports Med. 2019;27(2):166–81. 4. Volek JS, Noakes T, Phinney SD. Rethinking fat as a fuel for endurance exercise. Eur J Sport Sci. 2015;15(1):13–20. 5. Ross C, et al. Modern nutrition in health and disease. 11th ed. Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins; 2014. 6. Hawley JA, et al. Nutritional modulation of training-induced skeletal muscle adaptations. J Appl Physiol (1985). 2011;110(3):834–45. 7. Carr AJ, et al. Chronic ketogenic low carbohydrate high fat diet has minimal effects on acid-base status in elite athletes. Nutrients. 2018;10(2):236. 8. Ma S, et al. An 8-week ketogenic diet alternated interleukin-6, ketolytic and lipolytic gene expression, and enhanced exercise capacity in mice. Nutrients. 2018;10(11):1696. 9. Mestel S. Zach bitter is the 100-mile American record holder. He also eats almost no carbs. Men’s Fitness; 2017. 10. Roberts, M. and T. Wood, The IRONMAN guide to ketosis. nourishbalancethrive.com. 2017. 11. Loftin M, et al. Comparison of VO2 peak during treadmill and cycle ergometry in severely overweight youth. J Sports Sci Med. 2004;3(4):554–60. 12. Burke LM, et al. Low carbohydrate, high fat diet impairs exercise economy and negates the performance benefit from intensified training in elite race walkers. J Physiol. 2017;595(9):2785–807. 13. Heatherly AJ, et al. Effects of ad libitum low-carbohydrate high-fat dieting in middle-age male runners. Med Sci Sports Exerc. 2018;50(3):570–9. 14. McSwiney FT, et al. Keto-adaptation enhances exercise performance and body composition responses to training in endurance athletes. Metabolism. 2018;81:25–34. 15. Phinney SD, et al. The human metabolic response to chronic ketosis without caloric restriction: preservation of submaximal exercise capability with reduced carbohydrate oxidation. Metabolism. 1983;32(8):769–76. 16. Shaw DM, et al. Effect of a ketogenic diet on submaximal exercise capacity and efficiency in runners. Med Sci Sports Exerc. 2019;51(10):2135–46. 17. Zinn C, et al. Ketogenic diet benefits body composition and well-being but not performance in a pilot case study of New Zealand endurance athletes. J Int Soc Sports Nutr. 2017;14:22. 18. Ma S, Suzuki K. Keto-adaptation and endurance exercise capacity, fatigue recovery, and exercise-induced muscle and organ damage prevention: a narrative review. Sports (Basel). 2019;7(2):40. 19. Jeukendrup AE. Modulation of carbohydrate and fat utilization by diet, exercise and environment. Biochem Soc Trans. 2003;31(Pt 6):1270–3. 20. Williams CJ, et al. Genes to predict VO2max trainability: a systematic review. BMC Genomics. 2017;18(Suppl 8):831. 21. Smith AD, et al. Genetic and environmental influences on food preferences in adolescence. Am J Clin Nutr. 2016;104(2):446–53. 22. McSwiney FT, et al. Impact of ketogenic diet on athletes: current insights. Open Access J Sports Med. 2019;10:171–83. 23. Chang CK, Borer K, Lin PJ. Low-carbohydrate-high-fat diet: can it help exercise performance? J Hum Kinet. 2017;56:81–92. 24. Wilkinson DJ, Smeeton NJ, Watt PW. Ammonia metabolism, the brain and fatigue; revisiting the link. Prog Neurobiol. 2010;91(3):200–19. 25. Paoli A, et al. Ketosis, ketogenic diet and food intake control: a complex relationship. Front Psychol. 2015;6:27. 26. Fogelholm M. Effects of bodyweight reduction on sports performance. Sports Med. 1994;18(4):249–67. 27. Tenforde AS, et al. Parallels with the female athlete triad in male athletes. Sports Med. 2016;46(2):171–82. 28. Mann TN, Lamberts RP, Lambert MI. High responders and low responders: factors associated with individual variation in response to standardized training. Sports Med. 2014;44(8):1113–24. 29. Stanley J, Peake JM, Buchheit M. Cardiac parasympathetic reactivation following exercise: implications for training prescription. Sports Med. 2013; 43(12):1259–77. 30. Short KR, Sedlock DA. Excess postexercise oxygen consumption and recovery rate in trained and untrained subjects. J Appl Physiol (1985). 1997;83(1):153–9. 31. Tomlin DL, Wenger HA. The relationship between aerobic fitness and recovery from high intensity intermittent exercise. Sports Med. 2001;31(1):1–11. 32. Hautala A, et al. Effects of habitual physical activity on response to endurance training. J Sports Sci. 2012;30(6):563–9. 33. Shephard RJ. Tests of maximum oxygen intake: a critical review. Sports Med. 1984;1(2):99–124. 34. Noakes T. The lore of running. 4th ed. Southern Africa: Oxford University Press; 2003. 35. Kenney WL, Wilmore JH, Costill DL. Physiology of sport and exercise. Champaign: Human Kinetics; 2015. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Bailey and Hennessy Journal of the International Society of Sports Nutrition (2020) 17:33 Page 11 of 11

A review of the ketogenic diet for endurance athletes: performance enhancer or placebo effect?

06-22-2020

Bailey, Caitlin P,Hennessy, Erin

eng

PMC4574154

RESEARCH ARTICLE Dynamic Patterns of Forces and Loading Rate in Runners with Unilateral Plantar Fasciitis: A Cross-Sectional Study Ana Paula Ribeiro1,2*, Silvia Maria Amado João1, Roberto Casanova Dinato1, Vitor Daniel Tessutti1, Isabel Camargo Neves Sacco1 1 University of Sao Paulo, Physical Therapy, Speech and Occupational Therapy Department, School of Medicine, São Paulo, Brazil, 2 University of Santo Amaro, Physical Therapy Department, School of Medicine, São Paulo, Brazil * [email protected] Abstract Aim/Hypothesis The etiology of plantar fasciitis (PF) has been related to several risk factors, but the magni- tude of the plantar load is the most commonly described factor. Although PF is the third most-common injury in runners, only two studies have investigated this factor in runners, and their results are still inconclusive regarding the injury stage. Objective Analyze and compare the plantar loads and vertical loading rate during running of runners in the acute stage of PF to those in the chronic stage of the injury in relation to healthy runners. Methods Forty-five runners with unilateral PF (30 acute and 15 chronic) and 30 healthy control run- ners were evaluated while running at 12 km/h for 40 meters wearing standardized running shoes and Pedar-X insoles. The contact area and time, maximum force, and force-time inte- gral over the rearfoot, midfoot, and forefoot were recorded and the loading rate (20–80% of the first vertical peak) was calculated. Groups were compared by ANOVAs (p<0.05). Results Maximum force and force-time integral over the rearfoot and the loading rate was higher in runners with PF (acute and chronic) compared with controls (p<0.01). Runners with PF in the acute stage showed lower loading rate and maximum force over the rearfoot compared to runners in the chronic stage (p<0.01). PLOS ONE | DOI:10.1371/journal.pone.0136971 September 16, 2015 1 / 9 OPEN ACCESS Citation: Ribeiro AP, João SMA, Dinato RC, Tessutti VD, Sacco ICN (2015) Dynamic Patterns of Forces and Loading Rate in Runners with Unilateral Plantar Fasciitis: A Cross-Sectional Study. PLoS ONE 10(9): e0136971. doi:10.1371/journal.pone.0136971 Editor: Keith Stokes, University of Bath, UNITED KINGDOM Received: February 10, 2015 Accepted: August 11, 2015 Published: September 16, 2015 Copyright: © 2015 Ribeiro et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and on Figshare at http://dx.doi.org/ 10.6084/m9.figshare.1399148. Funding: The Agency Coordination of Improvement of Higher Education Personnel (CAPES) provided support for Ana Paula Ribeiro's scholarship (2011/ 03069-6) and Sao Paulo State Research Foundation (FAPESP) provided support for Roberto C. Dinato's scholarship (2010/14044-1). Competing Interests: The authors have declared that no competing interests exist. Conclusion Runners with PF showed different dynamic patterns of plantar loads during running over the rearfoot area depending on the injury stage (acute or chronic). In the acute stage of PF, run- ners presented lower loading rate and forces over the rearfoot, possibly due to dynamic mechanisms related to pain protection of the calcaneal area. Introduction Running is one of the most popular sport activities worldwide, since it is available for all ages at a low cost, and is versatile and associated with health benefits [1–3]. The prevalence of lower limb injuries has risen with running’s increased popularity over the last 30 years [1,2], among which plantar fasciitis (PF) is one of the most prevalent [3, 4]. Plantar fasciitis is a musculoskeletal disorder characterized by localized pain on the plantar fascia insertion, which is exacerbated in the mornings after getting up or after long rest periods [5, 6, 7]. Although there are several intrinsic and extrinsic factors related to the development of PF [5], some have drawn more attention in both clinical and research settings, such as longitu- dinal plantar arch alterations [4, 6, 7], rearfoot pronation [8], and magnitude of plantar loads [8–11]. Among all of the factors, plantar loads over the calcaneal area have been described as one of the primary risk factors for PF development [12–14]. Excessive loads promote stretching of the plantar fascia, which stimulates microtraumas and subsequent changes in the connective tis- sues, which in turn initiates an acute inflammatory response with fibroblast proliferation [15– 17]. The repetitive impact of the heel can result in a chronic process, followed by degeneration and fragmentation of the plantar fascia and by fibrosis formation without inflammatory response in the medial calcaneal tuberosity [17, 18]. Because PF is the third most-common injury in runners [3, 4], one would expect that this motor task—running—would be the focus of studies that investigate the risk factors of PF development in the population. However, the majority of studies addressing PF biomechanical issues have investigated the effect of plantar loads during walking in non-athletes with plantar fasciitis [9–11]. In particular, these walking studies have observed that the pain stimulus in the feet of individuals with PF (inflammatory stage) promoted changes in plantar loads during the support phase of walking. These changes resulted in higher loads over the more anterior parts of the foot, such as the midfoot [11], forefoot [9], and toes [10], but not over the calcaneal area (rearfoot region), as expected in the pathophysiology development of PF [8–10]. According to Wearing et al. (2007) [11], symptomatic feet make some adaptations during gait to reduce the load on the rearfoot. This study proposed two possible theories. First, it is not possible to infer if increased loads in other areas of the foot, such as the midfoot and forefoot, as described by some authors, in fact contribute to the development of plantar fasciitis by inducing the stretching of the fascia and increasing the tension stress on its insertion into the medial tuberosity of the calcaneus. Second, it is also not possible to infer whether the presence of pain in the rearfoot would promote protective mechanisms, which could reduce the plantar load in this area [11], or whether these anterior loads are a contributing factor to the develop- ment of PF. Therefore, it is important to investigate whether even in the absence of pain, during the chronic stage of PF, the dynamic load distribution pattern observed while walking in the acute stage of PF is similar to that during running. Recently, the effects of the different stages of PF Plantar Fasciitis and Loading Rates PLOS ONE | DOI:10.1371/journal.pone.0136971 September 16, 2015 2 / 9 and the presence or absence of pain have been studied during running [6, 8]; however their findings were contradictory. The first study showed that women with a history of PF (in the chronic stage of PF), without the presence of pain, showed higher loading rates compared to controls [6]. This study did not include runners with PF and presence of pain. The second study found that runners with acute PF (with pain) and chronic PF (without pain) had a simi- lar dynamic pressure distribution pattern in comparison to controls [8]. The difference in the results of both studies may be due to the variables chosen to represent loads and to the environ- ment used to evaluate the runners. The first study used the vertical ground reaction force from a force plate in a laboratory environment [6], whereas the second used plantar pressure vari- ables from instrumented insoles and a running track for training and competition [8]. The importance of studying the dynamic plantar loads in natural environments for training and competition was recently highlighted by Hong et al. (2012) [19], who found that the distri- bution of these loads during running on a treadmill were not the same as those observed during running on fixed ground surfaces. According to these authors, running on treadmills could even be employed in rehabilitation programs to help reduce plantar loads. However, for indi- viduals with lower limb injuries, the research has shifted the paradigm from the treadmill to the ground, as a more ecologically valid environment is crucial to better understand the causal factors involved in the runners’ daily routines. Because running is a cyclic modality, whose impacts on the heel, plantar ligaments and plantar fascia are of great magnitude (3.7 to 4.8 times the body weight) [20], its continuous practice could be directly related to the onset and progression of PF. A better understanding of the plantar load patterns during running in natu- ral environments could lead to better therapeutic benefits and better-designed rehabilitation programs for lower limb injuries, such as PF. The purpose of this study was to analyze and compare the dynamic patterns of plantar loads over foot areas and the loading rate during running of runners with acute-stage PF to those in the chronic stage of the injury in relationship to healthy runners. The hypotheses of the study were: (1) runners with both acute and chronic PF would show higher dynamic plantar loads over the rearfoot, compared to controls; (2) runners in the acute stage of PF would have lower levels of dynamic plantar loads over the rearfoot compared to runners in the chronic stage; and (3) runners in the acute stage would present higher plantar loads over the midfoot and forefoot, and lower loads over the rearfoot, due to pain caused by inflammation. Materials and Methods Participants Seventy-five recreational runners of both sexes (45 with PF and 30 healthy controls) were recruited by specific electronic media related to running activities and from the Rehabilitation Center of Sport Rheumatology of the University Hospital of São Paulo, Brazil. The mean run- ning speed of their last 10-km competition was 11.7± 0.6 km/h, as reported by the subjects. For inclusion in this study, the runners had to: have run at least 20km weekly for at least one year; be experienced in long-distance competitions; have a rearfoot strike pattern; have had no his- tory of prior surgery, traumas, or fractures of the lower limbs in the prior six months; have a maximum leg length discrepancy of 1cm; and had no other musculoskeletal disorders, such as neuropathies, obesity, rheumatoid arthritis, or calcaneus spurs. All participants provided writ- ten consent, based upon ethical approval by the Human Research Board of the School of Medi- cine, University of São Paulo (approval the protocol of research, number: 384/10; title: Support standard and impact of the feet with the ground during the running of runners with history and symptoms of plantar fasciitis and its relationship to the medial longitudinal arch). Plantar Fasciitis and Loading Rates PLOS ONE | DOI:10.1371/journal.pone.0136971 September 16, 2015 3 / 9 All 45 runners had a clinical diagnosis of unilateral PF, which was confirmed by ultrasonog- raphy [21] to better differentiate between the different stages of the injury. Thirty runners showed inflammatory processes in the ultrasound (hypoechoic changes, perifascial fluid collec- tion, and fibroblast proliferation), and were considered in the acute stage of the injury (acute PF group). They presented pain symptoms over the heel for more than four months (mean of 5.3±2.2 months), with an intensity of 7.8cm, assessed by means of a visual analog scale. The pain was present during palpation of the plantar fascia, after waking up in the mornings, while remaining in the standing position, or when performing the first steps of walking, as well as while maintaining long periods in a static standing position or sitting position, and after physi- cal activities of short duration [11, 22]. Fifteen runners with unilateral PF showed plantar fascia thickness, fragmentation, and degeneration in the ultrasound, but no signs of acute inflammatory processes [17]. They had a mean diagnosis time of 1.5±3.3 years and were pain-free for more than two months. These run- ners were considered in the chronic stage of the injury (chronic PF group). No differences among groups were found for demographic and anthropometric characteris- tics, as demonstrated in Table 1. Procedures and Instruments for the Assessment of Plantar Pressures The plantar pressure distributions were obtained during running with insoles of the Pedar-X sys- tem (Novel, Munich, Germany) at frequencies of 100Hz. All runners wore standardized running shoes (Rainha System, Rainha, Alpargatas, São Paulo, Brazil, USA sizes 7–12). The shoe charac- teristics included an insole made by ethylene vinyl acetate (EVA with compression set: 56%, hardness: 57 Asker C and density = 0.21 g/cm3) throughout the entire shoe sole, composed of light and highly resilient plastic that disperses the impact horizontally before returning quickly to the initial state. It is recommended by manufacturers for those seeking a running shoe with a neutral strike. The instrumented insoles were placed between the socks and the shoes and were connected to the 1.5-kg equipment inside a backpack [8]. The insoles were 2.5mm thick and con- tained a matrix of 99 capacitive pressure sensors with a spatial resolution of 1.6 to 2.2cm2. The runners underwent a pre-trial adaptation phase, using the required footwear and the backpack with the equipment. Subjects ran a distance of 40 meters on a regular asphalt surface at 12 km/h ±5%km/h. The runners were considered adapted to the environment (backpack and shoes) when the mean speed of three consecutive trials over 40 meters was 12 km/h±5% [8, 23]. Two observers used a digital stopwatch to control the speed simultaneously, within the central 20 meters. The inter-rater agreement was found to be excellent, with an intra-class cor- relation coefficient of 0.96. Approximately 30 steps were acquired and the variables of interest were calculated using a custom-written MATLAB function. The mean value of the 30 steps per subject was used for statistical purposes. The contact area (cm2), contact time (ms), maximum force (times body weight), and force-time integral (times body weight.ms) over the rearfoot, midfoot, and fore- foot were recorded. The force data were analyzed by a MATLAB routine and normalized by the body weight (BW). The plantar loading rate was calculated from the vertical force; loading rate was 80% [BW.s-1], defined as the force rate between 20% and 80% of the contact time from heel strike to the first vertical peak. Statistical Analyses The sample size calculation of the 75 runners was based upon the maximal force variable, was carried out using G-Power 3.0 software, and considered a moderate effect size (F = 0.25), a power of 80%, and a significance level of 5%. Plantar Fasciitis and Loading Rates PLOS ONE | DOI:10.1371/journal.pone.0136971 September 16, 2015 4 / 9 All outcome measures showed normal distributions (Shapiro-Wilk test) and homogeneity of variances (Levene’s test). For the control group, force data of one foot per subject was ran- domly selected for statistical comparisons with the PF groups (acute and chronic). For the PF groups, force data from the affected foot (unilateral PF) was analyzed and compared to the other groups. One-way ANOVAs followed by Newman-Keuls post-hoc tests were employed to compare groups regarding the anthropometric, demographic, and running practice character- istics and plantar loading rate. Groups and plantar areas were compared using two-way ANO- VAs for repeated measures (3 groups × 3 plantar areas) for force and contact area/time variables, followed by Newman-Keuls post-hoc tests. To describe the effect size between stud- ied groups, the Cohen’s d coefficients were calculated. All analyses were carried out with Statis- tica software (version 7.0). We adopted a significance level of 5%. Results The plantar loading rate of 20–80% (F = 7.16, DF = 2, p = 0.001) was higher in both PF groups compared to the controls, with effect sizes from moderate to large. The high plantar load rate (20–80%), with large effect sizes, was observed in the chronic PF group compared to controls. Runners with PF in the acute stage showed lower loading rates compared to runners in the chronic phase, with a moderate effect size (Table 2). The maximum force (F = 3.81, DF = 4, p = 0.005), force-time integral (F = 2.70, DF = 4, p = 0.047), and contact area over the rearfoot (F = 9.10, DF = 4, p = 0.002) were higher in run- ners with PF (acute and chronic) compared to controls, with effect sizes ranging from moder- ate to large (Table 3). The contact time over the rearfoot (F = 2.75, DF = 4, p = 0.212), midfoot (F = 6.27, DF = 4, p = 0.082) and forefoot (F = 1.23, DF = 4, p = 0.245) were similar among the acute- and chronic-stage PF and control groups. Runners with PF in the acute stage showed lower maximum force over the rearfoot compared to runners in the chronic stage, with a small Table 1. Descriptive statistics (mean ± standard deviation) and comparisons between acute plantar fasciitis (PF), chronic PF and control groups regarding their demographic, anthropometric, and running practice characteristics. Variables Acute PF (1) Chronic PF (2) Controls (3) p& Age (years) 45.4±8.1 38.3±3.3 37.0±2.0 0.191 Sex (%) F (40); M (60) F (36,6); M (63,4) F (36,6); M (63,4) - Body mass (kg) 69.6±14.0 72.3±10.0 60.5±5.0 0.585 Height (m) 1.68±9.2 1.76±7.8 1.74±7.0 0.173 Body mass index (kg/m2) 24.3±2.9 23.0±2.0 25.5±2.0 0.307 Training volume (Km/week) 40.0±12.0 45.0±10.0 42.0±8.5 0.110 Practice time (years) 7.0±5.0 6.2±5.0 5.1±3.8 0.140 &p value were calculated using one-way ANOVAs. doi:10.1371/journal.pone.0136971.t001 Table 2. Descriptive statistics (mean ± standard deviation) and comparisons between acute plantar fasciitis (PF), chronic PF and control groups regarding the plantar loading rate normalized by body weight (BW). Variable Acute PF (1) Chronic PF (2) Controls (3) p-values& Effect size (Cohen`s d) Loading rate (20–80%) (BW/s) 0.76±0.20 0.89±0.27 0.64±0.16 0.047 (1–2) 0.59 (1–2) (medium) 0.001 (1–3) 0.67 (1–3) (medium) 0.034 (2–3) 1.26 (2–3) (large) &p value were calculated using ANOVAS test and Newman-Keuls post-hoc test. doi:10.1371/journal.pone.0136971.t002 Plantar Fasciitis and Loading Rates PLOS ONE | DOI:10.1371/journal.pone.0136971 September 16, 2015 5 / 9 effect size (Table 3). The total contact time over the foot (F = 0.55, DF = 4, p = 0.867) also was similar among the acute- and chronic-stage PF and control groups, as shown in Table 4. Discussion The main findings of this study confirmed the first and the second hypotheses, and showed that runners with PF presented higher dynamic plantar loads over the rearfoot compared to controls, regardless of the stage of the injury (chronic and acute), and, among runners with PF, those in the chronic stage showed higher plantar loads compared to those in the acute stage. However, different from what was expected for the third hypothesis, runners in the acute stage did not show increased plantar loads over the midfoot and forefoot, but over the calcaneal area, as expected by the typical pathophysiology development of PF. This was different from what Table 3. Descriptive statistics (mean ± standard deviation) and comparisons between acute plantar fasciitis (PF), chronic PF and control groups regarding their maximum force and force-time integral (normalized by body weight, BW) and contact area in each plantar area. Variables Groups Rearfoot Midfoot Forefoot Effect size (Cohen´s d) rearfoot Maximum force (BW) Acute PF (1) 1.34 ±0.29 0.55±0.17 1.73±0.48 0.30 (1–2) (small) Chronic PF (2) 1.46 ±0.46 0.40±0.09 1.31±0.30 0.64 (1–3) (medium) Control (3) 1.19±0.17 0.46±0.10 1.49±0.21 0.93 (2–3) (large) p&-value 0.020 (1–2) > 0.05 > 0.05 0.029 (1–3) > 0.05 > 0.05 0.001 (2–3) > 0.05 > 0.05 Force-time integral (BW/ms) Acute PF (1) 77.51±19.22 38.43±9.38 168.05±41.15 0.22 (1–2) (small) Chronic PF (2) 74.01±14.71 48.87±16.24 208.29±60.36 0.79 (1–3) (large) Control (3) 64.40±14.09 46.27±11.75 192.96±27.14 0.69 (2–3) (medium) p&-value 0.718 (1–2) > 0.05 > 0.05 0.045 (1–3) > 0.05 > 0.05 0.040 (2–3) > 0.05 > 0.05 Contact area (cm2) Acute PF (1) 36.6±3.9 45.0± 5.9 65.3±5.8 0.45 (1–2) (medium) Chronic PF (2) 34.7±5.1 42.5± 7.8 65.2±6.5 1.00 (1–3) (large) Control (3) 40.3±3.6 44.5± 5.2 67.1± 5.5 1.45 (2–3) (large) p&-value > 0.05 > 0.05 > 0.05 > 0.05 > 0.05 > 0.05 Contact time (ms) Acute PF (1) 147.0± 16.9 182.8± 37.1 165.3± 25.2 0.38 (1–2) (small) Chronic PF (2) 151.0±16.1 179.2± 38.2 165.1±24.7 0.35 (1–3) (small) Control (3) 153.3±18.1 196.9± 34.1 175.1±24.0 0.13 (2–3) (small) p&-value > 0.05 > 0.05 > 0.05 &p value were calculated using ANOVAS test and Newman-Keuls post-hoc test. doi:10.1371/journal.pone.0136971.t003 Table 4. Descriptive statistics (mean ± standard deviation) and comparisons between acute plantar fasciitis (PF), chronic PF and control groups regarding their total contact time of the foot. Variable Acute PF (1) Chronic PF (2) Control (3) Value p& Total Contact time (ms) 230.4± 27.9 239.6± 24.5 234.0± 21.3 0.842 (1–2) 0.894 (1–3) 0.933 (2–3) &p value were calculated using ANOVAS test and Newman-Keuls post-hoc test. doi:10.1371/journal.pone.0136971.t004 Plantar Fasciitis and Loading Rates PLOS ONE | DOI:10.1371/journal.pone.0136971 September 16, 2015 6 / 9 has been observed in studies of plantar fasciitis during gait, in which the rate of plantar load remained in areas such as the forefoot [10, 11], midfoot [9], and toes [10, 11]. In the present study, the fact that during running the plantar loading rate was higher over the rearfoot (calcaneus) in the chronic stage of PF compared to both controls and runners in the acute stage of PF, may account for previous physiological results in subjects with PF. The loss of elasticity of the heel pad due to deposit fibrosis lead to a failure in the shock-absorbing mechanism, which resulted in higher loads over the rearfoot, as observed in the present study, followed by degeneration of the plantar fascia [13, 22, 24–26]. The result of a higher loading rate (20–80%) in runners with PF are in agreement with results reported by Pohl et al. (2009) [6], who also found higher loading rate (20–80%) during running in women with PF without pain (chronic stage). In addition to the reported results, the present study demonstrated that in the presence of pain (acute PF group), the plantar load- ing rate (20–80%) was lower compared to runners without pain (chronic PF group). The for- mer findings suggested that the presence of pain symptom in the rearfoot (acute stages of PF) could lead to antalgic mechanisms that reduce plantar loads in the calcaneous area. The plantar fascia elasticity was reduced in individuals with pain when compared to individ- uals without pain in Sahin’s study [27]. In addition, the pain associated with acute inflamma- tion of the plantar fascia increases its thickness, which in turn decreases its capacity to support plantar loads [28, 29]. This morphological change of the plantar fascia generates an increase in the stretching tension of the fascia tissue during dynamic activities, and runners may adapt their running pattern by shortening the foot contact to the ground to avoid pain. These dynamic adaptations in runners with pain can be considered an antalgic protective mechanism that results in lower plantar loading over the rearfoot, as we observed in runners with acute PF in the present study. However, in the chronic stage of PF there are different degenerative changes in the foot tis- sues compared to what happened in the acute stage. Degeneration and fibrosis of the plantar fascia, reduction in its thickness, and atrophy of the intrinsic foot muscles were observed in individuals in the chronic stage of PF [30]. These chronic tissue changes in runners without pain may lead to a withdrawal of the antalgic protective mechanism, resulting in higher plantar loads over the rearfoot, as observed in runners in the chronic stage compared to the acute PF and control groups. An appropriate plantar loading distribution during running is also a result of the cushioning properties of the footwear and the integrity of the heel tissues [31]; we stan- dardized the type of running shoe used for all individuals assessed in the present study to mini- mize the footwear influence in the investigation of the injury stages. A limitation of this study was the estimation of the loading rate using a plantar pressure system with a sampling rate of 100Hz. The differences observed between runners with chronic PF and controls in the present study were also found in a previous study that calculated the loading rate using force plates at a 1000Hz [6]. These findings are clinically important for promoting better therapeutic protocols for run- ners with PF in the plantar loading reduction strategies during running practice. These thera- peutic strategies to reduce plantar loading in runners has already been used with success in individuals with previous stress fractures [32]. The present results have the potential to improve therapeutics during different PF stages (acute and chronic) [33], especially in relation to recommendations for orthotics and insoles [34, 35]. Conclusions Runners with PF showed different dynamic patterns of plantar loads over the rearfoot area depending on which stage of the injury they were experiencing, but plantar load was always Plantar Fasciitis and Loading Rates PLOS ONE | DOI:10.1371/journal.pone.0136971 September 16, 2015 7 / 9 higher for the PF group than for the control runners. In the acute stage of PF, runners pre- sented a lower loading rate and forces over the rearfoot area, possibly due to dynamic mecha- nisms of plantar fascia during running related to pain protection of the calcaneal area. Acknowledgments The authors acknowledge the Agency Coordination of Improvement of Higher Education Per- sonnel (CAPES) for Ana Paula Ribeiro’s scholarship (2011/03069-6) and Sao Paulo State Research Foundation (FAPESP) for Roberto C. Dinato’s scholarship (2010/14044-1). Author Contributions Conceived and designed the experiments: APR SMAJ ICNS. Performed the experiments: APR SMAJ ICNS. Analyzed the data: APR SMAJ ICNS RCD VDT. Contributed reagents/materials/ analysis tools: APR SMAJ ICNS RCD VDT. Wrote the paper: APR SMAJ ICNS RCD VDT. Designed and elaborated methods and statistical analysis: APR RCD SMAJ ICNS. References 1. Van Middelkoop M, Kolkman J, Van Ochten J, Bierma-Zeinstra SM, Koes BW. Risk factors for lower extremity injuries among male marathon runners. Scand J Med Sci Sports. 2008; 18(6):691–7. doi: 10. 1111/j.1600-0838.2007.00768.x PMID: 18266787 2. van Gent RN, Siem D, van Middelkoop M, van Os AG, Bierma-Zeinstra SMA, Koes BW. Incidence and determinants of lower extremetiy runnig injures in long distance runners a systematic review. Br J Sports Med. 2007 2007; 41:469–80. PMID: 17473005 3. Lopes AD, Junior LCH, Yeung SS, Costa LOP. What are the Main Running-Related Musculoskeletal Injuries? A Systematic Review. Sports Med. 2012; 42(10):891–905. PMID: 22827721 4. Taunton JE, Ryan MB, Clement DB, McKenzie DC, Lloyd-Smith R. Plantar fasciitis: a retrospective analysis of 267 cases. Physical Therapy in Sport. 2002a; 3(2):57–65. 5. Rome K, Howe T, Haslock I. Risk factors associated with the development of plantar heel pain in ath- letes. The foot 2001; 11:119–25. 6. Pohl MB, Hamil J, Davis IS. Biomechanical and anatomic factors associated with a history of plantar fasciitis in female runners. Clin J Sport Med. 2009; 19(5):372–6. PMID: 19741308 7. Ribeiro AP, Trombini-Souza F, Tessutti VD, Lima FR, Sacco ICN, João SMA. Rearfoot alignment and medial longitudinal arch configurations of runners with symptoms and histories of plantar fasciitis. Clin- ics. 2011; 66(6):1027–33. PMID: 21808870 8. Ribeiro AP, Trombini-Souza F, Tessutti VD, Lima FR, João SMA, Sacco IC. The effects of plantar fasci- itis and pain on plantar pressure distribution of recreational runners. Clin Biomech. 2011; 26(2):194–9. 9. Bedi HS, Love BR. Differences in impulse distribution in patients with plantar fasciitis. Foot Ankle Int. 1998; 19(3):153–6. PMID: 9542986 10. Wearing SC, Smeathers JE, Urry SR. The effect of plantar fasciitis on vertical foot-ground reaction force. Clin Orthop Relat Res. 2003; 409:175–85. PMID: 12671500 11. Wearing SC, Smeathers JE, Sullivan PM, Yates B, Urry SR, Dubois P. Plantar fasciitis: are pain and fascial thickness associated with arch shape and loading? Phys Ther. 2007; 87(8):1002–8. PMID: 17553919 12. Chandler TJ, Kibler WB. A biomechanical approach to the prevention, treatment and rehabilitation of plantar fasciitis. Sports Med. 1993; 15(5):344–52. PMID: 8100639 13. Kibler WB, Goldberg C, Chandler TJ. Functional biomechanical deficits in running athletes with plantar fasciitis. Am J Sports Med. 1991; 19(1):66–71. PMID: 1672577 14. Cornwall MW, McPoil TG. Plantar fasciitis: etiology and treatment. J Orthop Sports Phys Ther. 1999; 29 (12):756–60. PMID: 10612073 15. Warren BL. Plantar fasciitis in runners. Treatment and prevention. Sports Med. 1990; 10:338–45. PMID: 1979886 16. Grasel RP, Schweitzer ME, Kovalovich AM, Karasick D, Wapner K, Hecht P, et al. MR imaging of plan- tar fasciitis: edema, tears, and occult marrow abnormalities correlated with outcome. AJR Am J Roent- genol. 1999; 173(3):699–701. PMID: 10470906 Plantar Fasciitis and Loading Rates PLOS ONE | DOI:10.1371/journal.pone.0136971 September 16, 2015 8 / 9 17. Lemont H, Ammirati K, Usen N. A Degenerative Process (Fasciosis) Without Inflammation. J Am Podi- atric Med Assoc. 2003; 93(3):234–7. 18. Schepsis AA, Leach RE, Gorzyca J. Plantar fasciitis. Etiology, treatment, surgical results, and review of the literature. Clin Orthop Relat Res. 1991; 266:185–96. PMID: 2019049 19. Hong Y, Wang L, Li JX, Zhou JH. Comparison of plantar loads during treadmill and overground running. J Sci Med Sport. 2012; 15(6):554–60. doi: 10.1016/j.jsams.2012.01.004 PMID: 22652147 20. Giddings VL, Beaupre GS, Whalen RT, Carter DR. Calcaneal loading during walking and running. Med Sci Sports Exerc. 2000; 32(3):627–34. PMID: 10731005 21. Akfirat M, Sen C, Gunes T. Ultrasonographic appearance of the plantar fasciitis. Clin Imaging. 2003; 27 (5):353–7. PMID: 12932690 22. Wearing SC, Smeathers JE, Urry SR, Hennig EM, Hills AP. The pathomechanics of plantar fasciitis. Sports Med. 2006; 36(7):585–611. PMID: 16796396 23. Tessutti V, Ribeiro AP, Trombini-Souza F, Sacco IC. Attenuation of foot pressure during running on four different surfaces: asphalt, concrete, rubber, and natural grass. J Sports Sci. 2012; 30(14):1545– 50. doi: 10.1080/02640414.2012.713975 PMID: 22897427 24. Viel E, Esnault M. The effect of increased tension in the plantar fascia: a biomechanical analysis. Physi- other Theory Pract. 1989; 5:69–73. 25. Prichasuk S, Subhadrabandhu T. The relationship of pes planus and calcaneal spur to plantar heel pain. Clin Orthop Relat Res. 1994; 306:192–6. PMID: 8070194 26. Warren BL. Anatomical factors associated with predicting plantar fasciitis in long-distance runners. Med Sci Sports Exerc. 1984; 16(1):60–3. PMID: 6708780 27. Sahin N, Ozturk A, Atici T. Foot mobility and plantar fascia elasticity in patients with plantar fasciitis. Acta Orthop Traumatol Turc. 2010; 44(5):385–391. doi: 10.3944/AOTT.2010.2348 PMID: 21343689 28. Rome K, Campbell R, Flint A, Haslock I. Heel Pad Thickness—A Contributing Factor Associated with Plantar Heel Pain in Young Adults. Foot & Ankle International 2002; 23(12):142–7. 29. Mahowald S, Legge BS, Grady JF. The correlation between plantar fascia thickness and symptoms of plantar fasciitis. J Am Podiatr Med Assoc. 2011; 101(5):385–389. PMID: 21957269 30. Chang R, Kent-Braun JA, Hamil J. Use of MRI for volume estimation of tibialis posterior and plantar intrinsic foot muscles in healthy and chronic plantar fasciitis limbs. Clin Biomech. 2012; 27(5):500–505. 31. Whittle MW. Generation and attenuation of transient impulsive forces beneath the foot: a review. Gait and Posture 1999; 10(3):264–275. PMID: 10567759 32. Crowell HP, Milner CE, Hamill J, Davis IS. Reducing Impact Loading During Running With the Use of Real-Time Visual Feedback. J Orthop Sports Phys Ther. 2010; 40(4):206–13. doi: 10.2519/jospt.2010. 3166 PMID: 20357417 33. Klein SE, Dale AM, Hayes MH, Johnson JE, McCormick JJ, Racette BA. Clinical Presentation and Self- Reported Patterns of Pain and Function in Patients with Plantar Heel Pain. Foot Ankle Int. 2012; 33 (9):693–8. PMID: 22995253 34. Fong DT, Pang KY, Chung MM, Hung AS, Chan KM. Evaluation of combined prescription of rocker sole shoes and custom-made foot orthoses for the treatment of plantar fasciitis. Clin Biomech. 2012; 27 (10):1072–7. 35. Landorf KB, Keenan AM, Herbert RD. Orthotics not effective for plantar fasciitis. J Family Practice 2006; 55(10):844. Plantar Fasciitis and Loading Rates PLOS ONE | DOI:10.1371/journal.pone.0136971 September 16, 2015 9 / 9

Dynamic Patterns of Forces and Loading Rate in Runners with Unilateral Plantar Fasciitis: A Cross-Sectional Study.

09-16-2015

Ribeiro, Ana Paula,João, Silvia Maria Amado,Dinato, Roberto Casanova,Tessutti, Vitor Daniel,Sacco, Isabel Camargo Neves

eng

PMC7555508

1 Vol.:(0123456789) Scientific Reports | (2020) 10:17154 | https://doi.org/10.1038/s41598-020-74097-7 www.nature.com/scientificreports Adding carbon fiber to shoe soles may not improve running economy: a muscle‑level explanation Owen N. Beck1,2*, Pawel R. Golyski1,3 & Gregory S. Sawicki1,2,3 In an attempt to improve their distance‑running performance, many athletes race with carbon fiber plates embedded in their shoe soles. Accordingly, we sought to establish whether, and if so how, adding carbon fiber plates to shoes soles reduces athlete aerobic energy expenditure during running (improves running economy). We tested 15 athletes as they ran at 3.5 m/s in four footwear conditions that varied in shoe sole bending stiffness, modified by carbon fiber plates. For each condition, we quantified athlete aerobic energy expenditure and performed biomechanical analyses, which included the use of ultrasonography to examine soleus muscle dynamics in vivo. Overall, increased footwear bending stiffness lengthened ground contact time (p = 0.048), but did not affect ankle (p ≥ 0.060), knee (p ≥ 0.128), or hip (p ≥ 0.076) joint angles or moments. Additionally, increased footwear bending stiffness did not affect muscle activity (all seven measured leg muscles (p ≥ 0.146)), soleus active muscle volume (p = 0.538; d = 0.241), or aerobic power (p = 0.458; d = 0.04) during running. Hence, footwear bending stiffness does not appear to alter the volume of aerobic energy consuming muscle in the soleus, or any other leg muscle, during running. Therefore, adding carbon fiber plates to shoe soles slightly alters whole‑body and calf muscle biomechanics but may not improve running economy. In competitive athletics, marginal differences distinguish champions from their competitors. For instance, if any of the top-five 2016 Olympic women’s marathon finishers ran 0.51% faster, they would have been crowned Olympic champion. Such miniscule differences highlight the importance for athletes to optimize all factors that influence race performance. One way to optimize athletic performance is to don the best footwear. Using footwear that reduces athlete aerobic energy expenditure at a given running speed (improves athlete running economy) can augment distance-running performance by decreasing user relative aerobic intensity1–3. An established method of improving footwear to augment athlete distance-running performance is to reduce its mass1,2,4,5. Based on literature values, if an aforementioned Olympic marathoner re-raced in shoes that were 100 g less than their original footwear, they would have expended aerobic energy at an ~ 0.8% slower rate5, run the marathon ~ 0.56% faster6, and taken the gold medal back to their country. A longstanding footwear technology that has polarized the running community is the incorporation of car- bon fiber plates in shoe soles7. Despite the rampant use of carbon fiber plates in athletics8–10, policy makers are regulating the use of these plates in distance-running footwear based on the notion that they provide wearers an ‘unfair advantage’ over competitors without such technology11. These views persist even though it is inconclusive whether adding carbon fiber to shoe soles improves running economy12–16 or distance-running performance. To date, two studies have reported that adding optimally stiff carbon fiber plates to shoe soles improves running economy by 0.812 and 1.1%13, while data from four other studies suggest that adding carbon fiber plates to shoe soles does not affect running economy14–17. Moreover, neither study that improved athlete running economy by adding carbon fiber plates to their shoes measured a physiologically-relevant link between the footwear-altered biomechanics and aerobic energy expenditure12,13. Namely, the first study did not identify a biomechanical mechanism12 while the second study suggested that adding carbon fiber plates to shoe soles improves running economy by altering a parameter that likely does not affect metabolism13. Specifically, the second study reported that adding carbon fiber plates to OPEN 1George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA. 2School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA. 3Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA. *email: obeck3@ gatech.edu 2 Vol:.(1234567890) Scientific Reports | (2020) 10:17154 | https://doi.org/10.1038/s41598-020-74097-7 www.nature.com/scientificreports/ shoe soles improves running economy by decreasing the leg-joint’s summed angular impulse (integral of torque with respect to time) during push-off13. However, decreasing angular impulse via greater peak torque and much shorter durations worsen running economy18–20. Consequently, it remains uncertain whether adding carbon fiber plates to shoe soles improves running economy, and if so how—we need a muscle-level explanation. Muscle contractions drive whole-body aerobic energy expenditure during locomotion21. To date, no study has assessed muscle fascicle dynamics from athletes running with shoes that have carbon fiber soles. Based on leg-joint analyses, which do not necessarily reflect the underlying fascicle dynamics22,23, metatarsophalangeal- and ankle-joint dynamics are more affected during running with the addition of carbon fiber plates to shoe soles than knee- and hip-joint dynamics13,14,24–26. Since intrinsic foot muscles do not directly affect running economy27, altered plantar flexor fascicle dynamics may help explain changes in running economy with versus without carbon fiber plates added to shoe soles. How does adding carbon fiber plates to shoe soles affect athlete plantar flexor dynamics during running? Adding carbon fiber plates to shoe soles increases the footwear’s 3-point bending stiffness12,13,15,17,24,25,28 and typically shifts the athlete’s center of pressure more anterior along the foot during ground contact24,25,28,29. These altered biomechanics generally yield a longer moment arm between the ground reaction force (FGRF) and the ankle-joint center ( RGRF)13,24. Longer moment arms lead to greater GRF-induced ankle-joint moments12,13,24,29. To prevent the ankle-joint from collapsing, plantar flexor muscle-tendons (MTs) need to generate a greater force ( FMTs ) and apply an equal and opposite moment about the joint throughout ground contact. The moment arm between the plantar flexor MTs and ankle-joint center is indicated by rMT30. Increased MT force is driven by greater plantar flexor muscle fascicle force ( FM ), which increases metabolic energy expenditure31 and can be calculated using the following (Eq. (2)): plantar flexor MT force ( FMT) , its physiological cross-sec- tional area relative to respective agonist muscles  PCSA m tot  30, and pennation angle ( θM). Adding carbon fiber plates to footwear may also cause plantar flexors to operate at relatively shorter lengths; incurring less economical muscle force production32–35. That is because running in footwear that have carbon fiber plates elicits similar leg-joint angles12,13 and MT lengths ( LMT)36 versus running in footwear absent of carbon fiber plates. Hence, reasoning that muscle pennation changes are relatively small, increased MT force may further stretch spring-like tendons (tendon length: LT ) and yield shorter in-series muscles lengths ( LM). Lastly, adding carbon fiber plates to shoe soles may decrease plantar flexor muscle fascicle shortening velocity during ground contact14,29, and elicit more economical force production33,34. Absent of meaningful changes in ankle-joint mechanical power ( Pank ) and plantar flexor MT moment arms ( rMTs ), increasing plantar flexor MTs force ( FMTs ) decreases ankle-joint angular velocity ( ωank)14. In turn, decreased ankle-joint angular velocity may translate to slower MT and muscle fascicle shortening velocities. Perhaps adding carbon fiber to shoe soles can optimize the trade-off between active muscle force (Fact ), force–length ( FL ) and force–velocity ( FV ) potential to minimize the active plantar flexor muscle volume ( Vact )37 (Eq. (5)) and whole-body aerobic energy expenditure during running12,13. σ is muscle stress and lm is optimal fascicle length. Conceptually, active muscle volume is the quantity of muscle that has adenosine triphosphate (ATP) splitting actin-myosin cross-bridges37. Hence, active muscle volume is proportional to metabolic energy expenditure. The purpose of this study was to reveal if and how adding carbon fiber plates to shoe soles alters running biomechanics and economy. In particular, we sought to investigate how footwear 3-point bending stiffness affects soleus fascicle dynamics and running economy. Based on the reported interactions between adding carbon fiber plates to shoe soles, footwear 3-point bending stiffness12–15,17,24,25,28,29, and ankle-joint dynamics13,14,24,29, we hypothesized that running with shoes that have stiffer carbon fiber plates would increase soleus fascicle force generation while decreasing its operating length and shortening velocity during the ground contact. We also hypothesized that an optimal footwear bending stiffness would minimize soleus active muscle volume and aerobic energy expenditure. To test our hypotheses, we quantified ground reaction forces, stride kinematics, limb-joint biomechanics, soleus dynamics, muscle activation patterns, and aerobic energy expenditure from 15 athletes running at 3.5 m/s using four separate footwear conditions that spanned a 6.4-fold difference in bend- ing stiffness (Table 1). (1) rMTs · FMTs = RGRF · FGRF (2) FM = FMTPCSA m tot cos (θM) (3) LM = (LMT − LT) cos(θM) (4) ωank = Pank rMTs · FMTs (5) Vact = Fact · lm σ · FL · FV 3 Vol.:(0123456789) Scientific Reports | (2020) 10:17154 | https://doi.org/10.1038/s41598-020-74097-7 www.nature.com/scientificreports/ Results Footwear conditions. Each athlete ran in the Adidas Adizero Adios BOOST 2 running shoes (Adidas) without carbon fiber plates, as well as in the Adidas with 0.8, 1.6, and 3.2 mm thick carbon fiber plates. The Adidas’ average ± SD 3-point bending stiffness was 13.0 ± 1.0 kN/m, and adding 0.8, 1.6, and 3.2 mm thick car- bon fiber plates to the shoes soles increased the average ± SD footwear 3-point bending stiffness to 31.0 ± 1.5, 43.1 ± 1.6, and 84.1 ± 1.1 kN/m, respectively. Further, the slope of each footwear-condition’s 3-point bending force–displacement profile was well-characterized by a linear function (average ± SD; Adidas R2: 0.97 ± 0.02; Adidas plus in-soles: R2: 0.99 ± 0.01). Limb‑joint dynamics. Footwear bending stiffness did not affect hip, knee, or ankle angles or moments (Fig. 1). Specifically, footwear bending stiffness was not associated with average, minimum, or maximum ankle (all p ≥ 0.121) (Fig. 1e and Fig. 2g,h), knee (all p ≥ 0.128) (Fig. 1c), or hip (all p ≥ 0.076) angle (Fig. 1a). Simi- larly, footwear bending stiffness did not affect average or maximum ankle (both p ≥ 0.060) (Fig. 1f), knee (both p ≥ 0.239) (Fig. 1d), or hip (both p ≥ 0.112) (Fig. 1b) moment. Stride kinematics and ground reaction forces. Increased footwear bending stiffness was associated with longer ground contact time (p = 0.048), but not step time (p = 0.956). Regarding GRFs, neither stance aver- age vertical (p = 0.209) (Fig. 2a,b), braking (p = 0.441) (Fig. 2c,d), nor propulsive (p = 0.133) (Fig. 2c,d) GRF differed across footwear bending stiffness conditions. Additionally, footwear bending stiffness did not affect the fraction of vertical (p = 0.881) or horizontal (p = 0.816) GRF exhibited during the first half of ground contact. Muscle–tendon dynamics. Footwear bending stiffness did not affect soleus muscle–tendon (MT) dynam- ics (Fig. 3). Neither average soleus MT force (p = 0.080) (Fig. 3a,b), length (p = 0.150) (Fig. 3c,d), nor velocity (p = 0.719) (Fig. 3e,f) during ground contact changed with altered footwear bending stiffness. Additionally, the ratio of the GRF versus soleus MT moment arms to the ankle-joint center (gear ratio, also known as 1/effec- tive mechanical advantage) was not affected by footwear bending stiffness (average and maximum gear ratio p = 0.371 and p = 0.752, respectively) (Fig. 2e,f). Soleus dynamics. Footwear bending stiffness did not influence average or maximum soleus fascicle pen- nation angle (both p ≥ 0.476) (Fig. 4a,b), force (both p ≥ 0.115) (Figs. 4c,d, 5b), length (p ≥ 0.286) (Fig. 4e,f and Fig. 5a), or velocity (both p ≥ 0.224) (Fig. 4g,h and Fig. 5c). As such, footwear bending stiffness did not affect stride-average soleus active muscle volume (p = 0.538; d = 0.241) (Figs. 5d, 6b). Muscle activation. Footwear bending stiffness did not affect stance- or stride-averaged activation of any measured muscle: soleus (both p ≥ 0.315) (Fig. 7a), medial gastrocnemius (both p ≥ 0.538) (Fig. 7b), tibialis ante- rior (both p ≥ 0.445) (Fig. 7c), biceps femoris (both p ≥ 0.190) (Fig. 7d), vastus medialis (both p ≥ 0.146) (Fig. 7e), gluteus maximus (both p ≥ 0.603) (Fig. 7f), or rectus femoris (both p ≥ 0.406) (Fig. 7g) (Table 2). Running economy. Footwear bending stiffness did not affect gross aerobic power (p = 0.458; d = 0.04) (Fig. 6a). Only the 84.1 ± 1.1 kN/m footwear bending stiffness condition elicited a mean gross aerobic power Table 1. Participant characteristics. Four and eleven participants initiated ground contact with a mid/forefoot strike (M/FFS) and heel strike (HS), respectively. All participants maintained the same foot strike pattern across footwear conditions. Participant Age (yrs) Height (m) Mass (kg) Leg length (m) US men’s shoe size Initial foot strike Standing aerobic power (W/kg) 1 20 1.65 57.0 0.89 9 HS 1.37 2 27 1.73 65.6 0.91 10 M/FFS 1.21 3 19 1.77 60.0 0.91 9 HS 1.98 4 27 1.88 66.4 0.97 12 M/FFS 1.44 5 27 1.70 58.9 0.83 10 HS 1.42 6 23 1.80 72.8 0.97 10 HS 1.70 7 19 1.76 71.5 0.91 10 HS 1.67 8 24 1.78 71.3 0.93 10 HS 1.58 9 20 1.80 66.5 0.95 10 HS 1.72 10 28 1.80 73.2 0.98 9 M/FFS 1.76 11 28 1.74 74.5 0.83 10 HS 1.32 12 28 1.89 75.4 0.95 12 M/FFS 1.21 13 42 1.74 73.6 0.90 11 HS 1.04 14 26 1.79 62.2 0.90 10 HS 1.38 15 23 1.78 65.2 0.89 9 HS 1.28 Average ± SD 25.4 ± 5.7 1.77 ± 0.06 67.6 ± 6.1 0.91 ± 0.05 10.1 ± 1.0 4 M/FFS 11 HS 1.47 ± 0.26 4 Vol:.(1234567890) Scientific Reports | (2020) 10:17154 | https://doi.org/10.1038/s41598-020-74097-7 www.nature.com/scientificreports/ value that was numerically less (non-significantly) than the footwear condition without a carbon fiber plate (13.0 ± 1.0 kN/m). Compared to the 13.0 ± 1.0 kN/m footwear condition, the 84.1 ± 1.1 kN/m footwear bending stiffness condition yielded 0.3 ± 2.2% lower gross aerobic power (paired t-test p = 0.663). To achieve a strong statistical power regarding the gross aerobic power elicited from the 84.1 ± 1.1 kN/m versus 13.0 ± 1.0 kN/m footwear bending stiffness condition (statistical power = 0.8), post-hoc analyses suggest that we would need to test 9104 participants. Individually, the footwear condition that minimized running economy was 13.0 ± 1.0 kN/m for 1 participant, 31.0 ± 1.5 kN/m for 4 participants, 43.1 ± 1.6 kN/m for 4 participants, and 84.1 ± 1.1 kN/m for 6 participants. Also, the footwear bending condition that elicited the worst running economy was 13.0 ± 1.0 kN/m for 5 participants, 31.0 ± 1.5 kN/m for 3 participants, 43.1 ± 1.6 kN/m for 3 participants, and 84.1 ± 1.1 kN/m for 4 participants (Supplementary Fig. S1a–o). Discussion Across a 6.4-fold increase in footwear bending stiffness, our participants ran with nearly identical body, limb- joint, and calf muscle mechanics, as well as elicited non-different running economy values. Footwear bending stiffness did not affect participant GRFs, limb-joint kinematics, or kinetics. Similarly, soleus MT and fascicle dynamics were unaltered across conditions. Regarding our hypotheses, running in stiffer footwear did not affect soleus fascicle force, length, or velocity; leading us to reject our initial hypothesis. While no previous study has quantified muscle fascicle dynamics from athletes running in shoes that varied in bending stiffness, our participant’s unaltered ankle-joint dynamics contrasts some previous reports12,13,24. Yet, the only biomechanical difference between our study and the classic investigation that reported that adding carbon fiber plates to shoe soles improve running economy12 is that the classic investigation found an increased maximum ankle moment with the use of stiffer footwear, whereas we did not. Further, while there are likely covariates, one previous study reported that athletes running in commercial shoes with curved carbon fiber plates embedded in their soles exhibited shorter GRF-ankle joint moment arms during ground contact compared to without carbon fiber plates38. Therefore, footwear with increased bending stiffness may not universally increase ankle-joint gear ratio. Despite controlling for shoe mass, adding carbon fiber plates to footwear did not affect running economy nor soleus active muscle volume. Thus, we rejected our second hypothesis. Because footwear bending stiffness did not affect the stride-average activation for any of the measured muscles (Table 2, Fig. 7), none of the respective active muscle volumes changed across footwear conditions (active muscle volume = total muscle volume × relative activation)37. This is now the fourth study that failed to replicate Roy and Stefanyshyn’s classic investigation12, Figure 1. Average (a,b) hip, (c,d) knee, and (e,f) ankle angle and net moment versus time during running with footwear of varied 3-point bending stiffness: 13.0 (black), 31.0 (blue), 43.1 (green), and 84.1 kN/m (orange). Vertical lines indicate the average end of ground contact for the respective footwear condition. Flexion (Flx) and Extension (Ext). 5 Vol.:(0123456789) Scientific Reports | (2020) 10:17154 | https://doi.org/10.1038/s41598-020-74097-7 www.nature.com/scientificreports/ which stated that adding carbon fiber plates to shoe soles improves running economy14–17. Since the classic investigation, only Oh and Park13 reported that adding carbon fiber plates to running shoes elicited a relative footwear stiffness that improves running economy at 2.4 m/s. Moreover, the classic investigation12 reported that participant body mass was inversely correlated with the change in oxygen uptake at their intermediate footwear stiffness condition (38 kN/m) relative to footwear condition that did not have a carbon fiber plate (18 kN/m). Hence, compared to their smaller participants, the running economy of their larger participants improved more by adding carbon fiber plates to their shoe soles. In the present study, post-hoc analyses revealed that participant body mass was independent to the change in aerobic power during the most compliant footwear condition ver- sus any of the stiffer footwear conditions (all p ≥ 0.502). Moreover, due to the implications of muscle dynamics on aerobic power37, we performed post-hoc linear regressions which revealed that the change in aerobic power from the footwear condition that did not contain a carbon fiber insole (13.0 ± 1.0 kN/m) was not correlated to the corresponding change in contact time (p = 0.135), soleus force generation (p = 0.614), or soleus velocity (p = 0.324). Further, there were two a potentially spurious weak correlations: (1) soleus active muscle volume versus gross aerobic power (r = -0.329; p = 0.039) and (2) change in soleus length versus change in gross aerobic power (r = 0.311, p = 0.040). Thus, we did not uncover any reasonable muscle-level parameters that correlated with the aerobic power when athletes ran in footwear conditions using carbon fiber plates versus without carbon fiber plates. Figure 2. (Left) Average (a,b) vertical and (c,d) horizontal ground reaction force (GRF), (e,f) soleus muscle tendon (MT) gear ratio, and (g,h) net ankle moment versus time and (right) footwear 3-point bending stiffness (right): 13.0 (black), 31.0 (blue), 43.1 (green), and 84.1 kN/m (orange). Vertical lines indicate the average end of ground contact for the respective condition and error bars indicate SE when visible. 6 Vol:.(1234567890) Scientific Reports | (2020) 10:17154 | https://doi.org/10.1038/s41598-020-74097-7 www.nature.com/scientificreports/ If footwear bending stiffness does not affect running economy, why does wearing Nike prototype footwear with carbon fiber plates embedded in their midsole (Nike) improve running economy compared to wearing Adidas footwear?39 Perhaps Nike’s carbon fiber plate provide the structure necessary for the midsole foam to function. Despite a 264% increased bending stiffness, when athletes run in Nike they elicit slightly shorter GRF to ankle-joint moment arms compared to running in Adidas footwear38. This increased footwear bending stiffness and shortened ankle-joint moment arm may be related to Nike’s curved carbon-fiber midsole plates39. Additionally, compared to the Adidas footwear, the respective Nike soles are ~ 8 mm taller (35–62% taller depend- ing on midsole location), the midsole foam is roughly half as stiff (in-series linear stiffness, not bending), and its hysteresis is 11.1% less during vertical loading and unloading39. Altogether, because both decreased linear stiffness40–42 and relative mechanical energy dissipation43 in-series to the stance-limb are associated with more economical running, Nike footwear may elicit superior running economy values than Adidas footwear due to their relatively compliant and resilient midsole foam—not increased bending stiffness. This study has potential limitations. First, our carbon fiber plates were located between the athlete’s sock and the Adidas midsole foam. The lack of cushioning on top of the stiffer carbon fiber plates may have elicited less comfortable footwear compared to the more compliant footwear conditions. Second, prior to the experimental trials, each participant performed a five-minute treadmill running habituation trial in the Adidas footwear without a carbon-fiber in-sole. Thus, differences in the habituation time between the footwear bending stiffness conditions may have affected our results. Even though humans adapt their biomechanics in just one step when landing onto terrain with different compliance44–46, running with carbon fiber insoles may require a more exten- sive habituation period, like that of more complicated lower-limb devices (e.g. exoskeletons)47–49. Additionally, we quantified soleus dynamics and not gastrocnemius dynamics because the soleus is the largest ankle plantar flexor50, it is the primary muscle that lifts and accelerates the participant’s center of mass during locomotion51,52, it likely generates the greatest muscle force of any plantar flexor30, and it is often estimated to consume the most metabolic energy of any plantar flexor during running30,53,54. Consistent with previous running studies that related longitudinal bending stiffness to metabolic energy expenditure12,13, we used a controlled laboratory environment and adequate sample size to relate metabolic energy expenditure collected in one session to biomechanical data Figure 3. (Left) Average (a,b) soleus muscle–tendon (MT) force, (c,d) length, and (e,f) velocity versus time and (right) footwear 3-point bending stiffness (right): 13.0 (black), 31.0 (blue), 43.1 (green), and 84.1 kN/m (orange). Vertical lines indicate the average end of ground contact for the respective footwear condition and error bars indicate SE when visible. 7 Vol.:(0123456789) Scientific Reports | (2020) 10:17154 | https://doi.org/10.1038/s41598-020-74097-7 www.nature.com/scientificreports/ collected from a separate session55. Moreover, regardless of how little footwear technology improves metabolic energy expenditure, even small improvements help separate champions from their peers in competitive athletics. Conclusion Changing footwear bending stiffness hardly changes athlete biomechanics and may not improve running econ- omy. Therefore, if competitive distance runners went back in time, added carbon fiber plates to their footwear, and re-raced, their performance would likely not change. Methods Participants. Fifteen males participated (Table 1). All participants were apparently free of cardiovascular, orthopedic, and metabolic disorders, and could run 5 km in < 25 min. Prior to the study, each participant gave informed written consent in accordance with the Georgia Institute of Technology Central Institutional Review Board. During the study. We followed the Georgia Institute of Technology Central Institutional Review Board’s approved protocol and carried out the study in accordance with these approved guidelines and regulations. Figure 4. (Left) Average (a,b) soleus (Sol) fascicle angle, (c,d) force, (e,f) length, and (g,h) velocity versus time and (right) footwear 3-point bending stiffness (right): 13.0 (black), 31.0 (blue), 43.1 (green), and 84.1 kN/m (orange). Vertical lines indicate the average end of ground contact for the respective footwear condition and error bars indicate SE. 8 Vol:.(1234567890) Scientific Reports | (2020) 10:17154 | https://doi.org/10.1038/s41598-020-74097-7 www.nature.com/scientificreports/ Figure 5. Estimated (a) Soleus (Sol) force–length and (c) force–velocity relationships during ground contact. The marker indicates soleus initial ground contact and the horizontal line indicates soleus operating range during ground contact. (b) Sol force and (d) volume (Vol) throughout ground contact and vertical lines indicate the average end of ground contact. Figure 6. Average (± SE) (a) gross aerobic power and (b) activated soleus (Sol) volume (Vol) per stride. Right axis: Percent difference in the respective variables from the Adidas condition without a carbon fiber plate versus shoe bending stiffness. 9 Vol.:(0123456789) Scientific Reports | (2020) 10:17154 | https://doi.org/10.1038/s41598-020-74097-7 www.nature.com/scientificreports/ Footwear. We acquired the Adidas Adizero Adios BOOST 2 (Adidas) running shoes in US men’s size 9, 10, 11, and 12. The Adidas are the same shoe model that Dennis Kimetto wore to set a previous marathon (42.2 km) world record (2:02:57 h:min:s). Next, we fabricated sets of custom carbon fiber in-soles that were 0.8, 1.6, and 3.2 mm thick to fit the Adidas shoes. We characterized the 3-point bending stiffness of each shoe and in-sole condition following previously described methods12,25,29. Briefly, we performed 3-point bending tests by placing each footwear condition in a frame with two supporting bars 80 mm apart. We applied a vertical force to the top of each footwear condition Figure 7. Average (a) soleus (Sol), (b) medial gastrocnemius (MG), (c) tibialis anterior (TA), (d) biceps femoris (BF), (e) vastus medialis (VM), (f) gluteus maximus (GM), and (g) rectus femoris (RF) versus time (left) across footwear 3-point bending stiffness: 13.0 (black), 31.0 (blue), 43.1 (green), and 84.1 kN/m (orange). Vertical lines indicate the average end of ground contact and stride for the respective footwear condition. Table 2. Stride averaged normalized muscle activation ± SD normalized to the respective muscle’s average maximum value during running with the Adidas (13.0 kN/m) footwear condition. Footwear bending stiffness (kN/m) Tibialis anterior (%) Soleus (%) Medial gastroc- nemius (%) Vastus medialis (%) Rectus femoris (%) Biceps femoris (%) Gluteus maximus (%) 13.0 ± 1.0 37 ± 7 24 ± 16 24 ± 5 19 ± 2 27 ± 10 40 ± 14 31 ± 15 31.0 ± 1.5 37 ± 9 21 ± 5 26 ± 3 20 ± 3 22 ± 12 39 ± 14 28 ± 9 43.1 ± 1.6 39 ± 9 21 ± 3 28 ± 4 19 ± 4 26 ± 12 38 ± 10 23 ± 6 84.1 ± 1.1 38 ± 9 20 ± 3 25 ± 3 20 ± 3 27 ± 13 40 ± 10 26 ± 7 10 Vol:.(1234567890) Scientific Reports | (2020) 10:17154 | https://doi.org/10.1038/s41598-020-74097-7 www.nature.com/scientificreports/ midway between the two supporting bars, approximately where the foot’s metatarsophalangeal joint would be located using a materials testing machine (Instron, Norwood, MA, USA). We applied force three consecutive times to displace each shoe 10 mm following a 2 N preload (loading rate: 8 mm/s). We calculated footwear 3-point bending stiffness during loading using the average linear slope of the force–displacement data (100 Hz) from the following displacement range: 5 to 9 mm. We also set each athlete’s footwear mass equal to their largest footwear condition, which was the Adidas plus thickest carbon fiber in-sole. For example, the size 9 Adidas shoe is 199 g and its stiffest in-sole was 60 g. Accordingly, we set all size 9 footwear conditions to 259 g by securing mass to the tongue of each shoe. Protocol. Each participant completed two experimental sessions. During the first session (aerobic session), participants performed a 5-min standing trial followed by five 5-min treadmill (Bertec Corporation, Columbus, OH, USA) running trials at 3.5 m/s. Prior to each trial, participants rested for at least 5 min. The first running trial served as habituation to treadmill running in the Adidas footwear (no carbon fiber in-sole). During each subsequent trial, participants ran using a different footwear condition: Adidas as well as Adidas with 0.8, 1.6, and 3.2 mm thick carbon fiber in-soles. We randomized footwear trial order. Each participant’s second session (biomechanics session) occurred at the same time of day and < 10 days following their first session. During the second session, participants performed four 2-min treadmill running trials at 3.5 m/s using the same footwear conditions as the first session in a re-randomized order. We performed separate aerobic and biomechanics ses- sions to mitigate the potential for technical difficulties to arise by measuring biomechanics over a briefer session than needed for accurate metabolic measurements. Aerobic energy expenditure. We asked participants to arrive to their aerobic session 3-h post-prandial. Throughout each of the aerobic session’s trials, we used open-circuit expired gas analysis (TrueOne 2400, Parvo- Medic, Sandy, UT, USA) to record the participant’s rates of oxygen uptake (V̇o2) and carbon dioxide production (V̇co2). We monitored each participant’s respiratory exchange ratio (RER) throughout each trial to ensure that everyone primarily relied on aerobic metabolism during running; indicated by an RER ≤ 1.031. Next, we averaged V̇o2 and V̇co2 over the last 2-min of each trial and used a standard equation56 to calculate aerobic power (W). Subsequently, we subtracted the corresponding session’s standing aerobic power (Table 1) from each running trial and divided by participant mass to yield mass-normalized aerobic power (W/kg). Biomechanics. Prior to the biomechanics session’s running trials, we placed reflective markers on the left and right side of each athlete’s lower body following a modified Helen Hayes marker set: superficial to the head of the 1st and 5th metatarsal, posterior calcaneus, medial and lateral malleoli, lateral mid-shank, medial and lateral knee-joint center, lateral mid-thigh, greater trochanter, anterior superior iliac crest, posterior superior iliac crest, and superior iliac crest. During the ensuing trials, we recorded vertical and anterior–posterior GRFs (1000 Hz) as well as motion capture (200 Hz) data during the last 30 s of each trial. We performed a fast fourier transform on the raw GRF data from six random participants and then filtered the raw GRFs and center-of-pressure data appropriately: using a fourth-order low-pass critically damped filter (14 Hz)54,57,58. We filtered motion capture using a fourth-order low-pass Butterworth filter (7 Hz)57,59–62. Using the filtered GRFs, we calculated whole-body stride kinematics (stance and stride time) and GRF parameters (stance average vertical and resultant GRF, as well as mean braking and propulsive horizontal GRFs63) with a custom MATLAB script (Mathworks, Natick, MA) that detected periods of ground contact using a 30 N vertical GRF threshold. We categorized each partici- pant as a heel striker or mid/forefoot striker based on visual inspection and whether their vertical GRF trace had an impact peak or not (Table 1). If the participant visually appeared to contact the ground with their heel and displayed a vertical GRF impact peak they were deemed a heel striker64. Participants that did not satisfy these criteria were deemed a mid/forefoot strikers. We performed inverse dynamics and determined limb joint kinematics (limb joint angles and GRF-to-joint- center moment arms) and kinetics (limb joint moments) (C-motion Inc., Germantown, MD; Mathworks Inc., Natick, MA, USA). Subsequently, we computed each participant’s instantaneous soleus muscle–tendon (MT) moment arm, length, velocity, and force. We used participant anthropometric data and limb-joint angles to cal- culate the respective soleus MT length36, velocity, and moment arm36,65. Next, we used each soleus MT moment arm (r) and net ankle-joint moment (M) to calculated soleus MT force (F) by deeming that the soleus generates 54% of total plantar flexor force based on its relative physiological cross sectional area66. Prior to the biomechanics session’s trials, we secured a linear-array B-mode ultrasound probe (Telemed, Vilnius, Lituania) to the skin superficial of each athlete’s right soleus. Using ultrasonography, we recorded mid- soleus fascicle images (100 Hz) during at least five consecutive strides per trial. We processed the images using a semi-automated tracking software67 to determine instantaneous soleus pennation angle and fascicle length. For semi-automated images that did not accurately track the respective soleus fascicle angle and/or length, we manually redefined the respective fascicle’s parameters. We used soleus MT force and fascicle angle to calculate soleus fascicle force, length, and velocity in congruence with previous studies37,57. We filtered soleus fascicle angle and length using a fourth-order low-pass Butterworth filter (10 Hz) and took the derivative of fascicle length with respect to time to determine fascicle velocity. Subsequently, we determined relative soleus fascicle length and velocity by deeming that soleus fascicles are at 97% of their optimal length at initial ground contact in the Adidas condition32 and that their maximum velocity is 6.77 L0/s53, respectively. We deemed average ± SD maximum soleus velocity to equal 297.1 ± 16.5 mm/s. Due to technical difficulties, we were unable to compute accurate active soleus volume during 18 of 60 trials; spanning 5 participants. We recorded surface EMG signals from the biomechanics session’s running trials using the standard proce- dures of the International Society for Electrophysiology and Kinesiology68. Prior to the first trial, we shaved and 11 Vol.:(0123456789) Scientific Reports | (2020) 10:17154 | https://doi.org/10.1038/s41598-020-74097-7 www.nature.com/scientificreports/ lightly abraded the skin superficial to the medial gastrocnemius, soleus, tibialis anterior, vastus medialis, rectus femoris, biceps femoris, and gluteus maximus of each participant’s left leg with electrode preparation gel (NuPrep, Weaver and Co., Aurora, CO). Next, we placed a bipolar surface electrode (Delsys Inc., Natick, MA) over the skin superficial to each respective muscle belly and in the same orientation as the respective muscle fascicle. We recorded EMG signals at 1000 Hz and verified electrode positions and signal quality by visually inspecting the EMG signals while participants contracted the respective muscle. Based on visual inspection and technical difficulties, we removed 97 of 420 potential muscle activation signals due to their poor signal quality; spanning 4 participants. To analyze EMG signals from the running trials, we band-pass filtered the raw EMG signals to retain frequencies between 20 and 450 Hz, full-wave rectified the filtered EMG signals, and then calculated the root mean square of the rectified EMG signals with a 40 ms moving window69,70. Lastly, we normalized each muscle activation to the average maximum activation of the respective muscle during running in the Adidas condition sans carbon fiber plates70. Statistics. An a priori analysis on Roy and Stefanyshyn’s data12, suggested that fifteen participants would achieve a strong statistical power (0.895) between footwear bending stiffness and metabolic power. We per- formed a linear regression on the footwear’s force–displacement profile, which was measured from a materials testing device. We performed independent repeated measures ANOVAs to determine whether footwear bend- ing stiffness (independent variable) affected athlete running biomechanics (hip, knee, and ankle stance average, minimum, and maximum angle; hip, knee, and ankle stance average and maximum moment; ground contact time; step time; stance average vertical, braking, and propulsive GRF; fraction of vertical and horizontal GRF during the first half of stance; stance average muscle–tendon force, length, velocity, and gear ratio; stance aver- age and maximum soleus fascicle pennation angle, force, length, velocity; stance average, stride average soleus active muscle volume; stance average and stride average soleus, medial gastrocgnemius, tibialis anterior, biceps femoris, vastus medialis, gluteus maximus, and rectus femoris; and gross aerobic power (dependent variables). We presented cohen’s d effect size for gross metabolic power and stride average soleus active muscle volume. We performed all statistical tests using R-studio (R-Studio Inc., Boston, USA) and G*Power software. Received: 23 October 2019; Accepted: 21 September 2020 References 1. Hoogkamer, W., Kipp, S., Spiering, B. A. & Kram, R. Altered running economy directly translates to altered distance-running performance. Med. Sci. Sports Exerc. 48, 2175–2180. https ://doi.org/10.1249/mss.00000 00000 00101 2 (2016). 2. Fuller, J. T., Thewlis, D., Tsiros, M. D., Brown, N. A. T. & Buckley, J. D. Effects of a minimalist shoe on running economy and 5-km running performance. J. Sports Sci. 34, 1740–1745. https ://doi.org/10.1080/02640 414.2015.11360 71 (2016). 3. Beck, O. N., Kipp, S., Byrnes, W. C. & Kram, R. Use aerobic energy expenditure instead of oxygen uptake to quantify exercise intensity and predict endurance performance. J. Appl. Physiol. 125, 672–674. https ://doi.org/10.1152/jappl physi ol.00940 .2017 (2018). 4. Franz, J. R., Wierzbinski, C. M. & Kram, R. Metabolic cost of running barefoot versus shod: Is lighter better?. Med. Sci. Sport Exerc. 44, 1519–1525 (2012). 5. Frederick, E., Daniels, J. & Hayes, J. The effect of shoe weight on the aerobic demands of running. Curr. Top. Sports Med. 15, 616–625 (1984). 6. Kipp, S., Kram, R. & Hoogkamer, W. Extrapolating metabolic savings in running: implications for performance predictions. Front. Physiol. https ://doi.org/10.3389/fphys .2019.00079 (2019). 7. Barry, D. T., Fredericksen, R., Soutas-Little, R. W. & Peterson, R. R. Google Patents (1991). 8. Stefanyshyn, D. & Fusco, C. Athletics: Increased shoe bending stiffness increases sprint performance. Sports Biomech. 3, 55–66 (2004). 9. Stefanyshyn, D. J. & Wannop, J. W. The influence of forefoot bending stiffness of footwear on athletic injury and performance. Footwear Sci. 8, 51–63. https ://doi.org/10.1080/19424 280.2016.11446 52 (2016). 10. Gregory, R. W., Axtell, R. S., Robertson, M. I. & Lunn, W. R. The effects of a carbon fiber shoe insole on athletic performance in collegiate athletes. J. Sports Sci. 6, 219–230 (2018). 11. Burns, G. T. & Tam, N. Is it the shoes? A simple proposal for regulating footwear in road running. Br. J. Sports Med. https ://doi. org/10.1136/bjspo rts-2018-10048 0 (2019). 12. Roy, J.-P.R. & Stefanyshyn, D. J. Shoe midsole longitudinal bending stiffness and running economy, joint energy, and EMG. Med. Sci. Sports Exerc. 38, 562–569 (2006). 13. Oh, K. & Park, S. The bending stiffness of shoes is beneficial to running energetics if it does not disturb the natural MTP joint flexion. J. Biomech. 53, 127–135 (2017). 14. Madden, R., Sakaguchi, M., Wannop, J. & Stefanyshyn, D. Forefoot bending stiffness, running economy and kinematics during overground running. Footwear Sci. 7, S11–S13 (2015). 15. Flores, N., Delattre, N., Berton, E. & Rao, G. Does an increase in energy return and/or longitudinal bending stiffness shoe features reduce the energetic cost of running?. Eur. J. Appl. Physiol. 119, 429–439 (2019). 16. Day, E. & Hahn, M. Optimal footwear longitudinal bending stiffness to improve running economy is speed dependent. Footwear Sci. 12, 1–11 (2019). 17. McLeod, A. R., Bruening, D., Johnson, A. W., Ward, J. & Hunter, I. Improving running economy through altered shoe bending stiffness across speeds. Footwear Sci. 12, 1–11 (2020). 18. Bergstrom, M. & Hultman, E. Energy cost and fatigue during intermittent electrical stimulation of human skeletal muscle. J. Appl. Physiol. 65, 1500–1505 (1988). 19. Hogan, M. C., Ingham, E. & Kurdak, S. S. Contraction duration affects metabolic energy cost and fatigue in skeletal muscle. Am. J. Physiol. 274, E397-402. https ://doi.org/10.1152/ajpen do.1998.274.3.E397 (1998). 20. Chasiotis, D., Bergstrom, M. & Hultman, E. ATP utilization and force during intermittent and continuous muscle contractions. J. Appl. Physiol. 63, 167–174. https ://doi.org/10.1152/jappl .1987.63.1.167 (1987). 12 Vol:.(1234567890) Scientific Reports | (2020) 10:17154 | https://doi.org/10.1038/s41598-020-74097-7 www.nature.com/scientificreports/ 21. Joyner, M. J. & Casey, D. P. Regulation of increased blood flow (hyperemia) to muscles during exercise: A hierarchy of competing physiological needs. Physiol. Rev. 95, 549–601. https ://doi.org/10.1152/physr ev.00035 .2013 (2015). 22. Ferris, D. P., Sawicki, G. S. & Daley, M. A. A physiologist’s perspective on robotic exoskeletons for human locomotion. Int. J. Hum. Robot 4, 507–528 (2007). 23. Farris, D. J. & Sawicki, G. S. Human medial gastrocnemius force–velocity behavior shifts with locomotion speed and gait. Proc. Natl. Acad. Sci. U.S.A. 109, 977–982 (2012). 24. Willwacher, S., König, M., Braunstein, B., Goldmann, J.-P. & Brüggemann, G.-P. The gearing function of running shoe longitudinal bending stiffness. Gait Posture 40, 386–390 (2014). 25. Willwacher, S., König, M., Potthast, W. & Brüggemann, G.-P. Does specific footwear facilitate energy storage and return at the metatarsophalangeal joint in running?. J. Appl. Physiol. 29, 583–592 (2013). 26. Wannop, J. W., Killick, A., Madden, R. & Stefanyshyn, D. J. The influence of gearing footwear on running biomechanics. Footwear Sci. 9, 111–119. https ://doi.org/10.1080/19424 280.2017.13427 05 (2017). 27. Farris, D. J., Kelly, L. A., Cresswell, A. G. & Lichtwark, G. A. The functional importance of human foot muscles for bipedal locomo- tion. Proc. Natl. Acad. Sci. U.S.A. 116, 1645–1650. https ://doi.org/10.1073/pnas.18128 20116 (2019). 28. Ray, S. F. & Takahashi, K. Z. Gearing up the human ankle-foot system to reduce energy cost of fast walking. Sci. Rep. 10, 8793. https ://doi.org/10.1038/s4159 8-020-65626 -5 (2020). 29. Takahashi, K. Z., Gross, M. T., Van Werkhoven, H., Piazza, S. J. & Sawicki, G. S. Adding stiffness to the foot modulates soleus force-velocity behaviour during human walking. Sci. Rep. 6, 29870 (2016). 30. Biewener, A. A., Farley, C. T., Roberts, T. J. & Temaner, M. Muscle mechanical advantage of human walking and running: Implica- tions for energy cost. J. Appl. Physiol. 97, 2266–2274. https ://doi.org/10.1152/jappl physi ol.00003 .2004 (2004). 31. Brooks, G. A., Fahey, T. D. & White, T. P. Exercise Physiology: Human Bioenergetics and Its Applications (Mayfield Publishing Company, Mountain View, 1996). 32. Rubenson, J., Pires, N. J., Loi, H. O., Pinniger, G. J. & Shannon, D. G. On the ascent: The soleus operating length is conserved to the ascending limb of the force–length curve across gait mechanics in humans. J. Exp. Biol. 215, 3539–3551 (2012). 33. Rall, J. A. Energetic aspects of skeletal muscle contraction: Implications of fiber types. Exerc. Sport Sci. Rev. 13, 33–74 (1985). 34. Hill Archibald, V. The heat of shortening and the dynamic constants of muscle. Proc. R. Soc. B 126, 136–195. https ://doi.org/10.1098/ rspb.1938.0050 (1938). 35. Hilber, K., Sun, Y. B. & Irving, M. Effects of sarcomere length and temperature on the rate of ATP utilisation by rabbit psoas muscle fibres. J. Physiol. 531, 771–780. https ://doi.org/10.1111/j.1469-7793.2001.0771h .x (2001). 36. Hawkins, D. & Hull, M. L. A method for determining lower extremity muscle-tendon lengths during flexion/extension movements. J. Biomech. 23, 487–494 (1990). 37. Beck, O. N., Punith, L. K., Nuckols, R. W. & Sawicki, G. S. Exoskeletons improve locomotion economy by reducing active muscle volume. Exerc. Sport Sci. Rev. 47, 237–245. https ://doi.org/10.1249/jes.00000 00000 00020 4 (2019). 38. Hoogkamer, W., Kipp, S. & Kram, R. The biomechanics of competitive male runners in three marathon racing shoes: A randomized crossover study. Sports Med. 49, 133–143. https ://doi.org/10.1007/s4027 9-018-1024-z (2019). 39. Hoogkamer, W. et al. A comparison of the energetic cost of running in marathon racing shoes. Sports Med. 48, 1009–1019. https ://doi.org/10.1007/s4027 9-017-0811-2 (2018). 40. Smith, J. A. H., McKerrow, A. D. & Kohn, T. A. Metabolic cost of running is greater on a treadmill with a stiffer running platform. J. Sports Sci. 35, 1592–1597. https ://doi.org/10.1080/02640 414.2016.12259 74 (2017). 41. Kerdok, A. E., Biewener, A. A., McMahon, T. A., Weyand, P. G. & Herr, H. M. Energetics and mechanics of human running on surfaces of different stiffnesses. J. Appl. Physiol. 92, 469–478 (2002). 42. Beck, O. N., Taboga, P. & Grabowski, A. M. Reduced prosthetic stiffness lowers the metabolic cost of running for athletes with bilateral transtibial amputations. J. Appl. Physiol. 122, 976–984. https ://doi.org/10.1152/jappl physi ol.00587 .2016 (2017). 43. Lejeune, T. M., Willems, P. A. & Heglund, N. C. Mechanics and energetics of human locomotion on sand. J. Exp. Biol. 201, 2071–2080 (1998). 44. Ferris, D. P., Liang, K. & Farley, C. T. Runners adjust leg stiffness for their first step on a new running surface. J. Biomech. 32, 787–794 (1999). 45. Ferris, D. P., Louie, M. & Farley, C. T. Running in the real world: Adjusting leg stiffness for different surfaces. Proc. R. Soc. B 265, 989–994 (1998). 46. Moritz, C. T. & Farley, C. T. Passive dynamics change leg mechanics for an unexpected surface during human hopping. J. Appl. Physiol. 97, 1313–1322. https ://doi.org/10.1152/jappl physi ol.00393 .2004 (2004). 47. Sawicki, G. S. & Ferris, D. P. Mechanics and energetics of level walking with powered ankle exoskeletons. J. Exp. Biol. 211, 1402–1413 (2008). 48. Koller, J. R., Jacobs, D. A., Ferris, D. P. & Remy, C. D. Learning to walk with an adaptive gain proportional myoelectric controller for a robotic ankle exoskeleton. J. Neuroeng. Rehabil. 12, 97. https ://doi.org/10.1186/s1298 4-015-0086-5 (2015). 49. Panizzolo, F. A. et al. Metabolic cost adaptations during training with a soft exosuit assisting the hip joint. Sci. Rep. 9, 9779. https ://doi.org/10.1038/s4159 8-019-45914 -5 (2019). 50. Albracht, K., Arampatzis, A. & Baltzopoulos, V. Assessment of muscle volume and physiological cross-sectional area of the human triceps surae muscle in vivo. J. Biomech. 41, 2211–2218. https ://doi.org/10.1016/j.jbiom ech.2008.04.020 (2008). 51. Dorn, T. W., Schache, A. G. & Pandy, M. G. Muscular strategy shift in human running: Dependence of running speed on hip and ankle muscle performance. J. Exp. Biol. 215, 1944–1956 (2012). 52. Hamner, S. R. & Delp, S. L. Muscle contributions to fore-aft and vertical body mass center accelerations over a range of running speeds. J. Biomech. 46, 780–787. https ://doi.org/10.1016/j.jbiom ech.2012.11.024 (2013). 53. Bohm, S., Mersmann, F., Santuz, A. & Arampatzis, A. The force length velocity potential of the human soleus muscle is related to the energetic cost of running. Proc. R. Soc. B 286, 20192560. https ://doi.org/10.1098/rspb.2019.2560 (2019). 54. Kipp, S., Grabowski, A. M. & Kram, R. What determines the metabolic cost of human running across a wide range of velocities?. J. Exp. Biol. 221, 184218 (2018). 55. Morgan, D. W., Martin, P. E., Krahenbuhl, G. S. & Baldini, F. D. Variability in running economy and mechanics among trained male runners. Med. Sci. Sports Exerc. 23, 378–383 (1991). 56. Peronnet, F. & Massicotte, D. Table of nonprotein respiratory quotient: An update. Can. J. Sport Sci. 16, 23–29 (1991). 57. Lai, A. et al. In vivo behavior of the human soleus muscle with increasing walking and running speeds. J. Appl. Physiol. 118, 1266–1275 (2015). 58. Lai, A. K. M., Lichtwark, G. A., Schache, A. G. & Pandy, M. G. Differences in in vivo muscle fascicle and tendinous tissue behavior between the ankle plantarflexors during running. Scand. J. Med. Sci. Sports 28, 1828–1836. https ://doi.org/10.1111/sms.13089 (2018). 59. Bohm, S., Marzilger, R., Mersmann, F., Santuz, A. & Arampatzis, A. Operating length and velocity of human vastus lateralis muscle during walking and running. Sci. Rep. 8, 5066. https ://doi.org/10.1038/s4159 8-018-23376 -5 (2018). 60. Beck, O. N., Taboga, P. & Grabowski, A. M. How do prosthetic stiffness, height and running speed affect the biomechanics of athletes with bilateral transtibial amputations?. J. R. Soc. Int. https ://doi.org/10.1098/rsif.2017.0230 (2017). 13 Vol.:(0123456789) Scientific Reports | (2020) 10:17154 | https://doi.org/10.1038/s41598-020-74097-7 www.nature.com/scientificreports/ 61. Czerniecki, J. M., Gitter, A. & Munro, C. Joint moment and muscle power output characteristics of below knee amputees during running: The influence of energy storing prosthetic feet. J. Biomech. 24, 63–75. https ://doi.org/10.1016/0021-9290(91)90327 -j (1991). 62. Nikolaidou, M. E., Marzilger, R., Bohm, S., Mersmann, F. & Arampatzis, A. Operating length and velocity of human M. vastus lateralis fascicles during vertical jumping. R. Soc. Open Sci. 4, 170185. https ://doi.org/10.1098/rsos.17018 5 (2017). 63. Crowell, H. P., Milner, C. E., Hamill, J. & Davis, I. S. Reducing impact loading during running with the use of real-time visual feedback. J. Orthop. Sport Phys. 40, 206–213. https ://doi.org/10.2519/jospt .2010.3166 (2010). 64. Cavanagh, P. R. & Lafortune, M. A. Ground reaction forces in distance running. J. Biomech. 13, 397–406. https ://doi. org/10.1016/0021-9290(80)90033 -0 (1980). 65. Bobbert, M. F., Huijing, P. A. & van Ingen Schenau, G. J. A model of the human triceps surae muscle-tendon complex applied to jumping. J. Biomech. 19, 887–898 (1986). 66. Fukunaga, T. et al. Physiological cross-sectional area of human leg muscles based on magnetic resonance imaging. J. Orthop. Res. 10, 926–934 (1992). 67. Farris, D. J. & Lichtwark, G. A. UltraTrack: Software for semi-automated tracking of muscle fascicles in sequences of B-mode ultrasound images. Comput. Method Prog. Bio 128, 111–118. https ://doi.org/10.1016/j.cmpb.2016.02.016 (2016). 68. Merletti, R. & Di Torino, P. Standards for reporting EMG data. J Electromyogr. Kinesiol. 9, 3–4 (1999). 69. Carlo, J. D. L. The use of surface electromyography in biomechanics. J. Appl. Biomech. 13, 135–163. https ://doi.org/10.1123/ jab.13.2.135 (1997). 70. Yang, J. F. & Winter, D. A. Electromyographic amplitude normalization methods: Improving their sensitivity as diagnostic tools in gait analysis. Arch. Phys. Med. Rehab. 65, 517–521 (1984). Acknowledgements We thank Dr. Frank Hammond and Lucas Tiziani for the use of the materials testing machine, in addition to Dr. Young-Hui Chang for the use of his analysis software. We thank PoWeR and EPIC Lab members, namely Lindsey Trejo for assisting data collection and Krishan Bhakta for assisting data analysis. This study was supported by O.N.B.’s National Institute of Health’s, Institute of Aging Fellowship: F32AG063460; P.R.G.’s National Science Foundation Graduate Research Fellowship: DGE-1650044; G.S.S’s National Institute of Health’s, Institute of Aging Award: R0106052017; and G.S.S.’s support from the George W. Woodruff School of Mechanical Engineering. Author contributions O.N.B. and G.S.S. came up with the study design. O.N.B. and P.R.G. acquired and analyzed the study’s data. O.N.B. and G.S.S. interpreted the data. O.N.B. drafted the manuscript, P.R.G. and G.S.S. edited the manuscript. All authors approved the manuscript, agree to be accountable for their contributions, and will ensure that ques- tions related to the accuracy or integrity of any part of the study are appropriately investigated, resolved, and the resolution documented in the literature. Competing interests The authors declare no competing interests. Additional information Supplementary information is available for this paper at https ://doi.org/10.1038/s4159 8-020-74097 -7. Correspondence and requests for materials should be addressed to O.N.B. Reprints and permissions information is available at www.nature.com/reprints. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/. © The Author(s) 2020

Adding carbon fiber to shoe soles may not improve running economy: a muscle-level explanation.

10-13-2020

Beck, Owen N,Golyski, Pawel R,Sawicki, Gregory S

eng

PMC9112246

1 Vol.:(0123456789) Scientific Reports | (2022) 12:8179 | https://doi.org/10.1038/s41598-022-12403-1 www.nature.com/scientificreports Energy supply and influencing factors of mountain marathon runners from Baiyin marathon accident in China Jichao Sun High temperature impacts the performance of marathon athletes, and hypothermia harms athletes. Twenty-one runners died, and eight were injured in the China Baiyin marathon on May 22, 2021. It’s a typical human life test. The energy equations are combined with the maximum energy supply of Chinese male athletes to study this accident. We analyze the human body’s route slope, travel speed, and heat dissipation under low temperatures in this marathon. The study shows that the large slope and long-distance of CP2 to CP3 section and the low temperature during the competition are the main reasons for the accident. The method of quantifying the slope and temperature and calculating the percentage of athletes’ physical consumption proposed in this paper can evaluate the route design of field marathons. We suggest that the physical energy consumption ratio of 90%, i.e. 315 cal/min/kg, should be taken as the maximum energy supply for Chinese male marathon runners. Dangerous risk zones for wind speed and temperature on dangerous path sections are also formulated for athletes to make their assessments. This paper’s theories and methods can effectively help design the marathon route and determine the race time. Sports is an activity developed to meet the needs of human production, military, and health1. Physical fitness is the premise to maintain and complete the human body’s essential life activities and physical skills. Marathon runs for a long time and consumes much energy. The performance of athletes is closely related to physical fitness2. Physical performance is also affected by the surrounding environment, including temperature, humidity, road slope, road friction, and so on3, 4. The relationship between human energy expenditure and the external environ- ment needs to be studied to assess the individual strength of marathon runners. The war between Russia and Ukraine is going on, and now ordinary Ukrainian residents are also participating in the war. These people lack military training and actual combat experience and have limited combat power to participate in the war. Obtain- ing the maximum energy supply of the human body also helps assess the combat effectiveness of a soldier5 and ordinary people to participate in war. For many years, sports researchers around the world have been conducting similar studies, including pre- competition hypothermia, post-competition hypothermia, and pre-competition and post-competition warm- ing studies6 at the cold Winter Olympics, as well as various Oxygen inhalation, high-intensity training at high altitudes7–10, etc., these studies have achieved positive results. High temperature impacts the performance of marathon athletes, and hypothermia harms athletes11, 12. The research on the limited energy supply means the final limit of the human body’s skills, and it is the research at the expense of the human body’s death. When evaluating the energy supply of marathon runners or ordinary people, especially their limited energy supply, it is impossible to carry out limit tests to ensure personnel safety. At the same time, there is still a lack of published literature on this kind of extreme energy supply research. From some marathon fatalities, we can get some trial-like results. Analyzing these marathon accidents, one can obtain data on the extreme energy supply of the personnel. Although these tests are not obtained from actual tests, these data are relatively close to the limits of the human body and are very precious. At the same time, such data is essential to the military and sports. After obtaining such data, it is possible to design better the marathon running route and guide the athletes to conduct self-assessments whether they can refer to relevant competitions, which will have positive significance and value. It is also a good guide for evaluating ordinary people who participated in the war. OPEN School of Water Resource & Environment, China University of Geosciences, Beijing 100083, China. email: sunjc@ cugb.edu.cn 2 Vol:.(1234567890) Scientific Reports | (2022) 12:8179 | https://doi.org/10.1038/s41598-022-12403-1 www.nature.com/scientificreports/ To increase the influence and popularity of the city, local governments in China have held marathons one after another and invited famous runners from all over the world to participate, which has indeed boosted the local economy in China. In 2018 and 2019, there were 278, 330, and 27, 61 registrations for Category A and Category B events of the Chinese Athletics Association respectively13. As of March 19, 2022, the running competitions held in China include various walks, half marathons, 50 km, 30 km, 28 km, 21 km, 16 km, 15 km, 10 km, 8, 8 km, 3.8 km, and so on to 2625 times14. But there have been very individual race accidents, such as the marathon accident in Baiyin, Gansu of China15. On May 22, 2021, the fourth Yellow River Shilin Mountain Marathon 100 km cross-country race (Abbreviated as Baiyin marathon) was held in Jingtai County, Baiyin City of China. 172 participants took part in the 100-km cross-country race. During the race, public safety responsibility events occurred due to sudden cooling, pre- cipitation, and strong wind, resulting in the death of 21 participants and the injury of 815. China’s State Sports Administration issued a notice on May 28 to comprehensively ‘one-on-one’ check road running (including marathon, half marathon, 10 km, 5 km) and suspend the competitions that do not meet the requirements. On June 25, the government arrested five officials and dismissed, removed, demoted, warned, and demerit recorded twenty-seven leaders15. It can be seen from Fig. 1 that due to COVID-19, the number of marathons has significantly been reduced since 2020. Due to the Baiyin marathon accident in 2021, the State Sport General Administration of China immediately stopped many marathons, so the number of races in 2021 was reduced. Data for 2022 is not final yet, and complete data will be available in 2023. On the one hand, marathon accidents are not good for the development of marathon sports, and on the other hand, it is also bad for the health of the athletes participating in the competition. The high temperature in this kind of competition, like a marathon, or long-distance races, has attracted the attention of athletes and relevant media16. However, low temperature also does great harm to athletes17. There are many reasons for the accident in the Baiyin marathon. However, the low temperature and the energy supply of the athletes’ bodies are less than the energy dissipation is one of the main reasons. Because of this severe sports accident, it is necessary to conduct scientific research, analyze the causes of the accident, and avoid such accidents. Local governments hold various competitions to promote tourism devel- opment, stimulate the economy, and improve local popularity. Ignoring the potential hazards is an important reason why many Gansu province officials were held accountable for an ultramarathon tragedy18. At the same time, the Baiyin marathon accident also has several adverse natural conditions: high altitude, large slope, and low temperature. This paper studies the route slope and temperature from the human body’s heat dissipation and energy supply to reveal the cause of the accident. In this accident, the athlete’s death has already shown that the energy supply has reached its limit. It is simply a life experiment of the human body, which is very cruel. Therefore, it is necessary to study the energy supply limit of athletes in this competition through this marathon accident and analyze the cause of the accident from the human energy supply limit. Therefore, the main goal of this paper is to study and obtain the human body’s limited supply of energy for remote mobilization and combine the slope of the track and the temperature of the environment to study and analyze the setting of the way. Avoid recurrence of such accidents through research. At the same time, we also put forward some sugges- tions for athletes to participate in similar competitions. We set up a self-assessment chart for whether sports can participate based on temperature, slope, and speed during the competition. It is hoped that casualties in future competitions can be avoided through this accident analysis. Figure 1. Many marathons held in China. 3 Vol.:(0123456789) Scientific Reports | (2022) 12:8179 | https://doi.org/10.1038/s41598-022-12403-1 www.nature.com/scientificreports/ Methods The elevation and landform of the Biayin marathon. Baiyin marathon in Gansu of China was held four times in 2018 (Fig. 2a–c), 2019, 2020, and 2021. The first three sessions went well, and an accident occurred in the fourth session (some runners were warming themselves in the cave-dwelling, Fig. 2d). According to the race route of the Baiyin marathon, the elevation map in the race route area is drawn, and a map of vegetation along the way is attached (as shown in Fig. 3). Baiyin city is mountainous primarily, and a broad valley plain coexists. The northern part is an alluvial diluvial inclined plain, the central part is a low hill, and the southern part is loess beam, replat, and tableland landform. Figure 2. Baiyin marathon photos of pre-race, disaster, and rescue. (a–c) Baiyin marathon on May 20, 2018. (d) cave-dwelling, some runners were warming themselves by the fire on May 22, 2021. 4 Vol:.(1234567890) Scientific Reports | (2022) 12:8179 | https://doi.org/10.1038/s41598-022-12403-1 www.nature.com/scientificreports/ The climate of Baiyin city is a transition zone from a middle temperate semi-arid zone to an arid zone in China. The annual average temperature is 6–9 ℃, and the yearly rainfall is 180–450 mm, mainly in July, August, and September, accounting for more than 60% of the annual precipitation. It belongs to the northwest margin of the southeast monsoon climate, and the yearly evaporation reaches 1500–1600 mm, 4.5 times the average precipitation. Jingtai County in the north has maximum annual evaporation of 3390 mm. Baiyin has four seasons with plenty of sunshine, no hot summer, and no cold winter. The topographic features of Baiyin City are mainly bedrock mountains (as shown in Figs. 2c, 3a) and inter- mountain basins. The horizontal distribution of vegetation in the city gradually transitions from south to north to grassland deserts (as shown in Fig. 3b), and the transition between zones is not apparent. The tectonic structure in the northwest belongs to the eastward extension of the Qilian Mountains, with bare ground bedrock (as shown in Fig. 2c) and natural vegetation on the shady slopes (as shown in Figs. 2a,b, 3c). The southeast is dominated Figure 3. Marathon route and start, check, supply and endpoint and pictures along the route. (a) Photo of the race route on CP1. (b,c) photos of the race route on CP2–CP3. ‘Start’ is the starting point, CP1–CP9 is the checkpoint, SP is the supply point, and EP is the endpoint. 5 Vol.:(0123456789) Scientific Reports | (2022) 12:8179 | https://doi.org/10.1038/s41598-022-12403-1 www.nature.com/scientificreports/ by loess beam, replat and tableland landform, rivers, flats, and valleys. The tectonic structure belongs to the Longzhong Basin. Except for a few bedrock mountains, the ground is covered by loess (Fig. 2d). Energy consumption at a particular slope and speed. Marathon runner’s energy consumption includes heat dissipation and kinetic energy consumption of the human body. The athletes need to supply more energy to meet heat dissipation and kinetic energy consumption when the temperature is lower, the travel speed is faster, and the road is steeper. The athlete can complete sports at low temperatures, large slopes, and high speeds only when the speed and intensity of the energy supplied by the human body meet these energy require- ments. At the general running speed, the prediction formula of human exercise energy consumption19, 20 is: where M is the energy consumption rate of the human body (w), W is the body weight (kg), L is the bear load (kg), V is the running speed (m/s), G is the slope (%), η is the surface condition coefficient, η = 1 in the case of horizontal hard road and η = 2 on loose sand. Figures 2a–c and 3a–c, η = 1.2. People move slower on a 12° gradient slope uphill and downhill than on a flat road21. When going downhill, the energy consumption decreases22, but with the increase of the descending slope, the energy consumption also increases to improve the friction and reduce the risk of falling23. But the increase is not as significant as the upslope walking. This paper selects the positive slope for calculation. Human body surface heat dissipation. Baiyin Marathon runners are not equipped for the cold, wearing simple clothes or directly exposed24–26. The heat dissipation is, where Wci is the air-cooling index (kcal/m2/h), F is the wind speed (m/s), T is the air temperature (℃). It repre- sents the heat dissipation rate of the human body surface with a skin temperature of 33 ℃. The maximum energy supply of Chinese runners. Accurate measurement of lipid or carbohydrate metabolism during exercise is also complex. Marathon running is fueled principally by the oxidation of intra- muscular glycogen and lipids to a lesser extent27, 28. Therefore, there is no need to distinguish between carbohy- drate and lipid metabolism and their percentages. At the same temperature, the energy consumption positively correlates with the maximum oxygen uptake29. According to the test results30, 31, perform linear formula fitting at the same temperature to obtain the energy consumption at 100% VO2max, namely the maximum energy supply rate. Table 1 lists the data. In reference29, the performance at different temperatures shows a similar and linear relationship. Rising and falling external temperatures increase the body’s energy expenditure (Table 1). The linear relationship between the total energy consumption of Chinese male athletes at different tem- peratures of 100%VO2max and temperature is not significant. On the contrary, the total energy consumption at different temperatures remains stable32. It isn’t easy to obtain data on the maximum energy supply at different temperatures. Therefore, this paper takes the full energy supply as a fixed value. The energy supply range of Chinese male athletes is 291.7–356.7 cal/min/kg. Therefore, this paper considers that Chinese male athletes’ maximum energy supply rate is 350 cal/min/kg. Other data. Select terrain data from ASTERGDEMv2.0, land DEM, with an accuracy of about 30 m, and the download address is https:// earth explo rer. usgs. gov/. Figure 3 is generated by ArcGIS V10.3. (1) M = 1.5W + 2.0(W + L)  L W 2 + η(W + L)[1.5V2 + 0.35VG], (2) Wci = (10.45 + 10 √ F − F) × (33 − T), Table 1. Reference data of human of Chinese male athletes’ energy supply. Intensity (% of VO2max) Total energy consumption (cal/min/kg) Temperature (°C) References 65%VO2max 240.0 12 30 80%VO2max 290.0 100%VO2max 356.7 This research 40%VO2max 134.8 23 31 60%VO2max 185.4 80%VO2max 239.8 100%VO2max 291.7 This research 40%VO2max 126.6 33 31 60%VO2max 188.3 80%VO2max 236.7 100%VO2max 294.0 This research 6 Vol:.(1234567890) Scientific Reports | (2022) 12:8179 | https://doi.org/10.1038/s41598-022-12403-1 www.nature.com/scientificreports/ Liang Jing, China’s leading marathon runner who died in the accident, ran at 100 km for 7 h in previous com- petitions at a speed of 4 m/s. This research takes the value of 1.5 m/s according to the running speed of ordinary athletes. The weight of male athletes is 70 kg, the wind speed is 0, the height is 170 cm, the body surface area is 1.82 m2, and the temperature comes from Baiyin meteorological observation station. Reference15 provides the temperature data of the day at point CP3. Baiyin Weather Station and Reference33 offer the average temperature, maximum and minimum temperature of 21 days before the competition. Figure 4 lists the temperature curves15. Results According to Eqs. (1) and (2) calculate the total energy required by running energy consumption and surface heat dissipation along the elevation of the running route. And the energy supply rate is 350 cal/min/kg (1715 J/s, max supply energy) according to the maximum oxygen uptake. Figure 5 lists the results. The slope increases significantly in the CP2–CP3 section (24–32.5 km), and the required energy rises. Accord- ing to the speed of 1.5 m/s, runners run in this section from 13:26 to 15:01. At this time, the temperature in Baiyin is 6.9–8.2 ℃. In Fig. 5, many parts of the blue curve of CP2–CP3 section are more significant than the pink curve, indicating that the energy demand is greater than the energy supply. The external performance is that the athletes gradually lose temperature and enter a dangerous state. In the section before CP1 (5–10 km), there is also a part where the energy demand is greater than the energy supply. Still, this section is at the initial running stage, and the gap between energy demand and energy supply is not too large. Athletes can well avoid temperature loss in this section by unconscious deceleration. The energy demand is greater than the energy supply from CP4 to SP and SP to CP5, but the two sections are mainly the downhill section. Since calculating in Figure 4. The temperature of Baiyin and CP3 on race day and the average temperature, maximum and minimum temperature of 21 days before the competition, Baiyin Weather Station. Figure 5. Marathon route altitude, maximum energy supply, and energy demand curve, replenishment points, and check-in points. Calculate the energy required according to the actual temperature at CP3. Since there is no temperature data later, the following data is vacant. 7 Vol.:(0123456789) Scientific Reports | (2022) 12:8179 | https://doi.org/10.1038/s41598-022-12403-1 www.nature.com/scientificreports/ this paper is based on the absolute value of the slope, athletes can slow down and travel downhill slowly. At the same time, the distance is not long, and the actual energy demand is not high. Therefore, we believe that the design of the route is seriously unreasonable and unscientific in CP2–CP3. And the design at 5–10 km is also absurd. The main reason is that the slope is too large, and the distance with a significant slope is too long34. From Fig. 4, when passing through CP2–CP3, the temperature is lower than the minimum temperature in the previous 21 days, which is also a significant factor leading to the accident. Therefore, it is necessary to design the running route with the local historical temperature and avoid the month with the lowest temperature and the continuous path with a steep slope. In the last three marathons, the competition time is May 20, 2018, June 8, 2019, and September 29, 2020. The maximum and minimum temperatures in these three times are 14–27 ℃, 15–29 ℃, 14–20 ℃. Choose the minimum value of temperature for calculation. In each interval, calculate the ratio of the total energy consumed to the maximum supply energy according to Eqs. (1) and (2), shown in Fig. 6. C1 is from the Start point to CP1 point [Start CP1]. C2: [CP1, CP2]. C3: [CP2, CP3]. C4: [CP3, CP4]. C5: [CP4, SP]. C6: [SP, CP5]. C7: [CP5, CP6]. C8: [CP6, CP7]. C9: [CP7, CP8]. C10: [CP8, CP9].C11: [CP9, End point]. The CP3 T is missing from C9 to C11 from the CP7 to the end. Baiyin T is the temperature of Baiyin. CP3 T is the temperature of CP3 point. The lowest temperature is 14 °C, 15 °C, and 14 °C on the 1st, the competition on May 20, 2018, the second on June 8, 2019, and the third on September 29, 2020. The horizontal black line is 90%. Section C3 in Fig. 6, that is, from CP2 to CP3, the calculation on the day of the first, second, and third mara- thon shows that the ratio is close to 90%. Calculate these three results according to the minimum temperatures of 14 °C, 15 °C and 14 °C on the day of the competition. The temperature of the race day is higher than and not constant equal to this minimum temperature. The energy consumption rate in this competition period is less than 90%. At the same time, the span of the minimum and maximum temperature is close to the optimal temperature of 18.6 °C35, so the athletes can pass smoothly. In Fig. 6, in Section C3, according to the temperature at Baiyin meteorological station and CP3 on the day of the fourth competition in 2021, the physical energy consumption of athletes on the race day has been greater than 90%, or even reached 113%, indicating that the physical energy consumption of athletes is too large. This distance is 8.5 km, which takes 1.6 h according to the speed of 1.5 m/s. At this time, the athletes are in danger. In section C11, in 2021, the physical consumption of athletes has also reached 94%, which is also a danger- ous section. In other areas (C1, C2, C4–C10), the physical consumption of athletes does not exceed 90%, and athletes are in a safe state. The first three were safe from the four Baiyin marathons in 2018, 2019, 2020, and 2021, and the fourth had accidents. Combining Fig. 6 with the above analysis, we conclude that choosing 350 cal/min/kg as Chinese male athletes’ maximum energy supply rate is reasonable under 100% VO2max. It is reasonable to take 90% of the energy consumption ratio as Chinese marathon athletes’ maximum energy supply rate 315 cal/min/kg. Discussion The purpose of this study is to prevent the recurrence of such accidents. The energy consumption of the human body: First, the heat dissipation on the surface of the body is directly related to the outside air temperature. The body heat dissipation is significant when the outside air temperature is low. The second is the path slope. The large slope requires large energy. The third is the moving speed. The speed is large, and the energy required is large. The speed is small, and the energy required is small. The fourth is the wind speed, in Eq. (2). The lost energy is large when the wind speed is large, and the heat loss is slight when the wind speed is negligible. Figure 6. The ratio of the total energy consumed for a marathon to the maximum energy supply T is the temperature, and LT is the lowest temperature. 8 Vol:.(1234567890) Scientific Reports | (2022) 12:8179 | https://doi.org/10.1038/s41598-022-12403-1 www.nature.com/scientificreports/ If completely avoid such accidents, one needs to limit the temperature, slope, moving speed, and wind speed. The above analysis is based on the fact that the long-distance moving speed of ordinary people is 1.5 m/s. The previous analysis has determined that the limit energy supply of personnel is 350 cal/min/kg. According to the calculation of 90%, the maximum energy supply is 315 cal/min/kg to be calculated. The marathon path is ups and downs with different slopes. The comprehensive slope characterizes the overall slope of the path, which is as follows: where Gz is the comprehensive slope; ΔLi is the ist distance, in the research, ΔLi = 200 m; Gi is the slope of the ist distance, only Gi > 0 is selected for calculation. The slop of the CP2-CP3 is the largest in the entire marathon route (Fig. 4). This part is chosen to calculate the comprehensive slope according to Eq. (2). The comprehensive slope of the CP2–CP3 is 0.17197. Suppose we determine all the slopes, including the Gi > 0 and Gi < 0, and take − Gi for Gi < 0, the comprehensive slope is 0.21152. It doesn’t take much energy to go downhill, so we don’t take downhill into account. If we take Gi for Gi < 0, the comprehensive slope is 0.132424. This calculation ignores the difficulty of the climb. So we choose 0.17197 to evaluate. When the temperature is 15 °C, the travel speed is 1.5 m/s, and the wind speed is 0, the required energy is 1546.3 w, which is close to 315 cal/min/kg of the energy supplied by the ordinary human body, and the calculated energy supply rate is 1543.5w. According to Eqs. (1), (2), and (3), in the CP2–CP3 section, the slope value is 0.17197, and the temperature and wind speed risk zoning of this section is shown in Fig. 7. The athlete’s speed is 0, 0.5 m/s, and the maximum energy supply is 1543.5 w, according to 70% and 100% VO2max. It is roughly divided into three zones: danger zone, transition zone, and safety zone. When the Baiyin Marathon was held on May 22, 2021, the temperature range was [− 4.1 ℃, − 1.2 ℃]. Because this paper could not get the wind speed data at that time, according to the photos of the participating remote mobilizers, the wind speed was very large. The wind speed F is greater than 5.5–8 m/s, or greater than 14–17 m/s. The dangerous risk zones in CP2–CP3 were shown in “Baiyin CP2–CP3” in Fig. 7. The rectangular area shown in the figure indicates that the site is hazardous, and as the wind speed increases, the danger increases. In CP2–CP3, marathon runners can make adjustments and actions by assessing their zone based on the tem- perature and wind speed at that time. Figure 7 has good instructive value and is very actionable. It is convenient for athletes to evaluate whether they can participate in the marathon according to their situation and prepare corresponding equipment. Conclusions ‘522’ Baiyin marathon is a typical ‘human life test’ and an ‘energy supply human experiment’. The lessons of the marathon accident can provide a reference for the marathon. In the first three Baiyin marathons, no accidents occurred. The main reason is that the temperature on the race day is relatively high, which is 14–29 °C. The energy consumption rate of athletes is lower than or temporarily reaches 90%, and the athletes are in a safe state all the time. An important reason for the accident of the fourth silver marathon in 2021 is the low temperature15. In the marathon route design, from CP2 to CP3, the terrain is continuously uphill at a large angle, and the athletes consume much physical energy. From CP2 to CP3, with the most accidents, the athletes’ physical consumption rate is greater than 90% or even 113%, and the athletes are in a dangerous state, resulting in hypothermia. Fatigue (3) Gz =  GiLi  Li × 100%, Figure 7. The dangerous risk zones of wind speed and temperature in the CP2–CP3 section of the Baiyin marathon. 9 Vol.:(0123456789) Scientific Reports | (2022) 12:8179 | https://doi.org/10.1038/s41598-022-12403-1 www.nature.com/scientificreports/ leads to insufficient physical strength to resist a sudden low temperature, resulting in hypothermia36. Runners used fire to keep warm in a cave-dwelling, shown in Fig. 2d. There are many reasons15 for the 2021 marathon accident, including unscientific route design, unreasonable holding time, etc. These complex factors bring great trouble to the route designer, which is challenging. The method proposed in this paper, which fully considers the slope and temperature, quantitatively calculates the percentage of athletes’ physical consumption, and then designs the route design and evaluation of field marathons through the physical consumption ratio, is a valuable method. The physical energy consumption ratio of 90%, i.e., 315 cal/min/kg, should be taken as the maximum energy supply rate of Chinese marathon athletes and the maximum energy supply rate at the maximum oxygen intake of Chinese male athletes is 350 cal/min/kg. When designing the marathon route in the future, avoiding continuous uphill with a large slope is necessary. Designers can refer to this literature34 for route design. At the same time, the race time should entirely avoid high temperatures and, more importantly, avoid low-temperature times. At the same time, the dangerous risk zones for the wind speed and temperature of marathon runners are established on the most hazardous path section so that the runners can evaluate whether they can participate in the marathon according to their conditions and prepare corresponding equipment. Since it is an accident-based analysis, much data is lacking, and life experiments are impossible. The calcula- tion involves a lot of content and is very complex. Nonetheless, such a limited analysis of life is valuable. The guiding significance of the paper is that the organizer needs to calculate the current situation synchronously according to the real-time situation, and broadcast the information in real time. Received: 17 December 2021; Accepted: 3 May 2022 References 1. Moskalenko, N., Otkydach, V., Korchagin, M., Khlimantsov, T. & Kuryshko, E. Influence of military classes sports matt on the health of students of higher education institutions. Sports Bull. Prydniprovya Sci. Pract. J. 1, 345–351 (2020). 2. Smith, C., Doma, K., Heilbronn, B. & Leicht, A. Effect of exercise training programs on physical fitness domains in military per- sonnel: A systematic review and meta-analysis. Mil. Med. https:// doi. org/ 10. 1093/ milmed/ usac0 40 (2022). 3. Saidi, O. et al. Effect of morning versus evening exercise training on sleep, physical activity, fitness, fatigue and quality of life in overweight and obese adults. Chronobiol. Int. 38, 1537–1548. https:// doi. org/ 10. 1080/ 07420 528. 2021. 19359 88 (2021). 4. Ferguson, D. P. & Myers, N. D. Physical fitness and blood glucose influence performance in indycar racing. J. Strength Cond. Res. 32, 3193–3206. https:// doi. org/ 10. 1519/ jsc. 00000 00000 002879 (2018). 5. Prokopczyk, A. & Sokolowski, M. Impact of training experience and training total time on aerobic capacity and level of effective restitution of female Polish Judo National Team athletes during the preparation for the Olympic Games. Arch. Budo 17, 35–41 (2021). 6. Ishide, Y. et al. The effect of GaAIAs diode laser on pre-sports warming up and post-sports cooling down. Laser Therapy 17, 187–192 (2008). 7. Fudge, B. et al. Evidence of negative energy balance in elite Kenyan endurance runners during intense high-altitude training. Med. Sci. Sports Exerc. 37, S142–S142. https:// doi. org/ 10. 1097/ 00005 768- 20050 5001- 00774 (2005). 8. Woolley, K. & Martin, T. High intensity interval training and acute altitude exposure in a masters athlete: A case study. Med. Sci. Sports Exerc. 51, 404–404. https:// doi. org/ 10. 1249/ 01. mss. 00005 61712. 41536. 2e (2019). 9. Shah, N. et al. The effects of apnea training, using voluntary breath holds, on high altitude acclimation: Breathe-high altitude study. High. Alt. Med. Biol. 21, 152–159. https:// doi. org/ 10. 1089/ ham. 2019. 0087 (2020). 10. Zaman, G. S. et al. The impact of body resistance training exercise on biomedical profile at high altitude: A randomized controlled trial. Biomed. Res. Int. https:// doi. org/ 10. 1155/ 2021/ 66841 67 (2021). 11. Castro, R. R. T., Mendes, F. & Nobrega, A. C. L. Risk of hypothermia in a new Olympic event: The 10-km marathon swim. Clinics 64, 351–356. https:// doi. org/ 10. 1590/ s1807- 59322 00900 04000 14 (2009). 12. Drigny, J. et al. Risk factors and predictors of hypothermia and dropouts during open-water swimming competitions. Int. J. Sports Physiol. Perform. 16, 1692–1699. https:// doi. org/ 10. 1123/ ijspp. 2020- 0875 (2021). 13. China Marathon. https:// www. runch ina. org. cn (Accessed 19 March 2022). 14. Zhixingheyi. http:// www. zx421 95. com (Accessed 19 March 2022). 15. Group, G. P. P. C. A. P. G. J. I. Baiyin Jingtai "5·22" Yellow River Shilin 100 km Cross-country Race Public Safety Responsibility Incident Investigation Report (2021) http:// www. gansu. gov. cn/ gsszf/ c1000 02/ c1000 10/ 202107/ 16435 66. shtml (Accessed 25 June 2021). 16. O’Meara, S. The heat is on. Nature 592, S2–S3. https:// doi. org/ 10. 1038/ d41586- 021- 00815-4 (2021). 17. Maughan, R. J. Thermoregulation in marathon competition at low ambient temperature. Int. J. Sports Med. 6, 15–19. https:// doi. org/ 10. 1055/s- 2008- 10258 06 (1985). 18. Ma, J. & Zhang, Y. 27 Gansu Officials Held Accountable for Marathon Deaths (2021). https:// www. china daily. com. cn/a/ 202106/ 12/ WS60c 32672 a3102 4ad0b ac668a. html (Accessed 12 June 2021). 19. Pandolf, K. B., Givoni, B. & Goldman, R. F. Predicting energy expenditure with loads while standing or walking very slowly. J. Appl. Physiol 43, 577 (1977). 20. Pimental, N. A. & Pandolf, K. B. Energy expenditure while standing or walking slowly uphill or downhill with loads. Ergonomics 22, 963–973 (1979). 21. Kawamura, K., Tokuhiro, A. & Takechi, H. Gait analysis of slope walking—A study on step length, stride width, time factors and deviation in the center of pressure. Acta Med. Okayama 45, 179–184 (1991). 22. Zai, C. Z. & Grabowski, A. M. The metabolic power required to support body weight and accelerate body mass changes during walking on uphill and downhill slopes. J. Biomech. 103, 6 (2020). 23. Minetti, A. E. Optimum gradient of mountain paths. J. Appl. Physiol. 79, 1698–1703 (1995). 24. Zhang, S. P., Meng, G. L., Wang, Y. W. & Li, J. Study of the relationships between weather conditions and the marathon race, and of meteorotropic effects on distance runners. Int. J. Biometeorol. 36, 63–68 (1992). 25. Siple, P. A. & Passel, C. F. Excerpts from: Measurements of dry atmospheric cooling in subfreezing temperatures. Wilderness Environ. Med. 89, 177–199 (1945). 26. Swarnkar, C. P., Prince, L. L. L. & Sonawane, G. G. Wind chill index and neonatal lamb mortality at an organized farm in semi-arid Rajasthan. Biol. Rhythm Res. https:// doi. org/ 10. 1080/ 09291 016. 2018. 14275 99 (2018). 27. Burke, L. M. & Hawley, J. A. Swifter, higher, stronger: What’s on the menu? Science 362, 781–787. https:// doi. org/ 10. 1126/ scien ce. aau20 93 (2018). 10 Vol:.(1234567890) Scientific Reports | (2022) 12:8179 | https://doi.org/10.1038/s41598-022-12403-1 www.nature.com/scientificreports/ 28. Hargreaves, M. & Spriet, L. L. Skeletal muscle energy metabolism during exercise. Nat. Metab. 2, 817–828. https:// doi. org/ 10. 1038/ s42255- 020- 0251-4 (2020). 29. Layden, J. D., Patterson, M. J. & Nimmo, M. A. Effects of reduced ambient temperature on fat utilization during submaximal exercise. Med. Sci. Sports Exerc. 34, 774 (2002). 30. Aiqi, Y., Qirong, W. & Wenping, F. Study on energy consumption characteristics of different intensity exercise in low temperature environment. Chin. J. Sports Med. 37, 59–61 (2018). 31. Jintao, Q., Fanxing, Z., Wenping, F. & Xun, L. The difference of energy consumption between high temperature and high humidity environment and normal temperature environment. Chin. J. Sports Med. 34, 164–169 (2015). 32. King, A. The key to endurance. Nature 592, S7–S9. https:// doi. org/ 10. 1038/ d41586- 021- 00817-2 (2021). 33. Media, T. N. C. F. How Cold It was During the Baiyin Marathon Cross-Country race in Gansu Province? (2021). 34. Sun, J. Design theories and methods of running routes and gradients. Int. J. Ground Sedim. Water 16, 1025–1035. https:// doi. org/ 10. 5281/ zenodo. 63894 77 (2022). 35. Scheer, V. et al. The optimal ambient conditions for world record and world class performances at the Berlin marathon. Front. Physiol. 12, 1–8. https:// doi. org/ 10. 3389/ fphys. 2021. 654860 (2021). 36. Ainslie, P. N., Campbell, I. T., Lambert, J. P., MacLaren, D. P. M. & Reilly, T. Physiological and metabolic aspects of very prolonged exercise with particular reference to hill walking. Sports Med. 35, 619–647. https:// doi. org/ 10. 2165/ 00007 256- 20053 5070- 00006 (2005). Acknowledgements Thank Zhang Xiaotao and Dongbei Lowang 01 for the pictures. Author contributions S.J. did and finished all the work. Competing interests The author declares no competing interests. Additional information Correspondence and requests for materials should be addressed to J.S. Reprints and permissions information is available at www.nature.com/reprints. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. © The Author(s) 2022

Energy supply and influencing factors of mountain marathon runners from Baiyin marathon accident in China.

05-17-2022

Sun, Jichao

eng

PMC7497021

1360 | Scand J Med Sci Sports. 2020;30:1360–1368. wileyonlinelibrary.com/journal/sms 1 | INTRODUCTION The medial longitudinal arch is a unique structure in the human foot. During weight-bearing exercises, the foot arch lowers while being stretched out and then recoils as the load is removed. Such a spring-like property of the foot arch helps to attenuate impact forces and store/release elas- tic strain energy leading to energy saving during running.1 It is known, however, that long-distance running (LDR) imposes repetitive mechanical stress on the foot, thereby inducing transient lowering of the foot arch.2,3 As the foot arch is temporarily collapsed, its force attenuation capacity Received: 14 December 2019 | Revised: 1 April 2020 | Accepted: 15 April 2020 DOI: 10.1111/sms.13690 O R I G I N A L A R T I C L E Acute effects of long-distance running on mechanical and morphological properties of the human plantar fascia Hiroto Shiotani1,2 | Tomohiro Mizokuchi3 | Ryo Yamashita3 | Munekazu Naito4,5 | Yasuo Kawakami5,6 This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2020 The Authors. Scandinavian Journal of Medicine & Science In Sports published by John Wiley & Sons Ltd 1Graduate School of Sport Sciences, Waseda University, Saitama, Japan 2Research Fellow of Japan Society for the Promotion of Science, Tokyo, Japan 3School of Sport Sciences, Waseda University, Saitama, Japan 4Department of Anatomy, Aichi Medical University, Aichi, Japan 5Human Performance Laboratory, Organization for University Research Initiative, Waseda University, Tokyo, Japan 6Faculty of Sport Sciences, Waseda University, Saitama, Japan Correspondence Yasuo Kawakami, Faculty of Sport Sciences, Waseda University, 2-579-15 Mikajima, Tokorozawa, Saitama 359-1192, Japan. Email: [email protected] Funding information Japan Society for the Promotion of Science, Grant/Award Number: 16H01870 and 19J14912 Long-distance running (LDR) can induce transient lowering of the foot arch, which may be associated with mechanical fatigue of the plantar fascia (PF). However, this has not been experimentally tested in vivo. The purpose of this study was to test our hypothesis that LDR induces transient and site-specific changes in PF stiffness and morphology and that those changes are related to the lowering of the foot arch. Ten male recreational long-distance runners and 10 untrained men were requested to run overground for 10 km. Before and after running, shear wave velocity (SWV: an index of soft tissue stiffness) and thickness of PF at three different sites from its proximal to distal end were measured using supersonic shear imaging and B-mode ultrasonography. Foot dimensions including the navicular height were measured using a three-dimensional foot scanner. SWV at the proximal site of PF and navicu- lar height was significantly decreased in both groups after running, with a higher degree in untrained men (−21.9% and −14.1%, respectively) than in runners (−4.0% and −6.3%, respectively). The relative change (%Δ) in SWV was positively cor- related with %Δnavicular height in both groups (r = .69 and r = .65, respectively). Multiple regression analysis revealed that %ΔSWV at the proximal site solely ex- plained 72.7% of the total variance in %Δnavicular height. It is concluded that LDR induces transient and site-specific decreases in PF stiffness. These results suggest that the majority of running-induced lowering of the foot arch is attributable to the reduction of PF stiffness at the proximal site. K E Y W O R D S elasticity, mechanical fatigue, medial longitudinal arch of the foot, plantar aponeurosis, stiffness, supersonic shear imaging, thickness, ultrasound shear wave elastography  | 1361 SHIOTANI eT Al. is compromised. A collapsed foot arch (eg, pes planus) is known to increase the risk of injury around the lower limb and foot.4,5 Previous work indicates that the foot arch elasticity is pri- marily attributed to the plantar fascia (PF).1,6,7 PF behaves visco-elastically under tension and contributes to the elas- tic recoil of the foot arch.1,8-11 During each foot contact of running, PF repetitively experiences tension as high as 0.6- 3.7 times the body weight, with its longitudinal strain up to 6%.12-15 Simulation studies have shown that the tension and peak stress along PF concentrate at its proximal sites.16-18 Accumulation of such repetitive and site-specific stress on PF can induce mechanical fatigue (ie, reduction of stiffness and increased strain upon loading).19-21 This can be a major factor for the lowering of the foot arch during LDR. This potential mechanism should be experimentally tested, but no study has ever attempted to quantitatively evaluate the running-induced mechanical fatigue of PF in vivo and relate it to the lowering of the foot arch. Long-distance runners, regardless of their performance level, are known to be the most prevalent and vulnerable population to plantar fasciitis.5,22,23 On the other hand, the plasticity of connective tissues’ mechanical and morpho- logical properties allows them to adapt to chronic mechan- ical loading (eg, increases in stiffness and cross-sectional area).24,25 Therefore, it is possible that well-experienced long-distance runners possess PF and a foot arch that are adapted to LDR (smaller changes in PF properties and arch deformation) as compared to untrained individuals. Available knowledge is limited for this issue. The purpose of the present study was to investigate the acute effects of LDR on the mechanical and morphological properties of PF and the foot arch in untrained individuals and long-distance runners. The hypotheses were (a) LDR induces transient and site-specific changes in PF stiffness and morphology, (b) those changes in PF are related to the indices of lowering of the foot arch, and (c) LDR shows smaller changes in PF characteristics and foot dimensions after running in runners than in untrained individuals. 2 | MATERIALS AND METHODS 2.1 | Participants Twenty healthy young men (10 recreational long-distance runners and 10 untrained individuals; Table 1) participated in this study. All participants had no lower extremity injury in the past 12 months or subjective symptom that would impede running at the time of measurement. The runners had kept habitual running for at least 10 km/wk for the year, and their running experiences ranged between 9 and 16 years. The un- trained participants were either sedentary or lightly active, and none of them had been involved in any structured LDR program or continuous sports participation at least 12 months before the measurement. This study was approved by the Institutional Human Research Ethics Committee and was carried out in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants before data collection. 2.2 | Protocol Prior to visiting the laboratory, participants were asked not to perform any strenuous exercises for at least 24 hours before the measurements. A test-retest protocol was used to exam- ine the acute effects of LDR. Participants were requested to run for 10 km on a 700 m outdoor asphalt-surface circular path adjacent to the laboratory. The protocol was assumed to be identical regarding the mechanical loading for the two groups given that they possess comparable stature (Table 1), and the condition was standardized with the running velocity set at 10 km/h. Lap time was recorded, and running veloc- ity was adjusted by oral instruction. Participants wore their own sports clothes and running shoes during running, but no one used unsuitable shoes that could confound the results (eg, the minimalist, high cushion or high motion control shoes). The foot strike pattern of participants was visually confirmed throughout the running task. All participants were able to complete this 10-km running task without resting or walking. The average completion time was 0:59:57 (0:59:04-1:00:57). Before (Pre), immediately after (Post), and 30 and 60 min- utes after the termination of running, participants underwent the measurement to examine PF stiffness, thickness, and foot dimensions of their right feet. Care was taken to ensure that participants were in the same posture during pre- and post- running measurements. TABLE 1 Physical characteristics of participants Variable Runners Untrained men P value n 10 10 - Age (y) 22.0 ± 0.7 22.5 ± 1.4 .31 Height (m) 1.68 ± 0.04 1.70 ± 0.05 .39 Body mass (kg) 55.5 ± 4.2 58.4 ± 5.6 .06 BMI (kg/m2) 19.6 ± 1.2 20.3 ± 1.7 .11 Running experience (y) 11.0 ± 2.2 - - Running distance (km/wk) 43.7 ± 35.4 - - RFS: FFS (n) 7:3 10:0 .06 Note: Data are shown as mean ± SD. Abbreviations: BMI, body mass index; FFS, forefoot strikers; RFS, rearfoot strikers. 1362 | SHIOTANI eT Al. 2.3 | Ultrasound measurements To measure mechanical and morphological properties of PF, supersonic shear imaging (SSI) and B-mode ultrasonography were used. SSI is a valid and reliable technique to evaluate stiffness and morphology of skeletal muscles, tendons, and fasciae in vivo.26-28 SSI uses acoustic push pulses that propa- gate in the soft tissues and measures their velocity (ie, shear wave velocity: SWV) as an index of stiffness.29,30 We re- cently developed a technique using SSI to measure localized SWV values and thickness of PF with high repeatability.31 Details of measurement and data processing are reported elsewhere31; but briefly, B-mode images and shear wave data were obtained using an Aixplorer ultrasound scanner (version 6.4; Supersonic Imagine) with a linear array transducer (SL 15-4; Supersonic Imagine). Participants were requested to rest in a supine position on the examination bed with the knee fully extended, and their ankle and toe digits were secured to a custom-made fixture at the neutral position (Figure 1). PF was scanned in the longitudinal direction at the proximal (in the proximity to the calcaneus), middle (the level of navicu- lar tuberosity), and distal (proximity to the second metatar- sal head) sites. The locations of the transducer were marked on the skin surface using a waterproof marker for identical measurement locations. For each measurement site, B-mode images and shear wave data were recorded for 7 seconds with a system operating at 12 Hz (ie, the default sampling rate of SSI measurement of the current version of the ultrasound scanner). After the data collection, three images separated by 12 frames from the middle of the 7 seconds recording were picked up and used for further analysis. SWV was obtained as the mean value within the region of interest (ROI) which was manually traced over the fascial boundaries of PF using a measurement tool included in the Aixplorer software (Q-boxTM Trace). Mean ROI size at the proximal, middle, and distal sites were approximately 0.34, 0.22, and 0.17 cm2, respectively. Care was taken to exclude any rejection (ie, the area with pixels having no color) within ROI to avoid underestimation of SWV (33 of the 720 im- ages used in this study had rejection areas of approximately 0.01 cm2). To assess PF morphology, the distance between the superficial and deep fascial boundaries was measured to determine thickness using a measurement tool (distance). Three images were analyzed for SWV and thickness at each measurement site of PF; then, the three values were averaged to obtain the representative value for each site. 2.4 | Measurements of foot dimensions A three-dimensional foot scanner (JMS-2100CU; Dream GP) was used to obtain foot dimensions. The scanning and analy- sis procedure were based on previous studies which reported transient changes in the foot shapes after LDR using the same system.2,3 The foot was scanned both in a sitting and standing positions. A laser scanner moved around the foot in an oval trajectory, measuring the foot dimensions and the anatomical marker positions based on laser line triangulation with high accuracy.2,3 After the scanning, foot length, dorsal height, and navicular height were obtained. The arch height ratio was calculated as the navicular height normalized to the foot length. Navicular drop was calculated as the difference in the navicular height between sitting and standing positions. Foot dimensions in the standing position are reported as the repre- sentative values unless otherwise noted. 2.5 | Statistical analysis An unpaired t test was performed to test the difference in physical characteristics between groups. The fractions of rearfoot (RFS) and forefoot strikers (FFS) within each FIGURE 1 Experimental setup and representative ultrasound B-mode and shear wave images of the plantar fascia (PF) at the proximal (P), middle (M), and distal (D) sites. ROI, region of interest  | 1363 SHIOTANI eT Al. group were compared with a Pearson chi-squared test. Changes in SWV, thickness, and foot dimensions were compared by a two-way (4 time points x 2 groups) re- peated measures analysis of variance (ANOVA). If signifi- cant main effects and/or interactions were found, Dunnett's test or an unpaired t test was performed as a post-hoc test, where appropriate. If there was no significant main effect or interactions for ANOVA or significant difference for a post-hoc test, a post-hoc power analysis (G*Power v3.1; Heinrich Heine-Universität) was performed to test our sample size was sufficient for 80% statistical power at a significance level of α = .05. To examine the difference between groups in the degree of running-induced changes in SWV, thickness, and foot di- mensions, relative change (%Δ) from pre- to post-running were calculated for these variables and were compared by an unpaired t test between groups. As indices of effect size, partial η2 (for ANOVA) and Cohen's d (for a post-hoc test) were calculated. A post-hoc power analysis estimated that the effect size needed for 80% power was d ≥ 0.577. To exam- ine the relationships of individual %Δnavicular height and %Δarch height ratio with %ΔSWV and %Δthickness at each measurement site, Pearson product-moment correlation coef- ficients were calculated. Moreover, to determine predictive variables for %Δnavicular height and %Δarch height ratio, six independent variables (%ΔSWV and %Δthickness at each measurement site) with combining data of both groups were entered into a forward stepwise multiple regression model with %Δnavicular height and %Δarch height ratio as the dependent variables. The criteria used for entering and removing the stepwise regression model were F ≤ 0.05 and F ≥  0.10, respectively. Statistical significance was set at α = .05. Statistical analysis was performed using SPSS soft- ware (SPSS Statistics 25; IBM). 3 | RESULTS Age, height, body mass, BMI, and fractions of the foot strike patterns were not significantly different between runners and untrained men (Table 1). Figure 2 shows the changes in SWV at each measurement site in runners and untrained men. SWV at the proximal site showed a significant time-group interac- tion (P = .001, η2 = 0.374). In runners, SWV at the proximal site significantly decreased at Post (P = .045, d = 1.026), but not at 30 or 60 minutes after running (P ≥ .346, d ≤ 0.208). In untrained men, SWV at the proximal site significantly decreased at Post (P  =  .003, d  =  1.541) and 30  minutes (P = .011, d = 1.309), but not at 60 minutes after running (P = .101, d = 0.719). %ΔSWV at the proximal site was sig- nificantly smaller in runners than in untrained men (P < .001, d  =  1.912). SWV at the middle site showed a significant main effect of time (P = .010, η2 = 0.321), without a main effect of group (P = .401, η2 = 0.040), or their interaction (P = .165, η2 = 0.266). Dunnett's test with combining data of both groups found that SWV at the middle site significantly decreased at Post (P = .036, d = 0.643), but not at 30 or 60 minutes after running (P ≥ .455, d ≤ 0.300). Figure 3 shows the changes in PF thickness at each mea- surement site in runners and untrained men. PF thickness at the proximal site showed a significant main effect of time (P = .012, η2 = 0.344) and group (P = .048, η2 = 0.200), without their interaction (P = .072, η2 = 0.121). However, Dunnett's test did not find a significant change in PF thick- ness at the proximal site in runners (P ≥ .196, d ≤ 0.603) or untrained men (P ≥ .196, d ≤ 0.603). PF thickness at the mid- dle site showed a significant main effect of time (P = .015, η2  =  0.174), without a main effect of group (P  =  .947, η2  <  0.001) or their interaction (P  =  .115, η2  =  0.103). However, Dunnett's test with combining data of both groups FIGURE 2 Shear wave velocity of the plantar fascia at the proximal, middle, and distal sites in runners (closed circles) and untrained men (opened circles) measured before (Pre), immediately after (Post), and 30 and 60 min after the termination of running. *Significantly different from pre (P < .05). †Combining data of both groups show significant difference from pre (P < .05) 1364 | SHIOTANI eT Al. did not find a significant change in PF thickness at the middle site (P = .211, d = 0.429). A post-hoc power analysis using the parameters of PF thickness revealed that a total of 18 par- ticipants (nine participants per each group) were required for 80% statistical power at a significance level of α = .05. Table 2 shows the changes in foot dimensions of runners and untrained men. Navicular height showed a significant time-group interaction (P < .001, η2 = 0.342). In runners, navicular height significantly decreased at Post (P = .042, d  =  1.129), but not at 30 or 60  minutes after running (P ≥ .576, d ≤ 0.163). In untrained men, navicular height significantly decreased at Post (P  =  .036, d  =  2.029) and 30 minutes (P = .043, d = 1.506), but not at 60 minutes after running (P = .566, d = 0.200). Arch height ratio showed a significant time-group interaction (P  <  .001, η2  =  0.329). In runners, arch height ratio significantly decreased at Post (P = .044, d = 1.050), but not at 30 or 60 minutes after run- ning (P ≥ .614, d ≤ 0.161). In untrained men, arch height ratio significantly decreased at Post (P = .020, d = 3.773) and 30 minutes (P = .048, d = 1.364), but not at 60 min- utes after running (P = .564, d = 0.200). %Δnavicular height (P = .002, d = 1.655) and %Δarch height ratio (P = .001, FIGURE 3 Thickness of the plantar fascia at the proximal, middle, and distal sites in runners (closed circles) and untrained men (opened circles) measured before (Pre), immediately after (Post), and 30 and 60 min after the termination of running TABLE 2 Changes in the foot dimensions in response to long-distance running Variable Runners (n = 10) Untrained men (n = 10) Pre Post 30 min 60 min Pre Post 30 min 60 min Foot length (mm) 245.9 ± 8.6 245.7 ± 8.5 244.3 ± 8.2 244.5 ± 7.7 248.3 ± 8.1 248.5 ± 7.8 248.4 ± 7.3 248.2 ± 7.2 Dorsal height (mm) 60.8 ± 4.2 59.6 ± 4.1 60.4 ± 3.9 60.0 ± 4.3 61.1 ± 4.0 60.0 ± 4.6 60.8 ± 4.3 60.8 ± 4.1 Navicular height (mm)a,b,c 41.9 ± 6.8 39.4 ± 7.3*,† 40.6 ± 7.4 41.1 ± 7.1 40.9 ± 5.4 35.2 ± 5.6*,† 37.7 ± 5.7*,† 39.8 ± 5.6 Arch height ratio (%) a,b,c 17.1 ± 3.0 16.3 ± 3.1*,† 16.6 ± 3.2 16.8 ± 3.1 16.4 ± 1.9 14.1 ± 2.0*,† 15.2 ± 2.1*,‡ 16.0 ± 2.1 Navicular height in sitting position (mm) 46.9 ± 5.8 45.5 ± 6.7 46.5 ± 5.7 46.0 ± 6.4 45.9 ± 5.7 42.6 ± 5.5 45.0 ± 5.2 45.4 ± 5.3 Navicular drop (mm)a 5.0 ± 2.0 6.1 ± 2.1*,† 5.9 ± 2.8*,‡ 5.0 ± 2.1 5.1 ± 1.2 7.4 ± 1.5*,† 7.3 ± 2.3*,‡ 5.6 ± 2.3 Note: Data are shown as mean ± SD. aSignificant main effect of time (P < .05). bSignificant main effect of group (P < .05). cSignificant time-group interaction (P < .05). *Significantly different from pre-running (P < .05). †,‡Effect size is interpreted as large (d ≥ 0.8) and medium (0.8 > d ≥ 0.5), respectively.  | 1365 SHIOTANI eT Al. d = 1.679) were significantly smaller in runners than in un- trained men. Navicular drop showed a significant main ef- fect of time (P < .001, η2 = 0.341), without a main effect of group (P = .281, η2 = 0.064) or their interaction (P = .330, η2 = 0.061). Dunnett's test with combining data of both groups found that navicular drop significantly increased at Post (P = .014, d = 0.880) and 30 minutes (P = .025, d = 0.624), but not at 60 minutes after running (P = .586, d = 0.742). %ΔSWV at the proximal site was positively correlated with %Δnavicular height and %Δarch height ratio in both runners and untrained men (Figure 4). Stepwise multiple re- gression analysis revealed that %ΔSWV at the proximal site was selected as the single predictor of %Δnavicular height and %Δarch height ratio explaining 72.7% and 74.4% of the variance, respectively. 4 | DISCUSSION The most important finding of the present study was that LDR induced transient decreases of both the foot arch and PF stiffness in both runners and untrained individuals and that the two variables were inter-related. This suggests that me- chanical fatigue of PF is one of the causes of foot arch flatten- ing, and in fact, the change in PF stiffness at the proximal site solely explained approximately 70% of the total variance in the measures of lowering of the foot arch. These results sup- port the notion that PF provides a primary supporting base for the foot arch,1,6,7 and our study further adds the possibility that mechanical fatigue of PF, in its proximal part in particu- lar, is the key factor for lowering of the foot arch. According to simulation studies, the proximal site of PF is where the mechanical loading is concentrated.16-18 Such site-specific stress accumulation during LDR could be the cause of site- specificity of mechanical fatigue of PF. It may be worthwhile also to note that the proximal site of PF is the common site of plantar fasciitis.32 Reduction of PF stiffness can lead to an in- crease in its strain during running. Mechanical overload and excessive strain can produce microscopic damage within PF which eventually leads to plantar fasciitis,33 and our findings are in support of such pathogenesis. Additionally, lowering of the foot arch during running would induce greater ever- sion of the foot. This can be related to a previous finding that lower extremity joint kinematics in the frontal plane gradu- ally changed (ie, greater eversion of the ankle, greater abduc- tion of the knee, and greater adduction of the hip) throughout a 10-km running.34 Since these kinematic features are known to be the risk factors for the running-related injuries,5 me- chanical fatigue of PF and lowering of the foot arch may also increase the injury risk of proximal joints. Theoretically, repetitive mechanical stress can induce thinning of PF by mechanical fatigue and/or creep deforma- tion.19-21 Our results did not show this, which was against our hypothesis but it is in line with a previous study on acute effects of walking and running on PF thickness.35 A post-hoc power analysis revealed that our sample size was sufficient to confidently accept or reject our null hypotheses. Our results suggest that PF thickness is unsuitable as an indicator of its mechanical fatigue. Runners showed smaller changes in PF stiffness and foot arch deformation after LDR than untrained men. This suggests adaptability of PF mechanical properties: runners may have a more resilient PF to protect against the risk of running-related injury, which was not the case for untrained individuals. However, there was no statistical group differ- ence in the baseline measures. This suggests that the adapt- ability of PF lies in the way it is under mechanical stress and a potentially faster recovery rate. A previous animal study demonstrated that not only its stiffness but also collagen FIGURE 4 Relationship between the relative change (%Δ) in shear wave velocity (SWV) of the plantar fascia at the proximal site and %Δnavicular height (A) and %Δarch height ratio (B) in runners (closed circle) and untrained men (opened circle). The regression lines are shown with correlation coefficients in runners (bold line) and untrained men (dotted line) 1366 | SHIOTANI eT Al. content, stress-relaxation, and hysteresis of connective tis- sues were affected by the external loading during daily exer- cises.25 It is speculated that the parameters such as viscosity, stress-relaxation, and hysteresis of PF are potential factors for the difference in mechanical fatigue response between runners and untrained individuals. Future studies address- ing chronic effects of running on the viscoelastic properties of PF are needed. It should be mentioned that smaller change in PF stiff- ness in runners might also be attributable to the biomechan- ical differences (eg, kinematics) during running, and indeed, runners showing higher values of SWV and navicular height were FFS while all untrained men were RFS (Figure 5). FFS are considered to receive higher mechanical stress on PF during running with higher velocity,12,14,15 which can be one of the reasons for the present results. Chronic effects of run- ning with different patterns on PF properties are unknown at the moment, and further research is warranted to establish the optimal training and conditioning programs that allow injury prevention while being able to run faster. PF stiffness as well as the foot arch recovered within 60 minutes after running in both runners and untrained men. A previous study on collegiate runners reported that lowering of the foot arch remained for more than a week after a full marathon.2 It may be that the persistence of running-induced fatigue of PF and the arch flattening is related to the duration and intensity of running. As we attempted to set the protocol on overground running, mechanical loads were not measured. This is one of the limitations of the present study. However, it has been shown that there are differences in lower extremity kinematics between overground and treadmill running.36-38 In addition, treadmill running has negligible effects on the foot arch flattening.35,39 Based on these findings, we decided to set the protocol at overground. Previous studies of 10-km running on a force-instrumented treadmill at a controlled intensity showed that even in competitive and recreational runners, there were different fatigue responses in kinematics and kinetics.34,40 Thus, it seems to be reasonable that runners and untrained individuals showed different fatigue responses in the present results. Since LDR is most often performed overground, we feel that our overground running setting was appropriate to investigate the association of PF and the foot arch in an athletic context. The effect of running duration/in- tensity on PF and foot arch will lead to a better understanding of running-induced mechanical fatigue of PF. In summary, this study revealed that LDR induced tran- sient and site-specific decreases in PF stiffness, indicating occurrence of mechanical fatigue. Furthermore, the majority of running-induced lowering of foot arch can be attributed to the reduction of PF stiffness. Long-distance runners showed smaller changes in PF properties and foot deformation after running compared with untrained individuals. Our results strongly support a current concept that PF is a primary sup- porting structure of the foot arch, both of which have positive adaptability in response to running training. 5 | PERSPECTIVES There are clinical implications for our findings. First, our results highlight that LDR brings about mechanical fatigue primarily in the proximal site of PF. This finding coincides with the pathology of plantar fasciitis. Second, such hetero- geneous mechanical properties of the PF depend on run- ning experience. Well-experienced runners can build up resilient PF that minimize the risk of running-related inju- ries. Future studies will enable a better understanding of the optimal training/conditioning schemes that allow PF injury prevention while improving its function as a spring during running. ACKNOWLEDGEMENTS This study was supported by JSPS KAKENHI Grant Numbers 19J14912 and 16H01870. This study was part of research ac- tivities of the Human Performance Laboratory, Organization for University Research Initiatives, Waseda University. The authors express their gratitude to Dr Pavlos Evangelidis and Dr Takaki Yamagishi for grammatical corrections of the manuscript. FIGURE 5 Individual patterns of response in shear wave velocity of the plantar fascia at the proximal site and navicular height in runners and untrained men at pre- (closed and opened circle) and post-running (closed and opened square). Individual changes from pre- to post-running in runners and untrained men are connected with bold and dotted lines, respectively. Runners, forefoot strikers (FFS) in particular, show relatively higher shear wave velocity and navicular height at the baseline  | 1367 SHIOTANI eT Al. CONFLICT OF INTERESTS No conflict of interest, financial or otherwise, is declared by the authors. ORCID Hiroto Shiotani  https://orcid.org/0000-0001-9214-4068 Munekazu Naito  https://orcid.org/0000-0003-0618-0607 Yasuo Kawakami  https://orcid.org/0000-0003-0588-4039 REFERENCES 1. Ker RF, Bennett MB, Bibby SR, Kester RC, Alexander RM. The spring in the arch of the human foot. Nature. 1987;325:147-149. 2. Fukano M, Inami T, Nakagawa K, Narita T, Iso S. Foot posture alteration and recovery following a full marathon run. Eur J Sport Sci. 2018;18(10):1338-1345. 3. Fukano M, Iso S. Changes in foot shape after long-distance run- ning. J Funct Morphol Kinesiol. 2016;1(1):30-38. 4. Michelson JD, Durant DM, McFarland E. The injury risk associated with pes planus in athletes. Foot Ankle Int. 2002;23(7):629-633. 5. Taunton JE, Ryan MB, Clement DB, McKenzie DC, Lloyd-Smith DR, Zumbo BD. A retrospective case-control analysis of 2002 run- ning injuries. Br J Sports Med. 2002;36(2):95-101. 6. Cifuentes-De la Portilla C, Larrainzar-Garijo R, Bayod J. Analysis of the main passive soft tissues associated with adult acquired flat- foot deformity development: A computational modeling approach. J Biomech. 2019;84:183-190. 7. Huang CK, Kitaoka HB, An KN, Chao EY. Biomechanical evalua- tion of longitudinal arch stability. Foot Ankle. 1993;14(6):353-357. 8. Erdemir A, Hamel AJ, Fauth AR, Piazza SJ, Sharkey NA. Dynamic loading of the plantar aponeurosis in walking. J Bone Joint Surg Am. 2004;86(3):546-552. 9. Farris DJ, Kelly LA, Cresswell AG, Lichtwark GA. The functional importance of human foot muscles for bipedal locomotion. Proc Natl Acad Sci USA. 2019;116(5):1645-1650. 10. Pavan PG, Stecco C, Darwish S, Natali AN, De Caro R. Investigation of the mechanical properties of the plantar aponeuro- sis. Surg Radiol Anat. 2011;33(10):905-911. 11. Stecco C, Corradin M, Macchi V, et al. Plantar fascia anatomy and its relationship with Achilles tendon and paratenon. J Anat. 2013;223(6):665-676. 12. Chen TL, Wong DW, Wang Y, Lin J, Zhang M. Foot arch deforma- tion and plantar fascia loading during running with rearfoot strike and forefoot strike: a dynamic finite element analysis. J Biomech. 2019;83:260-272. 13. Giddings VL, Beaupr GS, Whalen RT, Carter DR. Calcaneal loading during walking and running. Med Sci Sports Exerc. 2000;32(3):627-634. 14. McDonald KA, Stearne SM, Alderson JA, North I, Pires NJ, Rubenson J. The role of arch compression and metatarsophalangeal joint dynamics in modulating plantar fascia strain in running. PLoS One. 2016;11(4):e0152602. 15. Wager JC, Challis JH. Elastic energy within the human plantar apo- neurosis contributes to arch shortening during the push-off phase of running. J Biomech. 2016;49(5):704-709. 16. Chen YN, Chang CW, Li CT, Chang CH, Lin CF. Finite element analysis of plantar fascia during walking: a quasi-static simulation. Foot Ankle Int. 2015;36(1):90-97. 17. Cheng HY, Lin CL, Wang HW, Chou SW. Finite element anal- ysis of plantar fascia under stretch-the relative contribution of windlass mechanism and Achilles tendon force. J Biomech. 2008;41(9):1937-1944. 18. Lin SC, Chen CPC, Tang SFT, et al. Stress distribution within the plantar aponeurosis during walking - a dynamic finite element analysis. J Mech Med Biol. 2014;14(04):1450053. 19. Ker RF, Wang XT, Pike AVL. Fatigue quality of mammalian ten- dons. J Exp Biol. 2000;203:1317-1327. 20. Wang XT, Ker RF, Alexander RM. Fatigue rupture of wallaby tail tendons. J Exp Biol. 1995;198:847-852. 21. Wren TA, Lindsey DP, Beaupre GS, Carter DR. Effects of creep and cyclic loading on the mechanical properties and failure of human Achilles tendons. Ann Biomed Eng. 2003;31(6):710-717. 22. Nielsen RO, Ronnow L, Rasmussen S, Lind M. A prospective study on time to recovery in 254 injured novice runners. PLoS One. 2014;9(6):e99877. 23. van Gent RN, Siem D, van Middelkoop M, et al. Incidence and de- terminants of lower extremity running injuries in long distance run- ners: A systematic review. Br J Sports Med. 2007;41(8):469-480. 24. Bohm S, Mersmann F, Arampatzis A. Human tendon adapta- tion in response to mechanical loading: a systematic review and meta-analysis of exercise intervention studies on healthy adults. Sports Med Open. 2015;1(1):7. 25. Khayyeri H, Blomgran P, Hammerman M, et al. Achilles tendon compositional and structural properties are altered after unloading by botox. Sci Rep. 2017;7(1):13067. 26. Aubry S, Nueffer JP, Tanter M, Becce F, Vidal C, Michel F. Viscoelasticity in Achilles tendonopathy: quantitative assess- ment by using real-time shear-wave elastography. Radiology. 2015;274(3):821-829. 27. Lacourpaille L, Hug F, Bouillard K, Hogrel JY, Nordez A. Supersonic shear imaging provides a reliable measurement of resting muscle shear elastic modulus. Physiol Meas. 2012;33(3):N19-28. 28. Otsuka S, Shan X, Kawakami Y. Dependence of muscle and deep fascia stiffness on the contraction levels of the quadriceps: an in vivo supersonic shear-imaging study. J Electromyogr Kinesiol. 2019;45:33-40. 29. Eby SF, Song P, Chen S, Chen Q, Greenleaf JF, An KN. Validation of shear wave elastography in skeletal muscle. J Biomech. 2013;46(14):2381-2387. 30. Helfenstein-Didier C, Andrade RJ, Brum J, et al. In vivo quantifi- cation of the shear modulus of the human Achilles tendon during passive loading using shear wave dispersion analysis. Phys Med Biol. 2016;61(6):2485-2496. 31. Shiotani H, Yamashita R, Mizokuchi T, Naito M, Kawakami Y. Site- and sex-differences in morphological and mechanical prop- erties of the plantar fascia: a supersonic shear imaging study. J Biomech. 2019;85:198-203. 32. League AC. Current concepts review: plantar fasciitis. Foot Ankle Int. 2008;29(3):358-366. 33. Wearing SC, Smeathers JE, Urry SR, Hennig EM, Hills AP. The pathomechanics of plantar fasciitis. Sports Med. 2006;36(7):585-611. 34. Willwacher S, Sanno M, Bruggemann GP. Fatigue matters: an intense 10 km run alters frontal and transverse plane joint kine- matics in competitive and recreational adult runners. Gait Posture. 2019;76:277-283. 1368 | SHIOTANI eT Al. 35. Welk AB, Haun DW, Clark TB, Kettner NW. Use of high-reso- lution ultrasound to measure changes in plantar fascia thickness resulting from tissue creep in runners and walkers. J Manipulative Physiol Ther. 2015;38(1):81-85. 36. Nigg BM, De Boer RW, Fisher V. A kinematic comparison of overground and treadmill running. Med Sci Sports Exerc. 1995;27(1):98-105. 37. Wank V, Frick U, Schmidtbleicher D. Kinematics and electromy- ography of lower limb muscles in overground and treadmill run- ning. Int J Sports Med. 1998;19(7):455-461. 38. Riley PO, Dicharry J, Franz J, Della Croce U, Wilder RP, Kerrigan DC. A kinematics and kinetic comparison of overground and tread- mill running. Med Sci Sports Exerc. 2008;40(6):1093-1100. 39. Boyer ER, Ward ED, Derrick TR. Medial longitudinal arch me- chanics before and after a 45-minute run. J Am Podiatr Med Assoc. 2014;104(4):349-356. 40. Sanno M, Willwacher S, Epro G, Bruggemann GP. Positive Work Contribution Shifts from Distal to Proximal Joints during a Prolonged Run. Med Sci Sports Exerc. 2018;50(12):2507-2517. How to cite this article: Shiotani H, Mizokuchi T, Yamashita R, Naito M, Kawakami Y. Acute effects of long-distance running on mechanical and morphological properties of the human plantar fascia. Scand J Med Sci Sports. 2020;30:1360–1368. https://doi.org/10.1111/ sms.13690

Acute effects of long-distance running on mechanical and morphological properties of the human plantar fascia.

05-20-2020

Shiotani, Hiroto,Mizokuchi, Tomohiro,Yamashita, Ryo,Naito, Munekazu,Kawakami, Yasuo

eng

PMC9794057

1 S10 Table. Low level of agreement factors. Factors that achieved a level of agreement of 0-39% after all three rounds (n=54). Factor Level of agreement (%) Training Power capacity 33,3 Heart volume 33,3 Lung volume 16,7 Strength capacity 16,7 Metabolism Myoglobin storage capacity 33,3 Lactate dehydrogenase metabolism 33,3 Thermogenesis 5,6 Body Subcutaneous adipose tissue 16,7 Muscle fibres - contraction velocity capacity 11,1 Muscle fibres - hypertrophy capacity 11,1 Hormones Oestradiol level 33,3 Thyroid hormones level 27,8 Gonadotropin-releasing hormone level 22,2 Dihydrotestosterone level 11,1 Epinephrine level 11,1 Norepinephrine level 11,1 Progesterone level 11,1 Gonad corticoids level 11,1 Androstenedione level 11,1 Follicle-stimulating hormone level 11,1 Ghrelin level 5,6 Dehydroepiandrosterone level 5,6 Human chorionic gonadotropin level 5,6 Nutrition Magnesium deficiency 38,9 Steroid metabolism 33,3 Cell hydration status 33,3 Caffeine metabolism 33,3 Zinc deficiency 27,8 Bicarbonate level 27,8 Leucine level 22,2 Creatine level 22,2 Antioxidant level 22,2 Vitamin C deficiency 22,2 Cholesterol level 22,2  2 Carnosine level 16,7 Folic acid deficiency 16,7 Unsaturated fat metabolism 16,7 Omega 3 level 16,7 Saturated fat metabolism 11,1 Beta carotene deficiency 11,1 Vitamin A deficiency 11,1 Vitamin E deficiency 11,1 Selenium deficiency 11,1 Omega 6 level 11,1 L-carnitine level 5,6 Valine level 5,6 Immune system Cytokine responses 27,8 Detoxification process 11,1 Injuries Risk of left ventricular hypertrophy 27,8 Risk of metabolic myopathy 11,1 Psychological Risk of eating disorders 16,7 Environment Alcohol usage 22,2 Smoking behaviour 11,1 Proposed factor (Sedentary) lifestyle 16,7

Factors associated with high-level endurance performance: An expert consensus derived via the Delphi technique.

12-27-2022

Konopka, Magdalena J,Zeegers, Maurice P,Solberg, Paul A,Delhaije, Louis,Meeusen, Romain,Ruigrok, Geert,Rietjens, Gerard,Sperlich, Billy

eng

PMC9794057

1 S4 Table. Survey outline. Delphi Study - round 1 (click on link to access survey) Delphi Study - round 2 (click on link to access survey) Illustration of round 3 Factor Level of agreement (%) Rating round 2 Rating round 3 Endurance capacity 61,1 N Recovery speed 61,1 N Angiogenesis (=formation of new blood vessels) 50,0 Y Muscle fibres - transformation capacity (type 1 vs. type 2) 55,6 N Weight / BMI 44,4 N Total fat mass 50,0 Y Lean mass (=mass of all organs except body fat including bones, muscles, blood, skin) 44,4 Y Tendon stiffness 55,6 N Insulin-like growth factor- 1 (IGF-1) level 55,6 N Growth hormone level 66,7 Y Vitamin B complex vitamins (B1-12) deficiency 50,0 N Blood pressure regulation 50,0 N Healing function of soft tissue 50,0 N Risk of joint injuries 66,7 Y Risk of upper respiratory tract infections 61,1 N Emotion regulation 66,7 N Pain sensitivity 44,4 Y Self-control 50,0 N Resilience capacity 50,0 Y  2 Concentration capacity 44,4 Y Heat resistance capacity 50,0 Y Altitude training sensitivity 55,6 N Y=Yes (Factor is relevant and should be included in the consensus report). N=No (Factor is not relevant and should not be included in the consensus report).

Factors associated with high-level endurance performance: An expert consensus derived via the Delphi technique.

12-27-2022

Konopka, Magdalena J,Zeegers, Maurice P,Solberg, Paul A,Delhaije, Louis,Meeusen, Romain,Ruigrok, Geert,Rietjens, Gerard,Sperlich, Billy

eng

PMC3735486

RESEARCH ARTICLE Open Access Effectiveness of Start to Run, a 6-week training program for novice runners, on increasing health-enhancing physical activity: a controlled study Linda Ooms1,2*, Cindy Veenhof1 and Dinny H de Bakker1,2 Abstract Background: The use of the organized sports sector as a setting for health-promotion is a relatively new strategy. In the past few years, different countries have been investing resources in the organized sports sector for promoting health-enhancing physical activity. In the Netherlands, National Sports Federations were funded to develop and implement “easily accessible” sporting programs, aimed at the least active population groups. Start to Run, a 6-week training program for novice runners, developed by the Dutch Athletics Organization, is one of these programs. In this study, the effects of Start to Run on health-enhancing physical activity were investigated. Methods: Physical activity levels of Start to Run participants were assessed by means of the Short QUestionnaire to ASsess Health-enhancing physical activity (SQUASH) at baseline, immediately after completing the program and six months after baseline. A control group, matched for age and sex, was assessed at baseline and after six months. Compliance with the Dutch physical activity guidelines was the primary outcome measure. Secondary outcome measures were the total time spent in physical activity and the time spent in each physical activity intensity category and domain. Changes in physical activity within groups were tested with paired t-tests and McNemar tests. Changes between groups were examined with multiple linear and logistic regression analyses. Results: In the Start to Run group, the percentage of people who met the Dutch Norm for Health-enhancing Physical Activity, Fit-norm and Combi-norm increased significantly, both in the short- and longer-term. In the control group, no significant changes in physical activity were observed. When comparing results between groups, significantly more Start to Run participants compared with control group participants were meeting the Fit-norm and Combi-norm after six months. The differences in physical activity between groups in favor of the Start to Run group could be explained by an increase in the time spent in vigorous-intensity activities and sports activities. Conclusions: Start to Run positively influences levels of health-enhancing physical activity of participants, both in the short- and longer-term. Based on these results, the use of the organized sports sector as a setting to promote health-enhancing physical activity seems promising. Keywords: Sports setting, Sporting organizations, Running, Health-enhancing physical activity, Controlled study, Follow-up * Correspondence: [email protected] 1Netherlands Institute for Health Services Research (NIVEL), PO Box 1568, 3500 BN, Utrecht, The Netherlands 2Scientific Center for Transformation in Care and Welfare (Tranzo), Tilburg University, PO Box 90153, 5000 LE, Tilburg, The Netherlands © 2013 Ooms et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Ooms et al. BMC Public Health 2013, 13:697 http://www.biomedcentral.com/1471-2458/13/697 Background The positive effects of physical activity on health and mortality have been well established. Participation in regular physical activity decreases the risk of coronary heart disease, stroke, type 2 diabetes mellitus, certain cancers (e.g. breast cancer, colon cancer), osteoporosis, obesity and falls [1-7]. Moreover, there is some evidence that physical activity is positively associated with mental health and quality of life [8,9]. Given the numerous health benefits of physical activity participation, various guidelines have been published on the recommended volume and intensity of physical activity for healthy adults. Commonly used guidelines are those de- veloped by the American College of Sports Medicine (ACSM) and the American Heart Association (AHA). To promote and maintain health, the ACSM and AHA recom- mend that: “All healthy adults aged 18 to 65 years need moderate-intensity aerobic (endurance) physical activity for a minimum of 30 minutes on at least five days each week or vigorous-intensity aerobic physical activity for a minimum of 20 minutes on at least three days each week. Also, combinations of moderate- and vigorous-intensity activity can be performed to meet this recommenda- tion.” [10] Similar guidelines have been adopted in the Netherlands and are referred to as the Dutch Norm for Health-enhancing Physical Activity (DNHPA) and the Fit-norm. Someone who meets at least one of the two guidelines adheres to the so-called “Combi-norm”, the third norm used in the Netherlands (see Table 1) [11]. Despite the existence of these guidelines, more than one third of the Dutch adult population does not engage in sufficient physical activity: in 2009, 58% of the Dutch adult population met the DNHPA, 33% met the Fit- norm, and 62% met the Combi-norm [11]. One of the ways of being physically active is through organized sports. There is large potential for the orga- nized sports sector as a setting in which to promote health-enhancing physical activity to the general popula- tion, given the large numbers of participants, the extent of community reach and the availability of many differ- ent sports and professional trainers. Moreover, physical activity opportunities are provided on a continuous basis (i.e. people can play sport on a weekly basis at a sports club). This is in contrast with physical activity interven- tions, which are mostly of short or limited duration. In this way, the organized sports sector can also play an important role in maintaining physical activity levels. Another positive aspect of the organized sports setting is the possibility to socially interact with other people. As social support has been identified as a determinant of physical activity [12-14], participation in organized sports may lead to greater physical activity benefits than other forms of physical activity. It is, for example, well known that people who are involved in (organized) sports are significantly more likely to meet physical ac- tivity guidelines than those people who are not [11]. However, there are still people who are doing sports activities below the recommend levels of physical activity (i.e. with regard to frequency, duration and/or intensity) and there are also people who never play sports at all. According to recent data, 56% of the Dutch population plays sports at least once a week. For the European Union countries combined this percentage is only 40% [15]. This shows the importance of further increasing participation rates in (organized) sports. Sports promotion has a long history in many coun- tries, but the use of the organized sports sector as a set- ting to gain control over health issues and unhealthy behaviors, like physical inactivity, is a relatively new strategy [16-19]. This settings-based health promotion approach is based on the idea that changes in people’s health and health behavior are easier to achieve if health promoters focus on settings instead of individuals. It has also been applied to other settings, like schools and Table 1 Dutch physical activity guidelines for adults Norm Description Dutch Norm for Health-enhancing Physical Activity (DNHPA) Adults (18-54 years): Thirty minutes or more of at least moderate-intensity aerobic (endurance) physical activity (≥ 4 MET; combined intensity score SQUASH ≥ 3) on at least five days each week. Adults (55 years and older): Thirty minutes or more of at least moderate-intensity aerobic (endurance) physical activity (≥ 3 MET; combined intensity score SQUASH ≥ 3) on at least five days each week. Fit-norm Adults (18-54 years): Twenty minutes or more of vigorous-intensity physical activity (≥ 6.5 MET; combined intensity score SQUASH ≥ 6) on at least three days each week. Adults (55 years and older): Twenty minutes or more of vigorous-intensity physical activity (≥ 5 MET; combined intensity score SQUASH ≥ 6) on at least three days each week. Combi-norm Meeting at least one of the previous mentioned norms (i.e. the DNHPA or Fit-norm). Ooms et al. BMC Public Health 2013, 13:697 Page 2 of 12 http://www.biomedcentral.com/1471-2458/13/697 workplaces [20]. The approach builds on the Ottawa Charter of 1986 that stated: “Health is created and lived by people within the settings of their everyday life; where they learn, work, play and love.” [21] In the past few years, different countries have been investing resources in the organized sports sector for pro- moting health-enhancing physical activity: in Australia, for example, State Sporting Associations were funded to de- velop healthy (e.g. smoke-free settings) and welcoming en- vironments in their associated clubs, to ultimately increase participation in sport for health benefits [16,18]. In the Netherlands, the Dutch Ministry of Health, Welfare and Sport initiated the National Action Plan for Sport and Ex- ercise (NAPSE). This program was aimed at increasing the number of Dutch people meeting physical activity guide- lines [17]. Within the NAPSE, National Sports Federations were funded to develop and implement sporting programs tailored to the needs and abilities of the least active popula- tion groups, i.e. making sports activities easily accessible and creating a welcoming sports environment for these tar- get groups. A total of fourteen “easily accessible” sporting programs were developed and implemented in different lo- cations in the Netherlands. Start to Run, a 6-week training program for novice runners, developed by the Dutch Ath- letics Organization, is one of these programs. Participants are given the opportunity to become acquainted with the different aspects of running. Afterwards, they are stimu- lated to continue running as member of a local athletics club or the Dutch Athletics organization. Running is a feasible form of a vigorous-intensity phys- ical activity; it is not time consuming, it can be done any- where and at any time, and only a pair of running shoes is needed. As a result, running is a popular way to become physically active, and there are many different training pro- grams for novice runners available. There is strong litera- ture on the health benefits of running in general and different studies have been published about (the prevention of) running related injuries [e.g. 22-26]. So far, no studies have been conducted, however, about the effectiveness of running programs on increasing health-enhancing physical activity levels. In general, there is a lack of research and evaluation of activities conducted in sports settings. Im- provements in the research in this area are desirable. Par- ticularly, there is a need for controlled study designs, incorporating both the short- and longer-term effects of sporting programs and activities, to move towards provid- ing evidence-based programs [27,28]. Therefore, the aim of this study was to assess the effect- iveness of Start to Run on increasing health-enhancing physical activity, both in the short- and longer-term, and in comparison with a control group. The results of the current study will contribute to the knowledge base concerning the effectiveness of programs initiated in sports settings, and will, consequently, provide further insight into the role of the organized sports sector in promoting health- enhancing physical activity. The study findings may be of interest to policy makers in the areas of sports and health. Also, sporting organizations may use the results when de- veloping and implementing similar sporting programs. Methods Study design To assess the effectiveness of Start to Run on increasing health-enhancing physical activity, a controlled study de- sign was used. The study was performed according to Dutch legislation on privacy. The privacy regulations of the study were approved by the Dutch Data Protection Authority. According to Dutch legislation, approval by a medical ethics committee was not obligatory, as partici- pants were not subjected to procedures, nor were they required to follow rules of behavior (i.e. participants were approached for the study after they had voluntarily registered for the Start to Run training program). Study population Start to Run participants Start to Run is aimed at adult novice runners who want to learn to run continuously for at least three kilometers. The program is offered two times a year (in March and Septem- ber) by athletics clubs and running stores in more than hundred different locations in the Netherlands. Partici- pants are recruited locally using different recruitment strat- egies (e.g. by advertisements in local media, posters, and flyers). For this study, the Dutch Athletics Organization provided data (i.e. name, email address, sex and age) of 513 individuals who had registered for the Start to Run pro- gram in March 2009. These individuals were sent an email with information about the study and a link to an online baseline questionnaire. By completing the baseline ques- tionnaire, the Start to Run participants gave consent for participation in the study. Control group participants The control group consisted of members of the Dutch Health Care Consumer Panel of the Netherlands Insti- tute for Health Services Research (NIVEL). This panel contains about three thousand individuals aged 18 years and older and is representative for the Dutch population with regard to age and sex. The panel members are questioned four times a year about health care, health insurance, and other related issues [29]. For the current study, 1328 panel members were approached. Control group participants did not receive any intervention. Moreover, they were asked if they had participated in the Start to Run program or any of the other NAPSE sporting programs before or during the study period, as this could influence results. Subsequently, control group members who had done so were excluded from the Ooms et al. BMC Public Health 2013, 13:697 Page 3 of 12 http://www.biomedcentral.com/1471-2458/13/697 study. Compared with the Start to Run group, the con- trol group members were significantly older, and were more likely to be male. As physical activity levels differ by age and sex [15,30], the control group was matched with the Start to Run group on age and sex. Start to Run program During the 6-week training period, except for the last week, participants trained three times a week: one time in a group under guidance of one or more professional coaches (i.e. one coach per 15 participants), and two times individually. As a rule, training days were followed by rest days. In the last week, participants could test their running abilities in a three kilometers test run. Participation in this run, however, was not obligatory. A guided training ses- sion lasted approximately 90 minutes and consisted of a theoretical part (20-30 minutes), followed by a practical part (60-70 minutes). During the theoretical part one of the following theory items was discussed: health benefits of running and (prevention of) running-related injuries, running clothes and shoes, proper food and drinks (before, during and after training), physiological changes during running and training with a heart rate monitor. The prac- tical part consisted of a warming-up, a run-walk part and a cooling-down. Participants were instructed to walk and perform light (stretching) exercises to warm up and to cool down. During the warming-up also attention was paid to running technique (e.g. proper posture, stride, foot strike, breathing) and running technique exercises. The run-walk part consisted of a combination of running and walking, whereby running time and distance were grad- ually increased during the training period. On average there were 35 participants per group session, guided by two professional coaches. An individual training session lasted approximately 45 minutes and consisted, just as the practical part of the group sessions, of a warming-up, a run-walk part and a cooling-down. Participants received instructions (e.g. training schedule, running tips) for the individual training sessions during the group sessions from their coach(es) and through weekly emails from the Dutch Athletics Organization. After completing the program, participants were stimulated to continue running. Partici- pants were personally informed by their coach(es) about membership from this or other local athletics clubs. Add- itionally, participants received several emails from the Dutch Athletics Organization with information about local athletics clubs and an individual runner membership of the Dutch Athletics Organization. Outcome measures Demographic data were collected for each participant, including age and sex. The level of physical activity was assessed by the Short QUestionnaire to ASsess Health- enhancing physical activity (SQUASH). This instrument has proven to be fairly reliable and reasonably valid in ordering subjects according to their level of physical ac- tivity in an adult population [31]. The SQUASH mea- sures the amount of physical activity for five domains: commuting activities, leisure-time activities, sports activ- ities, household activities, and activities at work and school. It consists of three main queries, namely days per week, average time per day, and self-reported inten- sity (light, moderate or vigorous). An average week in the past month was taken as reference period. Using the Ainsworth Compendium of Physical Activities, a meta- bolic equivalent (MET) value was assigned to all physical activities [32]. Based on age and assigned MET values, physical activities were subdivided into three intensity categories: light, moderate and vigorous. For adults aged 18-54 years, the following cut-off values were used: < 4.0 MET (light), 4.0 to 6.5 MET (moderate), ≥ 6.5 MET (vigorous). For adults aged ≥ 55 years, the cut-off values were: < 3.0 MET (light), 3.0 to 5.0 MET (moderate), ≥ 5.0 MET (vigorous). This MET category was combined with self-reported intensity for each activity, resulting in a combined intensity score ranging from 1 to 9, with 1 being light MET and light self-reported intensity to 9 be- ing vigorous MET and vigorous self-reported intensity. The classification of physical activities according to the combined intensity score was as follows: < 3 (light), 3 to 6 (moderate), ≥ 6 (vigorous). Subsequently, the following outcome measures were calculated: compliance with the Dutch physical activity guidelines (see Table 1); minutes per week spent in light-, moderate- and vigorous-intensity activities; minutes per week spent in commuting activities, leisure-time activities, sports activities, household activities, and activities at work and school; and total minutes per week spent in physical activity. Compliance with the Dutch physical activity guidelines was seen as the primary out- come measure, as these guidelines specify the amount of physical activity necessary to obtain health benefits. The other physical activity outcome measures were used to ex- plain possible changes in physical activity behavior in more detail. Start to Run participants were assessed by means of an online questionnaire at baseline (t = 0), immediately after completing the program (t = 6 weeks) and six months after baseline (t = 6 months: i.e. 4.5 months after cessation of the Start to Run training program). Control group partici- pants were assessed at the start of the study (t = 0) by means of a postal questionnaire and six months later (t = 6 months) by means of a postal or an online questionnaire. The assessments of the control group were performed in the same months as the assessments of the Start to Run group. To increase response rates, reminders were sent one week (for online questionnaires) or two weeks (for postal questionnaires) later. Ooms et al. BMC Public Health 2013, 13:697 Page 4 of 12 http://www.biomedcentral.com/1471-2458/13/697 Sample size The sample size was based on detecting a difference in habitual physical activity according to the Fit-norm. As running is a vigorous-intensity activity, it was expected that the Start to Run program would mostly affect the percentage of people who met the Fit-norm. To detect a 20% difference between the Start to Run group and the control group six months after baseline, with a two- sided 5% significance level and a power of 80%, a sample size of 89 participants per group was necessary. Given the sample size of both the Start to Run group (n=513) and the control group (n=1328), it was expected that sufficient participants were included in the study. Statistical analysis All statistical analyses were performed using Stata stat- istical software version 10.1 (Stata Corporation, College Station, Texas). Descriptive statistics were used to de- scribe the main characteristics of each group and to ex- plore baseline comparability. Means and standard deviations were calculated for continuous measures, while percentages were calculated for dichotomous measures. Differences between groups with regard to age and sex were tested with an independent t-test and chi-squared test, respectively. Changes in physical activ- ity within groups were examined with paired t-tests for continuous physical activity measures and McNemar tests for dichotomous physical activity measures. To compare changes in physical activity between groups, multiple regression analyses (linear regression was used for continuous measures and logistic regression was used for dichotomous measures) were performed with physical activity level at six months as the dependent variable and group (Start to Run group versus control group, with the control group as the reference category) as the independent variable. Adjustments were made for baseline physical activity levels, by using this vari- able as a covariate in the regression model. To check if the results of the continuous physical activity outcome measures were influenced by outliers, also more robust regression techniques were applied: these techniques in- cluded the use of robust standard errors (i.e. Huber- White robust estimates of the standard errors and boot- strap estimates of the standard errors). As these robust regression techniques did not yield different results and conclusions, these results will not be presented here. P- values less than 0.05 were considered statistically significant. Results Study participants The flow of participants through the study is shown in Figure 1. Start to Run participants Of 513 persons approached, 244 completed the baseline assessment. Of these 244 persons, 125 completed the as- sessment at six weeks. Two persons were excluded from analysis, because compliance with the Dutch physical ac- tivity guidelines could not be calculated. Therefore, data of 123 persons were available to evaluate changes in physical activity after six weeks. All persons who com- pleted the baseline assessment (n=244) were also approached for the assessment at six months, irrespect- ive if they had completed the assessment at six weeks. This was done to get an optimal response for compari- sons with the control group. Of 244 persons approached, 104 completed the assessment at six months. Subse- quently, four persons were excluded from analysis, be- cause compliance with the Dutch physical activity guidelines could not be calculated. Consequently, data of 100 persons were available to evaluate changes in phys- ical activity after six months and to make comparisons with the control group. There were 78 Start to Run par- ticipants who completed all three assessments (not shown in Figure 1). However, to optimally use data and maintain study power (i.e. for comparisons with the control group a sample size of 89 participants per group was necessary), all available cases were included in the analyses. This means that analyses were performed on 123 and 100 Start to Run participants for effects after six weeks and six months, respectively. Non-response analyses revealed that Start to Run participants who did not complete the assessment after six months were sig- nificantly younger (37 ± 9 years vs. 40 ± 10 years) and were more likely to be female (92.4% female vs. 70.0% female) compared with those who did complete this as- sessment. There were no significant differences in base- line physical activity levels between respondents and non-respondents. Control group participants Of 1328 persons approached, 940 completed the base- line assessment. Of these 940 persons, 745 completed the assessment at six months. Subsequently, 46 persons were excluded from analysis due to participation in the Start to Run program (n=2) or any of the other NAPSE sporting programs (n=44). In addition, six other persons were excluded, because compliance with the Dutch physical activity guidelines could not be calculated. Of the remaining 693 persons, 100 were matched to the Start to Run group on age and sex. Baseline characteristics of study participants The baseline characteristics of the Start to Run group (i.e. the participants who completed the six months Ooms et al. BMC Public Health 2013, 13:697 Page 5 of 12 http://www.biomedcentral.com/1471-2458/13/697 assessment) and the control group are shown in Table 2. The Start to Run participants had a mean age of 40 years (SD=10) and the control group participants had a mean age of 42 years (SD=9). The percentage of women was 70.0% in both groups. There were no significant differ- ences in age and sex between groups. Matching was there- fore successful. With regard to baseline physical activity levels, the Start to Run participants spent significantly less time in moderate-intensity physical activities (213 ± 453 min/week vs. 406 ± 596 min/week, p=0.01) and household activities (552 ± 780 min/week vs. 919 ± 968 min/week, p=0.004) compared with controls. For the remaining phys- ical activity outcome measures, no significant differences were found between groups at baseline. Changes in physical activity Changes in physical activity after six weeks In Table 3, physical activity outcome measures are presented for the Start to Run group at baseline and after six weeks. At baseline, 43.9% of the Start to Run partici- pants met the DNHPA, 53.7% met the Fit-norm, and 57.7% met the Combi-norm. After six weeks, these per- centages increased significantly (p<0.0001) to 74.8%, 87.0%, and 91.1% for the DNHPA, Fit-norm, and Combi- norm, respectively. Although more Start to Run partici- pants met physical activity guidelines after six weeks, the total time spent in physical activity did not change signifi- cantly (2237 ± 1183 min/week vs. 1996 ± 1451 min/week, p=0.08). However, there were significant changes in Approached for study (n=513) Start to Run group Start to Run training program Completed assessment after Start to Run training program (n=125) Completed assessment six months after baseline (i.e. 4.5 months after cessation of the Start to Run training program) (n=104) Completed baseline assessment (n=244) Analyzed (n=100) • Excluded from analysis because compliance with the Dutch physical activity guidelines could not be calculated (n=4). Analyzed (n=123) • Excluded from analysis because compliance with the Dutch physical activity guidelines could not be calculated (n=2). Approached for study (n=1328) Completed assessment six months after baseline (n=745) Completed baseline assessment (n=940) Unmatched control group (n=693) • Excluded from analysis because of participation in Start to Run (n=2) or another NAPSE sporting program (n=44). • Excluded from analysis because compliance with the Dutch physical activity guidelines could not be calculated (n=6). Control group t = 0 t = 6 weeks t = 6 months Analyzed (n=100) • Control group matched by age and sex. 4.5 months Figure 1 Flow of participants through the study. Ooms et al. BMC Public Health 2013, 13:697 Page 6 of 12 http://www.biomedcentral.com/1471-2458/13/697 physical activity behavior within physical activity intensity categories and domains, i.e. after six weeks, the Start to Run participants spent more time in vigorous-intensity ac- tivities (200 ± 205 min/week vs. 410 ± 298 min/week, p<0.0001), commuting activities (70 ± 110 min/week vs. 98 ± 155 min/week, p=0.01), leisure-time activities (240 ± 268 min/week vs. 301 ± 343 min/week, p=0.02) and sports activities (101 ± 143 min/week vs. 243 ± 173 min/week, p<0.0001), while less time was spent in light-intensity ac- tivities (1827 ± 1192 min/week vs. 1423 ± 1296 min/week, p=0.002) and activities at work and school (1293 ± 940 min/week vs. 792 ± 794 min/week, p<0.0001). Changes in physical activity after six months: comparisons within groups In Table 4, physical activity outcome measures are presented for both the Start to Run group and control group at baseline and after six months. In the Start to Run group, the percentage of people who met the DNHPA (48.0% vs. 64.0%, p=0.004), Fit-norm (56.0% vs. 82.0%, p<0.0001), and Combi-norm (58.0% vs. 84.0%, p<0.0001) increased significantly between baseline and six months. These changes were accompanied by a sig- nificant increase in the total time spent in physical activ- ity (2265 ± 1251 min/week vs. 2536 ± 1210 min/week, p=0.04). Also, significant changes in physical activity be- havior were observed within physical activity intensity categories and domains, i.e. after six months, the Start to Run participants spent more time in vigorous- intensity activities (238 ± 250 min/week vs. 382 ± 306 min/week, p<0.0001), commuting activities (88 ± 137 min/week vs. 132 ± 181 min/week, p=0.006) and sports activities (126 ± 166 min/week vs. 225 ± 182 min/week, p<0.0001). In contrast, the control group participants did not significantly change their physical activity behav- ior between baseline and six months. Changes in physical activity after six months: comparisons between groups The results of the multiple linear and logistic regression analyses are presented in Table 5. After six months, sig- nificantly more Start to Run participants compared with control group participants were meeting the Fit-norm (OR=5.1; 95% CI: 2.3-11.1, p<0.001) and Combi-norm (OR=3.3; 95% CI: 1.4-7.7, p=0.006). In addition, signifi- cant effects were found in favor of the Start to Run group concerning physical activity intensity categories and domains: after six months, the Start to Run partici- pants were spending more time in vigorous-intensity ac- tivities (an average of 152 min/week more: b=152; 95% CI: 80-223, p<0.001) and sports activities (an average of 107 min/week more: b=107; 95% CI: 69-145, p<0.001) compared with controls. For the remaining physical ac- tivity outcome measures, no significant differences were found between groups. Discussion The aim of this study was to assess the effectiveness of Start to Run, a 6-week training program for novice run- ners, on increasing health-enhancing physical activity, both in the short- and longer-term. In the Start to Run group, short- and longer-term beneficial within group ef- fects were observed. In the control group, however, there were no significant changes in physical activity behavior within a period of six months. When comparing results between groups, the Start to Run program produced sig- nificant positive changes in health-enhancing physical activity levels: after six months, significantly more Start to Run participants compared with control group partic- ipants were meeting the Fit-norm and Combi-norm. The differences in the amount of physical activity between groups in favor of the Start to Run group could be explained by an increase in the time spent in vigorous- Table 2 Baseline characteristics of the Start to Run group and control group Start to Run groupa Control group P Sample size (n) 100 100 Age (years) Mean ± SD 40 ± 10 42 ± 9 0.12 Min-max 21-71 23-77 Sex (%) Male 30.0 30.0 1.0 Female 70.0 70.0 Dutch physical activity guidelines (%) Compliance with DNHPA 48.0 59.0 0.12 Compliance with Fit-norm 56.0 55.0 0.89 Compliance with Combi-norm 58.0 65.0 0.31 Physical activity by intensity, mean ± SD (min/week) Light-intensity activities 1814 ± 1224 1958 ± 1263 0.42 Moderate-intensity activities 213 ± 453 406 ± 596 0.01* Vigorous-intensity activities 238 ± 250 253 ± 337 0.73 Physical activity by domain, mean ± SD (min/week) Commuting activities 88 ± 137 117 ± 251 0.30 Leisure-time activities 257 ± 296 328 ± 407 0.16 Sports activities 126 ± 166 107 ± 147 0.40 Household activities 552 ± 780 919 ± 968 0.004* Activities at work and school 1309 ± 935 1182 ± 951 0.35 Total time spent in physical activity, mean ± SD (min/week) 2265 ± 1251 2616 ± 1356 0.06 aStart to Run participants who completed the six months assessment. *Significant (p<0.05) difference between groups. Ooms et al. BMC Public Health 2013, 13:697 Page 7 of 12 http://www.biomedcentral.com/1471-2458/13/697 intensity activities (physical activity intensity category) and sports activities (physical activity domain). As running is a vigorous-intensity sports activity, these results are not surprising. This is especially true for the assessment after six weeks. More interesting is the fact that these outcome measures were also positively af- fected at the six months assessment. Considering the higher levels of vigorous-intensity physical activity and sports activity, the results suggest that most Start to Run participants were still running even 4.5 months after ces- sation of the Start to Run training program. Some add- itional results, not presented in the results section, confirm that this was indeed the case: at the six months assessment, running behavior was also directly assessed Table 4 Changes in physical activity after six months: comparisons within groups Outcome measures Start to Run group (n=100) Control group (n=100) Baseline After six months Pa Baseline After six months Pb Dutch physical activity guidelines (%) Compliance with DNHPA 48.0 64.0 0.004* 59.0 62.0 0.68 Compliance with Fit-norm 56.0 82.0 <0.0001* 55.0 57.0 0.83 Compliance with Combi-norm 58.0 84.0 <0.0001* 65.0 73.0 0.10 Physical activity by intensity, mean ± SD (min/week) Light-intensity activities 1814 ± 1224 1947 ± 1043 0.31 1958 ± 1263 1972 ± 1181 0.90 Moderate-intensity activities 213 ± 453 206 ± 369 0.88 406 ± 596 450 ± 740 0.47 Vigorous-intensity activities 238 ± 250 382 ± 306 <0.0001* 253 ± 337 238 ± 286 0.60 Physical activity by domain, mean ± SD (min/week) Commuting activities 88 ± 137 132 ± 181 0.006* 117 ± 251 124 ± 215 0.80 Leisure-time activities 257 ± 296 276 ± 358 0.48 328 ± 407 325 ± 515 0.91 Sports activities 126 ± 166 225 ± 182 <0.0001* 107 ± 147 108 ± 142 0.96 Household activities 552 ± 780 585 ± 597 0.66 919 ± 968 807 ± 856 0.15 Activities at work and school 1309 ± 935 1381 ± 864 0.49 1182 ± 951 1322 ± 887 0.11 Total time spent in physical activity, mean ± SD (min/week) 2265 ± 1251 2536 ± 1210 0.04* 2616 ± 1356 2660 ± 1126 0.73 aP-value for difference in physical activity within the Start to Run group. bP-value for difference in physical activity within the control group. *Significant (p<0.05) change in physical activity after six months within the Start to Run group. Table 3 Start to Run group: changes in physical activity after six weeks Outcome measures Start to Run group (n=123) Baseline After six weeks Pa Dutch physical activity guidelines (%) Compliance with DNHPA 43.9 74.8 <0.0001* Compliance with Fit-norm 53.7 87.0 <0.0001* Compliance with Combi-norm 57.7 91.1 <0.0001* Physical activity by intensity, mean ± SD (min/week) Light-intensity activities 1827 ± 1192 1423 ± 1296 0.002* Moderate-intensity activities 209 ± 462 163 ± 253 0.21 Vigorous-intensity activities 200 ± 205 410 ± 298 <0.0001* Physical activity by domain, mean ± SD (min/week) Commuting activities 70 ± 110 98 ± 155 0.01* Leisure-time activities 240 ± 268 301 ± 343 0.02* Sports activities 101 ± 143 243 ± 173 <0.0001* Household activities 563 ± 759 614 ± 887 0.43 Activities at work and school 1293 ± 940 792 ± 794 <0.0001* Total time spent in physical activity, mean ± SD (min/week) 2237 ± 1183 1996 ± 1451 0.08 aP-value for difference in physical activity within the Start to Run group. *Significant (p<0.05) change in physical activity after six weeks within the Start to Run group. Ooms et al. BMC Public Health 2013, 13:697 Page 8 of 12 http://www.biomedcentral.com/1471-2458/13/697 by a single question: “Are you (still) running at this mo- ment?” The results of this question showed that 69.0% of the Start to Run participants was still performing run- ning activities [see Additional file 1 - Additional results evaluation Start to Run program]. Based on these find- ings, it seems that Start to Run can recruit people that are insufficiently active; motivate them to take up run- ning; and also frequently and long enough to meet levels of health-enhancing physical activity (as measured by the Fit-norm and Combi-norm). Consequently, Start to Run can positively contribute to improving health of participants. To sustain health benefits, however, it is important that this running behavior is maintained, i.e. that the Start to Run participants continue to run on a regular basis. Often maintenance is defined as implementing be- havior change for at least six months after cessation of intervention [33]. Since the last assessment of physical activity was 4.5 months after cessation of the Start to Run training program, it is difficult to ascertain whether sustained changes in physical activity behavior have been reached according to this definition of maintenance. Others, however, do not define maintenance as sustain- ing behavior change over a specified period of time. Rothman (2000), for example, rather looks at the pro- cesses that govern behavioral maintenance and he argues that people will maintain a change in behavior only if they are satisfied with the new behavior [34]. The Start to Run participants gave the overall training program a rating of 8.2 (scale 0-10; 0 being very poor and 10 being excellent) [see Additional file 1 - Additional results evaluation Start to Run program]. Moreover, the fact that most Start to Run participants were still running 4.5 months after cessation of the Start to Run training pro- gram, may on its own indicate that they were satisfied with their new running behavior and thus will continue running. Nonetheless, definite conclusions cannot be drawn and follow-up assessments over longer periods of time are necessary to establish if the Start to Run partici- pants continue their newly acquired physical activity behavior. With regard to maintaining physical activity levels, the organized sports sector itself may play an important role. In this sector, physical activity opportunities are provided on a continuous basis (i.e. people can play sports on a weekly basis at a sports club). When first providing an eas- ily accessible sporting program, like Start to Run, the next step, i.e. participation in organized sports on a continuous basis, may be facilitated. After completing the program, the Start to Run participants were stimulated to continue run- ning as a member of a local athletics club or the Dutch Athletics Organization. At the six months assessment, 41.0% of the Start to Run participants reported that they became (and still were) a member of a local athletics club or the Dutch Athletics Organization, as a result of partici- pation in the Start to Run training program [see Additional Table 5 Changes in physical activity after six months: comparisons between groups Dichotomous outcome measures OR (group variable)a 95% CI P (group variable) Dutch physical activity guidelines Compliance with DNHPA 1.5 0.8-3.0 0.22 Compliance with Fit-norm 5.1 2.3-11.1 <0.001* Compliance with Combi-norm 3.3 1.4-7.7 0.006* Continuous outcome measures b-coefficient (group variable)a,b 95% CI P (group variable) Physical activity by intensity Light-intensity activities 38 −234-311 0.78 Moderate-intensity activities −126 −265-12 0.07 Vigorous-intensity activities 152 80-223 <0.001* Physical activity by domain Commuting activities 22 −27-71 0.37 Leisure-time activities 19 −63-101 0.65 Sports activities 107 69-145 <0.001* Household activities −45 −220-131 0.62 Activities at work and school 5 −216-226 0.96 Total time spent in physical activity 20 −274-313 0.90 aMultiple (linear or logistic) regression analyses were conducted with physical activity level at six months as the dependent variable, and group (Start to Run group versus control group, with the control group as the reference category) as the independent variable. Adjustments were made for baseline physical activity levels. bUnstandardized regression coefficient. *Significant (p<0.05) difference in physical activity between groups. Ooms et al. BMC Public Health 2013, 13:697 Page 9 of 12 http://www.biomedcentral.com/1471-2458/13/697 file 1 - Additional results evaluation Start to Run program]. These data suggest that an easily accessible sporting pro- gram, like Start to Run, may indeed facilitate participation in organized sports. The role of the organized sports sector in both increasing and maintaining health-enhancing phys- ical activity levels should therefore be further explored. Next to significant increases in vigorous-intensity physical activity and sports activity, the study had some other interesting findings: after six weeks, the Start to Run participants were spending significantly more time in commuting activities and leisure-time activities. These results suggest that Start to Run may have led to in- creases in physical activity in these domains. However, in the same period, there was also a significant decrease in the time spent in work and school activities and, con- sequently, light-intensity activities. These results indicate that, at six weeks, physical activity levels may have been influenced by other factors, like weather conditions, sea- son and/or holidays. The influence of these factors on commuting activities, leisure-time activities and activities at work and school seems plausible, since no effects were found on these outcome measures at the six months assessment when compared with the control group. Yet, without an assessment of the control group at six weeks, some uncertainty remains. Another interesting finding is that Start to Run did not directly affect the total time spent in physical activ- ity. Despite no significant increases in the total time spent in physical activity, additional health benefits are obtained due to participation in Start to Run: as men- tioned before, the increases in sports activity/vigorous- intensity physical activity were substantial, and resulted in more Start to Run participants meeting minimum recommended amounts of vigorous-intensity physical activity for health benefits. Also, there is evidence that vigorous-intensity physical activities, like running, lead to even greater improvements in aerobic fitness and greater reductions in cardiovascular disease and mortal- ity risk than moderate- or light-intensity physical activ- ities, which is independent of their contribution to energy expenditure [35-37]. To our knowledge, this is the first study evaluating the effectiveness of a training program aimed at novice run- ners on increasing health-enhancing physical activity. In general, there is a lack of research and evaluation of ac- tivities conducted in sports settings, especially of con- trolled study designs incorporating both the short- and longer-term effects [27,28]. Therefore, it is difficult to compare these results with those of previous studies. Most comparable studies would be physical activity intervention studies, and a lot of research has been done in this area [e.g. 38,39]: some physical activity interven- tions that prescribed running positively affected physical activity behavior of participants. However, comparability is still limited, as these physical activity interventions did not focus on running per se, were often multi-component, took place in non-sports settings and used different out- come measures. There are some limitations to this study that should be mentioned. First of all, the design of the study does not allow drawing any conclusions on which specific as- pect of the Start to Run program (e.g. group sessions, in- dividual sessions, test run) is most important for increasing (and continuing) physical activity. Moreover, participants’ compliance with the different program components was not measured, making it even more dif- ficult to disentangle the most effective program parts. Second, in this study, a self-report measure of physical activity was used. Despite their common use, there are several limitations of self-report tools, including inaccur- ate recall of the frequency, duration and intensity of physical activity, problems with question comprehension and interpretation, and social desirability bias which can lead to over-reporting of physical activity [40]. However, any inaccuracies are assumed to be random and among both groups. It is therefore unlikely that these potential sources of bias explain the differences in physical activity between the Start to Run group and control group. Self- report measures have the advantage that they are able to collect data from a large number of people at low costs. The SQUASH questionnaire itself has some distinct ad- vantages compared with other physical activity question- naires: it is short, quick to fill in (3-5 minutes), it measures the amount of physical activity (separately) for five different domains and provides the opportunity to estimate compliance with physical activity guidelines. An alternative to self-report measures is to use more ob- jective instruments to measure physical activity, like ac- celerometers and heart rate monitors. Compared with self-report measures, objective instruments are more ex- pensive and logistically more difficult to administer on a large scale. For these reasons, it was decided to use a self-report measure. Nonetheless, it would be interesting to see if the results of this study could be replicated with such an objective measure. Third, due to the voluntary nature of participation in the Start to Run training pro- gram, the possibility of selection bias cannot be entirely excluded. It could be that people who registered for Start to Run were already highly motivated to increase phys- ical activity levels. Therefore, the findings of this study may not pertain to inactive individuals, i.e. the ones who are often less motivated to increase physical activity levels. On the other hand, also a large group of people who did not meet physical activity guidelines was attracted by the Start to Run training program (i.e. al- most half of the Start to Run participants), which may indicate that the program is also suited for this popula- tion group. Although this voluntary participation into Ooms et al. BMC Public Health 2013, 13:697 Page 10 of 12 http://www.biomedcentral.com/1471-2458/13/697 Start to Run might have caused selection bias, it is a strength of the study as well. First of all, behavior was not forced. Next to that, the study population of Start to Run was a sample of the actual Start to Run population. The Start to Run participants in this study had a mean age of 40 years and the percentage of women was 70.0%. Demographic data collected by the Dutch Athletics Organization of the entire Start to Run population in March 2009 (n=4230) show that the study sample is rep- resentative for the entire Start to Run population with regard to age and sex: the average age of the entire Start to Run population was also 40 years and 77.8% of partic- ipants was female. Thus, the study was performed in a generalizable group. Moreover, since the study was performed in a real-world setting, namely the sports set- ting, results are directly transferable into practice. Finally, in this study, it was not possible to ascertain why more than half of the Start to Run participants dropped out of the study between the baseline and six months assessment. It is very difficult to determine why participants did not fill in this questionnaire, because no follow-up data were avail- able of these persons. There could be cases that did not re- spond to the invitation to fill in this questionnaire because they stopped running (e.g. due to an injury or a bad run- ning experience). Given the very low drop-out rate of the Start to Run training program (according to the Dutch Athletics Organization, only 2.2% of the participants dropped out of the Start to Run training program) and the (already) relatively high drop-out in this the study after six weeks, this seems not a plausible explanation. With regard to baseline characteristics, non-respondents were some- what younger and more likely to be female. There were, however, no significant differences in baseline physical activity levels between respondents and non-respondents. Therefore, the most likely explanation for the non- response is that participants were not motivated to participate in a scientific study and filling in a question- naire. Furthermore, since no differences in baseline phys- ical activity levels were found between respondents and non-respondents, it is unlikely that these losses to follow- up influenced study results substantially. Conclusions Considering the above-mentioned limitations, this study does add to the knowledge base concerning the effective- ness of programs initiated in sports settings. The results indicate that an easily accessible program, like Start to Run, organized by a sporting organization, can positively influence levels of health-enhancing physical activity of participants, both in the short- and longer-term. Conse- quently, Start to Run can lead to tangible health benefits among its participants. Based on these results, the use of the organized sports sector as a setting to promote health- enhancing physical activity seems promising. However, further research is needed to establish maintenance of physical activity behavior and generalizability of these re- sults to other (easily accessible) sporting programs. More- over, the role of the organized sports sector in maintaining health-enhancing physical activity levels should be further explored. In future studies, it is also recommended to in- clude more in-depth analyses. It is, for example, important to investigate which population groups benefit most from a program like Start to Run (e.g. men vs. women, young adults vs. older adults, obese vs. non-obese people) and to establish the relative effectiveness of program parts. Re- search in the area of effectiveness of sporting programs in increasing health-enhancing physical activity is still lacking. These data will hopefully encourage policy makers and sporting organizations to both develop and rigorously evaluate easily accessible sporting programs. In this way, more knowledge about the role of the orga- nized sports sector in both promoting and maintaining health-enhancing physical activity can be acquired. Additional file Additional file 1: Additional results evaluation Start to Run program. In the additional file, results can be found concerning the evaluation of the Start to Run program that are not shown in the results section of the article. Abbreviations ACSM: American College of Sports Medicine; AHA: American Heart Association; DNHPA: Dutch Norm for Health-enhancing Physical Activity; MET: METabolic equivalent; NAPSE: National Action Plan for Sport and Exercise; NIVEL: Netherlands Institute for Health Services Research; SQUASH: Short QUestionnaire to ASsess Health-enhancing physical activity. Competing interests The authors declare that they have no competing interests. Authors’ contributions LO contributed to the design of the study, participated in the data collection process, performed data analysis, and drafted the manuscript. CV and DHB contributed to the design of the study, advised on the analytical approach, and reviewed and commented on the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors gratefully acknowledge the contribution of the study participants. This study was funded by the Netherlands Olympic Committee and Netherlands Sports Federation (NOC*NSF). NOC*NSF had no role in study design, data collection and analysis, interpretation of data, decision to publish or preparation of the manuscript. Received: 28 May 2013 Accepted: 26 July 2013 Published: 31 July 2013 References 1. Gill JM, Cooper AR: Physical activity and prevention of type 2 diabetes mellitus. Sports Med 2008, 38:807–824. 2. Jakicic JM: The effect of physical activity on body weight. Obesity (Silver Spring) 2009, 17(Suppl 3):S34–S38. 3. Schmitt NM, Schmitt J, Doren M: The role of physical activity in the prevention of osteoporosis in postmenopausal women-An update. Maturitas 2009, 63:34–38. Ooms et al. BMC Public Health 2013, 13:697 Page 11 of 12 http://www.biomedcentral.com/1471-2458/13/697 4. Sherrington C, Whitney JC, Lord SR, Herbert RD, Cumming RG, Close JC: Effective exercise for the prevention of falls: a systematic review and meta-analysis. J Am Geriatr Soc 2008, 56:2234–2243. 5. Sofi F, Capalbo A, Cesari F, Abbate R, Gensini GF: Physical activity during leisure time and primary prevention of coronary heart disease: an updated meta-analysis of cohort studies. Eur J Cardiovasc Prev Rehabil 2008, 15:247–257. 6. Thune I, Furberg AS: Physical activity and cancer risk: dose–response and cancer, all sites and site-specific. Med Sci Sports Exerc 2001, 33:S530–S550. 7. Wendel-Vos GC, Schuit AJ, Feskens EJ, Boshuizen HC, Verschuren WM, Saris WH, Kromhout D: Physical activity and stroke. A meta-analysis of observational data. Int J Epidemiol 2004, 33:787–798. 8. Bize R, Johnson JA, Plotnikoff RC: Physical activity level and health-related quality of life in the general adult population: a systematic review. Prev Med 2007, 45:401–415. 9. Strohle A: Physical activity, exercise, depression and anxiety disorders. J Neural Transm 2009, 116:777–784. 10. Haskell WL, Lee IM, Pate RR, Powell KE, Blair SN, Franklin BA, Macera CA, Heath GW, Thompson PD, Bauman A: Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Med Sci Sports Exerc 2007, 39:1423–1434. 11. Hildebrandt VH, Chorus AMJ, Stubbe JH: Trend report of physical activity and health 2008/2009. Leiden, The Netherlands: TNO Quality of Life; 2010. 12. Eyler AA, Brownson RC, Donatelle RJ, King AC, Brown D, Sallis JF: Physical activity social support and middle- and older-aged minority women: results from a US survey. Soc Sci Med 1999, 49:781–789. 13. Sallis JF, Hovell MF, Hofstetter CR: Predictors of adoption and maintenance of vigorous physical activity in men and women. Prev Med 1992, 21:237–251. 14. Wendel-Vos W, Droomers M, Kremers S, Brug J, van Lenthe F: Potential environmental determinants of physical activity in adults: a systematic review. Obes Rev 2007, 8:425–440. 15. European Commission: Sport and Physical Activity. Special Eurobarometer 334/Wave 72.3. Brussels, Belgium: TNS Opinion & Social; 2010. 16. Dobbinson SJ, Hayman JA, Livingston PM: Prevalence of health promotion policies in sports clubs in Victoria, Australia. Health Promot Int 2006, 21:121–129. 17. Dutch Ministry of Health, Welfare and Sport: Time for sport: exercise, participate, perform. The Hague, The Netherlands: Dutch Ministry of Health, Welfare and Sport; 2005. 18. Eime RM, Payne WR, Harvey JT: Making sporting clubs healthy and welcoming environments: a strategy to increase participation. J Sci Med Sport 2008, 11:146–154. 19. Kokko S, Kannas L, Villberg J: The health promoting sports club in Finland–a challenge for the settings-based approach. Health Promot Int 2006, 21:219–229. 20. Whitelaw S, Baxendale A, Bryce C, MacHardy L, Young I, Witney E: 'Settings' based health promotion: a review. Health Promot Int 2001, 16:339–353. 21. World Health Organization (WHO): Ottawa Charter for Health Promotion. Geneva: WHO; 1986. 22. Buist I, Bredeweg SW, van Mechelen W, Lemmink KA, Pepping GJ, Diercks RL: No effect of a graded training program on the number of running- related injuries in novice runners: a randomized controlled trial. Am J Sports Med 2008, 36:33–39. 23. Chakravarty EF, Hubert HB, Lingala VB, Fries JF: Reduced disability and mortality among aging runners: a 21-year longitudinal study. Arch Intern Med 2008, 168:1638–1646. 24. Koplan JP, Powell KE, Sikes RK, Shirley RW, Campbell CC: An epidemiologic study of the benefits and risks of running. JAMA 1982, 248:3118–3121. 25. Taunton JE, Ryan MB, Clement DB, McKenzie DC, Lloyd-Smith DR, Zumbo BD: A prospective study of running injuries: the Vancouver Sun Run "In Training" clinics. Br J Sports Med 2003, 37:239–244. 26. Williams PT: A cohort study of incident hypertension in relation to changes in vigorous physical activity in men and women. J Hypertens 2008, 26:1085–1093. 27. Priest N, Armstrong R, Doyle J, Waters E: Interventions implemented through sporting organisations for increasing participation in sport. Cochrane Database Syst Rev 2008(3). Art. No.: CD004812. DOI: 10.1002/ 14651858.CD004812.pub3. 28. Priest N, Armstrong R, Doyle J, Waters E: Policy interventions implemented through sporting organisations for promoting healthy behaviour change. Cochrane Database Syst Rev 2008.(3). Art. No.: CD004809. DOI: 10.1002/14651858.CD004809.pub3. 29. Reitsma-van Rooijen M, de Jong JD: Health Care Consumer Panel: basic report with information about the panel, 2009. Utrecht, The Netherlands: Netherlands Institute for Health Services Research (NIVEL); 2009. 30. Bauman A, Bull F, Chey T, Craig CL, Ainsworth BE, Sallis JF, Bowles HR, Hagstromer M, Sjostrom M, Pratt M: The International Prevalence Study on Physical Activity: results from 20 countries. Int J Behav Nutr Phys Act 2009, 6:21. 31. Wendel-Vos GC, Schuit AJ, Saris WH, Kromhout D: Reproducibility and relative validity of the short questionnaire to assess health-enhancing physical activity. J Clin Epidemiol 2003, 56:1163–1169. 32. Ainsworth BE, Haskell WL, Whitt MC, Irwin ML, Swartz AM, Strath SJ, O'Brien WL, Bassett DR Jr, Schmitz KH, Emplaincourt PO, Jacobs DR Jr, Leon AS: Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc 2000, 32:S498–S504. 33. Marcus BH, Dubbert PM, Forsyth LH, McKenzie TL, Stone EJ, Dunn AL, Blair SN: Physical activity behavior change: issues in adoption and maintenance. Health Psychol 2000, 19:32–41. 34. Rothman AJ: Toward a theory-based analysis of behavioral maintenance. Health Psychol 2000, 19:64–69. 35. Lee IM, Paffenbarger RS Jr: Associations of light, moderate, and vigorous intensity physical activity with longevity. The Harvard Alumni Health Study. Am J Epidemiol 2000, 151:293–299. 36. Swain DP: Moderate or vigorous intensity exercise: which is better for improving aerobic fitness? Prev Cardiol 2005, 8:55–58. 37. Swain DP, Franklin BA: Comparison of cardioprotective benefits of vigorous versus moderate intensity aerobic exercise. Am J Cardiol 2006, 97:141–147. 38. Foster C, Hillsdon M, Thorogood M: Interventions for promoting physical activity. Cochrane Database Syst Rev 2005(1). Art. No.: CD003180. DOI: 10.1002/14651858.CD003180.pub2. 39. Marcus BH, Williams DM, Dubbert PM, Sallis JF, King AC, Yancey AK, Franklin BA, Buchner D, Daniels SR, Claytor RP: Physical activity intervention studies: what we know and what we need to know: a scientific statement from the American Heart Association Council on Nutrition, Physical Activity, and Metabolism (Subcommittee on Physical Activity); Council on Cardiovascular Disease in the Young; and the Interdisciplinary Working Group on Quality of Care and Outcomes Research. Circulation 2006, 114:2739–2752. 40. Sallis JF, Saelens BE: Assessment of physical activity by self-report: status, limitations, and future directions. Res Q Exerc Sport 2000, 71:S1–14. doi:10.1186/1471-2458-13-697 Cite this article as: Ooms et al.: Effectiveness of Start to Run, a 6-week training program for novice runners, on increasing health-enhancing physical activity: a controlled study. BMC Public Health 2013 13:697. Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit Ooms et al. BMC Public Health 2013, 13:697 Page 12 of 12 http://www.biomedcentral.com/1471-2458/13/697

Effectiveness of Start to Run, a 6-week training program for novice runners, on increasing health-enhancing physical activity: a controlled study.

07-31-2013

Ooms, Linda,Veenhof, Cindy,de Bakker, Dinny H

eng

PMC9566386

Citation: Arede, J.; Fernandes, J.F.T.; Schöllhorn, W.I.; Leite, N. Differential Repeated Sprinting Training in Youth Basketball Players: An Analysis of Effects According to Maturity Status. Int. J. Environ. Res. Public Health 2022, 19, 12265. https://doi.org/10.3390/ ijerph191912265 Academic Editors: Krzysztof Ma´ckała and Hubert Makaruk Received: 12 August 2022 Accepted: 21 September 2022 Published: 27 September 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). International Journal of Environmental Research and Public Health Article Differential Repeated Sprinting Training in Youth Basketball Players: An Analysis of Effects According to Maturity Status Jorge Arede 1,2,3,4,* , John F. T. Fernandes 5, Wolfgang I. Schöllhorn 6 and Nuno Leite 4,7 1 Department of Sports Sciences, Exercise and Health, University of Trás-os-Montes and Alto Douro, 5001-801 Vila Real, Portugal 2 School of Education, Polytechnic Institute of Viseu, 3504-501 Viseu, Portugal 3 Department of Sports, Higher Institute of Educational Sciences of the Douro, 4560-708 Penafiel, Portugal 4 School of Sports Sciences, Universidad Europea de Madrid, Campus de Villaviciosa de Odón, 28670 Villaviciosa de Odón, Spain 5 School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff CF23 6XD, UK 6 Institute of Sport Science, Training and Movement Science, University of Mainz, 55122 Mainz, Germany 7 Research Center in Sports Sciences, Health Sciences and Human Development, CIDESD, University of Trás-os-Montes and Alto Douro, 5001-801 Vila Real, Portugal * Correspondence: [email protected] Abstract: The differential learning approach, which includes fluctuations that occur without move- ment repetitions and without corrections has received growing interest in the skill acquisition field. This study aimed to determine the effects of a 9-week training intervention involving differential repeated sprint training on a series of physical tests in youth basketball players. A total of 29 par- ticipants with different maturity statuses (pre-peak height velocity (PHV), n = 7; mid-PHV, n = 6; post-PHV, n = 16) completed 2 sessions per week of differential repeated sprint training for a period of 9 weeks. Sessions consisted of 2 × 10 repetitions sprints of 20-m whereby participants were instructed to perform various additional fluctuations for each repetition. Before and after the training intervention, participants completed jumping tests (countermovement jump (CMJ), single-leg CMJs, the modified 505 agility test, and straight sprinting tests (0–10 splits time), and maturity status was evaluated as well. Within-group analysis showed improvement in CMJ asymmetries and changes in direction asymmetries and 10-m sprint performance for the pre-, mid-, and post-PHV groups, respectively (p < 0.05), with large to very large effects. Analysis of covariance demonstrated that changes in sprint time in post-PHV players were greater than in the pre- and mid-PHV groups (p < 0.05), with moderate effect. Adding random fluctuations during repeated sprint training appear to be a suitable and feasible training strategy for maintaining and enhancing physical performance in youth basketball players, irrespective of maturity status. Furthermore, the present findings en- courage practitioners to implement the present approach in youth athletes to improve their physical performance, but they should be aware that training response can vary according to maturity status. Keywords: team sports; variation; movement variability; puberty; adolescence; growth; maturation; bilateral asymmetry 1. Introduction The requirement for high-intensity running and longer sprint distances has increased in basketball [1]. Consequently, practitioners have been developing methods of enhancing sprint and repeated sprint ability (RSA) in team-sports athletes. Since sprinting in basketball is not exclusively straight-line, it is considered beneficial to prepare athletes to sprint in different directions and challenge the technical model. Nevertheless, practitioners should be aware that adolescent youth basketball players experience puberty, one period of accelerated somatic growth promoted by the syner- gistic effect of gonadal hormones with growth hormone and insulin-like growth factor Int. J. Environ. Res. Public Health 2022, 19, 12265. https://doi.org/10.3390/ijerph191912265 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2022, 19, 12265 2 of 15 1 (IGF-1) [2]. During this period, several physical changes in height, weight, and strength are observed, resulting in decreased coordination and fine motor control [2]. These changes could result in increased injury risk, and individualized prevention strategies to reduce the likelihood of injury should be implemented [2,3]. This approach seems particularly imperative in “high-risk” or “load-sensitive” athletes, who simultaneously experience the period of accelerated growth and hold a high degree of sport specialization [4], such as often occurs with basketball players [5]. This is owing to the few opportunities to experience a variety of load-adaptive stimuli, resulting in fully developed neuromuscular patterns that protect against injury [4]. Nonetheless, the body of evidence suggest that underlying mech- anisms to explain training adaptations are different according maturity status [6]. Whereas pre-pubertal training adaptations result primarily from nervous system development, pu- bertal and post-pubertal adaptations are more associated with increases in sex androgen concentrations (e.g., testosterone, growth hormone, and insulin-like growth factor) [6]. Thus, including variation during training (e.g., using differential learning principles) could be a suitable strategy for addressing individual needs, mitigating neuromuscular deficits, and reducing the chance of overloading. However, further studies are needed for better understanding of the effectiveness of this approach in youth development, whether it is related to physical performance or health related issues. In contrast to the traditional training strategy, where fluctuations (i.e., different from biomechanical models) are viewed as errors that must be minimized, the differential learning approach [7,8] considers the fluctuations in moving systems as crucial sources for learning. A major purpose of differential learning is increasing the possibilities of movement rather than constraining them. Meanwhile, evidence has been provided that increased movement fluctuations can be quantified by the amount or structure of noise and can also be increased or modified by means of emotions [9] or fatigue [10]. Increasing noise serves to destabilize the learning system and to launch a genuine self-organizing process. In its most extreme form, differential learning includes movement variations without repetition and without correction [11]. Movement corrections in differential learning are avoided to enable the athlete to find their own optimal solution (which would not be the case if the athletes were being guided by information about “errors”). Also according to differential learning theory, increased fluctuations result in better skill acquisition and better learning rates than traditional models [12,13]. Thereby, according to the stochastic resonance principle within the differential learning theory, the noise is to be optimized rather than maximized [11]. In this regard, the benefits of the training programs based on differential learning in both technical and physical skills have been reported in team sports [14]. Related to differential sprint training, the studies by Schöllhorn et al. [12] and Arede et al. [15] are of special interest. In the first study, the effect of an intensive sprint training 5 times a week for 6 months, based on repetitions and corrections, was compared with a differential sprint training twice a week for the same duration in 2 youth male groups [12]. After 6 months, both groups improved their maximum running speed, but the differential group improved significantly more. Additionally, a pilot study of basketball-specific sprint training in differential form in female adolescents [15] provided evidence of beneficial applications in physical performance. The extent to which differential sprint training depends on the maturity level of the athletes has not yet been investigated. Based on the previous findings, including differential learning approach fluctuations in repeated sprints in a training program is assumed to have the potential for eliciting physical performance improvements. Therefore, the aim of this study was to examine the effect of a 9-week training interven- tion involving repeated differential sprint training on a series of physical tests (i.e., jumping, sprinting, and change-of-direction) but also in bilateral asymmetries, according to player maturity status. A better understanding of the effects of differential repeated sprint train- ing on various aspects of physical performance may help practitioners to better design training tasks to improve these aspects, considering individual needs based on growth Int. J. Environ. Res. Public Health 2022, 19, 12265 3 of 15 and maturation. Given the lack of previous comparable reports, we expect that repeated differential sprint training effects are independent of maturity status. 2. Materials and Methods 2.1. Participants A group of 38 male basketball players from the under-14 to under-18 age groups were recruited from the Portuguese Basketball Academy to participate in this study. All partic- ipants completed a total of ~270 min of basketball training (3 basketball sessions/week, 90 min/session) and 1 to 2 competitive matches per week. All participants were healthy, free of any injury within the last three months, and without previous history of injury or surgery that might have affected their physical performance. Only participants who participated in at least 90% of the workouts were considered for data analysis, which resulted in the exclusion of 1 player from post-testing analysis (Figure 1). Thirty-one play- ers completed the training program, but only twenty-nine players were finally assessed (Figure 1). Post hoc observed power calculations (G*Power, version 3.1.9.8; University of Düsseldorf; Düsseldorf, Germany) for analysis of covariance (ANCOVA), including three groups and one covariate (α = 0.05, d = 0.25), revealed power (β) of 0.09. Written and informed consent was obtained from all participants’ parents, and player approval was obtained before the beginning of this investigation. The present study was approved by the Institutional Research Ethics Committee and conformed to the recommendations of the Declaration of Helsinki. according to player maturity status. A better understanding of the effects of differential repeated sprint training on various aspects of physical performance may help practitioners to better design training tasks to improve these aspects, considering individual needs based on growth and maturation. Given the lack of previous comparable reports, we expect that repeated differential sprint training effects are independent of maturity status. 2. Materials and Methods 2.1. Participants A group of 38 male basketball players from the under-14 to under-18 age groups were recruited from the Portuguese Basketball Academy to participate in this study. All participants completed a total of ~270 min of basketball training (3 basketball sessions/week, 90 min/session) and 1 to 2 competitive matches per week. All participants were healthy, free of any injury within the last three months, and without previous history of injury or surgery that might have affected their physical performance. Only participants who participated in at least 90% of the workouts were considered for data analysis, which resulted in the exclusion of 1 player from post-testing analysis (Figure 1). Thirty-one players completed the training program, but only twenty-nine players were finally assessed (Figure 1). Post hoc observed power calculations (G*Power, version 3.1.9.8; University of Düsseldorf; Düsseldorf, Germany) for analysis of covariance (ANCOVA), including three groups and one covariate (α = 0.05, d = 0.25), revealed power (β) of 0.09. Written and informed consent was obtained from all participants’ parents, and player approval was obtained before the beginning of this investigation. The present study was approved by the Institutional Research Ethics Committee and conformed to the recommendations of the Declaration of Helsinki. Figure 1. Flowchart of participant recruitment and follow-up. Figure 1. Flowchart of participant recruitment and follow-up. 2.2. Procedures This experimental study incorporated a parallel-group, repeated-measures design, whereby participants were divided into three groups with repeated sprinting training based on differential learning principles [8] (Pre-PHV, n = 7; Mid-PHV, n = 8; Post-PHV, n = 20). The groups were clustered according to the percentage of predicted adult height (% PAH). Int. J. Environ. Res. Public Health 2022, 19, 12265 4 of 15 The training period lasted 9 weeks and was carried out within the regular in-season training sessions. The tests were performed one and two weeks before the commencement of the training period and one week after the intervention. Physical performance tests were conducted under the same experimental conditions (training session time and indoor basketball court). Testing sessions were completed on the same time interval (between 6:30 p.m. and 9:30 p.m.). A 10-min standardized warm-up was performed (5 min jogging, dynamic stretching, 10 bilateral squats, core exercises, 10 unilateral squats, and 3 vertical unilateral jumps) before all testing. Tests were conducted in the following order, respecting the principles of the National Strength and Conditioning Association for testing order [16]: anthropometrical measurements, jumping tests (countermovement jump (CMJ), single-leg countermovement jumps (SLCMJs), the modified 505 agility test, and straight sprinting tests (0–10 splits time). 2.3. Training Program The athletes included in the different training groups participated in two weekly training sessions during in-court training sessions (Supplementary Table S1). All the intervention drills were performed at the beginning of the training session, after the warm- up period. The differential repeated sprint training comprised 2 sets of 10 sprints for 20 m with 30 s of passive recovery between sprints and 3 min of passive recovery between sets. Before each repetition, all participants were verbally instructed by the main researcher to perform a different fluctuation (Supplementary Table S2) or a combination of fluctuations. No instructed movement fluctuation was repeated more than once in each training session. These fluctuations were selected based on previous studies involving the differential learning approach exercises for motor skills [11,15,17]. While differential learning theory, based on findings from biomechanical studies on motor learning and neuroanatomical development [18–20], suggests a coarse orientation on fluctuations that depend on the learning status [8], in our study, all participants, independent of their maturity status, executed the same structure of fluctuations. According to differential learning theory, beginners should focus more on varying variables that are associated with the geometry of a movement, and with advancing learning status, the focus shifts to variables that are related to velocity, acceleration, and rhythm [21]. Whether the maturity status corresponds to learning status in the investigated range of ages needs future extensive research and is beyond the scope of this study. 2.4. Measurements Somatic maturation. Height was recorded using a commercially portable stadiometer (Tanita BF-522W, Japan, nearest 0.1 cm). Body mass was estimated using a scale (Tanita BF-522W, Japan, nearest 0.1 kg). All measurements were taken following the guidelines outlined by the International Society for the Advancement of Kinanthropometry (ISAK) by the same researcher, who holds an ISAK Level 1 accreditation. Players’ height, weight, chronological age, and mid-parent height were used to predict the adult height of each player [22]. The heights of the biological parents of each player were self-reported and adjusted for over-estimation using the previously established equations [23]. The current height of each player was then expressed as a percentage of their predicted adult height (% PAH), which can then be used as an index of somatic maturation [24]. Players were grouped into three maturity bands based on the percentage of predicted adult height attained at the time of the tournament [25]: <86% (Pre-PHV), 86–95% (Mid-PHV) and >95% (Post-PHV) of predicted adult stature (Table 1). Only for descriptive reasons, maturity timing was estimated for each player based on z-scores: average or on-time (z-score between +0.5 and −0.5), early (z-score > +0.5), and late (z-score < −0.5). Int. J. Environ. Res. Public Health 2022, 19, 12265 5 of 15 Table 1. Descriptive data of the subjects (Mean ± SD). Variables Pre-PHV (n = 7) Mid-PHV (n = 6) Post-PHV (n = 16) Biological age (years) 12.01 ± 0.36 13.32 ± 0.58 16.97 ± 1.15 Height (cm) 149.14 ± 7.31 160.67 ± 7.99 179.31 ± 8.68 Body mass (kg) 39.86 ± 10.78 53.83 ± 10.80 74.13 ± 15.09 PAH (%) 83.71 ± 1.11 88.67 ± 2.50 98.69 ± 1.70 Timing −0.01 ± 0.50 1.75 ± 0.63 0.90 ± 0.39 Maturity Timing (Z-score) Early = 1 On-time = 4 Late = 2 Early = 6 On-time = 0 Late = 0 Early = 14 On-time = 2 Late = 0 Training experience (years) 4.86 ± 0.38 3.50 ± 1.38 5.94 ± 2.79 Legend: PHV = Peak of height velocity; PAH = Percentage of Adult Height. Note: A z-score < −0.5 is late, > +0.5 is early, and between +0.5 and −0.5 is average or on-time. Bilateral and Unilateral Countermovement Jumps (CMJ). CMJs were assessed according to the Bosco Protocol [26]. Participants performed three successful single leg CMJs (SLCMJs) with each leg in the vertical and horizontal directions. Participants began by standing on one leg, then descended into a countermovement before extending the stance leg to jump as far or as high as possible in the vertical and horizontal directions. The landing was performed on both feet simultaneously. A successful trial included hands remaining on the hips throughout the movement and balance being maintained for at least 3 s after landing. If the trial was considered unsuccessful, a new trial was performed. In the horizontal direction, the participants started with the selected leg positioned just behind a starting line. The jump height was recorded using an infrared optical system (OptoJump Next—Microgate, Bolzano, Italy). The modified 505 agility test (COD). Each participant was instructed to run to a mark situated 5 m from the starting line, perform a 180◦ COD using the right or left leg to push off, and return to the starting line, covering a total of 10 m [27]. The participants were asked to pass the line indicated on the ground with their entire foot at each turn. The modified 505 agility test total time was recorded with 90 cm height photoelectric cells separated by 1.5 m (Witty, Microgate, Bolzano, Italy). Each participant performed 2 sprints with COD for each side with 2 min of rest between them. Players began each trial in standing staggered position with their front feet 0.5 m behind the first timing gate. The lower limb asymmetry index (ASI) was determined using the following formula [28]: ASI = 100/Max Value (right and left)*Min Value (right and left)* − 1 + 100. The COD deficit (CODD) for the double 180◦ COD test for each leg was calculated via the following formula: mean double modified 505 agility test time—mean 10 m time [27]. Sprint test. The running speed was evaluated as 10 m (0–10 m) split time. Running times were recorded with single pairs of 90 cm high photoelectric cells separated by 1.5 m. Each participant performed 2 trials with 2 min of rest between each trial. Players began each trial in an upright standing position with their feet 0.5 m behind the first timing gate. 2.5. Statistical Analyses Descriptive data are presented as mean (M) ± standard deviation (SD). The reliability of test measures was computed using an average-measures two-way random intraclass correlation coefficient (ICC) with absolute agreement, inclusive of 95% confidence inter- vals (CI), and the coefficient of variation (CV). The ICC was interpreted as poor (<0.5), moderate (0.5–0.74), good (0.75–0.9), or excellent (>0.9) [29]. Coefficients of variation were considered acceptable if <10% [30]. The normality of the data distribution and spheric- ity were confirmed using the Shapiro–Wilk statistic and Levene’s test for the equality of variances, respectively. The analysis of variance (ANOVA) with bootstrapping was used to compare the groups at baseline, and Tukey’s post hoc test was used in conjunction Int. J. Environ. Res. Public Health 2022, 19, 12265 6 of 15 to examine the differences between groups. Effect sizes were evaluated using an omega squared (ω2), with <0.06, 0.06–0.14, and >0.14 indicating a small, medium, or large effect, respectively. A paired-samples t-test with bootstrapping was used to analyse within-group changes [31]. Percentage changes were calculated as ([post-training value—pretraining value]/pre-training value) × 100. Differences between pre- and post-test were calculated according to criteria described elsewhere [32]. Effect sizes (ES) of the within-group changes were evaluated using Hedges’ g correcting small sample biases [33]. The effect sizes were considered <0.2 trivial, >0.2–0.5 small, >0.5–0.8 medium, >0.8–1.3 large, and >1.3 very large [34]. An ANCOVA with Bonferroni-adjusted post hoc tests was performed to examine the differences between groups (Pre-PHV, Mid-PHV, and Post-PHV) in post-training values where the pre-training score was used as a covariate, the post-test scores as the dependent variable and the maturity status as the independent variable [35]. ES was evaluated with partial eta squared (η2p), and the threshold values were no effect (η2p < 0.04), minimum ef- fect (0.04 < η2p < 0.25), moderate effect (0.25 < η2p < 0.64), and strong effect (η2p > 0.64) [36]. This measure has been widely cited as a measure of ES and predominantly provided by statistical software [37]. All statistical analyses were performed using the SPSS software (version 28 for Windows; SPSS Inc., Chicago, IL, USA). 3. Results 3.1. Tests Reliability All ICCs were excellent (ICC range = 0.97–0.99), and most (5 of the 6) of the CVs were acceptable (CV range = 1.34–10.11%) (Table 2). Table 2. Reliability data for test variables. Data are presented as value with lower- and -upper confidence limits. Test Variables ICC (95% CL) CV (%) (95% CL) CMJ (cm) 0.98 (0.97; 0.99) 5.66 (3.81; 7.52) 0–10 m (s) 0.99 (0.98; 0.99) 1.34 (0.88; 1.80) CMJR (cm) 0.98 (0.97; 0.99) 8.19 (6.47; 9.92) CMJL (cm) 0.98 (0.97; 0.99) 10.11 (7.82; 12.39) M505R (s) 0.97 (0.92; 0.98) 2.58 (2.01; 3.14) M505L (s) 0.98 (0.95; 0.99) 1.96 (1.36; 2.56) Abbreviations: ICC = Intraclass correlation coefficient; CV = Coefficient of variation; CL = Confidence limits; CMJ = Countermovement jump height; 0–10 m = 0–10 m sprint time; M505 = Modified 505 agility test; R = Right; L = Left. 3.2. Tests Outcomes At baseline, the training groups were significantly different in CMJ, 0–10 m sprint time, CMJR, CMJL, M505R, and M505L (p ≤ 0.05; large effect; see Table 3). Tukey’s post hoc analysis revealed significant differences between the Pre-PHV and Post-PHV training groups on these physical performance tests. Within-group changes for both training groups are described in Table 3. The Pre-PHV training group showed a significant decrease in CMJASY (p ≤ 0.05, large effect), and the Mid-PHV training group showed a significant decrease in CODASY (p ≤ 0.05, very large effect). Finally, the Post-PHV training group showed significant improvement in 0–10 m sprint time (p ≤ 0.01, large effect). According to the ANCOVA results, significant differences were observed in 0–10 m sprint time (p ≤ 0.05; moderate effect), with higher results for the Post-PHV than the Pre-PHV. Int. J. Environ. Res. Public Health 2022, 19, 12265 7 of 15 Table 3. Inferences of the training programs intervention on subject’s performance measures. Variables Pretest, Mean ± SD Postest, Mean ± SD ∆ % p Hedge’s g Between- Groups Pretest Differences (p) ω2 ANCOVA (p) η2p CMJ (cm) Pre-PHV 22.06 ± 6.55 23.44 ± 5.41 6.28 0.171 0.007 * 0.26 (−0.04; 0.47) 0.466 Mid-PHV 25.23 ± 6.80 26.37 ± 6.63 4.49 0.444 Post-PHV 34.73 ± 9.95 35.51 ± 8.61 2.23 0.372 0–10 m (s) Pre-PHV 2.18 ± 0.29 2.17 ± 0.28 −0.46 0.731 0.014 * 0.22 (−0.06; 0.43) 0.020 * 0.27 Mid-PHV 2.07 ± 0.16 2.03 ± 0.17 −2.25 0.138 Post-PHV 1.88 ± 0.21 1.81 ± 0.17 −3.53 0.000 1.05 (0.42; 1.65) CMJR (cm) Pre-PHV 11.74 ± 3.90 12.44 ± 3.99 5.96 0.222 0.014 * 0.22 (−0.06; 0.44) 0.072 Mid-PHV 12.28 ± 2.79 14.22 ± 3.21 15.74 0.205 Post-PHV 19.66 ± 8.01 20.86 ± 6.35 6.14 0.052 CMJL (cm) Pre-PHV 13.67 ± 3.61 13.61 ± 4.17 −0.42 0.945 0.027 * 0.18 (−0.07; 0.40) 0.332 Mid-PHV 12.72 ± 2.27 14.82 ± 2.93 16.51 0.131 Post-PHV 19.67 ± 7.45 20.85 ± 7.64 6.01 0.098 CMJASY (%) Pre-PHV 26.96 ± 6.61 19.67 ± 7.56 −27.04 0.046 0.88 (−0.03; 1.75) 0.195 0.095 Mid-PHV 27.57 ± 12.77 26.57 ± 8.28 −3.62 0.819 Post-PHV 20.33 ± 9.96 18.61 ± 11.34 −8.47 0.611 M505R (s) Pre-PHV 3.15 ± 0.35 3.06 ± 0.38 −2.90 0.112 0.026 * 0.18 (−0.07; 0.40) 0.430 Mid-PHV 3.07 ± 0.17 3.01 ± 0.18 −1.90 0.321 Post-PHV 2.78 ± 0.32 2.72 ± 0.22 −2.22 0.164 M505L (s) Pre-PHV 3.13 ± 0.36 3.10 ± 0.38 −0.91 0.365 0.023 0.19 (−0.00; 0.41) 0.177 Mid-PHV 3.15 ± 0.13 3.00 ± 0.22 −4.56 0.063 Post-PHV 2.80 ± 0.33 2.73 ± 0.25 −2.41 0.091 CODASY (%) Pre-PHV 4.85 ± 4.05 5.54 ± 2.19 14.42 0.648 0.594 0.326 Mid-PHV 6.34 ± 1.62 4.02 ± 1.41 −36.56 0.015 1.94 (0.50; 3.32) Post-PHV 5.33 ± 2.18 5.68 ± 2.96 6.43 0.705 CODDR (s) Pre-PHV 1.03 ± 0.14 0.94 ± 0.13 −8.97 0.172 0.192 0.664 Mid-PHV 1.04 ± 0.17 0.99 ± 0.12 −4.94 0.326 Post-PHV 0.93 ± 0.14 0.94 ± 0.10 1.00 0.804 CODDL (s) Pre-PHV 0.99 ± 0.13 0.96 ± 0.10 −2.88 0.541 0.115 0.864 Mid-PHV 1.09 ± 0.12 0.97 ± 0.14 −10.70 0.075 Post-PHV 0.95 ± 0.14 0.93 ± 0.11 −1.19 0.782 Abbreviations: CMJ = Countermovement jump height; 0–10 m = 0–10 m sprint time; M505 = Modified 505 agility test; COD = Change of direction test; CODD = COD deficit; R = Right; L = Left; ASI = Bilateral asymmetry; * Pre-PHV vs. Post-PHV (p < 0.05). Figure 2 displays the individual changes in performance from pre- to post-test for each training group. Most Pre-PHV subjects were better at CMJ (57%) and CMJR (71%) on the post-test compared with the pre-test. In the same training group, all subjects improved CMJL. Furthermore, within Pre-PHV, distinct training responses were observed for 0–10 sprint time. The majority of Mid-PHV subjects were better in M505L (50 %), when comparing to the pre-test values. However, in the same training group many subjects kept the same performance in CMJL (33%) and 0–10 sprint time (33%), in post-test. Nevertheless, in CMJ distinct training response were observed in Mid-PHV subjects. In the post-PHV group, many subjects improved 0–10 sprint time (63%); however, distinct training response was observed for different tests, with exception of CMJR. Int. J. Environ. Res. Public Health 2022, 19, 12265 8 of 15 Int. J. Environ. Res. Public Health 2022, 19, x 8 of 16 Figure 2. Percentage of athletes per training response. Legend: (A) Pre-PHV; (B) Mid-PHV; (C) Post- PHV. 4. Discussion The aim of this study was to examine possible group specific effects of differential repeated sprinting training dependent on maturity status. Although all groups had the same training content of differential sprint training exercises, every group had a different training response in distinct variables. We found that the presented training program resulted in significant decreases in bilateral asymmetries during the physical performance tests in the Pre- and Mid-PHV subjects. Moreover, the Post-PHV training group improved their 0–10 m sprint time significantly more than the Pre-PHV subjects. Furthermore, Mid- PHV had more homogenous training responses (better and/or same), whereas more diverse training responses were observed in Pre-PHV and Post-PHV. Whether these results depend on the different levels at the beginning or on the maturity status needs further research. Given the lack of comparative studies on the training response by maturity status, this study should be viewed as the starting point for further studies on this topic as a further intermediate step on the way to individuality of learning [38]. The results indicate that 9 weeks of differential repeated sprinting training of adolescent male basketball players had a beneficial impact in 0–10 m sprint time in different maturity statuses, especially in Post-PHV subjects, as the effect was significantly higher than Pre-PHV. The extent to which the lower increase in performance in the Pre-PHV group is indicative of either too much variation or wrong variations for the performance level and thus suggests that a more traditional approach or individually adapted variations to sprint training are recommended, which at this level still has sufficient variation for optimal learning even with repetition. Whether there is a principle level dependency, needs to be clarified in future studies [11,39]. However, performance advances should not forget the long-term development of athletes, where other parameters like higher symmetry in CMJs could be a preventive and precondition for further performance gains. The beneficial impact in 0– 10 m sprint time is in line with previously results obtained in a pilot study on female basketball players [15]. Nonetheless, results from other studies differ in magnitude. For Figure 2. Percentage of athletes per training response. Legend: (A) Pre-PHV; (B) Mid-PHV; (C) Post-PHV. 4. Discussion The aim of this study was to examine possible group specific effects of differential repeated sprinting training dependent on maturity status. Although all groups had the same training content of differential sprint training exercises, every group had a different training response in distinct variables. We found that the presented training program resulted in significant decreases in bilateral asymmetries during the physical performance tests in the Pre- and Mid-PHV subjects. Moreover, the Post-PHV training group improved their 0–10 m sprint time significantly more than the Pre-PHV subjects. Furthermore, Mid- PHV had more homogenous training responses (better and/or same), whereas more diverse training responses were observed in Pre-PHV and Post-PHV. Whether these results depend on the different levels at the beginning or on the maturity status needs further research. Given the lack of comparative studies on the training response by maturity status, this study should be viewed as the starting point for further studies on this topic as a further intermediate step on the way to individuality of learning [38]. The results indicate that 9 weeks of differential repeated sprinting training of adolescent male basketball players had a beneficial impact in 0–10 m sprint time in different maturity statuses, especially in Post-PHV subjects, as the effect was significantly higher than Pre-PHV. The extent to which the lower increase in performance in the Pre-PHV group is indicative of either too much variation or wrong variations for the performance level and thus suggests that a more traditional approach or individually adapted variations to sprint training are recommended, which at this level still has sufficient variation for optimal learning even with repetition. Whether there is a principle level dependency, needs to be clarified in future studies [11,39]. However, performance advances should not forget the long-term development of athletes, where other parameters like higher symmetry in CMJs could be a preventive and precondition for further performance gains. The beneficial impact in 0–10 m sprint time is in line with previously results obtained in a pilot study on female basketball players [15]. Nonetheless, results from other studies differ in magnitude. For example, 6 weeks of plyometric training resulted less effective improvement in 0–10 m Int. J. Environ. Res. Public Health 2022, 19, 12265 9 of 15 sprint time in youth basketball players [40], whereas other short- to medium-term training protocols (combined strength and conditioning, small-sided games training, high-intensity interval training, and plyometric, strength and change-of-direction training) were more effective at improving the 0–10 m sprint time in pubertal youth basketball players, based in their maturity offset [41–43]. In contrast, the current protocol gives indication to be superior to 6-week eccentric overload training that was direction-specific [44], and 10-week strength training program with random recovery times [45] to achieve gains in 0–10 m sprint time. These findings are in line with Rumpf and colleagues [46] who suggested that other methods (e.g., plyometrics and strength) can be more effective to improve speed during puberty, whereas the combination of methods in athletes with accumulated training and well-developed training skills combination of training methods, forms and purposes in a single drill can be particularly effective [47]. Thereby, discrepancies between studies, and between maturity status can have substantial influence on neuromotor development aspects which underlie possible training adaptations to the differential repeated sprint training. However, with respect to the actual more generally discussed replication crisis [48,49] and the critical discussion of the applied statistics therein the results are rather to be considered as helpful proposals and cannot be generalized [50]. Including our own investigation, they at best provide suggestions that it is worthwhile to conduct further research in this area. During differential repeated sprinting training, many alternating variants of sprinting occur in a single session. In this regard, in comparison with normal sprinting patterns, differential sprints provide a multitude of kinematic and kinetic changes [12,51–56]. Here, the general idea of differential learning theory is to also use restrictions in one area to increase fluctuations in another area in the short term and then increase the number of opportunities in general in the long term by combining the again released constrained area with the increased fluctuations in the other areas. Thereby it is important to notice that the restrictions are explicitly not used for guiding the system towards an externally given problem solution but to initiate a self-organizing process. For example, sprinting with the arms held across the chest or running with the arms held behind the back resulted in increased peak lateral ground reaction forces and higher peak hip internal rotation, and knee flexion [54]. Moreover, forward trunk lean sprinting resulted in greater lengths of all the three hamstring muscles at foot strike and toe-off [53]. Evidence was also provided that the restriction of scapula movement influenced the stance-leg motion and whole- body position during the first step, but also the sprint speed [55]. Restricted arm action (i.e., crossed arms) resulted in compensatory upper body motions that could provide the rotational forces needed to offset the lower body angular momentum generated by the swinging legs [52]. Adding “erroneous” and non-representative -movements by increasing the existing fluctuation during repeated sprint training generate short term co-contractions (i.e., simultaneous contraction of agonist and antagonist muscles around a joint), which provide more joint stability and higher accelerating forces [57,58]. However, combining higher levels of noise, speed, and co-contractions may reduce the momentary speed of movement but provide stronger and movement adequate stimuli for muscle groups that are requested in situations of high competitive stress [56,59]. Greater physical performance requires a balance between maximizing the movement intensity, controlling movement through co-contractions, faster relaxation, and reducing muscle slack [56,59]. In this regard, the transient shift from protective, long-latency reflexes to pre-active, short-latency reflex recruitment throughout maturation, particularly the reduction of inhibitory mechanisms to protect the Musculo-tendon unit [60] can result in more efficient stretch shortening cycle (SSC) actions [58], explaining better training response in 0–10 m sprint time of Post-PHV subjects comparing to the Pre-PHV. Closely connected to the increase in the multitude of muscle activation patterns due to changed joint lever conditions and, consequently, due to the changed proprioception are changes in the brain activation. Neurophysiological adaptations resulting from differential learning include electroencephalographic frequencies in the alpha- and theta-bands which benefits short-term memory and learning [61]. Moreover, there is evidence that differential Int. J. Environ. Res. Public Health 2022, 19, 12265 10 of 15 learning results in increased theta activity in contralateral parieto–occipital regions [61] but also stimulates the somatosensory and motor system and engages more regions of the cortex [62]. Notwithstanding these meaningful findings, brain activity after differential learning has been only analyzed in young adults, and the effects of differential learning, including the brain activity, may be different during neurodevelopment in childhood and puberty, resulting in inter-individual differences in terms of physical performance. Indeed, during young adulthood occurs a peaking of white matter volume [63], an area which controls the signals that neurons share, coordinating how well brain regions work together [64]; whereas, the peak of grey matter (i.e., area with large number of neurons) volume occurs before typical age of puberty onset [63]. Moreover, the process of myelination (i.e., acquisition of the highly specialized myelin membrane around axons) occurs from the back of the cerebral cortex to front, and from subcortical regions to higher centers of the central nervous system (e.g., cerebellum and cortex) [65]. This suggests that the learning of complex skills may lead to distinct neurological adaptations with respect to the maturity status. In fact, learning complex skills results in noticeable changes in the white matter; however, learning after adolescence is associated with increased white matter development in regions that are still undergoing myelination, such as the forebrain [64]. This region integrates different brain areas, such as the prefrontal cortex, the premotor cortex, and the primary motor cortex associated with voluntary movement [65]. Altogether, Post-PHV may have benefited from both brain maturity patterns and neurophysiological adaptations of training in specific brain areas, resulting in improved performance during voluntary actions, such as short sprinting. After the differential repeated sprint training program, irrespective of maturity status, participants displayed higher values of unilateral vertical jumping (except for CMJL in pre-PHV) compared to the pre-test values. Similar benefits for unilateral vertical jump- ing were observed in a pilot study [15]; whereas different effects occurred after a group of youth basketball players completed different training programs [44,45,66]. Albeit en- hanced neuromuscular qualities can be achieved using movement variability [67], overload and assist musculature of hip and knee regions involved in the SSC may be beneficial (e.g., higher peak activity of knee stabilizers muscles or considering concentric peak verti- cal power/body weight) [68,69], to have higher unilateral jumping height in youth athletes. Furthermore, differential repeated sprint training program was particularly beneficial for Mid-PHV athletes regarding vertical unilateral jumping. These results are particularly promising because using the present training strategy, practitioners can simultaneously achieve positive adaptations resulting from natural improvements in maximal muscular power during puberty [60], but also adjust load patterns considering particularities of accelerated growth period. Thereby, during puberty muscle strength increases, but there is no increase in proportion to limb inertial properties; and, excessive physical loading may cause skeletal injury, particularly through overuse mechanism. Therefore, jumping training during puberty should be carefully prescribed because the increased risk of joint overload, and coordination training (movement adaptability) should be particularly emphasized [2]. In this regard, practitioners can provide a relatively safe, enjoyable, and effective training program more based on individual needs, frequently alternating many variants of sprinting in a single session using differential learning, resulting in improved multifaceted adaptabil- ity, and consequently improved vertical unilateral jumping. Moreover, subjects may benefit from an immediate transfer to specific sport such as previously observed after differential learning based jumping training in handball [21]. In addition, it is widely established that change-of-direction speed is an essential skill among youth athletes engaging in team sports [70]. The current training program was beneficial (but without statistical significance) during the agility test including 180◦ change of direction. This change of direction involves a more aggressive cutting angle (≥75◦) which includes higher braking requirements [71]. In this regard, fastest performance in 180◦ change of direction includes higher propulsive and braking forces (particularly horizontal) on the final foot contact [72], and has been associated with higher eccentric Int. J. Environ. Res. Public Health 2022, 19, 12265 11 of 15 and isometric strength [70]. Moreover, the 180◦ change of direction involves high peak muscle activity of the knee stabilizers (vastus medialis and lateralis) which play a key role in frontal play control [69]. Thus, chronic exposure to frequently alternating variants of sprinting in a single session can generate structural and functional adaptations which positively influences biomechanical determinants of 180◦ change of direction, resulting in improved performance in a controlled setting. Furthermore, youth athletes may have bene- fited from continued neural development and hormonal changes throughout childhood and adolescence, resulting in improved change of direction performance [73]. Nevertheless, a previous study involving 16 years old male basketball players which included multidi- rectional eccentric overload training resulted in similar gains in the same 180◦ change of direction test [44]. Thus, older players may benefit from multidimensional adaptations (i.e., biomechanical, morphological, and neuromuscular levels) resulting from eccentric training [74], which could provide an advantage in high-intensity actions, such as cutting. Moreover, athletes may have benefited from performing resistance exercises (i.e., unilateral lateral eccentric overload training) including frontal and transverse plane-dominated tasks, similar to a 180◦ COD test. Notwithstanding, the present findings are promising because the training strategy is low in cost due to no equipment requirements and the results are bene- ficial in 180◦ change of direction, including the potential of increased neurophysiological adaptations [61]. Increases in bilateral asymmetries are observed during early stages of adolescence or in the period of accelerated growth, particularly when rapid gains in limb length occur [75]. In this regard, young athletes can be more predisposed to various injuries in high-intensity activities (e.g., cutting and landings), because of additional stress placed on the weaker leg due to bilateral asymmetry [75]. In the present study, most of subjects had CMJASY above 10% cut-off criterion for bilateral asymmetries becoming more likely to have an injury. Indeed, the participants of our study had larger CMJASY and CODASY than previously observed in youth tennis players, irrespective of maturity status based in maturity off- set [76]. Notwithstanding, the applied training strategy was effective to decrease CMJASY and CODASY, in Pre- and Mid-PHV, respectively. In this regard, differential repeated sprint training which generates neurophysiological adaptations seems to be similarly beneficial to other methods to reduce discrepancies between lower limbs (e.g., bilateral and unilateral strength and plyometric training, and balance and core training) [77], in young subjects where neural mechanisms are mainly responsible for training adaptations [47]. On the contrary to previously observed after 10-week strength training program with random recovery times involving post-pubertal male basketball players [45], the decrease in CMJASY was substantially lower in the present study. This comparison between studies suggests that resistance training may be more effective to induce positive changes in CMJASY. In addition, contrary to what was observed in the pilot study, probably one reason of a lower starting performance level [15], the present protocol did not result in increased CMJ performance. In previous studies including young male basketball players, CMJ values showed higher improvements after completing different short- to medium-term training programs (most of them including jumps) compared to that of the present study, irrespective of maturity status [41–43,78–81]. It appears that a greater dynamic correspon- dence of CMJ with different exercises (vertical jumps, axial based resistance exercises, etc.) may be responsible for achieving the CMJ improvement. Also, in older basketball players (≥16 years old), short- to medium resistance training programs (6–10 week) in unilateral and bilateral fashion resulted more [45,82], albeit lower magnitude was observed in direction-specific eccentric overload training [44]. It suggests different between-studies underlying mechanisms explaining the adaptations in jumping performance, particularly in pre- and pubertal stages, where higher improvements were observed. Thereby, the adaptative response to frequently alternating variants of sprinting in a single session using differential learning which result in improved ability to use the positive effect of SSC to the vertical jumping performance, could be related to neural improvements in these stages [47]. Notwithstanding, in prepubertal and pubertal stages, the magnitude of improvement in Int. J. Environ. Res. Public Health 2022, 19, 12265 12 of 15 a key physiological mechanism underlying efficient movement, such as utilization of the SSC, seems to be greater according the level of neuromuscular load experienced in this plane, how occurs in plyometric training [60]. 5. Conclusions Often interpreted at a first superficial glance as arbitrary variations, on a slightly closer look the variations proposed under the differential learning approach turn out to be tar- geted interventions for a holistic neuromuscular and specific training that must be adjusted to every discipline and level of performance [8]. Through constantly changing postures within the context of the discipline (crossed arms in front of the body, arms above the head, etc.) there is not only a stronger tuning of targeted muscle groups through correspondingly changed levers, but also a more versatile or noisy tuning of the neuronal system (e.g., motor and somatosensory cortices), which thus becomes more robust against future disturbances. Thereby, the body and especially head rotations around various axis are of special im- portance since they train the versatile interactions of the vestibular apparatus with the activating and perceiving apparatus [20,56]. Our findings indicate that adding “erroneous” fluctuation and non-representative movements during repeated sprint training can result in a significant reduction of bilateral asymmetries during physical performance tests, in pre- and mid-PHV basketball players. Furthermore, Post-PHV athletes improved their 10-m sprint to a greater extent than Pre- and Mid-PHV. Generally, the Mid-PHV had more positive and homogenous training response (better and/or same), whereas more varied response was observed in their Pre- and Post-PHV counterparts. Indeed, the inclusion of these fluctuations within repeated sprint training may positively influence the basketball players’ movement patterns towards more effective and stabilized skills. The positive adaptations are potentially owing to concomitant neurophysiological adaptations induced by the differential repeated sprint training. Nonetheless, how much of the learning progress is influenced by the continuously changing biomechanical conditions and how much by the cognitive effect of not having errors corrected in connection with the accompanied disadvantageous brain activations needs to be clarified in future. Nevertheless, the present findings may encourage practitioners to implement similar protocols in youth athletes to improve physical performance, although always being aware that training response can be variable according to maturity status. Furthermore, the higher variability of stimuli during the training also suggests looking for additional effects on prevention of injuries, which have higher incidence during periods of accelerated growth. Finally, further studies should examine the real differences between the application of differential repeated sprint training to the natural development without any training protocol (i.e., control group), advancing towards more individuality in training. Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/ijerph191912265/s1, Table S1: Training program variations. Table S2: Examples of the fluctuations performed during differential repeated sprint training interventions. Author Contributions: Data curation, J.A.; Formal analysis, J.A.; Funding acquisition, N.L.; Inves- tigation, J.A. and N.L.; Methodology, J.A. and N.L.; Project administration, J.A.; Validation, J.A.; Visualization, J.A.; Writing—original draft, J.A., J.F.T.F., W.I.S. and N.L.; Writing—review & edit- ing, J.A., J.F.T.F., W.I.S. and N.L. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the Foundation for Science and Technology (FCT, Portugal), under the project UIDB 04045/2020. Institutional Review Board Statement: The present study was approved by the Institutional Re- search Ethics Committee and conformed to the recommendations of the Declaration of Helsinki. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data that support the findings of this study are available from the corresponding author, J.A., upon reasonable request. Int. J. Environ. Res. Public Health 2022, 19, 12265 13 of 15 Conflicts of Interest: The authors declare no conflict of interest. References 1. Ferioli, D.; Schelling, X.; Bosio, A.; la Torre, A.; Rucco, D.; Rampinini, E. Match activities in basketball games: Comparison between different competitive levels. J. Strength Cond. Res. 2020, 34, 172–182. [CrossRef] [PubMed] 2. Hume, P.; Russell, K. Overuse injuries and injury prevention strategies for youths. In Strength and Conditioning for Young Athletes: Science and Application; Routledge: Oxford, UK, 2014; pp. 200–212. 3. Johnson, D.M.; Cumming, S.P.; Bradley, B.; Williams, S. The influence of exposure, growth and maturation on injury risk in male academy football players. J. Sports Sci. 2022, 40, 1127–1136. [CrossRef] [PubMed] 4. Jayanthi, N.; Schley, S.; Cumming, S.P.; Myer, G.D.; Saffel, H.; Hartwig, T.; Gabbett, T.J. Developmental Training Model for the Sport Specialized Youth Athlete: A Dynamic Strategy for Individualizing Load-Response During Maturation. Sports Health 2022, 14, 142–153. [CrossRef] [PubMed] 5. Arede, J.; Esteves, P.; Ferreira, A.P.; Sampaio, J.; Leite, N. Jump higher, run faster: Effects of diversified sport participation on talent identification and selection in youth basketball identification and selection in youth basketball. J. Sports Sci. 2019, 37, 2220–2227. [CrossRef] [PubMed] 6. Lloyd, R.S.; Oliver, J.L. The youth physical development model: A new approach to long-term athletic development. Strength Cond. J. 2012, 34, 61–72. [CrossRef] 7. Schöllhorn, W. Practical consequences of biomechanically determined individuality and fluctuations on motor learning. In Proceedings of the International Society of Biomechanics Conference, Calgary, AB, Canada, 8–13 August 1999. 8. Schöllhorn, W. Applications of systems dynamic principles to technique and strength training. Acta Acad. Olymp. Est. 2000, 8, 67–85. 9. Janssen, D.; Schöllhorn, W.I.; Lubienetzki, J.; Fölling, K.; Kokenge, H.; Davids, K. Recognition of Emotions in Gait Patterns by Means of Artificial Neural Nets. J. Nonverbal Behav. 2008, 32, 79–92. [CrossRef] 10. Janssen, D.; Schöllhorn, W.I.; Newell, K.M.; Jäger, J.M.; Rost, F.; Vehof, K. Diagnosing fatigue in gait patterns by support vector machines and self-organizing maps. Hum. Mov. Sci. 2011, 30, 966–975. [CrossRef] [PubMed] 11. Schöllhorn, W.; Michelbrink, M.; Welminsiki, D.; Davids, K. Increasing stochastic perturbations enhances acquisition and learning of complex sport movements. Perspect. Cogn. Action Sport 2009, 59–73. 12. Schöllhorn, W.; Röber, F.; Jaitner, T.; Hellstem, W.; Käubler, W. Discrete and continuous effects of traditional and differential sprint training. In Proceedings of the 6th Annual Congress of the European College of Sport Science, Köln, Germany, 24–28 July 2001. 13. Tassignon, B.; Verschueren, J.; Baeyens, J.-P.; Benjaminse, A.; Gokeler, A.; Serrien, B.; Clijsen, R. An Exploratory Meta-Analytic Review on the Empirical Evidence of Differential Learning as an Enhanced Motor Learning Method. Front. Psychol. 2021, 12, 1186. [CrossRef] 14. Coutinho, D.; Santos, S.; Gonçalves, B.; Travassos, B.; Wong, D.P.; Schöllhorn, W.; Sampaio, J. The effects of an enrichment training program for youth football attackers. PLoS ONE 2018, 13, e0199008. [CrossRef] [PubMed] 15. Arede, J.; Poureghbali, S.; Freitas, T.; Fernandes, J.; Schöllhorn, W.I.; Leite, N. The Effect of Differential Repeated Sprint Training on Physical Performance in Female Basketball Players: A Pilot Study. Int. J. Environ. Res. Public Health 2021, 18, 12616. [CrossRef] 16. Haff, G.G.; Triplett, T. Essentials of Strength Training and Conditioning, 4th ed.; Human Kinetics Publishers Inc.: Champaign, IL, USA, 2016. 17. Schollhorn, W.I. The Nonlinear Nature of Learning - A Differential Learning Approach. Open Sports Sci. J. 2012, 5, 100–112. [CrossRef] 18. Creutzfeldt, O. Cortex Cerebri; Springer: Berlin/Heidelberg, Germany, 1983. 19. Nieuwenhuys, R.; Voogd, J.; Van Huijzen, C. The Human Central Nervous System: A Synopsis and Atlas; Springer: Berlin/Heidelberg, Germany, 1991. 20. Schöllhorn, W. Systemdynamische Betrachtung komplexer Bewegungsmuster im Lernprozess—Prozessorientierte Strukturierung der Entwicklung Eines Bewegungsablaufs Mit Hilfe Biomechanischer Beschreibungsgrößen; Peter Lang Verlag: Frankfurt, Germany, 1998. 21. Jainer, T.; Pfeiffer, M. Developing jumping strength based on systems dynamics principles. In European Workshop on Movement Science Mechanics and Physiology; Schöllhorn, W., Bohn, C., Jäger, J.M., Schaper, H., Alichmann, M., Eds.; Sportverlag Strauß: Münster, Germany, 2003. 22. Khamis, H.; Roche, A. Predicting Adult Stature Without Using Skeletal Age: The Khamis-Roche Method. Pediatrics 1994, 94, 504–507. 23. Epstein, L.H.; Valoski, A.M.; Kalarchian, M.; McCurley, J. Do Children Lose and Maintain Weight Easier Than Adults: A Comparison of Child and Parent Weight Changes From Six Months to Ten Years. Obes. Res. 1995, 3, 411–417. [CrossRef] [PubMed] 24. Roche, A.F.; Tyleshevski, F.; Rogers, E. Non-Invasive Measurements of Physical Maturity in Children. Res. Q. Exerc. Sport 1983, 54, 364–371. [CrossRef] 25. Cumming, S.P.; Lloyd, R.S.; Oliver, J.L.; Eisenmann, J.C.; Malina, R.M. Bio-banding in Sport: Applications to Competition, Talent Identification, and Strength and Conditioning of Youth Athletes. Strength Cond. J. 2017, 39, 34–47. [CrossRef] 26. Bosco, C.; Luhtanen, P.; Komi, P.V. A simple method for measurement of mechanical power in jumping. Eur. J. Appl. Physiol. Occup. Physiol. 1983, 50, 273–282. [CrossRef] Int. J. Environ. Res. Public Health 2022, 19, 12265 14 of 15 27. Nimphius, S.; Callaghan, S.J.; Spiteri, T.; Lockie, R.G. Change of Direction Deficit: A More Isolated Measure of Change of Direction Performance Than Total 505 Time. J. Strength Cond. Res. 2016, 30, 3024–3032. [CrossRef] [PubMed] 28. Bishop, C.; Read, P.; Lake, J.; Chavda, S.; Turner, A. Inter-limb asymmetries: Understanding how to calculate differences from bilateral and unilateral tests. Strength Cond. J. 2018, 40, 1–6. [CrossRef] 29. Koo, T.K.; Li, M.Y. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J. Chiropr. Med. 2016, 15, 155–163. [CrossRef] 30. Cormack, S.J.; Newton, R.U.; McGuigan, M.R.; Doyle, T.L.A. Reliability of Measures Obtained During Single and Repeated Countermovement Jumps. Int. J. Sports Physiol. Perform. 2008, 3, 131–144. [CrossRef] 31. Marôco, J. Análise Estatística com o SPSS Statistics, 6th ed.; ReportNumber Lda: Pêro Pinheiro, Portugal, 2014; p. 990. 32. Turner, A. But did my athlete improve? Assessing performance changes when N = 1. Prof. Strength Cond. 2022, 63, 27–31. 33. Cumming, G. Understanding the New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis; Routledge: New York, NY, USA, 2012. 34. Cohen, J. Statistical power analysis for the behavioral sciences. In Statistical Power Analysis for the Behavioral Sciences, 2nd ed.; Routledge: London, UK, 1988; p. 567. 35. Van Breukelen, V.; Gerard, P.J. ANCOVA versus change from baseline had more power in randomized studies and more bias in nonrandomized studies. J. Clin. Epidemiol. 2006, 59, 920–925. [CrossRef] 36. Ferguson, C.J. An Effect Size Primer: A Guide for Clinicians and Researchers. Prof. Psychol. Res. Pract. 2009, 40, 532–538. [CrossRef] 37. Lakens, D. Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t -tests and ANOVAs. Front. Psychol. 2013, 4, 863. [CrossRef] [PubMed] 38. Horst, F.; Janssen, D.; Beckmann, H.; Schöllhorn, W.I. Can Individual Movement Characteristics Across Different Throwing Disciplines Be Identified in High-Performance Decathletes? Front. Psychol. 2020, 11, 2262. [CrossRef] 39. Schöllhorn, W. Differenzielles Lehren und Lernen von Bewegung - durch veränderte Annahmen zu neuen Konsequenzen. In Zur Vernetzung von Forschung und Lehre in Biomechanik, Sportmotorik und Trainingswissenschaft; Gabler, H., Göhner, U., Schiebl, F., Eds.; DVS Symposium 2004 Tübingen; Czwalina: Hamburg, Germany, 2005; pp. 125–135. 40. Palma-Muñoz, I.; Ramírez-Campillo, R.; Azocar-Gallardo, J.; Álvarez, C.; Asadi, A.; Moran, J.; Chaabene, H. Effects of Progressed and Nonprogressed Volume-Based Overload Plyometric Training on Components of Physical Fitness and Body Composition Variables in Youth Male Basketball Players. J. Strength Cond. Res. 2021, 35, 1642–1649. [CrossRef] 41. Arede, J.; Vaz, R.; Franceschi, A.; Gonzalo-Skok, O.; Leite, N. Effects of a combined strength and conditioning training program on physical abilities in adolescent male basketball players. J. Sports Med. Phys. Fit. 2019, 59, 1298–1305. [CrossRef] 42. Arslan, E.; Kilit, B.; Clemente, F.M.; Murawska-Ciałowicz, E.; Soylu, Y.; Sogut, M.; Akca, F.; Gokkaya, M.; Silva, A.F. Effects of Small-Sided Games Training versus High-Intensity Interval Training Approaches in Young Basketball Players. Int. J. Environ. Res. Public Health 2022, 19, 2931. [CrossRef] 43. Sáez de Villarreal, E.; Molina, J.G.; de Castro-Maqueda, G.; Gutiérrez-Manzanedo, J.V. Effects of Plyometric, Strength and Change of Direction Training on High-School Basketball Player’s Physical Fitness. J. Hum. Kinet. 2021, 78, 175–186. [CrossRef] [PubMed] 44. Gonzalo-Skok, O.; Sánchez-Sabaté, J.; Tous-Fajardo, J.; Mendez-Villanueva, A.; Bishop, C.; Piedrafita, E. Effects of Direction- Specific Training Interventions on Physical Performance and Inter-Limb Asymmetries. Int. J. Environ. Res. Public Health 2022, 19, 1029. [CrossRef] 45. Arede, J.; Leite, N.; Tous-Fajardo, J.; Bishop, C.; Gonzalo-Skok, O. Enhancing High-Intensity Actions During a Basketball Game After a Strength Training Program with Random Recovery Times Between Sets. J. Strength Cond. Res. 2021, 36, 1989–1997. [CrossRef] [PubMed] 46. Rumpf, M.C.; Cronin, J.B.; Pinder, S.D.; Oliver, J.; Hughes, M. Effect of different training methods on running sprint times in male youth. Pediatr. Exerc. Sci. 2012, 24, 170–186. [CrossRef] [PubMed] 47. Oliver, J.L.; Rumpf, M.C. Speed development in youths. In Strength and Conditioning for Young Athletes: Science and Application; Lloyd, R.S., Oliver, J.L., Eds.; Routledge: London, UK, 2014. 48. Baker, M.; Penny, D. 1500 scientists lift the lid on reproducibility. Nature 2016, 533, 452–454. [CrossRef] [PubMed] 49. Ioannidis, J.P.A. Why Most Published Research Findings Are False. PLoS Med. 2005, 2, e124. [CrossRef] 50. Schöllhorn, W.I. Studies of specific movements and their stabilization do not allow general inferences on motor learning, even at the neurophysiological level - comment on Lage et al (2021). Braz. J. Mot. Behav. 2022, 16, 8–10. [CrossRef] 51. Schöllhorn, W. DVD—Schnelligkeitstraining: Wie Werde Ich Schneller? Sprint-Koordination; Prinzip: Differenzielles Lernen; Saller: Weikersheim, Germany, 2006. 52. Brooks, L.C.; Weyand, P.G.; Clark, K.P. Does restricting arm motion compromise short sprint running performance? Gait Posture 2022, 94, 114–118. [CrossRef] [PubMed] 53. Higashihara, A.; Nagano, Y.; Takahashi, K.; Fukubayashi, T. Effects of forward trunk lean on hamstring muscle kinematics during sprinting. J. Sports Sci. 2014, 33, 1366–1375. [CrossRef] [PubMed] 54. Miller, R.H.; Caldwell, G.E.; Van Emmerik, R.E.A.; Umberger, B.R.; Hamill, J. Ground Reaction Forces and Lower Extremity Kinematics When Running with Suppressed Arm Swing. J. Biomech. Eng. 2009, 131, 124502. [CrossRef] 55. Otsuka, M.; Ito, T.; Honjo, T.; Isaka, T. Scapula behavior associates with fast sprinting in first accelerated running. Springerplus 2016, 5, 1–9. [CrossRef] [PubMed] Int. J. Environ. Res. Public Health 2022, 19, 12265 15 of 15 56. Schöllhorn, W. Schnelligkeitstraining für alle Sportarten; Rowohlt: Reinbek, Germany, 1995. 57. Radnor, J.M.; Oliver, J.L.; Waugh, C.M.; Myer, G.D.; Moore, I.S.; Lloyd, R.S. The Influence of Growth and Maturation on Stretch-Shortening Cycle Function in Youth. Sports Med. 2017, 48, 57–71. [CrossRef] [PubMed] 58. Schöllhorn, W.; Frick, U.; Schmidtbleicher, D. Der Einfluß der Landetechnik im reaktiven Sprungkrafttraining auf biomechanische Kennwerte des Bewegungsverhaltens. In Proceedings of the Techniques in Athletics-The First International Conference, Cologne, Germany, 7–9 June 1990; Brüggemann, G.P., Rühl, J.K., Eds.; pp. 808–814. 59. Bosch, F. Strength Training and Coordination: An Integrative Approach; 2010 Publishers: Rotterdam, The Netherlands, 2010. 60. Lloyd, R.S.; Cronin, J.B. Plyometric development in youths. In Strength and Conditioning for Young Athletes: Science and Application; Routledge: London, UK, 2014; pp. 94–106. 61. Henz, D.; Schöllhorn, W. Differential training facilitates early consolidation in motor learning. Front. Behav. Neurosci. 2016, 10, 199. [CrossRef] 62. Henz, D.; John, A.; Merz, C.; Schöllhorn, W.I. Post-task Effects on EEG Brain Activity Differ for Various Differential Learning and Contextual Interference Protocols. Front. Hum. Neurosci. 2018, 12, 19. [CrossRef] 63. Bethlehem, R.A.I.; Seidlitz, J.; White, S.R.; Vogel, J.W.; Anderson, K.M.; Adamson, C.; Adler, S.; Alexopoulos, G.S.; Anagnostou, E.; Areces-Gonzalez, A.; et al. Brain charts for the human lifespan. Nature 2022, 604, 525–533. [CrossRef] 64. Fields, R.D. White matter matters. Sci. Am. 2008, 298, 54–61. [CrossRef] 65. Abernethy, B.; Kippers, V.; Hanrahan, S.; Pandy, M.; McManus, A.; Mackinnon, L. Biophysical Foundations of Human Movement; Human Kinetics: Champaign, IL, USA, 2013; p. 394. 66. Gonzalo-Skok, O.; Sánchez-Sabaté, J.; Izquierdo-Lupón, L.; De Villarreal, E.S. Influence of force-vector and force application plyometric training in young elite basketball players. Eur. J. Sport Sci. 2018, 19, 305–314. [CrossRef] 67. Arede, J.; Gonzalo-Skok, O.; Bishop, C.; Schöllhorn, W.I.; Leite, N. Rotational flywheel training in youth female team sport athletes: Could inter-repetition movement variability be beneficial? J. Sports Med. Phys. Fit. 2020, 60, 1444–1452. [CrossRef] 68. Meylan, C.M.P.; Nosaka, K.; Green, J.; Cronin, J.B. Temporal and kinetic analysis of unilateral jumping in the vertical, horizontal, and lateral directions. J. Sports Sci. 2010, 28, 545–554. [CrossRef] 69. Falch, H.N.; Rædergård, H.G.; Van Den Tillaar, R. Relationship of performance measures and muscle activity between a 180◦ change of direction task and different countermovement jumps. Sports 2020, 8, 47. [CrossRef] [PubMed] 70. Spiteri, T.; Newton, R.U.; Binetti, M.; Hart, N.H.; Sheppard, J.M.; Nimphius, S. Mechanical Determinants of Faster Change of Direction and Agility Performance in Female Basketball Athletes. J. Strength Cond. Res. 2015, 29, 2205–2214. [CrossRef] [PubMed] 71. Deweese, B.; Nimphius, S. Program Design and Technique for Speed and Agility Training. In Essentials of Strength and Conditioning; Haff, G.G., Triplett, T.N., Eds.; Human Kinetics: Champaign, IL, USA, 2016; pp. 521–558. 72. Dos’Santos, T.; Thomas, C.; Jones, P.A.; Comfort, P. Mechanical Determinants of Faster Change of Direction Speed Performance in Male Athletes. J. Strength Cond. Res. 2017, 31, 696–705. [CrossRef] 73. Lloyd, R.S.; Read, P.; Oliver, J.L.; Meyers, R.W.; Nimphius, S.; Jeffreys, I. Considerations for the Development of Agility During Childhood and Adolescence. Strength Cond. J. 2013, 35, 2–11. [CrossRef] 74. Douglas, J.; Pearson, S.; Ross, A.; McGuigan, M. Chronic Adaptations to Eccentric Training: A Systematic Review. Sports Med. 2016, 47, 917–941. [CrossRef] 75. Read, P.J.; Oliver, J.L.; Croix, M.B.A.D.S.; Myer, G.D.; Lloyd, R.S. Neuromuscular Risk Factors for Knee and Ankle Ligament Injuries in Male Youth Soccer Players. Sports Med. 2016, 46, 1059–1066. [CrossRef] [PubMed] 76. Madruga-Parera, M.; Romero-Rodríguez, D.; Bishop, C.; Beltran-Valls, M.R.; Latinjak, A.T.; Beato, M.; Fort-Vanmeerhaeghe, A. Effects of Maturation on Lower Limb Neuromuscular Asymmetries in Elite Youth Tennis Players. Sports 2019, 7, 106. [CrossRef] [PubMed] 77. Bishop, C.; Turner, A.; Read, P. Training Methods and Considerations for Practitioners to Reduce Interlimb Asymmetries. Strength Cond. J. 2018, 40, 40–46. [CrossRef] 78. Zribi, A.; Zouch, M.; Chaari, H.; Bouajina, E.; Ben Nasr, H.; Zaouali, M.; Tabka, Z. Short-Term Lower-Body Plyometric Training Improves Whole-Body BMC, Bone Metabolic Markers, and Physical Fitness in Early Pubertal Male Basketball Players. Pediatr. Exerc. Sci. 2014, 26, 22–32. [CrossRef] 79. Santos, E.J.A.M.; Janeira, M.A.A.S. Effects of complex training on explosive strength in adolescent male basketball players. J. Strength Cond. Res. Natl. Strength Cond. Assoc. 2008, 22, 903–909. [CrossRef] [PubMed] 80. Santos, E.J.; Janeira, M.A. The Effects of Plyometric Training Followed by Detraining and Reduced Training Periods on Explosive Strength in Adolescent Male Basketball Players. J. Strength Cond. Res. 2011, 25, 441–452. [CrossRef] 81. Latorre Román, P.Á.; Villar Macias, F.J.; García Pinillos, F. Effects of a contrast training programme on jumping, sprinting and agility performance of prepubertal basketball players. J. Sports Sci. 2017, 36, 802–808. [CrossRef] [PubMed] 82. Gonzalo-Skok, O.; Tous-Fajardo, J.; Suarez-Arrones, L.; Arjol, J.L.; Casajús, J.A.; Mendez-Villanueva, A. Single-Leg Power Output and Between-Limbs Imbalances in Team-Sport Players: Unilateral Versus Bilateral Combined Resistance Training. Int. J. Sports Physiol. Perform. 2017, 12, 106–114. [CrossRef] [PubMed]

Differential Repeated Sprinting Training in Youth Basketball Players: An Analysis of Effects According to Maturity Status.

09-27-2022

Arede, Jorge,Fernandes, John F T,Schöllhorn, Wolfgang I,Leite, Nuno

eng

PMC6366724

RESEARCH ARTICLE Cardiorespiratory fitness assessment and prediction of peak oxygen consumption by Incremental Shuttle Walking Test in healthy women Liliana Pereira Lima1,2☯, He´rcules Ribeiro Leite1,2☯, Mariana Aguiar de Matos2‡, Camila Danielle Cunha Neves2‡, Vanessa Kelly da Silva Lage2‡, Guilherme Pinto da Silva1,2‡, Gladson Salomão Lopes2‡, Maria Gabriela Abreu Chaves2‡, Joyce Noelly Vitor Santos2‡, Ana Cristina Resende CamargosID1,2‡, Pedro Henrique Scheidt Figueiredo1,2‡, Ana Cristina Rodrigues Lacerda1,2☯, Vanessa Amaral Mendonc¸aID1,2☯* 1 Programa de Po´s-Graduac¸ão em Reabilitac¸ão e Desempenho Funcional, Departamento de Fisioterapia, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, Minas Gerais, Brasil, 2 Laborato´rio de Inflamac¸ão e Metabolismo – LIM, CIPq Sau´de, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, Minas Gerais, Brasil ☯ These authors contributed equally to this work. ‡ These authors also contributed equally to this work. * [email protected] Abstract Introduction Preliminary studies have showed that the Incremental Shuttle Walking Test (ISWT) is a maximal test, however comparison between ISWT with the cardiopulmonary exercise test (CEPT) has not yet performed in the healthy woman population. Furthermore, there is no regression equation available in the current literature to predict oxygen peak consumption (VO2 peak). Thus, this study aimed to compare the ISWT with CEPT and to develop an equation to predict peak oxygen uptake (VO2 peak) in healthy women participants. Methods First, the VO2 peak, respiratory exchange ratio (R peak), heart rate max (HR max) and per- centage of predicted HR max (% predicted HR max) were evaluated in the CEPT and ISWT (n = 40). Then, an equation was developed to predict the VO2 peak (n = 54) and its validation was performed (n = 20). Results There were no significant differences between the ISWT and CEPT of VO2 peak, HR max and % predicted HR max values (P>0.05), except for R peak measure in the ISWT (1.22 ± 0.13) and CEPT (1.18 ± 0.1) (P = 0.022). Therefore, both tests showed a moderate positive correlation of VO2 peak (r = 0.51; P = 0.0007), HR max (r = 0.65; P<0.0001) and R peak (r = 0.55; P = 0.0002) and the Bland-Altman analysis showed agreement of VO2 peak (bias = -0.14). The distance walked on ISWT and age explained 36.3% (R2 Adjusted = 0.363) of the PLOS ONE | https://doi.org/10.1371/journal.pone.0211327 February 7, 2019 1 / 11 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Lima LP, Leite HR, Matos MAd, Neves CDC, Lage VKdS, Silva GPd, et al. (2019) Cardiorespiratory fitness assessment and prediction of peak oxygen consumption by Incremental Shuttle Walking Test in healthy women. PLoS ONE 14(2): e0211327. https://doi. org/10.1371/journal.pone.0211327 Editor: Gustavo Batista Menezes, UFMG, BRAZIL Received: May 23, 2018 Accepted: January 3, 2019 Published: February 7, 2019 Copyright: © 2019 Lima et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper. Funding: The author(s) received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. variance in VO2 peak. The equation developed was VO2 peak (predicted) = 19.793 + (0.02 x distance walked)—(0.236 x age). There was no statistically significant difference between the VO2 peak measured directly and the predicted, and the Bland-Altman analysis showed agreement (bias = 1.5 ml/kg/min). Conclusion ISWT is a maximal test showing similar results compared to the CEPT, and the predicted equation was valid and applicable for VO2 peak assessing in young adult healthy women. Introduction Cardiorespiratory fitness (CRF) is defined as the ability to sustain dynamic exercise by large muscle groups over time at moderate to high intensity levels [1]. Furthermore, CRF has been used to measure exercise capacity and provide information about physical limitation, morbid- ity prognosis, and responsiveness to treatment [2]. The current gold standard for the evalua- tion of CRF is the direct measurement of maximal oxygen uptake (VO2max) which represents the maximal achievable level of oxidative metabolism involving large muscle groups [3]. How- ever, in clinical testing situations, the exercise usually is limited by symptoms before the indi- vidual achieve the VO2max. Consequently, VO2 peak is often used as an estimate for VO2max and they are used interchangeably [3]. The laboratory assessment of CRF through maximal tests on treadmills or cycle ergometers (cardiopulmonary exercise testing-CEPT) has a high cost [4] and require specialized profes- sionals and equipments that is not always available [5]. Thus, field tests were developed and have been increasingly used in clinical practice, such as the Six-minute walk test and the Incre- mental Shuttle Walking Test (ISWT). ISWT was created by Singh et al. [6] to assess the CRF of patients with chronic pulmonary obstructive disease (COPD) and later used in other condi- tions or healthy subjects [7, 8, 9, 10, 11]. Several studies had already shown strong correlations between the performance on CEPT and ISWT [5, 12, 13, 14]. Some studies have showed that the ISWT is a maximal test in the pediatric and elderly population [15, 16, 17, 18], however the intensity of ISWT was often indirectly assessed by predictive equations [15, 16, 17]. Hence, our study group com- pared cardiorespiratory responses between ISWT and CEPT in healthy young adult men [14] and adolescent boys (data not published), where the results showed moderate to high significant correlation and agreement, concluding that the ISWT is a maximal test in these subjects. In addition, a VO2 peak prediction equation based on ISWT variables was devel- oped and it demonstrated feasibility and validity [14]. However, this study did not include women in the assessments, remaining a gap in the literature about the ISWT in healthy women. In this paper, we evaluate the CRF in healthy young women by comparing and correlating VO2 peak, respiratory quotient peak (R peak), maximum heart rate (HR max) and percentage of predicted maximum heart rate (% predicted HR max), between ISWT with CEPT through direct analysis of the exhaled gases, aiming to classify the ISWT intensity and to elaborate a predictive equation to estimate the VO2 peak in young adult women population. Incremental Shuttle Walking Test in healthy women PLOS ONE | https://doi.org/10.1371/journal.pone.0211327 February 7, 2019 2 / 11 Materials and methods Subjects Women between 18 and 45 years of age were recruited by convenience from Diamantina city, Minas Gerais state, Brazil. The inclusion criteria were: self-report of no acute or chronic dis- eases; eutrophic according to the body mass index (BMI between 18.5 and 24.9 kg/m2); no smoker; sedentary (not performing physical activity for 30 minutes or more at least three times a week) [19]. The participants were excluded from the study if did not reach the maximal test values on the treadmill (% predicted HR max higher than 90%) and those who failed to understand the tests. This study was approved by the Ethics and Research Committee of Uni- versidade Federal dos Vales do Jequitinhonha e Mucuri, Brazil (protocol 1.184.419/2015) and conducted in accordance with the Resolution N˚ 466/12 of the National Health Council and the Declaration of Helsinki. The participants were informed about the procedures and poten- tial risks associated with the study and all gave written informed consent. Stages of the study This was a cross-sectional study divided into three stages: (1) To compare the CEPT and the ISWT and evaluate the correlation and agreement between the variables VO2 peak, R peak, HR max and % predicted HR max, as well as determine the ISWT intensity in the female popu- lation; (2) To elaborate an equation to predict the VO2 peak; and (3) validate this equation. The sample size was calculated using the statistical program G.Power 3.1 and was based on the number of variables to be included in the multiple regression analysis and the minimum num- ber of observations required. Considering an effect size of 0.68 and power of 0.99, 54 volun- teers were required in order to develop a linear model including up to four variables [14]. To validate the equation, another 20 volunteers were required [14]. To evaluate the cardiorespiratory fitness, all participants were instructed to avoid physical activity and intake caffeine and alcohol in the 24 h prior to the test, to get at least 8 hours of sleep the night before, to eat a light meal and to ingest 500 ml of water two hours before the tests [19]. During all tests performed, the exhaled gases were collected and assessed by a porta- ble telemetric gas analysis system (K4b2, Cosmed, Rome, Italy). Among other variables, VO2, R and HR breath-by-breath were monitored. The data were tabulated and was defined as VO2 peak and R peak the highest value of these measures at peak effort [20]. Predicted HR max was calculated by the equation HR max = 220 –age [21]. The first stage of the study was performed on three consecutive days. On the first day, the anthropometric variables weight, height and BMI, were measured and a familiarization was performed. On subsequent days, the CEPT or the ISWT was performed by randomization. The ISWT was performed in a 10-m course identified by two cones placed 0.5 m from each end point, with an initial speed of 0.5 m/s, increasing 0.17 m/s every minute. The protocol used was composed of 15 stages of 1 min, to prevent the ceiling effect [10, 22] and the walking speed was dictated by a sound [6]. The test was interrupted if the volunteer did not reach the cone once, at the request of the volunteer or for some other reported symptom (dyspnea, dizzi- ness, vertigo, and angina). The CEPT protocol was based on the progression of the ISWT, with the same initial speed and the same speed increase every minute, without changing the incline of the treadmill. The criteria for interrupting the CEPT was systolic blood pressure (SBP) greater than 210 mm Hg; diastolic blood pressure greater than 120 mm Hg; sustained decrease in SBP; angina; dyspnea; cyanosis; nausea; dizziness; or by volunteer’s request [19]. In the second and third stage, the participants performed two ISWT with an interval of 30 minutes between then [23] and the results of the test with the longest walking distance were Incremental Shuttle Walking Test in healthy women PLOS ONE | https://doi.org/10.1371/journal.pone.0211327 February 7, 2019 3 / 11 used for the statistical analysis. To validate the equation, a different group of women was selected according to the same inclusion criteria of the study. The VO2 peak obtained by the gas analyzer was compared with the VO2 peak predicted by the elaborated equation. Statistical analysis Statistical analysis was performed with the Statistical Package for Social Sciences programs ver- sion 22.0 (SPSS Inc., Chicago, IL, USA) and GraphPad Prism 5.0 (Inc., USA). Data were pre- sented as mean (standard deviation). In the first stage the normality of the data was calculated by Shapiro-Wilk test. As the data presented normal distribution, the comparison between the means of the physiological variables evaluated (VO2 peak, R peak, HR max and % predicted HR max) were performed using Paired T-test. The correlation analysis of the variables col- lected was performed by Pearson‘s correlation. The agreement of the variables collected was performed by the Bland-Altman analysis. In the second stage, the Kolmogorov-Smirnov test was used, and the analysis of multiple linear regression was performed with the variables age, weight, height and distance walked defined a priori to elaborate the VO2 peak prediction equa- tion. For the validation of the equation, the Shapiro-Wilk test was performed and then the paired T-test to compare the mean values of the VO2 peak values obtained by the equation with those obtained by the analyzer of gases. In addition, the comparison between the women of first and third stages were realized using the Independent test t or Mann-Whitney test, according of normality of data. The level of statistical significance adopted was P <0.05. Results First stage: Comparison between CEPT and ISWT The general characteristics of the participants of first and second stage and their performance on ISWT are showed in Table 1. Forty volunteers performed both ISWT and CEPT and their cardiorespiratory responses are presented in Table 2. There was no statistically significant difference for any of the vari- ables, except for the R peak, which was higher in the ISWT. According to the percentage of predicted HR max (above 90%) and R peak (> 1.1), the ISWT could be considered a test of maximum intensity [14, 24, 25]. Blood pressure and heart rate were monitored during all tests and there were no intercurrences. Significant correlations were found for the variables VO2 peak, HR max and R peak (Fig 1). The Bland-Altman analysis also demonstrated agreement between the VO2 peak in the ISWT and in the CEPT (Fig 2). Table 1. General characteristics of participants study. Variable N = 54 Age (years) 26.41± 5.6 (24.89–27.92) Weight (kg) 56.56 ± 9.1 (54.08–59.05) Height (m) 1.63 ± 0.1 (1.608–1.641) BMI (kg/m2) 21.86 ± 1.8 (21.38–22.33) Distance walked (m) 821.10 ± 118.9 (788.7–853.6) Walking speed (m/s) 2.06 ± 0.2 (2.013–2.104) The data is presented as mean ± SD (95% CI). BMI = body mass index. https://doi.org/10.1371/journal.pone.0211327.t001 Incremental Shuttle Walking Test in healthy women PLOS ONE | https://doi.org/10.1371/journal.pone.0211327 February 7, 2019 4 / 11 Second stage: Reference equation for VO2 peak The univariate analysis was performed with the variables age, weight, height and distance walked (N = 54). A model of stepwise linear multiple regressions showed distance walked on ISWT and age explained 36.3% (Adjusted R Square = 0.363) of the variance in VO2 peak and this was significant (p = 0.014). The reference equation for the VO2 peak in the ISWT was: VO2 peakðpredictedÞ¼ 19:793þð0:02 x distance walkedÞwith COPD, cystic fibrosis and chronic heart failure, showing strong and significant correla- tions for VO2 peak of CEPT and ISWT [7, 9, 12]. In a study recently published by our research group, male healthy adults showed HR max, VO2 peak and R peak values with strong and significant correlations and agreement between the ISWT and the CEPT, with ISWT being a maximal test for this population [14]. Consider- ing that the maximum VO2 values for women are about 70% of the average values for men [27] and that is not known whether ISTW is a maximum test for healthy young women, we initially investigated the intensity of ISTW. Since the values of HR max above 90% of predicted and R peak > 1.1 [14, 24, 25], we estab- lish that this is a maximum test for this population, and similar VO2 peak results were found between CEPT and ISWT. Further tests carried out with patients with cardiopulmonary dis- eases concurred with our findings [6, 12, 28, 29]. However, data on the validity of the ISWT to evaluate VO2 peak in healthy individuals are scarce in the literature [18]. Gonc¸alves et. al [30], studying subjects of both sexs, different age ( 18 years old), who presented comorbidities such as arterial hypertension, peripheral vascular disease, arthritis and cardiopathies, also con- cluded that ISWT above 12 levels requires maximum effort in these individuals. As the direct analysis of the exhaled gases has a high cost, the use of prediction equations becomes more applicable due to the feasibility and low cost. Considering our results that Fig 1. Correlation between (A) VO2 peak, (B) HR max and (C) R peak in the ISWT and the CEPT. ISWT = Incremental Shuttle Walking Test; CEPT = cardiopulmonary exercise test; VO2 = oxygen uptake; HR max = maximum heart rate; R = respiratory exchange ratio. https://doi.org/10.1371/journal.pone.0211327.g001 Incremental Shuttle Walking Test in healthy women PLOS ONE | https://doi.org/10.1371/journal.pone.0211327 February 7, 2019 6 / 11 ISTW is a maximum test to healthy women, its usefulness is reinforced as a simple way of mea- suring CRF. In this context, an equation was then elaborated for the prediction of VO2 peak in ISTW. In our study, age and distance walked accounted for more than 30% of VO2 peak variance. In the literature it is reported that beyond gender, other factors that influence VO2 peak as genetic factors, age, weight, and training [31]. Findings similar to our study were found in obese women, where there was a significant correlation between the VO2 peak in the cardio- pulmonary exercise test with the ISWT VO2 peak and the ISWT distance [5]. In this same study, the variables age and distance walked by the ISWT explained the predictive model for the VO2 peak. Only two other studies have published a reference equation for VO2 peak using ISWT, highlighting the variables distance and body mass in the prediction [11, 32]. In the study of Fig 2. Bland-Altman agreement of VO2 peak in the ISWT and the CEPT. ISWT = Incremental Shuttle Walking Test; CEPT = cardiopulmonary exercise test; VO2 = oxygen uptake. https://doi.org/10.1371/journal.pone.0211327.g002 Table 3. General characteristics of the study participants. Variable N = 20 Age (years) 25.85 ± 5.6 (23,24–28,46) Weight (kg) 55.84 ± 5.7 (53.16–58.51) Height (m) 1.62 ± 0.04 (1.594–1.638) BMI (kg/m2) 21.34 ± 1.5 (20.61–22.07) Distance walked (m) 865 ± 100.2 (818.1–911.9) Walking speed (m/s) 2.11 ± 0.14 (2.049–2.181) The data is presented as mean ± SD (95% CI). BMI = body mass index. https://doi.org/10.1371/journal.pone.0211327.t003 Incremental Shuttle Walking Test in healthy women PLOS ONE | https://doi.org/10.1371/journal.pone.0211327 February 7, 2019 7 / 11 Dourado et. al [11] the distance in the ISWT was selected, the maximum walking velocity, and distance in the ISWT × body mass as the only determinants of the peak VO2. This is consistent with the variables selected in our study. However, they did not compare to another cardiopul- monary exercise test, nor did they validate the equation. As age is a determining factor for VO2 peak, it is important to highlight that several studies have used the ISWT in the older population [10, 11, 22, 23, 26] or in children and adolescents [15–17], and some evaluated stratifying age groups [2, 30]. Due to the influence of cardiorespi- ratory fitness on functional independence, there is great interest in describing age-related changes in maximum oxygen consumption. Evidences support a 10% per decade decline in VO2 max in men and women regardless of activity level [33]. For all the facts reported, it makes sense for age to be a predictor of VO2 peak in the elaborated equation. Our study presents differentials when proposing a prediction equation for VO2 peak, the main variable for evaluation of cardiorespiratory fitness [19, 34], since most of the studies with ISWT focus on the prediction of walking distance [2, 10, 15, 17, 22, 23]. In addition, those who did the VO2 peak prediction equation for women did not validate it [5, 11]. The equation devel- oped in this study was validated in other volunteers and the VO2 peak values obtained by the equation and the values of VO2 peak obtained by the gas analyzer were similar, indicating that the application of the equation is feasible to estimate the VO2 peak of the chosen population. The limitation of the study was the level of physical activity having been self-reported, but this strategy is adopted in scientific studies [35, 36]. Conclusion The Incremental Shuttle Walking Test was concordant with the CEPT, requiring maximum effort in young health women. The elaborated equation is valid and applicable, being a simple and inexpensive tool to evaluate the cardiorespiratory fitness in the study population. Fig 3. Bland-Altman agreement of VO2 peak in the validation of the reference equation. ISWT = Incremental Shuttle Walking Test; CEPT = cardiopulmonary exercise test; VO2 = oxygen uptake. https://doi.org/10.1371/journal.pone.0211327.g003 Incremental Shuttle Walking Test in healthy women PLOS ONE | https://doi.org/10.1371/journal.pone.0211327 February 7, 2019 8 / 11 Acknowledgments The authors are grateful to Centro Integrado de Po´s-Graduac¸ão e Pesquisa em Sau´de, Univer- sidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, Minas Gerais, Brazil, for providing equipment and technical support for the experiments. Author Contributions Conceptualization: Vanessa Amaral Mendonc¸a. Data curation: Liliana Pereira Lima. Formal analysis: Liliana Pereira Lima, Ana Cristina Resende Camargos, Pedro Henrique Scheidt Figueiredo. Investigation: Liliana Pereira Lima. Methodology: Liliana Pereira Lima, Camila Danielle Cunha Neves, Vanessa Kelly da Silva Lage, Guilherme Pinto da Silva, Gladson Salomão Lopes, Maria Gabriela Abreu Chaves, Joyce Noelly Vitor Santos, Ana Cristina Rodrigues Lacerda. Project administration: Vanessa Amaral Mendonc¸a. Supervision: He´rcules Ribeiro Leite, Vanessa Amaral Mendonc¸a. Writing – original draft: Liliana Pereira Lima, He´rcules Ribeiro Leite, Mariana Aguiar de Matos, Camila Danielle Cunha Neves, Vanessa Kelly da Silva Lage, Ana Cristina Resende Camargos, Pedro Henrique Scheidt Figueiredo, Ana Cristina Rodrigues Lacerda, Vanessa Amaral Mendonc¸a. Writing – review & editing: Liliana Pereira Lima, He´rcules Ribeiro Leite, Mariana Aguiar de Matos, Camila Danielle Cunha Neves, Vanessa Kelly da Silva Lage, Guilherme Pinto da Silva, Gladson Salomão Lopes, Maria Gabriela Abreu Chaves, Joyce Noelly Vitor Santos, Ana Cristina Resende Camargos, Pedro Henrique Scheidt Figueiredo, Ana Cristina Rodri- gues Lacerda, Vanessa Amaral Mendonc¸a. References 1. Despre´s JP. Physical Activity, Sedentary Behaviours, and Cardiovascular Health: When Will Cardiore- spiratory Fitness Become a Vital Sign? Can J Cardiol 2016 Apr; 32(4):505–13. https://doi.org/10.1016/ j.cjca.2015.12.006 PMID: 26907579 2. Agarwal B, Shah M, Andhare N, Mullerpatan R. Incremental Shuttle Walk Test: Reference values and predictive equation for healthy Indian adults. Lung India 2016 Jan-Feb; 33(1):36–41. https://doi.org/10. 4103/0970-2113.173056 PMID: 26933305 3. American Thoracic Society/ American College of Chest Physicians. ATS/ACCP statement on cardiopul- monary exercise testing. Am J Respir Care Med. 2003; 167 (2): 211–77. 4. American College of Sports Medicine. Diretrizes do ACSM para os testes de esforc¸o e sua prescric¸ão. Rio de Janeiro: Guanabara Koogan, 2003. 5. Ju¨rgensen SP, Trimer R, Di Thommazo-Luporini L, Dourado VZ, Bonjorno-Junior JC, Oliveira CR, et al. Does the incremental shuttle walk test require maximal effort in young obese women? Braz J Med Biol Res [online]. 2016; vol. 49, n.8, e5229. Epub 11-Jul-2016. 6. Singh SJ, Morgan MD, Scott S, Walters D, Hardman AE. Development of a shuttle walking test of dis- ability in patients with chronic airways obstruction. Thorax 1992; 47: 1019–1024. PMID: 1494764 7. Bradley J, Howard J, Wallace E, Elborn S. Validity of a modified shuttle test in adult cystic fibrosis. Tho- rax 1999; 54 (5): 437–439. PMID: 10212110 8. Morales FJ, Martı´nez A, Me´ndez M, Agarrado A, Ortega F, Ferna´ndez-Guerra J et al. A shuttle walk test for assessment of functional capacity in chronic heart failure. Am Heart J 1999; 138 (2): 291–298. Incremental Shuttle Walking Test in healthy women PLOS ONE | https://doi.org/10.1371/journal.pone.0211327 February 7, 2019 9 / 11 9. Lewis ME, Newall C, Townsend JN, Hill SL, Bonster RS. Incremental Shuttle walk test in the assess- ment of patients for heart transplantation. Heart 2001; 86:183–7. https://doi.org/10.1136/heart.86.2. 183 PMID: 11454837 10. Dourado VZ, Vidotto MC, Guerra RLF. Reference equations for the performance of healthy adults on field walking tests. J Bras Pneumol 2011; 37: 607–614. PMID: 22042392 11. Dourado VZ, Guerra RL, Tanni SE, Antunes LC, Godoy I. Reference values for the incremental shuttle walk test in healthy subjects: from the walk distance to physiological responses. J Bras Pneumol 2013; 39: 190–197. https://doi.org/10.1590/S1806-37132013000200010 PMID: 23670504 12. Singh SJ, Morgan MD, Hardman AE, Rowe C, Bardsley PA. Comparison of oxygen uptake during a conventional treadmill test and the shuttle walking test in chronic airflow limitation. Eur Respir J 1994; 7: 2016–2020. PMID: 7875275 13. Ju¨rgensen SP et al. Shuttle walking test in obese women: test-retest reliability and concurrent validity with peak oxygen uptake. Clin Physiol Funct Imaging 2015. 35:120–126. https://doi.org/10.1111/cpf. 12135 PMID: 24589245 14. Neves CDC, Lacerda AC, Lage VK, Lima LP, Fonseca SF, de Avelar NC, et al. Cardiorespiratory responses and prediction of peak oxygen uptake during the shuttle walking test in healthy sedentary adult men. PLoS One 2015 Feb 6; 10(2):e0117563. https://doi.org/10.1371/journal.pone.0117563 PMID: 25659094 15. Lanza FC, Zagatto Edo P, Silva JC, Selman JP, Imperatori TB, Zanatta DJ, et al. Reference Equation for the Incremental Shuttle Walk Test in Children and Adolescents. J Pediatr. 2015 Nov; 167(5):1057– 61. https://doi.org/10.1016/j.jpeds.2015.07.068 PMID: 26323195 16. Pinho TMP. Predictive Equation for Incremental Shuttle Walk Test in Adolescents Universidade de Aveiro. 2015 Available from: http://hdl.handle.net/10773/14580. 17. Vardhan V, Palekar T, Dhuke P, Baxi G. Normative Valeus of Incremental Shuttle Walk Test in Children AND Adolescents: Observational study. Int J Pharma Bio Sci 2017 October; 8(4): (B) 478–483. 18. Dourado VZ, and Guerra R.L.F. Reliability and validity of heart rate variability threshold assessment during an incremental shuttle-walk test in middle-aged and older adults. Braz J Med and Biol Res 2013; 46: 194–199. 19. ACSM, American College os Sports Medicine. Diretrizes do ACSM para os testes de esforc¸o e sua pre- scric¸ão. Balady Gary J, 6ªed., Guanabara Koogan; 2014. 20. Herdy AH, Caixeta A. Classificac¸ão Nacional da Aptidão Cardiorrespirato´ria pelo Consumo Ma´ximo de Oxigênio. Arq Bras Cardiol 2016; 106(5):389–395. 21. Karvonen MJ, Kental E, Mustala. The effects of on heart rate a longitudinal study. Ann Med Exper Fenn 1957 35: 307–315. 22. Ju¨rgensen SP, Antunes LC, Tanni SE, Banov MC, Lucheta PA, Bucceroni AF, Godoy I, Dourado VZ. The incremental shuttle walk test in older Brazilian adults. Respiration 2011; 81(3):223–8. https://doi. org/10.1159/000319037 PMID: 20639622 23. Probst VS, Hernandes NA, Teixeira DC, Felcar JM, Mesquita RB, Gonc¸alves CG, et al. Reference val- ues for the incremental shuttle walking test. Respiratory medicine 2012; 106(2): 243–248. 24. Robergs RA, Dwyer D, Astorino T. Recommendations for improved data processing from expired gas analysis indirect calorimetry. Sports Med 2010; 40: 95–111. https://doi.org/10.2165/11319670- 000000000-00000 PMID: 20092364 25. Sawyer BJ, Blessinger JR, Irving BA, Weltman A, Patrie JT, Gaesser GA. Walking and running econ- omy: inverse association with peak oxygen uptake. Med Sci Sports Exerc 2010; 42: 2122–2127. https://doi.org/10.1249/MSS.0b013e3181de2da7 PMID: 20351592 26. Harrison SL, Greening NJ, Houchen-Wolloff L, Bankart J, Morgan MD, Steiner MC, et al. Age- specific normal values for the incremental shuttle walk test in a healthy British population. J Cardio- pulm Rehabil Prev 2013; 33: 309–313. https://doi.org/10.1097/HCR.0b013e3182a0297e PMID: 23959208 27. Nunes RAM, Pontes GFR, Dantas PMS, Fernandes Filho J. Referencial table of cardiopulmonary fit- ness. Fitness & Performance Journal 2005; 4(1), 27–33. 28. Onorati P, Antonucci R, Valli G, Berton E, De Marco F, Serra P, et al. Non-invasive evaluation of gas exchange during a shuttle walking test vs. a 6-min walking test to assess exercise tolerance in COPD patients. Eur J Appl Physiol 2003; 89: 331–336. https://doi.org/10.1007/s00421-003-0803-9 PMID: 12736842 29. Palange P, Forte S, Onorati P, Manfredi F, Serra P, Carlone S. Ventilatory and metabolic adaptations to walking and cycling in patients with COPD. J Appl Physiol 2000; 88:1715–1720. https://doi.org/10. 1152/jappl.2000.88.5.1715 PMID: 10797134 Incremental Shuttle Walking Test in healthy women PLOS ONE | https://doi.org/10.1371/journal.pone.0211327 February 7, 2019 10 / 11 30. Gonc¸alves CG, Mesquita R, Hayashi D, Merli MF, Vidotto LS, Fernandes KBP, et al. Does the Incre- mental Shuttle Walking Test require maximal effort in healthy subjects of different ages? Physiotherapy 2015 Jun; 101(2):141–6. https://doi.org/10.1016/j.physio.2014.11.002 PMID: 25700634 31. American Thoracic Society/ American College of Chest Physicians. ATS/ACCP statement on cardiopul- monary exercise testing. Am J Respir Care Med. 2003; 167 (2): 211–77. 32. Jurio-Iriarte B, Gorostegi-Anduaga I, Aispuru GR, Pe´rez-Asenjo J, Brubaker PH, Maldonado-Martı´n S. Association between Modified Shuttle Walk Test and Cardiorespiratory Fitness in Overweight/Obese Adults with Primary Hypertension: EXERDIET-HTA Study. J Am Soc Hypertens 2017 Apr; 11(4):186– 195. https://doi.org/10.1016/j.jash.2017.01.008 PMID: 28302456 33. Hawkings S,Wiswell R. Rate and Mechanism of Maximal Oxygen Consumption Decline with Aging Implications for Exercise Training. Sports Med 2003; 33(12):877–88. https://doi.org/10.2165/ 00007256-200333120-00002 PMID: 12974656 34. American Thoracic Society/European Respiratory Society. American Thoracic Society/European Respiratory Society statement on pulmonary rehabilitation. Am J Resp Crit Care Med 2006; 173: 1390–1413. https://doi.org/10.1164/rccm.200508-1211ST PMID: 16760357 35. Lima VP, Velloso M, Almeida FD, Carmona B, Ribeiro-Samora GA, Janaudis-Ferreira T. Test–retest reliability of the unsupported upper-limb exercise test (UULEX) and 6-min peg board ring test (6PBRT) in healthy adult individuals. Physiotherapy Theory and Practice 2018 Jan; 19:1–7. 36. Robles P, Araujo T, Brooks D, Zabjek K, Janaudis-Ferreira T, Marzolini S, Goldstein R, Mathur S. Cardiorespiratory Responses to Short Bouts of Resistance Training Exercises in Individuals With Chronic Obstructive Pulmonary Disease: A COMPARISON OF EXERCISE INTENSITIES. J Cardio- pulm Rehabil Prev 2017 Sep; 37(5):356–362. https://doi.org/10.1097/HCR.0000000000000282 PMID: 28858033 Incremental Shuttle Walking Test in healthy women PLOS ONE | https://doi.org/10.1371/journal.pone.0211327 February 7, 2019 11 / 11

Cardiorespiratory fitness assessment and prediction of peak oxygen consumption by Incremental Shuttle Walking Test in healthy women.

02-07-2019

Lima, Liliana Pereira,Leite, Hércules Ribeiro,Matos, Mariana Aguiar de,Neves, Camila Danielle Cunha,Lage, Vanessa Kelly da Silva,Silva, Guilherme Pinto da,Lopes, Gladson Salomão,Chaves, Maria Gabriela Abreu,Santos, Joyce Noelly Vitor,Camargos, Ana Cristina Resende,Figueiredo, Pedro Henrique Scheidt,Lacerda, Ana Cristina Rodrigues,Mendonça, Vanessa Amaral

eng

PMC9819577

Citation: Muñoz-Pérez, I.; Varela-Sanz, A.; Lago-Fuentes, C.; Navarro-Patón, R.; Mecías-Calvo, M. Central and Peripheral Fatigue in Recreational Trail Runners: A Pilot Study. Int. J. Environ. Res. Public Health 2023, 20, 402. https:// doi.org/10.3390/ijerph20010402 Academic Editor: Antonio Sousa Received: 18 October 2022 Revised: 9 December 2022 Accepted: 22 December 2022 Published: 27 December 2022 Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). International Journal of Environmental Research and Public Health Article Central and Peripheral Fatigue in Recreational Trail Runners: A Pilot Study Iker Muñoz-Pérez 1 , Adrián Varela-Sanz 2,*, Carlos Lago-Fuentes 3 , Rubén Navarro-Patón 4 and Marcos Mecías-Calvo 4,* 1 Facultad de Ciencias de la Educación y Deporte, Universidad de Deusto, 48007 Bilbao, Spain 2 Physical and Sports Education Department, Faculty of Sport Sciences and Physical Education, University of A Coruna, 15179 A Coruna, Spain 3 Facultad de Ciencias de la Salud, Universidad Europea del Atlántico, 39011 Santander, Spain 4 Facultad de Formación del Profesorado, Universidade de Santiago de Compostela, 27001 Lugo, Spain * Correspondence: [email protected] (A.V.-S.); [email protected] (M.M.-C.); Tel.: +34-981167000 (ext. 4012) (A.V.-S.); +34-982821069 (M.M.-C.) Abstract: Background: Understanding fatigue mechanisms is crucial for exercise performance. How- ever, scientific evidence on non-invasive methods for assessing fatigue in trail running competitions is scarce, especially when vertical kilometer trail running races (VK) are considered. The main purpose of this study was to assess the autonomic nervous system (ANS) activity (i.e., central fa- tigue) and the state of muscle activation (i.e., peripheral fatigue) before and after a VK competition. Methods: A cross-sectional pilot study was performed. After applying inclusion/exclusion criteria, 8 recreational male trail runners (31.63 ± 7.21 yrs, 1.75 m ± 0.05 m, 70.38 ± 5.41 kg, BMI: 22.88 ± 0.48, running experience: 8.0 ± 3.63 yrs, weekly training volume: 58.75 ± 10.35 km) volunteered to participate and were assessed for both central (i.e., via heart rate variability, HRV) and peripheral (via tensiomyography, TMG) fatigue before and after a VK race. Results: After the VK, resting heart rate, RMSSD (p = 0.01 for both) and SDNN significantly decreased (p = 0.02), while the stress score and the sympathetic-parasympathetic ratio increased (p = 0.01 and p = 0.02, respectively). The TMG analyses suggest that runners already suffered peripheral fatigue before the VK and that 20–30 min are enough for muscular recovery after the race. In summary, our data suggest that participants experienced a pre-competition fatigue status. Further longitudinal studies are necessary to investigate the mechanisms underlying fatigue during trail running races, while training periodization and tapering strategies could play a key role for minimizing pre-competition fatigue status. Keywords: vertical kilometer; trail running; running performance; heart rate variability; muscular fatigue; tensiomyography 1. Introduction Vertical Kilometer (VK) running races are a trail running modality that has gained importance in the last few years and are characterized by the great gradient (1000 m) that runners have to cover over a distance of less than 5000 m (regulation of the Interna- tional Skyrunning Federation), usually performed in mountainous areas. While the main factors determining endurance running performance were exhaustively investigated in scientific literature (i.e., maximum oxygen consumption -VO2max-, velocity associated to VO2max-vVO2max-, lactate threshold -LT- and running economy -RE-) [1,2], the key factors affecting trail running performance were scarcely studied until recently. In this regard, studies have predominately focused on metabolic (e.g., VO2max, vVO2max, RE), biome- chanical (e.g., vertical running speed, ground contact time and flight time, stride length and frequency, ground technicity) and neuromuscular (e.g., stiffness, lower-limb muscular endurance and extensor muscles maximum strength) parameters during both uphill and downhill running [3–8]. Int. J. Environ. Res. Public Health 2023, 20, 402. https://doi.org/10.3390/ijerph20010402 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2023, 20, 402 2 of 11 Considering the aforementioned key factors affecting trail running performance and the specific characteristics of VK competitions (i.e., runners are used to face slopes of more than 40%, while the duration of these challenges can range between 29 and 60 min), the physiological and neuromuscular demands are maximized due to the accumulated gradient in competition, since runners must displace their body upward against gravity, increasing mechanical power in a manner proportionate to slope [9]. In this regard, scientific evidence shows a time-dependent relationship for both the development of muscle damage [3,10–12] and altered myocardial function [13] after ultramarathon races, even if the competition is performed at low intensity [14]. Nevertheless, there is no study that determines the degree of peripheral and central fatigue after shorter, more intense (>LT) competitions, such as VK races. Hence, knowing the degree of fatigue generated during trail running competitions is crucial to establish the optimal recovery time before applying high-demand training loads. On this point, a common approach to evaluate the acute changes in cardiac function and athletes’ readiness for training is the autonomic nervous system (ANS) activity monitoring via heart rate variability (HRV) evaluation. This method has proven to be valid and reliable to control and monitor endurance training, avoiding the development of non- functional overreaching and overtraining [15–18] by assessing the balance between the parasympathetic (PNS) and sympathetic nervous system (SNS) [19–21], thus allowing the establishment of optimal training conditions for supercompensation [22]. The use of HRV to assess the rest time required to restore ANS balance after competi- tion has been reported in several studies [23,24], ranging from 1 to 3 days depending on the competition characteristics (e.g., distance, race profile, etc). However, athletes’ self- perception of full recovery after a 24-h competition can be up to 12 days [25]. This difference between subjective perception and objective evaluation of recovery (i.e., measured by ANS activation) may be influenced by muscle damage associated with peripheral fatigue and therefore, cannot be detected by HRV measurement. In this regard, simultaneous assess- ment of muscle fatigue and HRV, both pre- and post-competition, could be a suitable strategy to determine the degree of fatigue and the minimum time required for optimal recovery and subsequent performance. To date, there are few studies using non-invasive methods, such as maximal voluntary contraction (MVC) determination and electrical stimulation, to assess the level of muscle fatigue after an endurance trail running competition [10,12,26]. Therefore, the use of a non- invasive method to assess muscle contractile capacity, such as tensiomyography (TMG), may be a novel strategy to analyze muscle activity before and after competition in order to establish the optimal relationship between activity and recovery [11,27–33]. Taking into account the benefits and practical applications of using non-invasive methods to evaluate performance variables (i.e., muscle fatigue and performance of ANS) during a competitive race, to the best of our knowledge, no studies have implemented these approaches in trail-mountain running races. For these reasons, the first objective of this study was to compare the state of activation of the ANS (via HRV measurement) before and after a VK trail running race in recreational trail runners (i.e., central fatigue). The second objective of the study was to assess the muscle fatigue caused by this type of competition in recreational trail runners (i.e., peripheral fatigue). We hypothesize that both HRV and TMG values will be negatively affected after the race (within 20–30 min after completion) when compared to those registered previous to the competition, but the magnitude of these changes will not be large. 2. Materials and Methods 2.1. Study Design A cross-sectional study was conducted with pre- and post-competition evaluations regarding central and peripheral fatigue in a group of experienced recreational trail runners to determine the objectives of this investigation. The athletes took part in the Vertical Int. J. Environ. Res. Public Health 2023, 20, 402 3 of 11 Kilometer of Fuente Dé (2018), an uphill trail running race 2.6 km in distance and with a positive slope of 972 m to reach an altitude of ~1877 m. 2.2. Participants Eleven recreational trail runners (10 men and 1 woman), with competition experience in these types of races of at least 3 years, voluntarily participated in this study. The inclusion criteria for the present study were: (1) to complete all the records, (2) to finish the competition, and (3) not to suffer any injury or illness during the measurements. Once the inclusion criteria were applied, the final sample consisted of 8 male participants with the following characteristics (mean ± SD): age 31.63 ± 7.21 yrs, height 1.75 m ± 0.05 m, body weight 70.38 ± 5.41 kg, BMI 22.88 ± 0.48, running experience 8.0 ± 3.63 yrs; weekly training volume 58.75 ± 10.35 km. The experimental procedures were explained in detail to all participants prior to the beginning of the study and they were free to withdraw from the study at any time. All of them signed a written informed consent form before the start of the study. The research was approved by the Ethics Committee of the Universidad Europea del Atlántico (CEI 21/2018), under the standards established in the Declaration of Helsinki. 2.3. Measurements 2.3.1. Central Fatigue Assessment: HRV To collect HRV data for each athlete and after a 1-min stabilization period, a 5-min measurement protocol was performed in the supine position in a dim light room with a temperature of 20–22 ◦C, with a relative humidity of 60–65% and after emptying their urinary bladder, as previously recommended [19,34]. During the recordings the authors encouraged participants to stay calm and not perform any movement throughout the measurements. Respiratory rate was not controlled during recording, these previous studies found only small differences between spontaneous and metronome-guided breathing on HRV variables [35]. The R-R intervals were registered using an HR band (Polar H10 band, Polar V800, Polar Electro Oy, Finland), with data downloaded using custom software (Polar Pro) and dumped into a .txt file without applying any filter for correction. Once .txt files were generated for each athlete and measurement (i.e., pre-post), these were imported into a specific software (HRV Kubios Version 3.5, Kuopio, Finland) [36] to process HRV data with artifact correction (i.e., settings: “custom” and “0.3”). The data processing configuration was carried out following the pre-established values by the Kubios software (Lambda = 500). Each R-R series were corrected by applying the medium threshold for beat correction, as suggested in the software. In this regard, the following variables were obtained for further analyses [37]: the square root of the mean of the squared differences between successive normal-to-normal intervals (RMSSD), the standard deviation of normal-to-normal intervals (SDNN), and the percentage of successive RR intervals that differ by more than 50 ms (pNN50) in the time domain. The stress score (SS), and the ratio that compares the activity of the SNS -measured by SS- vs. the activity of the PNS-measured by the variable SD1- (S/PS ratio), were obtained as non-linear measurements. 2.3.2. Peripheral Fatigue Assessment: Contractile Muscle Properties The muscular response of the rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM), and gastrocnemius medialis (GM) of both legs was measured by TMG (TMG-100 System electrostimulator, TMG-BMC d.o.o., Ljubljana, Slovenia). All measurements were performed under static conditions, and with the muscle totally relaxed. The RF, VL and VM were measured with the participant in the supine position and the knee joint flexed at a 40◦ angle by means of a wedge cushion designed for that purpose. The GM was measured with the participant in the prone position and the knee joint bent at an angle of 15◦, also through a specially-designed wedge cushion. A digital displacement transducer (Trans-Tek DC-DC; GK 40, Panoptik d.o.o. Ljubljana, Slovenia) incorporating a spring of 0.17 N·m−1 Int. J. Environ. Res. Public Health 2023, 20, 402 4 of 11 was used and placed perpendicular and directly on the skin at the area of maximal mus- cle mass of each muscle (established visually and on palpation of the muscle during a voluntary contraction), as previously described [38]. The two self-adhesive electrodes (5 × 5 cm2) (Compex Medical SA, Ecublens, Switzerland) were placed symmetrically to the sensor, following the arrangement of the fibers [39]. The positive electrode (anode) was placed in the proximal part and the negative (cathode) in the distal part, between 5–6 cm from the measurement point. The electrical stimulus (i.e., 1 ms) was applied with an electrostimulator (TMG-S1; Furlan Co., & Ltd., Ljubljana, Slovenia), while the intensity was varied (i.e., 50, 75 and 100 mAp). The intensity that reached the maximum response of the radial displacement of the muscle belly was selected [40]. In addition, periods of 10 s were established between consecutive measurements to minimize the possible effects of fatigue or muscle enhancement [40,41]. All measurements were performed by the same researcher, who had experience in collecting these types of measurements. None of the evaluated subjects presented discomfort during electrical stimulation. Maximal radial muscle-belly displacement (Dm); reaction or activation time (also known as time delay) between the initiation and 10% of Dm (Td); contraction time between 10 and 90% Dm (Tc); sustain time (Ts), as the interval in milliseconds (ms) between 50% of Dm on both the ascending and descending sides of the curve; and relaxation time (Tr), as the interval between 90% and 50% Dm of muscle reaction of the RF, VL, VM and GM, were recorded using TMG. The TMG-derived contraction velocity (Vc) was also calculated by dividing Dm by the sum of Tc and Td [39,42]. In this regard, previous evidence supports the use of Vc as a sensi- tive marker of acute variations in speed and power performance [39]. All TMG variables used had demonstrated a high intraclass correlation coefficient (ICC) (i.e., 0.86–0.98), as described in previous studies [43,44]. Finally, to assess peripheral fatigue of the lower limb, both legs were individually analyzed, and then the results obtained for each muscle of each leg were pooled, according to García-Manso et al. [29]. 2.4. Procedures Both central and peripheral fatigue tests were performed the day before the com- petition and immediately after it, within 20–30 min of the end of the race, as previously described [38]. Each test lasted no more than 10 min. Firstly, central fatigue was assessed by measuring HRV. Upon completion of this, peripheral fatigue was assessed using the contractile properties of the RF, VL, VM and GM of both legs. An experimental design scheme is presented in Figure 1. 2.5. Statistical Analysis Statistical analysis of the data was performed using the Jamovi 1.6.16 software (Sydney, Australia). The Shapiro-Wilk test was applied to establish whether the variances of the different variables correspond to a normal and homogeneous distribution. A T-test was performed for repeated samples, or its non-parametric counterpart, when applicable, to detect significant differences before and after the competition (i.e., pre-post) in the following variables: (1) SDNN, Pnn50, RSSMD, SS, and S/PS ratio for central fatigue assessment; and (2) Td, Tc, Ts, Tr, Dm, Vc for peripheral fatigue assessment. Cohen’s d was used to measure the effect size (ES) of the parametric meanings, using the small (d = 0.2), medium (d = 0.5) and large (d = 0.8) reference values, as Cohen suggested [45]. In the case of applying a non-parametric test, the ES was determined using a biserial correlation analysis [46]. The confidence interval for the differences was established at 95%. The significant difference for the value of α was established with a value of p < 0.05. Int. J. Environ. Res. Public Health 2023, 20, 402 5 of 11 Int. J. Environ. Res. Public Health 2023, 20, x FOR PEER REVIEW  5  of  12        Figure 1. Experimental design scheme. VK: vertical kilometer; HRV: heart rate variability; RMSSD:  square root of the mean of the squared differences between successive normal–to–normal intervals;  SDNN: standard deviation of normal–to–normal intervals; pNN50: percentage of successive RR in‐ tervals that differ by more than 50 ms; SS: stress score; S/PS ratio: ratio between the sympathetic  nervous system and the parasympathetic nervous system activity; TMG: tensiomyography; RF: rec‐ tus femoris; VL: vastus lateralis; VM: vastus medialis; GM: gastrocnemius medialis.  2.5. Statistical Analysis  Statistical analysis of the data was performed using the Jamovi 1.6.16 software (Syd‐ ney, Australia). The Shapiro‐Wilk test was applied to establish whether the variances of  the different variables correspond to a normal and homogeneous distribution. A T‐test  was performed for repeated samples, or its non‐parametric counterpart, when applicable,  to detect significant differences before and after the competition (i.e., pre‐post) in the fol‐ lowing variables: (1) SDNN, Pnn50, RSSMD, SS, and S/PS ratio for central fatigue assess‐ ment; and (2) Td, Tc, Ts, Tr, Dm, Vc for peripheral fatigue assessment. Cohen’s d was used  to measure the effect size (ES) of the parametric meanings, using the small (d = 0.2), me‐ dium (d = 0.5) and large (d = 0.8) reference values, as Cohen suggested [45]. In the case of  applying a non‐parametric test, the ES was determined using a biserial correlation analy‐ sis [46]. The confidence interval for the differences was established at 95%. The significant  difference for the value of α was established with a value of p < 0.05.  3. Results  3.1. Central Fatigue Assessment: HRV  Table 1 shows the fluctuation of the observed variables, referring to the ANS activity.  Resting heart rate (HR) significantly increased after the VK competition (⁓28%, p = 0.01).      Figure 1. Experimental design scheme. VK: vertical kilometer; HRV: heart rate variability; RMSSD: square root of the mean of the squared differences between successive normal–to–normal intervals; SDNN: standard deviation of normal–to–normal intervals; pNN50: percentage of successive RR intervals that differ by more than 50 ms; SS: stress score; S/PS ratio: ratio between the sympathetic nervous system and the parasympathetic nervous system activity; TMG: tensiomyography; RF: rectus femoris; VL: vastus lateralis; VM: vastus medialis; GM: gastrocnemius medialis. 3. Results 3.1. Central Fatigue Assessment: HRV Table 1 shows the fluctuation of the observed variables, referring to the ANS activity. Resting heart rate (HR) significantly increased after the VK competition (~28%, p = 0.01). Table 1. Central fatigue before and after the VK trail running race. Pre-VK Post-VK p-Value 95% CI Cohen’s d ES 95% CI (Mean ± SD) (Mean ± SD) Lower Upper Lower Upper HR (bpm) 56.10 ± 6.96 71.90 ± 11.78 0.01 −26.99 −4.51 −1.17 −2.07 −0.23 Time domain SDNN (ms) 58.10 ± 27.06 33.40 ± 18.99 0.02 4.79 44.52 1.04 0.14 1.89 pNN50 (%) 23.71 ± 12.56 8.26 ± 8.94 0.23 3.04 24.49 0.89 RMSSD (ms) 48.45 ± 19.29 27.21 ± 12.16 0.01 8.13 34.35 1.35 0.35 2.31 Non-linear measurements SS (a.u.) 14.97 ± 7.76 28.42 ± 13.66 0.01 −22.31 −4.59 −1.27 −2.20 −0.30 S/PS ratio (a.u.) 0.58 ± 0.45 2.36 ± 2.32 0.02 −3.89 −0.21 −0.94 HR: Heart Rate; SDNN: standard deviation of normal-to-normal intervals; pNN50: percentage of successive RR intervals that differ by more than 50 ms; RMSSD: square root of the mean of the squared differences between succes- sive normal-to-normal intervals; SS: Stress Score; S/PS ratio: ratio between the sympathetic nervous system and the parasympathetic nervous system activity; VK: vertical kilometer; SD: standard deviation; CI: confidence interval. The time domain variables showed a significant decrease of the PNS activity after the competition (Table 1). On the contrary, SS, an index related to the SNS activity, significantly increased after the race (Table 1). Regarding autonomic balance, S/PS ratio increased, denoting a significant predomi- nance of the SNS over the PNS (Table 1). Int. J. Environ. Res. Public Health 2023, 20, 402 6 of 11 3.2. Peripheral Fatigue Assessment: TMG Concerning TMG measures, there were several changes in the muscular response after the competition. Table 2 shows the differences between pre- and post-competition values obtained from the analysis of both legs pooled and for each muscle group. Significant dif- ferences were found only in TdRF [mean difference: 1.94 (95% CI: 0.41–3.47), t (10) = 2.8310; p = 0.018, d = 0.85)], TrVM [mean difference: 72.05 (95% CI: 18.01–126.1), t (10) = 2.9707; p = 0.014, d = 0.89)] and TsGM [mean difference: −139.274 (95% CI: −239.41–39.11), t (10) = −3.09; p = 0.011, d = 0.93)] when comparing pre- and post-competition values. Table 2. Peripheral fatigue before and after the VK trail running race for both legs. Muscle Group TMG Pre-VK (Mean ± SD) Post-VK (Mean ± SD) Difference p-Value 95% CI Cohen’s d ES 95% CI (%) Lower Upper Lower Upper Rectus Femoris (RF) Td 47.45 ± 2.93 45.34 ± 2.66 −4.7 0.02 * 0.41 3.47 0.85 0.14 1.54 Tc 57.19 ± 7.09 57.41 ± 8.40 0.4 0.88 −5.0 4.35 −0.05 −0.64 0.55 Ts 307.43 ± 599.85 143.03 ± 131.92 −114.9 0.17 −97.48 481.47 0.44 −0.19 1.06 Tr 121.65 ± 230.21 53.69 ± 55.11 −126.6 0.15 −33.34 188.73 0.47 −0.17 1.09 Dm 14.30 ± 3.43 15.60 ± 3.76 8.3 0.18 −3.10 0.68 −0.43 −1.04 0.20 Vc 0.27 ± 0.06 0.30 ± 0.06 9.2 0.12 −0.06 0.008 −0.50 −1.13 0.14 Vastus Lateralis (VL) Td 43.93 ± 2.31 42.81 ± 2.49 −2.6 0.09 −0.19 2.20 0.56 −0.09 1.19 Tc 45.85 ± 4.99 45.20 ± 5.88 −1.4 0.52 −1.22 2.24 0.19 −0.40 0.79 Ts 90.84 ± 47.23 89.07 ± 25.18 −2.0 0.81 −28.24 35.47 0.07 −0.52 0.67 Tr 38.82 ± 39.04 35.05 ± 17.45 −10.8 0.66 −21.23 32.03 0.13 −0.46 0.73 Dm 10.37 ± 1.82 11.16 ± 1.91 7.0 0.12 −1.68 0.23 −0.50 −1.13 0.13 Vc 0.23 ± 0.04 0.25 ± 0.04 9.2 0.06 −0.04 0.001 −0.63 −1.28 0.03 Vastus Medialis (VM) Td 42.83 ± 1.52 42.51 ± 1.01 −0.7 0.49 −0.72 1.38 0.21 −0.39 0.81 Tc 47.64 ± 4.52 48.15 ± 5.28 1.0 0.41 −1.89 0.83 −0.26 −0.86 0.34 Ts 411.90 ± 165.18 371.33 ± 59.77 −10.9 0.23 −34.64 127.56 0.38 −0.24 0.99 Tr 230.80 ± 65.42 151.23 ± 68.48 −52.6 0.01 * 18.01 126.10 0.89 0.17 1.59 Dm 15.58 ± 3.46 16.09 ± 3.38 3.2 0.24 −1.28 0.36 −0.37 −0.98 0.25 Vc 0.35 ± 0.09 0.36 ± 0.08 2.9 0.33 −0.03 0.01 −0.31 −0.91 0.30 Gastrocnemius Medialis (GM) Td 39.94 ± 1.91 37.72 ± 3.82 −5.9 0.10 −0.43 4.12 0.54 −0.10 1.17 Tc 43.22 ± 6.08 44.10 ± 13.28 2.0 0.66 −8.64 5.73 −0.13 −0.73 0.46 Ts 480.16 ± 242.51 622.28 ± 353.28 22.8 0.01 * −239.42 −39.12 −0.93 −1.63 −0.20 Tr 175.05 ± 129.45 143.02 ± 118.09 −22.4 0.61 −96.87 157.16 0.15 −0.44 0.75 Dm 5.07 ± 2.17 4.76 ± 2.14 −6.6 0.65 −1.07 1.65 0.14 −0.46 0.73 Vc 0.12 ± 0.05 0.12 ± 0.05 −4.8 0.71 −0.02 0.04 0.11 −0.48 0.71 VK: vertical kilometer; SD: standard deviation; CI: confidence interval; ES: effect size; TMG: variables derived from the tensiomyography measurements; Td: time delay between the initiation and 10% of maximal radial muscle-belly displacement; Vc: tensiomyography-derived contraction velocity; Tc: contraction time between 10% and 90% of maximal radial muscle-belly displacement; Ts: sustain time, the interval in milliseconds (ms) between 50% of Dm on both the ascending and descending sides of the curve; Tr: relaxation time, the interval between 90% and 50% Dm of muscle reaction. * p < 0.05. Table 3 shows the statistically significant differences obtained from the analysis of each leg individually (lateral symmetry). Table 3. Peripheral fatigue before and after the VK trail running race for each leg. Side & Muscle Group TMG Pre-VK (Mean ± SD) Post-VK (Mean ± SD) p-Value 95% CI Cohen’s d ES 95% CI Lower Upper Lower Upper Right RF Td 23.74 ± 1.70 22.38 ± 1.95 0.02 0.02 2.46 0.94 0.13 1.71 Vc 0.14 ± 0.04 0.16 ± 0.036 0.03 −0.04 −0.002 −0.86 −1.62 −0.07 Right VL Td 22.08 ± 1.48 21.13 ± 1.12 0.02 0.20 1.71 0.97 0.15 1.75 Tc 23.1 ± 1.83 21.85 ± 2.19 0.02 0.24 2.27 0.95 0.13 1.73 Left VM Tr 117.4 ± 48.07 67.18 ± 39.76 0.02 8.87 91.55 0.93 0.12 1.71 VK: vertical kilometer; SD: standard deviation; CI: confidence interval; ES: effect size; TMG: variables derived from the tensiomyography measurements; RF: rectus femoris; VL: vastus lateralis; VM: vastus medialis; Td: time delay between the initiation and 10% of maximal radial muscle-belly displacement; Vc: tensiomyography-derived contraction velocity; Tc: contraction time between 10% and 90% of maximal radial muscle-belly displacement; Ts: sustain time, the interval in milliseconds (ms) between 50% of Dm on both the ascending and descending sides of the curve; Tr: relaxation time, the interval between 90% and 50% Dm of muscle reaction. Int. J. Environ. Res. Public Health 2023, 20, 402 7 of 11 4. Discussion The main findings of our investigation were: (1) as expected, there was a significant increase in the SNS activity after the competition, which lasted up to 45–60 min; (2) simulta- neously, post-exercise PNS activity was significantly reduced; and (3) time-related variables and Dm levels presented by our runners suggest pre-competition peripheral fatigue. To the best of our knowledge, this is the first study evaluating central (i.e., ANS performance) and peripheral (i.e., muscle activity) fatigue before and immediately after (i.e., within 20–30 min after finishing) a VK trail running race in recreational trail runners. Although trail running is an emerging topic, the great majority of studies performed in the past few years have focused on metabolic, biomechanical and neuromuscular parameters. However, no studies have simultaneously assessed central and peripheral fatigue via non-invasive methods, especially when uphill trail running is considered (e.g., VK races). As expected, HRV parameters assessed after the competition showed significantly decreased values when compared to pre-competition levels. However, one interesting point is that when values of the variables related to the modulation of PNS (Table 1) are compared with previous studies [37,47,48], our runners showed lowered values before the VK competition (previous 24–36 h). In this regard, it should be considered that HRV is usually greater in active than sedentary individuals [21,47,49,50]. For instance, trained athletes show higher RMSSD values [21], thus training characteristics might influence HRV time and frequency domain measures [21]. The analyses of the pre-competition HRV time-domain variables (i.e., RMSSD, SDNN and Pnn50) traditionally related to PNS activity [37,51] showed that RMSSD values of our runners (48.45 ± 19.29 ms) would be considered within the average range (i.e., 50th percentile) for the age group when compared with previous studies performed with healthy non-athletes [48]. However, if our results are compared with another investigation carried out in a group of athletes with similar daily activity patterns [47], our runners would be located close to the 25th and 10th percentiles regarding RMSSD and SDNN values respectively. Similarly, Pnn50 values drops to below the 25th percentile when our results are compared with a previous work performed with professional athletes [37]. However, during the present study, a measurement of PNS-related variables was not performed continuously, establishing baseline values and the trend of these before the competition in each participant [52]. Therefore, it was not possible to assess intrasubject PNS activity and to determine a greater or lesser degree of PNS dominance in our runners in the hours prior to competition based only on the HRV measurements performed. Apart from that, all recorded variables related to time domain underwent a large change after the race (ES = 0.89–1.35), indicating a clear downregulation of the PNS (Table 1, time domain variables) and therefore, presumably, an upregulation of the SNS. Thus, it is clear that a VK is a very demanding competition, even if the average speed during the race is low. One of the limitations of this study was the lack of measurements throughout the days after the race, which would allow us to know how much time is needed for the PNS to reach its pre-race levels. Regarding the pre-competition balance between the SNS and PNS assessed through HRV non-linear measurements, our runners reported high SS values (14.97 ± 7.76) that are beyond the 90th percentile, which is related to a high level of sympathetic stress [37]. Further, the runners of the present study showed a clear disbalance of autonomic activity, reporting an S/PS ratio higher than 0.3 at rest (0.58 ± 0.45), which suggests an excess of the SNS activity or a lack of recovery of the PNS activity [37]. In addition to this, considering the variation of these two variables (i.e., S/PS and SS) before and after the competition, a clear increase in their values can be observed and therefore, a greater dominance of the SNS after the VK. This is in accordance with a lower activity of the PNS measured by time domain variables (Table 1). Taken together, in addition to the fatigue generated by the VK, both time domain and nonlinear measures may represent a sensitive downward modulation in the PNS of our runners and a lack of autonomic balance (i.e., central fatigue) prior to the race. This Int. J. Environ. Res. Public Health 2023, 20, 402 8 of 11 interpretation could suggest that a supercompensation status was not attained, which could be linked with fatigue. In this regard, some authors have suggested that recreational endurance runners who do not properly recover between training sessions, or those ex- periencing psychological stress or autonomic neuromuscular fatigue, are at higher risk of developing the so-called “overtraining syndrome”, which impairs endurance perfor- mance and leads to long-term fatigue [53]. Moreover, other studies have suggested that recreational runners try to imitate training practices performed by professional athletes, including high weekly volume (e.g., >70 km), which could lead to some health-related problems (e.g., injuries, overtraining) [54]. With this in mind, we speculate that many recreational runners are usually overtrained and therefore, unable to peak during the competition period. However, the absence of previous works in the field with runners with similar characteristics to those in our study and the lack of continuous pre-race HRV measurement, means it is not possible to draw a conclusion about the degree of stress and fatigue that runners experienced in our study previous to the race. Regarding peripheral fatigue, despite having analyzed six variables by TMG (i.e., Td, Tc, Ts, Tr, Vc and Dm) in four muscle groups (i.e., RF, VL, VM and GM), there were statistically significant differences only in TdRF (p = 0.018; d = 0.85), TrVM (p = 0.014; d = 0.89) and TsGM (p = 0.011; d = −0.93), when the pooled data of both legs were con- sidered. These variations in Td, Tr and Ts might be connected to metabolic changes in myoplasmic Ca2+ [55,56]. Some research in endurance sports suggests that the time-related variables (Td, Tc, Ts, Tr and Vc) tend to decrease after competition, while Dm increases, indicating a reduction of muscular stiffness and an increase of neuromuscular peripheral fatigue [11,29,38,55,57], according to the different muscles evaluated. In this regard, our results are partially in accordance with previous studies, since not all time-related variables showed decreased values after the VK race. For instance, after the race, Tc increased in all the muscles studied, except VL (−1.4%); Ts increased in GM (22.8%); and Vc increased in all the muscles analyzed (RF: 9.2%; VL: 9.2%; VM: 2.9%), except in GM, which decreased (−4.8%). Similarly, Dm increased in all the muscles studied (RF: 8.3%; VL: 7.0%; VM: 3.2%) except in GM, where it decreased (−6.6%). On the other hand, when analyzing each leg individually (i.e., lateral symmetry) we found statistically significant differences in the right leg in TdRF (0.023; ES = 0.94), VcRF (0.032; ES = 0.86), TdVL (0.02; ES = 0.97) and TcVL (0.021; ES = 0.95); and in the left leg in TrVM (0.023; ES = 0.93). These differences between segments may be due to ground surface irregularities, independent of differences in muscle strength, which may predispose the athlete to temporary asymmetric stimuli due to the activity being performed at a given time [58]. Based on our neuromuscular results, and the lack of consistency of previous studies, we speculate that: (1) 20–30 min is a sufficient period for experienced trail runners to recover at neuromuscular level after an intense effort with predominantly concentric contractions (i.e., uphill trail running); and (2) regarding HRV tendency, recreational runners might have already experienced peripheral fatigue before the race. Therefore, the peripheral fatigue generated during the competition did not represent an important stimulus at neuromuscular level. On this point, previous scientific evidence has suggested there is a link between central and peripheral fatigue. Thus, endurance training induces central fatigue adaptations, leading to improved tolerance of peripheral fatigue by the central nervous system [59]. One of the limitations of our study is the small sample size. However, it is important to consider that VK competitions are highly-demanding trail running races with less participation than other endurance running events. The complexity of assessing both central and peripheral fatigue immediately after the race (i.e., in the mountains, within 20–30 min after finishing) makes it difficult to evaluate a large number of participants. Future longitudinal studies are guaranteed for investigating the mechanisms underlying fatigue in endurance trail running events, especially when uphill running (i.e., VK races) is considered. In this regard, it would be of great interest to assess recovery variables over subsequent days after the competition to analyze recovery status evolution. Int. J. Environ. Res. Public Health 2023, 20, 402 9 of 11 5. Conclusions The present study demonstrates that a VK race affects the ANS system, downregulat- ing the PNS and upregulating the SNS. However, regarding peripheral fatigue, only small changes in the contractile capacity of specific muscle groups were detected. In addition, the pre-race measurements of HRV could suggest trail runners experienced a lack of recovery or non-functional overreaching before the race. Furthermore, while the neuromuscular stimulus of the competition did not seem to infer a great peripheral fatigue in these athletes, central fatigue significantly increased after the race. Considering the link between both central and peripheral fatigue, training periodization and new tapering strategies are called to play a key role in minimizing pre-competition fatigue status, thus favoring running performance. In this regard, monitoring HRV during the preparation period has been shown to be an effective strategy to avoid pre-competition fatigue. Author Contributions: Conceptualization, I.M.-P. and M.M.-C.; methodology, I.M.-P., R.N.-P. and M.M.-C.; validation, I.M.-P., A.V.-S., C.L.-F., R.N.-P. and M.M.-C.; formal analysis, I.M.-P. and R.N.-P.; investigation, I.M.-P., C.L.-F. and M.M.-C.; resources, I.M.-P. and M.M.-C.; data curation, I.M.-P. and M.M.-C.; writing—original draft preparation, I.M.-P., A.V.-S. and M.M.-C.; writing—review and editing, I.M.-P., A.V.-S., C.L.-F., R.N.-P. and M.M.-C.; visualization, I.M.-P., A.V.-S., C.L.-F., R.N.-P. and M.M.-C.; supervision, I.M.-P., A.V.-S. and M.M.-C. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Universidad Europea del Atlántico (protocol code CEI 21/2018 and 03/2018 of approval). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidentiality and anonymity of study participants. Conflicts of Interest: The authors declare no conflict of interest. References 1. Bassett, D.R.J.; Howley, E.T. Limiting factors for maximum oxygen uptake and determinants of endurance performance. Med. Sci. Sports Exerc. 2000, 32, 70–84. [CrossRef] [PubMed] 2. Casado, A.; González-Mohíno, F.; González-Ravé, J.M.; Foster, C. Training Periodization, Methods, Intensity Distribution, and Volume in Highly Trained and Elite Distance Runners: A Systematic Review. Int. J. Sports Physiol. Perform. 2022, 17, 820–833. [CrossRef] [PubMed] 3. Lazzer, S.; Salvadego, D.; Taboga, P.; Rejc, E.; Giovanelli, N.; di Prampero, P.E. Effects of the Etna Uphill Ultramarathon on Energy Cost and Mechanics of Running. Int. J. Sports Physiol. Perform. 2015, 10, 238–247. [CrossRef] 4. Ehrström, S.; Tartaruga, M.P.; Easthope, C.S.; Brisswalter, J.; Morin, J.-B.; Vercruyssen, F. Short Trail Running Race: Beyond the Classic Model for Endurance Running Performance. Med. Sci. Sports Exerc. 2018, 50, 580–588. [CrossRef] 5. Björklund, G.; Swarén, M.; Born, D.-P.; Stöggl, T. Biomechanical Adaptations and Performance Indicators in Short Trail Running. Front. Physiol. 2019, 10, 506. [CrossRef] 6. Bascuas, P.J.; Bataller-Cervero, A.V.; Gutierrez, H.; Berzosa, C. Modifications of viscoelastic properties and physiological parameters after performing uphill and downhill running trials. J. Sports Med. Phys. Fit. 2020, 61, 625–635. [CrossRef] 7. Lemire, M.; Hureau, T.J.; Favret, F.; Geny, B.; Kouassi, B.Y.L.; Boukhari, M.; Lonsdorfer, E.; Remetter, R.; Dufour, S.P. Physiological factors determining downhill vs uphill running endurance performance. J. Sci. Med. Sport 2021, 24, 85–91. [CrossRef] 8. Nicot, F.; Sabater-Pastor, F.; Samozino, P.; Millet, G.Y.; Rupp, T. Effect of ground technicity on cardio-respiratory and biomechanical parameters in uphill trail running. Eur. J. Sport Sci. 2021, 22, 1836–1846. [CrossRef] 9. Berzosa, C.; Gutierrez, H.; Bascuas, P.J.; Arbones, I.; Bataller-Cervero, A.V. Muscle Tone and Body Weight Predict Uphill Race Time in Amateur Trail Runners. Int. J. Environ. Res. Public Health 2021, 18, 2040. [CrossRef] 10. Millet, G.Y.; Tomazin, K.; Verges, S.; Vincent, C.; Bonnefoy, R.; Boisson, R.-C.; Gergelé, L.; Féasson, L.; Martin, V. Neuromuscular Consequences of an Extreme Mountain Ultra-Marathon. PLoS ONE 2011, 6, e17059. [CrossRef] 11. Giovanelli, N.; Taboga, P.; Rejc, E.; Simunic, B.; Antonutto, G.; Lazzer, S. Effects of an Uphill Marathon on Running Mechanics and Lower-Limb Muscle Fatigue. Int. J. Sports Physiol. Perform. 2016, 11, 522–529. [CrossRef] [PubMed] Int. J. Environ. Res. Public Health 2023, 20, 402 10 of 11 12. Millet, G.Y.; Martin, V.; Temesi, J. The role of the nervous system in neuromuscular fatigue induced by ultra-endurance exercise. Appl. Physiol. Nutr. Metab. 2018, 43, 1151–1157. [CrossRef] [PubMed] 13. Hewing, B.; Schattke, S.; Spethmann, S.; Sanad, W.; Schroeckh, S.; Schimke, I.; Halleck, F.; Peters, H.; Brechtel, L.; Lock, J.; et al. Cardiac and renal function in a large cohort of amateur marathon runners. Cardiovasc. Ultrasound 2015, 13, 13. [CrossRef] [PubMed] 14. Marklund, P.; Mattsson, C.M.; Wåhlin-Larsson, B.; Ponsot, E.; Lindvall, B.; Lindvall, L.; Ekblom, B.; Kadi, F. Extensive inflamma- tory cell infiltration in human skeletal muscle in response to an ultraendurance exercise bout in experienced athletes. J. Appl. Physiol. 2012, 114, 66–72. [CrossRef] 15. Kiviniemi, A.M.; Hautala, A.J.; Kinnunen, H.; Tulppo, M.P. Endurance training guided individually by daily heart rate variability measurements. Eur. J. Appl. Physiol. 2007, 101, 743–751. [CrossRef] 16. Pichot, V.; Roche, F.; Celle, S.; Barthélémy, J.-C.; Chouchou, F. HRVanalysis: A Free Software for Analyzing Cardiac Autonomic Activity. Front. Physiol. 2016, 7, 557. [CrossRef] 17. Varela-Sanz, A.; Tuimil, J.L.; Abreu, L.; Boullosa, D.A. Does Concurrent Training Intensity Distribution Matter? J. Strength Cond. Res. 2017, 31, 181–195. [CrossRef] 18. Vesterinen, V.; Nummela, A.R.I.; Heikura, I.D.A.; Laine, T.; Hynynen, E.S.A.; Botella, J.; Häkkinen, K. Individual Endurance Training Prescription with Heart Rate Variability. Med. Sci. Sports Exerc. 2016, 48, 1347–1354. [CrossRef] 19. Task Force of the European Society of Cardiology the North American Society of Pacing and Electrophysiology. Heart Rate Variability. Standards of measurement, physiological interpretation and clinical use. Circulation 1996, 93, 1043–1065. [CrossRef] 20. Carrasco-Poyatos, M.; González-Quílez, A.; Martínez-González-Moro, I.; Granero-Gallegos, A. HRV-Guided Training for Professional Endurance Athletes: A Protocol for a Cluster-Randomized Controlled Trial. Int. J. Environ. Res. Public Health 2020, 17, 5465. [CrossRef] 21. Altini, M.; Plews, D. What Is behind Changes in Resting Heart Rate and Heart Rate Variability? A Large-Scale Analysis of Longitudinal Measurements Acquired in Free-Living. Sensors 2021, 21, 7932. [CrossRef] [PubMed] 22. Pichot, V.; Busso, T.; Roche, F.; Garet, M.; Costes, F.; Duverney, D.; Lacour, J.-R.; Barthélémy, J.-C. Autonomic adaptations to intensive and overload training periods: A laboratory study. Med. Sci. Sports Exerc. 2002, 34, 1660–1666. [CrossRef] [PubMed] 23. Gratze, G.; Rudnicki, R.; Urban, W.; Mayer, H.; Schlögl, A.; Skrabal, F. Hemodynamic and autonomic changes induced by Ironman: Prediction of competition time by blood pressure variability. J. Appl. Physiol. 2005, 99, 1728–1735. [CrossRef] [PubMed] 24. Hautala, A.; Tulppo, M.P.; Mäkikallio, T.H.; Laukkanen, R.; Nissilä, S.; Huikuri, H.V. Changes in cardiac autonomic regulation after prolonged maximal exercise. Clin. Physiol. 2001, 21, 238–245. [CrossRef] [PubMed] 25. Nicolas, M.; Banizette, M.; Millet, G.Y. Stress and recovery states after a 24 h ultra-marathon race: A one-month follow-up study. Psychol. Sport Exerc. 2011, 12, 368–374. [CrossRef] 26. Easthope, C.S.; Hausswirth, C.; Louis, J.; Lepers, R.; Vercruyssen, F.; Brisswalter, J. Effects of a trail running competition on muscular performance and efficiency in well-trained young and master athletes. Eur. J. Appl. Physiol. 2010, 110, 1107–1116. [CrossRef] 27. Carrasco, L.; Sañudo, B.; de Hoyo, M.; Pradas, F.; Da Silva, M.E. Effectiveness of low-frequency vibration recovery method on blood lactate removal, muscle contractile properties and on time to exhaustion during cycling at VO2max power output. Eur. J. Appl. Physiol. 2011, 111, 2271–2279. [CrossRef] 28. García-García, O.; Cuba-Dorado, A.; Riveiro-Bozada, A.; Carballo-López, J.; Álvarez-Yates, T.; López-Chicharro, J. A Maximal Incremental Test in Cyclists Causes Greater Peripheral Fatigue in Biceps Femoris. Res. Q. Exerc. Sport 2020, 91, 460–468. [CrossRef] 29. García-Manso, J.M.; Rodríguez-Ruiz, D.; Rodríguez-Matoso, D.; de Saa, Y.; Sarmiento, S.; Quiroga, M. Assessment of muscle fatigue after an ultra-endurance triathlon using tensiomyography (TMG). J. Sports Sci. 2011, 29, 619–625. [CrossRef] 30. García-Manso, J.M.; Rodríguez-Matoso, D.; Sarmiento, S.; de Saa, Y.; Vaamonde, D.; Rodríguez-Ruiz, D.; Da Silva-Grigoletto, M.E. Effect of high-load and high-volume resistance exercise on the tensiomyographic twitch response of biceps brachii. J. Electromyogr. Kinesiol. 2012, 22, 612–619. [CrossRef] 31. Macgregor, L.J.; Ditroilo, M.; Smith, I.J.; Fairweather, M.M.; Hunter, A.M. Reduced Radial Displacement of the Gastrocnemius Medialis Muscle After Electrically Elicited Fatigue. J. Sport Rehabil. 2016, 25, 241–247. [CrossRef] [PubMed] 32. Raeder, C.; Wiewelhove, T.; Simola, R.Á.D.P.; Kellmann, M.; Meyer, T.; Pfeiffer, M.; Ferrauti, A. Assessment of Fatigue and Recovery in Male and Female Athletes After 6 Days of Intensified Strength Training. J. Strength Cond. Res. 2016, 30, 3412–3427. [CrossRef] [PubMed] 33. Wiewelhove, T.; Raeder, C.; Meyer, T.; Kellmann, M.; Pfeiffer, M.; Ferrauti, A. Markers for Routine Assessment of Fatigue and Recovery in Male and Female Team Sport Athletes during High-Intensity Interval Training. PLoS ONE 2015, 10, e0139801. [CrossRef] [PubMed] 34. González-Fimbres, R.A.; Hernández-Cruz, G.; Flatt, A.A. Ultrashort Versus Criterion Heart Rate Variability Among International- Level Girls’ Field Hockey Players. Int. J. Sports Physiol. Perform. 2021, 16, 985–992. [CrossRef] [PubMed] 35. Bloomfield, D.M.; Magnano, A.; Bigger, J.T.; Rivadeneira, H.; Parides, M.; Steinman, R.C. Comparison of spontaneous vs. metronome-guided breathing on assessment of vagal modulation using RR variability. Am. J. Physiol. Circ. Physiol. 2001, 280, H1145–H1150. [CrossRef] [PubMed] 36. Tarvainen, M.P.; Niskanen, J.-P.; Lipponen, J.A.; Ranta-aho, P.O.; Karjalainen, P.A. Kubios HRV—Heart rate variability analysis software. Comput. Methods Programs Biomed. 2014, 113, 210–220. [CrossRef] [PubMed] Int. J. Environ. Res. Public Health 2023, 20, 402 11 of 11 37. Naranjo Orellana, J.; de la Cruz Torres, B.; Sarabia Cachadiña, E.; de Hoyo, M.; Domínguez Cobo, S. Two New Indexes for the Assessment of Autonomic Balance in Elite Soccer Players. Int. J. Sports Physiol. Perform. 2015, 10, 452–457. [CrossRef] 38. Gutiérrez-Vargas, R.; Martín-Rodríguez, S.; Sánchez-Ureña, B.; Rodríguez-Montero, A.; Salas-Cabrera, J.; Gutiérrez-Vargas, J.C.; Simunic, B.; Rojas-Valverde, D. Biochemical and Muscle Mechanical Postmarathon Changes in Hot and Humid Conditions. J. Strength Cond. Res. 2020, 34, 847–856. [CrossRef] 39. Pereira, L.A.; Ramirez-Campillo, R.; Martín-Rodríguez, S.; Kobal, R.; Abad, C.C.C.; Arruda, A.F.S.; Guerriero, A.; Loturco, I. Is tensiomyography-derived velocity of contraction a sensitive marker to detect acute performance changes in elite team-sport athletes? Int. J. Sports Physiol. Perform. 2020, 15, 31–37. [CrossRef] 40. Rey, E.; Padrón-Cabo, A.; Barcala-Furelos, R.; Mecías-Calvo, M. Effect of High and Low Flexibility Levels on Physical Fitness and Neuromuscular Properties in Professional Soccer Players. Int. J. Sports Med. 2016, 37, 878–883. [CrossRef] 41. Rey, E.; Lago-Peñas, C.; Lago-Ballesteros, J. Tensiomyography of selected lower-limb muscles in professional soccer players. J. Electromyogr. Kinesiol. 2012, 22, 866–872. [CrossRef] 42. Loturco, I.; Pereira, L.A.; Kobal, R.; Kitamura, K.; Ramírez-Campillo, R.; Zanetti, V.; Abad, C.C.C.; Nakamura, F.Y. Muscle contraction velocity: A suitable approach to analyze the functional adaptations in elite soccer players. J. Sports Sci. Med. 2016, 15, 483. [PubMed] 43. De Paula Simola, R.Á.; Harms, N.; Raeder, C.; Kellmann, M.; Meyer, T.; Pfeiffer, M.; Ferrauti, A. Assessment of Neuromuscular Function After Different Strength Training Protocols Using Tensiomyography. J. Strength Cond. Res. 2015, 29, 1339–1348. [CrossRef] 44. De Paula Simola, R.Á.; Raeder, C.; Wiewelhove, T.; Kellmann, M.; Meyer, T.; Pfeiffer, M.; Ferrauti, A. Muscle mechanical properties of strength and endurance athletes and changes after one week of intensive training. J. Electromyogr. Kinesiol. 2016, 30, 73–80. [CrossRef] [PubMed] 45. Ellis, P.D. The Essential Guide to Effect Sizes; Cambridge University Press: Cambridge, UK, 2010; ISBN 9780511761676. 46. Kerby, D.S. The Simple Difference Formula: An Approach to Teaching Nonparametric Correlation. Compr. Psychol. 2014, 3, 11. [CrossRef] 47. Medina Corrales, M.; de la Cruz Torres, B.; Garrido Esquivel, A.; Garrido Salazar, M.A.; Naranjo Orellana, J. Normal values of heart rate variability at rest in a young, healthy and active Mexican population. Health 2012, 4, 377–385. [CrossRef] 48. Sammito, S.; Böckelmann, I. Reference values for time- and frequency-domain heart rate variability measures. Heart Rhythm 2016, 13, 1309–1316. [CrossRef] [PubMed] 49. Melanson, E.L. Resting heart rate variability in men varying in habitual physical activity. Med. Sci. Sports Exerc. 2000, 32, 1894–1901. [CrossRef] 50. Sandercock, G.R.H.; Hardy-Shepherd, D.; Nunan, D.; Brodie, D. The relationships between self-assessed habitual physical activity and non-invasive measures of cardiac autonomic modulation in young healthy volunteers. J. Sports Sci. 2008, 26, 1171–1177. [CrossRef] 51. Hoshi, R.A.; Pastre, C.M.; Vanderlei, L.C.M.; Godoy, M.F. Poincaré plot indexes of heart rate variability: Relationships with other nonlinear variables. Auton. Neurosci. 2013, 177, 271–274. [CrossRef] 52. Plews, D.J.; Laursen, P.B.; Kilding, A.E.; Buchheit, M. Heart rate variability in elite triathletes, is variation in variability the key to effective training? A case comparison. Eur. J. Appl. Physiol. 2012, 112, 3729–3741. [CrossRef] [PubMed] 53. Nicoll, J.X.; Hatfield, D.L.; Melanson, K.J.; Nasin, C.S. Thyroid hormones and commonly cited symptoms of overtraining in collegiate female endurance runners. Eur. J. Appl. Physiol. 2018, 118, 65–73. [CrossRef] [PubMed] 54. Boullosa, D.; Esteve-Lanao, J.; Casado, A.; Peyré-Tartaruga, L.A.; da Rosa, R.; Del Coso, J. Factors Affecting Training and Physical Performance in Recreational Endurance Runners. Sports 2020, 8, 35. [CrossRef] 55. Rodríguez-Matoso, D.; Mantecón, A.; Barbosa-Almeida, E.; Valverde, T.; García-Manso, J.M.; Rodríguez-Ruiz, D. Mechanical response of knee muscles in high level bodyboarders during performance. Rev. Bras. Med. Esporte 2015, 21, 144–147. [CrossRef] 56. Westerblad, H.; Lännergren, J.; Allen, D.G. Slowed Relaxation in Fatigued Skeletal Muscle Fibers of Xenopus and Mouse: Contribution of [Ca2+]i and Cross-bridges. J. Gen. Physiol. 1997, 109, 385–399. [CrossRef] [PubMed] 57. García-García, O.; Cuba-Dorado, A.; Álvarez-Yates, T.; Carballo-López, J.; Iglesias-Caamaño, M. Clinical utility of tensiomyogra- phy for muscle function analysis in athletes. Open Access J. Sports Med. 2019, 10, 49–69. [CrossRef] 58. Carpes, F.P.; Mota, C.B.; Faria, I.E. On the bilateral asymmetry during running and cycling—A review considering leg preference. Phys. Ther. Sport 2010, 11, 136–142. [CrossRef] 59. Zghal, F.; Cottin, F.; Kenoun, I.; Rebaï, H.; Moalla, W.; Dogui, M.; Tabka, Z.; Martin, V. Improved tolerance of peripheral fatigue by the central nervous system after endurance training. Eur. J. Appl. Physiol. 2015, 115, 1401–1415. [CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Central and Peripheral Fatigue in Recreational Trail Runners: A Pilot Study.

12-27-2022

Muñoz-Pérez, Iker,Varela-Sanz, Adrián,Lago-Fuentes, Carlos,Navarro-Patón, Rubén,Mecías-Calvo, Marcos

eng

PMC3638679

Hindawi Publishing Corporation Cardiology Research and Practice Volume 2013, Article ID 940170, 5 pages http://dx.doi.org/10.1155/2013/940170 Clinical Study Comparison of Predicted Exercise Capacity Equations and the Effect of Actual versus Ideal Body Weight among Subjects Undergoing Cardiopulmonary Exercise Testing H. Reza Ahmadian,1 Joseph J. Sclafani,1 Ethan E. Emmons,2 Michael J. Morris,2 Kenneth M. Leclerc,1 and Ahmad M. Slim1 1 Cardiology Service, Brooke Army Medical Center, 3551 Roger Brooke Drive, San Antonio, TX 78234-6200, USA 2 Pulmonary/Critical Care Service, Brooke Army Medical Center, 3551 Roger Brooke Drive, San Antonio, TX 78234-6200, USA Correspondence should be addressed to Ahmad M. Slim; [email protected] Received 2 January 2013; Accepted 13 March 2013 Academic Editor: Firat Duru Copyright © 2013 H. Reza Ahmadian et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. Oxygen uptake at maximal exercise (VO2 max) is considered the best available index for assessment of exercise capacity. The purpose of this study is to determine if the use of actual versus ideal body weight in standard regression equations for predicted VO2 max results in differences in predicted VO2 max. Methods. This is a retrospective chart review of patients who were predominantly in active military duty with complaints of dyspnea or exercise tolerance and who underwent cardiopulmonary exercise testing (CPET) from 2007 to 2009. Results. A total of 230 subjects completed CPET on a bicycle ergometer with a male predominance (62%) and an average age of 37 ± 15 years. There was significant discordance between the measured VO2 max and predicted VO2 max when measured by the Hansen and Wasserman reference equations (𝑃 < 0.001). Specifically, there was less overestimation when predicted VO2 max was based on ideal body weight as opposed to actual body weight. Conclusion. Our retrospective analysis confirmed the wide variations in predicted versus measured VO2 max based on varying prediction equations and showed the potential advantage of using ideal body weight as opposed to actual body weight in order to further standardize reference norms. 1. Introduction The determination of functional capacity to perform maximal exercise is one of the intended goals of any form of stress testing. Cardiopulmonary exercise testing (CPET) offers two specific advantages over conventional stress testing. During conventional testing, the degree of effort can be measured in several ways including subject report of volitional fatigue, ratings of perceived exertion, the percentage of predicted heart rate achieved, and the interpretation of the provider who is supervising the test. Another advantage of CPET is the direct measurement of maximal oxygen consumption as a measure of functional capacity, referred to as VO2 max. The objective of this study was to compare published reference values for VO2 max based on ideal versus actual body weight to determine the effect on interpretation of maximal exercise during CPET. 2. Methods 2.1. Study Protocol and Oversight. The study is a retrospective review of a series of CPET data initially utilizing pre- dicted VO2 max using the Jones 1983 reference equation: Male: VO2 (L/ min) = 4.2 − (0.032 ∗ age), and Female: VO2 (L/ min) = 2.6 − (0.014 ∗ age). Maximum VO2 was recalculated using different pre- diction equations and ideal versus actual body weight, respectively. Physicians trained in the interpretation of CPET determined if interpretation of maximal exercise differed using the various prediction equations (Jones et al., 1985; 2 Cardiology Research and Practice Hansen et al., 1984; Wasserman et al., 1999) as well as ideal versus actual body weight, respectively, as follows. Jones et al., 1985 [2]: VO2 (L/ min) = 0.046 (ht) − 0.21 (age) − 0.62 (sex) − 4.31, Hansen et al., 1984 [3]: Male: VO2 (L/ min) = wt ∗ (50.75 − (0.37 ∗ age))/1000, Female: VO2 (L/ min) = (wt + 43) ∗ (22.78 − (0.17 ∗ age))/1000, Wasserman et al., 1999 [4]: Male: VO2 (L/ min) = wt ∗ (50.72 − (0.372 ∗ age))/1000, Female: VO2 (L/ min) = (wt + 42.8) ∗ (22.78 − (0.17 ∗ age))/1000. 2.2. Data Collection. All CPET studies were performed in the Brooke Army Medical Center Pulmonary Function Labora- tory beginning in January 2007 through December 2009. The study group primarily consisted of active duty military being evaluated for dyspnea or exercise intolerance. Studies were performed on a graded exercise test using an incremental protocol on a cycle ergometer, and patients performed a maximal exercise test until limited by fatigue or symptoms. Oxygen saturation was monitored with the LifeStat 1600 pulse oximeter (Physio-Control; Redmond, WA), and 12- lead electrocardiograph monitoring was accomplished via the Marquette 2000 during the test. Blood pressures were taken before the test and immediately upon completion of exercise. All participants were exercised using a standard protocol with increases in resistance of 25 watts every minute and were asked to continue exercising until exhaustion or limited by symptoms. During the entire warm-up, exercise, and recovery phases of the test, expired gas analysis was performed through the 2900 Series Metabolic Cart (Sen- sormedics; Yorba Linda, CA). Gas analysis measurements included oxygen consumption (VO2), carbon dioxide pro- duction (VCO2), tidal volume (TV), respiratory rate (RR), and minute ventilation (VE). 2.3. Statistical Analysis. Data are presented as mean ± SD. Demographic comparisons between genders were analyzed by a two-tail Student’s 𝑡 test. Actual measured VO2 max was compared to predicted VO2 max between all methods using a one-way ANOVA with Holm-Sidak post hoc test. Clinical agreement between algorithms for VO2 max using actual versus ideal body weights using limit of VO2 max ≤ 84% predicted maximums for nominal data was assessed using Cohen’s kappa, and the McNemar test was employed to test discordance. 𝑃 values < 0.05 were considered significant. Regression analysis was employed to assess the strength of association between VO2 max predictors and actual VO2 max measurements, and a Bland-Altman test was employed to assess agreement throughout the range of predicted VO2 max for each algorithm. Table 1: Demographics gender variations. Column 1 Male (𝑛 = 142) Female (𝑛 = 88) 𝑃 value Age (yrs) 36 ± 14 40 ± 16 0.049 Height (cm) 176.9 ± 8.2 163.9 ± 7.9 <0.001 Weight (Kg) 89.4 ± 18.3 72.3 ± 13.8 <0.001 Ideal weight (Kg) 79.0 ± 6.6 64.1 ± 5.3 <0.001 BMI (Kg/M2) 28.6 ± 5.6 27.1 ± 5.7 <0.001 0 1 2 3 4 5 6 Actual VO2 max Jones et al., [2] Jones 83 Wasserman et al., [4] (wt) Wasserman et al., [4] (pred) Hansen et al., [3] (wt) Hansen et al., [3] (pred) Figure 1: Figure indicates significant overestimation of predictors of VO2 max compared with actual measured VO2 max in this population. Significant differences among test (𝑃 ≤ 0.001). 3. Results 3.1. Baseline Patient Characteristics. The study population consisted of 230 subjects with male predominance (62%) and a mean age of 37 ± 15 years. Table 1 illustrates differences among genders with the population. Figure 1 illustrates the marked variance among all VO2 predictive equations (𝑃 < 0.001), regardless of whether ideal body weight was used or not, as well as significant overestimation of predicted VO2 max compared with actual measured VO2 max in this pop- ulation. Figure 2 compares regression lines for the Hansen algorithm using either actual or ideal body weights to predict VO2 max. Although 𝑅2 was greater when using ideal body weight, the discordance of the estimates of true VO2 max, using ideal or actual body weights, was greater when VO2 max was low. Figures 3 and 4 indicate only a moderate agreement between Hansen algorithms using actual versus ideal body weights to predict VO2 max ≤ 84%. Although 80.8% of time results agreed (kappa = 0.566), there was significant discordance (19.2%, 𝑃 < 0.001) between tests. Cardiology Research and Practice 3 0 1 2 3 4 5 6 0 1 2 3 4 5 Hansen VO2 max True VO2 max 𝑦 = 0.8577𝑥 + 1.0685 𝑅2 = 0.4664 𝑦 = 0.7369𝑥 + 1.0483 𝑅2 = 0.5105 VO2 max (actual) VO2 Hansen et al., [3] (act) VO2 Hansen et al., [3] (pred) Linear (VO2 Hansen et al., [3] (act)) Linear (VO2 Hansen et al., [3] (pred)) Figure 2: Comparison among regression lines for the Hansen algorithm using either actual or ideal body weights to predict VO2 max. 4. Discussion VO2 max reflects the product of cardiac output and the arteriovenous oxygen difference at peak exercise. Clinically, it is usually expressed as a percentage of predicted since it is believed to be more appropriate for intersubject comparisons as opposed to the standardization by body mass [1]. Because this is a weight-indexed value, differences in weight alone can impact the calculation irrespective of other objective factors. This is illustrated by the observation that obese patients have lower VO2 max results than those of normal weight due to the fact that adipose tissue is relatively metabolically inactive. The measurement of VO2 max is influenced by many factors to include age, sex, body size and composition, and level of aerobic training. Consequently, different prediction equations can yield different predicted VO2 values based on which variables are used in the calculations. According to recommendations made by the Amer- ican Thoracic Society/American College of Chest Physi- cians (ATS/ACCP) in a statement on CPET, the two most widely used sets of references values, Jones et al. [2] and 0 0.5 1 0 1 2 3 4 5 Differences −0.5 −1 −1.5 −2 CI +95% Bias CI −95% Bias −ci Bias +ci Test range Bland-Altman test of agreement bias 𝑃 = 0.072 Mean difference Hansen predicted − actual VO2 Figure 3: Illustration of Bland-Altman test of agreement between Hansen predicted VO2 max using ideal versus actual body weight in calculations. 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 Disagree Disagree Agree Agree %VO2 max Hansen (wt) %VO2 max Hansen (predicted wt) Clinical interpretations Figure 4: Presentation of the level of agreement between Hansen algorithm using actual versus ideal body weights to predict VO2 max ≤ 84%. Hansen et al. [3], should be used clinically. Wasserman et al. published as well a different set of reference values used for VO2 max in addition to the ATS/ACCP endorsed references mentioned earlier [4]. At least one study has demonstrated that different sets of maximal reference values can have significant impact on interpretation of CPET results [5]. 4 Cardiology Research and Practice The ATS/ACCP guidelines address the issue of peak VO2 prediction based on weight and acknowledge the absence of standardization regarding the best index of body size. They acknowledge the known miscalculations of VO2 max in obese patients. They allude to recommendations made by several experts about referencing VO2 to fat-free weight (FFW) and believe that this index has the added advantage of accounting for gender differences in VO2 max. However, the ATS/ACCP stopped short in making this recommendation since the routine measurement of FFW would be difficult to implement in most conventional exercise laboratories. The ATS/ACCP therefore recommends that VO2 max be expressed as an absolute value and as a percentage of the predicted value. Maximum VO2 should also be referenced to body weight (in kilograms) and/or height in the formatting of the report so that the impact of body size on exercise results is readily recognized [6]. Several experts have examined the applicability of body size and the interpretation of VO2 max. Buskirk and Taylor made the observation that VO2 max was more closely rele- vant to fat-free weight (FFW) than to total body weight. FFW may not be related to level of conditioning. They stressed the importance of calculating VO2 max in relation to lean body mass to avoid misclassification of obese patients [2]. Hansen and associates studied 77 ex-shipyard workers, one- third of whom were obese, defined as weight greater than 120% of expected for height. In this population, he proposed that height should be used with age and sex as predictors of VO2. His theory was tested using the formulas of Bruce and coworkers who first showed the relationship between height and weight in a sedentary middle-aged male population [3]. They used height to estimate normal weight and used the normalized weight in all those above this value. In only 2 of 77 subjects did the measured VO2 differ widely from the predicted VO2. Maximum VO2 was poorly predicted if actual weight was used in their obese population. A recent study by Sill et al. examining CPET in a similar normal population of military personnel (mean age of 25.4 ± 4.3 years, body mass index of 24.4 ± 2.8, and percent body fat of 21.3 ± 6.1) found only a slight decrease in the predicted normal VO2 max to 82% predicted [7]. In a 1974 study in which 710 healthy, active duty Air Force personnel underwent maximal exercise testing, the authors published a regression equation used to predict VO2 max. However, the study population included only men, and the regression equation only factored in age, making no adjustments for height or weight. Another study evaluating exercise capacity in a military population included 1,514 male and 375 female active duty military personnel and reported VO2 max mean values of 51 and 37 mL/kg/weight/min for males and females, respectively [8]. In our study population, predicted VO2 max, when indexed to weight, was overestimated compared to measured VO2 max regardless of the predictive equation used. How- ever, there was less overestimation when predicted VO2 max was based on ideal body weight (IBW) as opposed to actual body weight. One of the limitations of this chart review is that approx- imately 70% of the study population failed to achieve 84% of predicted VO2 max. This could have been attributed to true pathology, decreased exercise capacity, obesity, or merely not being pushed to peak exertional capacity. The latter seems like the most plausible explanation for at least a portion of the subjects since evaluation of heart rates revealed that 31% of the study population also failed to achieve 84% of their target heart rate. Another important limitation of this study is that the study population included symptomatic subjects who were not an exclusively healthy group of young volunteers. This again highlights the need for a set of population-based norms for CPET evaluation and interpretation. Furthermore, physical fitness impacts the correlation among the reference equations, and the fact that only approximately one-third of men and women in our subgroup analysis met Air Force standards for fitness could be skewing our results. Despite the previous limitations, this study is unique since comparing predicted to measured VO2 max using the variety of known prediction equations has never been done previously. Our retrospective analysis confirmed the wide variations in predicted versus measured VO2 based on varying prediction equations and shows the potential advan- tage of using ideal body weight as opposed to actual body weight in order to further standardize reference norms. It also illustrates the need for having population-specific reference norms for the most relevant and accurate interpretation of cardiopulmonary exercise testing. Abbreviations CPET: Cardiopulmonary exercise testing RER: Respiratory exchange ratio VCO2: Carbon dioxide production VO2: Oxygen consumption VO2 max: Maximum oxygen consumption ATS: American Thoracic Society ACCP: American College of Chest Physicians TV: Tidal volume RR: Respiratory rate VE: Minute ventilation FFW: Fat-free weight. Disclaimer The opinions in this paper do not constitute endorsement by San Antonio Army Medical Center, the US Army Medical Department, the US Army Office of the Surgeon General, the Department of the Army, Department of Defense, or the US Government of the information contained therein. References [1] G. J. Balady, R. Arena, K. Sietsema et al., “Clinician’s guide to cardiopulmonary exercise testing in adults: a scientific state- ment from the American heart association,” Circulation, vol. 122, no. 2, pp. 191–225, 2010. [2] N. L. Jones, L. Makrides, C. Hitchcock, T. Chypchar, and N. McCartney, “Normal standards for an incremental progressive cycle ergometer test,” American Review of Respiratory Disease, vol. 131, no. 5, pp. 700–708, 1985. Cardiology Research and Practice 5 [3] J. E. Hansen, D. Y. Sue, and K. Wasserman, “Predicted values for clinical exercise testing,” American Review of Respiratory Disease, vol. 129, no. 2, pp. S49–S55, 1984. [4] K. Wasserman, J. E. Hansen, D. Y. Sue, R. Casaburi, and B. J. Whipp, Principles of Exercise Testing and Interpretation: Including Pathophysiology and Clinical Applications, Lippincott, Williams & Wilkins, Philadelphia, Pa, USA, 3rd edition, 1999. [5] I. M. Weisman and R. J. Zeballos :, “A step approach to the eval- uation of unexplained dyspnea: the role of cardiopulmonary exercise testing,” Pulmonary Perspective, vol. 15, pp. 8–11, 1998. [6] “American Thoracic Society/American College of Chest Physi- cians statement on cardiopulmonary stress testing,” American Journal of Respiratory and Critical Care Medicine, vol. 167, pp. 211–277, 2003. [7] J. M. Sill, M. J. Morris, J. E. Johnson, P. F. Allan, and V. X. Grbach, “Cardiopulmonary exercise test interpretation using age-matched controls to evaluate exertional dyspnea,” Military Medicine, vol. 174, no. 11, pp. 1177–1182, 2009. [8] J. A. Vogel, J. F. Patton, R. P. Mello, and W. L. Daniels, “An anal- ysis of aerobic capacity in a large United States population,” Journal of Applied Physiology, vol. 60, no. 2, pp. 494–500, 1985.

Comparison of Predicted Exercise Capacity Equations and the Effect of Actual versus Ideal Body Weight among Subjects Undergoing Cardiopulmonary Exercise Testing.

04-03-2013

Ahmadian, H Reza,Sclafani, Joseph J,Emmons, Ethan E,Morris, Michael J,Leclerc, Kenneth M,Slim, Ahmad M

eng

PMC5713493

sensors Article Estimating Stair Running Performance Using Inertial Sensors Lauro V. Ojeda 1,* ID , Antonia M. Zaferiou 2, Stephen M. Cain 1 ID , Rachel V. Vitali 1, Steven P. Davidson 1, Leia A. Stirling 3 ID and Noel C. Perkins 1 1 Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; [email protected] (S.M.C.); [email protected] (R.V.V.); [email protected] (S.P.D.); [email protected] (N.C.P.) 2 Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL 60612, USA; [email protected] 3 Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Boston, MA 02139, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-734-647-1803 Received: 10 October 2017; Accepted: 13 November 2017; Published: 17 November 2017 Abstract: Stair running, both ascending and descending, is a challenging aerobic exercise that many athletes, recreational runners, and soldiers perform during training. Studying biomechanics of stair running over multiple steps has been limited by the practical challenges presented while using optical-based motion tracking systems. We propose using foot-mounted inertial measurement units (IMUs) as a solution as they enable unrestricted motion capture in any environment and without need for external references. In particular, this paper presents methods for estimating foot velocity and trajectory during stair running using foot-mounted IMUs. Computational methods leverage the stationary periods occurring during the stance phase and known stair geometry to estimate foot orientation and trajectory, ultimately used to calculate stride metrics. These calculations, applied to human participant stair running data, reveal performance trends through timing, trajectory, energy, and force stride metrics. We present the results of our analysis of experimental data collected on eleven subjects. Overall, we determine that for either ascending or descending, the stance time is the strongest predictor of speed as shown by its high correlation with stride time. Keywords: wearable sensors; inertial measurement units; motion tracking; human performance; stair running 1. Introduction We present a method for using inertial measurement units (IMUs) to measure the kinematics and performance of stair running. Running on stairs is a mechanically challenging task. Stair ascent (both walking and running) challenges the body to achieve center of mass translation forward and upward against gravity (repeatedly generating upward ground reaction forces larger than the downward bodyweight force). Therefore, studying stair ascent can provide insights into an individual's aerobic conditioning [1], athletic strength and lower extremity power [2], and performance [3]. Stair descent, in contrast, challenges the body to achieve the desired forward and downward trajectory while controlling and leveraging the assistance of gravity. Therefore, stair descent performance is often studied in clinical populations to assess the level of lower extremity joint stability and control [4]. Furthermore, each footfall needs to land on the relatively small surface of each step, therefore, successful performance of both stair ascent and decent require body coordination across multiple body segments in order to avoid trips or falls. Overground running has been studied extensively from different points of view [5,6]; a detailed review of early research being provided by Novacheck [7]. Sensors 2017, 17, 2647; doi:10.3390/s17112647 www.mdpi.com/journal/sensors Sensors 2017, 17, 2647 2 of 14 On the other hand, in-depth biomechanical analysis of stair running has been limited by inadequate biomechanical tracking tools. Optical-based motion capture systems and instrumented walkways, which are commonly used for studying gait, are limited by practical challenges in order to appropriately position cameras for the desired motion capture volume. Consequently, past studies of stair climbing focus on the functional walking pace [8–10] and have estimated overall energy expenditure [11], basic timing measures [12], and joint angles [6]. In contrast, we propose using foot-mounted IMUs as a motion capture instrument. Body-worn IMUs enable human motion analysis in outdoor and other contextually-relevant settings (e.g., training facilities, game settings, obstacle courses) and have been used in a wide array of biomechanics applications; see, for example, [13–20]. Our approach uses foot-mounted IMUs to measure the foot kinematic variables (acceleration and angular velocity) during stair running. Doing so enables one to track a large number of steps, to understand transient and steady state running on stairs, and to also deduce performance measures. IMUs are portable, unobtrusive, and unconstrained (e.g., they do not need external references) motion tracking devices. However, IMU data (and quantities computed therefrom) are affected by several sources of error (e.g., bias instability, scale factor errors, acceleration, and temperature sensitivity) that must be accounted for during motion tracking applications [21]. In this paper, we present specialized algorithms that address these sources of error to estimate the foot trajectory and velocity during stair running. In particular we extend the Zero velocity UPdaTe (ZUPT) algorithm [22], which has been validated to provide accurate foot motions [14,23], by adding additional drift corrections specific to the constraints of stair running (known riser dimensions). We further employ those estimates to deduce metrics for evaluating stair running performance and explore the metrics utilizing experimental data collected on 11 subjects. We hypothesized that the metrics that could be defined were related to the overall speed, thereby providing an ability to assess stair running techniques. 2. Materials and Methods We tested 11 healthy volunteer subjects (three female, eight male; age: 25.6 ± 3.7 years; mean ± SD). The University of Michigan IRB approved the study and all subjects provided informed consent. Subjects were instructed to run up a long staircase at maximum speed, without skipping treads. After pausing for approximately ten seconds, the subjects ran down the same flight of stairs at maximum speed returning to the starting position, again without skipping treads. The staircase provided 16 strides total during the steady state (eight left and eight right). Subjects were not instructed which foot to begin stepping with for the task. The staircase rise height was 18 cm and the depth was 30 cm. The subjects wore two IMU data loggers (Opals, APDM, Portland OR, USA; 128 Hz sampling, ±6 g acceleration, ±2000 deg/s angular rate), one mounted on each shoe affixed using athletic tape to the top of the laces (see Figure 1). The IMUs measure and store three components of linear acceleration (af = [ax, ay, az]) from the on-board accelerometer and three components of angular velocity (ωf =  ωx, ωy, ωz ) from the on-board angular rate gyro, both relative to the sensor-fixed axes (x, y, z). These sensor axes define the IMU frame of reference. We also define a navigation frame that overlaps with the IMU frame during initialization. The navigation frame remains affixed to the world during the experiment, while the IMU frame moves with the subject’s foot. Since the IMU sensor measurements are relative, there is no need to follow a strict anatomical calibration. However, since the IMU reference frame determines the navigation frame during initialization, it is advisable to approximately align the IMU axes to the desired navigation frame (see Figure 1). In-depth explanations of how (strap-down) IMUs are used, particularly for navigation applications, are provided in [24,25]. Major results from this field that we employ are summarized below. Sensors 2017, 17, 2647 3 of 14 Sensors 2017, 17, 2647 3 of 14 (a) (b) (c) Figure 1. IMU data logger setup: (a) APDM IMU device showing the IMU sensor axes; (b) IMU attached to the shoe showing the IMU frame axes convention used in this paper; and (c) video stills showing the IMUs mounted on a subject's shoe climbing stairs ascent. 2.1. Orientation Estimation Estimating the foot trajectory from IMU data begins with first estimating the orientation of the foot-mounted IMU. For this purpose, we choose a quaternion (ࢗ) representation of the IMU orientation. Unlike the more common Euler angle representation that suffers from gimbal-lock, the quaternion representation readily describes any arbitrary sequence of rotations [26]. Quaternions represent an orientation as a rotation angle about a rotation axis. Thus, quaternions are defined using four parameters, one defining the angle of rotation and three defining the axis of rotation (e.g., three direction cosines). The four quaternion parameters satisfy the differential equation: ࢗሶ = ࢗ ∘ࢹ 2 (1) ࢹ=ൣ0,߱௫, ߱௬, ߱௭൧ (2) in which the operator ∘ denotes quaternion multiplication [25,27] and ࢹ is a four-element vector containing the aforementioned measured angular velocity components ൫߱௙൯. Thus, the solution of (1) using the measured ࢹ yields the gyro-estimated orientation of the IMU as a function of time. The gyro-estimated orientation will inevitably drift due to sensor errors, including bias drift, scale factor errors, and acceleration sensitivity. Our algorithm fuses the gyro-estimated orientation with accelerometer-estimated tilt angles from vertical (roll and pitch). This is achieved using a Kalman filter [28,29]. When the IMUs are mounted on the feet, the foot and the attached IMU are essentially stationary during specific time periods (ݐ௦) for the stance phase of each stride. The stationary periods are detected by observing the gyroscope and accelerometer measurements (see [14] Section 2.1 for more information about how ݐ௦ is determined). During stationary periods, the accelerometer measures the components of gravity (ܩ) along each sense axis. These measures are used to form accelerometer-estimated roll and pitch angles (ࢠ=[߶௔,ߠ௔]) per: ߶௔ = sinିଵ ቀܽ௫ ܩ ቁ (3) ߠ௔=−sinିଵ ൬ ܽ௬ܩ cos ߶௔ ൰ (4) Next, we use the gyroscope-estimated quaternion (ࢗ) value to calculate the equivalent Euler angles ൫࢞ = ൣ߶௚,ߠ௚,߰௚൧൯, which also includes the estimated yaw angle ൫߰௚൯ (that is temporarily ignored as it cannot be detected from the accelerometers). The Kalman filter states ൫࢞ෝ = ൣ߶෠,ߠ෠൧൯ are estimated as a combination of the gyroscope-based and accelerometer-based tilt estimates. We assume that all gyroscope error contributions and accelerometer-based tilt errors can be modeled as zero mean Gaussian noise. Since the process and measurement covariance errors are sensor- dependent only, once the Kalman filter is tuned the parameters are valid for all participants. The Figure 1. IMU data logger setup: (a) APDM IMU device showing the IMU sensor axes; (b) IMU attached to the shoe showing the IMU frame axes convention used in this paper; and (c) video stills showing the IMUs mounted on a subject's shoe climbing stairs ascent. 2.1. Orientation Estimation Estimating the foot trajectory from IMU data begins with first estimating the orientation of the foot-mounted IMU. For this purpose, we choose a quaternion (q) representation of the IMU orientation. Unlike the more common Euler angle representation that suffers from gimbal-lock, the quaternion representation readily describes any arbitrary sequence of rotations [26]. Quaternions represent an orientation as a rotation angle about a rotation axis. Thus, quaternions are defined using four parameters, one defining the angle of rotation and three defining the axis of rotation (e.g., three direction cosines). The four quaternion parameters satisfy the differential equation: .q = q ◦ Ω 2 (1) Ω =  0, ωx, ωy, ωz  (2) in which the operator ◦ denotes quaternion multiplication [25,27] and Ω is a four-element vector containing the aforementioned measured angular velocity components (ω f ). Thus, the solution of (1) using the measured Ω yields the gyro-estimated orientation of the IMU as a function of time. The gyro-estimated orientation will inevitably drift due to sensor errors, including bias drift, scale factor errors, and acceleration sensitivity. Our algorithm fuses the gyro-estimated orientation with accelerometer-estimated tilt angles from vertical (roll and pitch). This is achieved using a Kalman filter [28,29]. When the IMUs are mounted on the feet, the foot and the attached IMU are essentially stationary during specific time periods (ts) for the stance phase of each stride. The stationary periods are detected by observing the gyroscope and accelerometer measurements (see [14] Section 2.1 for more information about how ts is determined). During stationary periods, the accelerometer measures the components of gravity (G) along each sense axis. These measures are used to form accelerometer-estimated roll and pitch angles (z = [φa, θa]) per: φa = sin−1 ax G  (3) θa = − sin−1  ayG cos φa  (4) Next, we use the gyroscope-estimated quaternion (q) value to calculate the equivalent Euler angles (x = [φg, θg, ψg]), which also includes the estimated yaw angle Sensors 2017, 17, 2647 4 of 14 once the Kalman filter is tuned the parameters are valid for all participants. The updated state is then converted back to its corresponding quaternion value. Figure 2 illustrates a block diagram of this orientation estimation algorithm. Sensors 2017, 17, 2647 4 of 14 updated state is then converted back to its corresponding quaternion value. Figure 2 illustrates a block diagram of this orientation estimation algorithm. Figure 2. Angular velocity components measured by the gyroscope are integrated once to obtain orientation estimates ࢞. The accelerometer components are used to estimate tilt (roll and pitch) during stationary periods ݐ௦. The Kalman filter bounds the tilt errors by fusing the gyro-based orientation and accelerometer-based tilt to establish the “corrected orientation” ࢞ෝ. 2.2. Foot Trajectory Estimation The resulting orientation estimates are used to resolve the foot IMU frame-acceleration components ൫ࢇ௙൯ into the navigation frame acceleration components (ࢇ௡). The ݖ-axis component of the resultant world-referenced acceleration ࢇ௡ will be affected by gravity ܩ: ࢇ௪ = ࢘௙ ௡ࢇ௙+ܩ (5) in which ࢘௙ ௡ is the rotation matrix from the foot IMU frame to the navigation frame as computed from the quaternion ࢗ [24,25]. Next, integrating ࢇ௡ once and then twice yields the foot IMU velocity (࢜) and position (࢖): ࢜ = ࢜௢ + නࢇ௡݀ݐ ௧ ௧೚ (6) ࢖ = ࢖௢ + න ࢜݀ݐ ௧ ௧೚ (7) Since the experiment starts with a stationary phase, the initial velocity (࢜௢) is zero and at a position (࢖௢) also designated as zero. However, this can be generalized to a non-zero initial velocity or position for applications that require such. Examples of software implementations of (1)–(7) are found in [30,31]. The velocity estimated from (6) is often polluted by residual drift error (deriving from both the gyro and the accelerometer) which leads to (often slowly varying) velocity errors. The velocity drift error can be estimated and (approximately) eliminated using the following procedure. During the stationary times (ݐ௦) any remaining estimated velocity during these times can be assumed to be caused by drift error. These velocity errors are used to correct both the velocity (6) and position (7) estimates using an algorithm known as the Zero velocity UPdaTe (ZUPT). A block diagram for the ZUPT algorithm is illustrated in Figure 3 and further details of its implementation can be found in [14,22]. ࢞ න ݂(ݐ)݀ݐ ௧ ௧೚ Kalman Filter ࢞ Corrected Orientation ࢞ෝ Angular Velocity Tilt Estimation ࢠ Linear Acceleration Noise + Orientation ݐ௦ ࢇ௙ ࣓ࢌ Figure 2. Angular velocity components measured by the gyroscope are integrated once to obtain orientation estimates x. The accelerometer components are used to estimate tilt (roll and pitch) during stationary periods ts. The Kalman filter bounds the tilt errors by fusing the gyro-based orientation and accelerometer-based tilt to establish the “corrected orientation” ˆx. 2.2. Foot Trajectory Estimation The resulting orientation estimates are used to resolve the foot IMU frame-acceleration components (af ) into the navigation frame acceleration components (an). The z-axis component of the resultant world-referenced acceleration an will be affected by gravity G: aw = rn f af + G (5) in which rn f is the rotation matrix from the foot IMU frame to the navigation frame as computed from the quaternion q [24,25]. Next, integrating an once and then twice yields the foot IMU velocity (v) and position (p): v = vo + Z t to andt (6) p = po + Z t to vdt (7) Since the experiment starts with a stationary phase, the initial velocity (vo) is zero and at a position (po) also designated as zero. However, this can be generalized to a non-zero initial velocity or position for applications that require such. Examples of software implementations of (1)–(7) are found in [30,31]. The velocity estimated from (6) is often polluted by residual drift error (deriving from both the gyro and the accelerometer) which leads to (often slowly varying) velocity errors. The velocity drift error can be estimated and (approximately) eliminated using the following procedure. During the stationary times (ts) any remaining estimated velocity during these times can be assumed to be caused by drift error. These velocity errors are used to correct both the velocity (6) and position (7) estimates using an algorithm known as the Zero velocity UPdaTe (ZUPT). A block diagram for the ZUPT algorithm is illustrated in Figure 3 and further details of its implementation can be found in [14,22]. Sensors 2017, 17, 2647 5 of 14 Sensors 2017, 17, 2647 5 of 14 Figure 3. The accelerometer measurements are resolved in the world coordinate frame using the corrected orientation. The resultant accelerations are integrated twice to determine velocity and position. During stationary periods ݐ௦, any remaining velocity is considered an error and its value is used to reset the position and acceleration errors. 2.3. Elevation Correction Since the riser (step height) and tread (step depth) dimensions of the stairs are known, we add an additional correction to the position estimate. In particular, we designed a single-state Kalman filter that makes corrections to the IMU-derived vertical foot position (࢞ = [݌௭]) knowing the riser height (ܪ) and the number of steps (݊) to yield an elevation observation per footfall (ࢠ=[ܪ݊]). The filter makes corrections to its state (࢞ෝ = [݌௭ ෞ]) whenever the foot reaches a new tread during the stationary time (ݐ௦) . The filter assumes that the state and observation are both affected by uncorrelated white noise. A block diagram showing this filter is illustrated in Figure 4. Finally, we apply a linear interpolation in order to provide backward corrections to obtain the complete foot trajectory for each stride. Figure 4. A Kalman filter makes foot elevation corrections using the known step height (riser), during each stationary time ݐ௦. 2.4. Gait Timing Variables We used a wavelet analysis to establish the beginning (foot-strike) and end (toe-off) of each foot/ground contact period [32]. This approach is effective at identifying gait events because when the foot strikes or leaves the ground, the acceleration and angular velocity signals contain significantly more high-frequency content than at other times of the gait cycle. The wavelet analysis is used to identify time points when the measured signals contain significant content above 20 Hz, corresponding to either foot-strikes or toe-offs. Foot-strike time (ݐ௦௧௥௜௞௘) was defined as the time when the foot first contacts a tread. For running on stairs, the toe is more likely to contact the tread first (whereas, during flat-surface walking the heel contacts the ground first). The initial contact ݐ௦௧௥௜௞௘ estimation does not require it to be a heel or toe specifically. Toe-off time ൫ݐ௢௙௙൯ is defined as the time when the foot first loses contact with the tread. The durations of the major phases of the gait Corrected Orientation Linear Acceleration × ݐ௦ ࢇ௙ න ݂(ݐ)݀ݐ ௧ೞ ௧೚ න ݂(ݐ)݀ݐ ௧ೞ ௧೚ ࢘௙ ௡ Corrected Position ݂݀(ݐ) ݀ݐ න ݂(ݐ)݀ݐ ௧ೞ ௧೚ ࢖ ࢜ࢇ௡ Velocity Error Accel. Error Position Error − − Kalman Filter ࢞ Corrected Elevation ࢞ෝ Elevation ࢠ Stair Height Noise + ݐ௦ ܪ݊ Noise + ݌௭ Figure 3. The accelerometer measurements are resolved in the world coordinate frame using the corrected orientation. The resultant accelerations are integrated twice to determine velocity and position. During stationary periods ts, any remaining velocity is considered an error and its value is used to reset the position and acceleration errors. 2.3. Elevation Correction Since the riser (step height) and tread (step depth) dimensions of the stairs are known, we add an additional correction to the position estimate. In particular, we designed a single-state Kalman filter that makes corrections to the IMU-derived vertical foot position (x = [pz]) knowing the riser height (H) and the number of steps (n) to yield an elevation observation per footfall (z = [Hn]). The filter makes corrections to its state (ˆx = [ ˆpz]) whenever the foot reaches a new tread during the stationary time (ts). The filter assumes that the state and observation are both affected by uncorrelated white noise. A block diagram showing this filter is illustrated in Figure 4. Finally, we apply a linear interpolation in order to provide backward corrections to obtain the complete foot trajectory for each stride. Sensors 2017, 17, 2647 5 of 14 Figure 3. The accelerometer measurements are resolved in the world coordinate frame using the corrected orientation. The resultant accelerations are integrated twice to determine velocity and position. During stationary periods ݐ௦, any remaining velocity is considered an error and its value is used to reset the position and acceleration errors. 2.3. Elevation Correction Since the riser (step height) and tread (step depth) dimensions of the stairs are known, we add an additional correction to the position estimate. In particular, we designed a single-state Kalman filter that makes corrections to the IMU-derived vertical foot position (࢞ = [݌௭]) knowing the riser height (ܪ) and the number of steps (݊) to yield an elevation observation per footfall (ࢠ=[ܪ݊]). The filter makes corrections to its state (࢞ෝ = [݌௭ ෞ]) whenever the foot reaches a new tread during the stationary time (ݐ௦) . The filter assumes that the state and observation are both affected by uncorrelated white noise. A block diagram showing this filter is illustrated in Figure 4. Finally, we apply a linear interpolation in order to provide backward corrections to obtain the complete foot trajectory for each stride. Figure 4. A Kalman filter makes foot elevation corrections using the known step height (riser), during each stationary time ݐ௦. 2.4. Gait Timing Variables We used a wavelet analysis to establish the beginning (foot-strike) and end (toe-off) of each foot/ground contact period [32]. This approach is effective at identifying gait events because when the foot strikes or leaves the ground, the acceleration and angular velocity signals contain significantly more high-frequency content than at other times of the gait cycle. The wavelet analysis is used to identify time points when the measured signals contain significant content above 20 Hz, corresponding to either foot-strikes or toe-offs. Foot-strike time (ݐ௦௧௥௜௞௘) was defined as the time when the foot first contacts a tread. For running on stairs, the toe is more likely to contact the tread first (whereas, during flat-surface walking the heel contacts the ground first). The initial contact ݐ௦௧௥௜௞௘ estimation does not require it to be a heel or toe specifically. Toe-off time ൫ݐ௢௙௙൯ is defined as the time when the foot first loses contact with the tread. The durations of the major phases of the gait Corrected Orientation Linear Acceleration × ݐ௦ ࢇ௙ න ݂(ݐ)݀ݐ ௧ೞ ௧೚ න ݂(ݐ)݀ݐ ௧ೞ ௧೚ ࢘௙ ௡ Corrected Position ݂݀(ݐ) ݀ݐ න ݂(ݐ)݀ݐ ௧ೞ ௧೚ ࢖ ࢜ࢇ௡ Velocity Error Accel. Error Position Error − − Kalman Filter ࢞ Corrected Elevation ࢞ෝ Elevation ࢠ Stair Height Noise + ݐ௦ ܪ݊ Noise + ݌௭ Figure 4. A Kalman filter makes foot elevation corrections using the known step height (riser), during each stationary time ts. 2.4. Gait Timing Variables We used a wavelet analysis to establish the beginning (foot-strike) and end (toe-off) of each foot/ground contact period [32]. This approach is effective at identifying gait events because when the foot strikes or leaves the ground, the acceleration and angular velocity signals contain significantly more high-frequency content than at other times of the gait cycle. The wavelet analysis is used to identify time points when the measured signals contain significant content above 20 Hz, corresponding to either foot-strikes or toe-offs. Foot-strike time (tstrike) was defined as the time when the foot first contacts a tread. For running on stairs, the toe is more likely to contact the tread first (whereas, during flat-surface walking the heel contacts the ground first). The initial contact tstrike estimation does not require it to be a heel or toe specifically. Toe-off time (to f f ) is defined as the time when the foot first loses contact with the tread. The durations of the major phases of the gait cycle are important indicators of stair-climbing performance. In particular, we consider the durations of: (1) the entire stride; (2) the Sensors 2017, 17, 2647 6 of 14 stance phase; and (3) the swing phase. The stride time tstride is measured as the time it takes from one foot-strike to the next foot-strike of the same foot during steady state. The stance time tstance is the time difference between two consecutive foot-strike and toe-off events. The swing time tswing is the time difference between two consecutive toe-off and foot-strike events: tstride = ∆tstrike (8) tstance = to f f − tstrike (9) tswing = tstride − tstance (10) We calculate the percentage of time that the subjects remain in the stance phase: tps = 100 × tstance tstride (11) Assuming left-right gait symmetry [33], a tps value larger than 50% indicates the existence of a double support phase (when both feet are in contact with the ground simultaneously). 2.5. Gait Kinematic and Kinetic Variables Beyond the timing of gait events, our approach provides the full trajectory and orientation of the feet, which are useful for understanding stair running performance. Foot clearance (c) is defined as the foot height (pz) difference between the times of the local maximum (tmax) and minimum (tmin) around foot-strike: c = pz(tmax) − pz(tmin) (12) In particular, for every stride we identify the local minimum foot height (tmin) after the tstrike and before to f f . For stair ascending, tmax is defined as the time when the local maximum foot height occurs just prior to foot-strike (swing phase) while, for stair descending, it is identified after the foot strike and, in most cases, before toe-off (stance phase). Examples showing the typical distribution of local minimum and maximum times in the different gait cycles for stair running (both ascending and descending) are shown in Figure 5. One interpretation of the clearance, c is that it indicates how subjects minimize tripping risk as they plan for advancing to the next step (i.e., larger value of c could imply a more careful foot trajectory planning that provides a safer margin to clear the steps). Sensors 2017, 17, 2647 6 of 14 cycle are important indicators of stair-climbing performance. In particular, we consider the durations of: (1) the entire stride; (2) the stance phase; and (3) the swing phase. The stride time ݐ௦௧௥௜ௗ௘ is measured as the time it takes from one foot-strike to the next foot-strike of the same foot during steady state. The stance time ݐ௦௧௔௡௖௘ is the time difference between two consecutive foot-strike and toe-off events. The swing time ݐ௦௪௜௡௚ is the time difference between two consecutive toe-off and foot- strike events: ݐ௦௧௥௜ௗ௘ = Δݐ௦௧௥௜௞௘ (8) ݐ௦௧௔௡௖௘ =ݐ௢௙௙−ݐ௦௧௥௜௞௘ (9) ݐ௦௪௜௡௚ =ݐ௦௧௥௜ௗ௘ −ݐ௦௧௔௡௖௘ (10) We calculate the percentage of time that the subjects remain in the stance phase: ݐ௣௦ = 100 ×ݐ௦௧௔௡௖௘ ݐ௦௧௥௜ௗ௘ (11) Assuming left-right gait symmetry [33], a ݐ௣௦ value larger than 50% indicates the existence of a double support phase (when both feet are in contact with the ground simultaneously). 2.5. Gait Kinematic and Kinetic Variables Beyond the timing of gait events, our approach provides the full trajectory and orientation of the feet, which are useful for understanding stair running performance. Foot clearance (ܿ) is defined as the foot height (݌௭) difference between the times of the local maximum (ݐ௠௔௫) and minimum (ݐ௠௜௡) around foot-strike: ܿ = ݌௭(ݐ௠௔௫) − ݌௭(ݐ௠௜௡) (12) In particular, for every stride we identify the local minimum foot height (ݐ௠௜௡) after the ݐ௦௧௥௜௞௘ and before ݐ௢௙௙. For stair ascending, ݐ௠௔௫ is defined as the time when the local maximum foot height occurs just prior to foot-strike (swing phase) while, for stair descending, it is identified after the foot strike and, in most cases, before toe-off (stance phase). Examples showing the typical distribution of local minimum and maximum times in the different gait cycles for stair running (both ascending and descending) are shown in Figure 5. One interpretation of the clearance, ܿ is that it indicates how subjects minimize tripping risk as they plan for advancing to the next step (i.e., larger value of ܿ could imply a more careful foot trajectory planning that provides a safer margin to clear the steps). (a) (b) Figure 5. Estimated foot trajectory and speed for running over three treads during ascending (a) and descending (b). Close up of a steady state running gait showing the major stride events times: toe- off ݐ௢௙௙ (green dots), foot strike ݐ௦௧௥௜௞௘ (red dots), maximum elevation ݐ௠௔௫ (black dots), minimum elevation ݐ௠௜௡ (yellow dots); and gait phases: stance phase ݐݏݐܽ݊ܿ݁ (blue curves) and swing phase ݐݏݓ݅݊݃ (red curves). Figure 5. Estimated foot trajectory and speed for running over three treads during ascending (a) and descending (b). Close up of a steady state running gait showing the major stride events times: toe-off to f f (green dots), foot strike tstrike (red dots), maximum elevation tmax (black dots), minimum elevation tmin (yellow dots); and gait phases: stance phase tstance (blue curves) and swing phase tswing (red curves). Sensors 2017, 17, 2647 7 of 14 The estimated foot IMU velocity (6) is used to compute a proxy for the foot kinetic energy per unit of mass kem per using the following formulation: kem = k m = |v| 2 2 (13) where |v| denotes the average magnitude of the foot speed calculated over the duration of every stride tstride (8). During stair running, the foot rotates with the majority of rotation manifesting in changes in pitch θ. We estimate the “bounce angle” θbounce as the angular displacement in pitch from foot-strike to toe-off as follows: θbreak = |θ(tstrike) − θ(tmin)| (14) θprop = |θ(to f f ) − θ(tmin)| (15) θbounce = θbreak + θprop (16) Here, the “braking angle” θbreak is computed as the change in foot pitch from the contact time tstrike until the foot reaches its minimum elevation during the stance phase. The “propulsion angle” θprop is computed as the change in foot pitch from the time of minimum elevation until toe-off to f f . The resulting bounce angle could be related to ankle stiffness used during propulsion [34], which implicates performance outcomes [35] (i.e., stiffer ankles limit the time delay, or, “give” in the transmission of forces up the kinetic chain) or risk for injury [36]. By estimating the impulse between foot-strike and toe-off events, we also estimate the foot vertical ground reaction force per unit of mass g f m per: g f m = fz m = ∆vz ∆t (17) ∆vz = vz(to f f ) − vz(tstrike) (18) where the time increment ∆t equals the tstance (9). 2.6. Statistical Analysis In our analysis, we eliminated the first and the last step from each stair run, as we considered them to be transition steps that differ from the approximately steady state stepping that is the focus of our study. We also assumed left-right foot symmetry and pooled these data within the statistical analysis. This study does not consider or use the anthropometric characteristics of the participants. To evaluate how the gait timing, kinematic, and kinetic parameters were related to the stride times (speed), we performed a simple linear regression for each relationship to determine: the R-squared value (R2) to quantify the variation explained by the relation; the slope of the relation (b) between the metric of interest and the stride time; and the statistical significance of the slope (pb). The simple linear regression assumptions of normality and constant variance of the residual were assessed using the Lilliefors test and Engle’s Auto Regressive Conditional Heteroskedasticity (ARCH) test, respectively. When these conditions were not met, a transformation of the variables was performed and the simple linear regression was fit to the transformed variables to assess if the relationship trends were consistent. Comparison of the variation between tswing and tstance was assessed using an F-test. We use a two-sample t-test to compare the ascending and descending conditions for the tps, c, and θbounce variables. Finally, we use a one-sample t-test to determine if g f m was different than zero. 3. Results and Discussion Figure 5 shows an example of the estimated foot elevations and velocity magnitudes against time for a subject running while ascending (Figure 5a) and descending (Figure 5b) the stairs. The trajectories Sensors 2017, 17, 2647 8 of 14 illustrate several steady state strides with labelled times for foot-strike, toe-off, maximum elevation, minimum elevation, and gait phases. The above algorithm yields estimates of the full (three-dimensional) trajectories, as well as (three-dimensional) foot orientation angles. Figure 6 presents a foot trajectory in space (elevation plotted versus forward position) as well as the foot pitch angle and for the same sample steps considered in Figure 5. Using speed alone as the criterion, stair running performance can then be quantified by the stride time (shorter average stride time predicting greater average speed since step lengths are defined/constrained by the stairs geometry). Figures 7–12 compare the individual stride times (vertical axis) against all other metrics, including the additional gait timing, kinematic, and kinetic variables defined above (horizontal axes). In these figures, each dot represents one stride during steady state, and each color represents one subject. We also provide the equation of the linear fit, R2, and pβ for each relation. Sensors 2017, 17, 2647 8 of 14 The above algorithm yields estimates of the full (three-dimensional) trajectories, as well as (three-dimensional) foot orientation angles. Figure 6 presents a foot trajectory in space (elevation plotted versus forward position) as well as the foot pitch angle and for the same sample steps considered in Figure 5. Using speed alone as the criterion, stair running performance can then be quantified by the stride time (shorter average stride time predicting greater average speed since step lengths are defined/constrained by the stairs geometry). Figures 7–12 compare the individual stride times (vertical axis) against all other metrics, including the additional gait timing, kinematic, and kinetic variables defined above (horizontal axes). In these figures, each dot represents one stride during steady state, and each color represents one subject. We also provide the equation of the linear fit, R2, and ݌ఉ for each relation. (a) (b) Figure 6. Foot trajectory (black curve) and pitch angle ߠ (colored lines) for ascending (a) and descending (b) stairs. The colors distinguish the distinct gait cycles across successive treads. 3.1. Gait Timing Variables Our data analysis shows that in either direction (stair ascent or decent), the stride time ݐ௦௧௥௜ௗ௘ was mainly predicted by the stance time ݐ௦௧௔௡௖௘ as measured by high correlation (R2 value for ascent 0.84, ݌௕ < 0.001; R2 for descent 0.92, ݌௕ < 0.001); refer to Figure 7. Thus, shorter ݐ௦௧௔௡௖௘ values are strong predictors of overall speed (shorter stride times) during both stair ascent and decent. (a) (b) Figure 7. Stance ݐݏݐܽ݊ܿ݁ and stride time ݐݏݐݎ݅݀݁ relationship for ascending (a) and descending (b) stairs. Each dot represents one stride, and each color represents one subject. Overall speed is largely determined by the stance phase. Figure 6. Foot trajectory (black curve) and pitch angle θ (colored lines) for ascending (a) and descending (b) stairs. The colors distinguish the distinct gait cycles across successive treads. 3.1. Gait Timing Variables Our data analysis shows that in either direction (stair ascent or decent), the stride time tstride was mainly predicted by the stance time tstance as measured by high correlation (R2 value for ascent 0.84, pb < 0.001; R2 for descent 0.92, pb < 0.001); refer to Figure 7. Thus, shorter tstance values are strong predictors of overall speed (shorter stride times) during both stair ascent and decent. Sensors 2017, 17, 2647 8 of 14 The above algorithm yields estimates of the full (three-dimensional) trajectories, as well as (three-dimensional) foot orientation angles. Figure 6 presents a foot trajectory in space (elevation plotted versus forward position) as well as the foot pitch angle and for the same sample steps considered in Figure 5. Using speed alone as the criterion, stair running performance can then be quantified by the stride time (shorter average stride time predicting greater average speed since step lengths are defined/constrained by the stairs geometry). Figures 7–12 compare the individual stride times (vertical axis) against all other metrics, including the additional gait timing, kinematic, and kinetic variables defined above (horizontal axes). In these figures, each dot represents one stride during steady state, and each color represents one subject. We also provide the equation of the linear fit, R2, and ݌ఉ for each relation. (a) (b) Figure 6. Foot trajectory (black curve) and pitch angle ߠ (colored lines) for ascending (a) and descending (b) stairs. The colors distinguish the distinct gait cycles across successive treads. 3.1. Gait Timing Variables Our data analysis shows that in either direction (stair ascent or decent), the stride time ݐ௦௧௥௜ௗ௘ was mainly predicted by the stance time ݐ௦௧௔௡௖௘ as measured by high correlation (R2 value for ascent 0.84, ݌௕ < 0.001; R2 for descent 0.92, ݌௕ < 0.001); refer to Figure 7. Thus, shorter ݐ௦௧௔௡௖௘ values are strong predictors of overall speed (shorter stride times) during both stair ascent and decent. (a) (b) Figure 7. Stance ݐݏݐܽ݊ܿ݁ and stride time ݐݏݐݎ݅݀݁ relationship for ascending (a) and descending (b) stairs. Each dot represents one stride, and each color represents one subject. Overall speed is largely determined by the stance phase. Figure 7. Stance tstance and stride time tstride relationship for ascending (a) and descending (b) stairs. Each dot represents one stride, and each color represents one subject. Overall speed is largely determined by the stance phase. Sensors 2017, 17, 2647 9 of 14 Due to the restrictions imposed by the stair design, subjects are relatively constrained during the swing phase. Regardless of speed, the feet must travel approximately the same distance. Thus, one expects less variation in tswing than in tstance. This expectation is supported by the smaller standard deviation of tswing (SD for ascent 0.020 s, for descent 0.022 s) compared to that for the tstance (SD for ascent 0.044 s, for descent 0.049 s) across all subjects (F(153, 153) = 4.67, p < 0.001 for ascent; F(153, 153) = 5.06, p < 0.001 for descent). During stair ascent, subjects provide just enough speed to reach the next tread, since otherwise they risk missing, tripping, or overshooting, making the task either dangerous or inefficient. As a result, there is a lower correlation between tstride and tswing during ascent (R2 value 0.24) (see Figure 8a). During stair descent, however, subjects have more freedom to choose higher speeds during the swing phase by using their muscles to break less, as shown by the higher correlation between tswing and tstride for stair descent (R2 value 0.60) (see Figure 8b). This gain in speed comes at the expense of having to accommodate for higher foot-strike impacts and increasing fall risk. Sensors 2017, 17, 2647 9 of 14 Due to the restrictions imposed by the stair design, subjects are relatively constrained during the swing phase. Regardless of speed, the feet must travel approximately the same distance. Thus, one expects less variation in ݐ௦௪௜௡௚ than in ݐ௦௧௔௡௖௘. This expectation is supported by the smaller standard deviation of ݐ௦௪௜௡௚ (SD for ascent 0.020 s, for descent 0.022 s) compared to that for the ݐ௦௧௔௡௖௘ (SD for ascent 0.044 s, for descent 0.049 s) across all subjects (F(153, 153) = 4.67, p < 0.001 for ascent; F(153, 153) = 5.06, p < 0.001 for descent). During stair ascent, subjects provide just enough speed to reach the next tread, since otherwise they risk missing, tripping, or overshooting, making the task either dangerous or inefficient. As a result, there is a lower correlation between ݐ௦௧௥௜ௗ௘ and ݐ௦௪௜௡௚ during ascent (R2 value 0.24) (see Figure 8a). During stair descent, however, subjects have more freedom to choose higher speeds during the swing phase by using their muscles to break less, as shown by the higher correlation between ݐ௦௪௜௡௚ and ݐ௦௧௥௜ௗ௘ for stair descent (R2 value 0.60) (see Figure 8b). This gain in speed comes at the expense of having to accommodate for higher foot-strike impacts and increasing fall risk. (a) (b) Figure 8. Swing ݐ௦௪௜௡௚ and stride time ݐ௦௧௥௜ௗ௘ relationship for ascending (a) and descending (b) stairs. The greater correlation during stair descent indicates that subjects likely generate speed gains during the swing phase. Finally, we observe that when running downstairs, subjects do so more carefully, as manifested in a greater (t(306) = −15.65, ݌ < 0.001) percentage of time ݐ௣௦ (11) that the subjects remain in the stance phase while descending (ascending: 44.3 ± 10.8%, descending: 53.4 ± 13.5%; mean/SD). We conclude that ݐ௦௧௥௜ௗ௘ is highly correlated with ݐ௦௧௔௡௖௘ and therefore speed is determined largely by the ability of the subjects to generate enough impulse to reach the next step in the shortest period of time. Table 1 presents a summary of the gait timing variables. To summarize, both ݐ௦௧௔௡௖௘ and ݐ௦௪௜௡௚ have significant relationships to speed. However, ݐ௦௧௔௡௖௘ shows the highest correlation, indicating the potential to be a better predictor. Table 1. Gait cycle timing variables for running while ascending and descending stairs. Direction ࢚࢙࢚࢘࢏ࢊࢋvs ࢚࢙࢚ࢇ࢔ࢉࢋ R2/࢈ ࢚࢙࢚࢘࢏ࢊࢋvs ࢚࢙࢝࢏࢔ࢍ R2/࢈࢚࢙࢚ࢇ࢔ࢉࢋ SD (s) ࢚࢙࢝࢏࢔ࢍ SD (s) ࢚࢖࢙ Mean ± SD (%) Ascent 0.84/1.05 0.24/1.22 † 0.044 0.020 44.3 ± 10.8 Descent 0.92/1.25 † 0.60/2.27 † 0.049 0.022 53.4 ± 13.5 † Does not meet constant variance assumption. 3.2. Gait Kinematic and Kinetic Variables While the estimated slope between ݐ௦௧௥௜ௗ௘ (speed) and foot clearance ܿ for ascent was significant, there is a negligible relationship between these variables as seen by the low R2 value (R2 for ascent 0.03, ݌௕ = 0.05). There was a significant linear relation for descent (R2 = 0.34, ݌௕ < 0.001) (see Figure 9). During descent, subjects clear the steps with a smaller average clearance relative to ascent (ascending: 0.06 ± 0.02 m, descending: 0.02 ± 0.02 m; mean ± SD; t(306) = 17.49; ݌ < 0.001), in Figure 8. Swing tswing and stride time tstride relationship for ascending (a) and descending (b) stairs. The greater correlation during stair descent indicates that subjects likely generate speed gains during the swing phase. Finally, we observe that when running downstairs, subjects do so more carefully, as manifested in a greater (t(306) = −15.65, p < 0.001) percentage of time tps (11) that the subjects remain in the stance phase while descending (ascending: 44.3 ± 10.8%, descending: 53.4 ± 13.5%; mean/SD). We conclude that tstride is highly correlated with tstance and therefore speed is determined largely by the ability of the subjects to generate enough impulse to reach the next step in the shortest period of time. Table 1 presents a summary of the gait timing variables. To summarize, both tstance and tswing have significant relationships to speed. However, tstance shows the highest correlation, indicating the potential to be a better predictor. Table 1. Gait cycle timing variables for running while ascending and descending stairs. Direction tstride vs. tstance R2/b tstride vs. tswing R2/b tstance SD (s) tswing SD (s) tps Mean ± SD (%) Ascent 0.84/1.05 0.24/1.22 † 0.044 0.020 44.3 ± 10.8 Descent 0.92/1.25 † 0.60/2.27 † 0.049 0.022 53.4 ± 13.5 † Does not meet constant variance assumption. 3.2. Gait Kinematic and Kinetic Variables While the estimated slope between tstride (speed) and foot clearance c for ascent was significant, there is a negligible relationship between these variables as seen by the low R2 value (R2 for ascent 0.03, pb = 0.05). There was a significant linear relation for descent (R2 = 0.34, pb < 0.001) (see Figure 9). During descent, subjects clear the steps with a smaller average clearance relative to ascent (ascending: Sensors 2017, 17, 2647 10 of 14 0.06 ± 0.02 m, descending: 0.02 ± 0.02 m; mean ± SD; t(306) = 17.49; p < 0.001), in some cases by rolling the foot on the nose of the tread as they transition to the next tread. Smaller average clearance enables the foot to follow a more linear trajectory (see Figure 6), which can be more energy efficient as explained in the next section. Sensors 2017, 17, 2647 10 of 14 some cases by rolling the foot on the nose of the tread as they transition to the next tread. Smaller average clearance enables the foot to follow a more linear trajectory (see Figure 6), which can be more energy efficient as explained in the next section. (a) (b) Figure 9. Foot clearance ܿ and stride time ݐ௦௧௥௜ௗ௘ relationship for ascending (a) and descending (b) stairs. Descent is accomplished with an overall smaller clearance relative to ascent. A significant linear relationship between the foot kinetic energy ݇݁݉ (13) and ݐ௦௧௥௜ௗ௘ exists (R2 for ascent: 0.53, ݌௕ < 0.001; R2 for descent: 0.8, ݌௕ < 0.001) (see Figure 10), with faster subjects exhibiting higher kinetic energy. During stair ascent, a fraction of the kinetic energy is consumed just to clear the nose of the steps safely and, as a result, the foot describes a parabolic trajectory (see Figure 6a) in strong contrast with the linear trajectory exhibited during descent (see Figure 6b). (a) (b) Figure 10. Kinetic energy per unit of mass ݇݁݉ and stride time ݐݏݐݎ݅݀݁ relationship for ascending (a) and descending (b) stairs. In stair ascent, a fraction of the kinetic energy is consumed in order to safely clear the nose of the treads. Example variations in the pitch angle during stair running are illustrated in Figure 6. The pitch variations ߠ௕௢௨௡௖௘ (14)–(16) do not have a linear relationship with ݐ௦௧௥௜ௗ௘ (Figure 11) during ascending (R2 for ascent: 0.01, ܾ = 0, ݌௕ = 0.36) and have a moderate relationship during descending (R2 for descent: 0.32, ܾ = 3 × 10−3, ݌௕ < 0.001). The average bounce angle during ascent is smaller than the average bounce angles during descent (ascending: 45.0 ± 9.2 deg, descending: 66.2 ± 10.4 deg; mean ± SD; t(306) = −19.029; ݌ < 0.001). It is noteworthy that during the stair ascent smaller bounce angles are indicative of an increase in ankle stiffness which, in turn, increases vertical velocity [35]. Figure 9. Foot clearance c and stride time tstride relationship for ascending (a) and descending (b) stairs. Descent is accomplished with an overall smaller clearance relative to ascent. A significant linear relationship between the foot kinetic energy kem (13) and tstride exists (R2 for ascent: 0.53, pb < 0.001; R2 for descent: 0.8, pb < 0.001) (see Figure 10), with faster subjects exhibiting higher kinetic energy. During stair ascent, a fraction of the kinetic energy is consumed just to clear the nose of the steps safely and, as a result, the foot describes a parabolic trajectory (see Figure 6a) in strong contrast with the linear trajectory exhibited during descent (see Figure 6b). Sensors 2017, 17, 2647 10 of 14 some cases by rolling the foot on the nose of the tread as they transition to the next tread. Smaller average clearance enables the foot to follow a more linear trajectory (see Figure 6), which can be more energy efficient as explained in the next section. (a) (b) Figure 9. Foot clearance ܿ and stride time ݐ௦௧௥௜ௗ௘ relationship for ascending (a) and descending (b) stairs. Descent is accomplished with an overall smaller clearance relative to ascent. A significant linear relationship between the foot kinetic energy ݇݁݉ (13) and ݐ௦௧௥௜ௗ௘ exists (R2 for ascent: 0.53, ݌௕ < 0.001; R2 for descent: 0.8, ݌௕ < 0.001) (see Figure 10), with faster subjects exhibiting higher kinetic energy. During stair ascent, a fraction of the kinetic energy is consumed just to clear the nose of the steps safely and, as a result, the foot describes a parabolic trajectory (see Figure 6a) in strong contrast with the linear trajectory exhibited during descent (see Figure 6b). (a) (b) Figure 10. Kinetic energy per unit of mass ݇݁݉ and stride time ݐݏݐݎ݅݀݁ relationship for ascending (a) and descending (b) stairs. In stair ascent, a fraction of the kinetic energy is consumed in order to safely clear the nose of the treads. Example variations in the pitch angle during stair running are illustrated in Figure 6. The pitch variations ߠ௕௢௨௡௖௘ (14)–(16) do not have a linear relationship with ݐ௦௧௥௜ௗ௘ (Figure 11) during ascending (R2 for ascent: 0.01, ܾ = 0, ݌௕ = 0.36) and have a moderate relationship during descending (R2 for descent: 0.32, ܾ = 3 × 10−3, ݌௕ < 0.001). The average bounce angle during ascent is smaller than the average bounce angles during descent (ascending: 45.0 ± 9.2 deg, descending: 66.2 ± 10.4 deg; mean ± SD; t(306) = −19.029; ݌ < 0.001). It is noteworthy that during the stair ascent smaller bounce angles are indicative of an increase in ankle stiffness which, in turn, increases vertical velocity [35]. Figure 10. Kinetic energy per unit of mass kem and stride time tstride relationship for ascending (a) and descending (b) stairs. In stair ascent, a fraction of the kinetic energy is consumed in order to safely clear the nose of the treads. Example variations in the pitch angle during stair running are illustrated in Figure 6. The pitch variations θbounce (14)–(16) do not have a linear relationship with tstride (Figure 11) during ascending (R2 for ascent: 0.01, b = 0, pb = 0.36) and have a moderate relationship during descending (R2 for descent: 0.32, b = 3 × 10−3, pb < 0.001). The average bounce angle during ascent is smaller than the average bounce angles during descent (ascending: 45.0 ± 9.2 deg, descending: 66.2 ± 10.4 deg; Sensors 2017, 17, 2647 11 of 14 mean ± SD; t(306) = −19.029; p < 0.001). It is noteworthy that during the stair ascent smaller bounce angles are indicative of an increase in ankle stiffness which, in turn, increases vertical velocity [35]. Sensors 2017, 17, 2647 11 of 14 (a) (b) Figure 11. Bounce angle ߠ௕௢௨௡௖௘ and stride time ݐݏݐݎ݅݀݁ correlation for ascending (a) and descending (b) stairs. Lower bounce angle during stair ascent is related to impulsive motion. We calculated foot vertical ground force ݂݃݉ using (17), and determined that there was moderate correlation between ݐ௦௧௥௜ௗ௘ and ݂݃݉ for both ascent and descent (R2 for ascent: 0.45, ݌௕ < 0.001; R2 for descent: 0.21, ݌௕ < 0.001) (see Figure 12). The ݂݃݉ mean value shows that ascending stairs requires generating a non-zero reaction force (0.09 ± 0.03 N/kg; mean ± SD; t(153) = 40.97, p < 0.001), whereas the descending force was not statistically different from zero (0.0 ± 0.02 N/kg, mean ± SD; t(153) = −1.96, p = 0.052). This suggests distinct mechanisms for running on stairs with ascending requiring changes in momentum (impulses), while descending requires maintaining momentum. Ascending stairs requires generating the necessary force needed to propel the body upwards and forwards; conversely, during descending the muscles have less resistance (as supported by the increase in bounce angle) allowing gravity to do the work. The kinematic and kinetic variables are summarized in Table 2. In summary, we determine that clearance, ܿ, is only correlated to speed during stair descent. We found that some ݇݁݉ is lost during stair ascent because of the foot parabolic trajectory required to clear safely the steps. The foot angle ߠ௕௢௨௡௖௘ shows ankle stiffness during stair ascent versus compliance during stair descent. The effect of ߠ௕௢௨௡௖௘ is also evident in ground forces ݂݃݉ being large for stair ascent and negligible for stair descent. (a) (b) Figure 12. Vertical ground reaction force per unit of mass ݂݃݉ and stride time ݐݏݐݎ݅݀݁ relationship for ascending (a) and descending (b) stairs. Stair ascent employs significantly larger impulses relative to descent. Figure 11. Bounce angle θbounce and stride time tstride correlation for ascending (a) and descending (b) stairs. Lower bounce angle during stair ascent is related to impulsive motion. We calculated foot vertical ground force g f m using (17), and determined that there was moderate correlation between tstride and g f m for both ascent and descent (R2 for ascent: 0.45, pb < 0.001; R2 for descent: 0.21, pb < 0.001) (see Figure 12). The g f m mean value shows that ascending stairs requires generating a non-zero reaction force (0.09 ± 0.03 N/kg; mean ± SD; t(153) = 40.97, p < 0.001), whereas the descending force was not statistically different from zero (0.0 ± 0.02 N/kg, mean ± SD; t(153) = −1.96, p = 0.052). This suggests distinct mechanisms for running on stairs with ascending requiring changes in momentum (impulses), while descending requires maintaining momentum. Ascending stairs requires generating the necessary force needed to propel the body upwards and forwards; conversely, during descending the muscles have less resistance (as supported by the increase in bounce angle) allowing gravity to do the work. The kinematic and kinetic variables are summarized in Table 2. In summary, we determine that clearance, c, is only correlated to speed during stair descent. We found that some kem is lost during stair ascent because of the foot parabolic trajectory required to clear safely the steps. The foot angle θbounce shows ankle stiffness during stair ascent versus compliance during stair descent. The effect of θbounce is also evident in ground forces g f m being large for stair ascent and negligible for stair descent. Sensors 2017, 17, 2647 11 of 14 (a) (b) Figure 11. Bounce angle ߠ௕௢௨௡௖௘ and stride time ݐݏݐݎ݅݀݁ correlation for ascending (a) and descending (b) stairs. Lower bounce angle during stair ascent is related to impulsive motion. We calculated foot vertical ground force ݂݃݉ using (17), and determined that there was moderate correlation between ݐ௦௧௥௜ௗ௘ and ݂݃݉ for both ascent and descent (R2 for ascent: 0.45, ݌௕ < 0.001; R2 for descent: 0.21, ݌௕ < 0.001) (see Figure 12). The ݂݃݉ mean value shows that ascending stairs requires generating a non-zero reaction force (0.09 ± 0.03 N/kg; mean ± SD; t(153) = 40.97, p < 0.001), whereas the descending force was not statistically different from zero (0.0 ± 0.02 N/kg, mean ± SD; t(153) = −1.96, p = 0.052). This suggests distinct mechanisms for running on stairs with ascending requiring changes in momentum (impulses), while descending requires maintaining momentum. Ascending stairs requires generating the necessary force needed to propel the body upwards and forwards; conversely, during descending the muscles have less resistance (as supported by the increase in bounce angle) allowing gravity to do the work. The kinematic and kinetic variables are summarized in Table 2. In summary, we determine that clearance, ܿ, is only correlated to speed during stair descent. We found that some ݇݁݉ is lost during stair ascent because of the foot parabolic trajectory required to clear safely the steps. The foot angle ߠ௕௢௨௡௖௘ shows ankle stiffness during stair ascent versus compliance during stair descent. The effect of ߠ௕௢௨௡௖௘ is also evident in ground forces ݂݃݉ being large for stair ascent and negligible for stair descent. (a) (b) Figure 12. Vertical ground reaction force per unit of mass ݂݃݉ and stride time ݐݏݐݎ݅݀݁ relationship for ascending (a) and descending (b) stairs. Stair ascent employs significantly larger impulses relative to descent. Figure 12. Vertical ground reaction force per unit of mass g f m and stride time tstride relationship for ascending (a) and descending (b) stairs. Stair ascent employs significantly larger impulses relative to descent. Sensors 2017, 17, 2647 12 of 14 Table 2. Kinematic and kinetic variables for running while ascending and descending stairs. Direction tstride vs. c R2/b tstride vs. kem R2/b tstride vs. θbounce R2/b tstride vs. gfm R2/b c Mean ± SD (m) θbounce Mean ± SD (deg) gfm Mean ± SD (N/Kg) Ascent 0.03/−0.38 ‡,* 0.53/−0.11 ‡ 0.01/0.0 †,* 0.45/−1.21 0.06 ± 0.02 45.0 ± 9.2 0.09 ± 0.03 Descent 0.34/2.44 † 0.80/−0.17 ‡ 0.32/3 × 10−3 0.21/1.45 0.02 ± 0.02 66.2 ± 10.4 0.0 ± 0.02 * b Not statistically significant. † Constant variance assumption not met. ‡ Normality assumption not met. For every simple linear regression relation, the assumptions of normality and constant variance of the residuals were tested (see Tables 1 and 2). For the cases that did not meet the assumptions, we used data transformation algorithms to correct for distribution skewness as described in [37,38] and verified the significance of the relationships when assumptions were met. To facilitate the interpretation of the measures, we presented the relationships for the variables prior to transformation. It is important to note that while the slopes may differ with the transformed variables, the direction and significance of the relationship would not be expected change the results presented. Finally, it is important to note that the sensors that we use have limited operational range that may influence some of the outcomes, in particular the vertical acceleration during the foot-strike could be underestimated. We believe that the final effect of this limitation in our calculations is small due to the short duration of this event, the elevation correction that we perform, and our stride-by-stride basis analysis instead of the whole trajectory. 4. Conclusions This paper presents a method for understanding the task of running on stairs (both ascending and descending) from data harvested from foot-mounted IMUs. This understanding derives from an algorithm that estimates the foot velocity and trajectory while correcting for sensor drift errors using the ZUPT technique together with a known stair riser height. In studies of human mobility outside of a controlled experimental setup, during which stair height may not be known to the researchers, implementing a “standard” step height correction may still assist in calculating stride metrics. Timing, kinematic, and kinetic variables are proposed as metrics of stair running performance. Results on human subjects reveal that stair running speed is largely controlled by the stance phase, as opposed to the swing phase. An approximate measure of foot kinetic energy illustrates greater foot energy economy during descent versus ascent, which also follows from the near-linear foot trajectory during descent versus the parabolic path during ascent. The IMU-derived estimates for foot clearance may have future use in assessing trip/fall risks while the IMU-derived estimates of ground reaction and bounce angle may have future use in assessing injury potential. Acknowledgments: This material is based upon work supported by the US Army Contracting Command-APG, Natick Contracting Division, Natick, MA, under contract W911QY-15-C-0053. Author Contributions: L.V.O. developed and implemented the algorithms for computing foot trajectories and performance parameters. L.V.O., A.M.Z., and N.C.P. developed the biomechanical analysis. S.M.C. developed the gait event detection algorithms. L.V.O. and L.A.S. developed and completed the statistical analysis. L.V.O., A.M.Z, S.M.C., R.V.V., S.P.D., L.A.S. and N.C.P. provided valuable insight and/or assistance in collecting the data, as well as contributed to the preparation of the manuscript. Conflicts of Interest: The authors declare no conflict of interest. References 1. Kerr, J.; Eves, F.; Carroll, D. Six-Month Observational Study of Prompted Stair Climbing. Prev. Med. 2001, 33, 422–427. [CrossRef] [PubMed] 2. Huskey, T.; Mayhew, J.L.; Ball, T.E.; Arnold, M.D. Factors affecting anaerobic power output in the Margaria-Kalamen test. Ergonomics 1989, 32, 959–965. [CrossRef] [PubMed] Sensors 2017, 17, 2647 13 of 14 3. Harris, G.R.; Stone, M.H.; O’Bryant, H.S.; Johnson, R.L. Short-term performance effects of high power, high force, or combined weight-training methods. J. Strength Cond. Res. 2000, 14, 14–20. 4. Protopapadaki, A.; Drechsler, W.; Cramp, M.C.; Coutts, F.J.; Scott, O.M. Hip, knee, ankle kinematics and kinetics during stair ascent and descent in healthy young individuals. Clin. Biomech. 2007, 22, 203–210. [CrossRef] [PubMed] 5. De Wit, B.; De Clercq, D.; Aerts, P. Biomechanical analysis of the stance phase during barefoot and shod running. J. Biomech. 2000, 33, 269–278. [CrossRef] 6. Braunstein, B.; Arampatzis, A.; Eysel, P.; Brüggemann, G.P. Footwear affects the gearing at the ankle and knee joints during running. J. Biomech. 2010, 43, 2120–2125. [CrossRef] [PubMed] 7. Novacheck, T.F. The biomechanics of running. Gait Posture 1998, 7, 77–95. [CrossRef] 8. Reid, S.M.; Graham, R.B.; Costigan, P.A. Differentiation of young and older adult stair climbing gait using principal component analysis. Gait Posture 2010, 31, 197–203. [CrossRef] [PubMed] 9. Novak, A.C.; Brouwer, B. Kinematic and kinetic evaluation of the stance phase of stair ambulation in persons with stroke and healthy adults: A pilot study. J. Appl. Biomech. 2013, 29, 443–452. [CrossRef] [PubMed] 10. Benedetti, M.G.; Agostini, V.; Knaflitz, M.; Bonato, P. Muscle activation patterns during level walking and stair ambulation. In Applications of EMG in Clinical and Sports Medicine; Steele, C., Ed.; Available online: https://cdn.intechopen.com/pdfs-wm/25822.pdf (accessed on 12 September 2017). 11. Halsey, L.G.; David, A.R.; Watkins, D.A.; Duggan, B.M. The energy expenditure of stair climbing one step and two steps at a time: Estimations from measures of heart rate. PLoS ONE 2012, 9, e100658. [CrossRef] [PubMed] 12. Johnson, A.N.; Cooper, D.F.; Edwards, R.H. Exertion of stair climbing in normal subjects and in patients with chronic obstructive bronchitis. Thorax 1977, 32, 711–716. [CrossRef] [PubMed] 13. Beyea, J.; McGibbon, C.A.; Sexton, A.; Noble, J.; O’Connell, C. Convergent validity of a wearable sensor system for measuring sub-task performance during the timed up-and-go test. Sensors 2017, 17, 934. [CrossRef] [PubMed] 14. Rebula, J.R.; Ojeda, L.V.; Adamczyk, P.G.; Kuo, A.D. Measurement of foot placement and its variability with inertial sensors. Gait Posture 2013, 38, 974–980. [CrossRef] [PubMed] 15. Peruzzi, A.; Della Croce, U.; Cereatti, A. Estimation of stride length in level walking using an inertial measurement unit attached to the foot: A validation of the zero velocity assumption during stance. J. Biomech. 2011, 44, 1991–1994. [CrossRef] [PubMed] 16. Seel, T.; Schauer, T.; Raisch, J. Joint axis and position estimation from inertial measurement data by exploiting kinematic constraints. In Proceedings of the 2012 IEEE International Conference Control Application, Dubrovnik, Croatia, 3–5 October 2012. 17. Mariani, B.; Hoskovec, C.; Rochat, S.; Büla, C.; Penders, J.; Aminian, K. 3D gait assessment in young and elderly subjects using foot-worn inertial sensors. J. Biomech. 2010, 43, 2999–3006. [CrossRef] [PubMed] 18. McGinnis, R.S.; Cain, S.M.; Davidson, S.P.; Vitali, R.V.; McLean, S.G.; Perkins, N.C. Inertial sensor and cluster analysis for discriminating agility run technique. IFAC 2017, 32, 150–156. [CrossRef] 19. Chardonnens, J.; Favre, J.; Cuendet, F.; Gremion, G.; Aminian, K.A. System to measure the kinematics during the entire ski jump sequence using inertial sensors. J. Biomech. 2013, 46, 56–62. [CrossRef] [PubMed] 20. Vitali, R.V.; Cain, S.M.; McGinnis, R.S.; Zaferiou, A.M.; Ojeda, L.V.; Davidson, S.P.; Perkins, N.C. Method for estimating three-dimensional knee rotations using two inertial measurement units: Validation with a coordinate measurement machine. Sensors 2017, 17, 1970. [CrossRef] [PubMed] 21. El-Sheimy, N.; Hou, H.; Niu, X. Analysis and modeling of inertial sensors using Allan variance. IEEE Trans. Instrum. Meas. 2008, 57, 140–149. [CrossRef] 22. Ojeda, L.; Borenstein, J. Non-GPS Navigation for Security Personnel and First Responders. J. Navig. 2007, 60, 391–407. [CrossRef] 23. Ojeda, L.V.; Rebula, J.R.; Adamczyk, P.G.; Kuo, A.D. Mobile platform for motion capture of locomotion over long distances. J. Biomech. 2013, 46, 2316–2319. [CrossRef] [PubMed] 24. Titterton, D.; Weston, J. Strapdown Inertial Navigation Technology, 2nd ed.; American Institute of Aeronautics and Astronautics: Reston, VA, USA, 2004; ISBN 978-0863413582. 25. Rogers, R.M. Applied Mathematics in Integrated Navigation Systems, 3rd ed.; American Institute of Aeronautics and Astronautics: Reston, VA, USA, 2007; ISBN 9781563479274. Sensors 2017, 17, 2647 14 of 14 26. Shoemake, K. Animating rotation with quaternion curves. In Proceedings of the 12th Annual Conference on Computer Graphics and Interactive Techniques, New York, NY, USA, 22–26 July 1985; pp. 245–254. 27. Farrell, J.A.; Barth, M. The Global Positioning System & Inertial Navigation; McGraw-Hill Professional: New York, NY, USA, 1998; ISBN 0-07-022045-X. 28. Kalman, R. A New Approach to Linear Filtering and Prediction Problems. J. Basic Eng. 1960, 82, 35–45. [CrossRef] 29. Grover, R.G.; Hwang, P.Y. Introduction to Random Signals and Applied Kalman Filtering with Matlab Exercises, 4th ed.; John Wiley & Sons Inc.: New York, NY, USA, 2012; ISBN 978-1-118-21485-5. 30. Savage, P.G. Strapdown Inertial Navigation Integration Algorithm Design Part 1: Attitude Algorithms. J. Guid. Control Dyn. 1998, 21, 19–28. [CrossRef] 31. Savage, P.G. Strapdown Inertial Navigation Integration Algorithm Design Part 2: Velocity and Position Algorithms. J. Guid. Control Dyn. 1998, 21, 208–221. [CrossRef] 32. Cain, S.M.; McGinnis, R.S.; Davidson, S.P.; Vitali, R.V.; Perkins, N.C.; McLean, S.G. Quantifying performance and effects of load carriage during a challenging balancing task using an array of wireless inertial sensors. Gait Posture 2016, 43, 65–69. [CrossRef] [PubMed] 33. Sadeghi, H.; Allard, P.; Prince, F.; Labelle, H. Symmetry and limb dominance in able-bodied gait: A review. Gait Posture 2000, 12, 34–45. [CrossRef] 34. Arampatzis, A.; Brüggemann, G.P.; Metzler, V. The effect of speed on leg stiffness and joint kinetics in human running. J. Biomech. 1999, 32, 1349–1353. [CrossRef] 35. Charalambous, L.; Irwin, G.; Bezodis, I.N.; Kerwin, D. Lower limb joint kinetics and ankle joint stiffness in the sprint start push-off. J. Sports Sci. 2012, 30, 1–9. [CrossRef] [PubMed] 36. Butler, R.J.; Crowell, H.P.; Davis, I.M. Lower extremity stiffness: Implications for performance and injury. Clin. Biomech. 2003, 18, 511–517. [CrossRef] 37. Howell, D.C. Statistical Methods for Psychology, 6th ed.; Thomson Wadsworth: Belmont, CA, USA, 2007; ISBN 0495012874. 38. Tabachnick, B.G.; Fidell, L.S. Using Multivariate Statistics, 5th ed.; Allyn and Bacon: Boston, MA, USA, 2007; ISBN 0205459382. © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Estimating Stair Running Performance Using Inertial Sensors.

11-17-2017

Ojeda, Lauro V,Zaferiou, Antonia M,Cain, Stephen M,Vitali, Rachel V,Davidson, Steven P,Stirling, Leia A,Perkins, Noel C

eng

PMC8581565

ORIGINAL RESEARCH published: 28 October 2021 doi: 10.3389/fphys.2021.739745 Edited by: Gary W. Mack, Brigham Young University, United States Reviewed by: Filipe Dinato De Lima, University Center of Brasilia, Brazil Michael E. Tschakovsky, Queen’s University, Canada *Correspondence: Agnieszka Danuta Jastrz ˛ebska [email protected] Specialty section: This article was submitted to Exercise Physiology, a section of the journal Frontiers in Physiology Received: 11 July 2021 Accepted: 07 October 2021 Published: 28 October 2021 Citation: Hebisz P, Jastrz ˛ebska AD and Hebisz R (2021) Real Assessment of Maximum Oxygen Uptake as a Verification After an Incremental Test Versus Without a Test. Front. Physiol. 12:739745. doi: 10.3389/fphys.2021.739745 Real Assessment of Maximum Oxygen Uptake as a Verification After an Incremental Test Versus Without a Test Paulina Hebisz, Agnieszka Danuta Jastrz ˛ebska* and Rafał Hebisz Department of Physiology and Biochemistry, University School of Physical Education in Wrocław, Wrocław, Poland The study was conducted to compare peak oxygen uptake (VO2peak) measured with the incremental graded test (GXT) (VO2peak) and two tests to verify maximum oxygen uptake, performed 15 min after the incremental test (VO2peak1) and on a separate day (VO2peak2). The aim was to determine which of the verification tests is more accurate and, more generally, to validate the VO2max obtained in the incremental graded test on cycle ergometer. The study involved 23 participants with varying levels of physical activity. Analysis of variance showed no statistically significant differences for repeated measurements (F = 2.28, p = 0.118, η2 = 0.12). Bland–Altman analysis revealed a small bias of the VO2peak1 results compared to the VO2peak (0.4 ml·min−1·kg−1) and VO2peak2 results compared to the VO2peak (−0.76 ml·min−1·kg−1). In isolated cases, it was observed that VO2peak1 and VO2peak2 differed by more than 5% from VO2peak. Considering the above, it can be stated that among young people, there are no statistically significant differences between the values of VO2peak measured in the following tests. However, in individual cases, the need to verify the maximum oxygen uptake is stated, but performing a second verification test on a separate day has no additional benefit. Keywords: maximum oxygen uptake, VO2 plateau, physical fitness, cycle ergometer, verification phase, incremental test INTRODUCTION Maximum oxygen uptake (VO2max) is considered to be the gold standard in assessing oxygen capacity, as it reflects the efficiency of the respiratory and circulatory system and the efficiency of the muscular system in using oxygen whilst exercising (Bassett and Howley, 2000; Lucia et al., 2001; Martino et al., 2002; Joyner and Coyle, 2008). The incremental graded test (GXT) protocol is commonly used to assess the VO2max, which involves increasing the external load and continuing it until the subject reaches volitional exhaustion (Beltz et al., 2016). For years, the paradigm of the GXT was accepted and this form of VO2max testing was used. However, for several years, there has been a discussion of whether the GXT in each case allows for an accurate measurement of maximum oxygen uptake (Howley et al., 1995; Poole et al., 2008; Sánchez-Otero et al., 2014; Schaun, 2017). It was pointed out that subjects with no experience for maximal efforts and those with low Frontiers in Physiology | www.frontiersin.org 1 October 2021 | Volume 12 | Article 739745 Hebisz et al. Real Assessment of Maximum Oxygen Uptake motivation and low cardiorespiratory fitness may interrupt the test before reaching VO2max due to fatigue-related symptoms (Midgley et al., 2007b; Poole and Jones, 2017). Therefore, new criteria for the accuracy of VO2max measurements have been proposed (Howley et al., 1995; Sánchez-Otero et al., 2014; Beltz et al., 2016; Schaun, 2017). It has been suggested that achieving a VO2 plateau in the final phase of the GXT is proof that a VO2max measurement is accurate (Howley et al., 1995). However, it has been documented that in many subjects (both athletes and non-athletes), it is impossible to separate the plateau phase when reaching VO2max (Lucia et al., 2006; Schaun, 2017; Hebisz et al., 2018). The other criteria for accurately measuring VO2max–analysis of peak respiratory quotient, peak heart rate (HR), and post-workout lactate concentration–have also been widely discussed (Howley et al., 1995; Duncan et al., 1997; Beltz et al., 2016). Nonetheless, their high inter-subject variability may suggest that some subjects do not satisfy mentioned criterions even if their maximum effort is made, which lowers their value. It has been also demonstrated that the criterion of achieving a VO2 plateau in the final phase of the GXT frequently does not meet the criteria for HR and lactate concentration (Poole et al., 2008). These limitations reduce the certainty that subjects performing the GXT reach their “true” VO2max. Considering the doubts about the effectiveness of the above-mentioned criteria in verifying the accuracy of VO2max measurements, constant power verification tests were proposed (Midgley et al., 2006; Beltz et al., 2016; Poole and Jones, 2017; Schaun, 2017; Possamai et al., 2020). The idea is simply to provoke the VO2 plateau through constant-load effort performed with intensities ranging from submaximal to supramaximal effort (Barker et al., 2011; Nolan et al., 2014; Poole and Jones, 2017; Astorino and DeRevere, 2018). Usually, the verification tests are performed approximately 5–15 min after the incremental test (Schaun, 2017) and last several minutes (Barker et al., 2011; Nolan et al., 2014; Beltz et al., 2016; Schaun, 2017; Astorino and DeRevere, 2018). On the other hand, Possamai et al. (2020) suggests that the test to verify the VO2max obtained in the GXT should be performed on a different day, assuming that the subject’s exercise tolerance/capacity is higher then and that the peak oxygen uptake (VO2peak) measured in a verification test on another day are not lower than that from a verification test performed several minutes after the GXT. However, in both verification tests they used a power output level of 100% of maximal power–as measured in a previous incremental test–which could have contributed to similar values of oxygen uptake being recorded in the tests. Moreover, their results showed that the VO2peak achieved in the verification test performed on a separate day were closer to the VO2peak of the GXT than that of a verification test done several minutes after the GXT. More recently, in order to verify the VO2peak from the GXT, researchers proposed performing the verification test with a power level exceeding the power output of the GXT, but mainly several minutes after the GXT (Barker et al., 2011; Nolan et al., 2014; Schaun, 2017; Astorino and DeRevere, 2018). It seems that it would be worth using a higher load in the verification test performed on a separate day, as exercise tolerance is higher then. The aim of this study was to compare the values of VO2peak obtained from the incremental test and from two verification tests completed with a power output of 110% of the peak power output reached in a previous incremental test [the first one was performed 15 min after the progressive test (Tver-1), whilst the second one was performed on a separate day (Tver-2)]. It was hypothesized that in individual cases, the verification test performed on a separate day may allow for higher VO2peak values than the incremental test and the verification test performed several minutes after the incremental test. MATERIALS AND METHODS The study involved 23 participants: recreationally active individuals (n = 13, including 7 women and 6 men) and athletes (cyclists) (n = 10, including 4 women and 6 men). Each participant had been active recreationally or practicing sport (cyclists) for at least 3 years. The two groups, the recreationally active people and the athletes, were similar in regard to their anthropometric characteristics, whereas the parameters for physical capacity–VO2peak (p < 0.000) and power value (Pmax) (p < 0.000) differed significantly (Table 1). The study design was approved by the institutional review board and was conducted in accordance with the ethical standards established by the Declaration of Helsinki. Written informed consent was obtained from all participants after the study details, procedures, benefits, and risks were explained. Exercise Tests The study consisted of three exercise tests (Figure 1). On the first day of the study, each participant performed an incremental graded test (GXT) and a verification test (Tver-1). After a 48- h break, an additional verification test (Tver-2) was performed, which was only preceded by a warm-up. The tests (GXT and Tver-1) and Tver-2 were performed at a similar time of day (±30 min). All the tests were carried out using a Lode Excalibur Sport electronically braked cycloergometer (Lode BV, Groningen, Netherlands). The tests were performed in controlled laboratory conditions at an exercise laboratory (PN-EN ISO 9001:2001 certified). One week prior to the incremental graded test, the participants were familiarized with the protocol of the test. Incremental Exercise Test With Verification Test Performed on the Same Day The VO2peak was determined using a continuous GXT, with a self-selected pedal rate no lower than 60 rev/min. The test started with a 40-W or 50-W load (for women and men, respectively), and it was increased by 40 W or 50 W (for women and men, respectively) every 3 min until volitional exhaustion. Heart rate was recorded with a V800 cardiofrequencimeter (Polar, Oy, Finland). The respiratory parameters were measured breath-by- breath (Quark, COSMED, Milan, Italy) and averaged over 30-s intervals. The data recording began 2 min before GXT and ended Frontiers in Physiology | www.frontiersin.org 2 October 2021 | Volume 12 | Article 739745 Hebisz et al. Real Assessment of Maximum Oxygen Uptake TABLE 1 | Basic anthropological and physiological parameters characterizing the subjects. All (n = 23) Recreational active (n = 13) Athletes (n = 10) Females (n = 11) Males (n = 12) Age (years) 22.00 ± 3.79 21.23 ± 1.01 23.00 ± 5.64 21.64 ± 3.67 22.33 ± 4.03 Body height (m) 1.74 ± 0.10 1.76 ± 0.11 1.72 ± 0.08 1.67 ± 0.06 1.82 ± 0.07* Body mass (kg) 68.50 ± 9.96 70.64 ± 11.38 65.73 ± 7.39 61.75 ± 6.92 74.69 ± 8.22* VO2peak1 (ml·kg−1·min−1) 52.00 ± 13.31 42.62 ± 6.10 64.18 ± 9.58* 45.46 ± 8.44 57.98 ± 14.42* Pmax (W) 288.91 ± 77.71 244.23 ± 56.26 347.00 ± 62.51* 230.64 ± 49.31 342.33 ± 57.94* VO2peak1, the peak oxygen uptake in an incremental test; Pmax, the maximum aerobic power measured during the progressive test; data are presented as mean ± standard deviation. *p < 0.05 for the difference between groups. FIGURE 1 | Scheme of visit in laboratory. 5 min after the verification test (Tver-1). The device was calibrated with an atmospheric air and gas mixture: 5% CO2, 16% O2, and 79% N2. Oxygen uptake (VO2), exhaled carbon dioxide (VCO2), and minute pulmonary ventilation (VE) were measured. The highest VO2 recorded in the GXT was taken as the VO2peak, whilst the highest VO2 recorded in the Tver-1 was taken as the VO2peak1. Based on the respiratory data records from the GXT, the first ventilatory threshold (VT1) was determined at the point preceding the first non-linear increase in VE·VO2−1 without a concomitant increase in VE·VCO2−1 equivalent; the second ventilatory threshold (VT2) was at the point preceding the second non-linear increase in VE·VO2−1 accompanied by an increase of VE·VCO2−1 equivalent, according to the methodology described by Davis et al. (1980) and Beaver et al. (1986). The cycloergometer was controlled by a computer, which recorded instantaneous power and exercise time. The maximum aerobic Pmax was obtained by subtracting 0.22 W for women and 0.28 W for men for each missing second of the last performed load. After the end of the test, the subject rested for 15 min, with an active rest on a 20-W cycloergometer. Next, a 3-min, square- wave Tver-1 was performed with an intensity of 110% of Pmax with regards to Schaun (2017). Verification Test Performed on a Different Day The test was preceded by a 15-min warm-up consisting of 5 min of exercise at an intensity corresponding to the power achieved with the VT1, then 10 min at a power corresponding to half the distance between the VT1 and the VT2. The warm-up was followed by a 10-min passive break. Tver-2 was 3 min long and was performed at an intensity of 110% of Pmax, as determined by the results of the incremental graded test performed 2 days prior. The recording of respiratory parameters started 1 min before the verification test and ended 5 min after it was completed. The values averaged every 30 s were used in data analysis. The highest recorded oxygen uptake (from the averaging of 30-s intervals) was taken as the VO2peak in the verification test performed on a separate day (VO2peak2). Statistical Analysis The differences (expressed in %) between VO2peak and VO2peak1, as well as between VO2peak were calculated for each participant. The tolerance of measurement error was at 5% (Midgley et al., 2007a; Romero-Fallas et al., 2012; Hall-Lopez et al., 2015). Data normality was assessed through the Kolmogorov–Smirnov test with Lilliefors significance correction. Bland–Altman analysis was performed to determine the size of the difference shift between VO2peak and VO2peak1, as well as between VO2peak and VO2peak2. Pearson’s correlation and linear regression were performed for comparing the results of GXT and Tver-1 or Tver-2. STATISTICA 13.1 software (StatSoft Inc., Tulsa, OK, United States) was used for further statistical processing of the data. All data are reported as mean ± SD. Analysis of variance with repeated measurements and the Scheffe post hoc test were used to determine whether factors such as sex, athletic ability, or subsequent tests affected VO2peak. The results were considered statistically significant at an alpha level of p < 0.05. RESULTS The GXT and Tver-2 were performed by 23 participants, while Tver-1 was performed by 21 participants (2 participants refused to perform this test because of perceived fatigue). The analysis of the main effects showed statistically significant differences in oxygen uptake for sex (F = 25.02; p = 0.000; η2 = 0.60) and physical activity level (F = 74.24; p = 0.000; η2 = 0.81). There were no statistically significant differences for repeated measurements (F = 2.28, p = 0.118, η2 = 0.12) or mixed effects for repeated measurements and sex (F = 0.68, p = 0.516, η2 = 0.04), nor for mixed effects for repeated measurements and physical activity level (F = 0.20, p = 0.820, η2 = 0.01) (Table 2). The individual analysis showed that 2 subjects in the Tver-1 and 7 subjects in the Tver-2 had a higher VO2peak by 5% than in the GXT (Table 3). Bland–Altman analysis (Figure 2) revealed a small bias of the VO2peak1 results compared to the VO2peak Frontiers in Physiology | www.frontiersin.org 3 October 2021 | Volume 12 | Article 739745 Hebisz et al. Real Assessment of Maximum Oxygen Uptake TABLE 2 | Peak oxygen uptake value in the incremental test and in the verification tests in the entire group of subjects, as well as after dividing the group according to sex and physical activity level. Peak oxygen uptake (VO2peak) [ml·min−1·kg−1] Progressive test (n = 23) Verification test 1 (n = 21) Verification test 2 (n = 23) Whole group (n = 23ˆ) 51.99 ± 13.31 51.03 ± 13.73 52.75 ± 13.37 Females (n = 11ˆ) 45.46 ± 8.44 44.09 ± 7.79 45.08 ± 7.67 Males (n = 12ˆ) 57.98 ± 14.42 57.35 ± 15.19 59.78 ± 13.85 Athletes (n = 10ˆ) 64.18 ± 9.58 64.94 ± 10.30 64.52 ± 10.58 Recreationally active (n = 13) 42.62 ± 6.10 42.48 ± 6.66 43.70 ± 6.32 Data are presented as mean ± standard deviation. ∧-21 participants completed the verification test 1, two athletes (one woman and one man) refused to participate in this test. TABLE 3 | The number of people who achieved a lower, higher or equal peak oxygen uptake in the verification tests compared to the peak oxygen uptake achieved in the progressive test. Whole I division II division Group (n = 23) Females (n = 11) Males (n = 12) Athletes (n = 10) Recreationally active (n = 13) VO2peak < VO2peak1 2 1 1 0 2 VO2peak > VO2peak1 4 2 2 2 2 VO2peak = VO2peak1 15 7 8 6 9 VO2peak < VO2peak2 7 2 5 2 5 VO2peak > VO2peak2 3 2 1 2 1 VO2peak = VO2peak2 13 7 6 6 7 The analysis was performed taking into account the division of the study group according to sex (I) and physical activity level (II). VO2peak, the peak oxygen uptake in the progressive test; VO2peak1, the peak oxygen uptake in the verification test 1; VO2peak2, the peak oxygen uptake in the verification test 2; <, less than. . .; >, greater than. . .; =, equal. . .. (0.4 ml·min−1·kg−1) and VO2peak2 results compared to the VO2peak (−0.76 ml·min−1·kg−1). The raw test records that were performed in the studies described in this work are posted in the repository at https://repod.icm.edu.pl/dataset.xhtml?persistentId=doi: 10.18150/HGE2PK. DISCUSSION In order to assess the VO2peak, researchers traditionally use the GXT test until exhaustion. Since the primary criterion of VO2peak attainment–a VO2 plateau in exhaustion–is not always reached during the GXT, some researchers have postulated using subsequent verification tests (Niemelä et al., 1980; Midgley et al., 2007b; Poole and Jones, 2017). However, in the available literature, there are contradictory suggestions as to the need for verification tests. There are opinions that question the validity of performing tests to verify the VO2max obtained from a progressive test, due to the minimal individual differences between the results of progressive and verifying tests (Rossiter et al., 2006; Murias et al., 2018; Brito et al., 2019). Similar results, confirmed by Bland–Altman analysis, were presented by McGawley (2017) when he compared the VO2peak measured in the progressive test with the VO2peak measured in a 4-min time trial run, performed on a separate day. The data presented herein show no differences in mean VO2peak in the GXT and Tver-1 versus Tver-2 (Table 2). Bland–Altman analysis showed a small bias of VO2peak1 compared to VO2peak, as well as of VO2peak2 compared to VO2peak (Figure 2). However, several subjects (both recreationally active people and athletes) achieved higher VO2peak1 or VO2peak2 values than VO2peak. Therefore, we support the postulate of Poole and Jones (2017) about the need to perform tests verifying the values of VO2peak measured in progressive tests. In most available literature, VO2max verifier tests are performed on the same day as the progressive test (Midgley et al., 2007b; Astorino, 2009; Kirkeberg et al., 2011; Dalleck et al., 2012; Poole and Jones, 2017; Adam et al., 2018). The factor differentiating used procedures is the time between the tests. Intervals of between 5 and 15 min have commonly been used (Midgley et al., 2007b; Poole and Jones, 2017; Adam et al., 2018), although intervals ranging from 1 to 3 min (Kirkeberg et al., 2011) to even 60–90 min (Astorino, 2009; Dalleck et al., 2012; Nolan et al., 2014) have been used for verification tests performed on the same day. Nolan et al. (2014) reported no differences in VO2peak between verification tests performed with 105% Pmax after 20- and 60-min recovery periods. Thus, 20 min of recovery may be sufficient for physically active subjects. As noted by Scharhag-Rosenberger et al. (2011), comparable VO2peak values after an incremental test and verification test followed by a 10-min break indicates that even shorter breaks can be used. The results reported by Kirkeberg et al. (2011) show that even short recovery periods of 1–3 min turned out to be sufficient among physically active people. Regardless of the intervals used between the tests, it seems that the effectiveness of the VO2max Frontiers in Physiology | www.frontiersin.org 4 October 2021 | Volume 12 | Article 739745 Hebisz et al. Real Assessment of Maximum Oxygen Uptake FIGURE 2 | Bland-Altman plot showing: (A) Individual differences between the VO2peak values attained in the incremental and VO2peak1 from Tver-1 (B) individual differences between the VO2peak values attained in the incremental and VO2peak2 from Tver-2. Solid line show bias and dashed lines represent a 1.96 SD (standard deviation) for difference between peak oxygen uptakes. (C) Pearson correlation between VO2peak and VO2peak1. (D) Pearson correlation between VO2peak and VO2peak2. In (C,D) the dashed lines indicate the 5% threshold difference from VO2peak. verification tests we quote above was similar. Therefore, it could be concluded that VO2peak in a verification test seems not to be affected by the exhaustion caused by the incremental test. Schaun (2017) also stated that the time elapsed between an incremental test and a verification test is not a key aspect to achieving the verification criterion. Attempts were also made to perform tests to verify VO2max on a different day than the progressive test (Scharhag-Rosenberger et al., 2011; Possamai et al., 2020; Sawyer et al., 2020). Possamai et al. (2020) found that during the verification test performed on a separate day, the exercise capacity is greater than during the verification test performed several minutes after the progressive test. Such a conclusion was formulated on the basis of a longer effort time in a verification test performed on a separate day, compared to a test performed several minutes after the progressive test. However, the greater exercise capacity described by Possamai et al. (2020) did not affect the VO2peak values, which were similar in individual tests. Scharhag-Rosenberger et al. (2011) also performed verification tests on a separate day. Based on the results of these studies, it was also considered that VO2peak in the verification test performed on a separate day does not differ significantly from VO2peak from the verification test performed several minutes after the progressive test. However, in the studies described above, verification tests were preceded by a short warm-up. Another factor that may influence VO2peak values is the type of warm-up used before the verification test carried out on a separate day. Possamai et al. (2020) preceded the verification test with a warm-up of 6 min and measured the power at the lactate threshold, defined as the first sharp increase in lactate concentration in a progressive test. An even shorter warm-up, lasting 5 min, was used by Scharhag-Rosenberger et al. (2011) and Sawyer et al. (2020). In Scharhag-Rosenberger et al. (2011) study the warm-up was done at a speed higher than the lactate threshold speed. Also, a warm-up in the research of Sawyer et al. (2020) consisted of 5 min of exercise, however, at an intensity of 50 W (men) or 30 W (women) which is lower than those proposed by Scharhag-Rosenberger et al. (2011). Bishop (2003) stated that the optimal warm-up duration before intensive efforts with an average duration should be at least 10 min, which allows the subject to reach steady-state VO2. In our own studies, the warm-up lasted 15 min, including 5 min of VT1 effort and 10 min of effort measured halfway between VT1 and VT2. We concluded that such a warm-up, performed before the verification test on a separate day, may allow to obtain higher VO2peak values than in the above-cited works Frontiers in Physiology | www.frontiersin.org 5 October 2021 | Volume 12 | Article 739745 Hebisz et al. Real Assessment of Maximum Oxygen Uptake (Scharhag-Rosenberger et al., 2011; Possamai et al., 2020; Sawyer et al., 2020). This assumption was supported by the results of our own previous studies (Hebisz et al., 2017), in which we also used a long warm-up time. We then found that it is possible to achieve a higher VO2peak value even during a series of four short sprints (30-s each) in comparison to the progressive test. However, analysis of variance showed no statistically significant differences between VO2peak, VO2peak1 and VO2peak2 in the entire group of subjects. Moreover, Bland-Altman analysis revealed a bias of VO2peak1 compared to VO2peak, as well as of VO2peak2 compared to VO2peak was neglectable. Therefore, the research procedure we used produced similar statistical effects as the research results described by Scharhag-Rosenberger et al. (2011). The possibility that the training level meets the VO2peak verification criterion was also analyzed in this study. The above- cited studies (Scharhag-Rosenberger et al., 2011; Nolan et al., 2014; Possamai et al., 2020) involved physically active people, but they were not professional athletes. Only in a review, Costa et al. (2021) stated that concordance between VO2peak level from GXT and verification tests is not affected by the cardiorespiratory level of participants. In the present study, we compared athletes with recreationally active subjects. Analysis of variance showed no mixed effects on repeated measurements and level of physical activity. Therefore, the results of the studies described in this work support Costa et al.’s (2021) suggestion that the effects of VO2max verification are not related to the level of efficiency (cardio-respiratory level). LIMITATIONS In our research, we compared VO2peak values achieved by cyclists and amateurs. In this way, our research complements the knowledge about the effects of verification tests, because so far there has been little information in the literature about the results of verification tests performed by athletes. On the other hand, performing analyses on a group of respondents consisting of cyclists and amateurs is a factor limiting the certainty of our conclusions, because athletes and amateurs are characterized by a different level of physical performance (muscular power, VO2peak, VO2max). Different levels of exercise tolerance in our studies may affect the high variability of the obtained results and thus may affect the results of statistical analyses. The second factor limiting the certainty of our conclusions is the way the subjects are prepared for the verification test performed on a separate day. After warming up, and before the verification test, we used a passive break of 15 min. We decided that this way of preparing for the test is good, because in the literature there are suggestions that the type of break (active or passive) before a few minutes and intense efforts does not affect exercise capacity (McAinch et al., 2004; Fennell and Hopker, 2021). In addition, vasodilation of muscle vessels and the activity of histamine H1 and H2 receptors is high even for 90 min after exercise (Luttrell and Halliwill, 2017). However, the use of a passive break before the verification test performed on a separate day may have resulted in high variability of VO2peak–VO2peak2. CONCLUSION Among young people, there were no statistically significant differences between VO2peak measured in the progressive test and VO2peak measured in the verification tests (performed 15 min after the progressive test and performed on a separate day), in general. There are also no differences in peak oxygen consumption between the progressive test and the verification tests after dividing the group into athletes and recreationally active individuals in any of the above-mentioned groups. In individual cases, the need to verify the maximum oxygen uptake is stated, but performing a second verification test on a separate day does not bring additional benefits. DATA AVAILABILITY STATEMENT The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://repod.icm.edu. pl/dataset.xhtml?persistentId=doi:10.18150/HGE2PK. ETHICS STATEMENT The studies involving human participants were reviewed and approved by the Senate Research Ethics Committee at University School of Physical Education in Wrocław. The patients/participants provided their written informed consent to participate in this study. AUTHOR CONTRIBUTIONS PH contributed to the study design and data collection, and drafted the manuscript. AJ contributed to the data collection and made the critical revisions to the manuscript. RH contributed to the study design and data analysis, and drafted the manuscript. All authors discussed the results, commented and edited the manuscript at all stages, approved the final version and agreed to be accountable for all aspects of the work. FUNDING This work was supported by the University School of Physical Education in Wrocław under grant number PN/BK/2020/07. Frontiers in Physiology | www.frontiersin.org 6 October 2021 | Volume 12 | Article 739745 Hebisz et al. Real Assessment of Maximum Oxygen Uptake REFERENCES Adam, J., Causer, A. J., Shute, J. K., Cummings, M. H., Shepherd, A. I., Bright, V., et al. (2018). Cardiopulmonary exercise testing with supramaximal verification produces a safe and valid assessment of VO2max in people with cystic fibrosis: a retrospective analysis. J. Appl. Physiol. 125, 1277–1283. doi: 10.1152/ japplphysiol.00454.2018 Astorino, T. A. (2009). Alterations in VO2max and the VO2 plateau with manipulation of sampling interval. Clin. Physiol. Funct. Imaging. 29, 60–7. doi: 10.1111/j.1475-097X.2008.00835.x Astorino, T. A., and DeRevere, J. (2018). Efficacy of constant load verification testing to confirm VO2 max attainment. Clin. Physiol. Funct. Imaging 38, 703–709. doi: 10.1111/cpf.12474 Barker, A. R., Williams, C. A., Jones, A. M., and Armstrong, N. (2011). Establishing maximal oxygen uptake in young people during a ramp cycle test to exhaustion. Br. J. Sports Med. 45, 498–503. doi: 10.1136/bjsm.2009.063180 Bassett, D. R., and Howley, E. T. (2000). Limiting factors for maximum oxygen uptake and determinants of endurance performance. Med. Sci. Sports Exerc. 32, 70–84. Beaver, W. L., Wasserman, K., and Whipp, B. J. (1986). A new method for detecting anaerobic threshold by gas exchange. J. Appl. Physiol. 60, 2020–2027. doi: 10. 1152/jappl.1986.60.6.2020 Beltz, N. M., Gibson, A. L., Janot, J. M., Kravitz, L., Mermier, C. M., and Dalleck, L. C. (2016). Graded exercise testing protocols for the determination of VO2max: historical perspectives, progress, and future considerations. J. Sports Med. 2016:3968393. doi: 10.1155/2016/3968393 Bishop, D. (2003). Warm up II: performance changes following active warm up and how to structure the warm up. Sports Med. 33, 483–498. doi: 10.2165/00007256- 200333070-00002 Brito, J., Fernandes, L., Rebelo, A., Krustrup, P., Silva, G., and Oliveira, J. (2019). Heart rate kinetics response of pre-pubertal children during the Yo- Yo intermittent endurance test—level 1. Sports 7:65. doi: 10.3390/sports70 30065 Costa, V. A. B., Midgley, A. W., Carroll, S., Astorino, T. A., de Paula, T., Farinatti, P., et al. (2021). Is a verification phase useful for confirming maximal oxygen uptake in apparently healthy adults? A systematic review and meta-analysis. PLoS One 16:e0247057. doi: 10.1371/journal.pone.0247057 Dalleck, L. C., Astorino, T. A., Erickson, R. M., McCarthy, C. M., Beadell, A. A., and Botten, B. H. (2012). Suitability of verification testing to confirm attainment of VO2max in middle-aged and older adults. Res. Sports Med. 20, 118–128. doi: 10.1080/15438627.2012.660825 Davis, J. A., Whipp, B. J., and Wasserman, K. (1980). The relation of ventilation to metabolic rate during moderate exercise in man. Eur. J. Appl. Physiol. Occup. Physiol. 44, 97–108. doi: 10.1007/BF00421087 Duncan, G. E., Howley, E. T., and Johnson, B. N. (1997). Applicability of VO2max criteria: discontinuous versus continuous protocols. Med. Sci. Sports Exerc. 29, 273–278. doi: 10.1097/00005768-199702000-00017 Fennell, C. R. J., and Hopker, J. G. (2021). The acute physiological and perceptual effects of recovery interval intensity during cycling-based high-intensity interval training. Eur. J. Appl. Physiol. 121, 425–434. doi: 10.1007/s00421-020- 04535-x Hall-Lopez, J. A., Ochoa-Martinez, P. Y., Moncada-Jimenez, J., Mendez, M. A. O., Garcia, I. M., and Garcia, M. A. M. (2015). Reliability of the maximal oxygen uptake following two consecutive trials by indirect calorimetry. Nutr. Hosp. 31, 1726–1732. doi: 10.3305/nh.2015.31.4.8487 Hebisz, P., Hebisz, R., Borkowski, J., and Zato´n, M. (2018). Time of VO2max plateau and postexercise oxygen consumption during incremental exercise testing in young mountain bike and road cyclists. Physiol. Res. 67, 711–719. Hebisz, R., Hebisz, P., Zato´n, M., and Michalik, K. (2017). Peak oxygen uptake in a sprint interval testing protocol vs. maximal oxygen uptake in an incremental testing protocol and their relationship with cross-country mountain biking performance. Appl. Physiol. Nutr. Metab. 42, 371–376. doi: 10.1139/apnm- 2016-0362 Howley, E. T., Bassett, D. R., and Welch, H. G. (1995). Criteria for maximal oxygen uptake: review and commentary. Med. Sci. Sports Exerc. 27, 1292–1301. Joyner, M. J., and Coyle, E. F. (2008). Endurance exercise performance: the physiology of champions. J. Physiol. 586, 35–44. Kirkeberg, J. M., Dalleck, L. C., Kamphoff, C. S., and Pettitt, R. W. (2011). Validity of 3 protocols for verifying VO2max. Int. J. Sports Med. 32, 266–270. doi: 10.1055/s-0030-1269914 Lucia, A., Hoyos, J., and Chicharro, J. L. (2001). Physiology of professional road cycling. Sports Med. 31, 325–337. Lucia, A., Rabadan, M., Hoyos, J., Hernandez-Capilla, M., Perez, M., San Juan, A. F., et al. (2006). Frequency of the VO2max plateau phenomenon in world- class cyclists. Int. J. Sports Med. 27, 984–992. doi: 10.1055/s-2006-923833 Luttrell, M. J., and Halliwill, J. R. (2017). The intriguing role of histamine in exercise responses. Exerc. Sport. Sci. Rev. 45, 16–23. doi: 10.1249/JES.0000000000000093 Martino, M., Gledhill, N., and Jamnik, V. (2002). High VO2max with no history of training is primarily due to high blood volume. Med. Sci. Sports Exerc. 34, 966–971. McAinch, A. J., Febbraio, M. A., Parkin, J. M., Zhao, S., Tangalakis, K., Stojanovska, L., et al. (2004). Effect of active versus passive recovery on metabolism and performance during subsequent exercise. Int. J. Sport Nutr. Exerc. Metab. 14, 185–196. doi: 10.1123/ijsnem.14.2.185 McGawley, K. (2017). The reliability and validity of a four-minute running time- trial in assessing VO2max and performance. Front. Physiol. 8:270. doi: 10.3389/ fphys.2017.00270 Midgley, A. W., McNaughton, L. R., and Carroll, S. (2006). Verification phase as a useful tool in the determination of the maximal oxygen uptake of distance runners. Appl. Physiol. Nutr. Metab. 31, 541–548. Midgley, A. W., McNaughton, L. R., and Carroll, S. (2007a). Effect of VO2 time- averaging interval on reproducibility of VO2max in healthy athletic subjects. Clin. Physiol. Funct. Imaging 27, 122–125. doi: 10.1111/j.1475-097X.2007. 00725.x Midgley, A. W., McNaughton, L. R., Polman, R., and Marchant, D. (2007b). Criteria for determination of maximal oxygen uptake: a brief critique and recommendations for future research. Sports Med. 37, 1019–1028. doi: 10.2165/ 00007256-200737120-00002 Murias, J. M., Pogliaghi, S., and Paterson, D. H. (2018). Measurement of a true VO2max during a ramp incremental test is not confirmed by a verification phase. Front. Physiol. 9:143. doi: 10.3389/fphys.2018.00143 Niemelä, K., Palatsi, I., Linnaluoto, M., and Takkunen, J. (1980). Criteria for maximum oxygen uptake in progressive bicycle tests. Eur. J. Appl. Physiol. Occup. Physiol. 44, 51–59. doi: 10.1007/BF00421763 Nolan, P. B., Beaven, M. L., and Dalleck, L. (2014). Comparison of intensities and rest periods for VO2max verification testing procedures. Int. J. Sports Med. 35, 1024–1029. doi: 10.1055/s-0034-1367065 Poole, D. C., and Jones, A. M. (2017). Measurement of the maximum oxygen uptake ˙VO2max: ˙VO2peak is no longer acceptable. J. Appl. Physiol. 122, 997– 1002. doi: 10.1152/japplphysiol.01063.2016 Poole, D. C., Wilkerson, D. P., and Jones, A. M. (2008). Validity of criteria for establishing maximal O2 uptake during ramp exercise test. Eur. J. Appl. Physiol. 102, 403–410. doi: 10.1007/s00421-007-0596-3 Possamai, L. T., Campos, F. S., Salvador, P. C., de Aguiar, R. A., Guglielmo, L. G., de Lucas, R. D., et al. (2020). Similar maximal oxygen uptake assessment from a step cycling incremental test and verification tests on the same or different day. Appl. Physiol. Nutr. Metab. 45, 357–361. doi: 10.1139/apnm-2019-0405 Romero-Fallas, O., Soto-Arias, M., and Moncada-Jimenze, J. (2012). Maximal oxygen consumption in college students is reliable following four consecutive trials. J. Phys. Edu. Sport 12, 476–481. doi: 10.7522/jpes.2012.04069 Rossiter, H. B., Kowalchuk, J. M., and Whipp, B. J. (2006). A test to establish maximum O2 uptake despite no plateau in the O2 uptake response to ramp incremental exercise. J. Appl. Physiol. 100, 764–770. doi: 10.1152/japplphysiol. 00932.2005 Sánchez-Otero, T., Iglesias-Soler, E., Boullosa, D. A., and Tuimil, J. L. (2014). Verification criteria for the determination of VO2max in the field. J. Strength Cond. Res. 28:35443551. doi: 10.1519/JSC.0000000000000576 Sawyer, B. J., McMahon, N., Thornhill, K. L., Baughman, B. R., Mahoney, J. M., Pattison, K. L., et al. (2020). Supra-versus submaximal cycle ergometer verification of VO2max in males and females. Sports 8:163. doi: 10.3390/ sports8120163 Scharhag-Rosenberger, F., Carlsohn, A., Cassel, M., Mayer, F., and Scharhag, J. (2011). How to test maximal oxygen uptake: a study on timing and testing procedure of a supramaximal verification test. Appl. Physiol. Nutr. Metab. 36, 153–160. doi: 10.1139/H10-099 Frontiers in Physiology | www.frontiersin.org 7 October 2021 | Volume 12 | Article 739745 Hebisz et al. Real Assessment of Maximum Oxygen Uptake Schaun, G. Z. (2017). The maximal oxygen uptake verification phase: a light at the end of the tunnel? Sports Med. Open 3:44. doi: 10.1186/s40798-017-0112-1 Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher’s Note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Copyright © 2021 Hebisz, Jastrz˛ebska and Hebisz. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Frontiers in Physiology | www.frontiersin.org 8 October 2021 | Volume 12 | Article 739745

Real Assessment of Maximum Oxygen Uptake as a Verification After an Incremental Test Versus Without a Test.

10-28-2021

Hebisz, Paulina,Jastrzębska, Agnieszka Danuta,Hebisz, Rafał

eng

PMC9312819

Citation: Guerra, M.; Garcia, D.; Kazemitabar, M.; Lindskär, E.; Schütz, E.; Berglind, D. Effects of a 10-Week Physical Activity Intervention on Asylum Seekers’ Physiological Health. Brain Sci. 2022, 12, 822. https://doi.org/10.3390/ brainsci12070822 Academic Editor: Fiorenzo Moscatelli Received: 17 May 2022 Accepted: 20 June 2022 Published: 24 June 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). brain sciences Article Effects of a 10-Week Physical Activity Intervention on Asylum Seekers’ Physiological Health Matheus Guerra 1,2,*, Danilo Garcia 2,3,4,5,6,* , Maryam Kazemitabar 2,7, Erik Lindskär 2, Erica Schütz 2,8 and Daniel Berglind 1,9 1 Department of Global Health, Karolinska Institute, 171 77 Stockholm, Sweden; [email protected] 2 Promotion of Health and Innovation (PHI) Lab, International Network for Well-Being, New Haven, CT 06510, USA; [email protected] (M.K.); [email protected] (E.L.); [email protected] (E.S.) 3 Department of Behavioral Sciences and Learning, Linköping University, 581 83 Linköping, Sweden 4 Centre for Ethics, Law and Mental Health (CELAM), University of Gothenburg, 405 30 Gothenburg, Sweden 5 Department of Psychology, Lund University, 221 00 Lund, Sweden 6 Department of Psychology, University of Gothenburg, 405 30 Gothenburg, Sweden 7 School of Public Health, Yale University, New Haven, CT 06510, USA 8 Department of Psychology, Linnaeus University, 450 85 Kalmar, Sweden 9 Center for Epidemiology and Community Medicine (CES), Region Stockholm, 104 31 Stockholm, Sweden * Correspondence: [email protected] (M.G.); [email protected] (D.G.) Abstract: Introduction: The rise in armed conflicts has contributed to an increase in the number of asylum seekers. Prolonged asylum processes may negatively affect asylum seekers’ health and lead to inactivity. Studies show that physical activity interventions are associated with improvements in health outcomes. However, there are a limited number of studies investigating the associations of physical activity on asylum seekers’ health. Methods: Participants (263 males and 204 females), mostly from Syria, were assessed before and after a 10-week intervention for VO2 max, body mass index (BMI), skeletal muscle mass (SMM), body fat, and visceral fat. Linear mixed models were used to test differences within groups, and a linear regression model analysis was performed to test whether physiological variables predicted adherence. Results: Participants’ VO2 max increased: males by 2.96 mL/min/kg and females 2.57 mL/min/kg. Increased SMM percentages were seen in both genders: females by 0.38% and males 0.23%. Visceral fat area decreased: males by 0.73 cm2 and females 5.44 cm2. Conclusions: Participants showed significant increases in VO2 max and SMM and decreased visceral fat. This study provides an insight into asylum seekers’ health and serves as a starting point to new interventions in which physical activity is used as a tool to promote and improve vulnerable populations’ health. Keywords: physical activity; intervention; asylum seekers; physiological health; VO2 max 1. Introduction One of the biggest challenges in the 21st century is the increasing number of armed conflicts in the world [1], which has contributed to an increased number of displaced populations and asylum seekers. In 2015, about 163,000 individuals sought asylum in Sweden, compared to an average of 28,575 per year during 2000–2010 [2]. In Blekinge, Sweden, the number of asylum seekers registered in the county’s five municipalities in 2015 amounted to a total of 4069—which represents a significant increase when compared to the previous years (2014: 2126; 2013: 1280; and 2012: 1032) [2]. Lengthy application processes, which in some cases can take several years to complete, may adversely affect the physical and mental health of asylum seekers [3]. Additionally, other difficult and unusual situations, combined with limited language skills, may prevent asylum seekers from receiving the information required to navigate their newly adopted society [4]. Brain Sci. 2022, 12, 822. https://doi.org/10.3390/brainsci12070822 https://www.mdpi.com/journal/brainsci Brain Sci. 2022, 12, 822 2 of 15 Even though asylum seekers are a very diverse group regarding health status, research shows that they generally have more health problems, such as lower physical and mental health, than native populations [5–7]. For example, a previous study with an Iraq-born population in Sweden found a higher level of physical inactivity and a higher risk of the development of non-communicable diseases such as diabetes type 2 when compared with native Swedes [8]. Other studies have also found that Middle Eastern immigrants in Sweden have a four-fold higher risk of developing diabetes when compared with native Swedes [9], which can be partly attributed to a high prevalence of obesity in non-European immigrants [10]. Consequentially, there is a need to increase and promote physical activity among foreign-born populations in Sweden. It is a well-known fact that low cardiorespiratory fitness is one of the leading risk factors causing non-communicable diseases and overall mortality, while physical activity contributes to physical and mental well-being and increases the possibilities for creating social networks as well as being part of society [11]. For example, higher cardiorespiratory fitness is prospectively associated with lower all-cause mortality, in which a 1.0 mL/min/kg higher maximal oxygen consumption (VO2 max) is associated with a 9% relative risk reduction in all-cause mortality [12]. Moreover, the loss of skeletal muscle mass, together with the excessive gain of fat mass, is associated with several metabolic disorders such as metabolic syndrome, diabetes, and cardiovascular diseases [13]. However, skeletal muscle loss is, to a large extent, reversible through the adoption of resistance training and diet [14]. According to research conducted by Roth et al. [15], physical activity and resistance training are effective for the prevention of the loss of skeletal muscle mass which, in turn, improves quality of life. Last but not the least, excess visceral fat is closely associated with the development of many non-communicable diseases such as hypertension, type 2 diabetes, and hyper- lipidemia and constitutes an independent risk factor for developing heart disease [16]. Diet and regular exercise have been shown to be effective in reducing visceral fat, and the association between physical exercise and the reduction in visceral fat volume has been well established [17,18]. Despite the vast amount of evidence linking physical activity to improved quality of life and a reduced outcome of non-communicable diseases [19–21], there are few stud- ies exploring the associations of physical activity interventions with asylums seekers’ health [22,23]. One such study [24] investigated the impact of an eight-week training pro- gram in 45 young males (mean age = 25.6, SD = 7.1) living in a refugee camp in Greece. The participants were invited to engage in physical activities three to five times per week for approximately one hour, focusing on a combination of weight and endurance training. The study found that higher participation rates were associated with fewer anxiety symptoms, higher health-related quality of life, higher self-perceived fitness, greater handgrip strength, and improved cardiovascular fitness. The project “Health for Everyone-Sport, Culture and Integration” was an initiative created by the Ronneby municipality in Blekinge, Sweden, in partnership with the Blekinge Sports Association. Within the project, asylum seekers were invited to engage in physical activity once a week during a 10-week period in groups of 20 to 30 individuals. In addition to this, participants were invited to a once-a-week class on health promotion in their native language and a visit to the Blekinge Museum in order to introduce them to Scandinavian history and Blekinge’s cultural heritage. The aim of this study was to evaluate whether there were any significant changes in physiological health among asylum seekers who participated in the “Health for Everyone” project and to investigate whether physiological health measurements at baseline predicted adherence to the intervention. We expected to see improvements in most of the physiolog- ical health measures and hypothesized that individuals who were more physically fit at baseline would have higher rates of attendance. Brain Sci. 2022, 12, 822 3 of 15 2. Materials and Methods 2.1. Participants and Procedure The project “Health for Everyone-Sport, Culture and Integration” was created by the Ronneby municipality and carried out in partnership with the Blekinge Sports Association. It started in the fall of 2016 and lasted for two years, with a total of 18 months of active operation. The recruitment was carried by Blekinge’s municipalities, being scheduled within the framework of the social orientation for asylum seekers from countries currently experiencing armed conflicts. In total, 467 individuals (263 males and 204 females) with a mean age of 35.9 years (SD = 11.9) were enrolled in the project. The participants engaged in a combination of resistance and aerobic training designed in a circuit format alternating different exercise stations. The participants were provided with transportation from their settlements in each of the municipalities to the training facility in Karlskrona (Blekinge Health Arena) for the once-a-week training session. At the beginning of the intervention, the participants received information (verbally and written) about the activities in Arabic, Somali, and Persian languages, with the assis- tance of community-appointed translators, and they were subsequently asked to participate in the evaluation carried out by the research group. The participants were informed that their data were confidential, and that the data would be used for scientific analysis and publication. All the participants in this study gave their consent to participate in writing. The participants underwent physiological tests consisting of a bioelectrical impedance measurement using an InBody 720 body composition analyzer (Biospace Co., Ltd., Seoul, Korea) to measure body weight, body mass index (BMI), skeletal muscle mass (SMM), body fat, visceral fat, and cardiorespiratory fitness (VO2 max) through a beep test both at the beginning of the study and at the end of the intervention. The participants were also asked to answer questions regarding their background (demographical data, age, and gender, etc.) and other self-reports of validated psychological measures. In total, the data collection took approximately 1.5 h and was performed at baseline (week 0) and at endpoint (week 10), leading to a total of 8 training sessions within the intervention. 2.2. Measures 2.2.1. Attendance Attendance was logged by the Blekinge Health Arena instructors on each scheduled once-a-week training day. A total attendance of eight times was the maximum attendance rate. Therefore, we divided the number of recorded attendances for each participant by eight, which gave us the attendance percentage for each participant in the project. 2.2.2. Cardiorespiratory Fitness The multi-stage fitness test, also known as the beep test, was used to estimate the participants’ cardiorespiratory fitness (i.e., VO2 max). The test has been widely used due to its simplicity in providing an accurate approximation of an individuals’ VO2 max [25]. The test requires participants to run 20 m back and forth across a marked track, keeping time with beeps. Every minute, the next level starts; the time between beeps gets shorter, which requires participants to run faster to keep up with the next level. If the participant fails to reach the relevant marker in time, a first warning will be given, with a second warning meaning the end of the test. The number of shuttles successfully completed is therefore registered, and the final score is given according to which level and the to- tal number of shuttles the participant was able to complete. The following formula is used to transform the beep test results to VO2 max: VO2 max = 3.46 × (Level + No. of Shuttles/(Level × 0.4325 + 7.0048)) + 12.2. 2.2.3. Body Weight and Body Mass Index (BMI) BMI is a statistical index calculated by a person’s weight divided by height in square meters or BMI = weight (kg)/height2 (m) [26]. The number obtained by the equation is the individual’s BMI, and it is used to define an individual as underweight, normal weight, Brain Sci. 2022, 12, 822 4 of 15 overweight, or obese. A higher BMI indicates a higher likelihood of obesity. A commonly used reference range for normal weight is between 18.5 and 24.9 kg/m2 [26]. The BMI calculation was performed using measures obtained from a direct segmental multi-frequency bioelectrical impedance analysis (DSM-BIA) with an InBody 720 body com- position analyzer. The DSM-BIA technique provides an accurate assessment of segmental and body composition [27,28]. 2.2.4. Skeletal Muscle Mass (SMM) In humans, skeletal muscle is a type of striated muscle tissue which is under voluntary control of the somatic nervous system. It constitutes approximately 40% of the total body mass [29] and can be influenced by a person’s nutritional status, hormonal balance, physical activity levels, or disease. The SMM calculation was conducted using measures obtained from a DSM-BIA with an InBody 720 body composition analyzer. 2.2.5. Body Fat Mass Body fat mass refers to the amount of adipose tissue that constitutes the human body. The excessive accumulation of fat represents obesity, typically classified through the BMI with the underlying assumption that a higher BMI indicates increased body fat. However, BMI does not measure body fat mass directly and provides no information on the location of fat mass in different body sites. Hence, as a complement, body fat mass calculation was conducted using measures obtained from a DSM-BIA with an InBody 720 body composition analyzer. 2.2.6. Visceral Fat Visceral or abdominal fat refers to adipose tissue accumulated in the abdominal cavity between internal organs such as the liver, stomach, and intestines. The cut-off value of visceral fat area associated with an increased risk of obesity-related disorder, according to the receiver operating characteristics curve, was 103.8 cm2 [30]. The visceral fat calculation was conducted using measures obtained from a DSM-BIA with an InBody 720 body composition analyzer. 2.3. Statistical Analysis First, we removed the outliers. Outliers are values that deviate remarkably from other values [31], which make data distribution non-normal and create significant changes in parameter estimates, especially when the maximum likelihood estimation method is used [32]. In this study, outliers were detected using boxplots and scatterplots. A total of 93 extreme outliers in the variables of VO2 max, body weight, BMI, skeletal muscle mass (%), body fat mass (%), and visceral fat were removed in order to acquire normal distribution of the data. Then, the normality of the data was measured by investigating the skewness and kurtosis of the variables. All these values were within the range of ±1, and therefore, we considered the data distribution as normal for the dependent variables. Additionally, an MCAR test was conducted, and the results showed that the missing data were completely at random (Chi-Square = 51.65, df = 52, p = 0.49). The study had an interventional and longitudinal design. In short, a set of physiologi- cal variables was measured before and after the 10-week physical activity intervention for each participant. We used two linear mixed models to test differences within groups with regard to physiological health variables (i.e., cardiorespiratory fitness, body weight, BMI, SMM%, body fat mass%, and visceral fat) at the start (T1) and end (T2) of the intervention. In Models 1 and 2, T1 and T2 measures of physiological health were entered as dependent variables. In Model 2, gender, age, and attendance percentage were included as covariates into the regression model. The covariates’ intercepts effects were fixed, and individuals’ intercepts were set at random to test the differences within individuals with regard to the dependent variables. Brain Sci. 2022, 12, 822 5 of 15 We used the restricted maximum likelihood estimation method, which provides more accurate and unbiased results compared to other methods [33]. In addition, we used intraclass correlation coefficients (ICCs) as a measure of the variance explained by individuals; that is, an estimation of the group mean reliability across T1 and T2 [34]. This was the ratio of between-group variance to total variance. It was calculated using the following formula for each linear regression model: ICC = σ2 0 σ2 0 + σ2e in which σ2 0 is the variance of random intercept and σ2 0 + σ2 e is the total variance (i.e., random intercept variance and residual variance). The result is usually between 0 and 1; higher values suggest greater between-group variability. As the last analysis, we performed a linear regression model analysis to test whether variables in physiological health at baseline (i.e., T1) could predict adherence to the physical intervention (i.e., attendance percentage). All the statistical analyses were conducted using IBM SPSS Statistics v.26 software, and statistical significance for all analyses was set at p < 0.05. The statistical power (1 − β) for the total sample at α = 0.05 was equal to 0.99. 3. Results 3.1. Sample Characteristics Table 1 indicates the descriptive characteristics of the sample used in this study. The comparison of the mean differences in attendance percentage among females and males indicated that both genders participated in the physical activity sessions to roughly the same extent. Table 2 indicates the physiological health variables related to before (T1) and after (T2) the intervention. Table 1. Descriptive characteristics of the study sample. Variables Gender Mean/SD Age Female 40.29 ± 9.34 Male 39.28 ± 10.16 Total 39.70 ± 9.81 Attendance Percentage Female 70% ± 0.33 Male 76% ± 0.26 Total 73% ± 0.29 Differences between females and males in attendance percentage t-value p-value 1.99 0.05 Note: min: minimum, max: maximum, SD: standard deviation. Table 2. Physiological health measures at week 0 (T1) and week 10 (T2). Physiological Health Gender T1 Mean (SD) Total Mean T1 (SD) T2 Mean (SD) Total Mean T2 (SD) Mean Change T1 to T2 Cardiorespiratory Fitness (VO2 max; mL/min/kg) Female 28.58 (SD = 4.91) 31.46 (SD = 5.97) 31.22 (SD = 4.87) 34.35 (SD = 6.42) 2.64 Male 33.54 (SD = 5.81) 36.44 (SD = 6.49) 2.90 Body Weight (kg) Female 69.41 (SD = 11.93) 75.81 (SD = 14.64) 68.35 (SD = 10.29) 75.95 (SD = 13.98) −1.06 Male 80.37 (SD = 14.71) 80.83 (SD = 13.88) 0.46 Brain Sci. 2022, 12, 822 6 of 15 Table 2. Cont. Physiological Health Gender T1 Mean (SD) Total Mean T1 (SD) T2 Mean (SD) Total Mean T2 (SD) Mean Change T1 to T2 BMI (kg/m2) Female 26.57 (SD = 4.23) 26.56 (SD = 4.34) 26.34 (SD = 3.72) 26.63 (SD = 4.09) −0.23 Male 26.55 (SD = 4.43) 26.81 (SD = 4.31) 0.26 Skeletal Muscle Mass (%) Female 34.60 (SD = 3.50) 39.04 (SD = 5.51) 34.98 (SD = 3.46) 39.54 (SD = 5.52) 0.38 Male 42.21 (SD = 4.39) 42.45 (SD = 4.53) 0.24 Body Fat Mass (%) Female 36.69 (SD = 6.38) 30.01 (SD = 9.07) 35.96 (SD = 6.15) 29.21 (SD = 8.90) −0.73 Male 25.26 (SD = 7.58) 24.91 (SD = 7.63) −0.35 Visceral Fat (cm2) Female 126.60 (SD = 44.10) 107.91 (SD = 45.57) 114.96 (SD = 39.42) 104.53 (SD = 43.50) −11.64 Male 94.55 (SD = 41.83) 97.27 (SD = 44.84) 2.72 Note: SD: standard deviation. 3.2. Linear Mixed Model Analysis: Effect of the Intervention Table 3 shows the results of Models 1 (null model), 2 (random effects model), and 3 (fixed effects model) regarding predictors of physiological health. For Model 1, the results indicated that there were differences in physiological health measures between T1 and T2 within individuals (p < 0.00) f or all the various physiological measures (i.e., cardiorespiratory fitness, body weight, BMI, SMM%, body fat mass%, and visceral fat). In Model 2 (random effects model), we estimated the differences within individuals regarding physiological health measures in T1 and T2 by controlling for gender, age, and attendance percentage as predictors in the equation and putting individuals as random effects. The results showed that the predictor variables in Model 2 (i.e., gender, age, and attendance percentage) significantly affected the intercepts of the dependent variables in Model 1 (i.e., the physiological health measures). The ICCs for all the equations showed that the added predictors in Model 2 changed the independent intercepts in Model 1. To be more precise, the results of the linear mixed model for Model 2 showed that the changes from T1 to T2 regarding cardiorespiratory fitness, BMI, body weight, and SMM percentage were significant (p ≤ 0.01). Gender predicted changes in body fat percentage and SMM percentage (p < 0.05) from T1 to T2. Thus, this suggests that females had a greater relative increase in SMM percentage and a greater relative decrease in body fat percentage compared to males. All the intercepts for visceral fat and the predictors were non-significant (p > 0.05). Hence, there were no differences within individuals concerning visceral fat, and gender, age, and attendance percentage did not predict changes in visceral fat values. Impor- tantly, attendance percentage did not have any association with changes in physiological health variables. For a comparison of the fixed versus random effects models, the fixed effects model was also measured. The results of the linear mixed model for Model 3 (fixed effects model) yielded similar outputs as the random effects model with several differences. In the fixed effects models, age predicted cardiorespiratory fitness, body fat percentage, and SMM percentage. In addition, gender predicted cardiorespiratory fitness in addition to those significant relationships in the random effects model. The comparison of the Akaike Information Criteria (AIC) showed that the random effects model better fit to the data. In this study, the outputs of the random effects model were considered for further analysis with regard to the model’s advantages over the fixed effects model. The random effects model is capable of estimating shrunken residuals [35] and provides estimates that overall are closer to the true value in any particular sample [36]. Brain Sci. 2022, 12, 822 7 of 15 Table 3. Linear mixed model analysis of the predictors of physiological health. Variables Model 1 Model 2 (Random Effects) Model 3 (Fixed Effects) Est. SE p-Value ICC AIC Est. SE p-Value AIC Est. SE p-Value Cardiorespiratory Fitness (VO2 max) 32.38 0.33 0.00 0.80 3115.81 50.93 8.91 0.00 3222.57 54.13 7.97 0.00 Gender −9.01 5.52 0.10 −10.70 4.98 0.03 Age −0.43 0.24 0.07 −0.50 .22 0.02 Attendance Percentage 5.39 11.24 0.63 3.45 9.85 0.727 BMI 26.61 0.22 0.00 0.98 2664.25 20.58 6.94 0.00 3455.94 22.96 5.75 0.00 Gender −0.15 4.34 0.97 −1.84 3.70 0.62 Age 0.22 0.19 0.24 0.20 0.16 0.20 Attendance Percentage 2.20 8.81 0.80 −1.08 7.15 0.88 Body Fat Percentage 29.90 0.47 0.00 0.97 3492.50 −4.59 12.14 0.71 4057.93 −5.21 9.36 0.58 Gender 17.57 7.55 0.02 17.77 6.01 0.00 Age 0.56 0.30 0.06 0.65 0.26 0.01 Attendance Percentage −2.45 15.39 0.87 2.62 11.65 0.82 Body Weight (kg) 75.97 0.75 0.00 0.99 4020.39 79.26 24.14 0.00 4847.40 88.60 11.12 0.00 Gender −14.48 15.01 0.34 −20.20 11.65 0.08 Age 0.56 0.60 0.35 0.51 0.50 0.31 Attendance Percentage 0.66 30.63 0.98 −9.61 22.52 0.67 SMM Percentage 39.12 0.29 0.00 0.98 2830.52 61.28 6.90 0.00 3380.54 62.45 5.35 0.00 Gender −12.08 4.29 0.01 −12.64 3.43 0.00 Age −0.34 0.17 0.05 −0.41 0.15 0.00 Attendance Percentage 1.92 8.75 0.83 −1.99 6.66 0.77 Visceral Fat 107.66 2.34 0.00 0.98 5576.84 3.62 73.55 0.96 6331.85 67.66 55.91 0.23 Gender 30.16 45.76 0.51 10.62 35.45 0.77 Age 2.17 1.83 0.24 0.27 1.52 0.86 Attendance Percentage −40.44 93.34 0.67 −58.05 70.71 0.41 Note: Est.: regression coefficient, SE: standard error, SMM: skeletal muscle mass. ML: maximum likelihood. ICC: intraclass correlation coefficients. AIC: Akaike Information Criteria. 3.3. Effect Size and Minimum Detectable Change Calculation for Each Physiological Measure Table 4 indicates the effect size Cohen’s f2 for each measure across females and males, which were calculated using GPower v3.1. The level of effect sizes was small for VO2 max, skeletal muscle mass, body fat mass for females, and visceral fat. The effect sizes for body weight, BMI for both females and males, and body fat mass for males were not significant. Cohen [37] suggested cut-off points of f 2 ≥ 0.02, f2 ≥ 0.15, and f 2 ≥ 0.35 representing small, medium, and large effect sizes, respectively. Moreover, small differences in effect sizes between females and males with respect to skeletal muscle mass and body fat mass were observed. Changes in physiological health measures were also estimated through minimum detectable change (MDC)—a statistical estimate of the smallest amount of change that can be detected by a measure that corresponds to a noticeable change in the variable under study over time which is not related to measurement error. MDC is calculated using the following formula: MDC = SEM × 1.96 × square root of 2 SEM = SD × 2q (1 − r) where 1.96 is a z-score which represents the confidence interval from a normal distribution, SD is the standard deviation at baseline, r is the test–retest reliability coefficient, and SEM is the standard error of measurement. Brain Sci. 2022, 12, 822 8 of 15 Table 4. Effect size, SEM, and MDC for all physiological health measures. Physiological Health Gender Cohen’s f2 SEM MDC95 Cardiorespiratory Fitness (VO2 max; mL/min/kg) Female 0.04 1.38 1.96 Male 0.04 Body Weight (kg) Female 0.01 1.46 1.69 Male 0.01 BMI (kg/m2) Female 0.01 0.43 0.92 Male 0.01 Skeletal Muscle Mass (%) Female 0.03 0.55 1.04 Male 0.02 Body Fat Mass (%) Female 0.02 0.91 1.34 Male 0.01 Visceral Fat (cm2) Female 0.02 4.56 2.99 Male 0.02 Note: SEM: standard error of measurement, MDC95: minimum detectable change at 95% confidence interval. The MDC value was assumed to be the minimum amount of change that needs to be observed so that it could be considered a real change [38] or a change to which the amount of change in performance was likely to be greater than the amount of random measurement error. Since the MDC95 values were greater than the SEM values, the changes in physiological health measures were not related to measurement error and therefore could be considered as real changes. Moreover, the visceral fat’s SEM was greater than MDC95, thus being consistent with the results from the linear mixed method in which the changes in visceral fat were not significant. Finally, although the linear mixed analysis showed that changes in body fat percentage were not significant, the MDC95 was greater than the SEM values for this variable. Perhaps this reflects the significant body fat percentage reduction in females shown using the linear mixed model method. 3.4. Regression Analysis: Baseline Physiological Health as Predictor of Attendance We conducted a linear regression analysis to investigate whether physiological health variables at baseline (T1) as well as age and gender predicted the attendance percent- age. As expected, cardiorespiratory fitness at baseline was significantly and positively (F(1, 307) = 9.25, p < 0.01, adj. R2 = 0.03) related to asylum seekers’ attendance to the physical intervention sessions (p < 0.01). Nevertheless, this correlation was relatively low (r = 0.17, p < 0.01). See Table 5 for details. Table 5. Linear regression analysis of the predictors of attendance percentage. Parameter B Std. β Estimate Std. Error p-Value Cardiorespiratory fitness (VO2 max) 0.008 0.171 0.003 0.003 BMI 0.000 0.004 0.004 0.947 Body Weight 5.639 0.003 0.001 0.957 SMM Percentage 0.004 0.082 0.003 0.123 Body Fat Percentage −0.003 −0.079 0.002 0.140 Visceral Fat 0.000 −0.067 0.000 0.214 Age 0.002 0.071 0.002 0.171 Gender −0.060 −0.102 0.030 0.048 4. Discussion The study’s aim was to evaluate the significance of the 10-week physical activity inter- vention, “Health for Everyone”, on asylum seekers’ physiological health. The results of the linear mixed model confirmed that the “Health for Everyone” intervention was associated with a beneficial impact on the asylum seekers’ physiological variables in both males and females, with improvements in their body composition and cardiorespiratory fitness. In general, the participants showed a significant increase in cardiorespiratory fitness, with females showing a decrease in their total body weight, while males showed a slight increase. Both genders showed an increase in skeletal muscle mass and a decrease in body Brain Sci. 2022, 12, 822 9 of 15 fat mass percentages and in visceral fat area. For all the participants, the total attendance percentage for the duration of the program was 73%. Using minimum detectable changes (MDC) and effect sizes, we found significant differences in the physiological health variables over time. This justifies the changes we obtained from the linear mixed model analysis. Similar results have been presented in a systematic review and meta-analysis of 65 physical activity interventions [39], showing significant improvements in the car- diometabolic health of the participants. From baseline to post-intervention, short-term interventions of <12 weeks significantly improved cardiorespiratory fitness in populations with overweight/obesity, along with a decrease in cardiometabolic risk factors. Moreover, in a culturally adapted lifestyle intervention, including seven sessions addressing healthy diet and physical activity [40] in a sample of 96 Iraq-born immigrants residing in Malmö, Sweden, beneficial effects on insulin levels, body weight and LDL cholesterol were found. 4.1. Cardiorespiratory Fitness The participants showed significant increases in cardiorespiratory fitness from T1 (week 0) to T2 (week 10). At the end of the intervention, males showed an increase in VO2 max of 2.90 mL/min/kg (M = 36.44, SD = 6.49) and females an increase of 2.64 mL/min/kg (M = 31.22, SD = 4.87). For comparison, a population-based study of 579 men aged 42 to 60 [12] found that a 1.0 mL/min/kg increase in VO2 max was prospectively associated with a 9% risk reduction in all-cause mortality, emphasizing the importance of increasing and maintaining cardiorespiratory fitness levels to promote long-term health. Albeit an increase in cardiorespiratory fitness was seen, and considering the partici- pants’ mean age of 35.9 (SD = 11.9), the results show that males in our study population had lower cardiorespiratory fitness levels when compared to females. Using the cardiores- piratory fitness classification scale for the age group of 30–39 years old [41], males were classified as having poor cardiorespiratory levels at T1 and T2, with females being classified as having fair cardiorespiratory fitness level at both points in time. It is worth noting that although both groups remained in the same cardiorespiratory fitness classification category, males and females increased their VO2 max during the 10-week intervention period. Comparing our sample’s results with the Swedish population, a study [42] of relative VO2 max trends in the working force from 1995 to 2017 (N = 354 277 participants; 44% women, 56% men, aged 18–74 years) showed that within the age group of 35–49 years, females had a mean relative VO2 max of 35.7 mL/kg/min (SD = 1.23) and males a mean of 34.9 mL/kg/min (SD = 1.38). Hence, at least for the males in our asylum seeker sample, their VO2 max improved and reached similar levels to native Swedes. 4.2. Skeletal Muscle Mass In our study, both genders increased their skeletal muscle mass percentages from baseline to endpoint. While female participants at endpoint showed an increase of 0.38%, male participants showed an increase in skeletal muscle mass percentage of 0.24% from T1 to T2. Contrasting with the European native population, a study [43] including a total of 1664 Hungarian adults (1198 females and 466 males) found that the mean skeletal muscle mass for the age group of 20–40 years was 46.51% and 39.60% for males and females, respectively. Another investigation with a sample of 1 924 Serbian women with a mean age of 35.5 years [44] registered the average skeletal muscle mass for the total sample as 39.3%. Bearing in mind that our study’s population consisted of foreign-born asylum seekers, and although skeletal muscle mass percentages increased for both genders, our results showed considerably lower skeletal muscle mass percentages when compared to the native European population described above. 4.3. Body Fat Despite the relatively limited number of training sessions offered to the participants, both genders showed a reduction in total body fat percentages, with males registering a Brain Sci. 2022, 12, 822 10 of 15 decrease of 0.35% at T2 and females a decrease of 0.73%. At endpoint, females reduced more in body fat percentages than their male counterparts. Although both genders showed a reduction in percentual points, males and females still showed comparably higher per- centages at T2 when compared to European populations. Ihász et al. [43] showed that the mean body fat percentages in the age group of 20–40 years were 18.27% for males and 27.75% for females. In a study [45] consisting of 433 healthy Caucasians (253 men and 180 women) aged 18–94 years, in the age group of 35–59 years, the fat mass percentages values in males were determined to be 21.2% and 29.0% for females. Moreover, the American College of Sports Medicine [41], in their classification scale for body composition, states for the age group of 30–39 years that good body fat percentages are 15.9–18.4% for men and 17.5–21.0% for women. In this context, the males in our study presented poor body fat percentages followed by very poor percentages for females at T1 and T2, while the above-mentioned studies sampling European populations presented fair/good percentages for males and poor/fair percentages for females, according to their respective age groups. 4.4. Visceral Fat The current literature maintains that healthy levels for visceral fat area should be sustained at <100 cm2, with values of ≥100 cm2 associated with an increased risk of obesity- related disorders such as hypertension, hyperglycemia, and dyslipidemia [46]. In a study of 413 subjects (174 men and 239 women) to determine cut-off values for visceral fat area associated with an increase in the risk of obesity disorders and metabolic syndrome [30], the value of visceral fat area associated with an increased risk of obesity-related disorders was 103.8 cm2. A previous investigation with a sample of 233 middle-aged and older women (45 to 73 years) showed that a visceral fat area of ≥106 cm2 is associated with elevated risks for having low HDL cholesterol concentrations, hypertriglyceridemia, a high LDL/HDL cholesterol ratio, impaired glucose tolerance, and hyperinsulinemia [47], with a visceral fat area of ≥163 cm2 being predictive of even greater risks for metabolic risk factors for coronary heart disease when compared to lower visceral fat levels. In relation to the values presented above, in our study, males had healthy visceral fat levels at both measure points (94.55–97.27), while females, despite having a signif- icant reduction in their mean visceral fat area, continued to be at an elevated risk for cardiometabolic disorders (126.60–114.91). 4.5. Physiological Health Variables as Predictors of Attendance Before discussing which physiological health variables predicted attendance, we would like to point out that in our study, females registered a larger increase in skeletal muscle mass and a larger decrease in body fat when compared to males. While age pre- dicted a decrease in skeletal muscle mass in older individuals, a reduction in skeletal muscle mass was expected for older individuals since the association of aging and progressive muscle loss are well stablished [48], with skeletal muscle mass decreasing at a rate of approximately three to eight percent per decade after 30 years, and an even higher rate of decline after the age of 60 [49]. These results, albeit outside our aim, are important to understand further analyses. Although the results showed differences in physiological health from T1 to T2 within individuals for the variables analyzed, the attendance percentages did not have any as- sociations with changes in physiological variables. However, cardiorespiratory fitness had a significant relationship with an individual’s attendance, i.e., individuals with bet- ter cardiorespiratory fitness at baseline had higher attendance rates. Other studies have shown similar results, as seen in a previous review performed to determine exercise ad- herence rates and their predictors in 21 randomized control trials [50]. As per our results, individuals with better cardiorespiratory fitness at baseline had the best adherence to physical training. Brain Sci. 2022, 12, 822 11 of 15 Nevertheless, one of the challenges is to increase adherence to physical activity inter- ventions. Some studies indicate that, among different populations, the more physically fit a person is at the start of the program, the higher attendance rate they have [50,51]. Since asylum seekers in general present lower fitness levels when compared to native populations, it could be speculated that the group may not have a high adherence rate to physical activity interventions. 4.6. Strengths and Limitations The study’s strength was first and foremost its longitudinal study design with a predefined set of specific variables and objective measures. It allowed for an insight into physiological health developments over a 10-week period in a rarely studied population. The primary methodological limitation of the study refers to the lack of randomization and a control group providing a standard for comparison when measuring the physiological outcomes. This limitation did not allow us to draw definitive conclusions on the effects of physical activity in the group studied; thereby, the results can only be interpreted as associations. Moreover, the mixed models we applied w particularly useful in longitudinal studies and are often preferred to other approaches because they can be used with missing values. Nevertheless, even if in our models the covariates’ intercepts effects were fixed and individ- uals’ intercepts were set at random in order to test the differences within individuals with regard to the dependent variables, the lack of a control group still presents a limitation. Upon first glance, our results might suggest that a short physical activity intervention combining resistance and aerobic training yields small but positive results in physiological variables for this specific population. For instance, a short intervention (8 to 10 sessions in a 10-week period, for example) would allow a much larger number of people to take part of the training program and still have positive impacts on their health. Indeed, the amount of training in “Health for Everyone” was dictated by financial and logistical constraints. However, the amount of physical activity that is recommended for adults is significantly higher. In order to improve and sustain cardiorespiratory fitness and reduce the risk of non-communicable diseases, the WHO [52] recommends at least 150 min of moderate- intensity physical activity per week or at least 75 min of vigorous-intensity physical activity per week. Therefore, the volume of exercise recommended is considerably higher than the volume that was provided by the intervention. Hence, even if a short intervention would be economically and logistically more feasible and still give positive effects, it would probably not have any long-term physiological effects. In addition, socioeconomic variables, which could potentially have affected the par- ticipants’ physiological outcomes, were not taken into consideration in the present study. The participants’ educational levels were not analyzed, and as reported in previous stud- ies [53,54], lower educational levels are linked to higher risks of physical inactivity and a higher incidence of non-communicable diseases [55]. Income levels and participation in the workforce were also not examined in this study. Higher physical activity levels are noted among those with higher income and a steady source of revenue. Meanwhile, less disposable income and unemployment are strongly related to low physical activity levels [56]. Even though information on income levels was not collected, it could be assumed that the vast majority of the participants were still navigating the Swedish immigration system and relied heavily on subsidies and financial support from the Swedish state, since the recruitment was carried out by Blekinge’s municipalities within the framework of social orientation for asylum seekers from countries currently experiencing armed conflicts. Although this study provides a valuable insight into asylum seekers’ physiological health, due to the relatively small number of participants in the project, we do not aim to use the cardiorespiratory fitness values found in this investigation to set reference values for VO2 max for asylum seekers in Sweden. Based on its limitations and study design, caution should be applied when arriving to conclusions based on the study findings. Brain Sci. 2022, 12, 822 12 of 15 5. Conclusions The results from our study are in accordance with previous research on the associ- ations of physical activity interventions in the physical health of adult populations. The participants showed significant improvements in physiological health variables of physical fitness and body composition, most noticeably in a significant increase in cardiorespiratory fitness. Moreover, individuals with higher initial cardiorespiratory fitness levels were more likely to adhere to the intervention, which leads to the assumption that participants who were already physically active were more inclined to maintain a physically active lifestyle. Given the current format and the limitations of the project “Health for Everyone- Sport, Culture and Integration”, it would be of interest to investigate whether an extended version could lead to long-term improvements in the participants’ physiological health and whether it could beneficially impact their exercise attitudes and their psychological and social health. With the complexity of asylum seekers’ physical and mental health needs, barriers and facilitators could also be identified in order to increase participation in a physical activity intervention and achieve more significant and lasting results. For example, Haith- Cooper et al. [57] found that, among asylum seekers, stress, poverty, and temporary living conditions acted as barriers for participating in physical activity. A point to be explored in future studies is the potential effects physical activity interventions may have on immigrant populations in the context of language acquisition. Evidence shows that physical activity interventions in young asylum seekers have a positive impact on their second language learning outcomes [58]. Since higher physical fitness is associated with improved cognition and literacy [59], it may therefore facilitate integration into a new society. Overall, the results from this study provide an insight into asylum seekers’ health status and could serve as a base for implementing an intervention scale-up, where culturally sensitive approaches to physical activity are used to improve vulnerable populations’ physical and psychological health, and act as a guide for future policies towards health equality and inclusion in society. Author Contributions: M.G. analyzed and interpreted the data results and prepared the manuscript draft for submission. D.G. gave input on the initial versions of the manuscript and guided the interpretation of the results. M.K. is responsible for the final data analysis. E.L. is responsible for the preliminary data analysis. E.S. provided manuscript inputs and insights into data interpretation. D.B. provided manuscript inputs and guided the data analysis and interpretation of the results. All authors have read and agreed to the published version of the manuscript. Funding: The project “Health for Everyone-Sport, Culture and Integration” was financed by Region Blekinge and the County Administrative Board in Blekinge. The project was managed by the Ronneby municipality with the collaboration of the Blekinge Sports Association and Blekinge Museum. The financers had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Institutional Review Board Statement: The evaluation of the project “Health for Everyone-Sport, Culture and Integration” was approved by the Swedish Ethical Review Authority (Dnr. 2017/604). The study was conducted in accordance with the ethical standards of the 1964 Helsinki declaration and further amendments. Hence, all the participants were provided with the necessary information to obtain verbal and written informed consent (e.g., aims of the study, that participation was voluntary, confidential, etc.). Informed Consent Statement: Informed consent was obtained from all the participants. Data Availability Statement: The data supporting the findings of this study are available from the research group, but restrictions apply to the availability of these data, and the data are not publicly available. Acknowledgments: We would like to express our gratitude to Olof Ljungberg, Integration Coordi- nator at the Blekinge Sports Association, Henrik Lövgren, Head of Social Communication at the Brain Sci. 2022, 12, 822 13 of 15 Blekinge Integration and Education Center, and Region Blekinge for allowing us to investigate the “Health for Everyone-Sport, Culture and Integration” intervention. Last but not the least, we would like to thank the participants and the staff at Blekinge Health Arena for their assistance during the data collection stage. Conflicts of Interest: The authors declare that they have no conflict of interests. Abbreviations BMI Body mass index DSM-BIA Direct segmental multi-frequency bioelectrical impedance analysis ICC Intraclass correlation coefficients SMM Skeletal muscle mass VO2 max: Maximal oxygen consumption WHO World Health Organization References 1. Pettersson, T.; Wallensteen, P. Armed Conflicts, 1946–2014. J. Peace Res. 2015, 52, 536–550. [CrossRef] 2. Migrationsverket Översikter Och Statistik Från Tidigare År: Migrationsverket. Available online: https://www.migrationsverket. se/Om-Migrationsverket/Statistik.html (accessed on 24 July 2021). 3. Svensson, V. Hälso- Och Sjukvård Och Tandvård Till Asylsökande Och Nyanlända; Socialstyrelsen: Stockholm, Sweden, 2016; p. 138. 4. Gerritsen, A.A.M.; Bramsen, I.; Devillé, W.; van Willigen, L.H.M.; Hovens, J.E.; van der Ploeg, H.M. Physical and Mental Health of Afghan, Iranian and Somali Asylum Seekers and Refugees Living in the Netherlands. Soc. Psychiatry Psychiatr. Epidemiol. 2006, 41, 18–26. [CrossRef] 5. Bradby, H.; Humphris, R.; Newall, D.; Phillimore, J. Public Health Aspects of Migrant Health: A Review of the Evidence on Health Status for Refugees and Asylum Seekers in the European Region; WHO Health Evidence Network Synthesis Reports; WHO Regional Office for Europe: Copenhagen, Denmark, 2015; ISBN 978-92-890-5110-1. 6. Hollander, A.-C. Social Inequalities in Mental Health and Mortality among Refugees and Other Immigrants to Sweden: Epidemi- ological Studies of Register Data. Glob. Health Action 2013, 6, 21059. [CrossRef] [PubMed] 7. Lebano, A.; Hamed, S.; Bradby, H.; Gil-Salmerón, A.; Durá-Ferrandis, E.; Garcés-Ferrer, J.; Azzedine, F.; Riza, E.; Karnaki, P.; Zota, D.; et al. Migrants’ and Refugees’ Health Status and Healthcare in Europe: A Scoping Literature Review. BMC Public Health 2020, 20, 1039. [CrossRef] [PubMed] 8. Bennet, L.; Groop, L.; Lindblad, U.; Agardh, C.D.; Franks, P.W. Ethnicity Is an Independent Risk Indicator When Estimating Diabetes Risk with FINDRISC Scores: A Cross Sectional Study Comparing Immigrants from the Middle East and Native Swedes. Prim. Care Diabetes 2014, 8, 231–238. [CrossRef] [PubMed] 9. Wändell, P.E.; Johansson, S.E.; Gåfvels, C.; Hellénius, M.L.; de Faire, U.; Sundquist, J. Estimation of Diabetes Prevalence among Immigrants from the Middle East in Sweden by Using Three Different Data Sources. Diabetes Metab. 2008, 34, 328–333. [CrossRef] 10. Wändell, P.-E.; Wajngot, A.; de Faire, U.; Hellénius, M.-L. Increased Prevalence of Diabetes among Immigrants from Non-European Countries in 60-Year-Old Men and Women in Sweden. Diabetes Metab. 2007, 33, 30–36. [CrossRef] 11. Cavill, N.; Kahlmeier, S.; Racioppi, F.; Weltgesundheitsorganisation; Regionalbüro für Europa. Physical Activity and Health in Europe: Evidence for Action; WHO: Geneva, Switzerland, 2006; ISBN 978-92-890-1387-1. 12. Laukkanen, J.A.; Zaccardi, F.; Khan, H.; Kurl, S.; Jae, S.Y.; Rauramaa, R. Long-Term Change in Cardiorespiratory Fitness and All-Cause Mortality: A Population-Based Follow-up Study. Mayo Clin. Proc. 2016, 91, 1183–1188. [CrossRef] 13. Kim, G.; Kim, J.H. Impact of Skeletal Muscle Mass on Metabolic Health. Endocrinol. Metab. Seoul Korea 2020, 35, 1–6. [CrossRef] 14. Little, J.P.; Phillips, S.M. Resistance Exercise and Nutrition to Counteract Muscle Wasting. Appl. Physiol. Nutr. Metab. Physiol. Appl. Nutr. Metab. 2009, 34, 817–828. [CrossRef] 15. Roth, S.M.; Ferrell, R.F.; Hurley, B.F. Strength Training for the Prevention and Treatment of Sarcopenia. J. Nutr. Health Aging 2000, 4, 143–155. [PubMed] 16. Lee, J.J.; Pedley, A.; Hoffmann, U.; Massaro, J.M.; Fox, C.S. Association of Changes in Abdominal Fat Quantity and Quality with Incident Cardiovascular Disease Risk Factors. J. Am. Coll. Cardiol. 2016, 68, 1509–1521. [CrossRef] [PubMed] 17. Irwin, M.L.; Yasui, Y.; Ulrich, C.M.; Bowen, D.; Rudolph, R.E.; Schwartz, R.S.; Yukawa, M.; Aiello, E.; Potter, J.D.; McTiernan, A. Effect of Exercise on Total and Intra-Abdominal Body Fat in Postmenopausal Women: A Randomized Controlled Trial. JAMA 2003, 289, 323–330. [CrossRef] [PubMed] 18. Janiszewski, P.M.; Ross, R. Physical Activity in the Treatment of Obesity: Beyond Body Weight Reduction. Appl. Physiol. Nutr. Metab. Physiol. Appl. Nutr. Metab. 2007, 32, 512–522. [CrossRef] 19. Bize, R.; Johnson, J.A.; Plotnikoff, R.C. Physical Activity Level and Health-Related Quality of Life in the General Adult Population: A Systematic Review. Prev. Med. 2007, 45, 401–415. [CrossRef] 20. Omorou, Y.A.; Erpelding, M.-L.; Escalon, H.; Vuillemin, A. Contribution of Taking Part in Sport to the Association between Physical Activity and Quality of Life. Qual. Life Res. Int. J. Qual. Life Asp. Treat. Care Rehabil. 2013, 22, 2021–2029. [CrossRef] Brain Sci. 2022, 12, 822 14 of 15 21. Puciato, D.; Borysiuk, Z.; Rozpara, M. Quality of Life and Physical Activity in an Older Working-Age Population. Clin. Interv. Aging 2017, 12, 1627–1634. [CrossRef] 22. Persson, G.; Mahmud, A.J.; Hansson, E.E.; Strandberg, E.L. Somali Women’s View of Physical Activity—A Focus Group Study. BMC Womens Health 2014, 14, 129. [CrossRef] 23. Sjögren Forss, K.; Mangrio, E.; Leijon, M.; Grahn, M.; Zdravkovic, S. Physical Activity in Relation to Wellbeing Among Newly Arrived Refugees in Sweden: A Quantitative Study. Front. Public Health 2020, 8, 532883. [CrossRef] 24. Knappe, F.; Colledge, F.; Gerber, M. Impact of an 8-Week Exercise and Sport Intervention on Post-Traumatic Stress Disorder Symptoms, Mental Health, and Physical Fitness among Male Refugees Living in a Greek Refugee Camp. Int. J. Environ. Res. Public. Health 2019, 16, 3904. [CrossRef] 25. Mayorga-Vega, D.; Aguilar-Soto, P.; Viciana, J. Criterion-Related Validity of the 20-M Shuttle Run Test for Estimating Cardiorespi- ratory Fitness: A Meta-Analysis. J. Sports Sci. Med. 2015, 14, 536–547. [PubMed] 26. Weir, C.B.; Jan, A. BMI Classification Percentile and Cut Off Points. In StatPearls; StatPearls Publishing: Treasure Island, FL, USA, 2021. 27. Ling, C.H.Y.; de Craen, A.J.M.; Slagboom, P.E.; Gunn, D.A.; Stokkel, M.P.M.; Westendorp, R.G.J.; Maier, A.B. Accuracy of Direct Segmental Multi-Frequency Bioimpedance Analysis in the Assessment of Total Body and Segmental Body Composition in Middle-Aged Adult Population. Clin. Nutr. Edinb. Scotl. 2011, 30, 610–615. [CrossRef] [PubMed] 28. Ogawa, H.; Fujitani, K.; Tsujinaka, T.; Imanishi, K.; Shirakata, H.; Kantani, A.; Hirao, M.; Kurokawa, Y.; Utsumi, S. InBody 720 as a New Method of Evaluating Visceral Obesity. Hepatogastroenterology 2011, 58, 42–44. [PubMed] 29. Frontera, W.R.; Ochala, J. Skeletal Muscle: A Brief Review of Structure and Function. Calcif. Tissue Int. 2015, 96, 183–195. [CrossRef] [PubMed] 30. Kim, J.A.; Choi, C.J.; Yum, K.S. Cut-off Values of Visceral Fat Area and Waist Circumference: Diagnostic Criteria for Abdominal Obesity in a Korean Population. J. Korean Med. Sci. 2006, 21, 1048–1053. [CrossRef] [PubMed] 31. Bollen, K.A.; Jackman, R.W. Regression Diagnostics: An Expository Treatment of Outliers and Influential Cases. Sociol. Methods Res. 1985, 13, 510–542. [CrossRef] 32. Cohen, J.; Cohen, P.; West, S.G.; Aiken, L.S. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 3rd ed.; Routledge: New York, NY, USA, 2002; ISBN 978-0-203-77444-1. 33. Liao, J.G.; Lipsitz, S.R. A Type of Restricted Maximum Likelihood Estimator of Variance Components in Generalised Linear Mixed Models. Biometrika 2002, 89, 401–409. [CrossRef] 34. Bliese, P.D. Within-Group Agreement, Non-Independence, and Reliability: Implications for Data Aggregation and Analysis. In Multilevel Theory, Research, and Methods in Organizations: Foundations, Extensions, and New Directions; Jossey-Bass: San Francisco, CA, USA, 2000; pp. 349–381. ISBN 978-0-7879-5228-0. 35. Clarke, P.; Crawford, C.; Steele, F.; Vignoles, A.F. The choice between fixed and random effects models: Some considerations for educational research. SSRN 2010, 5287, 1–38. [CrossRef] 36. Clark, T.S.; Linzer, D.A. Should I use fixed or random effects? Political Sci. Res. Methods 2015, 3, 399–408. [CrossRef] 37. Cohen, J. Statistical Power Analysis for the Behavioral Sciences; Routledge: Abingdon, UK, 2013. 38. Portney, L.G.; Watkins, M.P. Foundations of Clinical Research: Applications to Practice; Pearson/Prentice Hall: Upper Saddle River, NJ, USA, 2009; Volume 892. 39. Batacan, R.B.; Duncan, M.J.; Dalbo, V.J.; Tucker, P.S.; Fenning, A.S. Effects of High-Intensity Interval Training on Cardiometabolic Health: A Systematic Review and Meta-Analysis of Intervention Studies. Br. J. Sports Med. 2017, 51, 494. [CrossRef] 40. Siddiqui, F.; Kurbasic, A.; Lindblad, U.; Nilsson, P.M.; Bennet, L. Effects of a Culturally Adapted Lifestyle Intervention on Cardio-Metabolic Outcomes: A Randomized Controlled Trial in Iraqi Immigrants to Sweden at High Risk for Type 2 Diabetes. Metabolism 2017, 66, 1–13. [CrossRef] [PubMed] 41. ACSM’s Guidelines for Exercise Testing and Prescription, 10th ed.; American College of Sports Medicine; Riebe, D.; Ehrman, J.K.; Liguori, G.; Magal, M. (Eds.) Wolters Kluwer: Philadelphia, PA, USA, 2018; ISBN 978-1-4963-3906-5. 42. Ekblom-Bak, E.; Ekblom, Ö.; Andersson, G.; Wallin, P.; Söderling, J.; Hemmingsson, E.; Ekblom, B. Decline in Cardiorespiratory Fitness in the Swedish Working Force between 1995 and 2017. Scand. J. Med. Sci. Sports 2019, 29, 232–239. [CrossRef] [PubMed] 43. Ihász, F.; Finn, K.J.; Lepes, J.; Halasi, S.; Szabó, P. Body Composition Comparisons by Age Groups in Hungarian Adults. Int. J. Morphol. 2015, 33, 850–854. [CrossRef] 44. Rakic, S.; Dopsaj, M.; Djordjevic-Nikic, M.; Vasiljevic, N.; Dopsaj, V.; Maksimovic, M.; Tomanic, S.M.; Miljus, D. Profile and Reference Values for Body Fat and Skeletal Muscle Mass Percent at Females, Aged from 18.0 to 69.9, Measured by Multichannel Segmental Bioimpedance Method: Serbian Population Study. Int. J. Morphol. 2019, 37, 1286–1293. [CrossRef] 45. Kyle, U.G.; Genton, L.; Hans, D.; Karsegard, L.; Slosman, D.O.; Pichard, C. Age-Related Differences in Fat-Free Mass, Skeletal Muscle, Body Cell Mass and Fat Mass between 18 and 94 Years. Eur. J. Clin. Nutr. 2001, 55, 663–672. [CrossRef] 46. Ryo, M.; Maeda, K.; Onda, T.; Katashima, M.; Okumiya, A.; Nishida, M.; Yamaguchi, T.; Funahashi, T.; Matsuzawa, Y.; Nakamura, T.; et al. A New Simple Method for the Measurement of Visceral Fat Accumulation by Bioelectrical Impedance. Diabetes Care 2005, 28, 451–453. [CrossRef] 47. Nicklas, B.J.; Penninx, B.W.J.H.; Ryan, A.S.; Berman, D.M.; Lynch, N.A.; Dennis, K.E. Visceral Adipose Tissue Cutoffs Associated with Metabolic Risk Factors for Coronary Heart Disease in Women. Diabetes Care 2003, 26, 1413–1420. [CrossRef] Brain Sci. 2022, 12, 822 15 of 15 48. Larsson, L.; Degens, H.; Li, M.; Salviati, L.; Lee, Y.I.; Thompson, W.; Kirkland, J.L.; Sandri, M. Sarcopenia: Aging-Related Loss of Muscle Mass and Function. Physiol. Rev. 2019, 99, 427–511. [CrossRef] 49. Volpi, E.; Nazemi, R.; Fujita, S. Muscle Tissue Changes with Aging. Curr. Opin. Clin. Nutr. Metab. Care 2004, 7, 405–410. [CrossRef] 50. Martin, K.A.; Sinden, A.R. Who Will Stay and Who Will Go? A Review of Older Adults’ Adherence to Randomized Controlled Trials of Exercise. J. Aging Phys. Act. 2001, 9, 91–114. [CrossRef] 51. Cadmus-Bertram, L.; Irwin, M.; Alfano, C.; Campbell, K.; Duggan, C.; Foster-Schubert, K.; Wang, C.-Y.; McTiernan, A. Predicting Adherence of Adults to a 12-Month Exercise Intervention. J. Phys. Act. Health 2014, 11, 1304–1312. [CrossRef] 52. WHO. Global Recommendations on Physical Activity for Health; WHO Guidelines Approved by the Guidelines Review Committee; World Health Organization: Geneva, Switzerland, 2010; ISBN 978-92-4-159997-9. 53. Droomers, M.; Schrijvers, C.T.; Mackenbach, J.P. Educational Level and Decreases in Leisure Time Physical Activity: Predictors from the Longitudinal GLOBE Study. J. Epidemiol. Community Health 2001, 55, 562–568. [CrossRef] 54. Talaei, M.; Rabiei, K.; Talaei, Z.; Amiri, N.; Zolfaghari, B.; Kabiri, P.; Sarrafzadegan, N. Physical Activity, Sex, and Socioeconomic Status: A Population Based Study. ARYA Atheroscler. 2013, 9, 51–60. [PubMed] 55. Oshio, T.; Kan, M. Educational Level as a Predictor of the Incidences of Non-Communicable Diseases among Middle-Aged Japanese: A Hazards-Model Analysis. BMC Public Health 2019, 19, 852. [CrossRef] [PubMed] 56. Puciato, D.; Rozpara, M.; Mynarski, W.; Ole´sniewicz, P.; Markiewicz-Patkowska, J.; D˛ebska, M. Physical Activity of Working-Age People in View of Their Income Status. BioMed Res. Int. 2018, 2018, 8298527. [CrossRef] [PubMed] 57. Haith-Cooper, M.; Waskett, C.; Montague, J.; Horne, M. Exercise and physical activity in asylum seekers in Northern England; using the theoretical domains framework to identify barriers and facilitators. BMC Public Health 2018, 18, 762. [CrossRef] [PubMed] 58. Krüger, M. Second Language Acquisition Effects of a Primary Physical Education Intervention: A Pilot Study with Young Refugees. PLoS ONE 2018, 13, e0203664. [CrossRef] 59. Cox, E.P.; O’Dwyer, N.; Cook, R.; Vetter, M.; Cheng, H.L.; Rooney, K.; O’Connor, H. Relationship between Physical Activity and Cognitive Function in Apparently Healthy Young to Middle-Aged Adults: A Systematic Review. J. Sci. Med. Sport 2016, 19, 616–628. [CrossRef]

Effects of a 10-Week Physical Activity Intervention on Asylum Seekers' Physiological Health.

06-24-2022

Guerra, Matheus,Garcia, Danilo,Kazemitabar, Maryam,Lindskär, Erik,Schütz, Erica,Berglind, Daniel

eng

PMC4213177

6   Supplementary Figures S1-S3 and Table S7: Model analysis and results   Figure S1. Force-posture relations for the actuated spring-mass-damper model with various and arbitrary actuator motions. Here, the model parameters and touch-down conditions have been held constant, and arbitrary actuator motions applied. This demonstrates a wide range of possible force-length relations with the mathematical model. The arrangement of the actuator in series with the spring and damper decouples posture from force, allowing for forces that deviate significantly from a Hooke’s law relation. The specific force-length trajectory of the simulation results arises from minimal-work optimisation. The Journal of Experimental Biology | Supplementary Material 7   Figure S2. Example work-optimal solutions for the mathematical model satisfying the level-running ostrich gait boundary conditions (touchdown conditions of current and subsequent step). By changing either the model stiffness or damping coefficient by a factor of two, different work-optimal solutions emerge from the control optimization, which all satisfy the boundary conditions (i.e. the problem is not over-constrained). The modelling methods allow for freedom in take-off conditions, such that the model solutions could yield longer or shorter flight phases that satisfy the touch-down conditions for the subsequent step. Thus, the modelling approach can yield solutions with gait parameters and GRFs that deviate substantially from observed data. To make choice of stiffness and damping parameters non-arbitrary, we choose the model parameters for which a work-optimal control matched the data best (Fig. S3). However, the set of solutions from which these parameters were chosen (e.g., Fig. S3) were all work- optimal for their respective parameter values, and were not constrained to fit the bird data. Consequently, the modelling approach could have failed to fit the data, potentially refuting the work-minimising hypotheses. The Journal of Experimental Biology | Supplementary Material 8   Figure S3: A typical example of a parameter-fitting surface for the reduced order model of avian running: The results of our search for the best fitting parameters to the simple model with minimal actuation (Fig. 6A), visualised as a fitting landscape. All solutions shown on the surface are work-optimal for their respective parameter values. In this example, computed using ostrich data, the surface shows a characteristic ‘trough’ of parameter fits that emerge when searching for knorm and cnorm that best fit bird data. The red ‘trough’ line connects the best fits for each value of cnorm. Parameters are normalised as described Table S7, and mean-squared error is computed between model and mean-measured GRF. While some regions of this fitting landscape clearly performed better than others, there was often a large set of solutions that performed similarly well. Given the non-unique nature of the parameter fits, we do not make scientific claims about the functional significance of the fit set of parameters. Nonetheless, we did find a relatively narrow range of damping ratios (a standard measure of decay in oscillating systems) resulting in fits consistent with bird running data (Table S7). We report this as a successful result for the general model, which yielded good match between bird and model GRF, given a two-parameter fit (MSE: quail: 0.0103, pheasant: 0.0280, guinea fowl: 0.0032, turkey: 0.0086, ostrich: 0.0063, calculated by force error normalised to body weight). The Journal of Experimental Biology | Supplementary Material 1   Supplementary Tables S1-S6: Statistical results from experimental data Dependent Variable Step Type Species Species X StepType θTD 20.60 3.47 4.49 HTD 41.85 0.07 2.77 αTO 15.59 1.69 4.60 ΔEP 31.25 0.27 3.55 ΔEK 7.51 0.78 3.24 ΔECoM 8.64 0.42 2.97 Fmax 5.06 6.07 2.69 *Bolding indicates a statistically significant result Table S1: ANOVA F-statistic results for 5 species, including ostriches, testing for effects of step type and species in 0.1Lleg obstacle terrain (see Methods). Degrees of freedom are as follows: step type = 3; species = 4; species x step type = 12; αTO total = 743; all other variables total = 790. The F-statistic for the effect of step type on leg posture (θTD, HTD) and change in potential energy (ΔEp), which are most indicative of obstacle negotiation strategy, are much larger than the corresponding F-statistics for species (F < 1) and species x step type (F < 5). This reflects a uniform obstacle negotiation strategy across species the species studied here (see posthoc comparisons in Tables S2- S3 for further detail). All species used a consistent balance of ‘vaulting’ and ‘crouching’ strategies (Figs, 1 and 2). Step 1 Step 2 θTD (degrees) HTD αTO (degrees) ΔEP ΔEK ΔECoM Fmax Level Step -1 -0.80 0.015 2.28 0.041 0.053 0.093 0.01 Level Step 0 2.57 -0.044 -0.36 0.015 -0.005 0.010 -0.09 Level Step 1 -2.41 0.056 -2.27 -0.051 0.065 0.014 0.06 Step -1 Step 0 3.37 -0.059 -2.63 -0.026 -0.057 -0.084 -0.10 Step -1 Step 1 -1.61 0.041 -4.55 -0.092 0.013 -0.079 0.05 Step 0 Step 1 -4.98 0.099 -1.91 -0.066 0.070 0.005 0.15 *Bolding indicates significant difference based on Bonferroni threshold of 0.0083, for 6 possible step type pairwise comparisons within level and 0.1 Lleg obstacle height. Table S2: Post hoc results on the ANOVA using pairwise mean differences between step types (column 2 - column 1), in normalised units.     The Journal of Experimental Biology | Supplementary Material 2   Species 1 Species 2 θTD (degrees) HTD αTO (degrees) ΔEP ΔEK ΔECoM Fmax Step -1 Quail Pheasant -6.89 -0.020 -3.73 0.016 0.117 0.133 -0.51 Quail Guinea fowl -5.26 -0.294 -1.05 0.021 0.021 0.042 -0.22 Quail Turkey -4.34 -0.037 -2.75 -0.025 0.009 -0.017 -0.14 Quail Ostrich -3.48 -0.126 0.36 0.027 0.030 0.056 -0.08 Pheasant Guinea fowl 1.63 -0.010 2.68 0.005 -0.096 -0.092 0.30 Pheasant Turkey 2.55 -0.018 -0.98 -0.042 -0.109 -0.150 0.38 Pheasant Ostrich 3.41 -0.107 4.10 0.010 -0.087 -0.077 0.43 Guinea fowl Turkey 0.91 -0.008 -1.70 -0.046 -0.012 -0.058 0.08 Guinea fowl Ostrich 1.77 -0.097 1.41 0.006 0.009 0.015 0.14 Turkey Ostrich 0.86 -0.089 3.12 0.052 0.021 0.073 0.06 Step 0 Quail Pheasant -4.81 -0.032 1.71 0.014 0.159 0.174 -0.35 Quail Guinea fowl -2.95 -0.029 4.57 0.045 0.095 0.141 -0.05 Quail Turkey -3.36 -0.014 3.73 0.013 -0.030 -0.017 0.09 Quail Ostrich -1.47 -0.058 -0.36 0.013 0.061 0.074 -0.22 Pheasant Guinea fowl 1.86 0.004 2.85 0.031 -0.064 -0.033 0.30 Pheasant Turkey 1.45 0.019 2.01 -0.002 -0.189 -0.191 0.45 Pheasant Ostrich 3.34 -0.025 -2.08 -0.001 -0.098 -0.099 0.13 Guinea fowl Turkey -0.41 0.015 -0.84 -0.033 -0.125 -0.158 0.14 Guinea fowl Ostrich 1.48 -0.029 -4.93 -0.032 -0.034 -0.067 -0.16 Turkey Ostrich 1.89 -0.044 -4.09 0.000 0.091 0.091 -0.31 Step +1 Quail Pheasant -6.46 0.027 -3.99 -0.067 0.123 0.056 -0.12 Quail Guinea fowl -7.77 0.024 -1.53 -0.038 0.069 0.031 -0.01 Quail Turkey -2.18 -0.023 0.93 0.015 -0.068 -0.053 0.00 Quail Ostrich -2.94 -0.024 -1.61 0.006 0.024 0.031 -0.01 Pheasant Guinea fowl -1.31 -0.003 2.46 0.028 -0.053 -0.025 0.12 Pheasant Turkey 4.28 -0.050 4.92 0.081 -0.190 -0.109 0.13 Pheasant Ostrich 3.52 -0.051 2.38 0.073 -0.098 -0.026 0.11 Guinea fowl Turkey 5.59 -0.047 2.46 0.053 -0.137 -0.084 0.01 Guinea fowl Ostrich 4.83 -0.048 -0.08 0.044 -0.045 -0.001 -0.00 Turkey Ostrich -0.76 -0.001 -2.54 -0.009 0.092 0.083 -0.01 *Bolding indicates significant difference based on Bonferroni threshold of 0.005, for 10 possible species pairwise comparisons within each step category. Table S3: Post hoc pairwise mean differences between species (column 2 - column 1), in normalised units. The Journal of Experimental Biology | Supplementary Material 3   Dependent Variable Step Type Species Obstacle Height Obstacle Height X Step Type Species X Step Type θTD 663.92 6.37 9.99 105.26 1.13 HTD 1421.61 1.32 8.20 223.14 0.98 αTO 584.10 9.00 1.60 115.29 6.53 ΔEP 1402.79 2.33 7.13 267.98 3.01 ΔEK 78.83 4.57 5.37 13.99 2.08 ΔECoM 217.03 2.49 9.39 34.40 3.58 Fmax 114.41 16.33 9.79 19.11 2.51 *Bolding indicates a statistically significant result Table S4: ANOVA F-statistic results for galliform birds, with obstacle heights from 0.1-0.5Lleg (see Methods). Degrees of freedom are as follows: step type = 2; species = 3; obstacle height = 5; obstacle height x step type = 10; species x step type = 6; αTO total = 2360; all other variables total = 2522. Most of the variance in the model is explained by step type and the interaction of obstacle height and step type, reflecting a consistent obstacle negotiation strategy across species. The F-statistics for the effects of step type and obstacle height on leg posture (θTD, HTD) and potential energy (ΔEp), which are most indicative of obstacle negotiation strategy, are much larger than the corresponding F-statistics for the effects of species. We did not observe a significant shift in obstacle negotiation strategy with body size between small and large birds (see Supplementary Table S6). The Journal of Experimental Biology | Supplementary Material 4   Terrain θTD (degrees) HTD αTO (degrees) ΔEP ΔEK ΔECoM Fmax Step -1 ObsH=0.1 -1.02 0.027 1.91 0.037 0.048 0.085 0.02 ObsH=0.2 0.16 0.005 3.98 0.085 0.034 0.119 0.14 ObsH=0.3 -1.68 0.016 5.99 0.141 0.066 0.207 0.26 ObsH=0.4 0.45 0.001 9.88 0.242 0.041 0.283 0.35 ObsH=0.5 -1.16 0.011 11.22 0.293 -0.048 0.245 0.30 Step 0 ObsH=0.1 2.78 -0.036 0.33 0.011 -0.042 -0.031 0.02 ObsH=0.2 7.20 -0.101 -0.07 0.018 -0.039 -0.020 -0.08 ObsH=0.3 8.16 -0.122 -0.69 0.001 -0.051 -0.050 -0.08 ObsH=0.4 10.43 -0.148 -2.75 -0.014 -0.044 -0.058 -0.14 ObsH=0.5 9.09 -0.156 -3.99 -0.042 -0.040 -0.082 -0.21 Step +1 ObsH=0.1 -1.79 0.051 -1.60 -0.043 0.037 -0.006 0.06 ObsH=0.2 -2.95 0.096 -4.24 -0.090 0.098 0.008 0.05 ObsH=0.3 -5.02 0.133 -4.44 -0.113 0.090 -0.023 0.14 ObsH=0.4 -6.54 0.181 -8.63 -0.164 0.121 -0.043 0.13 ObsH=0.5 -11.08 0.222 -9.02 -0.187 0.146 -0.041 0.10 *Bolding indicates a significant difference based on Bonferroni threshold of 0.0033, for 15 possible obstacle pairwise comparisons within each step category Table S5: Post hoc pairwise mean differences (Obs- Level) in normalised units, for obstacle heights by step type across galliform birds. The Journal of Experimental Biology | Supplementary Material 5   Species 1 Species 2 θTD (degrees) HTD αTO (degrees) ΔEP ΔEK ΔECoM Fmax Step -1 Quail Pheasant -- -- -1.91 0.015 -- 0.060 -0.28 Quail Guinea fowl -- -- -0.63 -0.000 -- 0.021 -0.07 Quail Turkey -- -- -0.31 0.011 -- 0.013 -0.01 Pheasant Guinea fowl -- -- 1.28 -0.015 -- -0.039 0.21 Pheasant Turkey -- -- 1.60 -0.004 -- -0.047 0.27 Guinea fowl Turkey -- -- 0.32 0.011 -- -0.008 0.06 Step 0 Quail Pheasant -- -- 0.14 -0.012 -- -0.037 -0.26 Quail Guinea fowl -- -- 1.33 -0.010 -- -0.013 -0.05 Quail Turkey -- -- 0.64 -0.018 -- -0.062 0.06 Pheasant Guinea fowl -- -- 1.19 0.003 -- 0.024 0.21 Pheasant Turkey -- -- 0.50 -0.005 -- -0.025 0.31 Guinea fowl Turkey -- -- -0.69 -0.008 -- -0.049 0.11 Step +1 Quail Pheasant -- -- -1.97 0.000 -- 0.005 -0.32 Quail Guinea fowl -- -- -1.73 -0.016 -- -0.001 -0.03 Quail Turkey -- -- -1.89 -0.013 -- -0.019 -0.04 Pheasant Guinea fowl -- -- 0.24 -0.016 -- -0.006 0.29 Pheasant Turkey -- -- 0.08 -0.013 -- -0.024 0.28 Guinea fowl Turkey -- -- 0.16 0.003 -- -0.019 -0.01 *Bolding indicates significant difference based on Bonferroni threshold of 0.0083, for 6 possible species pairwise comparisons within each step category. Table S6: Post hoc pairwise mean differences between galliform species (column 2-column 1) from ANOVA (Table S4). Notably, pairwise differences in leg posture (θTD, HTD) and change in potential energy (ΔEp), which are most indicative of obstacle negotiation strategy, do not significantly differ between species.   The Journal of Experimental Biology | Supplementary Material 9   Species Quail Pheasant Guinea fowl Turkey Ostrich Fitted Parameters Spring stiffness (knorm=k*Lleg /(m*g)) 8.0 11 15 10 12 Damping coefficient 0.10 0.20 0.40 0.20 0.40 Computed Property Damping ratio 0.018 0.020 0.052 0.032 0.058 Optimal trajectory performance Mean-squared error 0.0102 0.0266 0.0032 0.0081 0.0063 Net unsigned work (Joules / (m*g*Lleg)) 0.3149 0.1708 0.0992 0.0761 0.0081 Normalising Parameters m (kg) 0.200 1.02 1.48 2.96 116 Lleg (m) 0.117 0.201 0.228 0.287 0.974 g (m/s2) 9.81 Table S7: Normalised results of trajectory optimisation applied to the actuated model (Fig. 6A), resulting in the reported fits to bird GRF (Fig. 6B) and leg length trajectories (Fig. 6C). Bird size spanned over a 500-fold mass range, but the damping ratio remained with a factor of 3.27 across species. Average masses reported in this table differ somewhat from those reported in main text because here the mass averaging was weighted by number of level step samples, not by individual birds. Given the non-unique nature of the parameter fits (Fig. S3), we do not make scientific claims about the functional significance of any one particular set of parameters. Nonetheless, a relatively narrow range of damping ratios results in fits consistent with bird running data. The Journal of Experimental Biology | Supplementary Material

Don't break a leg: running birds from quail to ostrich prioritise leg safety and economy on uneven terrain.

[]

Birn-Jeffery, Aleksandra V,Hubicki, Christian M,Blum, Yvonne,Renjewski, Daniel,Hurst, Jonathan W,Daley, Monica A

eng

PMC9371152

Citation: Štuhec, S.; Planjšek, P.; Ptak, M.; ˇCoh, M.; Mackala, K. Application of the Laser Linear Distance-Speed-Acceleration Measurement System and Sport Kinematic Analysis Software. Sensors 2022, 22, 5876. https://doi.org/ 10.3390/s22155876 Received: 29 June 2022 Accepted: 3 August 2022 Published: 5 August 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). sensors Article Application of the Laser Linear Distance-Speed-Acceleration Measurement System and Sport Kinematic Analysis Software Stanko Štuhec 1, Peter Planjšek 2, Mariusz Ptak 3 , Milan ˇCoh 1 and Krzysztof Mackala 4,* 1 Faculty of Sport, University of Ljubljana, Gortanova 22, 1000 Ljubljana, Slovenia 2 Ljubljana School of Business, Management and Informatics, Tržaška cesta 42, 1000 Ljubljana, Slovenia 3 Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, Lukasiewicza 7/9, 50-371 Wrocław, Poland 4 Faculty of Physical Education and Sport, Wroclaw University of Health and Sport Science, Paderewskiego 35, 51-612 Wrocław, Poland * Correspondence: [email protected]; Tel.: +48-347-3147 Abstract: The industrial development of technology, with appropriate adaptation, enables us to discover possibilities in sport training control. Therefore, we have developed a new approach to linear running analysis. This study aims to determine the measurement possibilities using an LDM301A laser system in obtaining basic kinematic parameters. The second goal is the application of specialized computer programs based on appropriate algorithms to calculate a vast number of variables that can be used to adjust the training and the rivalry. It is a non-invasive, non-contact measurement method. We can also determine the influence of both subjective and objective external factors. In this way, we can also conduct training with real-time scientific feedback. This method is easy to use and requires very little time to set up and use. The efficiency and running economy can be calculated with various time, speed, acceleration, and length indexes. Calculating the symmetries between the left and right leg in velocity, stride lengths, support phase times, flight phase times, and step frequency are possible. Using the laser measurement method and detailed kinematic analysis may constitute a new chapter in measuring speed. However, it still has to compete with classic photocell measurement methods. This is mainly due to their high frequency of measurement used, despite some reservations about the scale of measurement errors. Keywords: speed; sprints; laser system; step kinematics; run measurements; biomechanics 1. Introduction Training control based on new technologies and technological–methodological solu- tions are essential in sport. These procedures aim to determine the relevant and objective parameters of the athlete’s current speed preparation. Without data on biomotor, mor- phological, physiological, biochemical, psychological, and sociological characteristics, it is unmanageable to plan, program, and model a modern training process [1–5]. Based on the measured variables, we can choose the most effective methods and means for planning training and, thereby, improving sport results. The athlete’s motor skill development and special conditioning preparation interact in the sport training process [6,7]. This relationship is dynamic and always different depending on the phases of the training process and the biological development of the athlete [1,7,8]. Given that automated stereotypes and the level of motor abilities are changing, the training process must be monitored, controlled, and finally, corrected. The speed of the sprint changes in the individual phases of the run, thus each stage deserves special treatment, both in terms of training control and training itself [9]. Notably, the sprint is considered as running up to 100 m. The advantage of our new training control method is the help of a laser measurement, which is very useful because, in many sport disciplines, the ability to accelerate well from a static position and quickly develop the highest running Sensors 2022, 22, 5876. https://doi.org/10.3390/s22155876 https://www.mdpi.com/journal/sensors Sensors 2022, 22, 5876 2 of 13 speed is the key to improving performance in both teams and individual sports. Running velocity is the composition of stride frequency and stride length; in studies where the same subjects ran at different speeds, both stride rate and stride length are highly correlated with increasing running speed [1,10]. Current information, biocybernetic, and visual technologies solve the most demanding movement problems in the diagnostics of the sport training process [11–14]. The conceptual- ized modern measurement allows for an objective analysis of the movement structures, the selection, and the application of the most suitable training control methods for individual modelling of athletic training [15]. Locomotor speed is undoubtedly one essential biomotor ability that improves sport performance. It occurs in various sport disciplines such as running, sprinting, or jumping. In recent decades, the main focus has been on diagnostic capabilities that allow monitoring changes in the kinematic values of sprint parameters. This applies to both the competition and the monitoring of the training process. It is only possible due to the application of appropriate measurement methods (fully automatic tim- ing systems, photocells, Optojump System, video-recording via high-speed camera, global positioning systems—GPS, or laser system) and proper software, enabling an insightful analysis. This last methodology was used in the most critical athletic competitions in the world (World Championships, Olympic Games). The LDM (laser distance measurement) device appears to be an entirely new chapter in developing speed training control methods. The most desirable information about a moving athlete can be obtained from the description of the linear speed—the movement of SG (center of gravity). Such a reference is essential when the direction of the human body with all segments is studied as a maximal speed of a single point [16]. With limitations, an LDM can also be used to set realistic conditions for acceleration—deceleration drills [17]. Results in research indicate that a low- cost and accessible laser system can be used to accurately determine walking and running speed [18]. Therefore, the aims of this study are twofold. First, to present the usefulness of data acquisition from the LDM 301A (ASTECH GmbH, Rostock, Germany) device during the maximum sprint. The second goal was to determine the analytical capabilities of a new software, and consequently, to evaluate the usefulness of this software for analysis of speed kinematic profiles. 2. Materials and Methods 2.1. Participants The measurement of the 100 m sprint with the LDM 301A (ASTECH GmbH, Rostock, Germany) system involved one national-level Slovenian sprinter (age 22.4 years, body height 177.6 cm, and body weight 74.9 kg; best result 10.39 s/100 m). Before the experiment, the subject had six years of active training and competition in sprinting (60, 100, and 200 m). The laser measurement occurred at the beginning of the competition period, at the Faculty of the Sport, University of Ljubljana stadium. It was a sunny day, and the wind was +1.2 m/s. Before the study, approval by the Human Ethics Committee of the University of Ljubljana was obtained for this experiment. The participant was notified about the risks associated with participating in this experiment, the purpose of the investigation, and the measuring procedures. He signed an informed consent document before any testing. 2.2. Description of the Laser Distance Measurement Device A laser distance measurement device is completely non-invasive, which in practice means that an athlete can run in competition conditions without any sensors on the body. The LDM301A with an invisible beam (Figure 1) is a Class 1 laser device based on the norm IEC 60825-1:2003. The connection to a computer was established via a particular base station using the RS232 to USB port converter. The pilot laser with a visible redpoint is a Class 2 laser device based on the norm IEC 60825-1:2007. The average angle of the laser beam spread to 1.7 mrad. The divergence of the laser was 1.7 mrad × 0.08 mrad (rectangle). The receiver divergence was 2.9 mrad (circle). The size and shape of the laser beam on the reflecting surface increased with distance. From ten Sensors 2022, 22, 5876 3 of 13 to one hundred meters, the surface of the laser beam increased by a factor of 12.9 (from 1548 mm2 to 19,900 mm2). The measurement precision of the device was ±20 mm in a mode measurement frequency of 2 kHz and measurement value output of 100 Hz. The LDM tool created a text file containing the measurement data (first row, time; second row, distance). A schematic representation of the official measurement compared to a laser distance measurement used in the study is depicted in Figure 2. Sensors 2022, 22, x FOR PEER REVIEW 3 of 14 base station using the RS232 to USB port converter. The pilot laser with a visible redpoint is a Class 2 laser device based on the norm IEC 60825-1:2007. Figure 1. Laser distance measurement system setup (left) and LDM301 device with pivot laser point and base station (right). The average angle of the laser beam spread to 1.7 mrad. The divergence of the laser was 1.7 mrad × 0.08 mrad (rectangle). The receiver divergence was 2.9 mrad (circle). The size and shape of the laser beam on the reflecting surface increased with distance. From ten to one hundred meters, the surface of the laser beam increased by a factor of 12.9 (from 1548 mm2 to 19,900 mm2). The measurement precision of the device was ±20 mm in a mode measurement frequency of 2 kHz and measurement value output of 100 Hz. The LDM tool created a text file containing the measurement data (first row, time; second row, dis- tance). A schematic representation of the official measurement compared to a laser dis- tance measurement used in the study is depicted in Figure 2. Figure 2. A schematic representation of the official measurement compared to a laser distance meas- urement used in the study. Start signal First movement Official line crossing Reaction time Running Running Lower back finish line crossing Lower back start line crossing Official time Laser time Figure 1. Laser distance measurement system setup (left) and LDM301 device with pivot laser point and base station (right). base station using the RS232 to USB port converter. The pilot laser with a visible redpoint is a Class 2 laser device based on the norm IEC 60825-1:2007. Figure 1. Laser distance measurement system setup (left) and LDM301 device with pivot laser point and base station (right). The average angle of the laser beam spread to 1.7 mrad. The divergence of the laser was 1.7 mrad × 0.08 mrad (rectangle). The receiver divergence was 2.9 mrad (circle). The size and shape of the laser beam on the reflecting surface increased with distance. From ten to one hundred meters, the surface of the laser beam increased by a factor of 12.9 (from 1548 mm2 to 19,900 mm2). The measurement precision of the device was ±20 mm in a mode measurement frequency of 2 kHz and measurement value output of 100 Hz. The LDM tool created a text file containing the measurement data (first row, time; second row, dis- tance). A schematic representation of the official measurement compared to a laser dis- tance measurement used in the study is depicted in Figure 2. Figure 2. A schematic representation of the official measurement compared to a laser distance meas- urement used in the study. Start signal First movement Official line crossing Reaction time Running Running Lower back finish line crossing Lower back start line crossing Official time Laser time Figure 2. A schematic representation of the official measurement compared to a laser distance measurement used in the study. 2.3. Measurement Method Before starting the measurement, we needed to calibrate the track with a particular calibrating device. The calibration was performed by placing a rectangular bar (height 1.5 m, width 0.03 m, and depth 0.03 m) in an exact vertical position on the starting line and measuring the distance to the bar with the laser. The laser must be in a precise horizontal position and at the exact height of the lumbar spine of the measured person (L1 in Figure 3). Sensors 2022, 22, 5876 4 of 13 This distance represents the basis for the measurement. When the sprinter passed the measured calibration distance, the sprinter entered the measurement zone (L2 in Figure 3). The measurement lasted as long as the sprinter’s lower back was in the measurement zone (L2). 2.3. Measurement Method Before starting the measurement, we needed to calibrate the track with a particular calibrating device. The calibration was performed by placing a rectangular bar (height 1.5 m, width 0 .03 m, and depth 0.03 m) in an exact vertical position on the starting line and measuring the distance to the bar with the laser. The laser must be in a precise horizontal position and at the exact height of the lumbar spine of the measured person (L1 in Figure 3). This distance represents the basis for the measurement. When the sprinter passed the measured calibration distance, the sprinter entered the measurement zone (L2 in Figure 3). The measurement lasted as long as the sprinter’s lower back was in the measurement zone (L2). Figure 3. Diagram showing a sprint measurement protocol using a laser distance measurement. Legend: A—laser position, B—starting line, C—distance to the lower back of the runner, D—finish line, L1—calibration distance, L2—measuring distance, L3—the actual measuring distance of lower back to the laser measurement (L2 is calculated as the difference between L3 and L1). Due to various factors, some errors were made in the raw measurement. The most common errors occurred due to a loss of laser contact with the lower back due to the movement of the sprinter left to right, interruptions of the laser beam by hand, low-reflec- tion material of the shirt, and other interruptions to the laser signal. 2.4. Data Processing For raw distance-time data capture, we used the original software LDMTool from ASTECH. A simple user interface displayed all available parameters of the currently con- nected sensor and all measured values, as well as the state of the different output signals. There were four main groups of direct communication: device parameters, device data, a graphical display of a distance-time diagram, and a log window. The raw displacement data obtained with the LDM device were captured with a fre- quency of 100 Hz. From the change in displacement, the speed sprint (vh) was calculated in every hundredth of a second of running. The curve was smoothed with a moving av- erage filter over a 0.1 s interval (n = 10, smoothing frequency m = 10) to eliminate any within-step velocity fluctuations. The polynomial startpoint was identified from where the raw displacement values increased and remained more significant than 2 SD above the mean noisy pre-start signal level. The endpoint was 50 data points after the displace- ment exceeded 60 m. 2.5. Software for Kinematic Laser Distance Linear Running Analysis At the University of Ljubljana, Faculty of Sports, Institute of Sports, we developed an entirely new approach to kinematic measurements of linear sprint running in the Biome- chanical Laboratory. The development of laser meters, which allows us to perform non- L1 L2 L3 A B C D Figure 3. Diagram showing a sprint measurement protocol using a laser distance measurement. Legend: A—laser position, B—starting line, C—distance to the lower back of the runner, D—finish line, L1—calibration distance, L2—measuring distance, L3—the actual measuring distance of lower back to the laser measurement (L2 is calculated as the difference between L3 and L1). Due to various factors, some errors were made in the raw measurement. The most common errors occurred due to a loss of laser contact with the lower back due to the movement of the sprinter left to right, interruptions of the laser beam by hand, low- reflection material of the shirt, and other interruptions to the laser signal. 2.4. Data Processing For raw distance-time data capture, we used the original software LDMTool from ASTECH. A simple user interface displayed all available parameters of the currently connected sensor and all measured values, as well as the state of the different output signals. There were four main groups of direct communication: device parameters, device data, a graphical display of a distance-time diagram, and a log window. The raw displacement data obtained with the LDM device were captured with a frequency of 100 Hz. From the change in displacement, the speed sprint (vh) was calculated in every hundredth of a second of running. The curve was smoothed with a moving average filter over a 0.1 s interval (n = 10, smoothing frequency m = 10) to eliminate any within-step velocity fluctuations. The polynomial startpoint was identified from where the raw displacement values increased and remained more significant than 2 SD above the mean noisy pre-start signal level. The endpoint was 50 data points after the displacement exceeded 60 m. 2.5. Software for Kinematic Laser Distance Linear Running Analysis At the University of Ljubljana, Faculty of Sports, Institute of Sports, we developed an entirely new approach to kinematic measurements of linear sprint running in the Biomechanical Laboratory. The development of laser meters, which allows us to perform non-contact measurements of horizontal position, speed, and acceleration, combined with our program, has enabled us to quickly and accurately diagnose running techniques and tactics. With entirely new algorithms and the already mentioned essential variables, we additionally calculate the support time, flight time, step time, and frequency within individual steps. Advanced functions allow us to calculate the symmetries between the left and right foot for each step and step phase. The horizontal running speed of the sprinter fluctuates from the maximum speed at the last contact of the push-off leg with the ground, Sensors 2022, 22, 5876 5 of 13 and then decreases due to air resistance and the force of gravity in the flight phase until the opposite leg touches the ground, where it starts to increase again due to the action of the muscles in the push-off phase until the last contact time when maximum running speed is again reached on the opposite push-off leg. The time of the support phase is defined from the moment when the slope of the speed reduction curve changes and starts to increase again until the moment of maximum speed. The time of the left step lasts from the first contact of the left foot with the ground through the push-off time (the time until the last contact with the ground) and until the first contact of the right foot with the ground. The time of the right step lasts from the first contact of the right foot with the ground through the push-off time (the time until the last contact with the ground) and until the first contact of the left foot with the ground. The flight time of the left step lasts from the last contact of the left foot with the ground to the first contact of the right foot with the ground. The flight time of the right step lasts from the last contact of the right foot with the ground to the first contact of the left foot with the ground. When we have certain time phases of the left and right steps, we also calculate the step frequency, where we can define the beginning and end of the left or right step in different ways (the last contact of the left foot with the ground, through the flight phase from the left to the right foot and until the last contact of the right foot with the ground or from the first contact of the left foot with the ground to the last contact of the left foot with the ground, through the flight phase from the left to the right foot and until the first contact of the right foot with the ground). In the following, we calculated the new shape of the separate curve for path, velocity, and acceleration by prescribing the best match of the curve shape according to the wavelength, amplitude, and direction of inclination of the average section of each variable. The first derivative of path change over time is velocity, and the second derivative of it is acceleration. The software was based on functionality divided into twelve modules: (1) data editing: data preview, data preparation, adding missing data, data cut, error data elimination, converting data to sprint format, and data save; (2) measurement description: place, date, wind, official time, reaction time, measured distance, name, birth date, country, height, weight, left leg length, right leg length, sport discipline, dominant hand, dominant leg, first leg on start, moving or static start, static high or low start, notes, calibration distance, and maximum speed tolerance; (3) data smoothing: the type and rate of the smoothing used for each variable; (4) data calculation: times, speed, acceleration, steps, zones, phases, sections, frequencies, symmetry index, efficiency index, effectiveness index, and performance index; (5) single analysis: analyze a single measurement; (6) databases: database organization; (7) multiple analysis: analyze and compare multiple measurements; (8) statistical: descriptive statistical data analysis; (9) artificial intelligence: with the use of machine learning and artificial intelligence, we developed a unique code to look for new ways of optimizing and improving the efficiency and effectiveness of running; (10) export: exported all raw and calculated data in txt, CSV, or xlsx data format; (11) report: graphical and numerical report (diagrams and tables); (12) print: print selected report options. 3. Results Regarding the maximum running speed, both timing and distance data are essential. Based on this, we can adjust the training in real time so that the athlete can check on which section of the track he reached the maximum speed on each sprint. Therefore, raw data obtained during the measurement is a critical issue. It was first processed with the raw data editing module. The next phase is the data cut module. In this phase, we removed unwanted data before movement started and unwanted data after the lower back crossed the finish line. We recommend keeping data for one second before point B and after point D in Figure 3. The measured raw data and filter data are depicted in Figure 4. Further, we can define the speed zones, which always appear in a 100 m race, no matter the performance level. It is possible to do this via smoothed speed data. The diagram shows the three primary phases of the 100 m run: red—ascending speed, blue—maintenance of the maximum speed, and green—descending speed (Figure 5). However, in this case, Sensors 2022, 22, 5876 6 of 13 the maximum speed phase is defined by a tolerance of 2% from the maximum speed. An essential element of these two graphs is that the course of the variability of the maximum speed is presented in relation to the time of exercise and the change in distance. Both charts overlap and are the same. Maintaining a maximum speed is one of the most important indexes in sprint performance. Sensors 2022, 22, x FOR PEER REVIEW 6 of 14 data editing module. The next phase was to cut the module. In this phase, we removed unwanted data before movement started and unwanted data after the lower back crossed the finish line. We recommend keeping data for one second before point B and after point D in Figure 3. The measured raw data and filter data are depicted in Figure 4. Figure 4. Measured raw distance-time data (blue line), calculated raw speed (thin green line), and averaged speed (bold green line). Further, we can define the speed zones, which always appear in a 100 m race, no matter the performance level. It is possible to do this via smoothed speed data. The dia- gram shows the three primary phases of the 100 m run: red—ascending speed, blue— maintenance of the maximum speed, and green—descending speed (Figure 5). However, in this case, the maximum speed phase is defined by a tolerance of 2% from the maximum speed. An essential element of these two graphs is that the course of the variability of the maximum speed is presented in relation to the time of exercise and the change in distance. Both charts overlap and are the same. Maintaining a maximum speed is one of the most important indexes in sprint performance. 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 1 20 39 58 77 96 115 134 153 172 191 210 229 248 267 286 305 324 343 362 381 400 419 438 457 476 495 514 533 552 571 590 609 628 647 666 685 704 723 742 761 780 799 818 837 856 875 894 913 932 951 970 989 1008 speed (m/s) distance (m) time (ms) distance speed (raw) speed (smooth) Figure 4. Measured raw distance-time data (blue line), calculated raw speed (thin green line), and averaged speed (bold green line). This part of the analysis of the collected data is based on smaller, 10 m long segments of the 100 m distance, which allows studying the nature of the maximum speed in more detail. Such an analysis differs significantly from the overall approach because the course of individual running parameters is more distinct and is even due to smaller differences in the values of the discussed variables. Figure 6 shows how the sprinter’s speed increases proportionally concerning the average and maximum zone speeds. In this way, it can be found at what distance and when the sprinter reached a certain speed. In addition, our analysis of the speed of the object’s movement allows us to determine the type of movement, whether the runner is in an acceleration or deceleration phase. Consequently, the acceleration value expresses the speed of changing the position of a given object or the direction of its movement. More precisely, linear acceleration can be defined as the change of speed over time (1): ai = v t = (vi + 1) − (vi − 1) (ti + 1) − (ti − 1) (1) where: a = acceleration, v = speed, t = time, and i = calculation point. The calculated acceleration is one of the important variables used to determine sprint performance. We have to remember that the greater initial acceleration and longer positive acceleration define better performance. In Figure 7, we can observe the runner’s speed and acceleration in time. The peak velocity is calculated from the smoothed speed curve. Sensors 2022, 22, 5876 7 of 13 Sensors 2022, 22, x FOR PEER REVIEW 7 of 14 Figure 5. Sprinting phases: the diagram of speed in relation to time (above) and distance (below), acceleration phase (red line), maximum speed phase (blue line), and descending speed phase (green line). This part of the analysis of the collected data is based on smaller, 10 m long segments of the 100 m distance, which allows studying the nature of the maximum speed in more detail. Such an analysis differs significantly from the overall approach because the course of individual running parameters is more distinct and is even due to smaller differences in the values of the discussed variables. Figure 6 shows how the sprinter’s speed increases proportionally concerning the average and maximum zone speeds. In this way, it can be found at what distance and when the sprinter reached a certain speed. Figure 5. Sprinting phases: the diagram of speed in relation to time (above) and distance (be- low), acceleration phase (red line), maximum speed phase (blue line), and descending speed phase (green line). Sensors 2022, 22, x FOR PEER REVIEW 8 of 14 Figure 6. Cont. Sensors 2022, 22, 5876 8 of 13 Figure 6. Speed-time (above) and speed-distance (below) with 10 m sections (blue lines) and point of maximum speed (green line). In addition, our analysis of the speed of the object’s movement allows us to determine the type of movement, whether the runner is in an acceleration or deceleration phase. Consequently, the acceleration value expresses the speed of changing the position of a given object or the direction of its movement. More precisely, linear acceleration can be defined as the change of speed over time (1): 𝑎𝑖 = 𝑣 𝑡 = (𝑣𝑖 + 1) − (𝑣𝑖 − 1) (𝑡𝑖 + 1) − (𝑡𝑖 − 1) (1) where: a = acceleration, v = speed, t = time, and i = calculation point. The calculated accel- eration is one of the important variables used to determine sprint performance. We have to remember that the greater initial acceleration and longer positive acceleration define better performance. In Figure 7, we can observe the runner’s speed and acceleration in time. The peak velocity is calculated from the smoothed speed curve. Figure 6. Speed-time (above) and speed-distance (below) with 10 m sections (blue lines) and point of maximum speed (green line). 2, x FOR PEER REVIEW 9 of 14 Figure 7. Speed and acceleration (raw and smoothed data). The primary criterion determining the effectiveness of a maximum speed is the length and frequency of steps and their mutual relations. High values of both these pa- rameters have a decisive influence on the maximum speed value; they are also an indica- tor of the correct running technique. Using our program, we can accurately determine the length of each step. The module for automatically determining the length of the steps di- vides the entire run into individual steps based on the fluctuation of the speed of the run between the phase of the support phase and the flight phase. By smoothing, we eliminate the wrong calculated step length (Figure 8). -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 0 50 100 150 200 250 300 350 400 450 500 550 600 650 speed (m/s) and acceleration (m/s2) time (ms) speed-raw speed-smooth acceleration-raw acceleration-smooth Figure 7. Speed and acceleration (raw and smoothed data). The primary criterion determining the effectiveness of a maximum speed is the length and frequency of steps and their mutual relations. High values of both these parameters have a decisive influence on the maximum speed value; they are also an indicator of the correct running technique. Using our program, we can accurately determine the length of each step. The module for automatically determining the length of the steps divides the entire run into individual steps based on the fluctuation of the speed of the run between the phase of the support phase and the flight phase. By smoothing, we eliminate the wrong calculated step length (Figure 8). Sensors 2022, 22, 5876 9 of 13 parameters have a decisive influence on the maximum speed value; they are also an indicator of the correct running technique. Using our program, we can accurately determine the length of each step. The module for automatically determining the length of the steps divides the entire run into individual steps based on the fluctuation of the speed of the run between the phase of the support phase and the flight phase. By smoothing, we eliminate the wrong calculated step length (Figure 8). Figure 8. Function determining step length (thin line—raw and bold line—smooth length). Figure 8. Function determining step length (thin line—raw and bold line—smooth length). By the same principle, the frequency of the steps and the time are also calculated (Figures 9 and 10). Length and frequency of steps are the basis for optimizing and adapting these two variables to achieve a higher average running speed. FOR PEER REVIEW 10 of 14 By the same principle, the frequency of the steps and the tie are also calculated (Figures 9 and 10). Length and frequency of steps are the basis for optimizing and adapting these two variables to achieve a higher average running speed. Figure 9. The function of determining step frequency (thin line—raw and bold line—smooth frequency). Figure 9. The function of determining step frequency (thin line—raw and bold line—smooth frequency). Sensors 2022, 22, 5876 10 of 13 Figure 9. The function of determining step frequency (thin line—raw and bold line—smooth frequency). Figure 10. The function of determining step time from take-off (thin line—raw and bold line— smooth values). In the descriptive statistical data processing module, it is possible to calculate mean, median, mode, minimum, maximum, standard deviation, skewness, and kurtosis. We can also calculate the linear correlation between the selected variables. Figure 10. The function of determining step time from take-off (thin line—raw and bold line— smooth values). In the descriptive statistical data processing module, it is possible to calculate mean, median, mode, minimum, maximum, standard deviation, skewness, and kurtosis. We can also calculate the linear correlation between the selected variables. 4. Discussion The aims of this study are twofold. First, assess the usefulness of a laser measuring system to record the time of a sprint run with maximum intensity. Secondly, propose an innovative computer software for the sprint kinematic analysis, based on the logarithm of the extraction of motion parameters, including information on the change in the course of the maximum speed in a straight line. The conclusion is that the time measurement made by using the LDM laser system is useful to provide data for a statistical analysis of the course of maximum speed changes during a sprint. Exploring diagnostic problems related to sprint performance assessment, specifically the impact and magnitude of various external conditions, technologies, and monitoring, is one of the essential factors in the training process. In this case, the focus is on the diagnostic possibilities related to monitoring changes in the value of the linear running speed during competition and speed training. It seems to be a key element in improving sprint performance. The cinematographic method (Omega’s fully automatic timing system, Fribourg, Switzerland or Sony DCR-PC105E, Japan) is one of the primary methods of studying and evaluating the movement technique [11]. The main advantage of this method is the lack of direct interference in the athlete’s motor task performance, which allows for the full and free use of acquired technical skills. In recent years, several technologies have appeared to measure a run at maximum speed in a non-invasive way, both in a straight line and in curvature. There is an advanced technology that provides positional data with high spatiotemporal resolution [19]. The data can be collected with a radio-based position detection system (RedFIR, Fraunhofer Institute, Germany). The second option is to apply inertial measurement units to capture multidimensional accelerometers and gyroscope data to measure the kinematic parameters of a system. During the last decade, GPS with integrated accelerometers was extensively applied in various team sports to measure running velocity during training sessions and games [19–22]. One time-effective Sensors 2022, 22, 5876 11 of 13 method for obtaining speed-time curves is applying a laser distance measurement (LDM) device [12,13]. The most recognizable and used at international athletic competitions (2008 World Championships and Olympic Games) is the LAVEG laser speed gun (LAVEG Sport, Jenoptik, Germany). The system is placed behind the starting line during the measurement, and the beam laser must be aimed (tracking the competitor) at his pelvis throughout the measurement [23]. The LAVEG system measures the positional information of an athlete at 100 Hz. Therefore, training with immediate feedback using a laser device can be of great help when we try to improve running performance. An essential component of all these measurement methods is the validity and relia- bility of measurement [24,25]. Velocity data obtained from sprint trials were previously assessed but were limited, by comparison, to linear velocities at the hip over a distance. According to Haugen et al. [11], Omega’s fully automatic timing system demonstrated that the measurement method was valid to the instrument’s precision (±0.01 s), which is a very accurate value. The RedFIR (a radio-based position detection system) can provide precise in-field performance data based on reference systems [14,26]. Regardless of the level of validity and reliability, one crucial thing is that all these methodologies of time measurement systems allow obtaining the data that can be subjected to multidirectional analysis. It applies to velocity profiles—calculation of acceleration, instantaneous speed, split time, and parameters—that determine the length and frequency of the step. The more precise this diagnostic, the better are the technology used, especially re- garding the software for kinematic analysis. A proper kinematic analysis software of a sprint run should be based on a reliable acquisition of the primary structural properties of the movement, its characteristic quantities (numerical values), and the relations between them. Often, the identical movements of a sprinter’s lower limbs differ in many respects. This is mainly not due to different methods of recording a sprint run, but to the computer software applications used for detail analysis. A detailed description of the software opera- tion can be found in the Materials and Methods section. However, this software aims to estimate the kinematic parameters (i.e., position, speed, acceleration, and possibly basic running step parameters) of the moving sprinter based on noisy measurements collected by the laser beam sensor. It is possible thanks to the use of a special tracking algorithm. It must consider the deterministic model of the dynamics of changes in the target’s position (e.g., model of acceleration or increasing speed). Such activity enables the estimation of the batch processing method (raw data from the running time) to obtain the target kinematic parameters of the sprint. Therefore, estimation of target kinematic parameters based on LDM measurements is not a template due to the linear nature of these measurements about the target kinematic parameters. Additionally, this software represents closed solutions and, thus, requires the repetition of numerical search algorithms to obtain accurate kinematic data. This makes this software highly effective. The main task of training in a sprint should be to raise the athlete’s movement potential to the highest possible level to achieve maximum sport results [4,6]. This is mainly related to conducting comprehensive activities to achieve optimal running efficiency, maintaining the highest possible values of the maximum running speed over the entire distance [12]. The more significant the correlation between the length and frequency of running steps, the better. The primary criterion determining the effectiveness of speed for a whole sprint run and the level of speed preparation of a competitor is the length and frequency of steps and their mutual relations [5,27]. High values of both these parameters have a decisive influence on the maximum speed value; they are also an indicator of the correct running technique. The research conducted by Luhtanen and Komi [10] and Mero and Komi [8] showed that the values of these parameters change with increasing speed, which has a linear course until the competitor develops a speed of about 7 m/s. As this value increases to about 9 m/s, the increment in the stride length is small, and the frequency increment is significant. This is even more evident when the competitors reach speeds of 11–12 m/s, which is only possible due to a substantial increase in pace [1]. In addition to the length of steps, the frequency Sensors 2022, 22, 5876 12 of 13 of steps is the primary parameter that allows developing the maximum speed of the run, thus improving its efficiency. The more significant the correlation between the length and frequency of running steps, the better. However, this somewhat contradicts Deleclus’s manuscript [28], which found a linear relationship between the length of the running step and the speed developed, saying that there is no significant correlation between the frequency of steps and the speed. The author also believes that in short runs, the pace of the run reaches its maximum value at the beginning of the sprint (after a few steps) and does not change significantly. On the other hand, the stride length increases almost the entire distance, which is an essential factor in developing maximum speed. The relationship between stride length and speed achieved in a 100 m run for men was determined based on linear regression analysis and was V = 0.79 + (3.89 Lk). The change in stride length can explain almost 85% of the variation in maximum running speed. In such an argument, it is essential to indicate whether the analysis considers only the distribution of the length of individual steps over the entire distance or the average value calculated for a 10 m section. The distribution curve shows a much greater variability (dispersion) between personal values for each competitor than the average value of the results obtained in the individual ten sections. 5. Conclusions A runner’s distance, time, speed, and acceleration vary from step to step and can be divided into several phases and sections. Each of these phases has a particular influence on the final result. With LDM301A, the measured and calculated variables can be closely monitored at all stages, and each phase can be analyzed and optimized during the training process. Determining the right goals based on previous tests is crucial for the optimal planning of the athlete’s development. Once a coach has an insight into each phase, they can use the information to decide how to adapt the training process. We will continue to develop the functions of the software in the future. One of the priorities is the variability of the take-off speed of the consistent steps and the take-off speed for the left and the right leg. Calculating acceleration within the steps can also give us information about the ratio of positive acceleration (propulsion phase—the second part of the support phase) to deceleration (flight phase and braking in the first part of the support phase). This way, a large amount of measurement data will give us a new insight into running optimization. We will also determine the new index between the maximum speed zone, step length, step frequency, and step time. Some of our data indicate a high probability of added value when planning sprint training with this approach. Author Contributions: Conceptualization, S.Š. and M. ˇC.; methodology, S.Š., M. ˇC. and P.P.; software, S.Š. and P.P.; validation, S.Š., P.P. and M.P.; formal analysis, S.Š., M. ˇC., M.P. and K.M.; investigation, S.Š., M. ˇC., P.P. and K.M., resources, M. ˇC.; data curation, M. ˇC. and S.Š., writing—original draft preparation, S.Š., M. ˇC. and K.M., writing—review and editing, K.M., M. ˇC. and M.P.; visualization, S.Š. and K.M.; supervision, K.M. and M. ˇC. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Human Ethics Committee of the University of Ljubljana. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are available on request from the corresponding author. Acknowledgments: Open access publishing of this article was supported by the Slovenian Research Agency (grant number P5-0147). The authors thank the athlete for his participation in the study. Conflicts of Interest: The authors declare no conflict of interest. Sensors 2022, 22, 5876 13 of 13 References 1. Mero, A.; Komi, P.V.; Gregor, R.J. Biomechanics of Sprint Running. Sport. Med. 1992, 13, 376–392. [CrossRef] [PubMed] 2. Vonstein, W. Some reflections on maximum speed sprinting technique. New Stud. Athl. 1996, 11, 161–165. 3. Summers, R. Physiology and Biophysics of the 100-m Sprint. Physiology 1997, 12, 131–136. [CrossRef] 4. Coh, M.; Babic, V.; Ma´ckała, K. Biomechanical, Neuro-muscular and Methodical Aspects of Running Speed Development. J. Hum. Kinet. 2010, 26, 73–81. [CrossRef] 5. Ma´ckała, K.; Fostiak, M. Acute Effects of Plyometric Intervention—Performance Improvement and Related Changes in Sprinting Gait Variability. J. Strength Cond. Res. 2015, 29, 1956–1965. [CrossRef] 6. Mackala, K.; Fostiak, M.; Schweyen, B.; Osik, T.; Coch, M. Acute Effects of a Speed Training Program on Sprinting Step Kinematics and Performance. Int. J. Environ. Res. Public Health 2019, 16, 3138. [CrossRef] 7. Latash, M.L. Control of Human Movement; Human Kinetics: Champaign, IL, USA, 2000. 8. Mero, A.; Komi, P. V Force-, EMG-, and elasticity-velocity relationships at submaximal, maximal and supramaximal running speeds in sprinters. Eur. J. Appl. Physiol. Occup. Physiol. 1986, 55, 553–561. [CrossRef] 9. Delecluse, C.; Van Coppenolle, H.; Willems, E.; Van Leemputte, M.; Diels, R.; Goris, M. Influence of high-resistance and high-velocity training on sprint performance. Med. Sci. Sports Exerc. 1995, 27, 1203–1209. [CrossRef] 10. Luhtanen, P.; Komi, P. Mechanical Factors Influencing Running Speed; University Park Press: Baltimore, MD, USA, 1978. 11. Haugen, T.A.; Tønnessen, E.; Seiler, S.K. The Difference Is in the Start: Impact of Timing and Start Procedure on Sprint Running Performance. J. Strength Cond. Res. 2012, 26, 473–479. [CrossRef] 12. Bezodis, N.; Salo, A.I.; Trewartha, G. Measurement Error in Estimates of Sprint Velocity from a Laser Displacement Measurement Device. Int. J. Sports Med. 2012, 33, 439–444. [CrossRef] 13. Parrington, L.; Phillips, E.; Wong, A.; Finch, M.; Wain, E.; MacMahon, C. Validation of inertial measurement units for tracking 100m sprint data. In Proceedings of the ISBS-Conference Proceedings Archive, Tsukuba, Japan, 18–22 July 2016. 14. Wixted, A.J.; Billing, D.C.; James, D.A. Validation of trunk mounted inertial sensors for analysing running biomechanics under field conditions, using synchronously collected foot contact data. Sport. Eng. 2010, 12, 207–212. [CrossRef] 15. Zatsiorsky, V.; Vladimir, M. Science and Practice of Strength Training; Human Kinetics: Champaign, IL, USA, 2020. 16. Krause, J. Biomechanics: A Qualitative Approach for Studying Human Movement. Quest 1986, 38, 80–81. [CrossRef] 17. Ashton, J.; Jones, P.A. The Reliability of Using a Laser Device to Assess Deceleration Ability. Sports 2019, 7, 191. [CrossRef] 18. Clark, R.A.; Pua, Y.-H.; Bower, K.J.; Bechard, L.; Hough, E.; Charlton, P.C.; Mentiplay, B. Validity of a low-cost laser with freely available software for improving measurement of walking and running speed. J. Sci. Med. Sport 2019, 22, 212–216. [CrossRef] 19. Seidl, T.; Russomanno, T.G.; Stöckl, M.; Lames, M. Assessment of Sprint Parameters in Top Speed Interval in 100 m Sprint—A Pilot Study Under Field Conditions. Front. Sport. Act. Living 2021, 3, 165. [CrossRef] 20. MacLeod, H.; Morris, J.; Nevill, A.; Sunderland, C. The validity of a non-differential global positioning system for assessing player movement patterns in field hockey. J. Sports Sci. 2009, 27, 121–128. [CrossRef] 21. Coutts, A.J.; Duffield, R. Validity and reliability of GPS devices for measuring movement demands of team sports. J. Sci. Med. Sport 2010, 13, 133–135. [CrossRef] 22. Schutz, Y.; Herren, R. Assessment of speed of human locomotion using a differential satellite global positioning system. Med. Sci. Sport. Exerc. 2000, 32, 642–646. [CrossRef] 23. Berthoin, S.; Dupont, G.; Mary, P.; Gerbeaux, M. Predicting sprint kinematic parameters from anaerobic field tests in physical education students. J. Strength Cond. Res. 2001, 15, 75–80. 24. Duthie, G.M.; Pyne, D.B.; Ross, A.A.; Livingstone, S.G.; Hooper, S.L. The Reliability of Ten-Meter Sprint Time Using Different Starting Techniques. J. Strength Cond. Res. 2006, 20, 246. [CrossRef] 25. Barbero-Álvarez, J.C.; Coutts, A.; Granda, J.; Barbero-Álvarez, V.; Castagna, C. The validity and reliability of a global positioning satellite system device to assess speed and repeated sprint ability (RSA) in athletes. J. Sci. Med. Sport 2010, 13, 232–235. [CrossRef] 26. Lee, J.B.; Burkett, B.J.; Thiel, D.V.; James, D.A. Inertial sensor, 3D and 2D assessment of stroke phases in freestyle swimming. Procedia Eng. 2011, 13, 148–153. [CrossRef] 27. Donati, A. The development of stride length and stride frequency in sprinting. New Stud. Athl. 1995, 10, 51–66. 28. Delecluse, C. Stride characteristics related to running velocity in maximal sprint running. In Proceedings of the Proceedings II of XVI International Symposium on Biomechanics in Sports, Konstanz, Germany, 21–25 July 1998; pp. 146–148.

Application of the Laser Linear Distance-Speed-Acceleration Measurement System and Sport Kinematic Analysis Software.

08-05-2022

Štuhec, Stanko,Planjšek, Peter,Ptak, Mariusz,Čoh, Milan,Mackala, Krzysztof

eng

PMC8543686

Physiological Reports. 2021;9:e15076. | 1 of 15 https://doi.org/10.14814/phy2.15076 wileyonlinelibrary.com/journal/phy2 RUNNING ECONOMY (RE) at a specific submaximal running velocity is defined as oxygen consumption (VO2) per minute per kg body mass. RE can also be normalized with respect to distance as VO2 kg−1 km−1. Normalization to body mass allows for comparisons between individu- als. RE is a complex measure, which reflects the combined functioning of biomechanical, anatomical, metabolic and cardio- respiratory factors (Tawa & Louw, 2018). Even among well- trained runners, RE can be seen to differ up to approximately 30% between individuals (Barnes et al., 2014; Larsen, 2003; Saunders et al., 2004a; Scholz et al., 2008). This makes RE a most decisive performance Received: 3 September 2021 | Accepted: 15 September 2021 DOI: 10.14814/phy2.15076 O R I G I N A L A R T I C L E Factors correlated with running economy among elite middle- and long- distance runners Cecilie E. Hansen1 | Martin Stensvig1 | Jacob Wienecke2 | Chiara Villa3 | Jakob Lorentzen1 | John Rasmussen4 | Erik B. Simonsen1 This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2021 The Authors. Physiological Reports published by Wiley Periodicals LLC on behalf of The Physiological Society and the American Physiological Society 1Department of Neuroscience, University of Copenhagen, Copenhagen N, Denmark 2Department of Sport and Nutrition, University of Copenhagen, Copenhagen N, Denmark 3Department of Forensic Medicine, University of Copenhagen, Copenhagen Ø, Denmark 4Department of Materials and Production, Aalborg University, Aalborg Ø, Denmark Correspondence Erik B. Simonsen, Department of Neuroscience, University of Copenhagen, Blegdamsvej 3, 2200 Copenhagen N, Denmark. Email: [email protected] Funding information No funding information provided. Abstract Running economy (RE) at a given submaximal running velocity is defined as oxy- gen consumption per minute per kg body mass. We investigated RE in a group of 12 male elite runners of national class. In addition to RE at 14 and 18 km h−1 we measured the maximal oxygen consumption (VO2max) and anthropometric meas- ures including the moment arm of the Achilles tendon (LAch), shank and foot volumes, and muscular fascicle lengths. A 3- D biomechanical movement analysis of treadmill running was also conducted. RE was on average 47.8 and 62.3 ml O2 min−1 kg−1 at 14 and 18 km h−1. Maximal difference between the individual athletes was 21% at 18 km h−1. Mechanical work rate was significantly correlated with VO2 measured in L min−1 at both running velocities. However, RE and rela- tive work rate were not significantly correlated. LAch was significantly correlated with RE at 18 km h−1 implying that a short moment arm is advantageous regard- ing RE. Neither foot volume nor shank volume were significantly correlated to RE. Relative muscle fascicle length of m. soleus was significantly correlated with RE at 18 km h−1. Whole body stiffness and leg stiffness were significantly corre- lated with LAch indicating that a short moment arm coincided with high stiffness. It is concluded that a short LAch is correlated with RE. Probably, a short LAch al- lows for storage of a larger amount of elastic energy in the tendon and influences the force– velocity relation toward a lower contraction velocity. K E Y W O R D S Achilles tendon moment arm, biomechanics, fascicle length, running economy, stiffness 2 of 15 | HANSEN et al. factor in competitive middle- and long- distance running together with VO2max and “Utilization of VO2max,” which often refers to the relative load corresponding to “onset of blood lactate.” (Larsen & Sheel, 2015). A few anatomical measures have been shown to re- late to RE. One is the moment arm of the Achilles ten- don (LAch) about the ankle joint, another is the ratio between the length of the forefoot and LAch (Scholz et al., 2008; Spurrs et al., 2003). The size of the LAch is highly determined by the size of the calcaneus bone and is regarded as a highly specialized feature of the human species for the evolution of Endurance Running and Persistence Hunting (PH) in the genus Homo (Raichlen et al., 2011). It is speculated that hominids during PH ran at speeds that forced animals to enter hyperthermia (Pontzer et al., 2009). Despite the remarkable differences in RE between run- ners, it is largely unknown, which factors are decisive for a high RE (low VO2 kg−1 at a specific velocity). Since 1968, African and especially Kenyan runners have dominated the international scene in middle- and long- distance races to a degree that has been termed the greatest geographical concentration of sports excellence in the annals of sports (Larsen & Sheel, 2015). Accordingly, the Kenyan runners have been subjected to research projects regarding their anatomy, physiological capabilities, and biomechani- cal characteristics. Saltin et al. (Saltin, 2003) concluded that no differences between Kenyan and European run- ners could be observed regarding VO2max, muscle fiber type distribution, number of capillaries or metabolic en- zymes (Saltin, Kim et al., 1995; Saltin, Larsen et al., 1995). Biomechanically, only contact time has been reported shorter in Kenyan runners (Santos- Concejero et al., 2017). Regarding anatomical differences, it has been reported that elite Kenyans had longer shanks and longer Achilles ten- dons than Japanese elite runners (Kunimasa et al., 2014; Sano et al., 2015). However, the Achilles tendon moment arm (LAch) was found to be longer in Kenyan than Japanese elite runners (Kunimasa et al., 2014), which is contradic- tory to studies reporting significant correlations between LAch and RE (Barnes et al., 2014; Scholz et al., 2008) show- ing a positive effect of a short LAch. Due to these discrepan- cies, it was decided to measure the LAch of the athletes in the present study and reinvestigate any possible correlation with RE. It seems obvious that RE somehow should relate to “running technique,” but no studies have been able to show a relation between the movement pattern of mid- dle- and long- distance running and RE. Within Track and Field Athletics it is well known that changing the move- ment pattern of a distance runner is “dangerous” and will often result in impaired performance. Most often run- ners successfully choose their step frequency and stride length from subjective criteria, which was shown already by Högberg (1952). Accordingly, one purpose of the pres- ent study was to relate biomechanical calculations of me- chanical energy during running to RE in elite middle- and long- distance runners. Lower leg thickness has been found to correlate signifi- cantly to RE and especially for Kenyan runners, who were claimed to have more slender legs than European runners (Saltin, 2003). Based on this finding it was suggested that it would be less energy demanding to move a lower leg mass back and forward during the swing phase of running (Larsen et al., 2004; Saltin, 2003). It was therefore decided to measure foot and lower leg volume of the athletes in the present study to see if this anatomical parameter would be significantly correlated with RE. Muscular fascicle length has been shown to correlate significantly with maximal sprint running speed and it was suggested that longer muscle fibers would infer a more beneficial force– velocity relationship of the leg mus- cles (Abe et al., 2001). As this mechanism also could cause the muscles to produce the same muscle force at a lower contraction velocity and thereby the use of fewer muscle fibers at a given running velocity, it was decided to mea- sure muscular fascicle length by use of ultrasonography and relate this parameter to RE. 1 | METHODS 1.1 | Subjects Twelve elite, male, middle- and long- distance runners (Table 1) gave their voluntary consent to participate in the study. The athletes competed at national or inter- national level in events ranging from 800 m to 10 km. Characteristics of the subjects are presented in Tables 1 and 2. The protocol was approved by the Research Ethics Committee for Science and Health, University of Copenhagen, Denmark. 1.2 | Experimental protocol The subjects visited the laboratory on 3 consecutive days. On day 1, a treadmill test was completed to de- termine running economy (RE) and VO2max. On day TABLE 1 Subject data Height (m) Weight (kg) BMI Age (y) VO2max Mean 1.82 68.5 20.54 22.4 67.0 SD 0.06 7.66 1.21 3.1 4.2  | 3 of 15 HANSEN et al. 2, anthropometric and muscular variables were deter- mined. On day 3, biomechanical variables related to run- ning were determined. 1.3 | Running economy and VO2max Running Economy was determined as the rate of oxygen consumption (VO2) per kg body mass while running at two different submaximal velocities on a motorized tread- mill (Woodway Desmo Pro Treadmill, Woodway Inc). The speed of 14 km h−1 was chosen as a “safe” velocity with regard to the expected aerobic capacity of the ath- letes. The speed of 18 km h−1 was chosen to represent a velocity close to the conditions during competition. After a standardized warm up on the treadmill, the subjects ran at two submaximal running speeds 14 and 18 km h−1, 0% grade, for 4 min separated by 1– 2 min rest. During the 4- min stages, in and expired gases were measured continu- ously by a gas analyzer (MasterScreen CPX, CareFusion). Breath- by- breath data were processed by the software sys- tem JLab (CareFusion). Running economy (RE) was de- termined as the mean VO2 (ml kg−1 min−1) during the last minute of each 4- min bout. A few minutes after the last submaximal run, an in- cremental test to exhaustion was completed to determine VO2max. The test started at 16 km h−1 and the speed was increased by 1 km h−1 each minute until 20 km h−1. After a minute at this velocity, the treadmill gradient was in- creased by 1% each minute until exhaustion. VO2max was determined as the highest mean VO2 over a 30 s period. Values of VO2expressing resting values were obtained from the difference between VO2at 14 and 18 km h−1 di- vided by 4 km h−1. These values were subtracted from all the measured values of VO2. 1.4 | Anthropometric measurements The subjects’ Achilles tendon moment arm (LAch) was measured by the method presented by Scholz et al. (2008). Briefly, the most prominent part of the lateral and medial malleolus of the subjects’ right foot was marked. The sub- jects were seated in a chair with their foot on a reference block. First, foot and leg were positioned so that the lateral edge of the foot was aligned with the reference block and the anterior border of the tibia was vertical. From this po- sition the lateral side of the foot and leg was photographed (Figure 1). The same procedure was used for the medial side. The medial edge of the foot was aligned with the ref- erence block, the anterior border of the tibia was vertical, and the medial side was photographed (Figure 1). The horizontal distance from the marked spot on the malle- olus to the posterior aspect of the Achilles tendon was measured on the pictures. This was performed for both the lateral and medial sides, and the LAch was determined as the mean of two values. From the picture of the lateral side, the length of the forefoot was also determined by measuring the horizontal distance from the marked spot on the lateral malleolus to the head of the fifth metatarsal (marked by a spot). The TABLE 2 Personal best results of the athletes Athlete 800 m 1500 m 5000 m 10.000 m 1 1.53.99 min 3.41.17 min 2 1.51.11 min 3.53.35 min 3 31.03.00 min 4 30.45.00 min 5 1.58.17 min 3.57.92 min 6 1.57.73 min 3.57.69 min 7 1.54.00 min 3.53.37 min 8 1.49.44 min 3.49.59 min 9 4.06.26 min 14.51.25 min 10 4.04.13 min 11 1.55.55 min 3.49.44 min 12 1.49.50 min FIGURE 1 The lateral Achilles tendon moment arm (a) (top) and (bottom) the medial Achilles tendon moment arm (c). The resulting Achilles tendon moment arm (LAch) was calculated as the mean of a and c. The length of the forefoot is shown as distance b 4 of 15 | HANSEN et al. length of the forefoot was determined as the mean of two consecutive measurements. Lower leg and foot volume were determined by scan- ning the lower leg and foot with a hand- held 3- D surface scanner (Artec Eva, Artec 3D, Luxembourg) (Tierney et al., 1996). Proximally, the lower leg was marked by two mark- ers, one on caput fibula and one on tibia at about equal heights. Distally, the lower leg was marked by two addi- tional markers, one on the lateral malleolus (of tibia) and one on the medial side at about equal heights (approxi- mately 1 cm below the medial malleolus). This procedure was used on both the right leg and the left leg. The distal markers on the lower legs were used to mark the feet as well. During the scanning of the lower legs, the subjects were instructed to stand in a relaxed upright position with enough space between the feet for the scanner to be able to scan the medial side of the lower legs. Tape was used to mark the subject's foot position to guarantee accuracy during and between the measurements. A minimum of three scans were applied to the lower legs. During the scanning of the feet, the subjects sat in a chair with their right lower leg resting on another chair, so that the foot was free from the chair. The subjects were instructed to relax their foot during all scans and the lower leg was fastened with sports tape, so that movement of the lower leg and foot was minimized. This was repeated for the left leg, and a minimum of three scans were performed on each foot. A 3- D model was constructed and further processed using Artec Studio (Artec 3D, Luxembourg). The lower legs were isolated from the 3- D model by cutting off ev- erything proximally and distally to the two marker pairs, respectively. The feet were isolated by cuts proximal to the distal markers. The volumes of the 3- D models of the isolated lower legs and feet were calculated using the Artec Studio software. The volumes of two successful scans of each lower leg were calculated, and the volume of the lower leg was determined as the mean of these. The same procedure was applied to calculate the volume of the foot. Body mass and height were measured using standard procedures and, in addition, the following anthropo- metric variables on the subjects’ right side were deter- mined: total leg length (from the ground to spina iliaca anterior superior), thigh length (from trochanter major to the lateral condyle of the femur), shank length (from caput fibulae to the lateral malleolus of the tibia), foot length (from the back of the heel to the tip of the lon- gest toe), forefoot length from the lateral malleolus to the fifth metatarsal joint (Figure 1), and toe length (from the head of the first metatarsal to the tip of the first pha- lanx distalis). 1.5 | Fascicle length Fascicle length (Lf) was estimated using a B- mode ultra- sound scanner (LS128, CEXT- 1Z, Telemed Ltd.) and trans- ducer (LV8- 5L60N- 2 veterinary, Telemed Ltd.). The vastus lateralis (VL), gastrocnemius medialis (GM), and soleus (SOL) muscle of the subject's right leg were scanned. For VL, the transducer was placed at a point midway between trochanter major and the lateral condyle of the femur. For GM and SOL, the transducer was placed at a point approxi- mately 30% proximally between the medial condyle and the medial malleolus of the tibia and midway between the medial and lateral borders of the GM (Abe, 2002). During the scans, the subjects stood in an upright relaxed position and the transducer was placed parallel to the muscle fibers and adjusted if necessary to get the optimal picture. The ultrasound images of the muscles were recorded by Echo Wave II software (3.4.0, Telemed Ltd.). The fascicle pen- nation angle (α) was determined as the angle between the deep aponeurosis and the fascicles of the specific muscle (Abe et al., 2000; Cronin & Lichtwark, 2013; Kawakami et al., 2002). The isolated muscle thickness (Tm) was de- termined by measuring the distance between the deep and superficial aponeurosis of the specific muscle (Aggeloussis et al., 2010). This was performed for both the proximal and distal ends of the muscle visualized in the ultrasound image, and a mean of these two distances was used as Tm. The Lf was estimated using the following equation: The Lf of each muscle was determined as a mean of three estimated Lf of the specific muscle and expressed both in absolute values (cm) and relative to the related segment length (cm cm−1). 1.6 | 3- D biomechanical movement analysis Due to injuries (not related to this study), only 10 of the 12 subjects managed to complete a 3- D biomechanical anal- ysis of treadmill running to determine stride frequency (fs), stride length (Ls), contact time (tc), swing time (ts), vertical oscillations, and mechanical work. Thirty- five spherical re- flective markers were placed on selected anatomical land- marks (Figure 2). After a standardized warm up, the subject ran at the two submaximal velocities from the RE protocol (14 and 18 km h−1, 0% grade) while recorded by a Qualisys system for movement analysis (Qualisys AB). Eleven high- speed infrared cameras (300 Hz) recorded a minimum of 15 steps at each velocity. Three- dimensional coordinates of Lf = Tm sin(훼)  | 5 of 15 HANSEN et al. the markers were exported to the software system AnyBody (AnyBody version 7.1, AnyBody Technology A/S), which was used to analyze the recordings. Simultaneously, the athletes were recorded on video (120 frames s−1) and these recordings were later used to obtain contact time, stride frequency, swing time, and stride length. Stride length was calculated as velocity divided by stride frequency. AnyBody is a multibody dynamics system, which dis- cretizes the body into links representing the bones as rigid segments articulating at the anatomical joints. To each bone was assigned the mass of the other tissues sur- rounding the bone, such that the sum of segment masses equaled the total body mass and the distribution of masses followed Dempster (Dempster, 1955). The potential energy of the system was computed as the sum of potential energies of the segments. Similarly, the kinetic energy was computed as the sum of segment kinetic energies, where each segment's kinetic energy con- tained translational and rotational contributions (Winter, 1979). The mechanical energy of each segment and of the entire system, Emech, was calculated as the sum of poten- tial and kinetic energy (Winter, 1979). During motion, energy is converted between kinetic and potential contributions, existing energy is exchanged between segments via joint reaction forces and muscle connections, and energy is produced or dissipated by positive and negative muscle work in a complex interplay. The internal exchange of energy between segments is complicated, but disregard- ing friction, air resistance, and other dissipative effects, the net change in mechanical energy of the entire system is at- tributed to muscle work (Winter, 1979). We therefore defined the mechanical muscle power of the whole system as follows: Ppos and Pneg were defined as the sum of the positive and negative increments in Pmech, respectively. Pmech = d Emech dt FIGURE 2 Reflective spherical markers were placed at anatomical landmarks. Reproduced with permission of Qualisys AB 6 of 15 | HANSEN et al. Subsequently, we computed the metabolic power as that is, different metabolic efficiencies for concentric and eccentric muscle work (Aura & Komi, 1986; Laursen et al., 2000). When expressing the mechanical work intensity as liter O2 min−1, an energetic value of 20 kJ per liter oxygen was used. A measure of gross efficiency was obtained by di- viding Pmech by Pmetab. 1.7 | Stiffness Stiffness of the whole body was measured during running as previously described (Cavagna et al., 1977; Ferris et al., 1998; McMahon & Cheng, 1990; Morin et al., 2005). The vertical ground reaction force was calculated in the AnyBody sys- tem by the methods described by Fluit et al. (2014) and by Skals et al. (2017). The vertical trajectory of the body center of mass (BCM) was also computed by the AnyBody system using anthropometrics from Dempster (1955). Thus, the ver- tical stiffness kvert in kN m−1 was calculated by the formula: where Δy is the vertical displacement of BCM from touch down (heel strike) till Fmax, which is the peak value of the vertical ground reaction force. Leg stiffness of the support leg during running was cal- culated by the formula: where: where L is leg length and Δy is the vertical displacement of the body center of mass at its lowest point during the contact phase. It has been shown that BCM is at its lowest point at the time of Fmax (Morin et al., 2005). At each running velocity, stiff- ness was measured in three consecutive steps and averaged. 1.8 | Statistics Spearman's rank correlation analysis was used to deter- mine the relationship between RE and the anthropometric, biomechanical, and muscular variables of the subjects (Matlab R2018a, The MathWorks Inc). The level of signifi- cance was set to p < 0.05. 2 | RESULTS Personal data and VO2max of the athletes are listed in Tables 1 and 2. The group mean value of VO2max was 67.0  ml O2  kg−1 (range: 61.7– 78.2), which confirmed that the athletes were all well- trained elite runners (Table 3). Resting values calculated from the difference be- tween VO2 at the two running velocities were on average 3.66 ml O2 kg−1 min−1 (±0.60). Running econ- omy (RE) corrected for resting values was (averaged across subjects) 44.1 and 58.7 ml O2 kg−1 min−1 at 14 and 18 km h−1, respectively (Table 3). This implied at 14 km h−1 a difference of 22% and at 18 km h−1 a dif- ference of 21% between the best athlete and the poor- est athlete. RE at 14 and 18  km  h−1 was significantly correlated (Rho = 0.79, p = 0.0021) indicating a linear relationship between RE and running velocity as shown before (Saltin, Kim, et al., 1995; Saltin, Larsen, et al., 1995; Saunders et al., 2004a, 2004b). Without correction for resting metabolism, RE was on average 47.8 (±2.8) ml O2 kg−1 min−1 and 62.4 (±3.6) ml O2  kg−1  min−1 at 14 and 18  km  h−1, respectively. Uncorrected VO2max was 70.8 (±4.7) O2 kg−1 min−1. The relative load of the athletes at 14 and 18 km h−1 was on average 66.1% (±5.0) and 87.9% (±4.8) with respect to VO2max (Table 3). Biomechanical and temporal parameters related to the step cycle (step rate, step length, contact time, swing phase, and BCM oscillations) were not correlated with RE (Table 4). The mechanical work intensity (Pmech) was 3.41 (0.28) and 3.79 (0.54) W kg−1 for 14 and 18 km h−1, respectively (Figure 3). None of the parameters expressing mechanical work intensity were significantly correlated with RE. However, body mass was significantly correlated with VO2 (L min−1) at 14 km h−1 (Rho = 0.89, p = 0.0014) and at 18 km h−1 (Rho = 0.93, p = 0.0001). Body mass was also significantly correlated with Pmech at 18 km h−1 (Rho = 0.71, p = 0.0275). When the mechanical work intensity was expressed as liter O2  min−1, significant correlations were found between mechanical work intensity and the measured VO2 in L min−1 (Figure 4). At 14 km h−1, Rho was 0.66 (p = 0.044) and at 18 km h−1 Rho was 0.84 (p = 0.0045) (Figure 4). Respiratory quotient ratio (RER) values were 0.85 (range: 0.68– 0.93) and 0.935 (range: 0.76– 1.04) for 14 and 18 km h−1, respectively. Gross efficiency calculated on mechanical data only was 41.4% and 41.9% at 14 and 18 km h−1, respectively. Pmetab = { Pmech∕0.25 if Pmech ≥0 Pmech∕−1.20 if Pmech <0 kvert = Fmax Δy kleg = Fmax ΔL ΔL = L − √ L2 − (v⋅tc 2 )2 + Δy  | 7 of 15 HANSEN et al. Pmech at 14 and 18  km  h−1 was significantly cor- related (Rho = 0.68, p = 0.055) as was Pneg (Rho = 0.66, p = 0.044). The Achilles tendon moment arm (LAch) was on aver- age 3.91 cm and was significantly correlated with RE at 18 km h−1 (Rho = 0.73; p = 0.007) (Figure 5) (Table 5). This implied that a short moment arm is an advantage regarding RE at 18 km h−1 while not at 14 km h−1. The LAch varied from 3.46 to 4.21 cm corresponding to a differ- ence of 17.8% between the extremes of the group (Table 5; Figure 5). Fascicle length of m. soleus (SO) was 4.1 cm on aver- age and varied from 3.2 to 4.9 cm corresponding to a 36% difference between the subject with the shortest and the subject with the longest fascicles. Similar differences were observed for the gastrocnemius (GM) (mean 5.6 cm; range: 4.5– 6.8) and the vastus lateralis (VL) (mean 6.6 cm; range: 5.6– 7.9). Individual range for the GM corresponded to 34% and for the VL 29%. No significant correlations were found between absolute fascicle length and RE, neither at 14 nor at 18 km h−1. However, when normalized to leg (shank) length the soleus fascicles showed a significant correlation with RE at 18 km h−1 (Rho = −0.62; p = 0.03) (Figure 6). Total leg length, shank length, foot length, and toe length were not significantly correlated with RE (Table 5). TABLE 3 Running economy at 14 and 18 km h−1, respectively Athlete VO2 ml kg−1 min−1 14 km h−1 VO2 ml kg−1 km−1 14 km h−1 VO2 ml kg−1 min−1 18 km h−1 VO2 ml kg−1 km−1 18 km h−1 VO2max ml kg−1 min−1 % VO2max 14 km h−1 % VO2max 18 km h−1 1 45.3 194 58.2 194 65.4 69.3 89.1 2 47.9 205 62.2 207 68.6 69.7 90.6 3 39.8 170 54.5 182 68.0 58.5 80.2 4 44.2 189 57.5 192 66.3 66.7 86.7 5 45.7 196 65.7 219 78.2 58.5 84.1 6 47.3 202 61.1 204 67.4 70.1 90.6 7 42.9 184 59.8 199 66.0 65.0 90.7 8 40.9 175 57.0 190 61.7 66.2 92.3 9 41.4 177 54.4 181 69.1 59.9 78.7 10 48.6 208 60.2 201 64.7 75.1 93.1 11 43.1 185 56.3 188 65.7 65.6 85.8 12 42.4 182 57.7 192 62.4 67.9 92.4 Mean 44.1 189 58.7 196 67.0 66.1 87.9 SD 2.9 12.3 3.3 10.9 4.2 5.0 4.8 TABLE 4 Running step parameters. “BCM oscillations” are body center of mass vertical oscillations. No significant correlations between these parameters and running economy were observed Step rate Step length Contact time Swing phase BCM oscillation 14 km h−1 2.82 (Hz) (0.12) 1.38 (m) (0.06) 171 (ms) (9.22) 541 (ms) (31.7) 8.8 (cm) (1.2) 18 km h−1 2.96 (Hz) (0.09) 1.70 (m) (0.05) 138 (ms) (10.4) 542 (ms) (31.7) 8.3 (cm) (1.0) FIGURE 3 Mechanical work intensity Pmech. The positive (Ppos), negative (Pneg), and the metabolic calculated work (Pmetab) are corrected by 25% efficiency for positive work and −120% for negative work. Error bars are one standard deviation 8 of 15 | HANSEN et al. The same was the case for shank and foot volumes (Table 5). However, the foot ratio between the forefoot and the Achilles tendon moment arm was significantly correlated with RE at 18 km h−1 (Rho = −0.64; p = 0.030) (Table 5) (Figure 5), that is, a greater ratio seems an advantage regarding RE. Whole body stiffness normalized to body mass was 930 (±227) N  m−1  kg−1 at 14  km  h−1 and 1240 (±240) N m−1 kg−1 at 18 km h−1. Leg stiffness (kleg) was 900 (±220) and 1200 (±230) N  m−1  kg−1. None of these stiffnesses were significantly correlated with RE (Rho  =  −0.18, p = 0.63 and Rho = −0.58, p = 0.088, respectively). Whole body stiffness at 14  km  h−1 (Rho  =  −0.69; p  =  0.035) and at 18 km h−1 (Rho = −0.75; p = 0.018) was signifi- cantly correlated with the Achilles tendon moment arm (LAch) (Figure 7) indicating that a short moment arm co- incided with high stiffness. Also leg stiffness was signifi- cantly correlated with the Achilles tendon moment arm at 14 km h−1 (Rho = −0.7; p = 0.025) and at 18 km h−1 (Rho = −0.83; p = 0.006). The ratio between whole body FIGURE 4 Left: Relation between mechanical work expressed as ml O2 min−1 kg−1 and measured VO2 min−1 kg−1 at 18 km h−1. Right: Relation between mechanical work expressed as liter O2 min−1 and actually measured VO2 at 18 km h−1 FIGURE 5 Relation (top) between Achilles tendon moment arm (LAch) and RE and (bottom) relation between foot ratio and RE. Foot ratio is forefoot LAch −1  | 9 of 15 HANSEN et al. stiffness and LAch was significantly correlated with RE at 18 km h−1 (Rho = −0.72, p = 0.024) (Figure 7). Absolute whole body stiffness (N m−1) was significantly correlated with body mass (Rho = 0.68, p = 0.035) and to absolute VO2 (L min−1) (Rho = 0.71, p = 0.028). 3 | DISCUSSION 3.1 | Mechanical power By use of 2- D biomechanical movement analysis, it has earlier been attempted to quantify mechanical power ex- erted by the muscles during human running. However, different approaches have been used as the mechani- cal work may be defined and/or divided into external work on the surroundings and internal work due to the movements of segments like arms, legs, and trunk. The external work has been measured by force platforms (Cavagna et al., 1976), accelerometers (Cavagna et al., 1964), or by movements of the center of mass of the whole body (Luhtanen & Komi, 1978). The internal work is cal- culated by summation of potential and kinetic energy of all body segments (Laursen et al., 2000; Winter, 1979). By use of these different approaches power values of 556 W (Cavagna & Kaneko, 1977), 172 W (Norman et al., 1976), 931 W (Luhtanen & Komi, 1978), and 396 W (Williams & Cavanagh, 1983) have been reported. These studies were based on 2- D cinematography except the study of Williams and Cavanagh (Williams & Cavanagh, 1983), which was three dimensional with running velocities var- ying between 3.6 and 3.9 m s−1 (13– 14 km h−1). Williams and Cavanagh subdivided their subjects into three groups based on RE at 3.57  m  s−1 (approximately 13  km  h−1) and observed a trend between relative positive power and three “Physiological Efficiency Groups” (Williams & Athlete Achilles tendon moment arm (cm) Shank volume (liters) Foot volume (liters) 1 3.74 3.59 1.24 2 3.96 3.01 1.00 3 3.46 2.78 0.81 4 3.69 2.93 0.89 5 4.21 2.78 0.92 6 4.03 2.28 0.77 7 4.14 2.71 0.97 8 4.50 2.65 0.90 9 3.60 2.39 0.83 10 4.05 2.64 0.88 11 3.60 2.01 0.76 12 3.91 2.49 0.85 Mean 3.91* 2.69 0.90 SD 0.30 0.40 0.13 TABLE 5 Soleus moment arm, leg (shank) and foot volumes. * denotes a significant correlation to running velocity at 18 km h−1 (Rho = −0.66; p = 0.02) FIGURE 6 Relation between RE and relative soleus fascicle length at 14 and 18 km h−1. The correlation at 18 km h−1 was statistically significant (Rho = −0.62; p = 0.03) 10 of 15 | HANSEN et al. Cavanagh, 1983) but, to the best of our knowledge, no- body has found a significant correlation between biome- chanical calculations of power and measured VO2 during running. In the present study, a 3- D modeling approach was ap- plied to velocities of 14 and 18 km h−1 and the mechanical power was found to be 237 (30.3) and 264 (53.9) Watt cor- responding to 3.41 (0.28) and 3.79 (0.54) W kg−1, respec- tively (Figure 3). When mechanical power was expressed as metabolic cost corresponding to liter O2 min−1 a signifi- cant correlation was found between the mechanical calcu- lations and the measured VO2 (Figure 4), indicating that there is a mechanical explanation behind RE. However, since body mass was also highly correlated with VO2 mea- sured in absolute values, it is possible that the correlation only reflects the fact that heavy subjects consume more oxygen and produce more mechanical energy. It was remarkable that VO2 calculated from mechan- ical power was almost twice as high as the actually mea- sured VO2. A fixed value of 20 kJ per liter O2 was used to “convert” power to VO2 but using the actually measured respiratory quotient ratios would only have changed the calculated VO2 a negligible degree. The most likely expla- nation for the high calculated values is that summation of segment energies cannot account for storage and reuse of elastic energy in the tendons. This energy should be subtracted, but there is no way we can calculate or esti- mate the size of it. When mechanical power was normalized to body mass, no significant correlations were found regarding RE, which could be due to oxygen consumption not being linearly related to body mass in terms of physiology. This is a well- known phenomenon and it has been suggested to use body mass0.75 (Bergh et al., 1991). However, even body mass0.66 did not improve the correlations of the present study. It is not straight forward to explain the missing correlation between RE and relative mechan- ical work rate, but it may be an inherent problem that most biomechanical methods use anthropometric tables, like Dempster (Dempster, 1955), to calculate segmental masses and moments of inertia. This is also the case for the method presented by Winter (1979), which was used in the present study. When these body parameters only vary with body mass and segment lengths, it is obvious that this causes individual subjects to become more iden- tical and thereby more difficult to separate mechanically regarding RE. A future approach to relate biomechanical movement analysis to RE should deal with individual differences between the real body segments of the sub- jects as we found an extreme difference of 56% between the highest and the lowest shank volume in the present study (Table 5). FIGURE 7 On top: relation between whole body stiffness and Achilles tendon moment arm. Bottom: relation between RE and the ratio between stiffness and Achilles tendon moment arm  | 11 of 15 HANSEN et al. The method used by the present study and by Williams & Cavanagh (1983) was introduced by Winter (1979, 2009). It accounts for exchange of energy both between and within segments, but it cannot deal with storage and reuse of elas- tic energy in the muscle– tendon unit. The method allows for calculating the positive and the negative mechanical work separately and by assuming a mechanical efficiency for eccentric and concentric work it is possible to estimate a net efficiency for running only based on biomechanical movement analysis. In the present study, net efficiency was 41.6 (0.26) % and 41.9 (1.09) % for 14 and 18 km h−1, respec- tively. This corroborated the net efficiency of 44% reported by Williams & Cavanagh (1983) and it indicates that the mechanical efficiency of running is significantly higher as the approximately 25% efficiency of pure concentric mus- cle work (Asmussen, 1953; Asmussen & Bonde- Petersen, 1974; Aura & Komi, 1986). More simple biomechanical parameters like contact time, stride rate, and stride length have been investigated on numerous occasions and have rarely been found to have any influence on RE (Barnes et al., 2014). One study found a shorter contact time in Kenyan runners and argued that this would influence stiffness and the ability to store and reuse elastic energy (Santos- Concejero et al., 2017). In the present study, no significant correlations between these parameters and RE were seen (Table 4). 3.2 | Achilles tendon moment arm and RE In the study of Scholz et al. (2008) a significant correlation (r = 0.75) was reported between running economy (RE) at 16 km h−1 and the Achilles tendon moment arm (LAch). In the present study, a significant correlation (Rho = 0.66) was found between LAch and RE at 18 km h−1. In an extensive study of RE, 63 runners (24 females, 39 males) of collegiate or national level were examined regarding RE and Achilles tendon moment arm (LAch) (Barnes et al., 2014). For all subjects, LAch showed a very high and significant correlation (r  =  0.90) with RE at 14 km h−1 implying that a short moment arm is advanta- geous regarding RE. The LAch was on average 4.4 cm for males and 3.5 cm for females with r- values of 0.82 and 0.81 between RE and LAch. Accordingly, males and fe- males had the same RE despite differences in LAch (Barnes et al., 2014). In a study of Kenyan and Japanese long- distance runners by Kunimasa et al. (2014) it was found that the Kenyan runners had significantly longer LAch (4.46 cm) than the Japanese runners (4,07  cm). LAch of the Kenyans ranged from approximately 3.6– 5.1 cm (Figure 3 in Kunimasa et al., (2014)) and, when both Kenyan and Japanese runners were pooled, a significant correlation (r  =  0.55) was found between LAch and a performance index (International Athletics Amateur Federation). This indicated a long moment arm to be an advantage, but notably, RE was not measured directly (Kunimasa et al., 2014; Spiriev, 2011). Considering the results of the present study with an r- value of 0.66, and the previous results from the litera- ture with even higher r- values (Barnes et al., 2014; Scholz et al., 2008), it appears safe to conclude that LAch is highly correlated with RE despite the results of (Kunimasa et al. (2014). 3.3 | Running economy RE at 16 km h−1 was 48 ml O2 kg−1 min−1 in Scholz et al. (2008) corresponding to 182 ml O2 kg−1 km−1 while in the present study RE was 44 ml O2 kg−1 min−1 at 14 km h−1 cor- responding to 189 ml O2 kg−1 km−1. This remarkable differ- ence is difficult to explain. The maximal oxygen uptake was 67 ml O2 min−1 kg−1 in the present study but only 55 ml O2 min−1 kg−1 in Scholz et al. (2008), so it cannot be excluded that a systematic difference existed between the appara- tus used for gas analysis during running, especially as the Dutch athletes were described as “highly trained” (Scholz et al., 2008). When RE is expressed as ml O2 kg−1 km−1, it is possible to compare RE at different running velocities. Accordingly, RE ranges from 170 to approximately 250 ml O2 kg−1 km−1 in the literature. The athletes of the present study ranged from 170 to 219 ml O2 kg−1 km−1 (Table 3) and the Olympic Champion Frank Shorter (USA, Olympic marathon winner, 1972) has been reported to have had a RE of 172 ml O2 kg−1 km−1 while Joseph Ngugi (Kenyan Olympic gold medalist on 5000 m, 1988) has been reported to have had a RE of 170 ml O2 kg−1 km−1 (Saltin, Larsen, et al., 1995). Besides the study of Scholz et al. (2008), one other study has reported very low values of VO2 during sub- maximal running (147– 157 ml O2 kg−1 km−1) (Spurrs et al., 2003) and correspondingly low values of VO2max (< 60 ml O2 min−1 kg−1). In fact, the runners in Spurrs et al. (2003) appeared to have a RE better than the best Kenyan and African runners ever measured (Larsen, 2003; Larsen & Sheel, 2015; Saltin, 2003; Saltin, Larsen, et al., 1995; Weston et al., 2000), which is highly unlikely. 3.4 | Foot lever ratio Kunimasa et al. found a significant correlation between IAAF score (Spiriev, 2011) and a ratio between the forefoot and the LAch (Kunimasa et al., 2014). It turned out that the Kenyans had a shorter forefoot and longer LAch than the Japanese 12 of 15 | HANSEN et al. runners. A contradictory and significant correlation was found between the same foot ratio and RE at 18 km h−1 in the present study (Figure 5). A possible explanation for this could be that RE was not measured in the study of Kunimasa et al. (2014) as an IAAF score was used instead. The foot lever ratio is an interesting property as it has been suggested that a certain gear ratio between the active muscles and the mo- ment arm of the external ground reaction force may affect the energy cost of locomotion (Biewener et al., 2004; Carrier et al., 1994; Karamanidis & Arampatzis, 2007). 3.5 | Leg volume A strong relation has been reported between RE and lower leg circumference, which further indicated a trend toward Kenyan elite runners having a lower (smaller) leg thick- ness than European runners (Saltin, 2003), and it has been suggested that lighter shanks could partly explain the superior RE observed in Kenyan runners (Larsen, 2003; Larsen et al., 2004; Saltin, 2003; Saltin, Kim, et al., 1995; Saltin, Larsen, et al., 1995). Supposedly, it should require less energy to accelerate a lighter lower leg back and for- ward due to a lower segment moment of inertia. Scholz et al. (2008) measured foot length, lower leg length, lower leg volume, and lower leg moment of inertia in 15 Dutch well- trained runners and found significant correlations between lower leg volume and RE and between lower leg moment of inertia and RE. However, after analysis for covariation with the Achilles tendon moment arm, they rejected the influence of these parameters (Scholz et al., 2008). In the present study, shank and foot volumes were measured by surface scanning, but no significant correla- tions were found between shank or foot volume and RE (Tables 5 and 6), although there were 56% difference be- tween the smallest and the largest shank volume. Running experiments have shown that shod running is less expen- sive compared to barefooted but adding an extra weight of 100 g per shoe increased VO2 by 1% (Franz et al., 2012). Different animal species exhibit often very different anatomy of the legs. Taylor et al. (1974) calculated that cheetahs, gazelles, and goats had equal energy cost mov- ing their limbs during running despite large anatomical differences regarding limb mass, length, and distance to limb center of mass. It was therefore suggested that most of the energy expended in running at constant speed is not used to accelerate and decelerate limbs. 3.6 | Fascicle length In the present study, the fascicles of the soleus muscle showed a significant correlation to RE at 18 km h−1 when fascicle length was normalized to shank length (Figure 6). This indicated that longer muscle fibers have a posi- tive influence on RE. However, this is not supported by the literature. On the contrary, Japanese runners were found to have longer GM fascicles than Kenyan runners (Sano et al., 2015). In the present study the medial gas- trocnemius (GM) fascicles were 5.62 (0.72) cm on average (range: 4.50– 6.84 cm), which corresponds very well the 5.36 (0.72) cm reported by Abe et al. (2000) for the GM in distance runners while 6.64 (1.32) cm for sprint runners. It was suggested that long muscle fibers would be beneficial for sprint runners due to a more optimal force– velocity relation (Abe et al., 2000; Lee & Piazza, 2009). Longer fas- cicles than in controls have also been reported for sumo wrestlers, and it was suggested that fascicle length may increase with strength training (Kearns et al., 2000). Long fascicles imply long muscle fibers and more sar- comeres in series. As longer muscle fibers can contract at a higher shortening velocity than shorter fibers this would indicate a more beneficial force– velocity relation- ship of the muscles in question (Abe et al., 2001). At sub- maximal muscle activation, this means that the muscle can generate more force at the same shortening velocity. However, it is an important question whether the mus- cle fibers of, for example, the GM actually lengthen and shorten during running or the ankle joint movements are accomplished only by elastic length changes of the Achilles tendon. Giannakou et al. have shown that the TABLE 6 Correlations (Spearman's Rho) between anthropometry and RE. * indicates a statistically significant correlation (Achilles tendon moment arm and RE at 18 km h−1). Foot ratio is Forefoot∙LAch −1 14 km∙h−1 18 km∙h−1 Leg length Rho = 0.06 p = 0.85 Rho = 0.34 p = 0.28 Thigh length Rho = 0.09 p = 0.77 Rho = 0.45 p = 0.14 Shank length Rho = 0.18 p = 0.59 Rho = 0.46 p = 0.164 Foot length Rho = 0.22 p = 0.49 Rho = 0.47 p = 0.12 Toe length Rho = 0.08 p = 0.80 Rho = 0.37 p = 0.24 LAch Rho = 0.33 p = 0.30 Rho = 0.66 p = 0.02* Shank vol. Rho = 0.19 p = 0.56 Rho = 0.33 p = 0.30 Foot vol. Rho = 0.27 p = 0.39 Rho = 0.49 p = 0.11 Foot ratio Rho = −0.41 p = 0.18 Rho = −0.60 p = 0.043*  | 13 of 15 HANSEN et al. GM fascicles stretch and shorten approximately 2.5 cm during running (11 km h−1) in 12 long- distance runners (Giannakou et al., 2011) and Lai et al. found that the so- leus fascicles covered 20% of the lengthening/shortening of the muscle– tendon unit during running at various speed (Lai et al., 2015). Since muscle strength is not re- lated to the length of the muscle fibers, it is certain that longer muscle fibers with more sarcomeres in series con- sume more energy than shorter fibers when producing the same force (Walmsley & Proske, 1981). The force– length relationship of the muscle fibers is, on the other hand, highly influenced by fiber length, as longer fibers exhibit a wider range of length for optimal force produc- tion (Walmsley & Proske, 1981). Finally, the most important feature of longer muscle fibers may be an altering of the force– velocity relation so that the muscle can produce more force at the same short- ening velocity (Abe et al., 2000; Lee & Piazza, 2009). Only the relative length of the soleus fascicles correlated signifi- cantly to RE at 18 km h−1 in the present study (Figure 4), so additional research is required to establish whether long muscle fibers are an advantage regarding RE. This is espe- cially interesting as reports exist showing that the number of sarcomeres in series can be increased in rats after down- hill running (Lynn & Morgan, 1994; Lynn et al., 1998). 3.7 | Stiffness and storage of elastic energy A significant correlation between muscle– tendon stiffness and RE was found by Barnes et al. (2014). It was, however, poorly described how, exactly, stiffness was measured, but it was a maximal stiffness measured during vertical jumping. Barnes et al. also found a significant correlation between LAch and stiffness (Barnes et al., 2014), which was also found in the present study (Figure 7) where stiffness was measured during running. In both cases it seems that a short moment arm and a high stiffness follow each other and are somehow beneficial for RE. In Scholz et al. it was argued that a short moment arm of the Achilles tendon would imply a higher muscle force when producing a cer- tain moment about the ankle joint as compared to a longer moment arm (Scholz et al., 2008). The higher muscle force would cause an increased stretch of the Achilles tendon during the eccentric contraction and thereby store more elastic energy in the tendon to be reused during the im- mediately following concentric contraction. This may cer- tainly be true, but it requires the length and the stiffness of the tendon to match the muscle force exactly, so that the required range of joint motion is achieved. Sano et al. found longer Achilles tendons in Kenyan than Japanese runners but also longer shanks. The cross- sectional area of the Achilles tendon was also significantly larger than that of the Japanese (Kunimasa et al., 2014; Sano et al., 2015). Interestingly, given an upper limit on allowable tis- sue stress, a longer tendon would allow for storage of more elastic energy as would a stiffer tendon, which is the impli- cation of a larger cross- sectional area. Another effect of a short Achilles tendon moment arm may be a positive influence on the force– velocity relation of skeletal muscles. At a given angular motion in the ankle joint during plantar flexion, a shorter LAch will cause a lower shortening velocity than a longer LAch, simply due to geom- etry. In this way the required muscle force may be produced by fewer motor units and thereby fewer muscle fibers. 4 | CONCLUSIONS As the first study, we were able to show a significant cor- relation between biomechanical calculations of mechani- cal power and absolute oxygen consumption. However, this correlation did not exist when data were normalized to body mass. This is probably partly due to differences in anthropometry not accounted for in biomechanical move- ment analysis. The Achilles tendon moment arm is considered highly important for RE as a short moment arm theoretically can be beneficial for both storage of elastic energy and for the force– velocity relation of skeletal muscles. The ratio be- tween forefoot and Achilles tendon moment arm is also significantly correlated with RE due to a beneficial gearing of the foot with respect to the external forces. Stiffness of the whole body and the stance leg is indirectly important for RE as stiffness and Achilles tendon moment arm are significantly correlated. High stiffness of the leg muscles is very likely to favor storage and reuse of elastic energy during running. CONFLICT OF INTEREST The authors declare no conflict of interest. AUTHOR CONTRIBUTION C. E. Hansen planned and conducted the experiments, participated in the calculations, discussion of results, and in writing the manuscript. E. B. Simonsen planned and conducted the experiments, participated in the calcula- tions, discussion of results, and in writing the manuscript. M. Stensvig participated in the biomechanical data col- lection and in writing the manuscript. J. Rasmussen par- ticipated in the biomechanical calculations, discussion of results, and in writing the manuscript. J. Wienecke participated in data collection and in writing the manu- script. J. Lorentzen participated in collection of data from ultrasonography, data interpretation, and in writing the 14 of 15 | HANSEN et al. manuscript. C. Villa participated in collection and inter- pretation of surface scans and in writing the manuscript. ORCID Erik B. Simonsen  https://orcid. org/0000-0002-6378-6595 REFERENCES Abe, T. (2002). Fascicle length of gastrocnemius muscles in monozy- gous twins. Journal of Physiological Anthropology and Applied Human Science, 21, 291– 295. https://doi.org/10.2114/jpa.21.291 Abe, T., Fukashiro, S., Harada, Y., & Kawamoto, K. (2001). Relationship between sprint performance and muscle fas- cicle length in female sprinters. Journal of Physiological Anthropology and Applied Human Science, 20, 141– 147. https:// doi.org/10.2114/jpa.20.141 Abe, T., Kumagai, K., & Brechue, W. F. (2000). Fascicle length of leg muscles is greater in sprinters than distance runners. Medicine and Science in Sports and Exercise, 32, 1125– 1129. https://doi. org/10.1097/00005 768- 20000 6000- 00014 Aggeloussis, N., Giannakou, E., Albracht, K., & Arampatzis, A. (2010). Reproducibility of fascicle length and pennation angle of gastrocnemius medialis in human gait in vivo. Gait & Posture, 31, 73– 77. https://doi.org/10.1016/j.gaitp ost.2009.08.249 Asmussen, E. (1953). Positive and negative muscular work. Acta Physiologica Scandinavica, 28, 364– 382. https://doi. org/10.1111/j.1748- 1716.1953.tb009 88.x Asmussen, E., & Bonde- Petersen, F. (1974). Apparent efficiency and storage of elastic energy in human muscles during exer- cise. Acta Physiologica Scandinavica, 92, 537– 545. https://doi. org/10.1111/j.1748- 1716.1974.tb057 76.x Aura, O., & Komi, P. V. (1986). Mechanical efficiency of pure positive and pure negative work with special reference to work inten- sity. International Journal of Sports Medicine, 7, 44– 49. Barnes, K. R., Mcguigan, M. R., & Kilding, A. E. (2014). Lower- body determinants of running economy in male and female distance runners. Journal of Strength and Conditioning Research, 28, 1289– 1297. https://doi.org/10.1519/JSC.00000 00000 000267 Bergh, U., Sjodin, B., Forsberg, A., & Svedenhag, J. (1991). The rela- tionship between body mass and oxygen uptake during running in humans. Medicine and Science in Sports and Exercise, 23, 205– 211. https://doi.org/10.1249/00005 768- 19910 2000- 00010 Biewener, A. A., Farley, C. T., Roberts, T. J., & Temaner, M. (2004). Muscle mechanical advantage of human walking and running: implications for energy cost. Journal of Applied Physiology, 97, 2266– 2274. Carrier, D. R., Heglund, N. C., & Earls, K. D. (1994). Variable gear- ing during locomotion in the human musculoskeletal system. Science, 265, 651– 653. https://doi.org/10.1126/scien ce.8036513 Cavagna, G. A., Heglund, N. C., & Taylor, C. R. (1977). Mechanical work in terrestrial locomotion: two basic mechanisms for min- imizing energy expenditure. American Journal of Physiology, 233, R243– 261. Cavagna, G. A., & Kaneko, M. (1977). Mechanical work and effi- ciency in level walking and running. Journal of Physiology, 268, 467– 481. https://doi.org/10.1113/jphys iol.1977.sp011866 Cavagna, G. A., Saibene, F. P., & Margaria, R. (1964). Mechanical work in running. Journal of Applied Physiology, 19, 249– 256. https://doi.org/10.1152/jappl.1964.19.2.249 Cavagna, G. A., Thys, H., & Zamboni, A. (1976). The sources of exter- nal work in level walking and running. Journal of Physiology, 262, 639– 657. https://doi.org/10.1113/jphys iol.1976.sp011613 Cronin, N. J., & Lichtwark, G. (2013). The use of ultrasound to study muscle- tendon function in human posture and locomotion. Gait & Posture, 37, 305– 312. https://doi.org/10.1016/j.gaitp ost.2012.07.024 Dempster, W. T. (1955). Space requirements of the seated operator. WADC Technical Report Ohio: Wright- Patterson Air Force Base. Ferris, D. P., Louie, M., & Farley, C. T. (1998). Running in the real world: adjusting leg stiffness for different surfaces. Proceedings of the Royal Society B: Biological Sciences, 265, 989– 994. Fluit, R., Andersen, M. S., Kolk, S., Verdonschot, N., & Koopman, H. F. (2014). Prediction of ground reaction forces and moments during various activities of daily living. Journal of Biomechanics, 47, 2321– 2329. https://doi.org/10.1016/j.jbiom ech.2014.04.030 Franz, J. R., Wierzbinski, C. M., & Kram, R. (2012). Metabolic cost of running barefoot versus shod: is lighter better? Medicine and Science in Sports and Exercise, 44, 1519– 1525. https://doi. org/10.1249/MSS.0b013 e3182 514a88 Giannakou, E., Aggeloussis, N., & Arampatzis, A. (2011). Reproducibility of gastrocnemius medialis muscle architecture during treadmill running. Journal of Electromyography & Kinesiology, 21, 1081– 1086. https://doi.org/10.1016/j.jelek in.2011.06.004 Högberg, P. (1952). How do stride length and stride frequency influ- ence the energy- output during running? Arbeitsphysiologie, 14, 437– 441. https://doi.org/10.1007/BF009 34423 Karamanidis, K., & Arampatzis, A. (2007). Aging and running expe- rience affects the gearing in the musculoskeletal system of the lower extremities while walking. Gait & Posture, 25, 590– 596. https://doi.org/10.1016/j.gaitp ost.2006.06.015 Kawakami, Y., Muraoka, T., Ito, S., Kanehisa, H., & Fukunaga, T. (2002). In vivo muscle fibre behaviour during counter- movement exercise in humans reveals a significant role for ten- don elasticity. Journal of Physiology, 540, 635– 646. Kearns, C. F., Abe, T., & Brechue, W. F. (2000). Muscle enlargement in sumo wrestlers includes increased muscle fascicle length. European Journal of Applied Physiology, 83, 289– 296. https:// doi.org/10.1007/s0042 10000298 Kunimasa, Y., Sano, K., Oda, T., Nicol, C., Komi, P. V., Locatelli, E., Ito, A., & Ishikawa, M. (2014). Specific muscle- tendon archi- tecture in elite Kenyan distance runners. Scandinavian Journal of Medicine and Science in Sports, 24, e269– e274. https://doi. org/10.1111/sms.12161 Lai, A., Lichtwark, G. A., Schache, A. G., Lin, Y. C., Brown, N. A., & Pandy, M. G. (2015). In vivo behavior of the human so- leus muscle with increasing walking and running speeds. Journal of Applied Physiology, 118(10), 1266– 1275. https://doi. org/10.1152/jappl physi ol.00128.2015 Larsen, H. B. (2003). Kenyan dominance in distance running. Comparative Biochemistry and Physiology Part A Molecular Integrative Physiology, 136, 161– 170. https://doi.org/10.1016/ S1095 - 6433(03)00227 - 7 Larsen, H. B., Christensen, D. L., Nolan, T., & Sondergaard, H. (2004). Body dimensions, exercise capacity and physical activity level of ad- olescent Nandi boys in western Kenya. Annals of Human Biology, 31, 159– 173. https://doi.org/10.1080/03014 46041 00016 63416 Larsen, H. B., & Sheel, A. W. (2015). The Kenyan runners. Scandinavian Journal of Medicine and Science in Sports, 25(Suppl 4), 110– 118. https://doi.org/10.1111/sms.12573  | 15 of 15 HANSEN et al. Laursen, B., Ekner, D., Simonsen, E. B., Voigt, M., & Sjogaard, G. (2000). Kinetics and energetics during uphill and downhill car- rying of different weights. Appl Ergon, 31, 159– 166. https://doi. org/10.1016/S0003 - 6870(99)00036 - 8 Lee, S. S., & Piazza, S. J. (2009). Built for speed: musculoskeletal structure and sprinting ability. Journal of Experimental Biology, 212, 3700– 3707. Luhtanen, P., & Komi, P. V. (1978). Mechanical energy states during running. European Journal of Applied Physiology, 38, 41– 48. https://doi.org/10.1007/BF004 36751 Lynn, R., & Morgan, D. L. (1994). Decline running produces more sarcomeres in rat vastus intermedius muscle fibers than does incline running. Journal of Applied Physiology, 77(3), 1439– 1444. https://doi.org/10.1152/jappl.1994.77.3.1439 Lynn, R., Talbot, J. A., & Morgan, D. L. (1998). Differences in rat skeletal muscles after incline and decline running. Journal of Applied Physiology, 85(1), 98– 104. https://doi.org/10.1152/ jappl.1998.85.1.98 McMahon, T. A., & Cheng, G. C. (1990). The mechanics of running: how does stiffness couple with speed? Journal of Biomechanics, 23(Suppl 1), 65– 78. https://doi.org/10.1016/0021- 9290(90)90042 - 2 Morin, J. B., Dalleau, G., Kyrolainen, H., Jeannin, T., & Belli, A. (2005). A simple method for measuring stiffness during running. J Appl Biomech, 21, 167– 180. https://doi.org/10.1123/jab.21.2.167 Norman, R. W., Sharratt, M. T., Pezzack, J. C., & Noble, E. G. (1976). Reexamination of the mechanical efficiency of horizontal treadmill running. In P. V. Komi, & M. D. Baltimore (Eds.), ISB V (pp. 87– 93). International Series on Biomechanics, University Park Press. Pontzer, H., Raichlen, D. A., & Sockol, M. D. (2009). The meta- bolic cost of walking in humans, chimpanzees, and early hominins. Journal of Human Evolution, 56, 43– 54. https://doi. org/10.1016/j.jhevol.2008.09.001 Raichlen, D. A., Armstrong, H., & Lieberman, D. E. (2011). Calcaneus length determines running economy: implications for endurance running performance in modern humans and Neandertals. Journal of Human Evolution, 60, 299– 308. Saltin, B. (2003). The Kenya project - Final report IAAF. Saltin, B., Kim, C. K., Terrados, N., Larsen, H., Svedenhag, J., & Rolf, C. J. (1995). Morphology, enzyme activities and buffer capacity in leg muscles of Kenyan and Scandinavian runners. Scandinavian Journal of Medicine and Science in Sports, 5, 222– 230. https://doi.org/10.1111/j.1600- 0838.1995.tb000 38.x Saltin, B., Larsen, H., Terrados, N., Bangsbo, J., Bak, T., Kim, C. K., Svedenhag, J., & Rolf, C. J. (1995). Aerobic exercise cap acity at sea level and at altitude in Kenyan boys, junior and senior runners compared with Scandinavian runners. Scandinavian Journal of Medicine and Science in Sports, 5, 209– 221. https:// doi.org/10.1111/j.1600- 0838.1995.tb000 37.x Sano, K., Nicol, C., Akiyama, M., Kunimasa, Y., Oda, T., Ito, A., Locatelli, E., Komi, P. V., & Ishikawa, M. (2015). Can measures of muscle- tendon interaction improve our understanding of the superior- ity of Kenyan endurance runners? European Journal of Applied Physiology, 115, 849– 859. https://doi.org/10.1007/s0042 1- 014- 3067- 7 Santos- Concejero, J., Tam, N., Coetzee, D. R., Olivan, J., Noakes, T. D., & Tucker, R. (2017). Are gait characteristics and ground re- action forces related to energy cost of running in elite Kenyan runners? Journal of Sports Sciences, 35, 531– 538. https://doi. org/10.1080/02640 414.2016.1175655 Saunders, P. U., Pyne, D. B., Telford, R. D., & Hawley, J. A. (2004a). Factors affecting running economy in trained distance runners. Sports Medicine, 34, 465– 485. https://doi.org/10.2165/00007 256- 20043 4070- 00005 Saunders, P. U., Pyne, D. B., Telford, R. D., & Hawley, J. A. (2004b). Reliability and variability of running economy in elite distance runners. Medicine and Science in Sports and Exercise, 36, 1972– 1976. https://doi.org/10.1249/01.MSS.00001 45468.17329.9F Scholz, M. N., Bobbert, M. F., van Soest, A. J., Clark, J. R., & van Heerden, J. (2008). Running biomechanics: shorter heels, better economy. Journal of Experimental Biology, 211, 3266– 3271. Skals, S., Jung, M. K., Damsgaard, M., & Andersen, M. S. (2017). Prediction of ground reaction forces and moments during sports- related movements. Multibody System Dynamics, 39, 175– 195. https://doi.org/10.1007/s1104 4- 016- 9537- 4 Spiriev, B. (2011). IAAF Scoring Tables of Athletics International Amateur Athletics Federation. Spurrs, R. W., Murphy, A. J., & Watsford, M. L. (2003). The effect of plyometric training on distance running performance. European Journal of Applied Physiology, 89, 1– 7. https://doi. org/10.1007/s0042 1- 002- 0741- y Tawa, N., & Louw, Q. (2018). Biomechanical factors associated with running economy and performance of elite Kenyan dis- tance runners: a systematic review. Journal of Bodywork and Movement Therapies, 22, 1– 10. Taylor, C. R., Shkolnik, A., Dmi'el, R., Baharav, D., & Borut, A. (1974). Running in cheetahs, gazelles, and goats: energy cost and limb configuration. American Journal of Physiology, 227, 848– 850. Tierney, S., Asiam, M., Rennie, K., & Grace, P. (1996). Infrared op- toelectronic volumetry, the ideal way to measure limb volume. European Journal of Vascular and Endovascular Surgery, 12, 412– 417. https://doi.org/10.1016/S1078 - 5884(96)80005 - 0 Walmsley, B., & Proske, U. (1981). Comparison of stiffness of soleus and medial gastrocnemius muscles in cats. Journal of Neurophysiology, 46, 250– 259. https://doi.org/10.1152/jn.1981.46.2.250 Weston, A. R., Mbambo, Z., & Myburgh, K. H. (2000). Running economy of African and Caucasian distance runners. Medicine and Science in Sports and Exercise, 32, 1130– 1134. https://doi. org/10.1097/00005 768- 20000 6000- 00015 Williams, K. R., & Cavanagh, P. R. (1983). A model for the calculation of me- chanical power during distance running. Journal of Biomechanics, 16, 115– 128. https://doi.org/10.1016/0021- 9290(83)90035 - 0 Winter, D. A. (1979). A new definition of mechanical work done in human movement. Journal of Applied Physiology, 46, 79– 83. https://doi.org/10.1152/jappl.1979.46.1.79 Winter, D. A. (2009). Biomechanics and motor control of human movement. John Wiley. How to cite this article: Hansen, C. E., Stensvig, M., Wienecke, J., Villa, C., Lorentzen, J., Rasmussen, J., & Simonsen, E. B. (2021). Factors correlated to running economy among elite middle- and long- distance runners. Physiological Reports, 9, e15076. https://doi.org/10.14814/ phy2.15076

Factors correlated with running economy among elite middle- and long-distance runners.

[]

Hansen, Cecilie E,Stensvig, Martin,Wienecke, Jacob,Villa, Chiara,Lorentzen, Jakob,Rasmussen, John,Simonsen, Erik B

eng

PMC9794057

1 S6 Table. Results of round 2. Table A. Factors rated as ‘relevant’ in round 2 (level of agreement 70-100%), n=24. Factor Level of agreement (%) Training Maximal oxygen consumption 94,4 Economy of movement (=energy utilization) 88,9 Lactate threshold 88,9 Metabolism Glycolysis capacity (=break down of glucose) 100,0 Mitochondrial biogenesis (=growth of pre-existing mitochondria) 88,9 Lactate buffering system (=regulation of lactate level) 88,9 Fat metabolism (break down of fat for energy) 88,9 Body Number of red blood cells (=erythrocytes) 100,0 Muscle fibres - type 1 vs. type 2a/x (=slow vs. fast twitch fibres) 94,4 Hormones Testosterone level 94,4 Cortisol level 77,8 Erythropoietin (EPO) level 83,3 Nutrition Carbohydrate metabolism 100,0 Iron deficiency 94,4 Electrolyte balance/ hydration status 77,8 Vitamin D deficiency 72,2 Immune system Healing function of skeletal tissue 88,9 Injuries Risk of non-functional overreaching 88,9 Risk of stress fractures 77,8 Psychological Stress resistance 88,9 Motivation capacity 94,4 Self-confidence 72,2 Environment Sleep quality 94,4 Level of fatigue 77,8  2 Table B. Factors rated as ‘moderate’ in round 2 (level of agreement 40-69%), n=22. Factor Level of agreement (%) Training Endurance capacity 61,1 Recovery speed 61,1 Metabolism Angiogenesis (=formation of new blood vessels) 50,0 Body Muscle fibres - transformation capacity (type 1 vs. type 2) 55,6 Weight / BMI 44,4 Total fat mass 50,0 Lean mass (=mass of all organs except body fat including bones, muscles, blood, skin) 44,4 Tendon stiffness 55,6 Hormones Insulin-like growth factor-1 (IGF-1) level 55,6 Growth hormone level 66,7 Nutrition Vitamin B complex vitamins (B1-12) deficiency 50,0 Immune system Blood pressure regulation 50,0 Healing function of soft tissue 50,0 Injuries Risk of joint injuries 66,7 Risk of upper respiratory tract infections 61,1 Psychological Emotion regulation 66,7 Pain sensitivity 44,4 Self-control 50,0 Resilience capacity 50,0 Concentration capacity 44,4 Environment Heat resistance capacity 50,0 Altitude training sensitivity 55,6  3 Table C. Factors rated as ‘not relevant’ in round 2 (level of agreement 0-39%), n=54. Factor Level of agreement (%) Training Power capacity 33,3 Heart volume 33,3 Lung volume 16,7 Strength capacity 16,7 Metabolism Myoglobin storage capacity (=iron/ oxygen-binding protein) 33,3 Lactate dehydrogenase metabolism 33,3 Thermogenesis (=production of heat in the body) 5,6 Body Muscle fibres – contraction velocity capacity 11,1 Subcutaneous adipose tissue (=fat under the skin) 16,7 Muscle fibres – hypertrophy capacity (=muscle growth) 11,1 Hormones Dihydrotestosterone level 11,1 Oestradiol level 33,3 Thyroid hormones level 27,8 Epinephrine level 11,1 Norepinephrine level 11,1 Progesterone level 11,1 Gonadocorticoids level 11,1 Gonadotropin-releasing hormone level 22,2 Androstenedione level 11,1 Ghrelin level 5,6 Dehydroepiandrosterone level 5,6 Follicle-stimulating hormone level 11,1 Human chorionic gonadotropin level 5,6 Nutrition Steroid metabolism 33,3 Cell hydration status 33,3 Leucine level 22,2 Zinc deficiency 27,8 Magnesium deficiency 38,9 L-carnitine level 5,6 Creatine level 22,2 Caffeine metabolism 33,3 Antioxidant level 22,2 Carnosine level 16,7 Saturated fat metabolism 11,1 Beta carotene deficiency 11,1  4 Vitamin C deficiency 22,2 Folic acid deficiency 16,7 Bicarbonate level 27,8 Unsaturated fat metabolism 16,7 Cholesterol level 22,2 Omega 3 level 16,7 Vitamin A deficiency 11,1 Vitamin E deficiency 11,1 Selenium deficiency 11,1 Valine level 5,6 Omega 6 level 11,1 Immune system Cytokine responses 27,8 Detoxification process 11,1 Injuries Risk of left ventricular hypertrophy 27,8 Risk of metabolic myopathy 11,1 Psychological Risk of eating disorders 16,7 Environment Alcohol usage 22,2 Smoking behaviour 11,1 Proposed item (Sedentary) lifestyle in amateur athletes 16,7

Factors associated with high-level endurance performance: An expert consensus derived via the Delphi technique.

12-27-2022

Konopka, Magdalena J,Zeegers, Maurice P,Solberg, Paul A,Delhaije, Louis,Meeusen, Romain,Ruigrok, Geert,Rietjens, Gerard,Sperlich, Billy

eng

PMC6664210

ORIGINAL RESEARCH Effects of all-out sprint interval training under hyperoxia on exercise performance Michihiro Kon1,2,* , Kohei Nakagaki2,3,* & Yoshiko Ebi2 1 School of International Liberal Studies, Chukyo University, Nagoya, Japan 2 Department of Sports Sciences, Japan Institute of Sports Sciences, Tokyo, Japan 3 Department of Sports Sciences, Yamanashi Gakuin University, Yamanashi, Japan Keywords Accumulated oxygen deficit, hyperoxic training, lactate curve, trained athletes. Correspondence Michihiro Kon, School of International Liberal Studies, Chukyo University, 101-2 Yagotohonmachi, Showa-ku, Nagoya, 466- 8666, Japan. Tel: +81-52-835-9864 Fax: +81-52-835-7197 E-mail: [email protected] Funding Information This work was supported by JSPS KAKENHI (Grant Numbers 22700644 and 11J09235) and a grant from research project of Japan Institute of Sports Sciences. Received: 3 May 2019; Revised: 20 June 2019; Accepted: 8 July 2019 doi: 10.14814/phy2.14194 Physiol Rep, 2019, 7(14), e14194, https://doi.org/10.14814/phy2.14194 *These authors contributed equally to this work. Abstract All-out sprint interval training (SIT) is speculated to be an effective and time- efficient training regimen to improve the performance of aerobic and anaero- bic exercises. SIT under hypoxia causes greater improvements in anaerobic exercise performance compared with that under normoxia. The change in oxygen concentration may affect SIT-induced performance adaptations. In this study, we aimed to investigate the effects of all-out SIT under hyperoxia on the performance of aerobic and anaerobic exercises. Eighteen college male ath- letes were randomly assigned to either the normoxic sprint interval training (NST, n = 9) or hyperoxic (60% oxygen) sprint interval training (HST, n = 9) group and performed 3-week SIT (six sessions) consisting of four to six 30-sec all-out cycling sessions with 4-min passive rest. They performed maximal graded exercise, submaximal exercise, 90-sec maximal exercise, and acute SIT tests on a cycle ergometer before and after the 3-week intervention to evaluate the performance of aerobic and anaerobic exercises. Maximal oxy- gen uptake significantly improved in both groups. However, blood lactate curve during submaximal exercise test significantly improved only in the HST group. The accumulated oxygen deficit (AOD) during 90-sec maximal exercise test significantly increased only in the NST group. The average values of mean power outputs over four bouts during the acute SIT test significantly improved only in the NST group. These findings suggest that all-out SIT might induce greater improvement in aerobic exercise performance (blood lactate curve) but impair SIT-induced enhancements in anaerobic exercise performance (AOD and mean power output). Introduction All-out sprint interval training (SIT) has gained attention as an exercise training regimen that enhances the perfor- mance of aerobic and anaerobic exercises despite lower training volume. Burgomaster et al. (2005) demonstrated that SIT consisting of four to seven 30-sec all-out cycling with 4-min recovery induces improvements in endurance time to fatigue during submaximal cycling, maximal oxy- gen uptake ( _VO2max), and power output during repeated sprint test. In addition, similar enhancements in 750 kJ cycling time (Gibala et al. 2006) and _VO2max (Burgomas- ter et al. 2008; Cocks et al. 2013; Shepherd et al. 2013) are induced following SIT and traditional endurance training although total training volume is lower for SIT versus endurance training. These results suggest that all- out SIT may be a more time-efficient and effective train- ing method to improve the performance of aerobic and anaerobic exercises. In recent years, all-out SIT under hypoxic condition has been reported to induce greater improvements in exercise performance compared with that under normoxic ª 2019 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. 2019 | Vol. 7 | Iss. 14 | e14194 Page 1 Physiological Reports ISSN 2051-817X condition. Recent studies have shown greater improve- ment in power output during repeated sprint test follow- ing SIT under hypoxia compared with that under normoxia in male sprinters (Kasai et al. 2017) and female lacrosse athletes (Kasai et al. 2015). In addition, SIT under hypoxia leads to greater increases in power output and number of sprint sets until exhaustion during repeated sprint tests in male cyclists (Faiss et al. 2013) and male and female cross-country skiers (Faiss et al. 2015). However, one study found no additional effect of hypoxic SIT on the degree of improvement in these per- formance parameters in endurance-trained male subjects (Montero and Lundby 2017). Based on these results, all- out SIT under hypoxia, when compared with that under normoxia, may be more useful for enhancing anaerobic exercise performance. Hypoxic exposure increases the contribution of the anaerobic energy system during all- out sprint exercise (Ogura et al. 2006). Therefore, researchers speculate that the increased stimulus to the anaerobic energy system may contribute to greater improvements in anaerobic exercise performance (Kasai et al. 2015). If the alteration in the energy system contri- butions during SIT due to the change in oxygen concen- tration affects the SIT-induced performance adaptations, all-out SIT under hyperoxia may cause greater SIT-in- duced improvement in aerobic exercise performance because hyperoxia exposure increases the percentage of energy supplied from the aerobic system during maximal exercise (Linossier et al. 2000). By contrast, SIT under hyperoxia may impair SIT-induced enhancement in anaerobic exercise performance because hyperoxia expo- sure decreases the percentage of energy supplied from the anaerobic system during maximal exercise (Linossier et al. 2000). In this study, we aimed to investigate the effects of all- out SIT under hyperoxia on the performance of aerobic and anaerobic exercises in trained athletes. We hypothe- sized that all-out SIT under hyperoxia would lead to greater enhancement in aerobic exercise performance but diminishes anaerobic exercise performance. Materials and Methods Subjects Eighteen healthy college male athletes participated in this study. None of the subjects were smokers or taking any medications. They belonged to the canoe club at the same university and performed canoe-specific training 5 days per week. The subjects were randomly assigned to either the normoxic sprint training (NST, n = 9) or the hyper- oxic sprint training (HST, n = 9) group. The physical characteristics of the subjects are shown in Table 1. The subjects were informed of the experimental procedures, as well as the purpose of the present study. Informed con- sent was subsequently obtained from all subjects. The Japan Institute of Sports Sciences Ethics Committee approved the study design (Approval no. 001). Training protocol This study was conducted using a single-blind design. The subjects performed 3-week all-out SIT on nonconsec- utive days (every Monday and Thursday or every Tuesday and Friday, six sessions in total). Before the training, the subjects in the NST and HST groups wore a face mask covering the nose and mouth. In the HST group, the sub- jects received hyperoxic gas (60% oxygen) from the mask via a hyperoxic generator (TOK-20DX-M; IBS Co., Ltd., Osaka, Japan). The subjects in the NST group received normoxic air from the mask. The HST group was exposed to hyperoxic condition from 10 min before the sprint interval exercise session until immediately after the exercise session. The SIT consisted of repeated 30-s all- out cycling bouts on a cycle ergometer (Excalibur Sport 925900; Lode BV, Groningen, The Netherlands) at resis- tance equivalent to 7.5% of their body mass with 4-min passive rest between bouts. The cycle ergometer was set to fixed torque mode. The number of cycling bouts per- formed during each training session increased from four during week 1, to five during week 2, and finally to six Table 1. Characteristics of the subjects NST (n = 9) HST (n = 9) Pre Post Pre Post Age (year) 20.7
0.9 19
0.4 Height (cm) 172.9
1.8 171.9
1.8 Body mass (kg) 72.8
3.1 72.7
3.2 70.6
1.6 70.8
1.6 Body mass index (kg/m2) 24.3
0.8 24.3
0.9 23.9
0.3 24.0
0.3 Values are presented as means
SE. NST, normoxic sprint training; HST hyperoxic sprint training. 2019 | Vol. 7 | Iss. 14 | e14194 Page 2 ª 2019 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society Adaptations to Sprint Training in Hyperoxia M. Kon et al. during week 3. The subjects were instructed to sprint as fast as possible against the resistance and were encouraged verbally. Maximal graded exercise test A maximal graded exercise test on the cycle ergometer was performed to determine _VO2max and maximal work- load. After a 5-min warm-up at 100 W, the power output was increased by 15 W every 30 sec until exhaustion. Res- piratory gas samples were collected in Douglas bags every 30 sec during the test. The highest 30-sec _VO2 was regarded as _VO2max of the test. The subjects were instructed to maintain a cadence of 90 rpm during the test. The test was terminated when they could not main- tain pedal frequency within 5 rpm of the required level for 5 sec despite vigorous encouragement. The highest workload maintained for 30 sec was defined as maximal workload. Submaximal intermittent incremental exercise test A submaximal intermittent incremental exercise test on the cycle ergometer was performed to determine both the blood lactate curve and _VO2-power output linear rela- tionship. The power output for each 6-min stage was cal- culated as a percentage of the maximal workload (first stage, 20%; second stage, 30%; third stage, 40%; fourth stage, 50%; and fifth stage, 60%). The subjects were instructed to maintain a cadence of 90 rpm during the test. A 2-min rest period between each 6-min stage was allowed for the sampling of capillary blood. The _VO2 val- ues for the last 2 min of each 6-min stage were recorded and used to determine the _VO2-power output linear rela- tionship for each subject. 90-sec maximal exercise test The subjects performed a 90-sec maximal exercise test on the cycle ergometer as described previously (Gastin and Lawson 1994). Before the trial, subjects were given a 5- min warm-up at 100 W and then a 3-min rest. The resis- tance was reduced from 9.5 to 7.5% of their body weight at 30 sec and further reduced to 5.5% of their body weight at 60 sec. For an all-out effort, the subjects were instructed and strongly encouraged to maintain the cadence as high as possible throughout the test. The esti- mated oxygen demand for the 90-sec maximal exercise test was then calculated by extrapolation from the _VO2- power output linear relationship. The accumulated oxy- gen deficit (AOD) was calculated as the difference between the estimated oxygen demand of exercise and the accumulated oxygen uptake (AOU). Average power out- put, estimated oxygen demand and oxygen uptake were calculated over 30-sec intervals. Sprint interval exercise test The subjects performed an all-out sprint interval exercise test under normoxic conditions, comprising four 30-sec maximal cycling bouts with 4-min passive rest between bouts using the cycle ergometer. The resistance was equiv- alent to 7.5% of their body weight. The peak and mean power output values of each bout were measured and recorded. Cardiorespiratory measurements _VO2 and _VCO2 were determined using the Douglas bag method. The O2 and CO2 fractions in the expired gas were measured with a calibrated gas analyzer (Aeromoni- tor AE310s; Minato Medical Science, Osaka, Japan). The expired gas volume was determined using a dry gas meter (Oval GAL-55; Oval Corp., Tokyo, Japan). Blood sampling and analysis Blood sampling was performed before (pre) and after (post) the 3-week intervention. In the morning (between 08:00 and 09:00), all subjects visited the laboratory after overnight fasting and rested for 30 min before the blood collection. The subjects were confirmed to ensure a 48-h period without any exercise activity prior to the blood collection. Subsequently, blood samples were collected from each subject’s forearm. Serum samples were obtained by centrifugation (3000 rpm for 15 min) and stored at 80°C until analysis. Serum derivatives of reactive oxygen metabolites (d-ROMs), which are mark- ers to evaluate hydroperoxide levels (oxidative stress), and serum biological antioxidant potential (BAP), which indicates antioxidant capacity, were measured using a FREE Carrio Duo (Wismerll Co., Ltd., Tokyo, Japan). The BAP/d-ROMs ratio was also measured to evaluate serum oxidant-antioxidant balance (Sone et al. 2019). Blood lactate concentration was determined using a Bio- sen S-Line (EKF-diagnostic GmbH, Barleben, Germany) from 20 µL of fingertip capillary blood sample. Oxyhe- moglobin saturation (SpO2) was measured using a fore- head pulse oximeter (Masimo Rad-57; Masimo Corp., CA). Nutritional and physical activity controls During the experimental period, the subjects were ordered to continue their normal diet and physical activity. They ª 2019 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society 2019 | Vol. 7 | Iss. 14 | e14194 Page 3 M. Kon et al. Adaptations to Sprint Training in Hyperoxia were also instructed not to perform any exercise for 48 h before the exercise performance tests. Statistical analysis Results are presented as means with standard errors (SE). All data were analyzed using a two-way ANOVA with repeated measures. When significant differences existed, a post hoc analysis test (Fisher’s least significant difference) was performed. The percent changes between the groups were compared using unpaired t tests. The level of statis- tical significance was set at P < 0.05. Results Body composition Before the 3-week training period, no significant differ- ence was found in the physical characteristics between the NST and HST groups (Table 1). Body mass (main effect, P = 0.57) and body mass index (main effect, P = 0.56) did not change after the training period in either group. Oxyhemoglobin saturation Figure 1 shows a typical example of changes in SpO2 dur- ing six repeated bouts of sprint exercise under normoxia and hyperoxia obtained from the same subject. The SpO2 during SIT under normoxia changed between 98% and 92%. By contrast, no change (almost 100%) in SpO2 was observed during SIT under hyperoxia. Training power output Figure 2 shows the average values of mean power outputs over four, five, or six bouts throughout the 3-week training period. No significant differences were found in the power outputs throughout the 3-week training period between the NST and HST groups (main effect, P = 0.31). _VO2maxand maximal workload during maximal graded exercise test _VO2max significantly improved in both the NST and HST groups (main effect, P < 0.05; Fig. 3). However, no significant difference was found in the degree of improve- ment in _VO2max between the NST (3.0
2.1%) and HST (6.0
1.8%) groups (main effect, P = 0.83). Maxi- mal workload during progressive exercise test to deter- mine _VO2max also significantly increased in both the NST and HST groups (P < 0.05; Fig. 3), with no differ- ence between the two groups (main effect, P = 0.97). Respiratory gas and blood lactate during submaximal exercise test Table 2 shows changes in _VO2, _VCO2, and respiratory exchange ratio (RER) during submaximal intermittent incremental exercise test. The _VO2, _VCO2, and RER pro- gressively increased during the submaximal intermittent incremental test in the NST and HST groups (main effect, P < 0.05). Significant differences were not observed in _VO2 (NST, main effect, P = 0.79; HST, main effect, P = 0.56) and _VCO2 (NST, main effect, P = 0.87; HST, main effect, P = 0.71) between before (pre) and after (post) training in normoxia and hyperoxia. By contrast, the RER at the first stage in the NST group significantly increased after the training (P < 0.05, effect size = 0.78). Figure 4 shows blood lactate data during the submaxi- mal intermittent incremental cycling test. Blood lactate also progressively increased in the NST and HST groups (main effect, P < 0.05). However, blood lactate levels at 84 88 92 96 100 0 5 10 15 20 25 Normoxia Hyperoxia SpO2 (%) Time (min) 1st 2nd 3rd 4th 5th 6th Figure 1. Typical example of changes in SpO2 during six repeated bouts of sprint exercise under normoxia and hyperoxia obtained from the same subject. 0 200 400 600 800 1 2 3 4 5 6 NST HST Training power output (W) Training times Figure 2. Training power outputs throughout the 3-week all-out sprint interval training. NST, normoxic sprint interval training (n = 9); HST, hyperoxic sprint interval training (n = 9). Values are presented as means
SE. 2019 | Vol. 7 | Iss. 14 | e14194 Page 4 ª 2019 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society Adaptations to Sprint Training in Hyperoxia M. Kon et al. the 4th (P < 0.05, effect size = 0.80) and 5th (P < 0.05, effect size = 0.86) stages after the training were signifi- cantly lower than those before the training in the HST group, but no significant differences were found in the NST group (P = 0.51). Power outputs, AOU, AOD, and %AOD during 90-s maximal exercise test Table 3 shows power outputs, AOU, AOD, and %AOD data during 90-sec maximal exercise test. Peak and mean power outputs significantly increased in both the groups (main effect, P < 0.05). Although no significant differences were found in the degree of improvements in peak and mean power outputs between the NST and HST groups, percent changes in the peak and mean power outputs in the NST group (peak power output; 10.9
3.5%, mean power output; 6.3
1.7%) tended to be higher than those in the HST group (peak power output; 3.5
2.3%, mean power output; 2.2
1.0%) (P < 0.10). AOU significantly increased in both the NST and HST groups after the train- ing (main effect, P < 0.05). However, AOD (P < 0.05, effect size = 0.85) and %AOD (P < 0.05, effect size = 0.61) significantly increased only in the NST group after the training. In addition, the NST group showed significantly greater percentage increases in AOD (P < 0.05, effect size = 1.07) and %AOD (P < 0.05, effect size = 0.92) than the HST group (Fig. 5). 40 45 50 55 60 NST HST Pre Post VO2max (ml/kg/min) * * 4.0 4.5 5.0 5.5 6.0 NST HST Pre Post Maximal workload (W/kg) * * . Figure 3. Maximal oxygen uptake ( _VO2max) and workload before (pre) and after (post) 3 weeks of all-out sprint interval training. NST, normoxic sprint interval training (n = 9); HST, hyperoxic sprint interval training (n = 9). Values are presented as means
SE. *P < 0.05 versus pre. Table 2. Changes in _VO2, _VCO2, and RER during submaximal intermittent incremental exercise test NST (n = 9) HST (n = 9) Pre Post Pre Post _VO2 (mL/kg/min) 1st 19.2
0.4 19.4
0.3 1st 19.4
0.4 19.1
0.5 2nd 23.9
0.6 23.7
0.4 2nd 24.3
0.5 23.8
0.6 3rd 29.2
0.8 29.1
0.5 3rd 29.4
0.7 28.8
0.9 4th 34.6
0.9 34.3
0.6 4th 35.0
0.9 34.2
1.1 5th 40.7
1.2 39.9
0.7 5th 41.0
1.0 40.0
1.2 _VCO2 (mL/kg/min) 1st 17.2
0.4 17.9
0.4 1st 17.3
0.6 17.4
0.5 2nd 21.5
0.5 22.0
0.5 2nd 22.3
0.6 22.3
0.5 3rd 27.1
0.8 27.2
0.5 3rd 27.8
0.8 27.4
0.9 4th 33.0
0.8 32.8
0.7 4th 34.3
1.2 33.4
1.2 5th 40.4
1.3 40.0
0.9 5th 41.6
1.4 40.6
1.2 RER 1st 0.89
0.01 0.93
0.02* 1st 0.89
0.02 0.91
0.01 2nd 0.90
0.01 0.93
0.02 2nd 0.92
0.01 0.94
0.01 3rd 0.93
0.01 0.94
0.01 3rd 0.94
0.01 0.95
0.01 4th 0.96
0.01 0.96
0.01 4th 0.98
0.01 0.98
0.01 5th 0.99
0.01 1.00
0.01 5th 1.01
0.01 1.01
0.01 Values are presented as means
SE. NST, normoxic sprint training; HST hyperoxic sprint training. *P < 0.05 versus Pre. ª 2019 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society 2019 | Vol. 7 | Iss. 14 | e14194 Page 5 M. Kon et al. Adaptations to Sprint Training in Hyperoxia Power outputs during acute sprint interval exercise test Table 4 shows peak and mean power output data during acute sprint interval test. In the NST and HST groups, the peak and mean power outputs gradually decreased before (pre) and after (post) the training period (main effect, P < 0.05). The average values of peak power out- puts over four bouts significantly increased after the training in both the NST and HST groups (main effect, 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 1st 2nd 3rd 4th 5th Pre Post 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 1st 2nd 3rd 4th 5th Pre Post Blood lactate (mmol/L) Blood lactate (mmol/L) NST HST * * (stage) (stage) Figure 4. Blood lactate curve during submaximal intermittent incremental cycling test before (pre) and after (post) 3 weeks of all-out sprint interval training. NST, normoxic sprint interval training (n = 9); HST, hyperoxic sprint interval training (n = 9). Values are presented as means
SE. *P < 0.05 versus pre. Table 3. Changes in peak and mean power outputs, AOU, AOD, and %AOD during 90-sec maximal exercise test NST (n = 9) HST (n = 9) Pre Post Pre Post Peak power (W/kg) 20.5
0.7 22.7
1.1* 20.3
0.6 21.0
0.7* Mean power (W/kg) 6.9
0.1 7.3
0.2* 7.0
0.1 7.2
0.1* AOU (mL/kg) 65.0
1.2 66.2
1.2* 68.1
1.5 69.7
1.3* AOD (mLO2eq/kg) 55.4
1.7 61.0
2.4* 55.6
1.2 56.3
1.9 %AOD 46.0
0.8 47.9
1.2* 45.0
0.9 44.6
1.2 Values are presented as means
SE. AOU, accumulated oxygen uptake; AOD, accumulated oxygen deficit; NST, normoxic sprint training; HST hyperoxic sprint training. *P < 0.05 versus Pre. 0 3 6 9 12 15 NST HST Percent changes of AOD (%) * Percent changes of %AOD (%) * –4 –2 0 2 4 6 8 NST HST Figure 5. Percent changes in AOD and %AOD during 90-sec maximal cycling test before (pre) and after (post) 3 weeks of all-out sprint interval training. NST, normoxic sprint interval training (n = 9); HST, hyperoxic sprint interval training (n = 9). Values are presented as means
SE. *P < 0.05 between the NST and HST. 2019 | Vol. 7 | Iss. 14 | e14194 Page 6 ª 2019 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society Adaptations to Sprint Training in Hyperoxia M. Kon et al. P < 0.05). By contrast, the average values of mean power outputs over four bouts significantly increased after the training only in the NST group (P < 0.05, effect size = 0.84). In addition, the NST group showed a signifi- cantly greater percentage increase in the average values of mean power output over four bouts than the HST group (P < 0.05, effect size = 1.12; Fig. 6). Serum d-ROM, BAP, and BAP/d-ROMs ratio Figure 7 shows serum d-ROMs, BAP, and BAP/d-ROMs data before (pre) and after (post) 3-week SIT. Serum d- ROMs did not change in either the NST or HST groups. By contrast, serum BAP significantly decreased after the training in both groups (main effect, P < 0.05). However, the BAP/d-ROMs ratio did not change in either the NST or HST groups. Discussion This study investigated the effects of all-out SIT under hyperoxia on the performance of aerobic and anaerobic exercises. The blood lactate curve during the submaximal intermittent incremental cycling test, which represents enhancement in aerobic endurance capacity (Faude et al. 2009), improved only in the HST group. By contrast, hyperoxia exposure impaired the SIT-induced enhance- ments in the AOD and %AOD during 90-s maximal exer- cise test. Moreover, the SIT-induced increase in average mean power output during sprint interval exercise test was also diminished by hyperoxia exposure (Table 4). These present results suggest that all-out SIT under hyperoxia may bring about greater improvement in aero- bic exercise performance (blood lactate curve) but impair SIT-induced enhancements in anaerobic exercise perfor- mance (AOD, %AOD, and mean power output). In the present study, the blood lactate curve during submaximal cycling improved after the training period only in the HST group, but no difference was found in the degree of improvement in _VO2max between the NST and HST groups. Perry et al. (2005) demonstrated that hyperoxic (60% oxygen) interval training (10 repeats of 4 min cycling at 90% heart rate max with 2 min recovery, 3 days/week for 6 weeks) leads to greater enhancement in cycling performance time to exhaustion at 90% _VO2max without greater improvement in _VO2max when compared with normoxic interval training. These present and previ- ous results suggest that hyperoxic interval training may induce greater improvement in aerobic exercise Table 4. Changes in peak and mean power during acute sprint interval exercise test NST (n = 9) HST (n = 9) Pre Post Pre Post Peak power (W/kg) Bout 1 20.0
0.6 21.8
0.8 19.4
0.7 20.0
0.5 Bout 2 18.3
0.5# 19.9
0.6# 18.0
0.5# 19.2
0.6# Bout 3 16.5
0.4# 17.9
1.0# 15.6
0.7# 17.1
0.6# Bout 4 14.7
0.5# 15.9
1.2# 14.0
0.7# 15.3
0.9# Average 17.4
0.4 18.9
0.8* 16.7
0.6 17.9
0.6* Mean power (W/kg) Bout 1 9.8
0.2 10.3
0.3 9.9
0.1 9.9
0.1 Bout 2 8.8
0.2# 9.3
0.2# 8.9
0.1# 9.0
0.1# Bout 3 7.9
0.2# 8.4
0.2# 7.8
0.1# 8.1
0.1# Bout 4 7.3
0.3# 8.0
0.3# 7.5
0.2# 7.6
0.2# Average 8.4
0.2 9.0
0.2* 8.5
0.1 8.7
0.1 Values are presented as means
SE. NST, normoxic sprint training; HST hyperoxic sprint training. #P < 0.05 versus Bout 1. *P < 0.05 versus Pre. 0 2 4 6 8 NST HST * Percent changes of mean power (%) Figure 6. Percent changes in average mean power outputs during acute sprint interval exercise test before (pre) and after (post) 3 weeks of all-out sprint interval training. NST, normoxic sprint interval training (n = 9); HST, hyperoxic sprint interval training (n = 9). Values are presented as means
SE. *P < 0.05 between the NST and HST. ª 2019 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society 2019 | Vol. 7 | Iss. 14 | e14194 Page 7 M. Kon et al. Adaptations to Sprint Training in Hyperoxia performance without greater enhancement of _VO2max. However, the underlying mechanism of improving aero- bic exercise performance (blood lactate curve) by SIT under hyperoxia was not elucidated in this study. The improvement in the lactate curve may be related to the enhancement of the mitochondrial oxidative capacity of working muscle by exercise training. Perry et al. (2007) demonstrated that hyperoxic (60% oxygen) interval train- ing (10 repeats of 4 min cycling at 90% _VO2max with 2 min recovery, 3 days/week for 6 weeks) does not affect the degree of improvement in mitochondrial oxidative capacity of working muscles when compared with normoxic interval training. However, their research used submaximal interval training model, and an investigation of an all-out SIT model has not been conducted so far. Thus, future research could investigate the effect of all- out SIT under hyperoxia on skeletal muscle mitochon- drial oxidative capacity. All-out SIT under hyperoxia impaired the improve- ments in anaerobic exercise performance, such as AOD, %AOD, and mean power output during cycling exercise tests, in this study. In a previous study, hyperoxia expo- sure decreases energy supply from the anaerobic system during maximal cycling exercise (Linossier et al. 2000). Therefore, in this study, hyperoxia exposure might decrease energy supply from the anaerobic system during SIT. The decreased anaerobic energy release due to hyper- oxia might induce impairment in SIT-induced improve- ments in anaerobic exercise performance in this study. Additionally, the all-out SIT-induced enhancements in anaerobic exercise performance may be related to the increase in adenosine triphosphate production via the gly- colytic system. Short-term all-out SIT increases glycogen content in muscles (Burgomaster et al. 2005; Gibala et al. 2006). The increased glycogen content in muscles may be induced by increases in muscle content and translocation to plasma membrane of glucose transporter 4 (GLUT4), which facilitates glucose uptake in the skeletal muscles. Hyperoxia exposure decreases GLUT4 content and/or translocation in the skeletal muscles (Bandali et al. 2003). Conversely, short-term all-out SIT increases GLUT4 con- tent in the skeletal muscles (Burgomaster et al. 2007). Thus, hyperoxia exposure may diminish the SIT-induced increase in GLUT4 and glycogen contents in skeletal mus- cle, which may lead to impaired SIT-induced enhance- ments of anaerobic exercise performance in the HST group. Future studies are needed to clarify the effects of all-out SIT under hyperoxia on the GLUT4 and glycogen contents in skeletal muscles. A previous study reported that 3-week all-out SIT (four to six 30-sec all-out cycling with 4-min recovery, 3 days/ week) attenuates oxidative stress in healthy humans (Bog- danis et al. 2013). In addition, 3-week endurance exercise training (30-min moderate-intensity cycling, 5 days/week) under hyperbaric hyperoxia does not increase systemic oxidative stress in young male soccer players (Burgos et al. 2016). However, to date, no study has investigated the effect of SIT under normobaric hyperoxia on oxida- tive stress in healthy humans. In this study, no significant difference was observed in oxidative stress (d-ROMs) and oxidant-antioxidant balance (BAP/d-ROMs) between before and after SIT. To the best of our knowledge, this study is the first to determine that exercise training under normobaric hyperoxia does not affect oxidative stress in healthy humans. 0 50 100 150 200 250 300 350 NST HST Pre Post d-ROMs (U.CARR.) 0 500 1000 1500 2000 2500 NST HST Pre Post BAP (µmol/L) 0 2 4 6 8 10 NST HST Pre Post BAP/d-ROMs ratio * * Figure 7. Serum derivatives of reactive oxygen metabolites (d- ROMs), biological antioxidant potential (BAP), and BAP/d-ROMs ratio before (pre) and after (post) 3 weeks of all-out sprint interval training. NST, normoxic sprint interval training (n = 9); HST, hyperoxic sprint interval training (n = 9). Values are presented as means
SE. *P < 0.05 versus pre. 2019 | Vol. 7 | Iss. 14 | e14194 Page 8 ª 2019 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society Adaptations to Sprint Training in Hyperoxia M. Kon et al. This study has a several limitations. First, the present study did not use a crossover design and had a small sample size. Future studies with a crossover design and larger sample size are necessary to confirm our findings. Second, this study could not elucidate the factors that induced the performance adaptations to hyperoxic all-out SIT because we could not investigate skeletal muscle adaptations by all-out SIT under hyperoxia. Therefore, detailed studies using human skeletal muscles are war- ranted in future research. Finally, the present study did not assess nutritional status, but we instructed the sub- jects to continue their normal diet. Thus, future studies including assessment of nutritional status are needed. Conclusions All-out SIT under hyperoxia might induce greater improvement in aerobic exercise performance (blood lac- tate curve), but impairs SIT-induced enhancements in anaerobic exercise performance (AOD, %AOD, and mean power output). Acknowledgments We thank the clinical laboratory technicians at the Japan Institute of Sports Sciences for their help in conducting the clinical portion of this study. We also thank Kosuke Taniguchi for his help with the analyses of oxidative stress and antioxidant capacity. Conflict of Interest None declared. References Bandali, K. S., M. P. Belanger, and C. Wittnich. 2003. Does hyperoxia affect glucose regulation and transport in the newborn? J. Thorac. Cardiovasc. Surg. 126:1730–1735. https://doi.org/10.1016/S0022-5223(03)01044-4. Bogdanis, G. C., P. Stavrinou, A. Philippou, A. Chatzinikolaou, D. Draganidis, G. Ermidis, et al. 2013. Short-term high-intensity interval exercise training attenuates oxidative stress responses and improves antioxidant status in healthy humans. Food Chem. Toxicol. 61:171–177. https://doi.org/10.1016/j.fct.2013.05.046. Burgomaster, K. A., S. C. Hughes, G. J. Heigenhauser, S. N. Bradwell, and M. J. Gibala. 2005. Six sessions of sprint interval training increases muscle oxidative potential and cycle endurance capacity in humans. J. Appl. Physiol. 98:1985–1990. https://doi.org/10.1152/japplphysiol.01095. 2004. Burgomaster, K. A., N. M. Cermak, S. M. Phillips, C. R. Benton, A. Bonen, and M. J. Gibala. 2007. Divergent response of metabolite transport proteins in human skeletal muscle after sprint interval training and detraining. Am. J. Physiol. Regul. Integr. Comp. Physiol. 292:R1970–R1976. https://doi.org/10.1152/ajpregu.00503.2006. Burgomaster, K. A., K. R. Howarth, S. M. Phillips, M. Rakobowchuk, M. J. Macdonald, S. L. McGee, et al. 2008. Similar metabolic adaptations during exercise after low volume sprint interval and traditional endurance training in humans. J. Physiol. 586:151–160. https://doi.org/10.1113/ jphysiol.2007.142109. Burgos, C., C. Henrıquez-Olguın, D. C. Andrade, R. Ramırez- Campillo, O. F. Araneda, A. White, et al. 2016. Effects of exercise training under hyperbaric oxygen on oxidative stress markers and endurance performance in young soccer players: a pilot study. J. Nutr. Metab. 2016:5647407. https://doi.org/10.1155/2016/5647407. Cocks, M., C. S. Shaw, S. O. Shepherd, J. P. Fisher, A. Ranasinghe, T. A. Barker, et al. 2013. Sprint interval and endurance training are equally effective in increasing muscle microvascular density and eNOS content in sedentary males. J. Physiol. 591:641–656. https://doi.org/10.1113/jphysiol. 2012.239566. Faiss, R., B. Leger, J. M. Vesin, P. E. Fournier, Y. Eggel, O. Deriaz, et al. 2013. Significant molecular and systemic adaptations after repeated sprint training in hypoxia. PLoS ONE 8:e56522. https://doi.org/10.1371/journal.pone.0056522. Faiss, R., S. Willis, D. P. Born, B. Sperlich, J. M. Vesin, H. C. Holmberg, et al. 2015. Repeated double-poling sprint training in hypoxia by competitive cross-country skiers. Med. Sci. Sports Exerc. 47:809–817. https://doi.org/10.1249/ MSS.0000000000000464. Faude, O., R. R€oßler, and A. Junge. 2009. Lactate threshold concepts: how valid are they? Sports Med. 39:469–490. https://doi.org/10.2165/00007256-200939060-00003. Gastin, P. B., and D. L. Lawson. 1994. Influence of training status on maximal accumulated oxygen deficit during all- out cycle exercise. Eur. J. Appl. Physiol. Occup. Physiol. 69:321–330. https://doi.org/10.1007/BF00392038. Gibala, M. J., J. P. Little, M. van Essen, G. P. Wilkin, K. A. Burgomaster, A. Safdar, et al. 2006. Short-term sprint interval versus traditional endurance training: similar initial adaptations in human skeletal muscle and exercise performance. J. Physiol. 575(Pt 3):901–911. https://doi.org/ 10.1113/jphysiol.2006.112094. Kasai, N., S. Mizuno, S. Ishimoto, E. Sakamoto, M. Maruta, and K. Goto. 2015. Effect of training in hypoxia on repeated sprint performance in female athletes. Springerplus 4:310. https://doi.org/10.1186/s40064-015-1041-4. Kasai, N., C. Kojima, D. Sumi, H. Takahashi, K. Goto, and Y. Suzuki. 2017. Impact of 5 days of sprint training in hypoxia on performance and muscle energy substances. Int. J. Sports Med. 38:983–991. https://doi.org/10.1055/s-0043-117413. Linossier, M. T., D. Dormois, L. Arsac, C. Denis, J. P. Gay, A. Geyssant, et al. 2000. Effect of hyperoxia on aerobic and ª 2019 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society 2019 | Vol. 7 | Iss. 14 | e14194 Page 9 M. Kon et al. Adaptations to Sprint Training in Hyperoxia anaerobic performances and muscle metabolism during maximal cycling exercise. Acta Physiol. Scand. 168:403–411. https://doi.org/10.1046/j.1365-201x.2000.00648.x. Montero, D., and C. Lundby. 2017. No improved performance with repeated-sprint training in hypoxia versus normoxia: a double-blind and crossover study. Int. J. Sports Physiol. Perform. 12:161–167. https://doi.org/10.1123/ijspp.2015- 0691. Ogura, Y., S. Katamoto, J. Uchimaru, K. Takahashi, and H. Naito. 2006. Effects of low and high levels of moderate hypoxia on anaerobic energy release during supramaximal cycle exercise. Eur. J. Appl. Physiol. 98:41–47. https://doi. org/10.1007/s00421-006-0283-9. Perry, C. G., J. Reid, W. Perry, and B. A. Wilson. 2005. Effects of hyperoxic training on performance and cardiorespiratory response to exercise. Med. Sci. Sports Exerc. 37:1175–1179. https://doi.org/10.1249/01.mss.0000169610.33318.54. Perry, C. G., J. L. Talanian, G. J. Heigenhauser, and L. L. Spriet. 2007. The effects of training in hyperoxia vs. normoxia on skeletal muscle enzyme activities and exercise performance. J. Appl. Physiol. 102:1022–1027. https://doi. org/10.1152/japplphysiol.01215.2006. Shepherd, S. O., M. Cocks, K. D. Tipton, A. M. Ranasinghe, T. A. Barker, J. G. Burniston, et al. 2013. Sprint interval and traditional endurance training increase net intramuscular triglyceride breakdown and expression of perilipin 2 and 5. J. Physiol. 591:657–675. https://doi.org/10.1113/jphysiol. 2012.240952. Sone, R., K. Matsuba, R. Tahara, N. Eda, K. Kosaki, S. Jesmin, et al. 2019. Assessment of salivary nitric oxide levels in elite university athletes in Japan: findings from a cross sectional study design. J Clin Med Res 11:114–120. https://doi.org/10. 14740/jocmr3670. 2019 | Vol. 7 | Iss. 14 | e14194 Page 10 ª 2019 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society Adaptations to Sprint Training in Hyperoxia M. Kon et al.

Effects of all-out sprint interval training under hyperoxia on exercise performance.

[]

Kon, Michihiro,Nakagaki, Kohei,Ebi, Yoshiko

eng

PMC4048241

Static Stretching Alters Neuromuscular Function and Pacing Strategy, but Not Performance during a 3-Km Running Time-Trial Mayara V. Damasceno1, Marcos Duarte2, Leonardo A. Pasqua1, Adriano E. Lima-Silva3, Brian R. MacIntosh4, Roˆ mulo Bertuzzi1* 1 Endurance Performance Research Group, School of Physical Education and Sport, University of Sa˜o Paulo, Sa˜o Paulo, Sa˜o Paulo, Brazil, 2 Biomedical Engineering, Federal University of ABC, Santo Andre´, Sa˜o Paulo, Brazil, 3 Sports Science Research Group, Department of Physical Education and Sports Science, Federal University of Pernambuco, Vitoria de Santo Anta˜o, Pernambuco, Brazil, 4 Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada Abstract Purpose: Previous studies report that static stretching (SS) impairs running economy. Assuming that pacing strategy relies on rate of energy use, this study aimed to determine whether SS would modify pacing strategy and performance in a 3-km running time-trial. Methods: Eleven recreational distance runners performed a) a constant-speed running test without previous SS and a maximal incremental treadmill test; b) an anthropometric assessment and a constant-speed running test with previous SS; c) a 3-km time-trial familiarization on an outdoor 400-m track; d and e) two 3-km time-trials, one with SS (experimental situation) and another without (control situation) previous static stretching. The order of the sessions d and e were randomized in a counterbalanced fashion. Sit-and-reach and drop jump tests were performed before the 3-km running time-trial in the control situation and before and after stretching exercises in the SS. Running economy, stride parameters, and electromyographic activity (EMG) of vastus medialis (VM), biceps femoris (BF) and gastrocnemius medialis (GA) were measured during the constant-speed tests. Results: The overall running time did not change with condition (SS 11:35600:31 s; control 11:28600:41 s, p = 0.304), but the first 100 m was completed at a significantly lower velocity after SS. Surprisingly, SS did not modify the running economy, but the iEMG for the BF (+22.6%, p = 0.031), stride duration (+2.1%, p = 0.053) and range of motion (+11.1%, p = 0.0001) were significantly modified. Drop jump height decreased following SS (29.2%, p = 0.001). Conclusion: Static stretch impaired neuromuscular function, resulting in a slow start during a 3-km running time-trial, thus demonstrating the fundamental role of the neuromuscular system in the self-selected speed during the initial phase of the race. Citation: Damasceno MV, Duarte M, Pasqua LA, Lima-Silva AE, MacIntosh BR, et al. (2014) Static Stretching Alters Neuromuscular Function and Pacing Strategy, but Not Performance during a 3-Km Running Time-Trial. PLoS ONE 9(6): e99238. doi:10.1371/journal.pone.0099238 Editor: Franc¸ois Hug, The University of Queensland, Australia Received November 28, 2013; Accepted May 12, 2014; Published June 6, 2014 Copyright:  2014 Damasceno, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This study was supported by Sa˜o Paulo Research Foundation (FAPESP: 2011/10742-9). Mayara Vieira Damasceno and Leonardo Alves Pasqua were supported by a master scholarship from Sa˜o Paulo Research Foundation (FAPESP: 2011/02769-4 and 2010/13913-6), www.fapesp.br. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: [email protected] Introduction The manner in which runners distribute their speed during a competition is defined as pacing strategy [1]. It has been widely recognized that the pacing strategy adopted by athletes can substantially impact performance in long-distance running [1]. During these competitive events, endurance athletes usually adopt a pacing strategy with a speed distribution consisting of three distinct phases. These phases are characterized by a fast start, followed by a period of slower speed during the middle of the race, and a significant increase in running speed towards the end [2,3]. It has been previously demonstrated that the strategy with the highest speeds reached during start phase (i.e. fast start) is advantageous for increasing oxygen uptake early and decreasing the use of anaerobic energy reserves [4]. Additionally, a fast acceleration relies on the ability to generate high forces, suggesting an importance of neuromuscular system for the start phase [5]. The rating of perceived exertion (RPE) [6] can be evaluated during a running race in order to verify how effort and perceived difficulty relate to actual speed. More recently, Faulkner et al. [7] observed that when speed distribution during long-distance running was characterized by the triphasic speed distribution profile described above (so-called ‘‘U shaped’’ pacing strategy), the RPE increases linearly. It has been hypothesized that this linear profile of RPE during a time trial test reflects a centrally-regulated control system that is dependent on a patterned disturbance to muscular homeostasis. It is believed that this system regulates the pattern and magnitude of muscular activation to maintain the PLOS ONE | www.plosone.org 1 June 2014 | Volume 9 | Issue 6 | e99238 physiological strain at a tolerable level and to prevent premature exercise termination [8]. In addition to a centrally-regulated system, it has been proposed that pacing strategy can be influenced by some physiological feedback [9,10] and neuromuscular [11] performance changes. For example, Lima-Silva et al. [12] observed that long-distance runners who had higher running economy (RE) were able to adopt a more aggressive pacing strategy, employing faster velocities at the start phase (first 400 m) in a 10-km running race. In addition, it has been proposed that neuromuscular factors related to force production and muscular recruitment must also be considered when investigating the determinants of endurance performance [2,13]. Considering the highest speeds reached during the start phase, it seems plausible to assume that the neuromuscular variables related to force production might also be important determinants of pacing strategy and success in long-distance events. Static stretching (SS) is commonly used as part of a warm-up routine for athletes, yet SS has received considerable attention because it seems to have an acute negative effect on activities that are strength- and power-dependent [14,15]. Previous studies have reported that an acute session of SS resulted in impairments on sprint performance [16,17] and in jump height [14,15,18]. For example, Sayers et al. [17] observed a negative effect on the acceleration phase of a sprint test after SS. This impaired performance induced by SS treatment may be related to an inability to maximally activate muscle. Avela et al. [19] reported that there were significant decreases in maximal voluntary contraction (23.2%) and EMG (19.9%) following 1 h of passive stretching of the triceps surae. Collectively, these data suggest that SS results in reduced capacity of the skeletal muscle to produce explosive force and this could result in a reduction in speed during the acceleration phase of a long-distance race. Furthermore, SS has been reported to increase the energy cost of running [20], but there is not universal agreement on this [21]. Collectively, these consequences of SS would be expected to alter pacing strategy and consequently the performance during a 3 km time-trial. To date, no study has considered the potential negative influence of SS on pacing strategy during a long-distance running event. Since force generating capability is an important determi- nant of endurance performance [13], it is attractive to suspect that prior SS treatment could alter the acceleration phase during a long-distance event. Thus, the main objective of the current investigation was to analyze the acute effect of SS on pacing strategy adopted during a 3-km running time-trial. It was hypothesized that SS would increase the energy cost of running, reduce the capacity of lower limbs to produce explosive force, and reduce the initial speed in a 3-km running time-trial. Methods Participants Eleven male, recreationally trained long-distance runners (mean age: 35.766.1 years; height: 1.7660.08 m; mass: 79.7611.3 kg; maximal oxygen uptake: 51.063.0 mlNkg21Nmin21) volunteered to participate in this study. All participants regularly competed in 10- km running races at regional levels, and their best performances in 10-km competitions ranged from 35–45 minutes. They were included if they had been training for the last 2 years without interruption and for at least three times per week with a minimum weekly volume above 30 km. The study was conducted at the beginning of the year during a non-competitive period. The subjects performed only low-intensity continuous aerobic training (,60% maximal oxygen uptake) and reported no previous strength or plyometric training experience. None of the partici- pants were receiving any pharmacological treatments or had any type of neuromuscular disorder or cardiovascular, respiratory or circulatory dysfunction. The participants received a verbal explanation about the possible benefits, risks and discomfort associated with the study and signed a written informed consent before participating in the study. Procedures were in accordance with the Helsinki Declaration of 1975, and this investigation was approved by the Ethics and Research Committee of the University of Sa˜o Paulo. Experimental design Participants visited the laboratory on five separate occasions, with at least 48 h between sessions, over a three-week period. Figure 1 gives a pictorial view of the experimental design. In the first session, the participants completed one 6-min, constant-speed test by running at 12 kmNh21 without previous SS treatment (control condition) and a maximal incremental treadmill test. Ten minutes of passive recovery was allowed between these two tests. In the second session, anthropometric measurements and one 6- min, constant-speed running test at 12 kmNh21 with previous SS treatment (experimental condition) was performed. Drop jump familiarizations were conducted at the end of the first and second visits after 20 minutes of passive recovery. The order of presentation for components of the first and second visits was counterbalanced. In the third session, the participants performed a 3-km time trial test familiarization on an outdoor 400-m track. In the fourth and fifth sessions, the participants performed a 3-km time trial test either with or without previous SS treatment. For the experimental condition, the runners performed a sit-and-reach test and a drop jump before and after SS to determine the impact of SS on range of motion and the stretch-shortening cycle, respectively. These tests were also performed in the control condition before the 3-km time trial. The order of presentation for components of the fourth and fifth visits was counterbalanced. The duration of each experimental session was approximately 50 minutes. All of the tests were performed at the same time of day for a given subject, at least 2 h after the most recent meal. The subjects were instructed to maintain their training program during the study period, but they were asked to refrain from any exhaustive or unaccustomed exercise during the preceding 48 h of any experimental test and to refrain from taking nutritional supplements during the experimental period. Laboratory tests Anthropometric measurements. Anthropometric mea- surements were performed according to the procedures described by Lohman [22]. Participants were weighed to the nearest 0.1 kg using an electronic scale (Filizola, model ID 1500, Sa˜o Paulo, Brazil). Height was measured to the nearest 0.1 cm using a stadiometer. Skinfold thickness was measured to the nearest 0.2 mm at eight body sites (i.e., triceps brachii, suprailiac, abdominal, chest, subscapular, midaxillar, anterior thigh and calf) using a Harpenden caliper (West Sussex, United Kingdom). The median of three values was used for data analysis. Measurements were performed by an experienced investigator. Body density was estimated using the equation of Jackson and Pollock [23], and body fat was estimated using the equation of Brozek et al. [24]. Maximal incremental test. A maximal incremental running test was performed on a motorized treadmill (model TK35, CEFISE, Nova Odessa, Brazil). After a warm-up at 8 kmNh21 for 5 min, the speed was increased by 1 kmNh21 every minute until exhaustion. The participants received strong verbal encourage- ment to continue as long as possible. Expired gases were measured Static Stretching and Pacing Strategy PLOS ONE | www.plosone.org 2 June 2014 | Volume 9 | Issue 6 | e99238 by a metabolic measurement cart (Cortex Metalyzer 3B, Cortex Biophysik, Leipzig Germany) to determine VO2 and carbon dioxide output (VCO2) and were subsequently averaged over 30-s intervals throughout the test. Before each test, the metabolic cart was calibrated using ambient air and a gas containing 12% O2 and 5% CO2. The turbine flowmeter was calibrated using a 3-L syringe (Quinton Instruments, Seattle, WA, USA). Heart rate (HR) was monitored during the test with a HR transmitter (model S810, Polar Electro Oy, Kempele, Finland) coupled to the gas analyzer. Maximal heart rate (HRmax) was defined as the highest value obtained at the end of the test. Maximal oxygen uptake (VO2max) was determined when two or more of the following criteria were met: an increase in VO2 of less than 2.1 mlNkg21Nmin21 between consecutive stages, a respiratory exchange ratio greater than 1.1, and reaching 610 bpm of the maximal age-predicted heart rate [25]. Constant-speed test. To analyze the impact of an SS bout on running parameters, the participants performed two constant- speed tests (experimental vs. control condition) on a motorized treadmill (model TK35, CEFISE, Nova Odessa, Brazil). Before the tests, the athletes performed a standardized warm-up consisting of a 5-min run at 8 kmNh21, followed by a 3-min passive recovery. The treadmill speed was adjusted to 12 kmNh21 after the warm-up, and the subjects ran for 6 minutes at this speed. The test began with the participant’s feet astride the moving belt and hands holding the handrail. For the experimental condition, the athletes performed the test immediately after the SS treatment. The VO2 over the final 30 seconds was taken as the steady-state VO2 for that speed. Running economy (RE) was defined as described by Fletcher et al. [26]. Taking the average RER over the same 30 seconds, the caloric equivalent of the VO2 (kcalNl21O2) was determined [27], and the caloric unit cost was calculated using equation 1: Caloric unit cost (kcal.kg1. km1) ~VO2 . caloric equivalent. s1. BM1. K ðEq:1Þ Where VO2 is measured in liters per minute, caloric equivalent is in kilocalories per liter, speed (s) is in meters per minute, body mass (BM) is in kilograms, and K is 1000 mNkm21. The resting metabolic rate was not subtracted because it cannot be confirmed that resting metabolic demand continues at the same rate while running. Stride parameters and EMG signals were simultaneously measured from the left leg during the last 10 s of the constant-speed tests. Disposable dual Ag/AgCl snap electrodes with a 1 cm diameter and a 2-cm center-to-center spacing (Noraxon, Scottsdale, AZ, USA) were placed on the belly of the vastus medialis (VM), biceps femoris (BF) and gastrocnemius medialis (GA) before starting the tests. The guidelines published by SENIAM [Surface Electromyography for the Non-Invasive Assessment of Muscles (SENIAM)] were followed for skin preparation, electrode placement and orientation. Electrode positions were marked with small ink tattoos on the skin during the first testing session to ensure that electrode placement over the entire experimental period would be consistent [28]. The EMG signals were recorded with a telemetric EMG system, which had a gain of 1000 times, a bandwidth (23 dB) over 10 to 500 Hz, and a common mode rejection ratio .85 dB and was relayed to the computer via a 16-bit A/D converter (Telemyo 900, Noraxon, Scottsdale, AZ, USA). The EMG data were band-pass filtered at 20–400 Hz, and an envelope representing the muscle activation was determined using a moving RMS filter with a window of 50 ms. The period of activation of each muscle during a stride was determined as the period where the signal was above a threshold of 15% of the Figure 1. Pictorial view of the experimental design. doi:10.1371/journal.pone.0099238.g001 Static Stretching and Pacing Strategy PLOS ONE | www.plosone.org 3 June 2014 | Volume 9 | Issue 6 | e99238 maximum activity of that muscle during the trial for at least 100 ms. These parameters were selected based on the signal-noise relationship of the EMG data and were visually verified to correctly identify periods of muscle activation. For each bout of EMG activation, we calculated the integrated EMG (iEMG), defined as the area under the EMG versus time curve divided by the period of activation. A video camera (GR-DVL9800U, JVC Inc., Wayne, NJ, USA) was used to record frontal plane images at 120 Hz during the last 10 s of running. Stride parameters were measured simultaneously with EMG using Noraxon’s Myoresearch software (Version 1.08). Using frame-to-frame video analysis, ten steps were analyzed. Contact time was defined as the time from ground contact of the left foot until the time that the same foot left the ground. The flight time was determined from the time when the left foot left the ground to the time prior to the next contact of the same foot. The stride time was defined as the time from ground contact of one foot to the next ground contact of the same foot (i.e., contact time+ flight time). Given that contact time was ,250 ms and images were captured at 120 Hz sampling rate, the experimental uncertainty of digital instruments in our case was ,8 ms, corresponding to an uncertainty of 3.2%. This is acceptable considering the between subjects variability of the measurements was larger. Field tests 3-km running test. To analyze the impact of SS on pacing strategy, participants individually performed a 3-km run on an outdoor 400-m track on three different days (familiarization, experimental, and control conditions). These running time-trial tests were performed with an interval of at least 48 h between them. Before each time-trial, the participants did 10 minutes of warm-up at 8 kmNh21. They were instructed to maintain regular water consumption within six hours of testing, and water was provided ad libitum during the entire event. The participants were instructed to finish the race as quickly as possible, as they would in a competitive event. Verbal encouragement was provided during the entire event. However, runners were not advised of their lap splits. Speed was registered every 100 m via a global positioning system (GPS Forerunner 305, Garmin, Kansas City, Oregon, EUA). RPE was reported by participants every 400 m using the Borg 15-point scale [6]. Copies of this scale were laminated and reduced to 10 cm by 5 cm, and they were affixed to the wrist of the dominant arm of the individuals. The 3-km running tests were performed at the same time of day and in similar conditions. Ambient temperature and humidity were provided by the Institute of Astronomy, Geophysics and Atmospheric Sciences of the University of Sa˜o Paulo, Brazil. The mean 6 SD values for temperature and humidity were 24.163.9uC and 63.069.7%, respectively. The sit-and-reach and drop jump tests were performed before and immediately after the SS in the 3-km running time-trial session with previous SS, and they were performed before the 3- km running time-trial session without SS. In the former situation, these tests were performed to verify the effectiveness of SS on the range of motion and capacity of lower limbs to produce explosive force, respectively. The sit-and-reach test was used because it provides a global measure of hamstring, hip, and lower back flexibility [29]. The participants sat with their bare feet pressed against the sit-and-reach box. Knees were extended, and the right hand was positioned over the left. Participants were then asked to push a ruler transversely located over the box as far as possible on the fourth bobbing movement. Each subject performed 3 trials of the sit-and-reach test, and the best trial was used for analysis. After the sit-and-reach test, the participants were instructed to perform a drop jump. The jump height was determined by flight time, which was measured by a contact mat (MultiSprint, Hidrofit, Brazil). Athletes stepped off a 40 cm box and attempted to achieve the greatest vertical height with a short ground contact time (close to 200 ms) [30]. Subjects were instructed to minimize knee flexion and extension during the drop jump, and a demonstration was provided by the investigators. All jumps were performed with hands on hips, and five repetitions were performed with a 30-s rest between jumps. The largest and smallest values were rejected, and the average of the remaining 3 jumps was calculated and used for statistical analysis. Intervention protocol Static stretching. The stretching treatment used in the present study was similar to that described in Samogin-Lopes et al. [31]. The SS involved seven different exercises for the lower limbs, including 5 unassisted and 2 assisted exercises. Briefly, the exercises performed were unassisted straight-leg stand and toe touch, unassisted standing quadriceps stretching, unassisted hamstrings and back stretching, unassisted hurdler’s stretching, unassisted standing calf stretching, assisted quadriceps and hip stretching, and assisted thigh stretching. Each exercise was performed three times, and each time the stretching position was maintained for 30 seconds. The magnitude of stretch was sufficient to yield a score of 8–9 on the Borg CR10 scale [6]. The total duration of time required for completion of the SS treatment was approximately 20 minutes. Statistical analyses. Data normality was assessed by the Shapiro-Wilk test, and all variables showed a normal distribution. All data are reported as means and standard deviations (6SD). A paired t- test was used to determine differences between non- stretching and SS treatments for RE, EMG, drop jump height, sit- and-reach test, flight time, contact time and stride time. Repeated measures analysis of variance with two factors (distance x condition), followed by a Bonferroni adjustment to compare the alterations in the speed and RPE during the 3-km time trials. The level of significance was set at p#0.05. All statistical analyses were conducted using the SPSS statistical package (version 16.0, Chicago, USA). Smallest worthwhile change (SWC; clinically beneficial effect) was also determined for performance parameters using the method described by Batterham and Hopkins (32). A Cohen’s unit of 0.2 was used to determine the SWC. The uncertainty in the effect was expressed as difference and 90% confidence limits (difference 695% CL) and as likelihoods that the true value of the effect represents substantial change (harm or benefit). When clear interpretation could be made, a qualitative descriptor was assigned to the following quantitative chances of benefit: ,1%, almost certainly not; 1–5%, very unlikely; 5–25%, unlikely or probably not; 25–75%, possibly or may be; 75–95%, likely or probably; 95–99%, very likely; .99%, almost certainly (32). Where the chances of benefit or harm were both calculated to be $5%, the true effect was deemed unclear. Results Laboratory tests Table 1 shows the anthropometric and physiological charac- teristics of the participants. Table 2 shows the variables measured during the RE tests. No significant differences were observed in the caloric unit cost of running (p = 0.128) or HR between the conditions (p = 0.317). The iEMG for the BF muscle was significantly higher in the SS condition, compared to the control condition (p = 0.031). No significant changes in iEMG were Static Stretching and Pacing Strategy PLOS ONE | www.plosone.org 4 June 2014 | Volume 9 | Issue 6 | e99238 observed in either VM (p = 0.419) or GA (p = 0.212). The stride time was significantly longer in the SS condition (p = 0.053) than in the control, but no differences were observed for contact time or flight time. Field tests Variables measured during time-trial tests performed with and without previous static stretching are presented in figure 2. The speed-distance curve during 3-km running showed a classical U- shape in both conditions. It was detected that the first section (100 m) was completed at a significantly slower speed in the SS condition (very likely harmful, 21.161.0 km.h21 95% CL), compared with the control condition (p = 0.036). However, the overall running time to cover 3-km running during the control condition (11:28600:41min:s) was not significantly different from that during the SS condition (11:35600:31min:s) (trivial, 7.0613.9 s 95% CL). The RPE increased significantly over time in both conditions (p = 0.001). The RPE in the SS condition was statistically greater than that in the control condition only during the first 800 m (p = 0.019). Following SS, the athletes also demonstrated reduced drop jump height (p = 0.001) and improved performance on the sit-and-reach test (p = 0.0001) relative to measures obtained prior to SS protocol. There were no differences in drop jump height (p = 0.351) and sit-and-reach test (p = 0.262) before the 3-km running when the control and experimental situations were compared. Discussion The main objective of the present study was to investigate the impact of SS on pacing strategy and performance during a 3-km running time-trial. To the best of our knowledge, this is the first study to analyze the influence of this exercise-induced impairment in neuromuscular function on the pacing strategy adopted during a long-distance run. The main finding was that the SS resulted in a slow-start strategy during a 3-km running time-trial. We also observed an impaired drop jump performance and a higher perceived exertion during the first 800 m. Acute effects in neuromuscular function after SS treatments have led to changes in isometric peak torque, range of motion, height in vertical and drop jumps [14,18,33]. In the current study, it was found that an SS bout resulted in an 11% increase in the sit- and-reach test and a 9.2% decrease in drop jump height before the 3-km running time-trial. These findings are similar to previous investigations. Young and Elliott [34] demonstrated that SS significantly decreased drop jump performance by 6.9% in recreational athletes. Furthermore, Behm and Kibele [18] demonstrated that SS for lower limbs significantly decreased the drop jump height (24.6%) and increased the range of motion measured by the sit-and-reach test (+12.1%) in physically active subjects. These data show that the magnitude of modification in flexibility and jump performance after SS treatment demonstrated in the current study was similar to that reported in the literature. Data from the current study also showed that reduced ability to produce force after the SS protocol was accompanied by a higher iEMG of the BF and stride time during constant-speed running test. It is well recognized that BF activation plays a key role in the control of knee extension and in the generation of the knee flexion force in the late swing phase before foot contact during running [35]. Thus, the higher iEMG of the BF after the SS protocol might have reflected an increased motor unit recruitment in order to maintain the running mechanics and compensate the reduced passive forces that would otherwise have served this purpose. In turn, this increased muscle activation may have contributed to the perception of effort as indicated by the RPE. Previous findings have indicated an inverse relationship between RE and flexibility [20]. In the present study, it was found that SS produced a significant increase in range of motion, as measured by the sit-and-reach test (362 cm), but the RE measured at 12 kmNh21 was not statistically altered. These data were similar to the study of Allison et al. [21], who showed that the statistical changes detected in range of motion (2.760.6 cm), isometric strength (25.663.4%) and countermovement jump Table 1. Anthropometric and physiological characteristics of the participants. Variable Mean ± SD Age (years) 35.766.1 Height (cm) 173.369.0 Body mass (kg) 67.967.4 Body fat (%) 10.062.7 VO2max (ml.kg21.min21) 51.063.0 HRmax (beats.min21) 18466 RERmax 1.260.1 HRmax: maximal heart rate, RERmax: maximal respiratory exchange ratio. doi:10.1371/journal.pone.0099238.t001 Table 2. Variables measured during the constant-speed tests with or without previous static stretching. Control Static stretching RE (ml.kg21.min21) 41.362.8 40.463.0 CUC (kcal.kg21.km21) 1.0360.07 1.0060.08 iEMGVM(mV) 60621 64623 iEMGGA (mV) 77627 95.3638 iEMGBF(mV) 73626 94631* Contact time (ms) 256626 250632 Flight time (ms) 443642 452637 Stride time (ms) 697641 710641* Values are means 6 SD; RE: running economy; CUC: caloric unit cost; VM: vastus medialis; GA: gastrocnemius medialis; BF: biceps femoris. *Significantly different from control condition (p#0.05). doi:10.1371/journal.pone.0099238.t002 Static Stretching and Pacing Strategy PLOS ONE | www.plosone.org 5 June 2014 | Volume 9 | Issue 6 | e99238 height (25.563.4%) were not accompanied by changes in the RE after SS. Similarly, Hayes and Walker [36] observed that static and dynamic stretching improved range of motion, but both treatments had no impact on the RE. Therefore, it seems that acute improvement in range of motion after SS is not associated with modification in the RE. In relation to pacing strategy, our results revealed that runners adopted a slow start after SS. This reduced running speed during initial phase of the 3-km running time trial seems to be related to lower ability to produce force, as evidenced by decreased drop jump height and increased stride time found after the SS protocol. Previous studies have suggested that the ability to produce force is an essential determinant of endurance performance without being necessarily related to energy demand of running (e.g. running economy) [37,38]. This occurs because middle and long-distance runners must be able to maintain a relatively high speed over the course of a race [39]. In particular, the acceleration phase requires a great level of muscle contraction in order to overcome inertia. Because the SS have a negative acute effect on the neuromuscular system [14,15], its deleterious effect might be more pronounced in the start phase of a long-distance running. These results are in accordance with previous studies that reported reduced running sprint performance after a SS protocol [16,17]. Taken together, these findings suggest that the SS induces a slow start in a 3-km running time trial due its negative influence in the neuromuscular system, impairing the acceleration phase of a long-distance event. Interestingly, our results showed that the slow start induced by SS treatment was accompanied by an increase in the RPE. It has been suggested that RPE may reflect increased motor unit recruitment [40]. It is believed that collateral innervations are sent directly from the motor to the sensory areas in the brain, contributing to the increase of the RPE response during exercise [41]. Thus, it is plausible to suggest that the brain might have interpreted the efferent signals from increased motor unit recruitment of the BF as the first cues for running speed adjustments. Based on this finding, it is plausible to suggest that the greater RPE found during the start phase after SS may reflect an increased neural drive resulting from intention to produce the same amount of force and thus maintain a high initial running speed. This is in agreement with a previous suggestion that RPE has a relevant role in the speed control during the start phase of a running race [42]. Results of the present study revealed that despite the slow start, runners were able to maintain the overall running performance. Figure 2. Variables measured with and without previous static stretching treatment. A and B panels show the running speed and rating of perceived exertion during a 3-km running time-trial, respectively. C and D panels show the drop jump and sit-and-reach tests performed prior to and immediately after static stretching treatment. * Significantly different from control situation (p#0.05). #Significant difference over time in each condition (p#0.05). doi:10.1371/journal.pone.0099238.g002 Static Stretching and Pacing Strategy PLOS ONE | www.plosone.org 6 June 2014 | Volume 9 | Issue 6 | e99238 This is consistent with the idea that the effects of SS were overcome during the event. Ryan et al. [43] showed that two 30- second bouts of SS were sufficient to induce a significant decrease in the passive musculotendinous stiffness of the plantar flexor muscles. However, these authors reported that although stiffness decreased immediately after 2 min, 4 min, and 8 min of SS, the effects of stretching disappeared within 10 min. In turn, Mizuno et al. [44] showed that SS for 5 minutes at maximal dorsiflexion resulted in significantly increased range of motion that persisted for 30 min, but significant decreases in musculotendinous stiffness returned to baseline levels within 10 min. Taking into consider- ation the fact that the 3-km running was performed with an average time of 11 min, it can be suggested that the negative effects of SS on overall exercise performance was negligible. Thus, the negative effect of the SS in running performance might be restricted to the initial phase of a middle-distance event when the metabolic cues are less important for the running pacing strategy [42]. The current study does have some limitations. It is important to note that our SS treatment was composed of seven different exercises for the lower limbs, performed three times in a serial fashion at ‘‘high-intensity’’, which was defined as scores of 8–9 on the Borg CR10 scale. This SS treatment may have resulted in a higher volume and intensity stretching protocol than those often used in the ‘‘real world’’. In addition, our sample was composed by recreational runners, which have a lower endurance training volume and did not perform other training routines (i.e. strength or stretching training). In this manner, the effects of static stretching on the neuromuscular variables and pacing strategy in highly-trained subjects could be distinct from those observed in the present study. Thus, caution should be exercised in extrapolating these findings to runners with a higher training level. In conclusion, the present study provides novel findings concerning the impact of stretching-induced impairment on neuromuscular function and pacing strategy. It was detected that SS resulted in a reduced capacity of the skeletal muscle to produce explosive force and a reduction in running speed during the acceleration phase of a time-trial. These results clearly show that, independent of the intention of the athletes to finish a race as quickly as possible, the neuromuscular system has a primary function in contributing to the chosen speed during the initial phase of the race, where the highest running speeds were reached. Acknowledgments The authors thank each of the individuals for their participation. Author Contributions Conceived and designed the experiments: MVD LAP AEL-S BRM RB. Performed the experiments: MVD LAP. Analyzed the data: MVD MD LAP RB. Contributed reagents/materials/analysis tools: MD AEL-S BRM RB. Wrote the paper: MVD MD LAP AEL-S BRM RB. References 1. Abbiss CR, Laursen PB (2008) Describing and understanding pacing strategies during athletic competition. Sports Med 38(3): 239–252. 2. Nummela A, Paavolainen L, Sharwood K, Lambert M, Noakes T, et al. (2006) Neuromuscular factors determining 5 km running performance and running economy in well-trained athletes. Eur J Appl Physiol 97: 1–8. 3. Tucker R, Lambert MI, Noakes TD (2006) An analysis of pacing strategies during men’s world record performances in track athletics. Int J Sports Physiol Perform 1: 233–245. 4. Jones AM, Wilkerson DP, Vanhatalo A, Burnley M (2008) Influence of pacing strategy on O2 uptake and exercise tolerance. Scand J Med Sci Sports 18:615– 626. 5. Mikkola J, Vesterinen V, Taipale R, Capostagno B, Ha¨kkinen K, et al. (2011) Effect of resistance training regimens on treadmill running and neuromuscular performance in recreational endurance runners. J Sports Sci 29(11): 1359–1371 6. Borg G (1982) Psychophysical bases of perceived exertion. Med Sci Sports Exerc 14(5): 377–381. 7. Faulkner J, Parfitt G, Eston R (2008) The rating of perceived exertion during competitive running scales with time. Psychophysiology 45(6): 977–85. 8. Ulmer HV (1996) Concept of an extracellular regulation of muscular metabolic rate during heavy exercise in humans by psychophysiological feedback. Experientia 52(5): 416–20. 9. Bishop D, Bonetti D, Dawson B (2002) The influence of pacing strategy on VO2 and supramaximal kaykak performance. Med Sci Sports Exerc 34(6): 1041– 1047. 10. Sandals LE, Wood DM, Draper SB, James DVB (2006) Influence of pacing strategy on oxygen uptake during treadmill middle-distance running. Int J Sports Med 27: 37–42. 11. Baron B, Deruelle F, Moullan F, Dalleau G, Verkindt C, et al. (2009) The eccentric muscle loading influences the pacing strategies during repeated downhill sprint intervals. Eur J Appl Physiol. 105: 749–757. 12. Lima-Silva AE, Bertuzzi RCM, Pires FO, Barros RV, Gagliardi JF, et al. (2010) Effect of performance level on pacing strategy during a 10-km running race. EurJ Appl Physiol 108(5): 1045–1053. 13. Paavolainen L, Hakkinen K, Hamalainen I, Nummela A, Rusko H (1999) Explosive-strength training improves 5-km running time by improving running economy and muscle power. J Appl Physiol 86(5): 1527–1533. 14. Bradley PS, Olsen PD, Portas MD (2007) The effect of static, ballistic, and proprioceptive neuromuscular facilitation stretching on vertical jump perfor- mance. J Strength Cond Res 21(1): 223–6. 15. Cornwell A, Nelson AG, Heise GD, Sidaway B (2001) Acute effects of passive muscle stretching on vertical jump performance. J Hum Mov Stud 40: 307–324. 16. Nelson AG, Driscoll NM, Landin DK, Young MA, Schexnayder IC (2005) Acute effects of passive muscle stretching on sprint performance. 23(5): 449–454. 17. Sayers AL, Farley RS, Fuller DK, Jubenville CB, Caputo JL (2008) The effect of static stretching on phases of sprint performance in elite soccer players. J Strength Cond Res 22(5): 1416–21. 18. Behm D, Kibele A (2007) Effects of differing intensities of static stretching on jump performance. Eur J Appl Physiol 101: 587–594. 19. Avela J, Kyro¨la¨inen H, Komi PV (1999) Altered reflex sensitivity after repeated and prolonged passive muscle stretching. J Appl Physiol 86(4): 1283–91. 20. Jones AM (2002) Running economy is negatively related to sit-and-reach test performance in international-standard distance runners. Int J Sports Med. 23(1): 40–3. 21. Allison SJ, Bailey DM, Folland JP (2008) Prolonged static stretching does not influence running economy despite changes in neuromuscular function. J Sports Sci 26(14): 1489–95. 22. Lohman TG (1981) Skinfolds and body density and their relation to body fatness: a review. Hum Biol 53(2): 181–225. 23. Jackson AS, Pollock ML (1985) Practical assessment of body composition. Physical of Sport Med 19: 76–90. 24. Brozek J, Grande F, Anderson J, Keys A (1963) Densitometric analysis of body composition: revision of some quantitative assumptions. Annals of the New York Academy of Science 110: 113–140. 25. Howley ET, Bassett DR Jr, Welch HG (1995) Criteria for maximal oxygen uptake: review and commentary. Med Sci Sports Exerc 27: 1292–1301. 26. Fletcher JR, Esau SP, MacIntosh BR (2009) Economy of running: beyond the measurement of oxygen uptake. J Appl Physiol 107:1918–1922. 27. Lusk G (1928) Science of Nutrition. Philadelphia, PA: Saunders; p. 177–178. 28. Hakkinen K, Komi PV (1983) Electromyographic changes during strength training and detraining. Med Sci Sports Exerc 15(6): 455–60. 29. Baltaci G, Un N, Tunay V, Besler A, Gercxeker S (2003) Comparison of three different sit and reach tests for measurement of hamstring flexibility in female university students. Br J Sports Med 37: 59–61. 30. Young WB, Pryor JF, Wilson GJ (1995) Effect of instructions on characteristics of countermovement and drop jump performance. J Strength Cond Res 9(4): 232–236. 31. Samogin-Lopes FA, Menegon EM, Franchini E, Tricoli V, de M Bertuzzi RC (2010) Is acute static stretching able to reduce the time to exhaustion at power output corresponding to maximal oxygen uptake? J Strength Cond Res 24(6): 1650–1656. 32. Batterham AM, Hopkins WG (2006) Making meaningful inferences about magnitudes. Int J Sports Physiol Perform 1(1): 50–57. 33. Ogura Y, Miyahara Y, Naito H, Katamoto S, Aoki J (2007) Duration of static stretching influences muscle force production in hamstring muscles. J Strength Cond Res 21(3): 788–792. 34. Young W, Elliott S (2001) Acute effects on static stretching, proprioceptive neuromuscular facilitation stretching, and maximum voluntary contractions on explosive force production and jumping performance. Res Q Exerc Sport 72: 273–279. 35. Higashihara A, Ono T, Kubota J, Okuwaki T, Fukubayashi T (2010) Functional differences in the activity of the hamstring muscles with increasing running speed. J Sports Sci 28(10): 1085–92 Static Stretching and Pacing Strategy PLOS ONE | www.plosone.org 7 June 2014 | Volume 9 | Issue 6 | e99238 36. Hayes PR, Walker A (2007) Pre-exercise stretching does not impact upon running economy. J Strength Cond Res. 21(4): 1227–32. 37. Nummela A, Heath KA, Paavolainen LM, Lambert MI, St Clair Gibson A, et al. (2008) Fatigue during a 5-km running time trial. Int J Sports Med 29: 738– 745. 38. Kyro¨la¨inen H, Belli A, Komi PV (2001) Biomechanical factors affecting running economy. Med Sci Sports Exerc. 33(8): 1330–7. 39. Billat V, Demarle A, Slawinski J, Paiva M, Koralzstein J (2001) Physical and training characteristics of top-class marathon runners. Med Sci Sports Exerc 33(12): 2089–2097. 40. DeMorree HM, Klein C, Marcora SM (2012) Perception of effort reflects central motor command during movement execution. Psychophysiology 49: 1242–1253. 41. Marcora SM, Staiano W, Manning V (2009) Mental fatigue impairs physical performance in humans. J Appl Physiol. 106(3): 857–864. 42. Bertuzzi R, Lima-Silva AE, de O Pires F, Damasceno MV, Bueno S, et al. (2013) Pacing strategy determinants during a 10-km running time-trial: contributions of perceived effort, physiological, and muscular parameters. J Strength Cond Res. [Epub ahead of print]. 43. Ryan ED, Herda TJ, Costa PB, Defreitas JM, Beck TW, et al. (2009) Determining the minimum number of passive stretches necessary to alter musculotendinous stiffness. J Sports Sci 27(9): 957–961. 44. Mizuno T, Matsumoto M, Umemura Y (2013) Decrements in stiffness are restored within 10 min. Int J Sports Med 34: 484–490. Static Stretching and Pacing Strategy PLOS ONE | www.plosone.org 8 June 2014 | Volume 9 | Issue 6 | e99238

Static stretching alters neuromuscular function and pacing strategy, but not performance during a 3-km running time-trial.

06-06-2014

Damasceno, Mayara V,Duarte, Marcos,Pasqua, Leonardo A,Lima-Silva, Adriano E,MacIntosh, Brian R,Bertuzzi, Rômulo

eng

PMC8523042

Distance runners Step length (m) Cadence (steps/s) S2 Fig 0.5 1.0 1.5 2.0 2.5 2 3 4 5 1 0.5 1.0 1.5 2.0 2.5 2 3 4 5 2 0.5 1.0 1.5 2.0 2.5 2 3 4 5 3 0.5 1.0 1.5 2.0 2.5 2 3 4 5 4 0.5 1.0 1.5 2.0 2.5 2 3 4 5 5 0.5 1.0 1.5 2.0 2.5 2 3 4 5 6 0.5 1.0 1.5 2.0 2.5 2 3 4 5 7 0.5 1.0 1.5 2.0 2.5 2 3 4 5 8 0.5 1.0 1.5 2.0 2.5 2 3 4 5 9 0.5 1.0 1.5 2.0 2.5 2 3 4 5 10 0.5 1.0 1.5 2.0 2.5 2 3 4 5 11 0.5 1.0 1.5 2.0 2.5 2 3 4 5 12 0.5 1.0 1.5 2.0 2.5 2 3 4 5 13 0.5 1.0 1.5 2.0 2.5 2 3 4 5 14 0.5 1.0 1.5 2.0 2.5 2 3 4 5 15 0.5 1.0 1.5 2.0 2.5 2 3 4 5 16 0.5 1.0 1.5 2.0 2.5 2 3 4 5 17 0.5 1.0 1.5 2.0 2.5 2 3 4 5 18 0.5 1.0 1.5 2.0 2.5 2 3 4 5 19 0.5 1.0 1.5 2.0 2.5 2 3 4 5 20

Spatiotemporal inflection points in human running: Effects of training level and athletic modality.

10-18-2021

Goto, Yuta,Ogawa, Tetsuya,Kakehata, Gaku,Sazuka, Naoya,Okubo, Atsushi,Wakita, Yoshihiro,Iso, Shigeo,Kanosue, Kazuyuki

eng

PMC8998726




 Citation: Kim, J.; Park, S.-K. Differences in Physical Characteristics of the Lower Extremity and Running Biomechanics Between Different Age Groups. Int. J. Environ. Res. Public Health 2022, 19, 4320. https:// doi.org/10.3390/ijerph19074320 Academic Editor: Sechang Oh Received: 6 February 2022 Accepted: 30 March 2022 Published: 4 April 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). International Journal of Environmental Research and Public Health Article Differences in Physical Characteristics of the Lower Extremity and Running Biomechanics Between Different Age Groups Jongbin Kim 1 and Sang-Kyoon Park 2,* 1 Division of Kinesiology, Silla University, Busan 46958, Korea; [email protected] 2 Motion Innovation Center, Korea National Sport University, Seoul 05541, Korea * Correspondence: [email protected]; Tel.: +82-10-5378-9617 Abstract: (1) Background: The objective of this study was to determine physical and biomechanical changes in age groups upon running. (2) Method: 75 male adults (20–80s) participated in the study. Bone mineral density and lower extremity joint strength were measured according to age-increase targeting. Based on age, correlations among running characteristics, impulse, impact force, maximum vertical ground reaction force, loading rate, lower extremity joint 3D range of motion, joint moment, and power upon running motion were calculated. (3) Result: Older runners tended to show lower bone mineral density, extremity maximum strength, stride time, and stride distance, with smaller RoM and joint power of ankle and knee joints in the sagittal plane, compared with younger subjects. However, there were no significant correlations between age and impact variables (i.e., impulse, impact force, peak GRF, and loading rate) during running. (4) Conclusion: Older runners tend to show weaker physical strength characteristics, such as bone mineral density and muscle strength and lower joint functionality of ankle and knee joints during running, compared with younger runners. Therefore, strengthening the lower extremity muscle and improving dynamic joint function, especially for ankle joints, can be helpful for injury prevention during running. Keywords: bone mineral density; maximal strength of lower joint; aging; running; kinematics 1. Introduction Physical aging causes high blood pressure, diabetes, obesity, and bone mineral density (BMD) decrease; thus, physical changes such as aerobic capacity decline, coordination decrease, muscle function weakening, decreased gait ability, and risk of chronic disease are induced [1–3]. Among the changes in physical characteristics due to aging, BMD gradually decreases from 35 years of age, and osteoporosis is caused from 50 years of age [4]. Muscle volume starts to decrease by 10% compared with people in their 20s and this decrease is accelerated from 65 years of age. At 70 years of age, the mean strength is 60% of that of people in their 20s [5]. Consequently, independent life becomes impossible due to fracture damage and mental health, and all this may work as a potential factor for life quality decline [6–8]. To delay physical aging, regular exercise is essential. Regular exercise furthers functional physical health, including physical strength retention and enhancement, cardiovascular function improvement, muscle strength increase, flexibility increase, and mental health. Likewise, regular runs are effective for health enhancement and aging prevention [9–13]. As interest in running has recently increased for health enhancement, participants in running races of various distances and courses have also steadily increased. Because running’s temporal, spatial, and cost limitations are slight, its universality is proven as a health enhancement exercise, compared to other physical activities [14]. Regular physical exercise and compound exercise are suitable methods to maintain BMD through proper physical activities in everyday life, and they are frequently used for BMD improvement and osteoporosis treatment [15]; however, bone health may be negatively affected [16]. Int. J. Environ. Res. Public Health 2022, 19, 4320. https://doi.org/10.3390/ijerph19074320 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2022, 19, 4320 2 of 12 BMD increase between the lumbar and the thigh showed no significant difference in endurance running but exhibited a remarkable variance in weight in a study targeting long-distance runners aged 18 to 44 [17]. Various types of studies are required to analyze the effects of exercises on BMD. According to physical aging, the decreased rate of lower extremity strength is higher than the upper extremity strength. The muscle thickness of the gastrocnemius and thigh skeletal muscles decreases as one gets older, and consequently, joint power gradually reduces [18–20]. The lower extremity strength is maintained and increases with resistance exercise and weight-bearing (load) exercise [21,22]. Previous studies reported an improvement of 27% total muscle strength, 27% knee joint flexion, and 17% extension strength among elderly people who performed lower extremity resistance exercises for 14 weeks [23]. The elderly’s ankle and knee joints’ flexion and extension force was 70–80% of that of male adults in general. Regular physical activities to reduce risk factors, revealed due to physiological aging, were found adequate for the retention of and delayed decrease in bone mineral and strength [24]. A study on running motions reported that sagittal plane movements impact absorption, foot stability, balancing, and acceleration in the stance section when running. Additionally, most injuries, such as ankle sprain, stress fracture, backache, and muscle rupture, occurred in the stance section [25]. According to the analysis of the result, depending on age, the range of motion of the hip joint was greater, and the range of motion of the knee and ankle joints was smaller in the elderly people group than the young adult group. The impact force occurring in the initial stage stance section was larger in the young adult group compared with the elderly group [26–30]. In a dynamic analysis targeting adults between 18 and 60 years of age, stride decreased along with vertical ground reaction force, ankle moment, and power [31]. If the impact was not absorbed by adequately increasing the range of motion of the knee and ankle joints while running, it was reported that a lower extremity joint injury might occur [32]. However, very low joint stiffness caused by an increased range of motion may induce running injuries of the soft tissues and musculoskeletal system of runners’ lower extremities. Regular physical activities positively affect the lower extremity BMD and joint strength. Therefore, regular physical activities reduce musculoskeletal system injuries through a reduction in physical change due to aging [33]. However, it is insufficient to explain aging through the fragmental comparison of males in their 20s and 80s biomechanically with running motions among regular physical activities through existing previous studies. This study examined the effect of aging on physical characteristics, joint movement, and joint power, targeting males from 20 to 80 who regularly ran. The objective of this study was to determine physical and biomechanical changes in age groups upon running. It was hypothesized that older runners might compromise their lower extremities’ dynamic joint function during running due to declined physical characteristics compared with younger runners. 2. Materials and Methods 2.1. Participants The participants in this study were 75 healthy males who did not receive treatment and had surgery history due to lower extremity musculoskeletal system problems within the past six months, who ran 10 km or more three times a week, who had participated at least once in a marathon race, and whose right leg was their dominant leg (Table 1). All the participants voluntarily participated in this study. The experiment was carried out after gaining approval from the IRB (20180611-046). Int. J. Environ. Res. Public Health 2022, 19, 4320 3 of 12 Table 1. Subject characteristics for each age group. Group 20s 30s 40s 50s 60s 70s~80s Total Number of participants 12 12 11 12 12 16 75 Age (year) 24.67 ±2.46 33.83 ±2.79 45.18 ±3.38 55.42 ±2.36 64.33 ±2.99 75.13 ±3.93 51.12 ±18.04 Height (m) 1.75 ±0.05 1.76 ±0.04 1.72 ±0.05 1.71 ±0.04 1.67 ±0.05 1.67 ±0.06 1.71 ±0.06 Weight (kg) 73.58 ±5.92 74.58 ±6.11 67.64 ±6.95 66.83 ±4.02 63.42 ±7.37 62.67 ±6.09 68.07 ±7.46 2.2. Procedure After explaining the procedure and purpose of this study to the participants, they consented to the test. To collect body composition information, their height and weight were measured. The participants’ thighs and ankles were secured with a fixing band in a state where they did not move once lying down on the examination table and looking at the ceiling, with their lower extremity joint BMD being measured using the Dual En- ergy X-ray Absorptiometry (DEXA, QDR-1000; Hologic, Waltham, MA, USA) equipment. Their chest, abdomen, and thigh were held with a fixing band to measure their maximum lower extremity joint strength. There was no movement in the other joints except in the observed joints upon matching the dynamometer axis and joint using isokinetic exercise (Humac Norm, Stoughton, MA, USA), as shown in Figure 1 [34]. The hip joint extension (gluteus maximus) and flexion (iliopsoas), knee joint extension (quadriceps) and flexion (hamstrings), and ankle joint dorsiflexion (tibialis anterior) and plantar flexion (gastroc- nemius) were measured once, setting the angle and speed at 60◦/s each time. In doing so, practices were performed three times, and peak torque was measured five times. To prevent fatigue, break time was given between measurements, while a loud voice offered motivation to exert maximum strength upon measurement. Table 1. Subject characteristics for each age group. Group 20s 30s 40s 50s 60s 70s~80s Total Number of participants 12 12 11 12 12 16 75 Age (year) 24.67 ±2.46 33.83 ±2.79 45.18 ±3.38 55.42 ±2.36 64.33 ±2.99 75.13 ±3.93 51.12 ±18.04 Height (m) 1.75 ±0.05 1.76 ±0.04 1.72 ±0.05 1.71 ±0.04 1.67 ±0.05 1.67 ±0.06 1.71 ±0.06 Weight (kg) 73.58 ±5.92 74.58 ±6.11 67.64 ±6.95 66.83 ±4.02 63.42 ±7.37 62.67 ±6.09 68.07 ±7.46 2.2. Procedure After explaining the procedure and purpose of this study to the participants, they consented to the test. To collect body composition information, their height and weight were measured. The participants’ thighs and ankles were secured with a fixing band in a state where they did not move once lying down on the examination table and looking at the ceiling, with their lower extremity joint BMD being measured using the Dual Energy X-ray Absorptiometry (DEXA, QDR-1000; Hologic, Waltham, MA, USA) equipment. Their chest, abdomen, and thigh were held with a fixing band to measure their maximum lower extremity joint strength. There was no movement in the other joints except in the observed joints upon matching the dynamometer axis and joint using isokinetic exercise (Humac Norm, Stoughton, MA, USA), as shown in Figure 1 [34]. The hip joint extension (gluteus maximus) and flexion (iliopsoas), knee joint extension (quadriceps) and flexion (hamstrings), and ankle joint dorsiflexion (tibialis anterior) and plantar flexion (gas- trocnemius) were measured once, setting the angle and speed at 60°/s each time. In doing so, practices were performed three times, and peak torque was measured five times. To prevent fatigue, break time was given between measurements, while a loud voice offered motivation to exert maximum strength upon measurement. Figure 1. Measurements of BMD and maximum joint strength: (a) BMD, (b) hip, (c) knee, and (d) ankle joint. After performing a warm-up, the participants wore upper and lower tights for the motion capture test, and 64 reflective markers were attached to the body (Figure 2). They wore personal standard running shoes, and the standing calibration was carried out to ensure the participant’s body anatomical position before the running’s measurement. Eight infrared cameras (Oqus 300, Qualisys, Sweden) captured the running motions. The sampling frequency was set at 100 Hz. They ran on a treadmill embedded with two force plates (Instrumented treadmill, Bertec, corporation, Columbus, OH, USA), and the sam- pling rate was set at 1000 Hz. After sufficient rest to minimize muscle fatigue, measure- ment was carried out by gradually increasing speed for five minutes to induce natural running before the measurement. The running speed was set at 3.1 m/s [26]. During their run on the treadmill with a selected speed of five minutes, the motion was collected from 30 gait cycles of the right leg (Figure 3). Figure 1. Measurements of BMD and maximum joint strength: (a) BMD, (b) hip, (c) knee, and (d) ankle joint. After performing a warm-up, the participants wore upper and lower tights for the motion capture test, and 64 reflective markers were attached to the body (Figure 2). They wore personal standard running shoes, and the standing calibration was carried out to ensure the participant’s body anatomical position before the running’s measurement. Eight infrared cameras (Oqus 300, Qualisys, Sweden) captured the running motions. The sampling frequency was set at 100 Hz. They ran on a treadmill embedded with two force plates (Instrumented treadmill, Bertec, corporation, Columbus, OH, USA), and the sampling rate was set at 1000 Hz. After sufficient rest to minimize muscle fatigue, measurement was carried out by gradually increasing speed for five minutes to induce natural running before the measurement. The running speed was set at 3.1 m/s [26]. During their run on the treadmill with a selected speed of five minutes, the motion was collected from 30 gait cycles of the right leg (Figure 3). Int. J. Environ. Res. Public Health 2022, 19, 4320 4 of 12 Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 4 Figure 2. Reflective markers on the body: (a) frontal view, (b) lateral view, (c) backward view. Figure 3. Experimental setup for running on a treadmill. 2.3. Data Processing The stance phase was defined as the moment a participant’s right heel struck the off on the treadmill to analyze biomechanical variables. Each joint’s position and gro reaction force data were obtained using Qualisys’s Qualisys Track Manager Program data processing. To remove the noise of the data, Butterworth second-order low-pas tering at 12 Hz was performed for the 3D position coordinate data [35]. The ground r tion force data were set at a power spectrum density (PSD) of 99% of the value of the off frequency [36]. Using Visual 3D (C-Motion, Germantown, MD, USA) and M R2014a (The Mathworks, Natick,USA), the (+) joint angle from the range of motion o ankle, knee, and hip in the sagittal plane means a flexion and dorsiflexion angle, whil (−) joint angle means an extension and plantar flexion angle. Joint moment (Nm/kg) joint power (W/kg) (+) mean a concentric contraction, while the (–) counterpart m eccentric contraction. For the ground reaction force direction the X-axis was set as le and right (+), the Y-axis was set as the front (+) and back (−), while the Z-axis was s vertical (+). 2.4. Statistical Processing For statistical processing, SPSS Ver. 25.0 software (IBM, Armonk, NY,USA) wa plied. Regarding the physical characteristics, kinematics, and kinematic data obta through the analysis program, Pearson’s product-moment correlation coefficients w calculated to find the relationship between age and physical characteristics, as well as mechanical variables. Based on the sample size calculation, a minimum of 42 subjects required for the expected correlation coefficient (R) of 0.35 with a power of 70% (β) A significant level of statistics was set at an alpha level of 0.05. 3. Results 3.1. Correlations between Age and Physical Characteristics (BMD) and Peak Torque Figure 2. Reflective markers on the body: (a) frontal view, (b) lateral view, (c) backward view. Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 4 Figure 2. Reflective markers on the body: (a) frontal view, (b) lateral view, (c) backward view Figure 3. Experimental setup for running on a treadmill. 2.3. Data Processing The stance phase was defined as the moment a participant’s right heel struck the off on the treadmill to analyze biomechanical variables. Each joint’s position and gr reaction force data were obtained using Qualisys’s Qualisys Track Manager Program data processing. To remove the noise of the data, Butterworth second-order low-pa tering at 12 Hz was performed for the 3D position coordinate data [35]. The ground tion force data were set at a power spectrum density (PSD) of 99% of the value of the off frequency [36]. Using Visual 3D (C-Motion, Germantown, MD, USA) and M R2014a (The Mathworks, Natick,USA), the (+) joint angle from the range of motion o ankle, knee, and hip in the sagittal plane means a flexion and dorsiflexion angle, whil (−) joint angle means an extension and plantar flexion angle. Joint moment (Nm/kg joint power (W/kg) (+) mean a concentric contraction, while the (–) counterpart m eccentric contraction. For the ground reaction force direction the X-axis was set as le and right (+), the Y-axis was set as the front (+) and back (−), while the Z-axis was s vertical (+). 2.4. Statistical Processing For statistical processing, SPSS Ver. 25.0 software (IBM, Armonk, NY,USA) wa plied. Regarding the physical characteristics, kinematics, and kinematic data obta through the analysis program, Pearson’s product-moment correlation coefficients calculated to find the relationship between age and physical characteristics, as well a mechanical variables. Based on the sample size calculation, a minimum of 42 subjects required for the expected correlation coefficient (R) of 0.35 with a power of 70% (β) A significant level of statistics was set at an alpha level of 0.05. 3. Results 3.1. Correlations between Age and Physical Characteristics (BMD) and Peak Torque Figure 3. Experimental setup for running on a treadmill. 2.3. Data Processing The stance phase was defined as the moment a participant’s right heel struck the toe- off on the treadmill to analyze biomechanical variables. Each joint’s position and ground reaction force data were obtained using Qualisys’s Qualisys Track Manager Program for data processing. To remove the noise of the data, Butterworth second-order low-pass filtering at 12 Hz was performed for the 3D position coordinate data [35]. The ground reaction force data were set at a power spectrum density (PSD) of 99% of the value of the cut-off frequency [36]. Using Visual 3D (C-Motion, Germantown, MD, USA) and Matlab R2014a (The Mathworks, Natick, MA, USA), the (+) joint angle from the range of motion of the ankle, knee, and hip in the sagittal plane means a flexion and dorsiflexion angle, while the (−) joint angle means an extension and plantar flexion angle. Joint moment (Nm/kg) and joint power (W/kg) (+) mean a concentric contraction, while the (–) counterpart means eccentric contraction. For the ground reaction force direction the X-axis was set as left (−) and right (+), the Y-axis was set as the front (+) and back (−), while the Z-axis was set as vertical (+). 2.4. Statistical Processing For statistical processing, SPSS Ver. 25.0 software (IBM, Armonk, NY, USA) was applied. Regarding the physical characteristics, kinematics, and kinematic data obtained through the analysis program, Pearson’s product-moment correlation coefficients were calculated to find the relationship between age and physical characteristics, as well as biomechanical variables. Based on the sample size calculation, a minimum of 42 subjects was required for the expected correlation coefficient (R) of 0.35 with a power of 70% (β) [37]. A significant level of statistics was set at an alpha level of 0.05. Int. J. Environ. Res. Public Health 2022, 19, 4320 5 of 12 3. Results 3.1. Correlations between Age and Physical Characteristics (BMD) and Peak Torque According to the correlation analysis between age and BMD, statistical significance was displayed among the total (r = −0.380, p ≤ 0.001), legs BMD (r = −0.506, p ≤ 0.000), and T-score (r = 0.442, p ≤ 0.000), and it showed a negative correlation. In the lower extremity strength relationship, statistically significant negative correlations were displayed between hip joint extension (r = −0.399, p ≤ 0.000) and flexion (r = −0.612, p ≤ 0.000), knee joint extension (r = −0.535, p ≤ 0.000) and flexion (r = −0.525, p ≤ 0.000), ankle joint dorsiflexion (r = −0.407, p ≤ 0.000), plantar flexion (r = −0.494, p ≤ 0.000) (Table 2). Table 2. Correlations (R, p-value) between age and the variables (i.e., BMD, strength, running parameters, and running biomechanics). Age BMD Total Legs T score −0.380 * −0.506 ** −0.422 * 0.001 0.000 0.000 Strength Gluteus Maximus Iliopsoas Quadriceps Hamstrings Tibialis Anterior Gastrocnemius −0.399 * −0.612 * −0.535 * −0.525 * −0.407 * −0.494 * 0.000 0.000 0.000 0.000 0.000 0.000 Running Parameter Stride Time StrideDistance −0.336 * −0.536 * 0.003 0.000 Impact Impulse Impact Force GRF Peak Loading Rate −0.021 0.100 0.018 0.033 0.864 0.417 0.881 0.784 Joint Angle Ankle Dorsi Flexion Ankle Plantar Flexion Ankle RoM Knee Flexion Knee Extension Knee RoM 0.001 0.321 * −0.352 * −0.115 0.181 −0.361 * 0.991 0.006 0.002 0.335 0.129 0.000 Hip Flexion Hip Extension Hip RoM 0.164 0.289 * −0.064 0.166 0.013 0.593 Joint Moments Ankle Dorsi Flexion Ankle Plantar Flexion Knee Flexion Knee Extension Hip Flexion Hip Extension 0.328 * −0.177 0.253 * −0.166 0.212 0.225 0.004 0.140 0.030 0.167 0.072 0.060 Joint Power Ankle Absorption Ankle Generation Knee Absorption Knee Generation Hip Absorption Hip Generation −0.334 * 0.326 * −0.185 0.357 * −0.044 0.082 0.004 0.006 0.115 0.002 0.711 0.494 BMD: bone mineral density, GRF: ground reaction force. * p < 0.05 indicates significant correlations between age and the variables. ** p < 0.01. Int. J. Environ. Res. Public Health 2022, 19, 4320 6 of 12 3.2. Correlations between Age and Running Characteristics and Impact As age increased, a statistically significant negative correlation was revealed between stride time (r = −0.336, p ≤ 0.003) and stride distance (r = −0.536, p ≤ 0.000) in running characteristics (Table 2). However, there were no significant correlations between age and impact variables (i.e., impulse, impact force, GRF (ground reaction force) peak, and loading rate). 3.3. Correlations between Age and Joint Kinetics of the Lower Extremity Joint According to age increase, statistical significances of correlation were found in the ranges of the ankle (r = −0.352, p ≤ 0.002) and knee joint motion (r = −0.361, p ≤ 0.000) in the sagittal plane (Table 2). In addition, ankle and knee moments (ankle dorsiflexion moment: r = 0.328, p ≤ 0.004, knee flexion moment: r = 0.253, p ≤ 0.030), as well as ankle joint power (absorption: r = −0.334, p ≤ 0.004, and generation: r = 0.326, p ≤ 0.006) in the sagittal plane, were significantly lower in older runners compared with younger runners, based on the analysis of correlations (Table 2, Appendix A; Figure A3). 4. Discussion This study examined the relationship between lower extremity joint physical charac- teristics and running biomechanical variables and age, targeting males aged 20 to 80 who regularly ran. The study aimed to discover physical changes and risk factors of running ac- cording to aging. Based on the findings of this study, the hypothesis was partially accepted as elderly runners showed lower physical characteristics and dynamic joint function while running compared with their younger counterparts. When looking at the relationship between age increase and BMD and maximum strength, targeting males who regularly ran, a negative correlation was found in total BMD, legs, and T-score. A negative correlation was found in the maximum strength of the ankle, knee, and hip joints with increased age, with the power being 41.2% in the hip joint flexion power (Appendix A, Figure A1). In comparing the analysis result of the leg BMD of 2657 general elderly males and the analysis result of the older adults who regularly performed running in this study, the leg BMD of this study was 31% higher [38]. Regarding why running exercise affects BMD, this study infers that bone density decreases if stress (shock) is given to bone repeatedly by running exercise, as shown in Wolff’s law [39]. Furthermore, due to age increase, the weakening strength of the lower extremity joint displays a considerable physiological change and may become a cause of fracture injury. As for males in general, knee joint extension strength decreases by 12–15% every 10 years [33,40]. In this study, the muscle flexibility of the males who ran regularly was reduced as they aged, especially if there was a large difference in ankle joint dorsiflexion (tibialis anterior) and plantar flexion (gastrocnemius) [27]. Therefore, it is plausible that more power can be exerted because of the compensation action of the hip joint. On the other hand, when an older adult continues to exercise, the gastrocnemius and thigh skeletal muscle develop. As a result of comparing the lower extremity joint maximum strength in this study to a previous study [41], a delayed reduction in muscle strength in elderly runners was found. Regular participation in physical activities is vital in reducing physical change due to aging and the risk factors of musculoskeletal injuries [27,42]. Running exercise is a valuable method to reduce and prevent BMD. If one steadily runs, diseases and secondary fracture injuries due to bone reduction symptoms such as osteoporosis can be prevented in advance, and BMD can be retained. Additionally, running exercise can reduce the risk of fracture injuries and improve quality of life. Although the BMD volume and strength of the elderly group who continued to run were lower than those of the young adult male group, their BMD volume in the lower extremity joint was more significant than that of the elderly group who did not exercise. As age increases, the knee joint extensor muscle (thigh skeletal muscle) decreases, the iliopsoas that flexes the hip joint weakens, and the hip joint flexor muscle seems to weaken. However, regular running in the elderly Int. J. Environ. Res. Public Health 2022, 19, 4320 7 of 12 would delay a rapid decrease in BMD and lower extremity muscle strength, which may be beneficial for reducing injury risk. Upon running, the primary cause of injuries is one’s foot repeatedly touching the ground; the impact (shock) causes stress fracture, patellofemoral disorder, cartilage de- struction, and lower back pain [43]. As the impact force is generated upon landing, it has been known that older adults’ injury ratio is high due to physical weakness [44,45]. A positive relationship was observed upon examining the ground reaction force variables with age, but a significant level was not meaningful after weight standardization. No statis- tically significant difference was revealed between the impact variables as age increases in the impulse, impact force, maximum ground reaction force, and loading rate. On the other hand, this study’s magnitude of impact variables was similar to that of the previous study [31] and, with increased age, the vertical ground reaction force becomes smaller in elderly runners. This condition is conjectured to occur because stride becomes shorter and stride frequency increases in elderly runners (Appendix A, Figure A2). However, there may be a higher risk of accumulated high magnitude of impact for elderly runners due to weaker BMD and muscle strength. The decreased range of ankle joint and knee joint motion showed an increase with age, but the hip joint range of motion showed no changes in the sagittal plane (Appendix A, Figure A3). Ankle joint range of motion is affected by aging most, and the knee range of motion becomes larger as running speed increases [30,46]. The result of this study is similar to the result of previous studies, and it was confirmed that the range of motion in the ankle joint becomes smaller as age increases [26,27,47]. Previous studies also suggested that the thigh skeletal muscle and hamstring strength weaken in elderly runners [48]. The result is linked with how the ankle joint flexor muscle negatively correlates with age. Due to reduced strength flexibility, an ankle joint sprain, and tibialis posterior and tibialis anterior injuries can be caused as the ankle joint range of motion becomes smaller. If the knee joint is not smoothly moved, the impact occurring during running is thought to be delivered to the whole body. A positive correlation between males running regularly, indicating a slowly decreased ankle joint plantar flexion moment and knee joint extension moment, was found as age increased (Appendix A, Figure A3). A negative correlation shows slowly decreased ankle joint dorsiflexion and plantar flexion power with increased age. Additionally, a negative correlation was revealed be- tween the knee extension power, indicating absorption of joint energy and age. The runners propel themselves forward with generations of joint power in the lower extremity during running. However, if the ankle joint’s power generating ability decreases, compensation occurs in the knee and hip joints [31,49]. On the other hand, many portions of impact absorbed in the lower extremity joints are also transferred to the whole body, so there is a relation between the level of impact of the body and the lower extremity joint angle [50]. If the load transfer is not effectively controlled, a substantial impediment is caused to the lower extremity musculoskeletal system due to repetitive high impact force [51]. The load is significantly buffered on the ankle joint and then transferred to the knee and hip joints. As age increases, the load ability of the ankle is reduced, and the risk of ankle injury may increase [52]. If enough ankle joint strength and bone intensity are not maintained in elderly runners, there is a high possibility that ankle joint injuries may occur due to impact occurring on the ground during running. As age increases, decreased dynamic joint function, showing lower joint power due to weak lower extremity joints, especially in the ankle, may compromise running performance and induce the risk of joint injuries. Synthesizing all these, this study found that the musculoskeletal system can be main- tained by running as age increases. However, the function of the lower extremity joints significantly weakens linearly, especially when the ankle joint function significantly de- creases. The load of the lower extremity joint upon running is thought to be eased by muscle strength in physical strength. If one does not run correctly due to weakening strength ac- cording to aging, the physical activity can be connected to injuries, not health enhancement. Int. J. Environ. Res. Public Health 2022, 19, 4320 8 of 12 There are some study limitations that need to be addressed for the future direction of this area. First, a slight difference in biomechanical variables may exist between treadmill and overground running as this study was conducted on a treadmill. Second, the skill level and experience of the runners were not controlled among different age groups, but they may reflect different characteristics of running patterns. Finally, a longitudinal study investigating the effect of aging on physical and biomechanical changes in running may require in future research. 5. Conclusions This study targeted males aged 20 to 80 who were regularly running, and it aimed to determine the relationship among physical characteristics, running variables, and age. First, the T-score of the males aged 40 to 80 who regularly ran was found to be in the normal scope. The lower extremity maximum strength decreased in hips and knees among people in their 20s and decreased in the ankle joints among those in their 60s. Specifically, a considerable decrease was found in males in their 70s in ankle joint plantar flexion. Second, the stride length in the running characteristics fell as age increased. Stride showed a difference among the groups, but stride frequency showed an increased trend as age increased. This is related to stride frequency when older adults run. Third, it was observed that the knee joint range of motion and ankle joint movement remarkably decreased in running biomechanics as age increased. When synthesizing the results, the physical characteristics gradually decreased as age increased, and the BMD and lower extremity strength of the males who regularly ran was maintained and improved compared to non-runners. Running characteristics improved if one regularly ran. Specifically, the ankle joint movements were remarkably reduced due to aging, and impact absorption was further shown on the ankle joint as age increased. As a result of examining physical characteristics and kinematic variables, the burden on the ankle joint was more evident among males in their 60s due to their weakening lower extremity joint strength. Therefore, proper running intensity and method may be applicable to prevent running-related injuries in older runners by distributing the impact and load among the lower extremity joints based on the findings. A further biomechanics study, considering the changes in physical strength and motion by aging, may suggest a more effective training method for elderly runners to maintain musculoskeletal health and ensure injury prevention. Author Contributions: Conceptualization, J.K. and S.-K.P.; methodology, J.K. and S.-K.P.; software, J.K.; validation, J.K. and S.-K.P.; formal analysis, J.K. and S.-K.P.; investigation, J.K. and S.-K.P.; resources, J.K. and S.-K.P.; data curation, J.K.; writing—original draft preparation, J.K. and S.-K.P.; writing—review and editing, J.K. and S.-K.P.; visualization, J.K.; supervision, S.-K.P. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Korea National Sport University (approval number 20180611-046 and date of approval 25 June 2018). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. Int. J. Environ. Res. Public Health 2022, 19, 4320 9 of 12 Appendix A y g g Declaration of Helsinki and approved by the Institutional Review Board of Korea National Sport University (approval number 20180611-046 and date of approval 25 June 2018). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. Appendix A (a) (b) (c) Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 9 of 12 (d) (e) (f) (g) (h) (i) Figure A1. Correlations between age and physical characteristics (BMD and peak muscle torque). (a) Total BMD, (b) legs BMD, (c) T-scope, (d) hip extensor, (e) hip flexor, (f) knee flexor, (g) knee extensor, (h) ankle dorsi flexor, (i) ankle plantar flexor. * p < 0.05 indicates significant correlations between age and the variables. (a) (b) Figure A2. Correlations between age and impact and running characteristics. (a) Stride time, (b) stride distance. * p < 0.05 indicates significant correlations between age and the variables. Figure A1. Correlations between age and physical characteristics (BMD and peak muscle torque). (a) Total BMD, (b) legs BMD, (c) T-scope, (d) hip extensor, (e) hip flexor, (f) knee flexor, (g) knee extensor, (h) ankle dorsi flexor, (i) ankle plantar flexor. * p < 0.05 indicates significant correlations between age and the variables. Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 9 of 12 (d) (e) (f) (g) (h) (i) Figure A1. Correlations between age and physical characteristics (BMD and peak muscle torque). (a) Total BMD, (b) legs BMD, (c) T-scope, (d) hip extensor, (e) hip flexor, (f) knee flexor, (g) knee extensor, (h) ankle dorsi flexor, (i) ankle plantar flexor. * p < 0.05 indicates significant correlations between age and the variables. (a) (b) Figure A2. Correlations between age and impact and running characteristics. (a) Stride time, (b) stride distance. * p < 0.05 indicates significant correlations between age and the variables. Figure A2. Correlations between age and impact and running characteristics. (a) Stride time, (b) stride distance. * p < 0.05 indicates significant correlations between age and the variables. Int. J. Environ. Res. Public Health 2022, 19, 4320 10 of 12 (a) (b) Figure A2. Correlations between age and impact and running characteristics. (a) Stride time, (b) stride distance. * p < 0.05 indicates significant correlations between age and the variables. (a) (b) (c) Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 10 of 12 (d) (e) (f) (g) (h) (i) Figure A3. Correlations between age and lower extremity joint biomechanics upon running. (a) An- kle ROM, (b) knee ROM, (c) hip ROM, (d) ankle moment, (e) knee moment, (f) hip moment, (g) ankle power, (h) knee power, (i) hip power. * p < 0.05 indicates significant correlations between age and the variables. References 1. Waneen, W.S.; Karen, L.F.; Priscilla, G.M. Physical Dimensions of Aging; Human Kinetics: New York IL, USA, 2005. 2. Stathokostas, L.; Jacob-Johnson, S.; Petrella, R.J.; Paterson, D.H. Longitudinal changes in aerobic power in older men and women. J. Appl. Physiol. 2004, 97, 781–789. https://doi.org/10.1152/japplphysiol.00447.2003. 3. Sui, X.; LaMonte, M.J.; Laditka, J.N.; Hardin, J.W.; Chase, N.; Hooker, S.P.; Blair, S.N. Cardiorespiratory fitness and adiposity as mortality predictors in older adults. JAMA 2007, 298, 2507–2516. https://doi: 10.1097/01.HCR.0000314211.24923.54. 4. Kim, Y.R.; Lee, T.Y.; Lee, J.H. Bone density change and bone loss rate by region according to age in Korean men. J. Korean Soc. Ind. -Acad. Technol. 2014, 15, 3781–3788. https://doi.org/10.5762/KAIS.2014.15.6.378. 5. Scott, D.; Jones, G. Impact of nutrition on muscle mass, strength, and performance in older adults. Osteoporos. Int. 2014, 25, 791– 792. https://doi.org/10.1007/s00198-012-2236-y. 6. Tinetti, M.E.; Speechley, M.; Ginter, S.F. Risk factors for falls among elderly persons living in the community. NE J. Med. 1988, 319, 1701–1707. https://doi.org/10.1056/NEJM198812293192604. 7. Camicioli, R.; Panzer, V.P.; Kaye, J. Balance in the healthy elderly: Posturography and clinical assessment. Arch. Neurol. 1997, 54, 976–981. https://doi:10.1001/archneur.1997.00550200040008. 8. Prohaska, T.R.; Leventhal, E.A.; Leventhal, H.; Keller, M.L. Health practices and illness cognition in young, middle aged, and elderly adults. J. Gerontol. 1985, 40, 569–578. https://doi.org/10.1093/geronj/40.5.569. 9. Spirduso, W.W.; Clifford, P. Replication of age and physical activity effects on reaction and movement time. J. Gerontol. 1978, 33, 26–30. https://doi.org/10.1093/geronj/33.1.26. 10. Bassey, E.J. Longitudinal changes in selected physical capabilities muscle strength, flexibility and body size. Age Aging 1998, 27, 12–16. https://doi.org/10.1093/ageing/27.suppl_3.12. 11. Voorrips, L.E.; Lemmink, K.A.; Bult, P. The physical condition of elderly women differing in habitual physical activity. Med. Sci. Sports Exerc. 1993, 25, 1152–1157. PMID: 8231760. 12. Stamatakis, E.; Chaudhury, M. Temporal trends in adults’ sports participation patterns in England between 1997 and 2006: The Health Survey for England Br J Sports Med 2008 42 901 908 https://doi org/10 1136/bjsm 2008 048082 Figure A3. Correlations between age and lower extremity joint biomechanics upon running. (a) Ankle ROM, (b) knee ROM, (c) hip ROM, (d) ankle moment, (e) knee moment, (f) hip moment, (g) ankle power, (h) knee power, (i) hip power. * p < 0.05 indicates significant correlations between age and the variables. References 1. Waneen, W.S.; Karen, L.F.; Priscilla, G.M. Physical Dimensions of Aging; Human Kinetics: New York, NY, USA, 2005. 2. Stathokostas, L.; Jacob-Johnson, S.; Petrella, R.J.; Paterson, D.H. Longitudinal changes in aerobic power in older men and women. J. Appl. Physiol. 2004, 97, 781–789. [CrossRef] [PubMed] 3. Sui, X.; LaMonte, M.J.; Laditka, J.N.; Hardin, J.W.; Chase, N.; Hooker, S.P.; Blair, S.N. Cardiorespiratory fitness and adiposity as mortality predictors in older adults. JAMA 2007, 298, 2507–2516. [CrossRef] 4. Kim, Y.R.; Lee, T.Y.; Lee, J.H. Bone density change and bone loss rate by region according to age in Korean men. J. Korean Soc. Ind.-Acad. Technol. 2014, 15, 3781–3788. [CrossRef] 5. Scott, D.; Jones, G. Impact of nutrition on muscle mass, strength, and performance in older adults. Osteoporos. Int. 2014, 25, 791–792. [CrossRef] [PubMed] 6. Tinetti, M.E.; Speechley, M.; Ginter, S.F. Risk factors for falls among elderly persons living in the community. NE J. Med. 1988, 319, 1701–1707. [CrossRef] [PubMed] 7. Camicioli, R.; Panzer, V.P.; Kaye, J. Balance in the healthy elderly: Posturography and clinical assessment. Arch. Neurol. 1997, 54, 976–981. [CrossRef] [PubMed] 8. Prohaska, T.R.; Leventhal, E.A.; Leventhal, H.; Keller, M.L. Health practices and illness cognition in young, middle aged, and elderly adults. J. Gerontol. 1985, 40, 569–578. [CrossRef] [PubMed] Int. J. Environ. Res. Public Health 2022, 19, 4320 11 of 12 9. Spirduso, W.W.; Clifford, P. Replication of age and physical activity effects on reaction and movement time. J. Gerontol. 1978, 33, 26–30. [CrossRef] [PubMed] 10. Bassey, E.J. Longitudinal changes in selected physical capabilities muscle strength, flexibility and body size. Age Aging 1998, 27, 12–16. [CrossRef] [PubMed] 11. Voorrips, L.E.; Lemmink, K.A.; Bult, P. The physical condition of elderly women differing in habitual physical activity. Med. Sci. Sports Exerc. 1993, 25, 1152–1157. [CrossRef] [PubMed] 12. Stamatakis, E.; Chaudhury, M. Temporal trends in adults’ sports participation patterns in England between 1997 and 2006: The Health Survey for England. Br. J. Sports Med. 2008, 42, 901–908. [CrossRef] [PubMed] 13. Shams, A.; Nobari, H.; Afonso, J.; Abbasi, H.; Mainer Pardos, E.; Pérez-Gómez, J.; Bayati, M.; Bahrami, A.; Carneiro, L. Effect of aerobic-based exercise on psychological well-being and quality of life among older people: A Middle East study. Front. Public Health 2021, 9, 764004. Available online: https://pubmed.ncbi.nlm.nih.gov/34938705 (accessed on 12 November 2021). [CrossRef] 14. Anbarian, M.; Esmaeili, H. Effects of running-induced fatigue on plantar pressure distribution in novice runners with different foot types. Gait Posture 2016, 48, 52–56. [CrossRef] [PubMed] 15. Overgaard, K.; Riis, B.J.; Christiansen, C.; Hansen, M.A. Effect of salcatonin given intranasally on early postmenopausal bone loss. BMJ 1989, 299, 477–479. [CrossRef] [PubMed] 16. Hetland, M.L.; Haarbo, J.; Christiansen, C. Low bone mass and high bone turnover in male long distance runners. J. Clin. Endocrinol. Metab. 1993, 77, 770–775. [CrossRef] 17. Nevill, A.M.; Burrows, M.; Holder, R.L.; Bird, S.; Simpson, D. Does lower-body BMD develop at the expense of upper-body BMD in female runners? Med. Sci. Sports Exerc. 2003, 10, 1733–1739. [CrossRef] [PubMed] 18. Azuma, K.; Katsukawa, F.; Oguchi, S.; Murata, M.; Yamazaki, H.; Shimada, A.; Saruta, T. Correlation between serum resistin level and adiposity in obese individuals. Obes. Res. 2003, 11, 997–1001. [CrossRef] [PubMed] 19. Larsen, R.G.; Callahan, D.M.; Foulis, S.A.; Kent-Braun, J.A. Age-related changes in oxidative capacity differ between locomotory muscles and are associated with physical activity behavior. Appl. Physiol. Nutr. Metab. 2012, 37, 88–99. [CrossRef] 20. Ikezoe, T.; Mori, N.; Nakamura, M.; Ichihashi, N. Atrophy of the lower limbs in elderly women: Is it related to walking ability? Eur. J. Appl. Physiol. 2011, 111, 989–995. [CrossRef] [PubMed] 21. Figueroa, J.D.; Cordero, K.; Serrano-Illan, M.; Almeyda, A.; Baldeosingh, K.; Almaguel, F.G.; De Leon, M. Metabolomics uncovers dietary omega-3 fatty acid-derived metabolites implicated in anti-nociceptive responses after experimental spinal cord injury. Neuroscience 2013, 255, 1–18. [CrossRef] [PubMed] 22. Lui, P.P.Y.; Qin, L.; Chan, K.M. Tai Chi Chuan exercises in enhancing bone mineral density in active seniors. Clin. Sports Med. 2008, 27, 75–86. [CrossRef] [PubMed] 23. Frontera, W.R.; Meredith, C.N.; O'Reilly, K.P.; Knuttgen, H.G.; Evans, W.J. Strength conditioning in older men: Skeletal muscle hypertrophy and improved function. J. Appl. Physiol. 1998, 64, 1038–1044. [CrossRef] [PubMed] 24. Abe, T.; DeHoyos, D.V.; Pollock, M.L.; Garzarella, L. Time course for strength and muscle thickness changes following upper and lower body resistance training in men and women. Eur. J. Appl. Physiol. 2000, 81, 174–180. [CrossRef] 25. Kim, H. The Effect of Lower Extremity Anatomical Injury Factors on Hindfoot Pronation, Tibia Medial Rotation, and Vertical Impact during Running. Master’s Thesis, Yonsei University, Seoul, Korea, 2001. 26. Fukuchi, R.K.; Duarte, M. Comparison of three-dimensional lower extremity running kinematics of young adult and elderly runners. J. Sports Sci. 2008, 26, 1447–1454. [CrossRef] [PubMed] 27. Fukuchi, R.K.; Stefanyshyn, D.J.; Stirling, L.; Duarte, M.; Ferber, R. Flexibility, muscle strength and running biomechanical adaptations in older runners. Clin. Biomech. 2014, 29, 304–310. [CrossRef] [PubMed] 28. Kline, P.W.; Williams, D.S. Effects of normal aging on lower extremity loading and coordination during running in males and females. International J. Sports Phys. Ther. 2015, 10, 901–909. [PubMed] 29. Nigg, B.M.; Baltich, J.; Maurer, C.; Federolf, P. Shoe midsole hardness, sex and age effects on lower extremity kinematics during running. J. Biomech. 2012, 45, 1692–1697. [CrossRef] [PubMed] 30. Silvernail, J.F.; Boyer, K.; Rohr, E.; Brüggemann, G.P.; Hamill, J. Running mechanics and variability with aging. Med. Sci. Sports Exerc. 2015, 47, 2175–2180. [CrossRef] 31. Devita, P.; Fellin, R.E.; Seay, J.F.; Ip, E.; Stavro, N.; Messier, S.P. The relationships between age and running biomechanics. Med. Sci. Sports Exerc. 2016, 48, 98–106. [CrossRef] [PubMed] 32. Pohl, M.B.; Hamill, J.; Davis, I.S. Biomechanical and anatomic factors associated with a history of plantar fasciitis in female runners. Clin. J. Sport Med. 2009, 19, 372–376. [CrossRef] [PubMed] 33. Bohannon, R.W. Sit-to-stand test for measuring performance of lower extremity muscles. Percept. Mot. Ski. 1995, 80, 163–166. [CrossRef] 34. Hoseini, A.; Zarei, M.; Nobari, H.; Hovanloo, F.; Abbasi, H.; Pérez-Gómez, J. Isokinetic muscle strength cannot be related to the odds ratio of musculoskeletal injuries in young elite wrestlers. BMC Sports Sci. Med. Rehabil. 2022, 14, 1–7. Available online: https://pubmed.ncbi.nlm.nih.gov/35193657 (accessed on 22 February 2022). [CrossRef] 35. Heiderscheit, B.C.; Chumanov, E.S.; Michalski, M.P.; Wille, C.M.; Ryan, M.B. Effects of step rate manipulation on joint mechanics during running. Med. Sci. Sports Exerc. 2011, 43, 296, PMCID: PMC3022995. [CrossRef] [PubMed] 36. Gruber, A.H.; Boyer, K.A.; Derrick, T.R.; Hamill, J. Impact shock frequency components and attenuation in rearfoot and forefoot running. J. Sport Health Sci. 2014, 3, 113–121. [CrossRef] Int. J. Environ. Res. Public Health 2022, 19, 4320 12 of 12 37. Hulley, S.B.; Cummings, S.R.; Browner, W.S.; Grady, D.G.; Newman, T.B. Designing Clinical Research: An Epidemiologic Approach, 4th ed.; Appendix 6C; Lippincott Williams & Wilkins: Philadelphia, PA, USA, 2013; p. 79. 38. Choi, S.Y.; Lee, Y.J.; Kim, J.M.; Kang, H.J.; Cho, S.H.; Chang, S.E. Epidermal Growth Factor Relieves Inflammatory Signals in Staphylococcus aureus-Treated Human Epidermal Keratinocytes and Atopic Dermatitis-Like Skin Lesions in Nc/Nga Mice. BioMed Res. Int. 2018, 2018, 9439182. [CrossRef] [PubMed] 39. Einhorn, T.A.; Azria, M.; Goldstein, S.A.; Steven, A. Bone Fragility: The Biomechanics of Normal and Pathologic Bone; Sandoz Pharma, Ltd.: West Princeton, NJ, USA, 1992; pp. 1–43. 40. Lindle, R.S.; Metter, E.J.; Lynch, N.A.; Fleg, J.L.; Fozard, J.L.; Tobin, J.; Hurley, B.F. Age and gender comparisons of muscle strength in 654 women and men aged 20–93 yr. J. Appl. Physiol. 1997, 83, 1581–1587. [CrossRef] 41. Kim, J.B.; Ha, S.H.; Park, S.H.; Yoon, S.H.; Ryu, J.S.; Park, S.K. Comparison of Physical Characteristics and Lower Extremity Biomechanics of Elderly and Young Adult Runners. Korean J. Sport Biomech. 2019, 29, 145–155. [CrossRef] 42. Bolam, K.A.; Van Uffelen, J.G.; Taaffe, D.R. The effect of physical exercise on bone density in middle-aged and older men: A systematic review. Osteoporos. Int. 2013, 24, 2749–2762. [CrossRef] [PubMed] 43. Seo, S.S.; Kim, J.H. Prevention and rehabilitation of knee injuries in running people. Korean J. Orthop. Sports Med. 2010, 9, 22–27. 44. Hamill, J.; Bates, B.T.; Knutzen, K.M.; Sawhill, J.A. Variations in ground reaction force parameters at different running speeds. Hum. Mov. Sci. 1983, 2, 47–56. [CrossRef] 45. Hintermann, B.; Nigg, B.M. Pronation in runners. Sports Med. 1998, 26, 169–176. [CrossRef] 46. Bus, S.A. Ground reaction forces and kinematics in distance running in older-aged men. Med. Sci. Sports Exerc. 2003, 35, 1167–1175. [CrossRef] 47. Conoboy, P.; Dyson, R. Effect of aging on the stride pattern of veteran marathon runners. Br. J. Sports Med. 2006, 40, 601–604. [CrossRef] [PubMed] 48. Noehren, B.; Schmitz, A.; Hempel, R.; Westlake, C.; Black, W. Assessment of strength, flexibility, and running mechanics in men with iliotibial band syndrome. J. Orthop. Sports Phys. Ther. 2014, 44, 217–222. Available online: https://www.jospt.or (accessed on 29 March 2022). [CrossRef] [PubMed] 49. Kulmala, J.P.; Korhonen, M.T.; Kuitunen, S.; Suominen, H.; Heinonen, A.; Mikkola, A.; Avela, J. Which musclescompromise human locomotor performance with age? J. R. Soc. Interface 2014, 11, 1–10. [CrossRef] [PubMed] 50. Liew, B.; Morris, S.; Netto, K. The effect of backpack carriage on the biomechanics of walking: A systematic review and preliminary meta-analysis. J. Appl. Biomech. 2016, 32, 614–629. [CrossRef] [PubMed] 51. Lee, S.Y.; Lee, S.M.; Choi, J.Y. Effect of landing method on shock absorption mechanism of lower extremity joints. J. Korean Soc. Kinesiol. 2001, 10, 77–97. 52. Williams III, D.B.; Green, D.H.; Wurzinger, B. Changes in lower extremity movement and power absorption during forefoot striking and barefoot running. Int. J. Sports Phys. Ther. 2012, 7, 525, PMCID: PMC3474309.

Differences in Physical Characteristics of the Lower Extremity and Running Biomechanics Between Different Age Groups.

04-04-2022

Kim, Jongbin,Park, Sang-Kyoon

eng

PMC6650599

1 Scientific RepoRts | (2019) 9:10660 | https://doi.org/10.1038/s41598-019-47162-z www.nature.com/scientificreports Kinematic Profile of Visually Impaired Football Players During Specific Sports Actions sara Finocchietti 1, Monica Gori1 & Anderson Souza Oliveira 2 Blind football, or Football 5-a-side, is a very popular sport amongst visually impaired individuals (VI) worldwide. However, little is known regarding the movement patterns these players perform in sports actions. Therefore, the aim of this study was to determine whether visually impaired players present changes in their movement patterns in specific functional tasks compared with sighted amateur football players. Six VI and eight sighted amateur football players performed two functional tasks: (1) 5 m shuttle test and (2) 60 s ball passing against a wall. The sighted players performed the tests while fully sighted (SIG) as well as blindfolded (BFO). During both tasks, full-body kinematics was recorded using an inertial motion capture system. The maximal center-of-mass speed and turning center-of-mass speed were computed during the 5 m shuttle test. Foot resultant speed, bilateral arm speed, and trunk flexion were measured during the 60 s ball passing test. The results showed that VI players achieved lower maximal and turning speed compared to SIG players (p < 0.05), but BFO were slower than the VI players. The VI players presented similar foot contact speed during passes when compared to SIG, but they presented greater arm movement speed (p < 0.05) compared to both SIG and BFO. In addition, VI players presented greater trunk flexion angles while passing when compared to both SIG and BFO (p < 0.05). It is concluded that VI players present slower speed while running and turning, and they adopt specific adaptations from arm movements and trunk flexion to perform passes. Football is the most practiced and followed sport in the world, in which players need to efficiently and effec- tively execute the skilled movement, applying cognitive, perceptual and motor skills in ever-changing gaming contexts1. Blind football (officially called Football 5-a-side) is currently a Paralympic sport that is a variation of futsal, designed for players who are visually impaired (VI)2. Players are assigned to one of three sport classes based on their level of visual impairment3: (1) B1 - totally blind; from no light perception up to light perception but inability to recognize the shape of a hand; (2) B2 - partially sighted; able to recognize the shape of a hand up to a visual acuity of 2/60 or a visual field of less than 5 degrees; (3) B3 - partially sighted; visual acuity from 2/60 to 6/60 or visual field from 5 to 20 degrees. It is a five against five games in a field measuring 40 m × 20 m. In blind football, the football contains ball bearings that rattle and make the ball’s location accessible for VI players through auditory stimuli4. Players call out “yeah” and their names to make teammates aware of their presence. As a result, spectators must remain silent whilst watching the game until a goal is scored. The goalkeeper is sighted or partially sighted, to allow for the guidance of the other players who wear eyeshades to account for differences in blindness severity3. Blind football is quite popular worldwide, having organized national leagues in France, Brazil, and England. The physical fitness of football athletes has been dramatically improved in the last decades, as players are able to run faster and farther during the matches5. Some of these advances were achieved by the use of biomechanical analysis that describes the player’s motion. Understanding movement patterns have been essential for coaches and athletes, as it allows proposing changes to these patterns to improve performance6,7. Despite the considerable popularity of blind football, there is limited information regarding movement patterns of VI players. It has been shown that VI goalball and football athletes have similar self-selected walking speed, but lower static balance, when compared to sighted individuals8. In addition, these authors showed that VI players presented a greater fear of falling during sports practices. Therefore, evaluating movement patterns of VI football players in specific sports actions can be valuable to describe their disability-related movement limitations. Subsequently, this information 1U-VIP: Unit for Visually Impaired People, Fondazione Istituto Italiano di Tecnologia, Genova, Italy. 2Department of Materials and Production, Aalborg University, Aalborg, Denmark. Correspondence and requests for materials should be addressed to A.S.O. (email: [email protected]) Received: 4 October 2018 Accepted: 4 July 2019 Published: xx xx xxxx opeN 2 Scientific RepoRts | (2019) 9:10660 | https://doi.org/10.1038/s41598-019-47162-z www.nature.com/scientificreports www.nature.com/scientificreports/ can help in designing novel training methods to maximize the performance of blind football players and playing experiences. It is widely believed that blind individuals are better than sighted in the audio skills but this is not always true and recent results show that in some cases they have big impairments in audio spatial skills9–11. During football, the lack of visual input for blind players changes the way they perceive the ball’s location for a kick or a pass, likely evoking greater participation from the auditory system and overall postural control through soma- tosensory information to maintain postural control with no visual inputs12. Therefore, it is essential to assess the movement patterns of VI players in the most natural conditions possible. Inertial motion capture systems (IMC) have become highly popular in recent years, providing acceptable measurements of human kinematics in differ- ent movement conditions13,14. Especially for sports activities, IMCs allows recordings of kinematic data in more natural conditions, such as open spaces like football courts. This feature from IMCs is highly suitable to record kinematic profiles of VI football players while they perform football movements. To date, there are no studies investigating the movement patterns of blind players during game situations. Therefore, the aim of this study was to determine whether visually impaired players present changes in their movement patterns in specific functional tasks when compared to sighted amateur football players. It was hypoth- esized that visually impaired players would run slower, take more time to perform turns and perform less correct passes than sighted players. Moreover, visually impaired players will present distinct kinematic patterns when compared to sighted players. In addition, we hypothesized that blindfolded players would be slower than visually impaired players and assume changes in body posture to being able to perform simple passes. The results of this study can contribute to increasing our understanding of the motor performance of VI individuals. Methods Participants. Six male visually impaired (VI, 2 blind, age range: 25–38 years) and eight age-matched healthy controls participated in the study. All participants were males and amateur players (age range: 26–40), practicing football 1–2 times per week and participating at the National Italian Blind Football league. The vision loss of the early blind had different etiology. One player was born blind whereas another lost his vision at the age of four, as indicated in Table 1. The healthy controls were amateur players that practice both football and five-a-side football 1–2 times per week. Both VI and sighted individuals have practiced football for at least 10 years. Written informed consent was obtained from each subject prior to inclusion in the study. The study was conducted in accordance with the Declaration of Helsinki and approved by the local ethics committee (ASL3 Genovese, Italy). Experimental design. In a single session, participants performed two functional tasks in a gymnasium containing an official futsal court: 5 m shuttle and ball pass against a wall. The control group performed the tasks at first without vision (blindfolded, BFO), and then with vision (SIG) so that the sighted blindfolded players could not know in advance the football area. Kinematic data were acquired using an inertial motion capture system, and the horizontal center-of-mass speed was extracted to describe the maximum speed and turning speed during the shuttle test. The number of passes, foot, and arms speed while passing, as well as the trunk flexion angle at the T8/ T9 vertebrae level, were computed from the ball passing test. Familiarization to blindfolded conditions. Following the appropriate placement of the inertial motion capture suit, all sighted participants were blindfolded and asked to familiarize to the environment (e.g., the court and the general sounds from their surroundings). Initially, all participants walked and ran throughout the court for approximately 10 minutes, familiarizing to the court limits and to moving without visual feedback, just following the voice commands from one experimenter. In addition, blind football handling and passing were introduced to the BFO participants. Each participant was familiarized to the sounds of the ball, and the timing required to decode when the ball was approaching them, as well as trying to pass the ball back to the experi- menter, being guided auditory clues. The familiarization to the blindfolded condition was considered successful when the participant felt comfortable to perform running, passing and changing directions following auditory clues. Additional time was allowed if a participant required more time to familiarize to restricted vision. The 5 m shuttle and ball passing tests. For the 5 m shuttle test (adapted from Boddington and co-workers15), participants were asked to perform a 10 m shuttle test by running in a 5 m track marked on the floor and back to the original position. The trial was considered successful if both feet have crossed the 5 m line while turning. For VI players, one experimenter was positioned parallel to the 5 m line and whistled when the participant was with the trunk over the 5 m line, which indicated that he could turn and run back. Moreover, this auditory signal minimized the possibility of non-straight running after turning. A total of 5 successful trials were recorded for each participant, and average across trials was computed for the maximum speed and turning speed for further statistical analysis. age Etiology Residual vision Age of complete blindness P1 38 Retinitis pigmentosa None 20 P2 32 Retinitis pigmentosa Light and shadows 25 P3 18 Leber amaurosis, nistagmus 1/20 / P4 25 Retinitis pigmentosa Lights and shadows 17 P5 48 Congenital Glaucoma None 6 Table 1. Age and visual impairment characteristics of the visually impaired players participating in this study. 3 Scientific RepoRts | (2019) 9:10660 | https://doi.org/10.1038/s41598-019-47162-z www.nature.com/scientificreports www.nature.com/scientificreports/ Regarding ball passes against a wall, participants were asked to perform passes at the floor level against a wall located 5 m in front of them. This wall was 10 m wide and the test started with the ball positioned at the central position. A preliminary study on 11 healthy, young and sighted recreational football players has shown a high intra-class correlation coefficient across three different test days (r = 0.995, see Supplementary Table 1). Participants were instructed to perform as many passes as possible for 60 seconds while keeping an approximate distance of 5 m from the wall. The test was conducted using an official blind football which contains rings embed- ded, therefore VI and BFO participants could hear the location of the ball to perform the passes. The inertial motion capture system. An IMC (Xsens MVN Link, Xsens Technologies BV, Enschede, The Netherlands) and its respective software (Xsens MVN Studio version 4.2.4, Enschede, The Netherlands) were used to record full-body kinematics at a sampling rate of 240 Hz. The IMC consisted of 17 inertial measurement unit modules (25 × 35 × 8 mm, 30 g) mounted on a tight-fitting Lycra suit containing pre-defined locations for sensor placement. The IMUs were placed bilaterally in the following locations: shoulder, arm, forearm, hand, thigh, shank, and foot. In addition, IMUs were placed on the head (using a headband), on the chest and on the sacrum. The manufacturer’s sensor calibration procedure was followed by asking participants to assume different body poses such as N-pose (quiet standing with arms alongside the body) and T-pose (quiet standing with arms abducted 90° and horizontally aligned in the frontal plane). This calibration procedure assured the different IMUs were correctly representing the body’s segments in the three-dimensional space16. The manufacturer’s recommen- dations to avoid sources of electromagnetic fields were followed to assure the quality of the acquired data. Data processing. The orientation of each inertial measurement units was obtained by fusing accelerometer, gyroscope and magnetometer signals using an extended Kalman filter embedded in the IMC recording soft- ware17. The IMC software computed the three-dimensional position vectors for all sensors. The software subse- quently computed automatically the center-of-mass position from each body segment, as well as the full-body center-of-mass (COM) from these position vectors. Moreover, the IMC software partitioned the trunk kinematic data into four different segments (L3, L5, T8, and T12 vertebrae), and generated joint angles for upper and lower limbs, as well as for trunk spinal joints. In this study, we focused on the displacement of the full-body COM, kicking foot as well as the ipsilateral and contralateral arms. In addition, we investigated trunk kinematics during ball passes through the flexion angle for the lumbar (L1/T12), thoracic (T9/T8) and cervical trunk levels (T1/C7). All data from position vectors and joint angles were low-pass filtered (6 Hz, second-order Butterworth zero-phase). The COM, foot and arm segments position vectors were derived to generate velocity vectors. The resultant trunk speed was subsequently defined as: = + + S i x i y i z i ( ) ( ) ( ) ( ) 2 2 2 where for each time frame (i), S was the resultant speed from the velocity vectors in the anterior-posterior (x), medial-lateral (y) and vertical directions (z). Data were analyzed using custom scripts programmed in MATLAB® (R2015b, Mathworks Inc., Natick, MA USA). Data analysis – 5-m shuttle test. From the kinematic data, the shuttle period was defined from the period where the COM resultant speed was greater than 0.25 m/s. The number of strides for the dominant leg was defined from the dominant foot displacement in the shuttle running direction. The maximum speed was defined as the maximum resultant speed achieved throughout the test (Fig. 1A). In addition, we defined the turning period from −500 to 500 ms around the instant where the COM position was the farthest from the origin in the shuttle running direction. The average COM resultant speed during this turning period was defined as the COM turning speed. Data analysis – ball passes. The instants of foot contact to the ball were defined as the peak horizontal foot acceleration throughout the 60-second recordings (Fig. 1B), followed by visual inspection of the time indexes using the graphical representation of the participant’s task in the recording software. The resultant foot speed at the moment of contact was found using the time indexes. The resultant trunk speed was defined from 0 to 1000 ms around foot contact to the ball. In addition, the resultant speed of the ipsilateral and contralateral arms was defined from −250 to 250 ms around foot contact to the ball. Finally, the trunk flexion angle data from L1/ T12, T9/T8, and T1/C7 were averaged within −250 to 250 ms around foot contact to the ball, to describe the trunk flexion during the passes. Statistical analysis. The Statistical Package for the Social Sciences (IBM SPSS Inc. Version 23.0, Chicago, IL, USA) was used for statistical analysis. The normality of the dependent variables (resultant speed and joint angles) was assessed using Shapiro-Wilk tests, where both variables demonstrated normal distribution (p > 0.05). The differences across the three different groups (VI vs BFO vs SIG) for each variable were assessed using ANOVA 1-way, followed by Bonferroni post-hoc tests when necessary. The significance level was set at <0.05. partial eta-squared values are reported (ŋp2). Results The 5-m shuttle test. The SIG group was significantly faster and performed fewer stride cycles during the shuttle test in comparison to VI and BFO (p = 0.00001, ŋp2 = 0.67, Fig. 2A,B), whereas VI was faster and per- formed fewer strides than BFO (p = 0.00005, ŋp2 = 0.67). The maximum speed (Fig. 2C) and the turning speed (Fig. 2D) were comparable between VI and BFO, while SIG ran at the highest speed, and at the fastest turning speed (p = 0.0002, ŋp2 = 0.63). 4 Scientific RepoRts | (2019) 9:10660 | https://doi.org/10.1038/s41598-019-47162-z www.nature.com/scientificreports www.nature.com/scientificreports/ Ball passes test. The SIG group performed the greatest number of passes (55 ± 7 passes) when compared to BFO and VI (7 ± 1 and 17 ± 7 passes respectively, p = 0.00001, ŋp2 = 0.94). The BFO group presented the fastest speed during foot contact to the ball (p = 0.035, ŋp2 = 0.29), whereas VI and SIG were similar (Fig. 3A). Regarding whole-body movements, the VI group demonstrated the faster COM speed after passing (Fig. 3B, p = 0.01, ŋp2 = 0.23), as well as the fastest ipsilateral (Fig. 3C, p = 0.012, ŋp2 = 0.30) and contralateral arm speed (Fig. 3D, p = 0.036, ŋp2 = 0.12). Trunk kinematics during passes. The trunk flexion angle at the lumbar (L1/T12, Fig. 4A) and thoracic levels (Fig. 4B) were significantly greater for VI in comparison to both BFO and SIG (p = 0.045, ŋp2 = 0.31). No significant changes were found for the trunk flexion angle at the cervical level (Fig. 4C). Discussion Here we tested for the first time the differences in movement patterns of VI players compared to sighted players with and without visual feedback. The main results from the kinematic analysis were that VI players reach slightly slower maximum speed and turning speed compared to sighted players in the shuttle test while performing a greater number of strides to cover the same distance. However, the BFO group was the slowest and presented a substantial increase in the number of strides to complete the task. Regarding ball passes, VI players hit the ball with similar speed compared to the SIG group, but they increase arms movement speed during passes. Moreover, VI players present greater COM speed, concomitant to increased trunk flexion, after passing. Increased trunk flexion after passing was also found for the BFO group, which seems an immediate adaptation to the lack of visual contribution to performing such a movement pattern. The results from this study can substantially contribute to increasing the understanding of the biomechanical demands of sports performance in blind athletes, potentially assisting coaches and product developers to adapt training procedures and equipment. Maximal and turning speed during 5-m shuttle run test. In walking, individuals with a visual impair- ment show adaptation strategies towards a more cautious pattern, as they seem to depend more on tactile feed- back information from the foot’s plantar surface18. In is also known that congenitally blind children tend to take shorter strides, walke slower, and spend more time in the support phase of the gait than sighted children19. However, results on adults are unclear, as visually impaired adults manage to maintain a similar20 or inferior21, or superior22 walking speed than sighted blindfolded adults. Some controversies in the literature may be related to different experimental protocols, as the work of Gori and co-workers involved two-dimensional shape reproduc- tion following a moving sound. Our results regarding maximum and turning speed during running suggested that Figure 1. Illustration of the center of mass (CoM) resultant speed during the 5-m shuttle run test (A, Top panel) used to compute average and maximal running speed, as well as turning speed (defined during the gray shaded area). In (B) (bottom panel) the use of anterior-posterior (AP) foot sensor acceleration and AP foot speed to define peak AP foot speed and subsequent foot acceleration. 5 Scientific RepoRts | (2019) 9:10660 | https://doi.org/10.1038/s41598-019-47162-z www.nature.com/scientificreports www.nature.com/scientificreports/ VI players ran approximately 30% slower when compared to SIG. However, the BFO group presented the lowest maximum and turning speed across groups, due to the lack of long-term adaptations to running blindfolded. Blindfolded football players presented a shorter stride length, which consequently reduces running speed. Furthermore, arm movements may be a key contributor to postural maintenance during ambulation of VI indi- viduals. In fact, it has been shown that young VI individuals run slower than sighted individuals19. These VI individuals ran using shorter stride length and lower range of motion of the hip joint when compared to sighted individuals. They also kept stride contact longer and were airborne for a shorter time than the other peers23. Blindfolded sighted people may present even greater motor adaptations in their gait patterns, as walking without visual feedback information is a novel situation. This observation points towards an important role for multisen- sory integration during development, whereby the other sensory modalities are able to, at least partially, take over the role of visual information in the control of walking. Ball passing performance and postural adjustments. As expected, SIG performed a greater number of passes against a wall compared to VI players, but sighted participants performed BFO had a reduction of 86 ± 2% in their passing performance, performing less than 50% of what VI players could achieve. Foot speed during penalty kicking can range from 13 to 21 m.s−1 in youth players7, but no literature has been found describ- ing foot speed during ball passing, in which our participants presented foot speed ranging from 4 to 8 m/s−1. Moreover, the BFO group presented greater foot speed while contacting the ball, which may indicate a lack of proper control to perform the passes compared to SIG and VI. Regarding posture, VI players presented greater arm movement speed to perform the passes. Previous studies have shown that arm movements are important to maintain and optimize postural control and reduce risks of falls24 which may be an additional strategy to improve balance control under restricted vision conditions. The VI presented greater trunk flexion at L1/T12 and T9/T8 spine segments when compared to sighted indi- viduals while performing passes. Vision is confined to frontal space, and mostly at head level in humans and most animals25,26. In the lower space actions are mediated by foot, and during ambulation, audio and motor feedback are linked. The representation of auditory frontal space around the chest is more accurate than the auditory fron- tal representation around the foot27. Therefore, forward leaning of the trunk seems to be a strategy related to max- imizing the quality of auditory inputs to guide postural control during passing/receiving the ball. Interestingly, there was also a trend for BFO individuals to lean the trunk forward during passes. There were no instructions on how sighted participants should behave while blindfolded, therefore this postural adaptation seems an immediate strategy from the CNS to cope with the lack of visual inputs when spatial orientation is needed. Our data provide Figure 2. Mean (SD) of total time (A), number of strides (B), maximum speed (C) and turning speed (D) during the 5-m shuttle run test for visually impaired individuals (VI), sighted blindfolded (BFO) and sighted individuals (SIG). *Denotes significant differences in relation to SIG (p < 0.05); †denotes a significant difference in relation to VI (p < 0.05). 6 Scientific RepoRts | (2019) 9:10660 | https://doi.org/10.1038/s41598-019-47162-z www.nature.com/scientificreports www.nature.com/scientificreports/ the first insights on the performance of VI players and can contribute to assisting coaches and product developers to adapt training procedures and equipment. Limitations. The limitations to the present study are (1) the limited number of football players. In Italy this kind of football is still at an amatorial stage, played mainly in spare time. This makes difficult to organize exper- imental settings with larger patient populations. As a consequence, the low number of participants limits the generalization of the findings; (2) Sighted participants were blindfolded and received a familiarization period in such condition. Therefore, a learning effect might have occurred during the BFO condition. This learning effect may be beneficial for the study design, as sighted players had to accommodate their sensory strategies to the novel vision-restricted condition. Furthermore, some of the results, such as the BFO forward trunk leaning during passes, indicated that BFO performance was changed towards the VI performance. This result is an indication that VI players may present the most effective adaptations to perform such motor tasks (3) The use of inertial motion capture for describing trunk flexion/extension may present limitations. There is an acceptable accuracy Figure 3. Mean(SD) kicking foot speed at the instant of contact to the ball (A), the center of mass (COM) speed 1 second after passing (B), ipsilateral (C) and contralateral (D) arm speed from −250 to 250 ms around passing. Data for each subject were averaged across all passes performed for 1 minute for visually impaired individuals (VI), sighted blindfolded (BFO) and sighted individuals (SIG). *Denotes significant differences in relation to BFO and SIG (p < 0.05); †denotes a significant difference in relation to VI and SIG (p < 0.05). Figure 4. Mean(SD) flexion angle at the L1/T12 level (A), T8/T9 level (B) and T1/C7 level (C). Data for each subject were averaged across all passes performed for 1 minute for visually impaired individuals (VI), sighted blindfolded (BFO) and sighted individuals (SIG). *Denotes significant differences in relation to BFO and SIG (p < 0.05). 7 Scientific RepoRts | (2019) 9:10660 | https://doi.org/10.1038/s41598-019-47162-z www.nature.com/scientificreports www.nature.com/scientificreports/ of inertial motion capture systems to estimate trunk flexion/extension angles28, but results must be considered protocol specific. Finally, the lack of validation tests for the ball passes on visually impaired players is a limitation. Therefore, the results of this test must be interpreted with caution. In summary, we found that visually impaired players presented slower running and turning speed when com- pared to sighted players, but sighted blindfolded participants were slower than the visually impaired players. The visually impaired players hit the ball with similar speed compared to the SIG group, but they increase arms movement speed during passes, likely to maximize postural stability. Moreover, visually impaired players present greater center-of-mass speed, concomitant to increased trunk flexion at lumbar and thoracic levels, after passing. Such change in trunk position was also found in the blindfolded group, suggesting that leaning forward may be an immediate adaptation to the lack of visual contribution when targeting an object traveling in the opposite direction. Practical applications. These results can have some practical application. The first one is to provide to blind football players some indexes about how their football activity is performed compared to sighted players. This might be important for football trainers who are usually sighted to train the sport activity to reach these indexes. On the other hand, it can be also used to try to correct the motor behaviors that differ between sighted and blind players to verify if a more sighted like performance can optimize the results of the game. Starting from these results it would also possible to develop an application for football trainers and also for self-evaluation to quantify and train motor abilities of blind football players to reach optimal performances. References 1. Koerte, I. K. et al. Cortical thinning in former professional soccer players. Brain Imaging Behav. 10, 792–798 (2016). 2. Velten, M. C. C., Ugrinowitsch, H., Portes, L. L., Hermann, T. & Bläsing, B. Auditory spatial concepts in blind football experts. Psychol. Sport Exerc. 22, 218–228 (2016). 3. Magno e Silva, M. P., Morato, M. P., Bilzon, J. L. J. & Duarte, E. Sports injuries in Brazilian blind footballers. Int. J. Sports Med. 34, 239–43 (2013). 4. Gamonales Puerto, J. M., Muñoz Jiménez, J., León Guzmán, K. & Ibáñez Godoy, S. J. Efficacy of shots on goal in football for the visually impaired. Int. J. Perform. Anal. Sport 18, 393–409 (2018). 5. Blickenstaff, B. A Sport on the Edge: How Much Soccer Is Too Much Soccer? Vice Sports (2015). Available at, https://sports.vice.com/ en_us/article/bmek9a/a-sport-on-the-edge-how-much-soccer-is-too-much-soccer (Accessed: 13th July 2018). 6. Tak, I. J. R. Hip and groin pain in athletes: morphology, function and injury from a clinical perspective. Br. J. Sports Med. bjsports-2017–098618, https://doi.org/10.1136/bjsports-2017-098618 (2018). 7. Vieira, L. H. P. et al. Kicking Performance in Young U9 to U20 Soccer Players: Assessment of Velocity and Accuracy Simultaneously. Res. Q. Exerc. Sport 89, 210–220 (2018). 8. da Silva, E. S. et al. Gait and functionality of individuals with visual impairment who participate in sports. Gait Posture 62, 355–358 (2018). 9. Finocchietti, S., Cappagli, G. & Gori, M. Auditory Spatial Recalibration in Congenital Blind Individuals. Front. Neurosci. 11, 76 (2017). 10. Gori, M., Sandini, G., Martinoli, C. & Burr, D. C. Impairment of auditory spatial localization in congenitally blind human subjects. Brain 137, 288–293 (2014). 11. Gori, M., Amadeo, M. B. & Campus, C. Temporal Cues Influence Space Estimations in Visually Impaired Individuals. iScience 6, 319–326 (2018). 12. van Dieën, J. H., van Leeuwen, M. & Faber, G. S. Learning to balance on one leg: motor strategy and sensory weighting. J. Neurophysiol. 114, 2967–82 (2015). 13. Karatsidis, A. et al. Estimation of Ground Reaction Forces and Moments During Gait Using Only Inertial Motion Capture. Sensors 17, 75 (2016). 14. Fleron, M. K., Ubbesen, N. C. H., Battistella, F., Dejtiar, D. L. & Oliveira, A. S. Accuracy between optical and inertial motion capture systems for assessing trunk speed during preferred gait and transition periods. Sport. Biomech., https://doi.org/10.1080/14763141.2 017.1409259 (2018). 15. Boddington, M. K., Lambert, M. I. & Waldeck, M. R. Validity of a 5-meter multiple shuttle run test for assessing fitness of women field hockey players. J. Strength Cond. Res. 18, 97 (2004). 16. Svenningsen, F. P., de Zee, M. & Oliveira, A. S. The effect of shoe and floor characteristics on walking kinematics. Hum. Mov. Sci. 66, 63–72 (2019). 17. Roetenberg, D., Luinge, H. J., Baten, C. T. M. & Veltink, P. H. Compensation of magnetic disturbances improves inertial and magnetic sensing of human body segment orientation. IEEE Trans. Neural Syst. Rehabil. Eng. 13, 395–405 (2005). 18. Kobayashi, Y., Takashima, T., Hayashi, M. & Fujimoto, H. Gait Analysis of People Walking on Tactile Ground Surface Indicators. IEEE Trans. Neural Syst. Rehabil. Eng. 13, 53–59 (2005). 19. McGowan, H. E. The kinematic analysis of the walking gait of congenitally blind and sightedchildren: Ages 6–10 years. (1984). 20. Hallemans, A., Ortibus, E., Meire, F. & Aerts, P. Low vision affects dynamic stability of gait. Gait Posture 32, 547–551 (2010). 21. Nakamura, T. Quantitative analysis of gait in the visually impaired. Disabil. Rehabil. 19, 194–197 (1997). 22. Gori, M., Cappagli, G., Baud-Bovy, G. & Finocchietti, S. Shape Perception and Navigation in Blind Adults. Front. Psychol. 8, 10 (2017). 23. Arnhold, R. W. & McGrain, P. Selected Kinematic Patterns of Visually Impaired Youth in Sprint Running. Adapt. Phys. Act. Q. 2, 206–213 (1985). 24. Pijnappels, M., Kingma, I., Wezenberg, D., Reurink, G. & Van Dieën, J. H. Armed against falls: The contribution of arm movements to balance recovery after tripping. Exp. Brain Res. 201, 689–699 (2010). 25. Kóbor, I., Füredi, L., Kovács, G., Spence, C. & Vidnyánszky, Z. Back-to-front: improved tactile discrimination performance in the space you cannot see. Neurosci. Lett. 400, 163–7 (2006). 26. Oldfield, S. R. & Parker, S. P. A. Acuity of Sound Localisation: A Topography of Auditory Space. I. Normal Hearing Conditions. Perception 13, 581–600 (1984). 27. Aggius-Vella, E., Campus, C., Finocchietti, S. & Gori, M. Audio Spatial Representation Around the Body. Front. Psychol. 8, 1932 (2017). 28. Morrow, M. M. B., Lowndes, B., Fortune, E., Kaufman, K. R. & Hallbeck, S. Validation of inertial measurement units for upper body kinematics. J. Appl. Biomech. 33, 227–232 (2016). 8 Scientific RepoRts | (2019) 9:10660 | https://doi.org/10.1038/s41598-019-47162-z www.nature.com/scientificreports www.nature.com/scientificreports/ Acknowledgements The author’s thanks to the Liguria Calcio non vedenti and the VI players for the support and availability in performing the study. Author Contributions A.S.O., S.F. and M.G. conceived and designed the experiments. A.S.O. and S.F. performed the experiments A.S.O., S.F. and M.G. analyzed and interpreted the data A.S.O., S.F. drafted the manuscript All authors reviewed and approved the manuscript. Additional Information Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-019-47162-z. Competing Interests: The authors declare no competing interests. Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre- ative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not per- mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2019

Kinematic Profile of Visually Impaired Football Players During Specific Sports Actions.

07-23-2019

Finocchietti, Sara,Gori, Monica,Souza Oliveira, Anderson

eng

PMC3024328

Regulation of Pacing Strategy during Athletic Competition Jos J. de Koning1,2*, Carl Foster1,2, Arjan Bakkum1, Sil Kloppenburg1, Christian Thiel3, Trent Joseph2, Jacob Cohen2, John P. Porcari2 1 Faculty of Human Movement Sciences, Research Institute MOVE, VU University Amsterdam, Amsterdam, The Netherlands, 2 Department of Exercise and Sport Science, University of Wisconsin La Crosse, La Crosse, Wisconsin, United States of America, 3 Department of Sportmedicine, Goethe-Universita¨t, Frankfurt, Germany Abstract Background: Athletic competition has been a source of interest to the scientific community for many years, as a surrogate of the limits of human ambulatory ability. One of the remarkable things about athletic competition is the observation that some athletes suddenly reduce their pace in the mid-portion of the race and drop back from their competitors. Alternatively, other athletes will perform great accelerations in mid-race (surges) or during the closing stages of the race (the endspurt). This observation fits well with recent evidence that muscular power output is regulated in an anticipatory way, designed to prevent unreasonably large homeostatic disturbances. Principal Findings: Here we demonstrate that a simple index, the product of the momentary Rating of Perceived Exertion (RPE) and the fraction of race distance remaining, the Hazard Score, defines the likelihood that athletes will change their velocity during simulated competitions; and may effectively represent the language used to allow anticipatory regulation of muscle power output. Conclusions: These data support the concept that the muscular power output during high intensity exercise performance is actively regulated in an anticipatory manner that accounts for both the momentary sensations the athlete is experiencing as well as the relative amount of a competition to be completed. Citation: de Koning JJ, Foster C, Bakkum A, Kloppenburg S, Thiel C, et al. (2011) Regulation of Pacing Strategy during Athletic Competition. PLoS ONE 6(1): e15863. doi:10.1371/journal.pone.0015863 Editor: Alejandro Lucia, Universidad Europea de Madrid, Spain Received August 19, 2010; Accepted November 25, 2010; Published January 20, 2011 Copyright:  2011 de Koning et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: These authors have no support or funding to report. Competing Interests: The authors have declared that no competing interests exist. * E-mail: [email protected] Introduction The observation of changes in the pattern of velocity during competition has led to interest in pacing strategy during athletic competitions [1–6]. There have been at least three basic types of pacing strategies identified (positive, negative and even pacing strategy), which depend on the duration of the event and the consequences of slowing with a loss in power output [2,7–9]. There is some agreement that pacing strategy is organized in an anticipatory way designed not only to optimize performance but also to prevent unreasonably large homeostatic disturbances during the exercise bout [5,6]. There is also some agreement that different elements of the physiologic response to exercise are involved in regulating pacing strategy. Just as the driver of a race car might monitor different gauges during a race, and pay more attention to one gauge during a short race and another during a longer race; it appears that intramuscular substrate/metabolite changes [1,10–11] are more likely to be determining of changes in muscular power output during shorter (1–30 min) competitions, with body core (or brain) temperature being more central during mid-duration (20– 120 min) events [12–17], and the availability of carbohydrate as an oxidizable substrate being critical in longer (.90 min) events [18– 19]. While the effect of these different physiological regulators doubtless overlaps, their net input appears to be integrated by the conscious brain using the Rating of Perceived Exertion (RPE) [20– 23]. In a recently proposed model, Tucker (6) suggested that changes in the homeostatic status, reflected by the momentary RPE, allows alteration of pacing strategy (power output) in both an anticipatory and responsive manner based on pre-exercise expec- tations and peripheral feedback from different physiological sensors. It has been shown that RPE increases in an approximately linear manner as a function of the proportion of an event completed [24–28], and has scalar characteristics when plotted against percentage of the exercise task completed (time or distance). Together these data suggest that the athlete is continuously comparing how they feel at any moment in a competition with how they expected to feel at that moment. If their RPE is larger than expected for that point in the event, then power output (e.g. running speed) will decrease, even if it means giving up on the competition. If RPE is less than expected, then power output will increase. The process of controlling muscular power output via RPE apparently occurs continuously throughout an exercise bout and almost certainly takes into account the amount of distance remaining to be covered, as well as the momentary value of RPE [6,23]. If an athlete approaches critical levels of homeostatic disturbance (which can vary depending on the length of a race; e.g. pH disturbances in a race of 2 min duration, temperature increases in a race of 90 min duration, or PLoS ONE | www.plosone.org 1 January 2011 | Volume 6 | Issue 1 | e15863 limited availability of carbohydrate as an oxidizable substrate in a race of 180 min duration) before nearing the end-point of a competition, a progressive reduction in power output may take place [8]. This protective mechanism is apparently quite effective as it is very unusual to see an athlete continue until muscle contractions entirely fail or to run to hyperthermic collapse. Indeed, perhaps the only time the regulatory mechanism completely fails is in pathological conditions such as those typified by neuromuscular diseases (e.g. myasthenia gravis, McArdle disease), musculoskeletal disease (acute muscle or tendon ruptures), cardiovascular disease (e.g. ventricular arrhythmia, myocardial infarction triggered by exertion, exertion related congestive heart failure, peripheral vascular disease) or respiratory disease (exer- tional bronchospasm). Likewise, as the distance remaining becomes sufficiently small, the athlete may choose to use their remaining energetic reserves in an endspurt, regardless of the level of homeostatic disturbance. Effectively the exerciser must compute the hazard (e.g. danger of a competitively decisive competitive collapse or health related collapse) if a certain pace is maintained during the early or mid-portion part of an event versus the ability to achieve their competitive goal. On a conceptual level it can be hypothesized that an athlete performing an event in a fast start manner compared to an even paced race will have higher RPE values throughout the entire race (Figure 1 A, E). The higher RPE will be the result of the sub-conscious detection of, for instance, a higher heart rate [29], a faster depletion of glycogen stores [30] and/or an earlier rise in core temperature [17] (Figure 1B-D). The higher RPE in the fast start event will result in a higher ‘hazard of catastrophic collapse’, compared to the ‘even’ or ‘normal’ paced trial. This ‘hazard’ could be seen as something that could be dangerous for the physiological system. However, the ‘hazard’ must also be understood from the perspective of the amount of the event remaining. Accordingly we conceptualized a measure for the ‘hazard’ as the product of the momentary RPE with the fraction of the remaining distance; the ‘Hazard Score’ (Figure 1F). Our hypothesis is that the value of the Hazard Score is associated with the ability to accelerate during the race or to the need to reduce the speed to values at which the homeostatic disturbances stay within acceptable limits (Figure 1G). We sought to determine whether simple integration of the momentary RPE by the percentage of distance remaining (e.g. Hazard Score) could adequately explain within event variations in velocity by competitive athletes. Figure 1. Relation between the Hazard Score and the change in pace. Schematic pace (A), heart rate (HRmax%) (B), muscle glycogen store (C), core temperature (D), Rating of Perceived Exertion (RPE) (E) and Hazard Score (F) as a function of percent distance completed of a hypothetical athlete performing a race with a fast start strategy and with an even strategy and the resulting relation between the Hazard Score and the change in pace during the race (G). doi:10.1371/journal.pone.0015863.g001 Regulation of Pacing Strategy PLoS ONE | www.plosone.org 2 January 2011 | Volume 6 | Issue 1 | e15863 Methods To accomplish this, we integrated the velocity and RPE data of 9 separate experiments, in which either cyclists or runners completed competitive simulations in the laboratory, in events that required from 4 to 60 minutes. The individual studies were approved by the Institutional Review Board for the Protection of Human Subjects of the University of Wisconsin-La Crosse, and each subject provided written informed consent prior to participation. Some of the original data have previously been published [24] but are analyzed with a different purpose here, and some represent previously unpublished data. All of the individual exercise studies involved closed loop time trials, with the subjects instructed to finish the complete test in the smallest possible time. Some of the studies were performed on a racing cycle with computerized measurement of power output, velocity and distance. Others were performed on a motor driven treadmill. During these competitive simulations, all of the subjects were well- trained sub-elite athletes who were task habituated to the test event. In some of the events, the subjects were free to self pace their effort, with the intent of minimizing the time to complete the event. In others we imposed, for experimental reasons, either a starting strategy that was more aggressive than normally chosen by the athlete or which included a mid-race increase in pace, or environmental challenges. In all studies, the Rating of Perceived Exertion was measured using the Category Ratio version of the RPE scale [31]. We integrated the mean value for each series of experiments (10–12 subjects in each series) with reference to predicting changes in velocity during the event in terms of the ‘Hazard Score’, calculated as the product of the momentary RPE and the fraction of the event remaining at the same point. As a general principle, measures of RPE were obtained at approxi- mately 10% of full distance increments during each competitive simulation. Results Serial changes in velocity, RPE and the computed Hazard Score in all nine experiments are presented in Figure 2. The growth of RPE in relation to the proportional distance matched previous observations [6,24,26,28]. The value for the Hazard Score reached peak values usually during the first half of each event, with the exception of experiments during which mid-race surges were performed. Since the computation of the Hazard Score necessarily includes a zero value for percent of distance remaining at the end of the race, the score necessarily decreases to zero at the conclusion of the event. When changes in velocity within each event were analyzed in terms of the Hazard Score, there was, as hypothesized, a regular relationship, with low values of the Hazard Score being associated with increases in running or cycling velocity (a positive value for change in pace), and high values being associated with decelerations (a negative value for change in pace) (Figure 3). There was a significant likelihood that the change in velocity would be positive (acceleration) when the Hazard Score was less than 1.5, negative when the Hazard Score was greater than 3 and unchanged when the Hazard Score was between 1–3. Discussion In this analysis, we have demonstrated that the tendency of athletes to change pace during competitive simulations is related both to how they feel momentarily (RPE) and to how much of the event remains. The calculation of a simple index combining these two predictors (the Hazard Score), which represents the hazard of Figure 2. Velolcity, RPE and the Hazard Score. Changes in velocity (A), Rate of Perceived Exertion (RPE) (B) and the Hazard Score (C) in 9 competitive simulations in running or cycling events that required from 4 to 60 minutes. doi:10.1371/journal.pone.0015863.g002 Regulation of Pacing Strategy PLoS ONE | www.plosone.org 3 January 2011 | Volume 6 | Issue 1 | e15863 a competitively catastrophic collapse faced by the athlete, allows a remarkably accurate prediction of subsequent behavior. Although athletes rarely completely collapse during competition, there are enough examples (e.g. the collapse of Englishman Jim Peters during the Commonwealth Games marathon in 1954) to support the concept that there is a ‘limit’ to which humans can push themselves which can cause catastrophic collapse even in a highly trained athlete. Although this type of analysis should be tested during actual competition, the clarity of results presented here suggests a very simple explanation of how the decision making process regarding distribution of effort during competition (e.g. very heavy exercise) is made. Until experimental tests of this hypothesis can be made, it may be instructive to evaluate data from high level competitions to Figure 3. The acceleration/deceleration of athletes as function of the Hazard score. doi:10.1371/journal.pone.0015863.g003 Figure 4. Velocity of the winner (bold) and the runners successively 5 places further back (e.g., 5th, 10th, 15th) from the Beijing Olympic men’s 10 km race. doi:10.1371/journal.pone.0015863.g004 Regulation of Pacing Strategy PLoS ONE | www.plosone.org 4 January 2011 | Volume 6 | Issue 1 | e15863 provide a real-world test of this concept. In Figure 4 we present publicly available (www.iaaf.org) data from the Beijing Olympic men’s 10 km race, with the running velocity of the winner plotted (bold) and runners successively 5 places further back noted. First, it is evident that the overall pace slowed prior to 2 km (when we would have predicted high hazard scores to emerge). Second, it is evident that many of the runners dropped off the pace (e.g. ran slower than the eventual winner) at approximately the mid-point of the event and just at the moment the eventual winner began a series of accelerations (e.g. increasing RPE and thus the hazard score). Third, it is evident that during the endspurt, a period of low hazard score because of the small distance remaining, the winner simply ran faster than the remaining competitors. From this, it appears that runners who drop off in the middle portions of the race must have been confronted with an unacceptably large hazard score (e.g homeostatic disturbance) and, faced with the choice of slowing their pace or not finishing, chose to reduce their pace. Similarly, the actions of the eventual winner beginning at approximately mid race, at a time when the hazard score might be expected to be declining, was to increase velocity, which would have increased the hazard score of his competitors to the point where they dropped out of contention for winning the race. A limitation of the Olympic 10 km data is that we have no RPE or physiological variables of the athletes during their race. However, a typical example of a subject running multiple 10 km races on a treadmill in the laboratory illustrates the line of our reasoning (Figure 5). To mimic a real race the subject was forced to run at a set velocity for the first 4 km, as if he was running ’in the pack’. After 4 km he was free to vary his pace. Velocity, RPE and blood lactate concentration were measured and consequently the Hazard Score was computed. During his run on a ‘bad day’ (broken line), compared with a better performance (solid line), the RPE and lactate were running high early in the race as was the Hazard Score. The predictor of the slowdown at 4 km is the high Hazard Score in the 3–4 km interval, which reflects larger homeostatic disturbances. The results of this study, in which the likelihood of changing velocity during a competitive simulation are related to a simple index of the momentary RPE and the percentage of the event remaining suggest the fundamental strategy by which athletes regulate their effort during competition. This suggests the possibility of correlative studies where the magnitude of homeo- static disturbances that contribute to the RPE can be understood as they change during the course of the race. Author Contributions Conceived and designed the experiments: JJdK CF. Performed the experiments: JJdK CF AB SK CT TJ JC JPP. Analyzed the data: JJdK CF AB SK CT TJ JC JPP. Wrote the paper: JJdK CF AB SK. References 1. Foster C, Schrager M, Snyder AC, Thompson NN (1994) Pacing strategy and athletic performance. Sports Med 17: 77–85. 2. Abbiss CR, Laursen PB (2008) Describing and understanding pacing strategies during athletic competition. Sports Med 38: 239–252. 3. Amann M, Eldridge MW, Lovering AT, Strickland MK, Pegelow DF, et al. (2006) Arterial oxygenation influences central motor output and exercise performance via effects on peripheral locomotor fatigue in humans. J Physiol 575: 937–952. Figure 5. Example of a subject running two 10 km races on a treadmill. The pace during the first 4 km was set and could be varied after this point. The predictor of the slowdown at 4 km during a run at a ‘bad day’ (broken line) is the high Hazard Score in the 3–4 km interval, which reflects larger homeostatic disturbances. doi:10.1371/journal.pone.0015863.g005 Regulation of Pacing Strategy PLoS ONE | www.plosone.org 5 January 2011 | Volume 6 | Issue 1 | e15863 4. Amann M, Romer LM, Subudhi AW, Pegelow DF, Dempsey JA (2007) Severity of arterial hypoxemia affects the relative contributions of peripheral muscle fatigue to exercise performance in healthy humans. J Physiol 581: 389–403. 5. Tucker R, Noakes TD (2009) The physiological regulation of pacing strategy during exercise: a critical review. Br J Sports Med 43: e1. DOI:10.1136/ bjsm.2009.057562. 6. Tucker R (2009) The anticipatory regulation of performance: the physiological basis for pacing strategies and the development of a perception based model for exercise performance. Br J Sports Med 43: 392–400. 7. Foster C, deKoning JJ, Hettinga F, Lampen J, Dodge C (2004) Effect of competitive distance on energy expenditure during simulated competition. Int J Sports Med 25: 198–204. 8. Hettinga FJ, De Koning JJ, Broersen FT, Van Geffen P, Foster C (2006) Pacing strategy and the occurrence of fatigue in 4000-m cycling time trial. Med Sci Sports Exerc 38: 1484–1491. 9. Tucker R, Lambert MI, Noakes TD (2006) An analysis of pacing strategies during men’s World-Record performances in track athletics. Int J Sports Physiol Perform 1: 233–245. 10. Jones AM, Wilkerson DP, DiMenna F, Fulford J, Poole DC (2007) Muscle metabolic responses to exercise above the ‘critical power’ assessed using 31P- MRS. Am J Physiol Regul Intergr Comp Physiol 294: R585–593. 11. Karlsson J, Saltin B (1970) Lactate, ATP, and CP in working muscles during exhaustive exercise in man. J Appl Physiol 29: 598–602. 12. Nielsen B, Hyldig T, Bidstrup F (2001) Brain activity and fatigue during prolonged exercise in the heat. Pflugers Arch 442: 41–48. 13. Nybo L, Nielsen B (2001) Perceived exertion is associated with an altered brain activity during exercise with progressive hyperthermia. J Appl Physiol 91: 2017–2023. 14. Cheung SS, Sleivert GG (2004) Multiple triggers for hyperthermia fatigue and exhaustion. Exerc Sport Sci Rev 32: 100–106. 15. Gonzalez-Alonzo J, Crandall CG, Johnson JM (2008) The cardiovascular challenge of exercising in the heat. J Physiol 586 1: 45–53. 16. Laursen PB, Watson G, Abbiss CR, Wall BA, Nosaka K (2009) Hyperthermic fatigue preceeds a rapid reduction in serum sodium in an ironman triathlete: a case report. Int J Sports Physiol Perf 4: 533–537. 17. Dugas JP (2010) How hot is too hot? Some considerations regarding temperature and performance. Int J Sports Physiol Perf. In Press. 18. Coyle EF, Hagberg JM, Hurley BF, Martin WH, Ehsani AA, et al. (1983) Carbohydrate feeding during prolonged strenuous exercise can delay fatigue. J Appl Physiol 55: 230–235. 19. Karlsson J, Saltin B (1971) Diet, muscle glycogen and endurance performance. J Appl Physiol 31: 203–206. 20. Borg G (1998) Borg’s Perceived Exertion and Pain Scales, Champaign, IL: Human Kinetics Press. 21. Ulmer HV (1996) Concept of an extracellular regulation of muscular metabolic rate during heavy exercise in humans by psychophysiological feedback. Experientia 52: 416–420. 22. Lambert EV, St Clair Gibson A, Noakes TD (2005) Complex system model of fatigue: integrative homeostatic control of peripheral physiological systems during exercise in humans. Br J Sports Med 39: 52–62. 23. St Clair Gibson A, Lambert EV, Rauch LHG, Tucker R, Baden DA, et al. (2006) The role of information processing between the brain and peripheral physiological systems in pacing and perception of effort. Sports Med 36(8): 705–722. 24. Joseph T, Johnson B, Battista RA, Wright G, Dodge C, et al. (2008) Perception of fatigue during simulated competition. Med Sci Sports Exerc 40: 381–386. 25. Noakes TD (2004) Linear relationship between the perception of effort and the duration of constant load exercise that remains. J Appl Physiol 96: 1571–1572. 26. Faulkner J, Parfitt G, Eston R (2008) The rating of perceived exertion during competitive running scales with time. Psychophysiology 45: 977–985. 27. Swart J, Lamberts RP, Lambert MI, St Clair Gibson A, Lambert EV (2009) Exercising with reserve: evidence that the central nervous system regulates prolonged exercise performance. Br J Sports Med 43: 782–788. 28. Foster C, Hendrickson K, Peyer K, Reinier B, de Koning JJ (2009) Pattern of developing the performance template. Br J Sports Med 43: 765–769. 29. Esteve-Lanao J, Lucia A, de Koning JJ, Foster C (2008) How do humans control physiological strain during strenuous endurance exercise? PLoS ONE 3(8): e2943. doi:10.1371/journal.pone.0002943. 30. Rauch HG, St Clair Gibson A, Lambert EV, Noakes TD (2005) A signaling role for muscle glycogen during prolonged exercise. Br J Sports Med 39: 34–38. 31. Borg GA (1982) Psychophysical basis of perceived exertion. Med Sci Sports Exerc 14: 377–381. Regulation of Pacing Strategy PLoS ONE | www.plosone.org 6 January 2011 | Volume 6 | Issue 1 | e15863

Regulation of pacing strategy during athletic competition.

01-20-2011

de Koning, Jos J,Foster, Carl,Bakkum, Arjan,Kloppenburg, Sil,Thiel, Christian,Joseph, Trent,Cohen, Jacob,Porcari, John P

eng

PMC8787207

161 Journal of Epidemiology Vol. 14, No. 5 September 2004 Anthropometric, Lifestyle and Biomarker Assessment of J apanese Non-professional Ultra-marathon Runners BACKGROUND: Anthropometric characteristics, lifestyle, and baseline biological markers of J apanese non-professional ultra-marathon runners have not been fully assessed. METHODS: We evaluated anthropometric characteristics, lifestyle, and baseline biological markers of 180 J apanese amateur ultra-marathon runners (144 males [mean age: 50.5± 9.4 (standard deviation) years] and 36 females [48.9± 6.9]), and compared them with those of participants in a community heath check-up program and with the figures in the literature. We furthermore evaluated baseline blood indices according to monthly running distance with analysis of variance adjusted for age, body mass index, smoking and alcohol drinking habits. RESULTS: The ultra-marathon runners demonstrated more favorable values for body mass index and bone density, and the proportion of smoking, and undertaking physical activity (for both sexes), eating breakfast (for males), and having daily bowel movements (for females), while greater proportion of alco- hol drinking habit (for both sexes), than the comparison group. Average monthly running distances and standard deviations (km) were 257.2± 128.9 for males and 209.0± 86.2 for females. Male runners pos- sessed beneficial markers, including lowered triglyceride and elevated high-density lipoprotein choles- terol, and their values showed hockey-stick (or inverse hockey-stick) patterns depending on their monthly running distance. Some subjects running more than 300 km/month exhibited signs of an over- reaching/training syndrome, including somewhat lowered hemoglobin, ferritin and white blood cell count, and elevated creatine kinase and lactate dehydrogenase. CONCLUSIONS: Together with a desirable lifestyle, J apanese non-professional ultra-marathon runners with vigorous exercise habit demonstrated a preferable health status according to biological indices. J Epidemiol 2004;14:161-167. Key words: biomarker measures, health indices, lifestyle-related diseases, physical activity, non-profes- sional ultra-marathon runners. Received April 5, 2004, and accepted August 6, 2004. This study was supported, in part, by a Grant-in-Aid from the Japan Society for the Promotion of Science under the auspices of the Ministry of Education, Culture, Science, Sports, and Technology, Japan. Biochemical analyses, in part, were conducted according to the Postal Check by the Public Health Research Foundation and SRL Shizuoka, Inc. 1 Department of Health Promotion and Preventive Medicine, Nagoya City University Graduate School of Medical Sciences. 2 Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute. 3 Kitasato University of Medical Technology. 4 Kasugai City Health Care Center. 5 Aichi Bunkyo Women’s College. 6 Department of Preventive Nutraceutical Sciences, Nagoya City University Graduate School of Pharmaceutical Sciences. 7 Yokohama Rehabilitation Center. 8 Nagoya Bunri University. 9 Department of Bone and Orthopaedics, Nagoya City University Graduate School of Medical Sciences. 10 Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health. 11 Department of Health Science, Faculty of Psychological and Physical Sciences, Aichi Gakuin University. Address for correspondence: Shinkan Tokudome, Department of Health Promotion and Preventive Medicine, Nagoya City University Graduate School of Medical Sciences, Mizuho-ku, Nagoya 467-8601, Japan. Shinkan Tokudome,1 Kiyonori Kuriki,2 Norihiro Yamada,3 Hiromitsu Ichikawa,1 Machiko Miyata,1 Kiyoshi Shibata,4 Hideki Hoshino,5 Shinji Tsuge,6 Mizuho Tokudome,7 Chiho Goto,8 Yuko Tokudome,8 Masaaki Kobayashi,9 Hideyuki Goto,9 Sadao Suzuki,1 Yoshihiro Okamoto,1 Masato Ikeda,10 and Yuzo Sato.11 Original A rticle morphisms, including human 8-oxoguanine DNA glycosylase 1 (h-OGG1), aldehyde dehydrogenase 2 (ALDH2), peroxisome pro- liferators-activated receptor gamma (PPARγ ), leptin, angiotensin converting enzyme (ACE), β -adrenergic receptor, and CD36 genes. The protocol was approved by the institutional review board of the Nagoya City University Graduate School of Medical Sciences and by the chairman and organizing committee of the race. We administered our questionnaire to 202 runners by mail and obtained information on anthropometric characteristics and lifestyle, including sex, age (date of birth), height, and dietary, smoking and alcohol drinking habits. We checked unfilled items on the race day along with securing information on smoking, alcohol drinking and supplements taken during the race. For external comparison, the values of anthropometric charac- teristics and lifestyle of the participants in a community health check-up program in 2002, except for calcaneal bone density, body temperature, and resting pulse rate, were utilized. We received written informed consent from these participants, and the protocol was approved by the institutional review board of the Nagoya City University Graduate School of Medical Sciences. For calcaneal bone density, body temperature, and resting pulse rate, we used the figures reported in the literature for compari- son.24-26 Energy intake was assessed by the short food frequency ques- tionnaire (FFQ).27 A regression equation was applied, adopting intake frequency of foods/food groups, average portion size and nutrient concentrations/100 g of foods28 as independent variables and energy intake as a dependent variable. Anthropometric mea- surements and sampling of blood, urine and saliva were per- formed at the pre- (baseline), mid-, and post-race stages. We mea- sured body weight, body temperature at the tympanum (Nipro 43- 130, Morishita Jintan, K.K.) and blood lactate (Lactate Pro, LT- 1710, Kyoto Daiichi Kagaku Co., Ltd.). Calcaneal bone density (Speed of Sound) (Stiffness [%]) was gauged ultrasonographical- ly once on three measurement occasions (A-1000 Express, GE LUNAR). We analyzed urine for protein, glucose, occult blood, urobilin, urobilinogen, and pH at the site (Urisys 2400, Sysmex K.K.). Baseline serum parameters, including total protein, blood urea nitrogen (BUN), uric acid, aspartate aminotransferase (AST), ala- nine aminotransferase (ALT), gamma-glutamyltransferase (GTT), lactate dehydrogenase (LDH), creatine kinase (CK), creatinine, total cholesterol, high-density lipoprotein cholesterol (HDL-C), total bilirubin, triglyceride, free fatty acid (Hitachi 7600, Hitachi K.K.), myoglobin (radioimmunoassay), lipid peroxide (enzyme method), white blood cells (WBCs), red blood cells (RBCs), hemoglobin (Hb), hematocrit, mean corpuscular volume, mean corpuscular hemoglobin, mean corpuscular hemoglobin concen- tration, platelets (XE2100, Sysmex K.K.), ferritin (chemical immunoluminiscence), HbA1c (HPLC analysis), and serum elec- trolytes, including sodium (Na), potassium (K), and chlorine (Cl) (Hitachi 7600, Hitachi K.K.), were assayed.29 Resting pulse in bed Lifestyle and Biomarkers of Ultra-marathon Runners There are advantages and disadvantages to physical activity, exer- cise, and sports. Advantages include elevated bowel motility,1,2 modification of lipid metabolism and amelioration of insulin resistance and glucose intolerance,3-5 improvement of cardiovas- cular parameters, and prevention of obesity.6,7 Decreased serum concentrations of arachidonic acid and prostaglandin E2, reduced generation of radical oxygen species,8-15 enhancing oxygen radi- cals absorbance capacity and immune surveillance,16-19 including an increased natural killer cell activity, and diminishing cancer risk1,2,20 may all be achieved. Thus, appropriate physical activity in the long-term may decrease mortality from lifestyle-related dis- eases, prolong active life expectancy, alleviate mental stress, sup- port mental health and self-efficacy, and finally, enhance quality of life.6,7,21-23 On the other hand, disadvantages include damage in the hematopoietic system, skeletal or muscular injuries, oxidative stress/damage, cardiac arrest, arrhythmia, and sudden death.2,6,7,9,11,13-18 As is well known, moreover, there exists an over- reaching/training syndrome, and research is needed to clarify the type, intensity, duration, and frequency of physical activity/exer- cise/sports favorable to our health. Here, we studied anthropometric characteristics, lifestyle, and baseline biomarker measures among non-professional but vigor- ously-trained runners entering an ultra-distance race, and com- pared them with those of people receiving an annual health check- up program and with reference values in the literature. We also assessed baseline blood indices according to their monthly run- ning distance. METHODS Ultra-marathon race The ultra-marathon race is not a competitive one. It is nicknamed "Maranic" (marathon and picnic), and the tenth race was held in Gifu Prefecture, Japan, during July 27-28, 2002. The midsummer weather was partly cloudy, very hot and sultry. According to the meteorological authority, the temperature was approximately 35℃, and the relative humidity was about 55% at noon on both days. The race covered 130 km of distance running and moun- taineering over two days. On the first day, at 11 a.m., the partici- pants started a full-length marathon race to be completed within 6 hr and 30 min. On the second morning, at 3:30 a.m., they resumed the race to run approximately 90 km, including climbing up to a mountain lake approximately 1,100 m high, then returning to the starting point within 15 hr and 30 min. Subjects and methods Six weeks prior to the race, we asked 325 ultra-marathon runners entering the Maranic race to enroll in our study. Of these, 202 runners agreed to participate in the project. We received written informed consent from them for completing a questionnaire sur- vey, measuring anthropometric characteristics and bone density, sampling of blood, urine, and saliva, and analyzing genetic poly- 162 function. The remaining 180 subjects (144 males and 36 females) were included in this study. Mean ages were 50.5 ± 9.4 (± standard deviation) years for males and 48.9 ± 6.9 for females, respectively. The differences of means between the groups were obvious: that is, the values for BMI, body temperature (℃), and resting pulse rate were smaller, while those for calcaneal bone density (Stiffness [%] ) (for both sexes) were greater (Table 1). The percentages of smoking and enjoying physical activity (for both sexes), eating breakfast (for males), and having daily bowel movements (for females) were more favorable than for the comparison group. However, the pro- portion of alcohol drinking habit (for both sexes) was greater in runners than the general public. Blood analysis Average figures for all blood measures were located within the ranges of the reference values (Table 2). They were mostly favor- able readings in both sexes. However, hematological markers, such as Hb, ferritin, and WBCs, shifted to be lower than the stan- dard values. On the other hand, damage/repair markers of the musculo-skeletal system, including CK and LDH, tended to be greater than the reference values. Urine analysis Positive rates for urine glucose of 9.9% for males and 14.6% for females were greater than those in the general people partly because urine was collected on a spot sampling basis (data not shown). Tokudome S, et al. in the morning was surveyed by mail after the race. Statistical analysis Anthropometric characteristics and lifestyle values, including bone density,24 body temperature at the tympanum,25 and resting pulse,26 were age-adjusted, adopting the reference population or the study subjects in the literature as standard. The means ± 95% confidence interval were computed, and contrasted with those of the participants in a community health check-up program in 2002 or with reference values in the literature. Baseline blood and urine biomarkers were compared with reference values.29 Full-length and ultra-marathon completion rates, time and blood indices among males were collated according to monthly running dis- tance (km/month) (≤100, 101-200, 201-300, and 301+) with analysis of variance adjusted for age, body mass index (BMI [kg/m2]), smoking, and alcohol drinking.30 Tukey's post hoc multi- ple t-test was performed to examine differences in the least square means, and the linear trends were statistically verified. The p val- ues smaller than 0.05 were considered statistically significant. RESULTS Anthropometric measures, and major lifestyle characteristics Of our participants, 187 runners actually attended the race, and anthropometric measures were taken together with sampling of biomaterials. Seven participants were excluded from the analysis: three with uncompleted questionnaires, and two who were late for the pre-race examination. One finally declined pre- and mid-race blood sampling, and one was excluded due to abnormal liver 163 Item Body mass index (kg/m2) Eating breakfast (%) Energy intake (kcal) Smoking habit (%) Alcohol drinking habit (%) Undergoing physical activity (%) Sleep duration (hours) Having daily bowel movements (%) Calcaneal bone density (Stiffness [%])‡ Body temperature at the tympanum (℃)§ Resting pulse (bpm)‖ Table 1. Comparison of anthropometric characteristics and lifestyle between Japanese non-professional ultra-marathon runners and people receiving an annual community health check-up program and other reference people. ultra-marathon runners* (n=144) 22.2 (21.7-22.8) 96.5 (94.2-98.9) 2,302 (1,936-2,668) 7.3 (1.7-12.9) 78.6 (70.4-86.7) 100 6.8 (6.6-7.1) 91.4 (86.0-96.8) 101.2 (97.6-104.7) 36.2 (36.1-36.4) 53.6 (52.5-54.8) reference values† 23.3 (23.1-23.4) 90.0 (88.5-91.6) 2,117 (2,095-2,139) 31.1 (28.6-33.6) 58.5 (55.9-61.2) 40.2 (37.7-42.8) 7.0 (6.9-7.0) 88.9 (87.3-90.6) 85.9 (83.9-87.9) 36.9 (36.9-36.9) 65.9 (64.2-67.5) ultra-marathon runners (n=36) 21.2 (20.3-22.1) 93.9 (89.5-98.4) 1,732 (1,428-2,037) 1.0 (0-2.4) 48.3 (24.1-72.5) 100 6.4 (5.9-6.8) 96.5 (92.0-100) 93.7 (84.6-102.8) 36.3 (35.9-36.6) 54.7 (52.5-56.9) reference values 22.5 (22.4-22.7) 92.4 (91.2-93.6) 1,956 (1,942-1,970) 6.6 (5.5-7.7) 18.7 (16.9-20.4) 42.1 (39.9-44.3) 6.6 (6.6-6.7) 70.5 (68.4-72.5) 73.5 (72.4-74.6) 36.9 (36.9-36.9) 66.5 (64.7-68.3) males females * : Age-adjusted means ± 95% confidence intervals adopting corresponding reference populations or study subjects in the literature as standard. † : Values of people receiving annual health check-up program among 1,346 males and 2,043 females, except for calcaneal bone densi- ty, body temperature, and resting pulse rate. ‡ : For comparison, values of calcaneal bone density were cited from the reference No. 24. § : For comparison, values of body temperature were cited from the reference No. 25. ‖ : For comparison, values of resting pulse were cited from the reference No. 26. Lifestyle and Biomarkers of Ultra-marathon Runners 164 Total protein (g/dL) Blood urine nitrogen (BUN, mg/dL) Uric acid (mg/dL) Aspartate aminotransferase (AST, IU/L) Alanine aminotransferase (ALT, IU/L) Gamma-glutamyltransferase (GTT, IU/L) Lactate dehydrogenase (LDH, IU/L) Creatine kinase (CK, IU/L) Creatinine (mg/dL) Myoglobin (ng/mL) Total cholesterol (mg/dL) High-density lipoprotein cholesterol (HDL-C, mg/dL) Total bilirubin (mg/dL) Triglyceride (mg/dL) Free fatty acid (mEq/L) Lipid peroxide (nmol/mL) White blood cell count (WBCs, /μ L) Red blood cell count (RBCs, 104/μ L) Hemoglobin (Hb, g/dL) Hematocrit (%) Mean corpuscular volume (fl) Mean corpuscular hemoglobin (pg) Mean corpuscular hemoglobin concentration (%) Ferritin (ng/mL) Platelet count (104/μ L) Hemoglobin A1c (HbA1c, %) Sodium (Na, mEq/L) Potassium (K, mEq/L) Chlorine (Cl, mEq/L) Table 2. Comparison of blood indices between Japanese non-professional ultra-marathon runners and reference values. Ultra-marathon runners (n=144) mean ± standard deviation 7.2 ± 0.4 18 ± 4 5.8 ± 1.3 25 ± 10 27 ± 13 44 ± 36 198 ± 35 183 ± 139 0.64 ± 0.14 43 ± 15 206 ± 34 62 ± 15 0.4 ± 0.2 121 ± 66 0.36 ± 0.16 2.9 ± 0.7 5,553 ± 1,188 457 ± 38 14.3 ± 1.1 42.9 ± 3.2 94.0 ± 4.9 31.2 ± 1.7 33.2 ± 0.9 55.4 ± 40.3 22.6 ± 4.6 5.1 ± 0.4 142 ± 2 4.1 ± 0.6 105 ± 2 Reference values* 6.7 - 8.3 6 - 20 3.7 - 7.6 10 - 40 5 - 40 ≤ 70 115 - 245 57 - 197 0.61 - 1.04 ≤ 60 150 - 219 41 - 86 0.2 - 1.0 50 - 149 0.14 - 0.85 1.8 - 4.7 3,900 - 9,800 427 - 570 13.5 - 17.6 39.8 - 51.8 82.7 - 101.6 28.0 - 34.6 31.6 - 36.6 27 - 320 13.1 - 36.2 4.3 - 5.8 136 - 147 3.6 - 5.0 98 - 109 Ultra-marathon runners (n=36) mean ± standard deviation 7.1 ± 0.4 17 ± 4 4.1 ± 0.8 22 ± 9 21 ± 13 21 ± 8 197 ± 29 150 ± 91 0.48 ± 0.09 30 ± 9 218 ± 34 69 ± 14 0.4 ± 0.2 93 ± 35 0.35 ± 0.17 2.6 ± 0.6 5,197 ± 1,281 412 ± 30 12.7 ± 1.1 39.1 ± 2.6 94.8 ± 4.2 30.7 ± 1.6 32.4 ± 1.0 20.4 ± 14.5 22.2 ± 4.0 4.8 ± 0.3 141 ± 2 4.1 ± 0.4 105 ± 2 Reference values 6.7 - 8.3 6 - 20 2.5 - 5.4 10 - 40 5 - 40 ≤ 30 115 - 245 32 - 180 0.47 - 0.79 ≤ 60 150 - 219 41 - 96 0.2 - 1.0 50 - 149 0.14 - 0.85 1.8 - 4.7 3,500 - 9,100 376 - 500 11.3 - 15.2 33.4 - 44.9 79.0 - 100.0 26.3 - 34.3 30.7 - 36.6 3.4 - 89 13.0 - 36.9 4.3 - 5.8 136 - 147 3.6 - 5.0 98 - 109 males females *: Reference values are from the Test Directory 2002.29 Tokudome S, et al. 165 Total protein (g/dL) Blood urine nitrogen (BUN, mg/dL) Uric acid (mg/dL) Aspartate aminotransferase (AST, IU/L) Alanine aminotransferase (ALT, IU/L) Gamma-glutamyltransferase (GTT, IU/L) Lactate dehydrogenase (LDH, IU/L) Creatine kinase (CK, IU/L) Creatinine (mg/dL) Myoglobin (ng/mL) Total cholesterol (mg/dL) High-density lipoprotein cholesterol (HDL-C, mg/dL) Total bilirubin (mg/dL) Triglyceride (mg/dL) Free fatty acid (mEq/L) Lipid peroxide (nmol/L) White blood cell count (WBCs, /μ L) Red blood cell count (RBCs, 104/μ L) Hemoglobin (Hb, g/dL) Hematocrit (%) Mean corpuscular volume (fl) Mean corpuscular hemoglobin (pg) Mean corpuscular hemoglobin concentration (%) Ferritin (ng/mL) Platelet count (104/μ L) Hemoglobin A1c (HbA1c, %) Sodium (Na, mEq/L) Potassium (K, mEq/L) Chlorine (Cl, mEq/L) Table 3. Blood indices according to average monthly running distance adjusted for age, body mass index, smoking and alcohol drinking in Japanese male non-professional ultra-marathon runners. -100 (n=20) 7.2 19 5.9 22 26 37 190 143 0.66 43.0 200 58 0.5 145 0.35 2.9 5656 456 14.2 42.4 93.2 31.2 33.5 60.8 22.5 5.1 142 4.1 105 101-200 (n=44) 7.2 18 5.9 26 25 47 196 169 0.61 41.8 203 61 0.4 # 127 0.34 2.9 5735 459 14.4 43.2 94.1 31.4 33.3 59.7 23.3 5.0 142 4.1 105 201-300 (n=46) 7.2 19 5.8 25 30 42 * 194 * 168 0.67 44.0 204 62 0.5 110 0.35 3.0 * 5601 462 * 14.4 43.5 94.3 31.3 33.2 # 58.5 22.7 5.2 142 4.2 105 301+ (n=34) 7.2 19 5.7 27 29 46 213 246 0.63 44.7 216 67 0.4 114 0.38 2.8 5191 449 13.9 42.1 94.1 31.1 33.0 42.6 21.7 5.1 141 4.2 105 * * * linear trend * ** * # # * * * Average monthly running distance (km/month) #: Marginally significant, * p<0.05, ** p<0.01． Lifestyle and Biomarkers of Ultra-marathon Runners 166 cal antioxidant molecules of uric acid and bilirubin.8,12 Ferritin levels, however, decreased in proportion to monthly running dis- tance, along with lower body temperature25 and resting pulse rates26,33 were noted among the subjects. Taking into account these findings, we are now planning to make pre-, mid- and post-race comparisons of blood, urine and saliva bio-parameters, including serum d-ROM and Mn-SOD, and urinary 8-OHdG and biopy- rrins, as markers of reactive oxygen species and oxygen radical absorbance capacity.8-15 Participants running more than 300 km/month exhibited signs of an over-reaching/training syndrome, including lowered Hb, ferritin and WBCs suggesting damage in the hematopoietic sys- tem to some degree, and elevated CK and LDH indicating injuries in musculo-skeletal organs. Vigorous exercisers running around 200 km/month, even those running less than 100 km/month, who were insufficiently trained to run an ultra-marathon race, had preferable biomarker indices, implying that such exercises are favorable to health. Namely, triglyceride was decreased and HDL-cholesterol was elevated according to their monthly running distance. Thus, people committed to vigorous exercise probably do not suffer from obesity, high lipidemia/cholesterol,6,7 high blood pressure or high insulin resistance.3-5 Furthermore, they undoubtedly enjoy a low risk of coronary heart disease, cere- brovascular diseases and fat-related cancers, including colon, prostate and breast cancer.1,2,20 In conclusion, the study subjects were admittedly rather self- selected as being non-professional marathon runners and pos- sessed desirable demographic characteristics and lifestyle, even when compared with health-conscious people receiving an annual health check-up program. Runners committing to vigorous run- ning up to around 200 km/month, but not over-reaching/training, appear to have preferable biomarker indices, suggesting that vig- orous aerobic exercise is favorable to health, particularly for sedentary or physically-inactive workers. Further research is war- ranted to elucidate the type, intensity, duration, and frequency of physical activity/exercise/sports beneficial to promote health, to reduce the risk of lifestyle-related diseases and to enhance the quality of life. ACKNOWLEDGMENTS We appreciate the runners having willingly participated in our study and the chairman and organizing committee of the Maranic race. We thank Dr. Nagaya T, Ms. Fujii T, Ms. Kubo Y, Ms. Nakanishi N, Ms. Ito Y, Ms. Higuchi K, Ms. Watanabe M, and Dr. Moore MA for their technical and language assistance. REFERENCES 1. Friedenreich CM, Orenstein MR. Physical activity and cancer prevention: etiologic evidence and biological mechanisms. J Nutr 2002;132 (suppl.):3456S-64S. 2. Moore MA, Park CB, Tsuda H. Physical exercise: a pillar for Completion rates and time according to monthly running dis- tance Average monthly running distances (km) were 257.2 ± 128.9 (98 - 444) (minimum - maximum) for males and 209.0 ± 86.2 (23 - 222) for females. They mainly selected running, although some concurrently chose swimming, bicycling and other aerobic exer- cises and occasionally took part in weight/resistance training. Ninety-three percent (167 out of 180 of the study subjects) com- pleted the first day full-length marathon, and 60% (108 out of 180) the 2-day ultra-marathon race. Full-length and ultra- marathon completion rates were positively, while completion time was inversely dependent on their monthly running distance for either sex (data not shown). Differences in blood indices according to monthly running dis- tance The number of male runners by monthly running distance (km/month) of ≤100, 101-200, 201-300, 301+ were 20, 44, 46, and 34, respectively. As a whole, there were no significant dis- crepancies in anthropometric characteristics, including BMI, body temperature, resting pulse rate and bone density, according to monthly running distance (data not shown). Most blood measure- ments also showed no remarkable differences in proportion to monthly running distance after adjustment for age, BMI, smok- ing, and alcohol drinking, either (Table 3). Triglyceride was decreased according to monthly running distance, while HDL- cholesterol was steadily elevated. Some readings, however, revealed hockey-stick (or inverse hockey-stick) patterns, includ- ing lowered Hb, ferritin and WBCs, and elevated CK and LDH. The linear trends were statistically/marginally significant except for Hb and triglyceride. DISCUSSION Most of the present subjects followed the recommended Seven Heath Practices, including (1) hours of sleep, (2) smoking, (3) body weight, (4) alcohol drinking, (5) physical exercise, (6) eat- ing breakfast, and (7) eating between meals, proposed by Breslow et al.31 They showed preferable demographic characteristics and lifestyle, even when compared with health-conscious people receiving an annual health check-up program. Above all, they engaged in vigorous exercise regularly. Most maintained a desir- able body weight and bone density,24 and smoked less;32 however, they experienced greater energy from alcoholic beverages than the general population, at least partly because they regularly expended energy by running. Alcohol consumption, however, up to 30 g net ethanol per day, may not be harmful, provided the sub- ject is not a carrier of hepatitis B/C viruses, or has not hetero/mutant type genetic polymorphisms of ALDH-2. Most blood measures showed no remarkable variation accord- ing to monthly running distance after adjustment for age, BMI, smoking, and alcohol drinking, in line with homeostasis and adaptation.9,13,14,18 No significant differences were observed in typi- system: regulation, integration, and adaptation. Physiol Rev 2000;80:1055-81. 19. Shephard RJ, Verde TJ, Thomas SG, Shek P. Physical activi- ty and the immune system. Can J Sports Sci 1991;16:169-85. 20. Thune I. Assessments of physical activity and cancer risk. Eur J Cancer Prev 2000;9:387-93. 21. Drewnowski A, Evans WJ. Nutrition, physical activity, and quality of life in older adults: summary. J Gerontol 2001;56A (Series A):89-94. 22. Fletcher JS, Banasik JL. Exercise self-efficacy. Clin Excell Nurse Practit 2001;5:134-43. 23. Shimomitsu T, Odagiri Y. Endocrinological assessment of extreme stress. Ad Psychosom Med 2001;22:35-51. 24. Takeda N, Miyake M, Kita S, Tomomitsu T, Fukunaga M. Sex and age patterns of quantitative ultrasound densitometry of the calcaneus in normal Japanese subjects. Calcif Tissue Int 1996;59:84-8. 25. Yoshiue S, Yoshizawa H, Ito H, Nagashima K, Takeda K, Yazumi T, et al. Body temperature. Sogorinsho 1985;13 (suppl):1599-606. (in Japanese) 26. Ozaki M, Kusukawa R. Change of cardiac function in aging. Sogorinsho 1981;30:35-7. (in Japanese) 27. Tokudome S, Goto C, Imaeda N, Tokudome Y, Ikeda M, Maki S. Development of a data-based short food frequency questionnaire for assessing nutrient intake by middle-aged Japanese. Asian Pacific J Cancer Prev 2004;5:40-3. 28. Tokudome S, Ikeda M, Tokudome Y, Imaeda N, Kitagawa I, Fujiwara N. Development of data-based semi-quantitative food frequency questionnaire for dietary studies in middle- aged Japanese. Jpn J Clin Oncol 1998;28:679-87. 29. Special Reference Laboratory. Test Directory 2002. Tachikawa, Tokyo: Special Reference Laboratory, 2002. 30. SAS Institute Inc. SAS/ATAT Userﾕs Guide, Version 8. Cary, NC: SAS Institute Inc, 1999. 31. Belloc NB, Breslow L. Relationship of physical health status and health practices. Prev Med 1972;1:409-21. 32. Iwai N, Yoshiike N, Saitoh S, Nose T, Kushio T, Tanaka H et al. Leisure-time physical activity and related lifestyle charac- teristics among middle-aged Japanese. J Epidemiol 2000;10:226-33. 33. Wannamethee G, Shaper AG, Macfarlane PW. Heart rate, physical activity, and mortality from cancer and other noncar- diovascular diseases. Am J Epidemiol 1993;137:735-48. Tokudome S, et al. cancer prevention? Eur J Cancer Prev 1998;7:177-93. 3. DeFronzo RA, Ferrannini E. Insulin resistance. A multifac- eted syndrome responsible for NIDDM, obesity, hyperten- sion, dyslipidemia, and atherosclerotic cardiovascular dis- ease. Diabetes Care 1991;14:173-94. 4. Kaplan NM. The deadly quartet. Upper-body obesity, glucose intolerance, hypertriglyceridemia, and hypertension. Arch Intern Med 1989;149:1514-20. 5. Reaven GM. Role of insulin resistance in human disease. Diabetes 1988;37:1595-607. 6. Pate RR, Pratt M, Blair SN, Haskell WL, Macera CA, Bouchard C, et al. Physical activity and public health. A rec- ommendation from the Centers for Disease Control and Prevention and the American College of Sports Medicine. J Am Med Assoc 1995;273:402-7. 7. Sallis JF, Owen N. Physical Activity and Behavioral Medicine. Thousand Oaks: SAGE Publications, 1999. 8. Cao G, Alessio HM, Cutler RG. Oxygen-radical absorbance capacity assay for antioxidants. Free Radical Biol Med 1993;14:303-11. 9. Alessio HM. Exercise-induced oxidative stress. Med Sci Sports Exerc 1993;25:218-24. 10. Fielding RA, Meydani M. Exercise, free radical generation, and aging. Aging-Clin Experim Res 1997;9:12-8. 11. Ji LL. Exercise and oxidative stress: role of the cellular antioxidant systems. Exerc Sports Sci Rev 1995;23:135-66. 12. König D, Wagner K-H, Elmadfa I, Berg A. Exercise and oxidative stress: significance of antioxidants with reference to inflammatory, muscular, and systemic stress. Exerc Immunol Rev 2001;7:108-33. 13. Leaf DA, Kleinman MT, Hamilton M, Deitrick RW. The exercise-induced oxidative stress paradox: the effects of physical exercise training. Am J Med Sci 1999;317:295-300. 14. Niess AM, Dickhuth H-H, Northoff H, Fehrenbach E. Free radicals and oxidative stress in exercise— Immunological aspects. Exerc Immunol Rev 1999;5:22-56. 15. Sen CK. Antioxidants in exercise nutrition. Sports Med 2001;31:891-908. 16. Mackinnon LT. Immunity in athletes. Int J Sports Med 1997;18 (suppl.):62S-68S. 17. Nieman DC. Exercise immunology: practical applications. Int J Sports Med 1997;18 (suppl.):91S-100S. 18. Pedersen BK, Hoffman-Goetz L. Exercise and the immune 167

Anthropometric, lifestyle and biomarker assessment of Japanese non-professional ultra-marathon runners.

[]

Tokudome, Shinkan,Kuriki, Kiyonori,Yamada, Norihiro,Ichikawa, Hiromitsu,Miyata, Machiko,Shibata, Kiyoshi,Hoshino, Hideki,Tsuge, Shinji,Tokudome, Mizuho,Goto, Chiho,Tokudome, Yuko,Kobayashi, Masaaki,Goto, Hideyuki,Suzuki, Sadao,Okamoto, Yoshihiro,Ikeda, Masato,Sato, Yuzo

eng

PMC4216092

A Fast-Start Pacing Strategy Speeds Pulmonary Oxygen Uptake Kinetics and Improves Supramaximal Running Performance Tiago Turnes, Amadeo Fe´lix Salvador, Felipe Domingos Lisboˆ a, Rafael Alves de Aguiar, Roge´rio Santos de Oliveira Cruz, Fabrizio Caputo* Human Performance Research Group, Center for Health and Sport Science, Santa Catarina State University, Floriano´polis, Brazil Abstract The focus of the present study was to investigate the effects of a fast-start pacing strategy on running performance and pulmonary oxygen uptake (V˙O2) kinetics at the upper boundary of the severe-intensity domain. Eleven active male participants (28610 years, 7065 kg, 17666 cm, 5764 mL/kg/min) visited the laboratory for a series of tests that were performed until exhaustion: 1) an incremental test; 2) three laboratory test sessions performed at 95, 100 and 110% of the maximal aerobic speed; 3) two to four constant speed tests for the determination of the highest constant speed (HS) that still allowed achieving maximal oxygen uptake; and 4) an exercise based on the HS using a higher initial speed followed by a subsequent decrease. To predict equalized performance values for the constant pace, the relationship between time and distance/speed through log-log modelling was used. When a fast-start was utilized, subjects were able to cover a greater distance in a performance of similar duration in comparison with a constant-pace performance (constant pace: 670 m622%; fast-start: 683 m622%; P = 0.029); subjects also demonstrated a higher exercise tolerance at a similar average speed when compared with constant-pace performance (constant pace: 114 s630%; fast-start: 125 s626%; P = 0.037). Moreover, the mean V˙O2 response time was reduced after a fast start (constant pace: 22.2 s628%; fast-start: 19.3 s629%; P = 0.025). In conclusion, middle-distance running performances with a duration of 2–3 min are improved and V˙O2 response time is faster when a fast-start is adopted. Citation: Turnes T, Salvador AF, Lisboˆa FD, de Aguiar RA, Cruz RSdO, et al. (2014) A Fast-Start Pacing Strategy Speeds Pulmonary Oxygen Uptake Kinetics and Improves Supramaximal Running Performance. PLoS ONE 9(10): e111621. doi:10.1371/journal.pone.0111621 Editor: Maria F. Piacentini, University of Rome, Italy Received June 5, 2014; Accepted October 6, 2014; Published October 31, 2014 Copyright:  2014 Turnes et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper. Funding: This project was supported by National Council of Scientific and Technological Development (CNPq). Website: (www.cnpq.br). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * Email: [email protected] Introduction The pattern of speed (s) distribution chosen during an exercise bout, i.e. pacing strategy, has been shown to have important implications for both activation and proportional contribution of oxidative metabolism to energy turnover [1]. The rationale behind this phenomenon is that the rate of increase in oxygen uptake at the exercise onset (V˙ O2 kinetics) is proportional to the rate of phosphocreatine breakdown in active muscles per unit change in time (i.e. the D[PCr]/Dt ratio) [2]. In this sense, adopting a higher initial speed during a fast-start pacing strategy (FS) is thought to increase D[PCr]/Dt ratios. This enhanced aerobic contribution during the first few seconds of exercise spares an equivalent amount of the anaerobic capacity that can then be used to improve exercise performance [3]. Accordingly, the pacing strategies employed to achieve times within two percent of the world record time in the 800-m track event in international athletics competitions demonstrate that a relatively fast-start over the initial 200 m is the preferred strategy for running performance [4]. Although non-running studies have indicated that a FS improves high-intensity exercise performance by increasing the speed of relatively slower V˙ O2 kinetics [mean response time (MRT) approximately 40–50 s] [1,3,5], high-intensity running exercises already possess a fast V˙ O2 response [6–8], which may not increase to an extent that affects performance. Presently, the only indirect evidence on this topic comes from Sandals et al. [4], who demonstrated that middle-distance runners attained a lower peak V˙ O2 during a constant speed 800-m pace time-to-exhaustion on a treadmill in comparison with a race simulation involving acceleration to a faster speed followed by a speed decline (i.e. FS). Despite the higher V˙ O2 peak indicating a likely higher aerobic contribution, V˙ O2 kinetics and total O2 consumed was not measured by Sandals et al. [4]. Consequently, the actual effect of FS on the overall V˙ O2 response during supramaximal running performance is still unknown. A testing protocol was designed to investigate the effects of a FS on aerobic metabolism and performance based on the highest constant speed (HS) that still allows achieving maximal oxygen uptake (V˙ O2max) during treadmill running. This is an important aspect of this study, since Sandals et al. [4] used running speeds that were not able to elicit V˙ O2max. Furthermore, HS is also a physiological index representing the constant running speed at which V˙ O2max is reached with the fastest V˙ O2 kinetics [9], PLOS ONE | www.plosone.org 1 October 2014 | Volume 9 | Issue 10 | e111621 adding potential concerns about the effects of a FS on metabolic control. Theoretically, the HS would be a suitable intensity in the evaluation of the effects of a FS on middle-distance performance and physiological responses because the physiological determi- nants of the HS are probably similar to those responsible for middle-distance running performance (i.e. an integrative contri- bution of aerobic and anaerobic energy systems) [10–12]. To predict equalized performance values for a constant pace that can be readily compared to those derived from a FS performance, the relationship between time (t) and distance (d) or t and s through log-log modelling from a series of time-to-exhaustion tests was used. For times to exhaustion in the 1–10-min range, the log-log model has been shown to be appropriate and superior to the critical-power model [13]. The focus of the present study was: 1) to compare performance parameters (i.e. distance covered and time-to-exhaustion) using a FS with those predicted from the log-log modelling for constant pace performance; and 2) to assess the effect of a FS on the aerobic contribution during running exercise crossing the upper boundary of the severe-intensity domain [9,14]. It was hypothesized that a FS would increase performance during supramaximal treadmill running exercises by allowing VO2max to be achieved more rapidly during the bout. Methods Subjects Eleven active male subjects (28610 years, 7065 kg, 17666 cm, 5764 mL/kg/min) volunteered for this study. All participants were apparently healthy, non-smokers, free from injury, not taking any medication, and participating in physical activity at least three times a week. Before commencing the study, all participants were informed of the proceedings but remained naive to the study rationale. Subjects were also instructed to avoid strenuous exercise in the 24-h period preceding a test session and to arrive at the laboratory in a rested and fully hydrated state. All volunteers gave written informed consent to participate in this study, which had been approved by the Santa Catarina State University Research Ethics Committee. This work was performed in accordance with the principals of the Declaration of Helsinki. Experimental design Subjects visited the laboratory for four phases of experimenta- tion within a 3-week period, with at least 48-h separating each visit (Figure 1). All tests were performed at the same time of day (62 h) on a motorized treadmill (Inbramed Millenium Super ATL, Porto Alegre, Brazil) set at a 1% gradient. The four phases of the study comprised: 1) an incremental test in order to determine V˙ O2max, maximal aerobic speed and the speed associated with lactate threshold; 2) three laboratory test sessions for the determination of the relationship between t6d, t6s, and additional values of V˙ O2max; 3) the determination of the HS from two to four constant speed tests; and 4) an exercise to exhaustion phase using a higher initial speed followed by a subsequent decrease in speed (i.e., FS protocol). During all tests, subjects were blinded to the time elapsed during exercise and encouraged to continue for as long as possible until volitional exhaustion. For phases two, three and four, the tests were preceded by a warm up consisting of 10 min of continuous running at the speed of lactate threshold followed by a 5-min rest period. This warm-up was employed because prior moderate-intensity exercise has been demonstrated to have no influence on V˙ O2 kinetics during subsequent severe-intensity running [15]. All the transitions from rest to running were performed by the participants using the support rails to suspend their body above the belt while they developed cadence in their legs. Time-to-exhaustion measure- ments started when the participant released the support rails and started running on the treadmill belt. Throughout each test, respiratory gas exchange was measured breath-by-breath using an automated open-circuit gas analysis system (Quark PFTergo, Cosmed Srl, Rome, Italy). Prior to each test, gas analysers were calibrated using ambient air and gases containing 16% oxygen and 5% carbon dioxide. The turbine flow meter used for the determination of minute ventilation was calibrated with a 3-L calibration syringe (Cosmed Srl, Rome, Italy). For phases one and two, V˙ O2 was reduced to 15-s average values and the highest 15-s V˙ O2 value of each test was used to calculate subject’s V˙ O2max. For the remaining phases, the achievement (or not) of V˙ O2max was calculated based on the highest 15-s rolling average [9]. Phase one - Incremental test. The initial treadmill speed was set at 8 km/h and was increased by 1 km/h every 3-min until subject exhaustion. At the end of each stage, a 30-s rest period was required in order to collect capillary blood samples (25 mL) from the non-hyperaemic earlobe in order to measure blood lactate concentration. The speed associated with the lactate threshold was defined as the speed maintained during the stage prior to which the first sudden and sustained increase in blood lactate above the baseline level was observed. The maximal aerobic speed was calculated according to the method of Kuipers et al. [16] as the final speed achieved during the test. All subjects fulfilled at least two of the following three criteria for achieving V˙ O2max during the incremental test: 1) respiratory exchange ratio greater than 1.1; 2) a blood lactate concentration greater than 8 mmol/L; and 3) a peak heart rate at least equal to 90% of the age-predicted maximal. Phase two - Predictive trials and V˙ O2max. On separate days and in a random order, each participant performed three constant speed tests at 95, 100 and 110% of the maximal aerobic speed. The time-to-exhaustion was measured to the nearest second of the subject’s exhaustion. V˙ O2max was then calculated for each subject by averaging the four V˙ O2max values obtained during the incremental test and the three predictive trials. The total error in the measurement of V˙ O2max was also calculated for each subject from the same data as a coefficient of variation (%) [9]. Phase three - HS determination. Subjects performed between two and four constant speed tests to exhaustion in order to determine the HS. To ensure whether subjects had (or had not) attained V˙ O2max during these tests, the following criterion was adopted: the maximal V˙ O2 value (calculated as the highest 15-s rolling average) reached in each test should be within the total error of measurement obtained for each subject during V˙ O2max determination [9]. In the first test, speed was calculated to result in exercise exhaustion within 120 s (as described below). If V˙ O2max was attained, further subsequent tests at a 5% higher speed were performed on separate days until V˙ O2max could not be reached. Conversely, if during the first constant speed test V˙ O2max was not reached, further tests were conducted with reduced speeds (5%) until V˙ O2max had been elicited. Phase four – Fast-start strategy protocol. Finally, subjects performed a FS protocol, in which the initial speed was set 10% above the HS and then decreased progressively throughout the test until reaching 90% of the HS at an exercise duration and distance matched to those performed at the HS (Figure 1). The speed of the treadmill was then maintained at 90% of the HS until voluntary exhaustion of the subject. Fast Start and Running Performance PLOS ONE | www.plosone.org 2 October 2014 | Volume 9 | Issue 10 | e111621 Data analysis procedures Log-log modeling. Predicting the intensity that would be expected to lead to exhaustion in 120 s was performed by fitting the predictive trials (t and s) with a least-squares straight line to the natural logarithms (log-log predictions) for each subject. The performance parameters for constant-pace running were also derived by log-log predictions (t vs. d or t vs. s), but using both the predictive trials and the HS. Log-log modelling has demonstrated good reliability in predicting time-trial performance over race- specific distances and seems to be a better predictor in comparison with the critical-power model [13,17]. Each runner’s times for the standard competition distance of 800-m were also predicted using both strategies. 800-m performance using a FS was predicted by calculating the amount of the intercept used in the extra-time, assuming that the FS does not change the slope of the relationship between t and d. Measures of goodness of fit for each set of four runs were the adjusted correlation coefficient (square root of the R2 adjusted for degrees of freedom) and the standard error of the estimate (SEE). V˙ O2 responses. To avoid being influenced by the amount of data used in the comparison between the FS and HS, all of the following calculations, except maximal accumulated O2 deficit (MAOD), were analysed to individually fix the time window to the shortest time to exhaustion recorded for each subject (i.e. iso-time). Occasional errant breath values were removed from the data set if they fell more than three standard deviations outside the local mean (i.e. five-point rolling mean), and the integral area under the V˙ O2 curve representing the total amount of O2 consumed during exercise was calculated (OriginPro 8, OriginLab, Massachusetts, USA). Thereafter, to characterize the V˙ O2 kinetics during the HS and FS, we calculated the MRT for V˙ O2 by fitting a mono- exponential curve to the raw data from the onset of exercise using iterative nonlinear regression procedures: VO2 tð Þ~VO2 p ð ÞzA(1{e{(t=t)) where V˙ O2(t) is V˙ O2 at time t, V˙ O2(p) is the pre-test V˙ O2; A is the asymptote of the increase in V˙ O2 above the pre-test value and t is the time constant (equivalent to the MRT in this model). For the measurement of V˙ O2(p), the participant remained standing on the treadmill belt for 5 min prior to the test and the V˙ O2 of the last two minutes were averaged. With only one transition performed in each condition, more complex models were not considered suitable [3]. In addition, because the two protocols resulted in the rapid attainment of the V˙ O2max, a single exponential function starting at the onset of exercise was considered the most appropriate approach for characterizing the overall MRT [18]. The energy cost of running (i.e. the accumulated O2 demand) was set in this study as 0.192 mL O2 per kg of body mass per meter by using the average value reported by di Prampero et al. [19] and correcting for the 1% treadmill gradient [20]. The intercept representing the energy cost at rest (5.1 mL/kg/min) comes from Medbo et al. [21]. The MAOD for each condition was estimated by subtracting the total amount of O2 consumed from the calculated O2 required. Statistical Analysis Calculations were performed with the aid of a spreadsheet for straightforward crossover trial analysis [22]. When no comparisons were involved, the means and between-subject standard deviations were derived from the raw values of the measures; for all other measures, they were derived by performing back-transformation of the log-transformed values and the standard deviations were presented as percentages. Data reliability was assessed by means of the retest correlation (intraclass correlation coefficient; ICC) and the measurement errors (typical error or SEE) along with 90% confidence limits. The inflated typical errors were reported because there were no identifiable individual responses to the treatment. Uncertainties in the measurement errors are presented as factors. To make inferences about true (population) values of the effect (%) of a FS on performance and physiological responses, the uncertainty in the effect was expressed as 90% confidence limits and as likelihoods that the true value of the effect denotes real positive (+ive) or negative (2ive) change; this was represented by the probability (P) value derived from the t statistic followed by qualitative interpretation [23]. To evaluate the relationship between performance variables and to assess the association between performance and a set of physiological variables, single and multiple (stepwise) linear regressions analyses were used, respectively. Results In the incremental test, subjects attained a maximal aerobic speed of 16.161.8 km/h and the speed at lactate threshold was 9.862.7 km/h. The time-to-exhaustion for exercise at 95, 100 and 110% of the maximal aerobic speed was 5616143 s, 369682 s and 214672 s, respectively. The calculated subject’s V˙ O2max was 39826429 mL/min. The individual error in the measurement of V˙ O2max (i.e. the coefficient of variation of the four V˙ O2max values) ranged between 0.7 and 7.8% (mean 6 SD of 3.062.3%). During the third phase of the experiment, subjects attained a HS at 20.162.0 km/h (time-to-exhaustion of 108634 s), representing 126613% of maximal aerobic speed. Figure 1. Schematic representation of the protocol timing during the four phases. The superimposed data points are merely illustrative data representing V˙O2 response during tests. V˙O2max, maximal oxygen uptake (dashed line); HS, highest speed (solid line); FS, fast-start pacing strategy. See ‘‘Methods’’ for more details on phases one, two, three and four. doi:10.1371/journal.pone.0111621.g001 Fast Start and Running Performance PLOS ONE | www.plosone.org 3 October 2014 | Volume 9 | Issue 10 | e111621 Table 1. Comparison analysis of the FS performance variables with those predicted for constant pace from the log-log modelling. Mean ± coefficient of variation (%) Performance Measures Constant Pace FS Correlation and 90%CL SEE (%)a % changes ±90%CL P value Qualitative Inference Exercise tolerance at mean FS speed (s) 114630 125626 0.89 (0.71–0.96) 8 967 0.037 Benefit very likely Time to cover FS distance (s) 128626 0.99 (0.98–1.00) 1.5 22.561.8 0.033 Distance covered at FS duration (m) 670622 683622 0.99 (0.98–1.00) 1.3 2.061.4 0.029 Predicted 800-mb (s) 155611 152610 0.97 (0.90–0.99) 1.5 22.061.6 0.046 Data are back-transformed means 6 coefficients of variation. aUncertainties in these errors: 6/4 1.2. Multiply and divide the error by this number to obtain the 90% confidence for the true error. bThe 800-m using a FS was predicted by calculating the amount of the intercept used in the extra-time assuming that the FS does not change the slope of the relationship between t and d. FS: fast-start pacing strategy. doi:10.1371/journal.pone.0111621.t001 Table 2. Observed changes in physiological responses after a FS in comparison with constant speed exercise. Mean ± coefficient of variation (%) Physiological Measures HS FS Correlation and 90%CL Inflated Errora (%) % changes ±90%CL P value Qualitative Inferenceb Pretest V˙O2 (mL/min) 555614 511618 0.51 (0.02–0.80) 11 2868 0.095 Very likely –ive V˙O2 Mean Response Time (s) 22.2628 19.3629 0.80 (0.50–0.93) 13 21368 0.025 Very likely –ive Amplitude (mL/min) 3396612 341968 0.74 (0.39–0.90) 5.5 0.764.1 0.769 Unclear V˙O2max (mL/min) 3871610 387468 0.93 (0.80–0.98) 2.6 0.162.0 0.941 Unclear O2 consumed at iso-time (mL) 5373645 5503646 1.00 (0.99–1.00) 2.6 2.462.0 0.051 Very likely +ive MAOD (mL) 2385634 2425630 0.89 (0.70–0.96) 11 268 0.713 Unclear Data are back-transformed means 6 coefficients of variation. aUncertainties in these errors: 6/41.5. Multiply and divide the error by this number to obtain the 90% confidence for the true error. bThe effect was deemed unclear if the chances that the true effect has the same sign than that of the observed effect were lower than 75%. FS: fast-start pacing strategy; HS: highest constant speed; MAOD: maximal accumulated O2 deficit. doi:10.1371/journal.pone.0111621.t002 Fast Start and Running Performance PLOS ONE | www.plosone.org 4 October 2014 | Volume 9 | Issue 10 | e111621 The adjusted correlation coefficients for the log-log modelling of the sets of four runs were all at least 0.999 for the relationships between t6d and averaged 0.993 (SD of 0.008) for the relationship between t6s. The application of the models revealed a very large correlation between the HS and predicted constant-pace 800-m performance [r = 20.80 (–0.93 to 20.43)]. Stepwise multiple regression analyses further demonstrated that the major predictors of both the HS and predicted 800-m performance were, in order of importance, relative MAOD, relative V˙ O2max and MRT. The increase in multiple correlation coefficients with the addition of each predictor was 0.59–0.81–0.89 for the HS and 0.59–0.88–0.89 for the predicted 800-m performance. The upper and lower 90% confidence limits for the full models were identical: 0.68–0.96. Table 1 shows the comparison of the various performance parameters obtained during the FS with the approximations derived by the models for constant pace exercise. The benefit of the FS was very likely for all performance variables. In addition, the performance improvement was also very likely beneficial when comparing the predicted 800-m performance using both strategies. The V˙ O2 responses observed during the HS and FS performances were compared at iso-time and iso-distance (Figure 2 and Table 2). The FS very likely reduced the MRT and increased the amount of O2 consumed. There was no clear difference in MAOD between the experimental conditions. Moreover, the accumulated O2 deficit spared at iso-time with the FS (1456179 mL) was quite similar to that used to maintain the exercise during the FS after the iso-time (1346185 mL). Discussion Non-running studies of similar duration, most of them conducted in cycling, reported that improvements in time trials or time-to-exhaustion performances with a FS are usually accompanied by faster V˙ O2 kinetics and higher O2 consumption for a given time [1,3,5]. The results of this study are in concordance with these previous reports demonstrating the benefits of a FS in comparison with more conservative pacing strategies during treadmill running. In spite of the lower magnitude of MRT reductions, which were not correlated with change in performance in the present study, the faster achieve- ment of V˙ O2max induced by the FS increased the aerobic contribution even in an already fast V˙ O2 response. This seems to have resulted in a spared quantity of the anaerobic capacity, measured in the present study by the oxygen deficit. This quantity was equivalent to those used to prolong the exercise tolerance at a running intensity correlated with that of 800-m performance. Consequently, these results are in accordance with the established models of mitochondrial respiratory control, in which changes in muscle [PCr], [ADP] and [Pi] per unit change in time are responsible for mitochondrial respiratory control through the rate of oxidative phosphorylation in the active muscles during exercise [24,25]. The approximations derived from the log-log modelling for changes in time trial performance in the present study (2.5%) are within the effects usually reported for human performance experiments where the end-point is known. Although our subjects were not competitive runners, the significance of this effect in terms of magnitude should be discussed from a practical Figure 2. Group mean pulmonary V˙ O2 response during the highest speed and fast-start pacing strategy. For graphical presentation, data were matched at the shortest time to exhaustion recorded and interpolated to show second-by-second values. The vertical solid line represents the onset of exercise and the horizontal dashed line is the mean V˙O2max. The mean 6 SD of pre-test V˙O2 in each condition are also shown. doi:10.1371/journal.pone.0111621.g002 Fast Start and Running Performance PLOS ONE | www.plosone.org 5 October 2014 | Volume 9 | Issue 10 | e111621 perspective. Hopkins et al. [26] have demonstrated through simulations, that the increase in the chances of winning an event varies uniformly when a particular subject benefits with an enhancement corresponding to multiples of the within-subject random variation within a group of identical subjects (i.e. between- subject variation of zero, equal to a repeated measures design). Even though a more careful analysis of the reliability of subject’s performance has not been conducted, the typical error of measurement usually lies around 2–3% for groups with similar characteristics [17]. Therefore, we can be confident that the observed enhancement is meaningful for this group of subjects because the ratio between the observed effect and the typical error should increase the chances of winning by approximately 30% in cases where the subject runs against himself in a hypothetical simulated event, which can be considered as a moderate effect [30]. For the log-log predictions of 800-m performance the benefits of a FS decreased slightly, yet were still meaningful, probably as a consequence of the amount of anaerobic energy spared with the higher aerobic contribution becoming relatively lower in comparison with the total energy cost as the time/ distance increases. Although the present study has demonstrated that a FS enhanced performance by speeding an already fast aerobic response to exercise in non-athletes, extrapolating these results to middle-distance runners is an interesting issue. Athletes present even faster V˙ O2 kinetics for a given running speed and obviously have higher absolute speeds during performances of similar distance [27,28], which could prevent meaningful accelerations in V˙ O2 kinetics. Indeed, Thomas et al. [29] observed that elite runners reached V˙ O2max in a very fast time during an 800-m race (around 45 s). Conversely, it was demonstrated that the time to reach V˙ O2max in runners is liable to be reduced as a function of the initial speed at very high intensities [30]. In addition, athletes have higher values of V˙ O2max relative to body mass and, consequently, they may still spare an important amount of energy, although with a lesser acceleration in V˙ O2 kinetics. In other words, since the athletes present higher values of V˙ O2 along the transition from rest to exercise, a lower effect of the FS in the speed of V˙ O2 kinetics may not be a problem because the absolute amount of energy spared would be similar between athletes and non-athletes [27]. Therefore, although it is recognized that athletes generally present lower performance improvements in terms of magnitude than non-athletes for a given intervention, it is hypothesized that they would also benefit from a FS, since enhancements as low as 0.5% are considered important to elite runners [31,32]. The linear regression analysis, irrespective of being single or multiple, demonstrated that both the HS and 800-m speeds are linked with each other and are highly influenced by the same physiological parameters. There is no novelty in the fact that success with middle-distance running is dependent on an integrative contribution from the aerobic and anaerobic variables that allow a runner to maintain a rapid velocity during a race [10– 12], and the results of this study are consistent with the notion that having fast V˙ O2 kinetics is also important. Similarly, it is intuitive that a large anaerobic capacity allows for greater endurance at any given intensity and thus to continue reaching V˙ O2max at higher relative intensities [33]. Therefore, large anaerobic energy stores combined with a high aerobic power should yield a higher HS, especially when allied to a fast V˙ O2 kinetics. Therefore, it is hypothesized that the HS may group together several intervening factors for middle-distance performance into a single physiological index, which has proven sensitive to high-intensity training in an ongoing study (unpublished observations). One possible limitation of the present investigation is the lack of randomization between the HS and FS. While the nature of the present experiment rendered it impossible to control any possible order effects, the subjects were mostly accustomed with exercising to exhaustion. Furthermore, it is likely that three predictive trials plus two-to-four tests for the determination of the HS, the latter of which were at or very close to the HS and FS intensities, provided enough familiarization to prevent learning effects in the last two non-randomized trials. Conversely, if the high number of tests performed until exhaustion had caused an accumulated fatigue in the subjects, the observed result should be the opposite to what was found in the present study if there was no systematic effect of the FS on performance. Therefore, it is unlikely that any order effects influenced these findings. In conclusion, the results generated suggest that running performance over 2–3 min is improved when a FS is adopted. The higher aerobic contribution resulting from faster V˙ O2 kinetics in the early phase of exercise spares an important amount of the finite anaerobic capacity, which can be used as an additional energy source to improve middle-distance performance. It is recommend that future studies investigate how these effects would behave/interact in the presence of other strategies that are commonly used to speed V˙ O2 kinetics such as prior exercise. Author Contributions Conceived and designed the experiments: FC. Performed the experiments: TT AFS FDL RAA RSOC FC. Analyzed the data: TT AFS FDL RAA RSOC FC. Contributed reagents/materials/analysis tools: FC. Contrib- uted to the writing of the manuscript: TT AFS FDL RAA RSOC FC. References 1. Bailey SJ, Vanhatalo A, DiMenna FJ, Wilkerson DP, Jones AM (2011) Fast-start strategy improves VO2 kinetics and high-intensity exercise performance. Med Sci Sports Exerc 43: 457–467. 2. Poole DC, Jones AM (2012) Oxygen uptake kinetics. Comprehensive Physiology 2: 933–996. 3. Jones AM, Wilkerson DP, Vanhatalo A, Burnley M (2008) Influence of pacing strategy on O2 uptake and exercise tolerance. Scand J Med Sci Sports 18: 615– 626. 4. Sandals LE, Wood DM, Draper SB, James DV (2006) Influence of pacing strategy on oxygen uptake during treadmill middle-distance running. Int J Sports Med 27: 37–42. 5. Bishop D, Bonetti D, Dawson B (2002) The influence of pacing strategy on V˙ O2 and supramaximal kayak performance. Med Sci Sports Exerc 34: 1041–1047. 6. Jones AM, McConnell AM (1999) Effect of exercise modality on oxygen uptake kinetics during heavy exercise. Eur J Appl Physiol Occup Physiol 80: 213–219. 7. Carter H, Jones AM, Barstow TJ, Burnley M, Williams CA, et al. (2000) Oxygen uptake kinetics in treadmill running and cycle ergometry: a comparison. J Appl Physiol (1985) 89: 899–907. 8. Caputo F, Denadai BS (2006) Exercise mode affects the time to achieve V˙ O2max without influencing maximal exercise time at the intensity associated with V˙ O2max in triathletes. Int J Sports Med 27: 798–803. 9. Caputo F, Denadai BS (2008) The highest intensity and the shortest duration permitting attainment of maximal oxygen uptake during cycling: effects of different methods and aerobic fitness level. Eur J Appl Physiol 103: 47–57. 10. di Prampero PE, Capelli C, Pagliaro P, Antonutto G, Girardis M, et al. (1993) Energetics of best performances in middle-distance running. J Appl Physiol (1985) 74: 2318–2324. 11. Brandon LJ (1995) Physiological factors associated with middle distance running performance. Sports Med 19: 268–277. 12. Billat VL, Lepretre PM, Heugas AM, Koralsztein JP (2004) Energetics of middle-distance running performances in male and female junior using track measurements. Jpn J Physiol 54: 125–135. 13. Hinckson EA, Hopkins WG (2005) Reliability of time to exhaustion analyzed with critical-power and log-log modeling. Med Sci Sports Exerc 37: 696–701. 14. Hill DW, Poole DC, Smith JC (2002) The relationship between power and the time to achieve.VO(2max). Med Sci Sports Exerc 34: 709–714. Fast Start and Running Performance PLOS ONE | www.plosone.org 6 October 2014 | Volume 9 | Issue 10 | e111621 15. Draper SB, Wood DM, Corbett J, James DV, Potter CR (2006) The effect of prior moderate- and heavy-intensity running on the V˙ O2 response to exhaustive severe-intensity running. Int J Sports Physiol Perform 1: 361–374. 16. Kuipers H, Verstappen FT, Keizer HA, Geurten P, van Kranenburg G (1985) Variability of aerobic performance in the laboratory and its physiologic correlates. Int J Sports Med 6: 197–201. 17. Laursen PB, Francis GT, Abbiss CR, Newton MJ, Nosaka K (2007) Reliability of time-to-exhaustion versus time-trial running tests in runners. Med Sci Sports Exerc 39: 1374–1379. 18. Wilkerson DP, Koppo K, Barstow TJ, Jones AM (2004) Effect of work rate on the functional ‘gain’ of Phase II pulmonary O2 uptake response to exercise. Respir Physiol Neurobiol 142: 211–223. 19. di Prampero PE, Atchou G, Bruckner JC, Moia C (1986) The energetics of endurance running. Eur J Appl Physiol Occup Physiol 55: 259–266. 20. di Prampero PE, Salvadego D, Fusi S, Grassi B (2009) A simple method for assessing the energy cost of running during incremental tests. J Appl Physiol (1985) 107: 1068–1075. 21. Medbo JI, Mohn AC, Tabata I, Bahr R, Vaage O, et al. (1988) Anaerobic capacity determined by maximal accumulated O2 deficit. J Appl Physiol 64: 50– 60. 22. Hopkins WG (2003) A spreadsheet for analysis of straightforward controlled trials. Sportscience 7. Available: sportsci.org/jour/03/wghtrials.htm. 23. Hopkins WG (2007) A spreadsheet for deriving a confidence interval, mechanistic inference and clinical inference from a p value. Sportscience 11: 16–20. Available: sportsci.org/2007/wghinf.htm. 24. Bose S, French S, Evans FJ, Joubert F, Balaban RS (2003) Metabolic network control of oxidative phosphorylation: multiple roles of inorganic phosphate. J Biol Chem 278: 39155–39165. 25. Chance B, Williams GR, Holmes WF, Higgins J (1955) Respiratory enzymes in oxidative phosphorylation. V. A mechanism for oxidative phosphorylation. J Biol Chem 217: 439–451. 26. Hopkins WG, Hawley JA, Burke LM (1999) Design and analysis of research on sport performance enhancement. Med Sci Sports Exerc 31: 472–485. 27. Caputo F, Mello MT, Denadai BS (2003) Oxygen uptake kinetics and time to exhaustion in cycling and running: a comparison between trained and untrained subjects. Arch Physiol Biochem 111: 461–466. 28. Caputo F, Denadai BS (2004) Effects of aerobic endurance training status and specificity on oxygen uptake kinetics during maximal exercise. Eur J Appl Physiol 93: 87–95. 29. Thomas C, Hanon C, Perrey S, Le Chevalier JM, Couturier A, et al. (2005) Oxygen uptake response to an 800-m running race. Int J Sports Med 26: 268– 273. 30. Hanon C, Thomas C (2011) Effects of optimal pacing strategies for 400-, 800-, and 1500-m races on the V˙ O2 response. J Sports Sci 29: 905–912. 31. Carr AJ, Hopkins WG, Gore CJ (2011) Effects of acute alkalosis and acidosis on performance: a meta-analysis. Sports Med 41: 801–814. 32. Van Montfoort MC, Van Dieren L, Hopkins WG, Shearman JP (2004) Effects of ingestion of bicarbonate, citrate, lactate, and chloride on sprint running. Med Sci Sports Exerc 36: 1239–1243. 33. Billat LV, Koralsztein JP (1996) Significance of the velocity at V˙ O2max and time to exhaustion at this velocity. Sports Med 22: 90–108. Fast Start and Running Performance PLOS ONE | www.plosone.org 7 October 2014 | Volume 9 | Issue 10 | e111621

A fast-start pacing strategy speeds pulmonary oxygen uptake kinetics and improves supramaximal running performance.

10-31-2014

Turnes, Tiago,Salvador, Amadeo Félix,Lisbôa, Felipe Domingos,de Aguiar, Rafael Alves,Cruz, Rogério Santos de Oliveira,Caputo, Fabrizio

eng

PMC7967426

International Journal of Environmental Research and Public Health Article Influence of Psychological Factors on the Success of the Ultra-Trail Runner David Méndez-Alonso * , Jose Antonio Prieto-Saborit, Jose Ramón Bahamonde and Estíbaliz Jiménez-Arberás



 Citation: Méndez-Alonso, D.; Prieto-Saborit, J.A.; Bahamonde, J.R.; Jiménez-Arberás, E. Influence of Psychological Factors on the Success of the Ultra-Trail Runner. Int. J. Environ. Res. Public Health 2021, 18, 2704. https://doi.org/10.3390/ ijerph18052704 Academic Editors: Zbigniew Wa´skiewicz and Aleksandra ˙Zebrowska Received: 31 December 2020 Accepted: 3 March 2021 Published: 8 March 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Faculty Padre Ossó, University of Oviedo, 33008 Oviedo, Spain; [email protected] (J.A.P.-S.); [email protected] (J.R.B.); [email protected] (E.J.-A.) * Correspondence: [email protected] Abstract: The aim of this study was to analyze the psychological variables of runners of ultra-trail mountain races and their association with athletic performance and success. The sample was made up of 356 mountain runners, 86.7% men and 13.2% women, with a mean age of 42.7 years and 5.7 years of experience. Using pre- and post-race questionnaires, data were collected regarding mental toughness, resilience, and passion. The performance of each runner in the race was also recorded. The results showed very high values in the psychological variables analyzed compared with other sports disciplines. Completion of the race (not withdrawing) and the elite quality of the runners were presented as the most relevant indicators in the processes of resilience, mental toughness, and obsessive passion. Differences were noted between the pre- and post-race results, suggesting that the competition itself is a means of training those psychological factors that are essential to this sports discipline. It can be concluded that psychological factors are decisive to athletic performance and race completion in mountain ultra-marathon races. Keywords: ultra-marathon; trail running; mental toughness; resilience; passion 1. Introduction At the finish line of any ultra-marathon race, it is common to hear participants saying things such as “ . . . the final km. the legs just stopped working and only my head got me over the finish line,” or “ . . . I was able to finish this race because I’m psychologically fit.” This study seeks to take an in-depth look at and analyze the effect of three psychological variables on running mountain ultra-marathon races, these variables being mental toughness, resilience, and passion. The existence of ultra-trail races has increased exponentially in recent years [1,2]. Consequently, interest in researching decisive factors to performance in these types of trials has become the focus of multiple research groups around the world. Ultra-endurance races are a multifactorial event that include physiological, neuromuscular, biomechanical, and psychological factors [3]. Multiple studies have focused on analyzing the physiologi- cal variables for improving performance in ultra-marathons [4], yet a lack of knowledge still abounds regarding the psychological factors that are unique to these runners, de- spite an increase in studies in recent years due to the rising popularity of these types of races [5,6]. In this sense, multiple mixed methods research projects have approached [7,8] the combination of physiological determinants (VO2 max, current economy, etc.) and psychological and motivational factors that have been shown to significantly influence the athletic performance of runners [4]. The influence of psychological factors on athletic performance in long-distance races has always been a widely-discussed topic, though very seldomly analyzed from a perspec- tive of its impact on athletic performance. Without out a doubt, the variables that influence the psychological processes of athletes in highly challenging disciplines are many. The lack of studies analyzing the psychological factors unique to these types of races [9] has led various groups to focus their work on analyzing said factors that manifest Int. J. Environ. Res. Public Health 2021, 18, 2704. https://doi.org/10.3390/ijerph18052704 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2021, 18, 2704 2 of 13 during these races [10]. Personality factors [11] with high extroversion traits; emotional factors with heightened emotional intelligence traits that can be associated with optimal adaptive psychological traits [12], motivational factors with heightened levels of intrinsic motivation [13,14], pain tolerance levels [15] and even moods that runners experience during the race and can noticeably interfere with their results [16]. Long-distance race events are one of the most stressful activities in which a human being can participate voluntarily [17] due to their intensity, duration, and potentially adverse weather conditions, in which sources of stress are in constant flux [5], and which require specific physical preparation and tremendous physical and psychological effort. It is also worth mentioning that, in the field of ultra-trail races, psychological factors have been found to play a role in the majority of cases of withdrawal [18]. For a high percentage of runners, finishing the race is the main objective increasing the number of runners reaching the goal [19]. Perceptions of success in these types of races differ according to the runner’s motivation when running the race [20]. On the one hand, there are elite runners with clear-cut performance-related motivation whose goals are completely related to achieving the best classification, while on the other hand is a separate yet large group of runners with a goal focused on completing the race; these latter will present more intrinsic motivation related to reaching the finish line [21]. Gucciardi et al. [22] define mental toughness as “a personal capacity to produce con- sistently high levels of subjective (e.g., personal goals or strivings) or objective performance (e.g., sales, race time, GPA) despite everyday challenges and stressors as well as significant adversities” (p. 28). Mental toughness has received considerable attention in sports as a key factor in goal achievement in the presence of various degrees of pressure, adversity, or ob- stacles [23]. In ultra-marathon races, mental toughness presents as a factor associated with athletic performance [24], in the same way that previous research has concluded that mental tenacity is a key factor to success in various sports disciplines [25–27]. Crust & Clough [28] showed evidence that the components of mental toughness are higher in individuals who can endure more extended periods of physical effort; however, subsequent studies have shown that mental toughness and self-efficacy were not significantly associated with ultra- marathon performance, although athletes require this to be of the necessary standard to prepare for and compete in elite ultra-marathon events [29]. Likewise, resilience has shown a positive correlation with sports performance and psychological well-being [30]. Windle [31], based on a review of 270 research articles, conceptualizes resilience as the process of negotiating, adapting to, or managing significant sources of stress through diverse internal psychosocial resources and contextual aspects that facilitate this capacity for adaptation and flexibility in such adverse situations. A definition of resilience that is commonly used in sports emphasizes “the role of mental processes and behavior in promoting personal assets and protecting an individual from the potential effect of negative stressors” [32]. In the field of sport, various studies have highlighted the relationship between resilience, athletic performance [33,34] and psychological well-being [30]. In addition, resilience also relates to variables such as stress-recovery levels of athletes during the competition [35]. The study of resilience could represent an advance in the improvement of training planning and organization as well as in the athlete’s competitive performance. As for passion, it represents a dual psychological factor, as it can be associated with either an obsessive state or a harmonious state. Passion is defined as “a strong inclination toward an activity that people like, that they find important, and in which they invest time and energy” [36]. Passion is a construct involved in psychological processes that appear in many fields of human activity such as physical activity and sports, the arts, leisure, or interpersonal relations [37–39]. In this sense, Vallerand et al. [39] found that, in the world of sports, both harmonious and obsessive passion were positive predictors for deliberate practice, which was simultaneously a positive predictor for objective performance. In addition, the results distinctly related the two passions to achievement goals and subjective well-being. Specifically, harmonious passion was a positive predictor for seeking mastery Int. J. Environ. Res. Public Health 2021, 18, 2704 3 of 13 goals while obsessive passion was a positive predictor for mastery from the performance perspective [38]. Even though the two forms of passion may be an integral part of elite sports, athletes scoring high on obsessive passion may be at greater risk of developing burnout than more harmoniously passionate athletes [40] Consequently, despite the increasing popularity of long-distance races, research into the psychological processes of ultra-trail runners is still quite scarce. In this sense, it is not only necessary to study the profile of long-distance runners, but also to analyze the influence of the race itself on the variability of psychological factors and their connection to performance. Psychological factors do not only present as indicators associated with performance– understood as a better overall classification–, but also with the runner’s ability to finish the race. Factors such as the vitality states runners experience [41], self-efficacy, and intent to finish the race [42] are associated with the possibility of reaching the finish line. In such long-distance races, controlling the emotional shifts that runners experience is considered important to being able to reach the finish line [12,43]; significant differences were found in runners for variables such as anger, confusion, or frustration between the start and end stages of a long-distance race. Similar results were found in cyclists after multi-day races involving accumulated significant loss of sleep [44]. Evidence indicates emotions associate with performance [45] and that athletes are more likely to try to regulate an emotion if they believe that doing so will facilitate performance. In the case of mental toughness, resilience, and passion, no studies have been found in which changes occurred as a result of the race. 2. Hypothesis and Objectives The aim of the study was to identify the psychological profile of ultra-trail runners and the relationship between these factors and athletic success and other variables such as age and sex. At the same time, we were interested in discovering whether indicators undergo changes after highly demanding trials such as ultra-trail races. Based on previous research in the sporting world suggesting the benefits of specific psychological factors for athletic performance and their connection with highly demanding physical activity, the following hypotheses were defined: As an initial Hypothesis 1 (H1), it was believed that the variables analyzed in the runners’ psychological profiles (mental toughness, resilience, and passion index) would show higher scores than the sedentary population and athletes of other disciplines different from ultra-distance and would positively relate to gender, age, the athlete’s level, and their experience. Regarding the second Hypothesis 2 (H2), the psychological factors analyzed were predicted to have a significant influence on performance and success in the race. It was expected that runners who finish the race or achieve a better time or rank would display higher scores in mental toughness and resilience. Lastly, as a third Hypothesis 3 (H3), it was deemed that there would be significant differences in mental toughness, resilience, and passion in the pre-test and post-test results due to the set of experiences and the effort made. 3. Materials and Methods 3.1. Participants Some 450 runners registered for the race. The study sample comprises 356 runners (79.1%) taken from the participants in the Travesera Integral Picos de Europa race, held in the Picos de Europa National Park in Spain, with an age range of 23 to 68 years and mean age of 42.73 ± 7.44. The mean number of years of experience running ultra-trail races was 5.7 years, with a minimum of 2 and a maximum of 16. The sample comprises 309 men (86.79%) and 47 women (13.2%). The mean ITRA performance index (International Trail Running Association tool for evaluating and comparing the speed of different trail runners around the world. This index compares the speed of each runner on a scale of 1000 points, Int. J. Environ. Res. Public Health 2021, 18, 2704 4 of 13 corresponding to their performance against the world record for that distance) was 620 points with a maximum of 890 and a minimum of 525. The race organization provided the research group with a list of all the runners and their ITRA score (this has to be included in the registration form) which was used to assign numbers and start times. The age and sex percentages are similar to those seen in other international races such as the Ultra-Trail du Mont-Blanc [46]. The race is considered one of the most demanding ultra-trail races in the world, categorized with 5 ITRA points with a mountain coefficient of 14 (scale of 1–12), a distance of 75.9 km, and positive elevation gain of 7180 m. The average time to finish was 17 h and 53 min, up to a maximum of 21 h. The organization requires runners to show accredited prior experience in mountain races. In terms of the event on which the study is based, the race was classified as a Spanish Championship for mountain races by the Spanish Federation for Mountain and Climbing Sports, meaning that the participants were the most elite representation of the discipline at a national level. A total of 148 runners finished the race (41.57%), of which 133 were men (89.86%) and 15 were women (10.13%). The average time of those who completed the race was 17.11 h with a standard deviation of 3.15, a minimum of 19.32 and a maximum of 21. 3.2. Instruments The tool used to gather the data was a survey that included questionnaires that requested sociodemographic and sport-related data (years of experience participating in long-distance mountain races), as well as various scales to assess exercise dependence, mental toughness, motivation, passion, and resilience. All the questionnaires were sent out in Spanish and 96% of the received responses were from Spanish speakers. Mental toughness was evaluated using the 7-item Mental Toughness Inventory [25], in which participants respond using a Likert scale where 1 = False, 100% of the time, to 7 = True, 100% of the time. The Spanish version of the 14-item Resilience Scale (RS-14) validated by Sánchez- Teruel and Robles-Bello (2014) [47] was used, derived from the original version [48] based on the 25-item Resilience Scale (RS-25) [49]. The former is a 14-item scale presented in a positive manner and with a Likert-style 7-point response format. The scale measures the degree of individual resilience, considered a positive personality trait that enables individuals to adapt to adverse situations. The RS-14 measures two factors: Factor I: Personal Competence (11 items, self-confidence, independence, decisiveness, inventiveness, and perseverance); Factor II: Self-acceptance and of life (3 items, adaptability, balance, flexibility, and perspective of a stable life). The passion questionnaire created by Chamarro, Penelo, Fornieles, Oberst, Vallerand, and Fernández-Castro (2015) [50] was used to evaluate passion and comprises three sub- scales: Harmonious Passion, Obsessive Passion, and Passion Criteria, each of which has six items. The participants responded on a 6-point Likert scale ranging from 1 (Strongly disagree) to 7 (Strongly agree). 3.3. Procedure Firstly, permission was obtained from the Ethics Committee of the research team’s university, and authorization was later requested from the race directors to administer the survey. The questionnaire was sent online using Google Forms the week prior to the race and the day after the competition, with the responses collected during the week after the race to ensure that there were no other competitions that could potentially falsify the post-race data. The responses were provided individually by each of the runners. The survey was sent in one single block with the various scales separated and includ- ing instructions on how to fill in the questionnaire. Said instructions indicated that the responder should try to avoid any possible distractions and not stop part way through the survey; an estimated time for completing the survey was included (15 min). The responses that took more than 25 min to complete were not considered (8 runners). The participants gave their informed consent and filled in the questionnaires in an individual and voluntary Int. J. Environ. Res. Public Health 2021, 18, 2704 5 of 13 manner during the weeks prior and subsequent to the race. Once the data were collected, the runners’ race times were added as well as their overall classification and whether they completed the course or not. As none of the missing values exceeded 5% in any of the variables, this data did not influence the results obtained [47]. 3.4. Data Analysis The study design was descriptive, comparative, correlational and cross-sectional. Descriptive analyses were conducted with means, typical deviation, frequencies, and percentages to determine prevalence and create the sample description. After performing the Kolmogorov-Smirnov test of normality and the Levene’s test for homogeneity of variance, it should be noted that the results obtained in both test show that the variables have a normal distribution and the variances are homogeneous, which allows us to carry out parametric statistics. For the first hypothesis, descriptive statistics for each variable were used (means, typical deviation), as well performed using mean comparison contrast statistics (Student’s t-test) to make the comparison by gender. The analysis of differences between those who finish the test and those who drop outwas performed using mean comparison contrast statistics (Student’s t-test). To establish associations between psychological variables (resilience, mental toughess and passion) and rank, and race time, correlational analyses were carried out using the Pearson correlation coefficient. For the second hypothesis, to analyze the incidence of psychological variables in the final result of the career, the sample was splited into quartiles according to the completion time in hours. First, a unidirectional Anova was used, observing if there are differences in the psychological variables between these groups of performance standards. Second, comparison contrast statistics (Student’s t-test) to make the comparison by quartiles. For the third hypothesis, finally the comparison between the pre and post results were analyzed with comparison contrast statistics (Student’s t-test). The program SPSS, version 25.0 (IBM, Armonk, NY, USA), was used to conduct the statistical analyses. For the purposes of data interpretation and analysis, the confidence level was 0.05 (p ≤ 0.05). An attempt was made to reduce the effect of the type I error by assuming p ≤ 0.01 in the correlations. The perspective followed was frequentist versus Bayesian. An effort was made to avoid the so-called inverse probability fallacy in which 1-p is the probability that the alternative hypothesis is true [48]. 4. Results Table 1 present the results of the psychological profile of the ultra-trail runner in terms of mental toughness, resilience, and level of passion, as well as the existing correlations between the different variables. The runner presents high levels of mental toughness, resilience, and harmonious passion and low levels of obsessive passion. Significant cor- relations are observed between various psychological factors such as resilience, mental strength, and harmonious passion. In turn, a significant inverse correlation is found be- tween resilience levels and obsessive passion. The results obtained are independent from the gender of the runner, except for those related to the harmonious passion, where women present significantly higher results than men (Table 2). Int. J. Environ. Res. Public Health 2021, 18, 2704 6 of 13 Table 1. Means, standard deviations and correlations among psychological variables. M (SD) Mental Toughness Resilience Harmonious Passion Obsessive Passion α Crombach Mental Toughness 6.84 (0.93) 1 0.87 Resilience 6.23 (0.65) 0.186 ** 1 0.90 Harmonious Passion 6.31(0.83) 0.128 * 0.127 * 1 0.88 Obsessive Passion 2.93 (1.16) −0.061 −0.177 * 0.106 * 1 0.84 * p < 0.05 (bilateral), ** p < 0.01 (bilateral). Table 2. Comparison of the results of the psychological variables according to gender. t gl Sig. (Bilateral) Mental Toughness −0.23 354 0.81 Resilience −0.28 354 0.77 Harmonious Passion −4.13 354 0.01 Obsessive Passion −0.71 354 0.47 The runner’s age and experience level appear as influential elements in some of the psychological factors evaluated (Table 3). We can state that the levels of mental toughness and resilience increase with age and with years of experience in ultra-trail races. The elite quality of the runner, identified using their performance level based on ITRA points, positively correlates with various psychological factors, such that better runners display superior results in mental toughness, resilience, and obsessive passion. Table 3. Correlations among psychological variables and years of experience/age/International Trail Running Association (ITRA) score. Years of Experience Age ITRA Score Mental Toughness 0.30 ** 0.33 ** 0.50 ** Resilience 0.14 ** 0.11 * 0.26 ** Harmonious Passion −0.070 −0.188 ** −0.070 Obsessive Passion 0.22 ** * p < 0.05 (bilateral). ** p < 0.01 (bilateral). Experience in these types of races manifests as an essential factor in the runner’s chances of completing the race. In Table 4, we can see the significant differences found between runners who finished the race and those who dropped out throughout the course according to their years of experience in ultra-marathon races. Table 4. Comparison of results in relation to finishers and withdrawals among years of experience in ultra-trail races. N M(SD) t gl Sig. Years of experience in Ultra-Trail Race Finishers 148 6.10 (2.25) Withdrawals 208 3.69 (2.22) 10.01 354 0.001 * * p < 0.001. In relation to Hypothesis 2, the results show how psychological factors play an impor- tant role in the runner’s possibility of success, both in terms of finishing the race and the time taken to complete the race, or what is considered the overall classification. “Finishers” present significantly higher psychological factors than those who withdraw in the middle of the race (Table 5). Significant differences can be observed in the factors of mental toughness, resilience, and harmonious passion, while those who withdrew from the race displayed higher results for obsessive passion. Int. J. Environ. Res. Public Health 2021, 18, 2704 7 of 13 Table 5. Comparison of psychological variables in relation to finishers and withdrawals. N t Sig. Mental Toughness Finishers 148 Withdrawals 208 4.25 0.01 Resilience Finishers 148 Withdrawals 208 3.42 0.01 Obsessive Passion Finishers 148 Withdrawals 208 −4.39 0.70 Harmonious Passion Finishers 148 Withdrawals 208 −0.41 0.01 Psychological factors are also associated with the time taken to complete the race and, therefore, the final classification obtained. The results show significant correlations (p ≤ 0.01) among psychological factors (mental toughness, resilience, and obsessive pas- sion) and race time. Upon establishing the comparison between quartiles according to overall classification time, the ANOVA results display (Table 6) the significant differences between the groups in the mental toughness, resilience, obsessive passion, and harmonious passion variables. Upon establishing the comparison between quartiles (Table 7), differences can be observed between the first group and the second and third groups and, likewise, between the fourth group and the second and third groups in mental toughness and resilience. This indicates to us that mental toughness and resilience are factors that, on the one hand, impact the ability to achieve a good classification and, on the other hand, present as factors that are essential to completing the race in the last group. Table 6. ANOVA comparative analysis by quartiles as a function of race time. Sum of Squares gl Quadratic Mean F Sig. Mental Toughness 15,219 1 15,219 18,121 0.01 Resilience 4934 1 4934 11,745 0.01 Harmonious Passion 12,666 1 12,666 19,285 0.01 Obsessive Passion 0.226 1 0.226 0.168 0.682 Table 7. Descriptive statistics and comparison of psychological variables and time race quartiles (expressed in hours). Time Race Quartiles Mental Toughness Harmonious Passion Obsessive Passion Resilience M (SD) M (SD) M (SD) M (SD) 10.32–15.01 6.58 (1.32) 5.47 (0.96) * 3.15 (1.09) 6.26 (0.39) 15.02–17.56 5.6 (0.96) * 5.15 (1.21) * 2.78 (1.44) 5.60 (0.81) * 17.57–19.10 5.74 (1.13) * 5.88 (0.80) 2.49 (0.92) * 6.04 (0.55) * 19.11–21.00 6.37 (1.08) 5.92 (0.85) 2.39 (1.22) * 6.18 (0.44) * p < 0.01. Statistically significant comparison for mental toughness: first and fourth quartiles with second and third quartiles. Statistically significant comparison for harmonious passion: third quartile with first and second quartiles; fourth quartile with first and second quartiles. Statistically significant comparison for obsessive passion: first quartile with third and fourth quartiles. Statistically significant comparison for resilience: first quartile with second and third quartiles; fourth quartile with second quartile. The results obtained in the comparisons between groups with the harmonious passion and obsessive passion variables reflect how obsessive passion is highly present in runners whose aim is to finish the race in one of the top positions, while harmonious passion strongly prevails in the groups whose goal is to complete the race. In relation to working Hypothesis 3, Table 8 displays the results of the post-race questionnaire, showing the comparison of the pre- and post-race results with significant Int. J. Environ. Res. Public Health 2021, 18, 2704 8 of 13 differences in distinct mental psychological factors between the week prior to the race and the week after. Table 8. Comparation of pre-/post-race. Moment M SD t Sig. Mental Toughness Pre test 5.62 1.04456 Post test 6.21 0.57666 2.35 0.01 Resilience Pre test 5.8614 0.65773 Post test 6.36 0.65683 2.82 0.01 Harmonious Passion Pre test 5.7344 0.86276 Post test 6.0336 0.70145 3.09 0.01 Obsesive Passion Pre test 2.9332 1.17083 Post test 2.9327 1.13836 −0.41 0.99 5. Discussion The aim of the study was to identify the psychological profile of ultra-trail runners and the relationship between these factors and athletic success and other variables such as age, sex, or experience. The study also aimed to find out if completing the race would lead to changes in the post-test results. The principal study findings verified the first two hypotheses proposed, highlighting mental toughness and resilience as predictive psychological factors for the success of ultra-trail runners. These results open new doors to preparation strategies for these types of races where runners tend to focus all their effort on physiological aspects. Nevertheless, psychological preparation takes on a decisive role in the achievement of performance-related goals. The first hypothesis predicted high scores in the psychological factors studied. The descriptive analyses supported this prediction and displayed very high values in the dimensions of mental toughness and resilience in comparison with other sports disciplines and sedentary individuals [49,50], without finding differences relative to the runner’s sex. In this sense, the results coincide with those obtained by [51] and previously by [52]. The only differences in relation to sex where those found in the harmonious passion component, in which women scored higher than men, which could suggest a greater inclination towards the pursuit of mastery goals as compared to men and a greater focus on performance [20,27]. Years of experience and age also presented strong correlations with mental toughness and resilience. These findings are in line with prior research, though in different sports disciplines [53,54]. It is possible that the uncertain environment in these types of races results in each of the competitions representing a training session in and of itself. In this sense, a higher number of races would represent more mental training. Likewise, obsessive passion in the runner correlated with the exercise addiction inventory according to that proposed by [55] and interpretable based on the large number of training hours that the discipline requires. Similar results were reported by [56], who found that endurance sports present the highest risk of developing exercise addiction. The present study also found positive correlations between mental toughness and the athlete’s level, meaning more elite runners scored higher on the MT scale, similar to the results found by [22,26,57,58] in various sports disciplines. Nevertheless, other studies did not find differences in the MT scale according to the athlete’s level [27,28]. It is possible that the type of sport makes a difference in this sense. The elite quality of the runner based on their ITRA score, as well as their experience, positively correlates with obsessive passion and addiction to practicing sports in line with that analyzed by [59] wherein obsessive passion can affect well-being and athlete performance over the long-term due to the associated strict exercise behavior. The second hypothesis was also confirmed. Unlike the results obtained in ultra- marathon runners [29], mental toughness and resilience were revealed to be decisive Int. J. Environ. Res. Public Health 2021, 18, 2704 9 of 13 factors for success in long-distance mountain races. The mental toughness and resilience factors manifested as decisive elements in achieving a better time and therefore higher classification given that the runners in the first quartile, elite, show significantly superior values than the runners in quartiles two and three. This also comes to light in the group in the last quartile (amateur runners whose only aspiration is to reach the finish line before the maximum time limit) in order to finish the race. Both factors present as essential elements for completing the race as differences are found between those who withdraw and those who finish. Brace et al. [29] did not find any relationship between mental toughness and performance in a 100-mile ultra-marathon race; however, in their study the runners had higher mental toughness than other sports. The authors suggest that the standard ultra-marathon runner must have heightened mental toughness but, once that threshold is reached, it is likely that other factors are more influential in determining elite ultra-marathon performance. Nevertheless, there are notable differences between ultra-marathon races on asphalt and those held in the mountains that could explain the discrepancies found in both studies. Height, elevation, and dirt trails hinder ultra-trail runners from maintaining a continuous or controlled pace, meaning it is much harder to know what pace could be maintained in the race. Self-sufficiency is another decisive factor in mountain races as compared with asphalt races, which are equipped with aid stations. In the mountain, the athletes themselves are required to plan out and carry their provisions, thereby entailing self-regulation as well as extra weight to be carried. Lastly, the solitary nature of the mountain is an additional component, unlike asphalt races where practically the entire race is completed among other runners and with pacemakers to set the pace; in the mountains it is common to run alone for long stretches of time. This accumulation of factors caused by the environment (mountain), provokes a level of uncertainty in ultra-trail runners that prevents adequate prior preparation based solely on physiological aspects and physical preparation. The highly demanding and rigorous nature of preparing for mountain races often leads runners to consider that simply completing the race is a success in and of itself. In this sense, this can be seen in the significant differences between those who complete the race and those who withdraw before finishing, as well as the significant inverse correlation between the times of the best finishers. Previous studies did not find differences in the physiological or fitness level between runners who reach the finish line and those who withdraw [60], however, the findings of this study are in line with other works [41] where differences are observed between those who finish and those who withdraw in terms psychological factors and how the runners approach the race. Race “finishers” score significantly higher for harmonious passion, which may be associated with higher levels of positive feelings after a successful race. On the other hand, those who withdraw from the race present significantly higher levels for obsessive passion in connection to perceptions of burnout. These results are in line with the work carried out by [61]. In the same vein, the higher results in the obsessive passion component displayed by runners who withdrew from the race can be interpreted as being at a higher risk of developing burnout than the more harmoniously passionate athletes [40]. Therefore, the study results suggest that psychological factors decisively condition athletic success, this being understood not only through the prism of overall time and classification, but also the mere fact of completing the race. Consequently, the findings confirming the second hypothesis bring to light the rele- vance that psychological factors such as mental toughness and resilience have on athletic success and performance in long-distance mountain races in a similar way as those obtained in other studies with athletes in similar disciplines [6,30]. Lastly, one of the aspects that most stood out in the present study are the significant differences found in the different variables between the pre- and post-race. These results confirm the third hypothesis presented. It is possible that the actual running of such a rigorous and demanding race is the reason behind the changes found in the levels of mental toughness, resilience, and addiction to physical exercise. In this sense, the Int. J. Environ. Res. Public Health 2021, 18, 2704 10 of 13 results would confirm the hypothesis regarding the importance of the race itself as part of the training regimen. There is no doubt that the race itself acts as a functional element and training session for the psychological factors analyzed in a manner similar to that found in factors such as emotional control, mood, anger, or tension in races with similar characteristics [12,44]. Overcoming the effects of sleep deprivation [62], dealing with the pain of neuromuscular damage that occurs after so many hours of effort [63], and experiencing the mood shifts that occur over the course of the race [16] can be key factors in the increase in mental toughness and resilience found. The increases in harmonious passion can be easily understood due to the feelings and sensations felt by runners who complete these types of races and the experience as a whole which many runners consider to be a major life experience [64]. The post-race changes to the levels of the psychological factors can be interpreted from the perspective of the potential these races present as an element of training those same traits. Limitations and Strengths The main study limitations were, on the one hand, using the results from a single ultra-trail race. Despite being one of the most prestigious national races and having the best national and international runners in attendance, studies that analyze a larger number of races are preferential. On the other hand, this research was focused exclusively on psy- chological aspects. Bearing in mind the multifactorial evidence of this sport, future research should analyze both physiological and psychological variables within the same study. On the contrary, one of the strengths was the access to a majority of elite-level athletes in such a prestigious race. It is also worth mentioning that, in general, post-race question- naires tend to notably downplay the participation of the sample; however, in this study, the sample did not display the same feeling. 6. Conclusions Based on the results obtained in this study, we can conclude that athletes who par- ticipate in ultra-trail races present very specific psychological traits that enable them to adapt to the extremely tough conditions of the races. However, despite the fact that mental toughness, resilience, addiction, and passion form part of the standard runner model, age, experience, and the elite quality of the athlete accentuate this condition even more. Mental strength and resilience are decisive factors in athletic success and performance in ultra-trail races. In this sense, athletic success should be considered in terms of both overall classification and race completion, the latter being the goal for a large part of the participants. Author Contributions: Conceptualization, D.M.-A.; methodology, J.A.P.-S.; software, D.M.-A.; val- idation, J.A.P.-S. and J.R.B.; formal analysis, E.J.-A.; investigation, D.M.-A.; resources, D.M.-A.; data curation, J.A.P.-S.; writing—original draft preparation, D.M.-A.; writing—review and editing, D.M.-A.; visualization, E.J.-A.; supervision, J.R.B. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Principado de Asturias (CEImPA 2020.454). Informed Consent Statement: All subjects gave their informed consent for inclusion before they participated in the study. Conflicts of Interest: The authors declare no conflict of interest. Int. J. Environ. Res. Public Health 2021, 18, 2704 11 of 13 References 1. Hoffman, M.D.; Ong, J.C.; Wang, G. Historical analysis of participation in 161 KM ultramarathons in North America. Int. J. Hist. Sport 2010, 27, 1877–1891. [CrossRef] 2. Peter, L.; Rüst, C.A.; Knechtle, B.; Rosemann, T.; Lepers, R. Sex differences in 24-hour ultra-marathon performance-A retrospective data analysis from 1977 to 2012. Clinics 2014, 69, 38–46. [CrossRef] 3. Garbisu-Hualde, A.; Santos-Concejero, J. What are the limiting factors during an ultra-marathon? A systematic review of the scientific literature. J. Hum. Kinet. 2020, 72, 129–139. [CrossRef] [PubMed] 4. Baumann, C.W.; Wetter, T.J. Aerobic and anaerobic changes in collegiate male runners across a cross-county season. Int. J. Exerc. Sci. 2010, 3, 225–232. 5. Buman, M.P.; Omli, J.W.; Giacobbi, P.R., Jr.; Brewer, B.W. Experiences and coping responses of “hitting the wall” for recreational marathon runners. J. Appl. Sport Psychol. 2008, 20, 282–300. [CrossRef] 6. Roebuck, G.S.; Urquhart, D.M.; Che, X.; Knox, L.; Fitzgerald, P.B.; Cicuttini, F.M.; Lee, S.; Segrave, R.; FitzGibbon, B.M. Psychological characteristics associated with ultra-marathon running: An exploratory self-report and psychophysiological study. Aust. J. Psychol. 2020, 72, 235–247. [CrossRef] 7. Nikolaidis, P.T.; Knechtle, B.; Quartiroli, A. Editorial: Who runs? Psychological, physiological and pathophysiological aspects of recreational endurance athletes. Front. Psychol. 2020, 11. [CrossRef] 8. Hauw, D.; Rochat, N.; Gesbert, V.; Astolfi, T.; Philippe, R.A.; Mariani, B. Putting together first- and third-person approaches for sport activity analysis: The case of ultra-trail runners’ performance analysis. Adv. Intell. Syst. Comput. 2017, 496, 49–58. [CrossRef] 9. Buck, K.; Spittler, J.; Reed, A.; Khodaee, M. Psychological attributes of ultramarathoners. Wilderness Environ. Med. 2018, 29, 66–71. [CrossRef] [PubMed] 10. McCormick, A.; Meijen, C.; Marcora, S. Psychological determinants of whole-body endurance performance. Sports Med. 2015, 45, 997–1015. [CrossRef] [PubMed] 11. Hughes, S.; Case, H.S.; Stuempfle, K.; Evans, D. Personality profiles of iditasport ultra-marathon participants. J. Appl. Sport Psychol. 2003, 15, 256–261. [CrossRef] 12. Lane, A.M.; Wilson, M. Emotions and trait emotional intelligence among ultra-endurance runners. J. Sci. Med. Sport 2011, 14, 358–362. [CrossRef] 13. Ruiz-Juan, F.; Sancho, A.Z. Análisis de la motivación en corredores de maratón españoles. Rev. Latinoam. Psicol. 2014, 46, 1–11. [CrossRef] 14. Knechtle, B. Ultramarathon runners: Nature or nurture? Int. J. Sports Physiol. Perform. 2012, 7, 310–312. [CrossRef] 15. Freund, W.; Weber, F.; Billich, C.; Birklein, F.; Breimhorst, M.; Schuetz, U.H. Ultra-marathon runners are different: Investigations into pain tolerance and personality traits of participants of the transeurope footrace 2009. Pain Pract. 2013, 13, 524–532. [CrossRef] [PubMed] 16. Tharion, W.J.; Strowman, S.R.; Rauch, T.M. Profile and changes in moods of ultramarathoners. J. Sport Exerc. Psychol. 1988, 10, 229–235. [CrossRef] 17. Eich, T.S.; Metcalfe, J. Effects of the stress of marathon running on implicit and explicit memory. Psychon. Bull. Rev. 2009, 16, 475–479. [CrossRef] 18. Philippe, R.A.; Rochat, N.; Vauthier, M.; Hauw, D. The story of withdrawals during an ultra-trail running race: A qualitative investigation of runners’ courses of experience. Sport Psychol. 2016, 30, 361–375. [CrossRef] 19. Knechtle, B.; Valeri, F.; Zingg, M.A.; Rosemann, T.; Rüst, C.A. What is the age for the fastest ultra-marathon performance in time-limited races from 6 h to 10 days? AGE 2014, 36, 9715. [CrossRef] [PubMed] 20. Wa´skiewicz, Z.; Nikolaidis, P.T.; Gerasimuk, D.; Borysiuk, Z.; Rosemann, T.; Knechtle, B. What motivates successful marathon runners? The role of sex, age, education, and training experience in polish runners. Front. Psychol. 2019, 10, 1671. [CrossRef] [PubMed] 21. Krouse, R.Z.; Ransdell, L.B.; Lucas, S.M.; Pritchard, E.M. Motivation, goal orientation, coaching, and training habits of women ultrarunners. J. Strength Cond. Res. 2011, 25, 2835–2842. [CrossRef] 22. Gucciardi, D.F.; Hanton, S.; Gordon, S.; Mallett, C.J.; Temby, P. The concept of mental toughness: Tests of dimensionality, nomological network, and traitness. J. Pers. 2015, 83, 26–44. [CrossRef] [PubMed] 23. Thom, C.B.; Guay, F.; Trottier, C. Mental toughness in sport: The goal-expectancy-self-control (Ges) Model. J. Appl. Sport Psychol. 2020, 1–17. [CrossRef] 24. Christensen, D.A.; Brewer, B.W.; Hutchinson, J.C. Psychological predictors of performance in a 161 KM ultramarathon run. Int. J. Sport Psychol. 2018, 49, 74–90. 25. Chen, M.A.; Cheesman, D.J. Mental Toughness of Mixed Martial Arts Athletes at Different Levels of Competition. 2013. Available online: https://journals.sagepub.com/doi/10.2466/29.30.PMS.116.3.905-917 (accessed on 28 December 2020). 26. Wieser, R.; Thiel, H. A survey of “mental hardiness” and “mental toughness” in professional male football players. Chiropr. Man. Ther. 2014, 22, 17. [CrossRef] 27. Cowden, R.G.; Meyer-Weitz, A.; Asante, K.O. Mental Toughness in Competitive Tennis: Relationships with Resilience and Stress. Front. Psychol. 2016, 7, 320. [CrossRef] [PubMed] 28. Crust, L.; Clough, P.J. Relationship between mental toughness and physical endurance. Percept. Mot. Ski. 2005, 100, 192–194. [CrossRef] [PubMed] Int. J. Environ. Res. Public Health 2021, 18, 2704 12 of 13 29. Brace, A.W.; George, K.; Lovell, G.P. Mental toughness and self-efficacy of elite ultra-marathon runners. PLoS ONE 2020, 15, e0241284. [CrossRef] 30. Hosseini, S.A.; Besharat, M.A. Relation of resilience whit sport achievement and mental health in a sample of athletes. Procedia Soc. Behav. Sci. 2010, 5, 633–638. [CrossRef] 31. Windle, G. What is resilience? A review and concept analysis. Rev. Clin. Gerontol. 2011, 21, 152–169. [CrossRef] 32. Fletcher, D.; Sarkar, M. A grounded theory of psychological resilience in Olympic champions. Psychol. Sport Exerc. 2012, 13, 669–678. [CrossRef] 33. Gould, D.; Dieffenbach, K.; Moffett, A. Psychological characteristics and their development in olympic champions. J. Appl. Sport Psychol. 2002, 14, 172–204. [CrossRef] 34. Galli, N.; Vealey, R.S. “bouncing back” from adversity: Athletes’ experiences of resilience. Sport Psychol. 2008, 22, 316–335. [CrossRef] 35. Secades, X.G.; Molinero, O.; Barquín, R.R.; Salguero, A.; la Vega, R.D.; Márquez, S. La resiliencia en el deporte: Fun-damentos teóricos, instrumentos de evaluación y revisión de la literatura. Cuad. Psicol. Deporte 2014, 14, 83–92. [CrossRef] 36. Vallerand, R.J.; Blanchard, C.; Mageau, G.A.; Koestner, R.; Ratelle, C.; Léonard, M.; Gagné, M.; Marsolais, J. Les passions de l’âme: On obsessive and harmonious passion. J. Personal. Soc. Psychol. 2003, 85, 756. [CrossRef] 37. Curran, T.; Hill, A.P.; Appleton, P.R.; Vallerand, R.J.; Standage, M. The psychology of passion: A meta-analytical review of a decade of research on intrapersonal outcomes. Motiv. Emot. 2015, 39, 631–655. [CrossRef] 38. Vallerand, R.J.; Verner-Filion, J. Theory and research in passion for sport and exercise. In Handbook of Sport Psychology; John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2020; pp. 206–229. [CrossRef] 39. Vallerand, R.J.; Mageau, G.A.; Elliot, A.J.; Dumais, A.; Demers, M.-A.; Rousseau, F. Passion and performance attainment in sport. Psychol. Sport Exerc. 2008, 9, 373–392. [CrossRef] 40. Gustafsson, H.; Hassmén, P.; Hassmén, N. Are athletes burning out with passion? Eur. J. Sport Sci. 2011, 11, 387–395. [CrossRef] 41. Rochat, N.; Hauw, D.; Philippe, R.A.; Von Roten, F.C.; Seifert, L. Comparison of vitality states of finishers and withdrawers in trail running: An enactive and phenomenological perspective. PLoS ONE 2017, 12, e0173667. [CrossRef] [PubMed] 42. Corrion, K.; Morales, V.; Bergamaschi, A.; Massiera, B.; Morin, J.-B.; D’Arripe-Longueville, F. Psychosocial factors as predictors of dropout in ultra-trailers. PLoS ONE 2018, 13, e0206498. [CrossRef] [PubMed] 43. Pedlar, C.R.; Lane, A.M.; Lloyd, J.C.; Dawson, J.; Emegbo, S.; Whyte, G.P.; Stanley, N. Sleep profiles and mood states during an expedition to the south pole. Wilderness Environ. Med. 2007, 18, 127–132. [CrossRef] 44. Lahart, I.M.; Lane, A.M.; Hulton, A.; Williams, K.; Godfrey, R.; Pedlar, C.; Wilson, M.G.; Whyte, G.P. Challenges in maintaining emotion regulation in a sleep and energy deprived state induced by the 4800km ultra-endurance bicycle race; The race across america (RAAM). J. Sports Sci. Med. 2013, 12, 481–488. 45. Beedie, C.J.; Terry, P.C.; Lane, A.M. The profile of mood states and athletic performance: Two meta-analyses. J. Appl. Sport Psychol. 2000, 12, 49–68. [CrossRef] 46. Varesco, G.; Sabater-Pastor, F.; Millet, G.Y.; Rozand, V. Age-related performance at the Ultra-Trail du Mont-Blanc®. Mov. Sport Sci. Sci. Mot. 2019, 13–19. [CrossRef] 47. Sánchez-Teruel, D. Escala de resiliencia 14 ítems (Rs-14): Propiedades psicométricas de la versión en Español. Rev. Iberoam. Diagnóstico Evaluación Avaliação Psicológica 2015, 40, 103–113. 48. Hubbard, R.; Parsa, R.A.; Luthy, M.R. The spread of statistical significance testing in psychology. Theory Psychol. 1997, 7, 545–554. [CrossRef] 49. Cuberos, R.C.; Castro-Sanchez, M.; Espejo-Garcés, T.; Ortega, F.Z. Estudio de la resiliencia en función de la modalidad deportiva: Fútbol, balonmano y esquí (Research on resilience depending on the type of sport: Football, handball and ski) Research of resilience depending on the type of sport: Football, handball and ski). Retos 2015, 157–161. [CrossRef] 50. Galli, N.; Gonzalez, S.P. Psychological resilience in sport: A review of the literature and implications for research and practice. Int. J. Sport Exerc. Psychol. 2015, 13, 243–257. [CrossRef] 51. Analysis of the Resilience Scale in Mountain Runners. Available online: https://www.researchgate.net/publication/340620780_ Analysis_of_the_resilience_scale_in_mountain_runners (accessed on 28 December 2020). 52. Lundman, B.; Strandberg, G.; Eisemann, M.; Gustafson, Y.; Brulin, C. Psychometric properties of the swedish version of the resilience scale. Scand. J. Caring Sci. 2007, 21, 229–237. [CrossRef] [PubMed] 53. Marshall, T.; Roberts, J.; Pack, S.; Basevitch, I.; Rossato, C.; Suckling, C.; Dawkins, T.; Roberts, M. The effect of long term physical training on the development of mental toughness in recreationally active participants. J. Multidiscip. Res. 2017, 9, 29–43. 54. Scharneck, L. The Mediating Effect of Self-Determined Motivation in Student-Athlete Perceptions of Coaching Behaviors and Its Effect on Grit and Mental Toughness. Master’s Thesis, Illinois State University, Chicago, IL, USA, 2017. 55. Nogueira López, A.; Molinero, O.; Salguero, A.; Márquez, S. Exercise Addiction in Practitioners of Endurance Sports: A Literature Review. Front. Psychol. 2018, 9. [CrossRef] 56. di lodovico, L.; Poulnais, S.; Gorwood, P. Which Sports Are More at Risk of Physical Exercise Addiction: A Systematic Review. Addict. Behav. 2018. [CrossRef] [PubMed] 57. Sheard, M. Hardiness commitment, gender, and age differentiate university academic performance. Br. J. Educ. Psychol. 2009, 79, 189–204. [CrossRef] [PubMed] 58. Weissensteiner, J.R.; Abernethy, B.; Farrow, D.; Gross, J. Distinguishing psychological characteristics of expert cricket batsmen. J. Sci. Med. Sport 2012, 15, 74–79. [CrossRef] Int. J. Environ. Res. Public Health 2021, 18, 2704 13 of 13 59. Schiphof-Godart, L.; Hettinga, F.J. Passion and pacing in endurance performance. Front. Physiol. 2017, 8, 83. [CrossRef] 60. Moncelon, L.; Millet, G.; Poletti, L.; Féasson, L.; Edouard, P. Facteurs prédictifs d’abandon chez les coureurs à la limite des barrières horaires en Ultra-Trail: Application à l’UTMB® et à la TDS® 2019. J. Traumatol. Sport 2020. [CrossRef] 61. Verner-Filion, J.; Schellenberg, B.J.I.; Rapaport, M.; Bélanger, J.J.; Vallerand, R.J. “The thrill of victory and the agony of defea”: Passion and emotional reactions to success and failure among recreational golfers. J. Sport Exerc. Psychol. 2018, 40, 280–283. [CrossRef] [PubMed] 62. Hurdiel, R.; Pezé, T.; Daugherty, J.; Girard, J.; Poussel, M.; Poletti, L.; Basset, P.; Theunynck, D. Combined effects of sleep deprivation and strenuous exercise on cognitive performances during The North Face® Ultra Trail du Mont Blanc® (UTMB&reg). J. Sports Sci. 2015, 33, 670–674. [CrossRef] [PubMed] 63. Alterations of Neuromuscular Function after the World’s Most Challenging Mountain Ultra-Marathon. Available online: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0065596 (accessed on 2 February 2021). 64. Holt, N.L.; Lee, H.; Kim, Y.; Klein, K. Exploring experiences of running an ultramarathon. Sport Psychol. 2014, 28, 22–35. [CrossRef]

Influence of Psychological Factors on the Success of the Ultra-Trail Runner.

03-08-2021

Méndez-Alonso, David,Prieto-Saborit, Jose Antonio,Bahamonde, Jose Ramón,Jiménez-Arberás, Estíbaliz

eng

PMC8432918

26 AJTCCM VOL. 24 NO. 1 2018 RESEARCH Background. It is a common, yet unproven, belief that patients with post-inflammatory lung disease have a better functional reserve than patients with lung cancer when compared with their respective functional parameters of operability – forced expiratory volume in one second (FEV1), maximum oxygen uptake in litres per minute (VO2max) and the diffusion capacity for carbon monoxide (DLCO). Objectives. The aim of this study was to compare a group of patients with lung cancer with a group with post-inflammatory lung disease according to their respective functional parameters of operability. We also aimed to investigate any associations of FEV1 and/or DLCO with VO2max within the two groups. Methods. We retrospectively included 100 adult patients considered for lung resection. All patients were worked up using a validated algorithm and were then sub-analysed according to their parameters of functional operability. Results. Two-thirds of patients had post-inflammatory lung diseases whilst the rest had lung cancer. The majority of the patients in the lung cancer group had coexistent chronic obstructive pulmonary disease (COPD) (n=18). Most (n=47) of the patients in the post-inflammatory group were diagnosed with a form of pulmonary TB (active or previous). Among the two groups, the lung cancer group had a higher median %FEV1 value (62.0%; interquartile range (IQR) 51.0 - 76.0) compared with the post-inflammatory group (52%; IQR 42.0 - 63.0; p=0.01). There was no difference for the %DLCO and %VO2max values. The lung cancer group also had higher predicted postoperative (ppo) values for %FEV1 (41.0%; IQR 31.0 - 58.0 v. 34.0%; IQR 23.0 - 46.0; p=0.03, respectively) and %VO2max (58.0%; IQR 44.0 - 68.0 v. 46.0%; IQR 35.0 - 60.0; p=0.02). There was no difference in the %DLCO ppo values between the groups. Conclusion. Patients with lung cancer had higher percentage values for FEV1 and ppo parameters for %FEV1 and %VO2max compared with those who had post-inflammatory lung disease. Our findings suggest that lung cancer patients have a better functional reserve. Afr J Thoracic Crit Care Med 2018;24(1):26-29. DOI:10.7196/AJTCCM2018.v24i1.158 Cancer is one of the leading causes of mortality worldwide. Lung cancer is the leading cause of cancer-related mortality globally, causing 1.6 million deaths in 2012.[1] However, in southern Africa, the relationship between lung cancer and its mortality rate remains low in comparison with other cancers and respiratory diseases.[2-5] According to the World Health Organization (WHO), an estimated 7.7 million cases of pulmonary tuberculosis (PTB) occurred worldwide in 2007[6] and South Africa (SA) had the third highest tuberculosis (TB) burden.[7,8] Treated PTB can lead to complications, including progressive loss of lung function, persistent pulmonary symptoms[9] and chronic pulmonary aspergillosis.[10-12] These complications frequently necessitate surgery. A study by Rizzi et al.[13] reported that patients with post tuberculous chronic haemoptysis (10.0%), lung destruction (8.1%), chest wall involvement (1.9%), suspected cancer (24.2%), cavitatory lung disease (21.9%) and bronchiectasis (16.1%) required elective surgery, whereas those with massive bleeding (5.4%) or a bronchopleural fistula (3.1%) required emergency surgery. Lung resection can be a high-risk procedure, especially in patients with underlying cardiopulmonary disease. Predictors of mortality include the extent of resection, comorbidities and cardiopulmonary reserve.[14,15] Ninety percent of lung cancer patients are current or past smokers, which is frequently associated with varying degrees of concomitant chronic obstructive pulmonary disease and/or ischaemic heart disease. Furthermore, many of these patients are of advanced age and this places them at an increased risk of post-operative complications and mortality.[16,17] A number of prospective studies have validated a percentage-predicted forced expiratory volume in one second predicted postoperative value (%FEV1 ppo) of <40% as a prohibitive threshold for pulmonary resection, with mortality rates as high as 50% in such patients. Ferguson et al.[18] demonstrated that a diffusion capacity for carbon monoxide (DLCO) of <60% of the predicted value was a cut-off value for major pulmonary resection. The maximum oxygen uptake in litres per minute predicted postoperative (VO2 max ppo) value of <10 ml/kg/min, obtained from either formal cardiopulmonary exercise testing (CPET) or low-technology (minimal achievement) exercise tests, is associated with a high risk of post-operative complications and death. Regarding the cardiac A comparison of the functional parameters of operability in patients with post-inflammatory lung disease and those with lung cancer requiring lung resection M H Amirali, MD, MMed (Int), FCP (SA); E M Irusen, MB ChB, FCP (SA), FCCP, PhD; C F N Koegelenberg, MB ChB, MMed (Int), FCP (SA), FRCP (UK), Cert Pulm (SA), PhD Division of Pulmonology, Department of Medicine, Stellenbosch University and Tygerberg Academic Hospital, Cape Town, South Africa Corresponding author: M H Amirali ([email protected]) AJTCCM VOL. 24 NO. 1 2018 27 RESEARCH risk assessment, the Revised Cardiac Risk Index (RCRI)[19] is used by many authorities. The criteria contain six independent variables that correlate with post-operative cardiac complications - these include a high-risk type of surgery, a history of ischaemic heart disease, cardiac failure, cerebrovascular disease, diabetes requiring treatment with insulin and pre-operative serum creatinine of >177 µmol/L. Patients with more than two variables have a postoperative cardiac complication rate >10% and are considered to be at high risk.[17] The validated algorithms used to assess candidates for lung resection are based on spirometry, the DLCO and the VO2 max.[14] One such algorithm proposed by Bolliger and Perruchoud[15] has been used widely as a tool for evaluating cardiorespiratory reserves of lung resection candidates. The algorithm proposes that patients undergo successive steps of functional testing, the results of which qualify them for varying extents of resection or alternatively preclude them from any surgery.[15] Apart from the underlying cardiopulmonary disease and other comorbidities, the calculated predicted postoperative (ppo) values for FEV1, VO2max and DLCO are directly proportional to postoperative functional state and mortality.[21] It is a commonly held belief by various experts in the field of pulmonology that patients with post-inflammatory lung disease have a better functional reserve postoperatively than patients with lung cancer, when comparing their respective FEV1, VO2max and DLCO values; however, there is limited evidence to support the belief.[16] The aim of the present study was to compare two groups of patients (i.e. patients with lung cancer v. patients with post-inflammatory lung disease), and to investigate the association of functional parameters of operability within these two groups of patients. Methods Study design and population We retrospectively enrolled adult patients who had been considered for lung resection and were referred to the Division of Pulmonology at Tygerberg Academic Hospital, Cape Town, with either lung cancer or post-inflammatory lung disease. Ethical approval for this retrospective analysis was obtained from the Stellenbosch University Research Ethics Committee (ref. no. S15/04/074). The application included a waiver of consent due to the retrospective nature and anonymity of the study design. Cases were identified from existing medical records; they were stratified into two groups, namely ‘A’ and ‘B’, where ‘A’ comprised patients with non-small-cell lung cancer while ‘B’ comprised patients with post-inflammatory lung disease (bronchiectasis, active/post tuberculous haemoptysis, and aspergilloma). After obtaining permission from the chief medical superintendent, the original medical records of all cases identified were requested and data were collected anonymously. The data collected included the demographics (age, gender), comorbidities of patients, indications for lung resection, extent of lung resection, and their pulmonary function test values (i.e. FEV1, FVC, DLCO and VO2max). The ppo value for these parameters can be calculated by the equation in Fig. 2, where the pulmonary function test (PFT) can either be %FEV1, %VO2max or %DLCO. We used three validated ways of estimating the relative functional contribution or split function, i.e. anatomical calculation, split radionucleotide perfusion scanning and quantitative computer tomography scanning and dynamic perfusion magnetic resonance imaging (MRI). Anatomical calculations of ppo values were performed on all patients who required pre-operative estimation of post-operative lung function. Patients who required further evaluation underwent either radionucleotide perfusion scanning or quantitative CT scanning. All patients were worked up for lung resection using the algorithm for the assessment of their cardiorespiratory reserves (functional operability).[17] Patients were generally followed up as outpatients and CPET was only performed once the risk of haemoptysis was Diagnosis • Stress ECG • Echo • Perfusion scan • Angiogram Treatment • Medical • Surgical High risk Resection up to calculated extent Split function VO2max, ppo Split function • FEV1, ppo • DLCO, ppo Exercise testing VO2max Lungs • FEV1 • DLCO Heart • History • ECH Pneumonectomy Positive Negative Negative Positive Yes No <40% or <10 mL.kg–1.min–1 Both <40% <35% or <10 mL.kg–1.min–1 >35% and >10 mL.kg–1.min–1 Either one >40% 40 - 75% and 10 - 20 mL.kg–1.min–1 Either one <80% >75% or >20 mL.kg–1.min–1 Both >80% Fig. 1. Algorithm proposed by Bolliger et al.,[15] adapted by Koegelenberg et al.[17] (ECG = electrocardiogram ; FEV1 = forced expiratory volume in one second ; DLCO = diffusion capacity for carbon monoxide; VO2max = maximum oxygen uptake in litres per minute; mL = millilitres; kg = kilograms; ) %PFT ppo = [%PFT – ((a/n) × %PFT)] × 100 where PFT = pulmonary function test a = number of segments to be resected n = total number of segments Fig. 2. Equation used to calculate %PFT ppo value. (ppo = predicted postoperative, PFT = pulmonary function test.) 28 AJTCCM VOL. 24 NO. 1 2018 RESEARCH evaluated (i. e. no haemoptysis for 2 weeks). Patients included in the study were then evaluated for their respective functional operability parameters. Statistical analysis χ2 comparisons and Pearson product-moment correlation coefficient (Pearson’s r or ‘r-squared’) of proportional data were performed. We did not make any assumptions for normality; hence, these non- parametric inferences were used for statistical analysis. A p-value <0.05 in a two-tailed test of proportions (χ2) was considered statistically significant. Unless stated otherwise, data are displayed as median with interquartile range (IQR) values. Results We included 100 patients in our study. The demographic data, primary diagnoses and comorbidities of the patients are summarised in Table 1.The majority of our patients were male (n=66/100); 51 were diagnosed with a post-inflammatory lung disease, while the rest had lung cancer. The most common diagnosis in the post-inflammatory group was that of haemoptysis (n=47). Bronchiectasis and aspergilloma were the second most common diagnoses, followed by post-TB bronchiectasis and destroyed lung. The majority of the patients in the lung cancer group had COPD (n=18), 11 of them were either active or previous smokers. Two of the patients had ischaemic heart disease. Most (n=47) of the patients in the post inflammatory group were diagnosed with some form of pulmonary TB (active or previous). COPD and smoking had the second and third highest prevalence, and 17 patients had no associated comorbidities. When comparing the various functional parameters of operability between the two groups, the lung cancer group had higher %FEV1 values (62.0%; IQR 51.0 - 76.0; p=0.01), there were no differences between the %DLCO (56.0%; IQR 44.0 - 75.0; p=0.509), and %VO2max values (80.0%; IQR 66.0 - 89.0; p=0.105). The lung cancer group also had higher ppo values for %FEV1 (41.0%; IQR 31.0 - 58.0; p=0.03), and %VO2max (58.0%; IQR 44.0 - 68.0; p=0.02); there was ,however, no difference for %DLCO ppo values 40.0% (IQR 23.0 - 51.0; p=0.849). The values for the post-inflammatory group were: %FEV1 52.0% (IQR 42.0 - 63.0); %DLCO 63.0% (IQR 51.0 - 75.0); and %VO2max 72.0% (IQR 59.0 - 82.0). The ppo values were: %FEV1 34.0% (IQR 23.0 - 46.0); %VO2max 46.0% (IQR 35.0 - 60.0); and %DLCO 39.0% (IQR 26.0 - 55.0). Correlation analysis did not show any correlation between the two groups. Table 1. Demographic and clinical data of study population (N=100) n (%)* Male 66 (66.0) Female 34 (34.0) Age (years), mean (range) 46.7 (17 - 72) Medical condition Lung cancer Male 15 (62.5) Female 9 (37.5) Comorbidities Hypertension 8 (19.0) HIV 0 (0.0) Pulmonary TB 1 (2.4) COPD 18 (42.9) Smoking 11 (26.2) CAD 2 (4.8) None 2 (4.8) Post-inflammatory Male 51 (67.1) Female 25 (32.9) Diagnoses Post-TB bronchiectasis 14 (19.7) Bronchiectasis 18 (25.3) Aspergillomata 18 (25.3) Destroyed lung 14 (19.7) Echinococcal cysts 3 (4.2) Empyema 1 (1.4) Adenomatoid malformation 1 (1.4) Post-TB upper-lobe changes 1 (1.4) MDR-TB 1 (1.4) Comorbidities Hypertension 6 (4.30) HIV 12 (8.70) Pulmonary TB (active and previous) 47 (34.0) COPD 30 (21.7) Smoking 23 (16.7) CAD 2 (1.4) Bronchiectasis 1 (0.7) None 17 (12.3) TB = tuberculosis; COPD = chronic obstructive pulmonary disease; CAD = coronary artery disease; MDR-TB = multidrug-resistant tuberculosis. *Unless otherwise specified. Table 2. Comparison of functional parameters of operability among the two groups   All, median (IQR) A,* median (IQR) B,† median (IQR) p-value %FEV1 55 (43 - 65) 62 (51 - 76) 52 (42 - 63) 0.01 %FEV1 ppo 35 (26 - 48) 41 (31 - 58) 34 (23 - 46) 0.03 %VO2max 73 (60 - 84) 80 (66 - 89) 72 (59 - 82) 0.105 %VO2max ppo 49 (38 - 63) 58 (44 - 68) 46 (35 - 60) 0.02 %DLCO 62 (50 - 75) 56 (44 - 75) 63 (51 - 75) 0.509 %DLCO ppo 40 (26 - 54) 40 (23 - 51) 39 (26 - 55) 0.849 IQR = interquartile range; %FEV1 = percentage predicted for forced expiratory volume in one second; %FEV1 ppo = percentage predicted for forced expiratory volume in one second predicted postoperative; %VO2max = percentage predicted for maximum oxygen uptake in litres per minute; %VO2max ppo = percentage predicted for maximum oxygen uptake in litres per minute predicted postoperative; %DLCO = percentage predicted for diffusion capacity for carbon monoxide; %DLCO ppo = percentage predicted for diffusion capacity for carbon monoxide predicted postoperative. *Non-small-cell lung cancer group. †Post-inflammatory group (bronchiectasis, post tuberculous haemoptysis, aspergilloma). AJTCCM VOL. 24 NO. 1 2018 29 RESEARCH Discussion We found statistically significant differences between the two groups when comparing the %FEV1, %FEV1 ppo, and %VO2max ppo; the lung cancer group had a higher %FEV1 (p=0.01), and higher ppo values for %FEV1 and %VO2max (p=0.03 and p=0.02, respectively). We found no statistically significant differences between the two groups when we compared the %DLCO, %DLCO ppo and %VO2max. No gender- based differences were observed. There was no correlation between the variables in either group. Therefore, both FEV1 and DLCO did not predict VO2max in either group. It is well-known that the pre-operative assessment predicts postoperative functional reserve, morbidity and mortality. Usually, a FEV1 ppo, DLCO ppo, and VO2max ppo <40% of normal values have all been found to indicate increased mortality.[22] We have shown that patients with lung cancer have a better functional reserve when compared with those who have post-inflammatory lung disease, and that neither FEV1 nor DLCO predicted VO2max in either group. There was also no predilection of the functional reserve towards the sex or age of our patients. We believe that these findings will have implications for the surgical management of patients with lung cancer, in that they may now be more readily considered for lung resection. Depending on the extent and the time elapsed from the operation, lung resections determine a variable reduction in functional reserve. A study by Brunelli et al.[23] showed that at one month after lobectomy, the FEV1, DLCO, and VO2max values were 79.5%, 81.5%, and 96% of preoperative values, respectively. These recovered to 84%, 88.5% and 97%, respectively, after 3 months. Regarding pneumonectomy, the %FEV1, %DLCO, and VO2max values were 65%, 75%, and 87% of pre- operative values at 1 month, respectively; at 3 months postoperatively, the values were 66%, 80%, and 89%, respectively. Other studies have shown similar results.[24-26] Inferring from these data, the lung cancer group in our study would most likely have a better overall functional reserve postoperatively. Therefore, the assumption that lung cancer patients have a worse functional reserve postoperatively when compared with patients who have post-inflammatory lung disease is untrue. Study strengths and limitations This was a single-centre study, which benefits from strict adherence to a validated algorithm. The retrospective nature of the study, as well as the potential selection bias, could be limiting as only patients who were deemed clinically fit were recruited as study participants. We did not collect data on postoperative complications and mortality. Conclusion We found that patients with lung cancer had higher percentage- predicted values for FEV1 and predicted postoperative values for %FEV1 and %VO2 compared with those who had post-inflammatory lung disease. Future prospective studies should preferably include the postoperative outcomes among the two groups to provide a comprehensive analysis. Acknowledgements. We would like to thank all members of the pulmonary function laboratory team of Tygerberg Academic Hospital for their assistance and Mr Maxwell Chirehwa and Ms Tonya Esterhuizen for help with the statistical analysis. Author contributions. MHA was the principal investigator, who collected the data and wrote the manuscript. CFNK assisted with data analysis and reviewed the manuscript. EMI reviewed the final manuscript. Funding. None. Conflicts of interest. None. 1. World Health Organization. The 10 leading Causes of Death by Broad Income Group. Geneva: WHO, 2011. 2. Steen TW, Aruwa JE, Hone NM. The epidemiology of adult lung disease in Botswana. Int J Tuberc Lung Dis 2001;5(5):775-782. 3. Groenewald P, Vos T, Norman R, et al. Estimating the burden of disease attributable to smoking in South Africa in 2000. S Afr Med J 2007;97(8 Pt 2):674-681. https:// doi:10.7196/SAMJ.661 4. Sitas F, Urban M, Bradshaw D, et al. Tobacco attributable deaths in South Africa. Tob Control 2004;13(4):396-399. https:// 10.1136/tc.2004.007682 5. Willcox PA, O’Brien JA, Abratt RP. Lung cancer at Groote Schuur Hospital – a local perspective. S Afr Med J 1990;78(12):716-720. https://doi.org/10.1016/0169- 5002(91)90384-I 6. United Nations. World Population Prospects. The 2008 Revision. New York: UN, 2009. 7. World Health Organization. Global Tuberculosis Control. A Short Update to the 2009 Report. Geneva: WHO, 2009. 8. World Health Organization. Global Tuberculosis Control 2009. Epidemiology, Strategy, Financing. Geneva: WHO, 2009. 9. J Ross, R I Ehrlich, E Hnizdo, N White, G J Churchyard. Excess lung function decline in gold miners following pulmonary tuberculosis. Thorax 2010;65(11):1010-1015. https://doi.org/10.1136/thx.2009.129999 10. Denning DW. Chronic aspergillosis. Washington: ASM Press, 2009. 11. Jewkes J, Kay PH, Paneth M, Citron KM. Pulmonary aspergilloma: Analysis of cavitating invasive pulmonary aspergillosis in immunocompromised patients. Thorax 1983;38(8):572-578. https://doi.org/10.1136/thx.38.8.572 12. Nam HS, Jeon K, Um SW, et al. Clinical characteristics and treatment outcomes of chronic necrotizing pulmonary aspergillosis: A review of 43 cases. Int J Infect Dis 2010;14(6):e479-e482. https://doi.org/10.1016/j.ijid.2009.07.011 13. Rizzi A, Rocco G, Massera F. Results of surgical management of tuberculosis: Experience in 206 patients undergoing operation. Ann Thorac Surg 1995; 59(4):896- 900. https://doi.org/10.1016/0003-4975(95)00011-9 14. Koegelenberg CFN, Diacon AH, Irani S, Bolliger CT. Stair climbing in the functional assessment of lung resection candidates. Respiration 2008;75(4):374-379. https://doi. org/10.1159/000116873 15. Bolliger CT, Perruchoud AP. Functional evaluation of the lung resection candidate. Eur Respir J 1998;11(1):198-212. https://doi.org/10.1183/09031936.98.11010198 16. Bello B, Fadahun O, Kielkowski K, Nelson G. Trends in lung cancer mortality in South Africa: 1995 - 2006. BMC Public Health 2011;11(1):209. https://doi:10.1186/1471- 2458-11-209. 17. Koegelenberg CFN, Plekker D, Bolliger CT. Functional evaluation for treatment. Eur Respir Monogr 2009;44:169-186. https://doi.org/10.1183/1025448x.00044010 18. Ferguson MK, Little L, Rizzo L, et al. Diffusing capacity predicts morbidity and mortality after pulmonary resection. J Thorac Cardiovasc Surg 1988;96(4):894-900 19. Lee TH, Marcantonio ER, Mangione CM, et al. Derivation and prospective validation of a simple index for prediction of cardiac risk of major noncardiac surgery. Circulation 1999;100(10):1043-1049. https://doi.org/10.1161/01.cir.100.10.1043. 20. Bolliger CT, Koegelenberg CFN, Kendal R. Preoperative assessment for lung cancer surgery. Curr Opin Pulm Med 2005;11(4):301-306. https://doi.org/10.1097/01. mcp.0000166588.01256.9c 21. Bolliger CT, Wyser C, Roser H, Solar M, Perruchoud AP. Lung scanning and exercise testing for the prediction of postoperative performance in lung resection candidates at increased risk for complications. Chest 1995;108(2):341-348. https://doi.org/10.1378/ chest.108.2.341 22. Algar FJ, Antonio A, Salvatierra A, et al. Predicting pulmonary complications after pneumonectomy for lung cancer. Eur J Cardiothorac Surg 2003;23(2):201-208. https:// doi.org/10.1016/s1010-7940(02)00719-4 23. Brunelli A, Xiume F, Refai M, et al. Evaluation of expiratory volume, diffusion capacity, and exercise tolerance following major lung resection. A prospective follow- up analysis. Chest 2007;131(1):141-147. https://doi.org/10.1378/chest.06-1345 24. Bolliger CT, Jordan P, Soler M, et al. Pulmonary function and exercise capacity after lung resection. Eur Respir J 1996;9(3):415-421. https://doi.org/10.1183/09031936.9 6.09030415 25. Nezu K, Kushibe K, Tojo T, Takahama M, Kitamura S. Recovery and limitation of exercise capacity after lung resection for lung cancer. Chest 1998;113(6):1511-1516. https://doi.org/10.1378/chest.113.6.1511 26. Bolliger CT, Guckel C, Engel H, et al. Prediction of functional reserves after lung resection: Comparison between quantitative computed tomography, scintigraphy, and anatomy. Respiration 2002;69(6):482-489. https://doi.org/10.1159/000066474 Accepted 10 October 2017.

A comparison of the functional parameters of operability in patients with post-inflammatory lung disease and those with lung cancer requiring lung resection.

04-03-2018

Amirali, M H,Irusen, E M,Koegelenberg, C F N

eng

PMC8761130

Vol.:(0123456789) Sports Medicine (2022) 52:189 https://doi.org/10.1007/s40279-021-01549-z LETTER TO THE EDITOR Response to: Comment on: “Sex‑Specific Differences in Running Injuries: A Systematic Review with Meta‑Analysis and Meta‑Regression” Karsten Hollander1 · Jan Wilke2 · Astrid Zech3 Accepted: 14 August 2021 / Published online: 4 September 2021 © The Author(s) 2021 Dear Editor, We really appreciate the Letter to the Editor by Nnamani Silva et al. [1], which added valuable information and dis- cussion to our systematic review titled “Sex-specific differ- ences in running injuries: a systematic review with meta- analysis and meta-regression” [2]. The unequal sample size of sexes, with more male run- ners in road racing events and more female novice runners, emphasizes the need to take a closer look at moderating fac- tors. Generally, detailed reporting of potential effect modi- fiers is highly encouraged in primary studies to increase the often limited power of meta-regressions. Regardless, in our meta-analysis, the exclusive inclusion of studies with both sexes for the same running background (level) and use of incidences for risk ratio calculation of each study should have reduced the influence of unequal sample size distribu- tion. However, we agree that the combination of studies with different running levels in the same pooled risk ratio calcula- tion may have led to a greater weighting of one running level (towards the level with the higher number of studies). In our meta-regression, we quantified the running level with the competition distance, training duration, and training mileage but cannot completely rule out that a differentiation for the competition level (road racing vs. novice) would have led to different results. In conclusion, the points raised by Nnamani Silva et al. [1] highlighted another important aspect in the relevant consider- ation of sex as a variable for equal sampling in addition to the possible impact of sex specificity in the etiology and probably prevention and rehabilitation of running-related injuries. Declarations Funding Open Access funding enabled and organized by Projekt DEAL. Conflict of interest Karsten Hollander, Jan Wilke, and Astrid Zech have no conflicts of interest relevant to the content of this letter. Open Access This article is licensed under a Creative Commons Attri- bution 4.0 International License, which permits use, sharing, adapta- tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. References 1. Nnamani Silva ON, Armijo PR, Feld LD, Mascarenhas Monteiro JS, Pham R, Tenforde AS. Comment on: “Sex-specific differences in running injuries: a systematic review with meta-analysis and meta-regression.” Sports Med. 2021. https:// doi. org/ 10. 1007/ s40279- 021- 01548-0. 2. Hollander K, Rahlf AL, Wilke J, Edler C, Steib S, Junge A, Zech A. Sex-specific differences in running injuries: a system- atic review with meta-analysis and meta-regression. Sports Med. 2021;51:1011–39. https:// doi. org/ 10. 1007/ s40279- 020- 01412-7. An author’s reply to this comment is available at https:// doi. org/ 10. 1007/ s40279- 020- 01412-7. * Karsten Hollander [email protected] 1 Institute of Interdisciplinary Exercise Science and Sports Medicine, MSH Medical School Hamburg, Am Kaiserkai 1, 20457 Hamburg, Germany 2 Institute of Occupational, Social and Environmental Medicine, Goethe University Frankfurt, Frankfurt, Germany 3 Department of Human Movement Science and Exercise Physiology, Institute of Sport Science, Friedrich Schiller University Jena, Jena, Germany

Response to: Comment on: "Sex-Specific Differences in Running Injuries: A Systematic Review with Meta-Analysis and Meta-Regression".

09-04-2021

Hollander, Karsten,Wilke, Jan,Zech, Astrid

eng

PMC4227876

Mean Platelet Volume (MPV) Predicts Middle Distance Running Performance Giuseppe Lippi1*, Gian Luca Salvagno2, Elisa Danese2, Spyros Skafidas3, Cantor Tarperi4, Gian Cesare Guidi2, Federico Schena4 1 Laboratory of Clinical Chemistry and Hematology, Academic Hospital of Parma, Parma, Italy, 2 Laboratory of Clinical Biochemistry, Department of Life and Reproduction Sciences, University of Verona, Verona, Italy, 3 CeRiSM (Centre for Mountain Sport and Health), Rovereto (TN), Italy, 4 Department of Neurological, Neuropsychological, Morphological and Movement Sciences, University of Verona, Verona, Italy Abstract Background: Running economy and performance in middle distance running depend on several physiological factors, which include anthropometric variables, functional characteristics, training volume and intensity. Since little information is available about hematological predictors of middle distance running time, we investigated whether some hematological parameters may be associated with middle distance running performance in a large sample of recreational runners. Methods: The study population consisted in 43 amateur runners (15 females, 28 males; median age 47 years), who successfully concluded a 21.1 km half-marathon at 75–85% of their maximal aerobic power (VO2max). Whole blood was collected 10 min before the run started and immediately thereafter, and hematological testing was completed within 2 hours after sample collection. Results: The values of lymphocytes and eosinophils exhibited a significant decrease compared to pre-run values, whereas those of mean corpuscular volume (MCV), platelets, mean platelet volume (MPV), white blood cells (WBCs), neutrophils and monocytes were significantly increased after the run. In univariate analysis, significant associations with running time were found for pre-run values of hematocrit, hemoglobin, mean corpuscular hemoglobin (MCH), red blood cell distribution width (RDW), MPV, reticulocyte hemoglobin concentration (RetCHR), and post-run values of MCH, RDW, MPV, monocytes and RetCHR. In multivariate analysis, in which running time was entered as dependent variable whereas age, sex, blood lactate, body mass index, VO2max, mean training regimen and the hematological parameters significantly associated with running performance in univariate analysis were entered as independent variables, only MPV values before and after the trial remained significantly associated with running time. After adjustment for platelet count, the MPV value before the run (p = 0.042), but not thereafter (p = 0.247), remained significantly associated with running performance. Conclusion: The significant association between baseline MPV and running time suggest that hyperactive platelets may exert some pleiotropic effects on endurance performance. Citation: Lippi G, Salvagno GL, Danese E, Skafidas S, Tarperi C, et al. (2014) Mean Platelet Volume (MPV) Predicts Middle Distance Running Performance. PLoS ONE 9(11): e112892. doi:10.1371/journal.pone.0112892 Editor: Pedro Tauler, University of the Balearic Islands, Spain Received August 19, 2014; Accepted October 16, 2014; Published November 11, 2014 Copyright:  2014 Lippi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper. Funding: The authors have no support or funding to report. Competing Interests: The authors have declared that no competing interests exist. * Email: [email protected] Introduction According to a recent on-line survey, recreational running is the most popular leisure sport activity, followed by lifting weights, biking, hiking and other outdoor activities [1]. More specifically, 75% of adults aged 24 to 44 years are engaged in outdoor running activities at least once a week in the US [2]. The typical middle distance runner is a ‘‘normal’’ trained adult subject, with few previous experiences in competitive sport and without special functional characteristics. The broad popularity of middle distance is mostly attributable to a variety of reasons, which include no need of special talent or highly-specialized and expensive equipment, and the remarkable benefits on health, fitness, stress reduction and weight control [2]. It is also noteworthy that the practice of habitual running has been associated with a significantly reduced risk of obesity, hypertension, diabetes, cardiovascular disease, cancer, osteoporosis, depression and several other chronic conditions, thus resulting in an overall 20% to 40% lower risk of mortality [3]. Both running economy and overall performance in middle distance running depend on a number of physiological factors, which are partially different from those required for short and long distance running [4,5]. The published research on half-marathon runners has mainly focused on a number of specific anthropo- metric variables (i.e., midaxillary skinfold, body mass index, percent body fat), functional characteristics (i.e., maximal aerobic power [VO2max)], body core temperature), volume and intensity in training [6–8]. Despite the well-established relationship existing PLOS ONE | www.plosone.org 1 November 2014 | Volume 9 | Issue 11 | e112892 between packed cell volume, VO2max, aerobic performance and maximal exercise capacity [9–11], a fact that has also contributed to the increase use of blood doping in sports during the past decades [12], there is little information about the association between hematological variables and middle distance running performance. As such, the aim of this study was to investigate whether some hematological parameters may predict half-mara- thon running time in a large sample of recreational runners. Materials and Methods The study was performed during a specific event called ‘‘Run For Science’’, held in Verona (Italy) in April 2014, with the purpose of analyzing the normal response of adult person to middle distance running. Forty three amateur runners were recruited (15 females and 28 males; median age 47 years and IQR 42–50 years; median body mass index 23 kg/m2 and IQR, 22– 25 kg/m2), who successfully concluded a 21.1 km half-marathon at 75–85% of their VO2max. All athletes were members of a non professional team, were habitually involved in recreational running (mean training regimen 222 min/week and IQR 191– 253 min/week; maximal oxygen uptake 50 mL/kg/min and IQR 46–55 mL/kg/min), and had rested for not less than 36 hours before the trial. Maximal aerobic capacity was individually measured in the last two weeks before the event by a running test on a treadmill using a breath by breath ergospirometric system (Quark B2, Cosmed Italy). After appropriate familiarization, each runner underwent a progressive incremental test, starting from habitual running pace and increasing speed of 0.5 km/h every min till reaching the volitional exhaustion. None of the subjects were taking medications known to alter erythrocyte or platelet metabolism, including antiplatelet or antihypertensive drugs and erythropoiesis stimulating substances. The trial started at 9.30 AM and the 21.1 km distance was covered on a relatively flat route near Verona (35 m vertical gain, with maximal slope of 1.8%), in a partially sunny day with temperatures between 12–19uC and humidity between 55–75%. Participants were free to drink ad libitum during the run. Blood was drawn in primary blood tubes containing K2EDTA (Terumo Europe N.V., Leuven, Belgium) 10 min before the start of the run and immediately thereafter (i.e., within 15 min after conclusion). The whole blood samples were immediately transported to the local laboratory under controlled conditions of temperature and humidity, where a complete blood cell count (CBC) was performed on Advia 2120 (Siemens Healthcare Diagnostics, Tarrytown NY, USA), which included measurement of hematocrit, hemoglobin, red blood cell (RBC) count, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), RBC distribution width (RDW), platelet count, mean platelet volume (MPV), white blood cell (WBC) count and differential, reticulocyte count and reticulocyte hemoglobin concentration (RetCHR). The analysis of blood specimens was concluded within 2 hours after sample collection and all results were finally expressed as median and interquartile range (IQR). Differences of pre-run and post-run values were analyzed with Wilcoxon’s test for paired samples. Univariate (i.e., Spearman’s correlation) and multivariate analysis (with adjustment for age, sex, blood lactate, body mass index, VO2max, mean training regimen and CBC parameters significantly associated with running time in univariate correlation) were performed, in order to identify potential predictors of running performance. The statistical analysis was performed with Analyse-it (Analyse-it Software Ltd, Leeds, UK) for Microsoft Excel (Microsoft Corporation, Red- mond, WA, USA). All subjects gave a written consent for being enrolled in this investigation. The study was approved by the local ethical committee (Department of Neurological, Neuropsycholog- ical, Morphological and Movement Sciences, University of Verona) and performed in accord with the Helsinki Declaration of 1975 (additional information can be downloaded from the institutional Website: http://www.dsnm.univr.it/?ent=iniziativa& id=5382, Last accessed, 10 October 2014). Results The 43 amateur runners completed the run in a median time of 113 min (IQR, 105–121 min). As predictable, the median running performance of the 28 male athletes (100 min and IQR 101– 118 min) was significantly better than that of the 15 females athletes (120 min and IQR 113–123 min; p,0.001). The median body weight decreased by 2.2% after the run (from 73.1 to 71.5 kg; p,0.001). The median lactate value measured in capillary blood at the end of the run was 4.0 mmol/L (IQR, 3.0–4.9 mmol/L). The variation of the CBC parameters after the run is shown in table 1. The values of lymphocytes and eosinophils exhibited a significant decrease compared to pre-run values, whereas those of MCV, platelets, MPV, WBC, neutrophils and monocytes were found to be significantly increased after the run. In univariate analysis, significant predictors of finishing time were the pre-run values of hematocrit, hemoglobin, MCH, RDW, MPV, RetCHR, whereas the post-run values of MCH, RDW, MPV, monocytes and RetCHR were also associated with running performance (Table 2). The VO2max was the best overall predictor of running time (r = 20.601; p,0.001), whereas neither body mass index or blood lactate at the end of the half-marathon were significantly associated with running performance (Table 2). In multivariate analysis, in which running time was entered as dependent variable whereas age, sex, blood lactate, body mass index, VO2max, mean training regimen and the CBC parameters significantly associated with running performance in univariate analysis were entered as independent variables, only MPV values before and after the trial remained significantly associated with running time (Table 3). After adjustment for the platelet count, the MPV value before the run (p = 0.042), but not thereafter (p = 0.247), remained significantly associated with running perfor- mance (Fig. 1). Neither the platelet count (r = 20.210; p = 0.303) or the MPV (r = 0.039; p = 0.851) were significantly associated with VO2max in univariate analysis. Discussion Due to the increasing popularity of recreational running as a form of leisure activity and health-promoting behavior, a large number of studies have been performed over the past decades to identify the most reliable predictors of running economy and performance. The large majority of these investigations focused on anthropometric variables, functional characteristics, as well as volume and intensity of training [13]. With the notable exception of hemoglobin and packed cell volume, little information is available on other hematological parameters that may predict middle distance running performance [14]. This investigation was hence specifically planned to establish whether some hematolog- ical parameters comprised within the CBC may be significantly associated with half-marathon running time. The leukocytes variations recorded in this study are not new, since an increase of total leukocyte, neutrophil and monocyte counts along with a decrease of lymphocyte and eosinophils values have already been reported in a number of previous investigations, and are prevalently attributable to the well-documented release of catecholamines and cortisol during exercise [8,15,16]. Mean Platelet Volume Predicts Running Performance PLOS ONE | www.plosone.org 2 November 2014 | Volume 9 | Issue 11 | e112892 The significant increase of both platelet count (median increase, 17%; IQR, 10–34%) and MPV (median increase, 6%; IQR, 1– 9%) recorded immediately after the half-marathon run substan- tially exceeded the inter-individual biological variation of these parameters (platelet count, 9.1%; MPV, 4.3%) [17], and is also consistent with the well established evidence that aerobic physical activity is effective to enhance circulating activated platelets, as well as platelet-platelet and platelet-leukocyte aggregates [18–22]. More specifically, it has been recently demonstrated that the hyperactive platelets generated during exercise are rapidly cleared by the spleen, which is also a dynamic reservoir of younger and larger platelets (i.e., the human spleen retains one-third of total body platelets, with MPV approximately 20% greater than that of circulating platelets) [23]. The younger platelets are then released into the circulation, thus explaining the significant increase of platelet count and MPV observed after endurance exercise in this and other previous studies [18–22]. Another putative mechanism that may contribute to increase the MPV has been reported by Hilberg et al. [24], who observed that moderate exercise increased both platelet reactivity and platelet-leukocyte conjugate formation, which both contribute to increase the measured value of MPV. Regardless of the underlying mechanism(s), the significant increase of MPV recorded after exercise in this and other studies [18–22] has meaningful clinical implications, suggesting that the enhanced risk of cardiovascular events that is occasionally observed in athletes may be at least in part mediated by platelet hyper- reactivity [20]. Indeed, further studies are advisable to define whether an improvement of physical fitness is also accompanied with an increased MPV. Interestingly, although the pre-run values of hematocrit, hemoglobin, MCH, RDW, MPV, RetCHR, along with the post-run values of MCH, RDW, MPV, monocytes and RetCHR were significantly associated with running time in univariate analysis, only the MPV values before and after the half-marathon remained significantly correlated with running performance in the fully-adjusted model. As predictable, both hemoglobin and hematocrit values were found to be positively correlated with running performance in univariate analysis, but the significance of these associations was lost in the fully adjusted model, especially when VO2max was entered as covariate. This is plausible, since VO2max and both hemoglobin and hematocrit clearly interplay in increasing sport performance, and VO2max is in fact enhanced by approximately 1% for each 3 g/L increase of hemoglobin [25]. As such, this is the first study demonstrating a direct correlation between platelet size and endurance performance to the best of our knowledge. It is noteworthy that the inverse association between pre-run MPV value and half-marathon running time remained significant after adjustment for a number of factors such as age, sex, blood lactate, body mass index, VO2max, mean training regimen and platelet count, thus confirming the existence of an effective interplay between platelet metabolism and aerobic performance. In univariate analysis, the correlation between running time and pre-run MPV value was the second highest overall, only preceded by that between running time and VO2max (Table 2). In agreement with a previous study [26], neither the platelet count or the MPV at baseline were significantly associated with VO2max, thus confirming that the influence of MPV on running performance may be virtually independent from the baseline cardiorespiratory fitness level. An increased platelet volume is a well established surrogate marker of platelet activation, wherein large platelets are reportedly more active than small platelets [27–29]. The association of this evidence with our data would imply that platelet hyperactivity may be a significant determinant of performance in medium distance running. The use of platelets in sports medicine has risen sharply in recent times. The platelet-rich plasma (PRP), an autologous blood fraction rich in platelets and associated cytokines and growth factors, is mainly used for treatment of sports related Table 1. Variation of the complete blood cell count after a 21.1 km half-marathon run in 43 amateur runners. Pre-run Post-run P Hematocrit 0.45 (0.44–0.47) 0.45 (0.43–0.47) 0.420 Hemoglobin (g/L) 148 (140–155) 148 (138–155) 0.137 RBC (1012/L) 4.8 (4.6–5.0) 4.8 (4.5–5.1) 0.162 MCV (fL) 94 (91–96) 95 (92–97) 0.004 MCH (pg) 31 (30–32) 31 (30–32) 0.400 MCHC (g/dL) 32.7 (32.4–33.2) 32.5 (3.19–3.32) 0.068 RDW (%) 13.4 (13.1–13.5) 13.5 (13.1–13.6) 0.001 Platelets (109/L) 260 (218–299) 321 (287–361) ,0.001 MPV (fL) 9.2 (8.6–9.8) 9.5 (8.9–10.1) ,0.001 WBC (109/L) 5.6 (4.9–6.4) 12.4 (9.8–13.9) ,0.001 Neutrophils (109/L) 3.1 (2.5–3.6) 9.3 (7.4–11.5) ,0.001 Lymphocytes (109/L) 2.0 (1.7–2.3) 1.8 (1.5–2.2) 0.037 Monocytes (109/L) 0.3 (0.2–0.4) 0.5 (0.4–0.6) ,0.001 Eosinophils (109/L) 0.2 (0.1–0.2) 0.1 (0.0–0.01) ,0.001 Basophils (109/L) 0.1 (0.1–0.1) 0.1 (0.0–0.1) 0.052 LUC (109/L) 0.01 (0.1–0.1) 0.01 (0.1–0.1) 0.063 Reticulocytes (109/L) 62 (54–74) 60 (52–73) 0.138 RetCHR (pg) 31 (31–32) 31 (31–32) 0.243 RBC, red blood cell; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin (MCH); MCHC, mean corpuscular hemoglobin concentration; (MCHC); RDW, red blood cell distribution width; MPV, mean platelet volume (MPV); WBC, white blood cell; LUC, large unstained cells; RetCHR, reticulocyte hemoglobin concentration. doi:10.1371/journal.pone.0112892.t001 Mean Platelet Volume Predicts Running Performance PLOS ONE | www.plosone.org 3 November 2014 | Volume 9 | Issue 11 | e112892 injuries [30–32]. It was recently proven that injection of PRP may also exert some ergogenic effects. In particular, Wasterlain et al. studied the effect of PRP injection on variation of performance- enhancing systemic growth factors in 25 patients [33], and observed that the administration of PRP increased the concentra- tion of insulin-like growth factor-1 (IGF-1), basic fibroblast growth factor (bFGF) and VEGF. Interestingly, Kasuya et al. also showed that a symptom-limited treadmill exercise test was effective to enhance the platelet release of nitric oxide (NO) [34], which would then contribute to raise exercise tolerance and performance [35]. Another mechanism by which platelets may contribute to enhance sport performance is the attenuation of neuropathic pain and/or fatigue during exercise [36]. Kennedy et al. studied platelet activation and function in 17 patients with chronic fatigue Table 2. Univariate correlation (r) analysis between running performance and parameters of the complete blood cell count in 43 amateur athletes who completed a 21.1 km half-marathon run. Pre-run value Post-run value r p r p Hematocrit 20.329 0.031 20.298 0.052 Hemoglobin 20.388 0.010 20.291 0.059 RBC 20.074 0.635 20.086 0.584 MCV 20.234 0.131 20.257 0.097 MCH 20.306 0.046 20.341 0.025 MCHC 20.240 0.122 20.199 0.200 RDW 0.316 0.039 0.336 0.027 Platelets 0.300 0.052 0.256 0.097 MPV 20.450 0.002 20.476 0.001 WBC 20.208 0.181 0.248 0.109 Neutrophils 20.142 0.365 0.262 0.090 Lymphocytes 20.072 0.647 20.028 0.861 Monocytes 20.262 0.090 0.361 0.017 Eosinophils 20.143 0.360 20.258 0.095 Basophils 20.096 0.538 20.197 0.207 LUC 20.039 0.805 0.185 0.234 Ret 0.290 0.059 0.208 0.181 RetCHR 20.390 0.001 20.379 0.012 Blood lactate 2 2 20.069 0.663 Body mass index 0.092 0.555 - - VO2max (mL/min/Kg) 20.601 0.001 - - RBC, red blood cell; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin (MCH); MCHC, mean corpuscular hemoglobin concentration; (MCHC); RDW, red blood cell distribution width; MPV, mean platelet volume (MPV); WBC, white blood cell; LUC, large unstained cells; RetCHR, reticulocyte hemoglobin concentration; VO2max, maximal aerobic power. doi:10.1371/journal.pone.0112892.t002 Table 3. Multivariate correlation analysis between running performance and parameters of the complete blood cell count in 43 amateur athletes who completed a 21.1 km half-marathon run. Pre-run value Post-run value p p Hematocrit 0.338 - Hemoglobin 0.216 - MCH 0.512 0.567 RDW 0.272 0.216 MPV 0.042 0.026 Monocytes - 0.080 RetCHR 0.967 0.925 Results were also adjusted for age, sex, body mass index, post-run blood lactate, maximal aerobic power (VO2max) and training regimen. MCH, mean corpuscular hemoglobin (MCH); RDW, red blood cell distribution width; MPV, mean platelet volume (MPV); RetCHR, reticulocyte hemoglobin concentration. doi:10.1371/journal.pone.0112892.t003 Mean Platelet Volume Predicts Running Performance PLOS ONE | www.plosone.org 4 November 2014 | Volume 9 | Issue 11 | e112892 syndrome and 16 healthy controls [37], reporting that patients displayed lower platelet aggregability and reduced MPV. This would be consistent with the fact that smaller and less active platelets may somehow increase the fatigue threshold, thus conditioning exercise output. A series of studies also demonstrated that platelet gel or autologous platelet tissue graft are effective to lower pain after surgery and are associated with less pain medications and broader range of motion prior to discharge [38–40]. As specifically regards sports, the use of PRP was proven to be effective in reducing pain and promoting function improvement in tennis elbow [41] and other painful tendinopa- thies [42], as well as for accelerating muscle recovery after acute injury [43]. According to these evidences, it seems hence plausible that hyperactive platelets may exert some pleiotropic effects on endurance sport performance, by both releasing ergogenic mediators as well as by triggering an increase in performance- enhancing substances such as NO into the circulation. Further studies, involving also different running distances, sports and different categories of athletes are needed to confirm these findings and to elucidate the potential underlining mechanisms linking platelet volume and aerobic performance. Author Contributions Conceived and designed the experiments: GL CT FS. Performed the experiments: GLS ED SS. Analyzed the data: GL GLS GCG FS. Wrote the paper: GL GCG FS. References 1. Reuters Press. Americans say they’re creatures of simple, solo exercise habits. Available at: http://www.reuters.com/article/2013/09/23/us-fitness-habits- idUSBRE98M0KN20130923. Accessed: 16 August 2014. 2. Knechtle B, Barandun U, Knechtle P, Zingg MA, Rosemann T, et al. (2014) Prediction of half-marathon race time in recreational female and male runners. Springerplus 3:248. 3. Kokkinos P, Myers J (2010) Exercise and physical activity: clinical outcomes and applications. Circulation 122:1637–1648. 4. Brandon LJ (1995) Physiological factors associated with middle distance running performance. Sports Med 19:268–277. 5. Saunders PU, Pyne DB, Telford RD, Hawley JA (2004) Factors affecting running economy in trained distance runners. Sports Med 34:465–485. 6. Williams C, Nute ML (1983) Some physiological demands of a half-marathon race on recreational runners. Br J Sports Med 1983;17:152–161. 7. Knechtle B, Knechtle P, Barandun U, Rosemann T (2011) Anthropometric and training variables related to half-marathon running performance in recreational female runners. Phys Sportsmed 39:158–166. 8. Del Coso J, Ferna´ndez D, Abia´n-Vicen J, Salinero JJ, Gonza´lez-Milla´n C, et al. (2013) Running pace decrease during a marathon is positively related to blood markers of muscle damage. PLoS One 8:e57602. 9. Kanstrup IL, Ekblom B (1984) Blood volume and hemoglobin concentration as determinants of maximal aerobic power. Med Sci Sports Exerc 16:256–262. 10. Joyner MJ (2003) VO2MAX, blood doping, and erythropoietin. Br J Sports Med 37:190–191. 11. Calbet JA, Lundby C, Koskolou M, Boushel R (2006) Importance of hemoglobin concentration to exercise: acute manipulations. Respir Physiol Neurobiol 151:132–140. 12. Lippi G, Franchini M, Salvagno GL, Guidi GC (2006) Biochemistry, physiology, and complications of blood doping: facts and speculation. Crit Rev Clin Lab Sci 43:349–391. Figure 1. Correlation (and 95% prediction interval, 95% PI) between running performance and baseline value of mean platelet volume (MPV) in 43 amateur athletes completing a 21.1 km half-marathon run. doi:10.1371/journal.pone.0112892.g001 Mean Platelet Volume Predicts Running Performance PLOS ONE | www.plosone.org 5 November 2014 | Volume 9 | Issue 11 | e112892 13. Midgley AW, McNaughton LR, Jones AM (2007) Training to enhance the physiological determinants of long-distance running performance: can valid recommendations be given to runners and coaches based on current scientific knowledge? Sports Med 37:857–880. 14. Joyner MJ, Coyle EF (2008) Endurance exercise performance: the physiology of champions. J Physiol 586:35–44. 15. Lippi G, Schena F, Salvagno GL, Aloe R, Banfi G, et al. (2010) Foot-strike haemolysis after a 60-km ultramarathon. Blood Transfus 10:377–383. 16. Lippi G, Salvagno GL, Danese E, Tarperi C, Guidi GC, et al. (2014) Variation of Red Blood Cell Distribution Width and Mean Platelet Volume after Moderate Endurance Exercise. Adv Hematol 2014:192173. doi:10.1155/2014/ 192173. 17. Rico´s C, Alvarez V, Cava F, Garcı´a-Lario JV, Herna´ndez A, et al. (1999) Current databases on biological variation: pros, cons and progress. Scand J Clin Lab Invest 59:491–500. 18. Knudsen JB, Brodthagen U, Gormsen J, Jordal R, Nørregaard-Hansen K, et al. (1982) Platelet function and fibrinolytic activity following distance running. Scand J Haematol 29:425–430. 19. Yilmaz MB, Saricam E, Biyikoglu SF, Guray Y, Guray U, et al. (2004) Mean platelet volume and exercise stress test. J Thromb Thrombolysis 17:115–120. 20. Li N, He S, Blomba¨ck M, Hjemdahl P (2007) Platelet activity, coagulation, and fibrinolysis during exercise in healthy males: effects of thrombin inhibition by argatroban and enoxaparin. Arterioscler Thromb Vasc Biol 27:407–413. 21. Whittaker JP, Linden MD, Coffey VG (2013) Effect of aerobic interval training and caffeine on blood platelet function. Med Sci Sports Exerc 45:342–350. 22. Kahan T, Schwieler JH, Walle´n H, Nussberger J, Hjemdahl P (2013) Platelet activation during exercise is not attenuated by inhibition of the renin angiotensin system: the role of physical activity. J Hypertens 31:2103–2104. 23. Bakovic D, Pivac N, Eterovic D, Breskovic T, Zubin P, et al. (2013) The effects of low-dose epinephrine infusion on spleen size, central and hepatic circulation and circulating platelets. Clin Physiol Funct Imaging 33:30–37. 24. Hilberg T, Menzel K, Gla¨ser D, Zimmermann S, Gabriel HH (2008) Exercise intensity: platelet function and platelet-leukocyte conjugate formation in untrained subjects. Thromb Res 122:77–84. 25. Otto JM, Montgomery HE, Richards T (2013) Haemoglobin concentration and mass as determinants of exercise performance and of surgical outcome. Extrem Physiol Med 2:33. 26. Cho HC, Kim J, Kim S, Son YH, Lee N, et al. (2012) The concentrations of serum, plasma and platelet BDNF are all increased by treadmill VO2max performance in healthy college men. Neurosci Lett 519:78–83. 27. Guthikonda S, Alviar CL, Vaduganathan M, Arikan M, Tellez A, et al. (2008) Role of reticulated platelets and platelet size heterogeneity on platelet activity after dual antiplatelet therapy with aspirin and clopidogrel in patients with stable coronary artery disease. J Am Coll Cardiol 52:743–749. 28. Mangalpally KK, Siqueiros-Garcia A, Vaduganathan M, Dong JF, Kleiman NS, et al. (2010) Platelet activation patterns in platelet size sub-populations: differential responses to aspirin in vitro. J Thromb Thrombolysis 30:251–262. 29. Colkesen Y, Muderrisoglu H (2012) The role of mean platelet volume in predicting thrombotic events. Clin Chem Lab Med 50:631–634. 30. Mei-Dan O, Lippi G, Sa´nchez M, Andia I, Maffulli N (2010) Autologous platelet-rich plasma: a revolution in soft tissue sports injury management? Phys Sportsmed 38:127–135. 31. Mishra A, Harmon K, Woodall J, Vieira A (2012) Sports medicine applications of platelet rich plasma. Curr Pharm Biotechnol 13:1185–1195. 32. World Anti-Doping Agency. The 2014 Prohibited List. International Standard. Available at: http://list.wada-ama.org/. Accessed: 16 August 2014. 33. Wasterlain AS, Braun HJ, Harris AH, Kim HJ, Dragoo JL (2013) The systemic effects of platelet-rich plasma injection. Am J Sports Med 41:186–193. 34. Kasuya N, Kishi Y, Sakita SY, Numano F, Isobe M (2002) Acute vigorous exercise primes enhanced NO release in human platelets. Atherosclerosis 161:225–232. 35. Jones AM (2013) Dietary nitrate supplementation and exercise performance. Sports Med 44 Suppl 1:S35–45. 36. Kuffler DP (2013) Platelet-rich plasma and the elimination of neuropathic pain. Mol Neurobiol 48:315–332. 37. Kennedy G, Norris G, Spence V, McLaren M, Belch JJ (2006) Is chronic fatigue syndrome associated with platelet activation? Blood Coagul Fibrinolysis 17:89– 92. 38. Gardner MJ, Demetrakopoulos D, Klepchick PR, Mooar PA (2007) The efficacy of autologous platelet gel in pain control and blood loss in total knee arthroplasty. An analysis of the haemoglobin, narcotic requirement and range of motion. Int Orthop 31:309–313. 39. Everts PA, Devilee RJ, Brown Mahoney C, van Erp A, Oosterbos CJ, et al. (2008) Exogenous application of platelet-leukocyte gel during open subacromial decompression contributes to improved patient outcome. A prospective randomized double-blind study. Eur Surg Res 40:203–210. 40. Fanning J, Murrain L, Flora R, Hutchings T, Johnson JM, et al. (2007) Phase I/ II prospective trial of autologous platelet tissue graft in gynecologic surgery. J Minim Invasive Gynecol 14:633–637. 41. Raeissadat SA, Sedighipour L, Rayegani SM, Bahrami MH, Bayat M, et al. (2014) Effect of Platelet-Rich Plasma (PRP) versus Autologous Whole Blood on Pain and Function Improvement in Tennis Elbow: A Randomized Clinical Trial. Pain Res Treat 2014:191525. 42. Andia I, Latorre PM, Gomez MC, Burgos-Alonso N, Abate M, et al. (2014) Platelet-rich plasma in the conservative treatment of painful tendinopathy: a systematic review and meta-analysis of controlled studies. Br Med Bull 110:99– 115. 43. Hamid MS, Yusof A, Mohamed Ali MR (2014) Platelet-rich plasma (PRP) for acute muscle injury: a systematic review. PLoS One 9:e90538. Mean Platelet Volume Predicts Running Performance PLOS ONE | www.plosone.org 6 November 2014 | Volume 9 | Issue 11 | e112892

Mean platelet volume (MPV) predicts middle distance running performance.

11-11-2014

Lippi, Giuseppe,Salvagno, Gian Luca,Danese, Elisa,Skafidas, Spyros,Tarperi, Cantor,Guidi, Gian Cesare,Schena, Federico

eng

PMC9140916

Citation: Puccinelli, P.J.; de Lira, C.A.B.; Vancini, R.L.; Nikolaidis, P.T.; Knechtle, B.; Rosemann, T.; Andrade, M.S. The Performance, Physiology and Morphology of Female and Male Olympic-Distance Triathletes. Healthcare 2022, 10, 797. https:// doi.org/10.3390/healthcare10050797 Academic Editors: Parisi Attilio, João Paulo Brito and Rafael Oliveira Received: 9 March 2022 Accepted: 21 April 2022 Published: 25 April 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). healthcare Article The Performance, Physiology and Morphology of Female and Male Olympic-Distance Triathletes Paulo J. Puccinelli 1 , Claudio A. B. de Lira 2 , Rodrigo L. Vancini 3 , Pantelis T. Nikolaidis 4 , Beat Knechtle 5,6,* , Thomas Rosemann 6 and Marilia S. Andrade 1 1 Programa de Pós-Graduação em Medicina Translacional, Department of Physiology, Federal University of São Paulo, São Paulo 04021-001, Brazil; [email protected] (P.J.P.); [email protected] (M.S.A.) 2 Human and Exercise Physiology Division, Faculty of Physical Education and Dance, Federal University of Goiás, Goiânia 74690-900, Brazil; [email protected] 3 Center of Physical Education and Sports, Federal University of Espírito Santo, Vitória 29075-910, Brazil; [email protected] 4 School of Health and Caring Sciences, University of West Attica, 12243 Athens, Greece; [email protected] 5 Medbase St. Gallen Am Vadianplatz, St. Gallen and Institute of Primary Care, 9100 St. Gallen, Switzerland 6 Institute of Primary Care, University of Zurich, 8091 Zurich, Switzerland; [email protected] * Correspondence: [email protected]; Tel.: +41-71-226-93-00 Abstract: Sex differences in triathlon performance have been decreasing in recent decades and little information is available to explain it. Thirty-nine male and eighteen female amateur triathletes were evaluated for fat mass, lean mass, maximal oxygen uptake (VO2 max), ventilatory threshold (VT), respiratory compensation point (RCP), and performance in a national Olympic triathlon race. Female athletes presented higher fat mass (p = 0.02, d = 0.84, power = 0.78) and lower lean mass (p < 0.01, d = 3.11, power = 0.99). VO2 max (p < 0.01, d = 1.46, power = 0.99), maximal aerobic velocity (MAV) (p < 0.01, d = 2.05, power = 0.99), velocities in VT (p < 0.01, d = 1.26, power = 0.97), and RCP (p < 0.01, d = 1.53, power = 0.99) were significantly worse in the female group. VT (%VO2 max) (p = 0.012, d = 0.73, power = 0.58) and RCP (%VO2 max) (p = 0.005, d = 0.85, power = 0.89) were higher in the female group. Female athletes presented lower VO2 max value, lower lean mass, and higher fat mass. However, females presented higher values of aerobic endurance (%VO2 max), which can attenuate sex differences in triathlon performance. Coaches and athletes should consider that female athletes can maintain a higher percentage of MAV values than males during the running split to prescribe individual training. Keywords: triathlon; sports medicine; sports physiology; female athlete; VO2 max 1. Introduction The participation of women in amateur and elite endurance sports events, includ- ing triathlon, has increased and their performance has improved during the last three decades [1–6]. Factors that are possibly associated with the increasing participation of women are the acceptance of female athletes in society, the importance of regular physical activity for the prevention and treatment of noncommunicable diseases, and the feeling of well-being that comes from a more active lifestyle [7]. Sex differences in triathlon performance seem to be decreasing, and currently vary between 12 and 18% [8,9], which seems to be influenced by distance, the level of competition, and the participation of the athletes [10,11]. Longer triathlon events, such as the Ironman (3.8 km of swimming, 180 km of cycling, and 42.2 km of running), or ultra-triathlons such as the Double Iron ultra-triathlon (7.6 km of swimming, 360 km of cycling, and 84.4 km of running) seem to be associated with decreased sex differences in performance, compared to shorter triathlon distances [12]. Healthcare 2022, 10, 797. https://doi.org/10.3390/healthcare10050797 https://www.mdpi.com/journal/healthcare Healthcare 2022, 10, 797 2 of 9 In addition, a lower tendency to sex difference was observed for elite athletes when com- pared to amateur athletes [12]. Some morphological sex differences related to body composition, such as lower fat mass percentage and higher muscle mass in the male sex [13–15], seem to be associated with better male performance [16]. Regarding the physiological factors that influence endurance performance, maximal oxygen uptake (VO2 max), ventilatory threshold (VT), and running economy (RE) are variables commonly investigated to predict aerobic performance [17]. In terms of VO2 max values, average values for females are approximately 75% of the values for males [18]. However, among athletes, these differences may be lower [19]. Lower blood hemoglobin concentrations typically found in women, as well as lower red cell mass and hematocrit level, which result in lower arterial oxygen content (CaO2) and lower O2 delivery to muscles during exercise [20,21] are the main factors responsible for these VO2 max gender differences. Differently from VO2 max, data about sex differences according to the %VO2 max at VT seems to be contradictory. Female athletes have 7 to 23% more type I muscle fibers than men [22–24]. This difference in muscle fiber composition means that women have a greater oxidation of fat [25] and faster oxygen consumption kinetics [26], which should directly impact the VT, since this is dependent on the oxidative capacity of the muscles during exercise [27]. In addition, female athletes also have a higher rate of mitochondrial respiration [28]. These differences impact muscle metabolism, making women more apt to resynthesize ATP through the oxidative metabolism. Considering these sex differences, higher VT could be expected for female athletes; however, literature data show conflicting results. [17–20]. Moreover, as for VO2 max, there are very few data for VT in Olympic- distance triathletes [19]. Women’s participation in amateur triathlon events has increased in recent years. As a result, women’s performance has also improved, and sex differences have decreased [19]. So far, it is not possible to define whether the difference is associated with training volume or physiological limitations. Therefore, understanding physiological differences between the sexes can help clarify this issue. Considering the importance of understanding the differences between sexes in en- durance sports performance and the lack of data regarding both Olympic-distance triath- letes and amateur athletes, we compared the physiological and morphological character- istics of male and female amateur triathletes of the same mean age who competed in an Olympic-distance event. Better knowledge about gender differences and female character- istics can explain the narrowing performance gap between the sexes of amateur triathlon athletes, and may help women reach their best performance. We hypothesized that male triathletes would present higher VO2 max, higher lean mass, and lower fat mass than female triathletes, but that there would be no sex differences according to VT. Because of their higher VO2 max levels and better body composition, we hypothesized that men would present lower overall race time and split times than female triathletes in the Olympic triathlon race. 2. Materials and Methods 2.1. Ethical Approval All experimental procedures were approved by the Human Research Ethics Commit- tee of the Federal University of Sao Paulo (approval number 1659697) and conformed to the principles outlined in the Declaration of Helsinki. The study was conducted in accordance with recognized ethical standards and national/international laws. After receiving instruc- tions regarding the experimental procedures, their possible risks and benefits, the objectives and justification of the research, and the principles of respect for persons involved, which encompassed a guarantee of privacy, confidentiality, and anonymity rights, the athletes signed the consent form. Healthcare 2022, 10, 797 3 of 9 2.2. Participants Ninety-three athletes who had applied for Olympic-distance triathlon races accepted an invitation to participate in the study. However, thirty-six did not meet the inclusion criteria. Therefore, fifty-seven athletes participated in the study. The inclusion criteria to participate in the study included having participated in at least one Olympic-distance triathlon race, with at least one year of triathlon practice. The exclusion criteria included having no medical approval for maximum effort, being pregnant, having acute pain in the lower limbs, edema, or not finishing the race. The main reasons for the thirty-six exclusions were giving up on participating in the Olympic-distance triathlon race (n = 21), not finishing the race (n = 6), having injuries during the training period (n = 4), absence on the day scheduled for laboratory evaluations (n = 4), and one woman got pregnant. Characterization of the sample according to the age and training habits are presented in Table 1. Table 1. Characteristics of participants. Male Triathletes (n = 39) Female Triathletes (n = 18) p Value Age (years) 38.8 ± 6.9 41.3 ± 6.68.4 0.210 Triathlon experience (years) 2.7 ± 1.7 3.3 ± 1.6 0.232 Training per week (hours) 13.2 ± 4.1 14.4 ± 3.5 0.287 Data are presented as mean ± standard deviations. As the number of female athletes who participated in the study was smaller than the number of male athletes, the power of the statistical analysis is shown with the p-value. This was employed to identify the possible lack of statistical difference between the groups due to the small sample size. 2.3. Procedures Each participant reported to the laboratory for one day, in which they answered a questionnaire about training habits. Afterwards, anthropometric data measurement and a cardiorespiratory maximal test on a treadmill were performed. The organizing committee of the races provided the overall triathlon race time and split times. Thirty-nine male and six female amateur triathletes participated in the same race. 2.4. Assessments 2.4.1. Questionnaires The athletes answered a questionnaire about training habits with the four following questions: (1) How many years have you been practicing triathlon? (2) How many hours a week do you train swimming? (3) How many hours a week do you train cycling? (4) How many hours a week do you train running? 2.4.2. Body Composition and Anthropometry The height and body mass of the participants were assessed using a calibrated sta- diometer and were measured to the nearest 0.1 kg and 0.1 cm, respectively. Dual energy X-ray absorptiometry (DXA, software version 12.3, Lunar DPX, GE Healthcare, Madison, WI, USA) was used to assess body composition (lean and fat mass). Athletes were instructed to follow their normal ad libitum hydration habits. They were evaluated after bladder voiding; no fasting or other limitations on their usual activities were implemented [29]. This method has been previously demonstrated as a reliable technique for body composition assessments [30,31]. Healthcare 2022, 10, 797 4 of 9 2.4.3. Cardiorespiratory Maximal Test on a Treadmill Cardiopulmonary exercise testing (CPET) was conducted on a motorized treadmill (Inbrasport, ATL, Porto Alegre, Brazil) using a computer-based metabolic analyzer (Quark, Cosmed, Italy). The calibration procedure was performed prior to each test, according to the manufacturer’s guidelines. CPET was used to measure VO2 max, VT, respiratory com- pensation point (RCP), and maximal aerobic velocity (MAV). The VO2 max was determined as the stabilization of VO2 (increase lower than 2.1 mL·kg−1·min−1) even after increasing the treadmill velocity during the last stage of the CPET [32]. All the volunteers reached VO2 max. VT was determined based on the following criteria: an increase in the ventilatory equivalent for oxygen without an increase in the ventilatory equivalent for carbon dioxide, and an increase in the partial pressure of exhaled oxygen. RCP was determined based on the increase in the ventilatory equivalent for carbon dioxide and the decrease in the partial pressure of exhaled carbon dioxide [33]. VT and RCP were determined separately by two experienced investigators; a third investigator was asked in cases of discordance. MAV was determined as the minimal velocity eliciting the VO2 max [34]. The percentage of MAV that the athlete maintained during the running split was also calculated. Athletes warmed up for 4 min at 10 km·h−1 (males) and 9 km·h−1 (females). After the warm-up period, the running velocity was increased by 1 km·h−1 every minute until voluntary exhaustion [35]. The entire CPET lasted between 8 and 12 min and treadmill grade was set at 1% to simulate the energetic cost of outdoor running [36]. The heart rate was measured by a monitor (Ambit 2S, Suunto, Finland) throughout the entire test, and perceived exertion was rated according to the Borg scale (a 10-point scale) [37]. 2.5. Statistical Analysis Data are presented as the mean and the standard deviations. All variables presented normal distribution and homogeneous variability according to the Shapiro–Wilk and Levene tests, respectively. In order to compare the triathlon race times and morphological and physiological characteristics of the sexes, Welch’s unequal variances t-test was used. This test was chosen because it is more reliable when the two samples have unequal sample sizes [38]. The measures of the effect size for differences between sexes were determined by calculating the mean difference between the two sexes, and then dividing the result by the pooled standard deviation. Calculating effect sizes, the magnitude of any change was judged according to the following criteria: d = 0.2 was considered a “small” effect size; 0.5 represented a “medium” effect size; and 0.8 a represented “large” effect size [39]. Considering that the study had a convenience sample, the power of all between-sex comparisons were calculated. Power analysis was performed using G*Power software [40]. The power of the tests varied from 0 to 1. Usually, researchers use 0.80 as the power level of the test [41]. Therefore, the same values were considered in this study to interpret the results. The level of significance was set at p < 0.05. 3. Results Female athletes presented significantly lower body mass (p < 0.01, d = 2.00, power = 0.99) and height (p < 0.01, d = 1.80, power = 0.99) than male athletes. There was no difference in mean age between the groups (p = 0.21, d = 0.35, power = 0.65). Overall race time and split times were compared for sexes who participated in the same triathlon event. Regarding performance, female athletes presented higher race times for swimming (+11%), cycling (+7.5%), running (+7%), and overall race time (+8%). According to morphologic characteristics, male athletes presented higher lean body mass (kg) (p < 0.01, d = 3.11, power = 0.99). According to fat mass distribution, the percentage of trunk fat mass was not different between sexes (p = 0.522, d = 0.17, power = 0.73), nor was the percentage of android fat mass (p = 0.921, d = 0.02, power = 0.74), but the percentage of gynoid fat mass was higher in female athletes (p < 0.01, d = 1.37, power = 0.98). VO2 max (p < 0.01, d = 1.46, power = 0.99), MAV (p < 0.01, d = 2.05, power = 0.99), and velocities associated with VT (p = 0.02, d = 1.26, power = 0.97) and RCP (p < 0.01, d = 1.53, power = 0.99) were significantly Healthcare 2022, 10, 797 5 of 9 higher in the male group. %VO2 max at VT (p = 0.012, d = 0.73, power = 0.58) and %VO2 max at RCP (p = 0.005, d = 0.85, power = 0.89) were higher in the female group. During the running split, female athletes were running at a higher percentage of MAV (75 ± 8%) than males (62 ± 6%) (p < 0.01, d = 1.83, power = 0.99) (Table 2). Table 2. Descriptive characteristics of the triathletes and comparison between the sexes. Male (n = 39) Female (n = 18) p Value d Value Power (1-Beta) Anthropometric profile Age (years) 38.9 ± 6.9 41.3 ± 6.6 0.21 0.35 0.65 Body mass (kg) 74.3 ± 8.8 * 59.5 ± 5.6 <0.01 2.00 0.99 Height (cm) 174.8 ± 6.5 * 164.5 ± 4.8 <0.01 1.80 0.99 Fat mass (%) 16.8 ± 5.6 * 23.2 ± 9.2 0.02 0.84 0.78 Lean Mass (kg) 59.0 ± 5.7 * 43.0 ± 4.5 <0.01 3.11 0.99 Trunk fat mass (%) 19.8 ± 6.8 21.3 ± 10.2 0.52 0.17 0.73 Android fat mass (%) 22.7 ± 8.6 22.4 ± 12.0 0.92 0.02 0.74 Gynoid fat mass (%) 21.9 ± 6.2 * 33.2 ± 9.8 <0.01 1.37 0.98 Maximal graded exercise test VO2 max (ml·kg−1·min−1) 59.9 ± 6.3 * 49.5 ± 7.8 <0.01 1.46 0.99 VT (%VO2 max) 74.4 ± 5.6 * 78.7 ± 6.1 0.01 0.73 0.58 Velocity at VT (km·h−1) 12.4 ± 1.4 * 10.5 ± 1.6 <0.01 1.26 0.97 RCP (%VO2 max) 87.5 ± 4.6 * 91.2 ± 4.1 0.01 0.85 0.89 Velocity at RCP (km·h−1) 14.8 ± 1.5 * 12.5 ± 1.5 <0.01 1.53 0.99 MAV (km·h−1) 17.8 ± 1.4 * 14.6 ± 1.7 <0.01 2.05 0.99 Running split %MAV 62 ± 6 * 75 ± 8 <0.01 1.83 0.99 Velocity (km·h−1) 11.0 ± 1.0 * 11.0 ± 1.8 0.99 0.00 0.99 Data are presented as mean ± standard deviations. d value: Effect size (Cohen’s D). VO2 max: maximal oxygen uptake. VT: ventilatory threshold. RCP: respiratory compensation point (RCP). MAV: maximal aerobic velocity. * significant difference between sexes (p < 0.05). 4. Discussion The primary aim of this study was to compare the sex differences of amateur Olympic- distance triathletes in relation to performance and physiological and morphological char- acteristics. The main findings were that: (i) the sex differences in performance were 8.0% for overall race time, 11% for swimming, 7.5% for cycling, and 7% for running; (ii) female athletes presented a lower VO2 max and a higher %VO2 max at VT and RCP than male athletes; (iii) female athletes presented lower lean mass than males; and (iv) female athletes presented higher total fat mass and gynoid fat mass than males, but the same android and trunk fat masses. The sex differences in 1.5 km of swimming, 40 km of cycling, 10 km of running, and overall race time were 11.0, 7.5, 7.0, and 8.0%, respectively. Higher sex differences were previously shown for the top 10 athletes of each age group of the World Championship from 2009 to 2011, with a 13.3% performance difference in swimming, 10.7% difference in cycling, 7.5% difference in running, and 12% difference in overall race time [42]. Higher sex difference between the top five athletes from the “Zurich triathlon”, which occurs in Zurich, Switzerland, in each category have also been shown (18.5% in swimming, 15.5% in cycling, 18.5% in running, and 17.1% in overall race time) [6]. Therefore, it is evident that the sex differences in a given performance depend on the race level (world, national or regional championship). In the present study, minor differences were found between the sexes; however, only amateur athletes were studied, which differs from the studies mentioned above that evaluated elite athletes. As expected, female athletes presented lower VO2 max and MAV values (49.5 ± 7.8 mL·kg−1·min−1 and 14.6 ± 1.7 km·h−1, respectively) than male athletes (59.9 ± 6.3 mL·kg−1·min−1 and 17.8 ± 1.0 km·h−1, respectively), showing a sex differ- Healthcare 2022, 10, 797 6 of 9 ence of approximately 19%. Similar sex difference have previously been shown for elite younger triathletes, reporting 20% lower values for females than for males (56.1 and 67.9 mL·kg−1·min−1) [43]. However, VO2 max values for ultra-endurance triathletes seem to be more similar between the sexes (68.8 and 65.9 mL·kg−1·min−1 for males and females, respectively), evidencing a sex difference of only 4.4% [44]. Besides maximal capacity for oxygen uptake, endurance performance also depends on VT. It has been suggested that 70% of success in endurance running depends on these physiological parameters [17]. An important new finding from the present study is that the female athletes presented higher values for VT (78.7 ± 6.1% for females and 74.4 ± 5.6% of VO2 max for males) and RCP (91.2 ± 4.1% for females and 87.5 ± 4.6% VO2 max for males) than male athletes. In addition, female athletes maintained a velocity corresponding to 75% of the MAV during the running split, which is higher than the value for males, who maintained 62% of their MAV [34]. The VT is limited by the peripheral conditions (i.e., mitochondrial volume, capillary density, oxidative enzyme capacity) [45,46]. Consider- ing this context, females present different metabolic (greater proportional area of type I fibers [22–24], greater whole-muscle oxidative capacity [26], and greater mitochondrial ox- idative function [28]), contractile (Ca2+ transients were smaller in magnitude and longer in duration in females [47]), and hemodynamic (greater vasodilatory responses of the arteries to muscles and higher density of capillaries per unit of skeletal muscle [22]) properties of skeletal muscles than males, favoring ATP resynthesis from oxidative phosphorylation during exercise [48,49], which could contribute to a higher VT. Triathlon performance is also associated with body composition [16,50]. In this study, female athletes presented lower lean mass than males and higher total fat mass and gynoid fat mass percentage. The android and trunk body mass did not differ between the sexes. Moreover, fat mass values for both sexes were higher than those reported for elite athletes (<13% for female and <5% for males) [43]. Therefore, female body composition seems to be disadvantageous for athletic performance [13,14,51]. Regarding the limitations of this study, the test measurements cited were performed only on a treadmill. Thus, as the physiologic characteristics were only measured during a running activity using a treadmill, it would be very interesting to identify sex differences with the same measurements in tests performed during cycling or swimming activities. The inclusion of amateur athletes rather than elite athletes was another study limitation. Furthermore, this was a cross-sectional study, which prevented us from the studying the performance difference between sexes over time. Considering the increased popularity of Olympic-distance triathlon, especially among women, who were underrepresented in this sport until recently [52], the findings of the present study have practical applications for training monitoring. Strength and conditioning coaches working with triathletes might develop separate exercise programs for each sex. Thus, an awareness of physiological sex differences related to performance would help coaches to prescribe sex-tailored training. In this context, the main finding from the present study was that the female athletes presented higher values of aerobic endurance (%VO2 max) than male athletes. These findings suggest that female athletes can maintain a higher percentage of MAV values than males during the running split; therefore, coaches could consider these findings to prescribe individual training. 5. Conclusions In summary, female athletes present lower VO2 max and lean mass, and higher fat mass. However, they present higher values of aerobic endurance (%VO2 max), which can attenuate sex differences in triathlon performance. However, the sex differences in VT require further investigation, as there are few data about this variable in the literature. Healthcare 2022, 10, 797 7 of 9 Author Contributions: Conceptualization, P.J.P. and M.S.A.; methodology, P.J.P. and C.A.B.d.L.; software, R.L.V.; validation, C.A.B.d.L. and M.S.A.; formal analysis, P.J.P. and R.L.V.; investigation, M.S.A.; data curation, P.T.N.; writing—original draft preparation, C.A.B.d.L.; writing—review and editing, P.T.N., B.K. and T.R.; visualization, B.K. and T.R.; supervision, M.S.A. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Human Research Ethics Committee of the University (approval number 1659697). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Data supporting reported results can be asked to corresponding author. Acknowledgments: We would like to thank all of the participants who volunteered their time to participate in the study, the Olympic Training and Research Center (Centro Olímpico de Treinamento e Pesquisa, COTP, São Paulo, Brazil). Conflicts of Interest: The authors declare no conflict of interest. References 1. Jokl, P.; Sethi, P.M.; Cooper, A.J. Master’s performance in the New York City Marathon 1983–1999. Br. J. Sports Med. 2004, 38, 408–412. [CrossRef] 2. Leyk, D.; Erley, O.; Ridder, D.; Leurs, M.; Rüther, T.; Wunderlich, M.; Sievert, A.; Baum, K.; Essfeld, D. Age-related changes in marathon and half-marathon performances. Int. J. Sports Med. 2007, 28, 513–517. [CrossRef] 3. Hoffman, M.D.; Wegelin, J.A. The western states 100-mile endurance run: Participation and performance trends. Med. Sci. Sports Exerc. 2009, 41, 2191–2198. [CrossRef] 4. Hoffman, M.D. Performance trends in 161-km ultramarathons. Int. J. Sports Med. 2010, 31, 31–37. [CrossRef] [PubMed] 5. Lepers, R.; Cattagni, T. Do older athletes reach limits in their performance during marathon running? Age 2012, 34, 773–781. [CrossRef] [PubMed] 6. Etter, F.; Knechtle, B.; Bukowski, A.; Rüst, C.A.; Rosemann, T.; Lepers, R. Age and gender interactions in short distance triathlon performance. J. Sports Sci. 2013, 31, 996–1006. [CrossRef] 7. Deaner, R.O. Distance running as an ideal domain for showing a sex difference in competitiveness. Arch. Sex. Behav. 2013, 42, 413–428. [CrossRef] 8. Lepers, R.; Knechtle, B.; Stapley, P.J. Trends in triathlon performance: Effects of sex and age. Sport. Med. 2013, 43, 851–863. [CrossRef] 9. Stiefel, M.; Rüst, C.A.; Rosemann, T.; Knechtle, B. A comparison of participation and performance in age-group finishers competing in and qualifying for Ironman Hawaii. Int. J. Gen. Med. 2013, 6, 67–77. [CrossRef] 10. Lepers, R.; Rüst, C.A.; Stapley, P.J.; Knechtle, B. Relative improvements in endurance performance with age: Evidence from 25 years of Hawaii Ironman racing. Age 2013, 35, 953–962. [CrossRef] 11. Knechtle, B.; Zingg, M.; Rosemann, T.; Rust, C. Sex difference in top performers from Ironman to double deca iron ultra-triathlon. Open Access J. Sport. Med. 2014, 5, 159–172. [CrossRef] 12. Joyner, M.J. Physiological limits to endurance exercise performance: Influence of sex. J. Physiol. 2017, 595, 2949–2954. [CrossRef] [PubMed] 13. Fleck, S.J. Body composition of elite American athletes. Am. J. Sports Med. 1983, 11, 398–403. [CrossRef] 14. Knechtle, B.; Wirth, A.; Baumann, B.; Knechtle, P.; Rosemann, T. Personal best time, percent body fat, and training are differently associated with race time for male and female ironman triathletes. Res. Q. Exerc. Sport 2010, 81, 62–68. [CrossRef] 15. Heydenreich, J.; Kayser, B.; Schutz, Y.; Melzer, K. Total Energy Expenditure, Energy Intake, and Body Composition in Endurance Athletes Across the Training Season: A Systematic Review. Sport. Med.-Open 2017, 3, 8. [CrossRef] [PubMed] 16. Landers, G.J.; Blanksby, B.A.; Ackland, T.R.; Smith, D. Morphology and performance of world championship triathletes. Ann. Hum. Biol. 2000, 27, 387–400. [CrossRef] 17. Millet, G.P.; Vleck, V.E.; Bentley, D.J. Physiological requirements in triathlon. J. Hum. Sport Exerc. 2011, 6, 184–204. [CrossRef] 18. Sharma, H.B.; Kailashiya, J. Gender difference in aerobic capacity and the contribution by body composition and haemoglobin concentration: A study in young Indian National hockey players. J. Clin. Diagnostic Res. 2016, 10, CC09–CC13. [CrossRef] [PubMed] 19. Lepers, R. Sex difference in triathlon performance. Front. Physiol. 2019, 24, 973. [CrossRef] [PubMed] 20. Cureton, K.; Bishop, P.; Hutchinson, P.; Newland, H.; Vickery, S.; Zwiren, L. Sex difference in maximal oxygen uptake—Effect of equating haemoglobin concentration. Eur. J. Appl. Physiol. Occup. Physiol. 1986, 54, 656–660. [CrossRef] Healthcare 2022, 10, 797 8 of 9 21. Ansdell, P.; Thomas, K.; Hicks, K.M.; Hunter, S.K.; Howatson, G.; Goodall, S. Physiological sex differences affect the integrative response to exercise: Acute and chronic implications. Exp. Physiol. 2020, 105, 2007–2021. [CrossRef] [PubMed] 22. Roepstorff, C.; Thiele, M.; Hillig, T.; Pilegaard, H.; Richter, E.A.; Wojtaszewski, J.F.P.; Kiens, B. Higher skeletal muscle α2AMPK activation and lower energy charge and fat oxidation in men than in women during submaximal exercise. J. Physiol. 2006, 574, 125–138. [CrossRef] [PubMed] 23. Simoneau, J.A.; Bouchard, C. Human variation in skeletal muscle fiber-type proportion and enzyme activities. Am. J. Physiol.- Endocrinol. Metab. 1989, 257, E567–E572. [CrossRef] 24. Staron, R.S.; Hagerman, F.C.; Hikida, R.S.; Murray, T.F.; Hostler, D.P.; Crill, M.T.; Ragg, K.E.; Toma, K. Fiber type composition of the vastus lateralis muscle of young men and women. J. Histochem. Cytochem. 2000, 48, 623–629. [CrossRef] 25. Tarnopolsky, M.A. Sex differences in exercise metabolism and the role of 17-beta estradiol. Med. Sci. Sports Exerc. 2008, 40, 648–654. [CrossRef] 26. Beltrame, T.; Rodrigo, V.; Hughson, R.L. Sex differences in the oxygen delivery, extraction, and uptake during moderate-walking exercise transition. Appl. Physiol. Nutr. Metab. 2017, 42, 994–1000. [CrossRef] 27. Weltman, A. The Blood Lactate Response to Exercise; Human Kinetics: Champaign, IL, USA, 1995; ISBN 0873227697 9780873227698. 28. Cardinale, D.A.; Larsen, F.J.; Schiffer, T.A.; Morales-Alamo, D.; Ekblom, B.; Calbet, J.A.L.; Holmberg, H.C.; Boushel, R. Superior intrinsic mitochondrial respiration in women than in men. Front. Physiol. 2018, 9, 1133. [CrossRef] [PubMed] 29. Sanfilippo, J.; Krueger, D.; Heiderscheit, B.; Binkley, N. Dual-Energy X-ray Absorptiometry Body Composition in NCAA Division I Athletes: Exploration of Mass Distribution. Sports Health 2019, 11, 453–460. [CrossRef] [PubMed] 30. Hill, A.M.; LaForgia, J.; Coates, A.M.; Buckley, J.D.; Howe, P.R.C. Estimating abdominal adipose tissue with DXA and anthropom- etry. Obesity 2007, 15, 504. [CrossRef] [PubMed] 31. Choi, Y.J.; Seo, Y.K.; Lee, E.J.; Chung, Y.S. Quantification of visceral fat using dual-energy x-ray absorptiometry and its reliability according to the amount of visceral fat in Korean Adults. J. Clin. Densitom. 2015, 18, 192–197. [CrossRef] [PubMed] 32. Longstreet Taylor, H.; Buskirk, E.; Henschel, A. Maximal oxygen intake as an objective measure of cardio-respiratory performance. J. Appl. Physiol. 1955, 8, 73–80. [CrossRef] 33. Whipp, B.J.; Ward, S.A.; Wasserman, K. Respiratory markers of the anaerobic threshold. Adv. Cardiol. 1986, 35, 47–64. [CrossRef] [PubMed] 34. Puccinelli, P.J.; Lima, G.H.O.; Pesquero, J.B.; de Lira, C.A.B.; Vancini, R.L.; Nikolaids, P.T.; Knechtle, B.; Andrade, M.S. Previous experience, aerobic capacity and body composition are the best predictors for Olympic distance triathlon performance: Predictors in amateur triathlon. Physiol. Behav. 2020, 225, 113110. [CrossRef] [PubMed] 35. de Lira, C.A.B.; Peixinho-Pena, L.F.; Vancini, R.L.; de Freitas Guina Fachina, R.J.; de Almeida, A.A.; Andrade, M.D.S.; da Silva, A.C. Heart rate response during a simulated Olympic boxing match is predominantly above ventilatory threshold 2: A cross sectional study. Open Access J. Sport. Med. 2013, 4, 175–182. [CrossRef] 36. Jones, A.M.; Doust, J.H. A 1% treadmill grade most accurately reflects the energetic cost of outdoor running. J. Sports Sci. 1996, 14, 321–327. [CrossRef] [PubMed] 37. Noble, B.J.; Borg, G.A.V.; Jacobs, I.; Ceci, R.; Kaiser, P. A category-ratio perceived exertion scale: Relationship to blood and muscle lactates and heart rate. Med. Sci. Sports Exerc. 1983, 15, 523–528. [CrossRef] [PubMed] 38. Ruxton, G.D. The unequal variance t-test is an underusedalternative to Student’s t-test and the Mann–Whitney U test. Behav. Ecol. 2006, 17, 688–690. [CrossRef] 39. Cohen, J. Statistical Power for the Behavioral Sciences, 2nd ed.; Lawrence Erlbaum Associates: Hillsdale, NJ, USA, 1988; ISBN 0-8058-0283-5. 40. Kang, H. Sample size determination and power analysis using the G*Power software. J. Educ. Eval. Health Prof. 2021, 18, 17. [CrossRef] [PubMed] 41. Banerjee, A.; Chitnis, U.; Jadhav, S.; Bhawalkar, J.; Chaudhury, S. Hypothesis testing, type I and type II errors. Ind. Psychiatry J. 2009, 18, 127–131. [CrossRef] 42. Stevenson, J.L.; Song, H.; Cooper, J.A. Age and sex differences pertaining to modes of locomotion in triathlon. Med. Sci. Sports Exerc. 2013, 45, 976–984. [CrossRef] 43. Bunc, V.; Heller, J.; Horcic, J.; Novotny, J. Physiological profile of best Czech male and female young triathletes. J. Sports Med. Phys. Fitness 1996, 36, 265–270. [PubMed] 44. O’toole, M.L.; Douglas, W.; Crosby, L.O.; Douglas, P.S. The ultraendurance triathlete: A physiological profile. Med. Sci. Sports Exerc. 1987, 19, 45–50. [CrossRef] [PubMed] 45. Holloszy, J.O.; Coyle, E.F. Adaptations of skeletal muscle to endurance exercise and their metabolic consequences. J. Appl. Physiol. Respir. Environ. Exerc. Physiol. 1984, 56, 831–838. [CrossRef] 46. Coyle, E.F.; Coggan, A.R.; Hopper, M.K.; Walters, T.J. Determinants of endurance in well-trained cyclists. J. Appl. Physiol. 1988, 64, 2622–2630. [CrossRef] 47. Wasserstrom, J.A.; Kapur, S.; Jones, S.; Faruque, T.; Sharma, R.; Kelly, J.E.; Pappas, A.; Ho, W.; Kadish, A.H.; Aistrup, G.L. Characteristics of intracellular Ca2+ cycling in intact rat heart: A comparison of sex differences. Am. J. Physiol.-Heart Circ. Physiol. 2008, 295, H1895–H1904. [CrossRef] 48. Mitchell, E.A.; Martin, N.R.W.; Bailey, S.J.; Ferguson, R.A. Critical power is positively related to skeletal muscle capillarity and type I muscle fibers in endurance-trained individuals. J. Appl. Physiol. 2018, 125, 737–745. [CrossRef] [PubMed] Healthcare 2022, 10, 797 9 of 9 49. Vanhatalo, A.; Black, M.I.; DiMenna, F.J.; Blackwell, J.R.; Schmidt, J.F.; Thompson, C.; Wylie, L.J.; Mohr, M.; Bangsbo, J.; Krustrup, P.; et al. The mechanistic bases of the power-time relationship: Muscle metabolic responses and relationships to muscle fibre type. J. Physiol. 2016, 594, 4407–4423. [CrossRef] 50. Ackland, T.R.; Blanksby, B.A.; Landers, G.; Smith, D. Anthropmetrlc profiles of elite trlathletes. J. Sci. Med. Sport 1998, 1, 52–56. [CrossRef] 51. Knechtle, B.; Knechtle, P.; Rosemann, T. Similarity of anthropometric measures for male ultra-triathletes and ultra-runners. Percept. Mot. Skills 2010, 111, 805–818. [CrossRef] 52. Wonerow, M.; Rüst, C.A.; Nikolaidis, P.T.; Rosemann, T.; Knechtle, B. Performance trends in age group triathletes in the olympic distance triathlon at the world championships 2009–2014. Chin. J. Physiol. 2017, 60, 137–150. [CrossRef] [PubMed]

The Performance, Physiology and Morphology of Female and Male Olympic-Distance Triathletes.

04-25-2022

Puccinelli, Paulo J,de Lira, Claudio A B,Vancini, Rodrigo L,Nikolaidis, Pantelis T,Knechtle, Beat,Rosemann, Thomas,Andrade, Marilia S

eng

PMC3081141

Low Anaerobic Threshold and Increased Skeletal Muscle Lactate Production in Subjects with Huntington’s Disease Andrea Ciammola, MD,1* Jenny Sassone, PhD,1 Monica Sciacco, MD,2 Niccolo` E. Mencacci, MD,1 Michela Ripolone, PhD,2 Caterina Bizzi, MD,3 Clarissa Colciago, PhD,1 Maurizio Moggio, MD,2 Gianfranco Parati, MD,3,4 Vincenzo Silani, MD,1 and Gabriella Malfatto, MD3 1Department of Neurology and Laboratory of Neuroscience, Centro ‘‘Dino Ferrari’’ Universita` degli Studi di Milano - IRCCS Istituto Auxologico Italiano, Milan, Italy 2Department of Neurological Sciences, Centro ‘‘Dino Ferrari,’’ Universita` di Milano, IRCCS Fondazione Ospedale Maggiore Policlinico, Mangiagalli and Regina Elena, Milan, Italy 3Department of Cardiology, S. Luca Hospital, IRCCS Istituto Auxologico Italiano, Milan, Italy 4Department of Clinical Medicine and Prevention, Universita` di Milano-Bicocca, Milan, Italy ABSTRACT: Mitochondrial defects that affect cel- lular energy metabolism have long been implicated in the etiology of Huntington’s disease (HD). Indeed, several studies have found defects in the mitochondrial functions of the central nervous system and peripheral tissues of HD patients. In this study, we investigated the in vivo oxi- dative metabolism of exercising muscle in HD patients. Ventilatory and cardiometabolic parameters and plasma lactate concentrations were monitored during incremen- tal cardiopulmonary exercise in twenty-five HD subjects and twenty-five healthy subjects. The total exercise capacity was normal in HD subjects but notably the HD patients and presymptomatic mutation carriers had a lower anaerobic threshold than the control subjects. The low anaerobic threshold of HD patients was associated with an increase in the concentration of plasma lactate. We also analyzed in vitro muscular cell cultures and found that HD cells produce more lactate than the cells of healthy subjects. Finally, we analyzed skeletal muscle samples by electron microscopy and we observed strik- ing mitochondrial structural abnormalities in two out of seven HD subjects. Our findings confirm mitochondrial abnormalities in HD patients’ skeletal muscle and sug- gest that the mitochondrial dysfunction is reflected func- tionally in a low anaerobic threshold and an increased lactate synthesis during intense physical exercise. V C 2010 Movement Disorder Society Key Words: Huntington’s disease; skeletal muscle; anaerobic threshold; mitochondria Huntington’s disease is an autosomal-dominant neu- rodegenerative disorder characterized by chorea, de- mentia, and psychiatric disturbances. The genetic mutation underlying the disease is the expansion of the triplet cytosine-adenosine-guanosine (CAG) in the IT-15 gene; this mutation encodes for an expanded polyglutamine (polyQ) tract in the huntingtin (htt) pro- tein.1 Htt is ubiquitously expressed in the brain as well as in many extra-CNS tissues such as skeletal muscle.2 The expression of mutated htt has deleterious effects on skeletal muscle; in particular, HD patients suffer from muscular weakness3,4 and undergo progressive muscular wasting.5,6 In addition to this clinical evidence, various abnormalities have been observed in the muscular tissues of HD patients and in HD mouse models. These abnor- malities include skeletal muscle atrophy7,8 and impair- ment of adenosine triphosphate (ATP) metabolism, which manifests as a reduced ratio of phosphocreatine to ------------------------------------------------------------ Additional Supporting Information may be found in the online version of this article. *Correspondence to: Andrea Ciammola, MD, Department of Neurology and Laboratory of Neuroscience, IRCCS Istituto Auxologico Italiano, via Spagnoletto 3, 20149 Milan, Italy; [email protected] Funding agencies: This work was supported in part by Italian Health Ministry (AC, Malattie Neurodegenerative ex art. 56 legge finanziaria 2004), and by Associazione Amici del Centro Dino Ferrari, University of Milan, the Telethon project GTB07001, the Eurobiobank project QLTR- 2001-02769 and R.F. 02.187 Criobanca Automatizzata di Materiale Biologico. Relevant conflict of interest: Nothing to report. Full financial disclosures and author roles may be found in the online version of this article. Received: 30 March 2010; Revised: 22 April 2010; Accepted: 26 April 2010 Published online 7 October 2010 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/mds.23258 R E S E A R C H A R T I C L E 130 Movement Disorders, Vol. 26, No. 1, 2011 inorganic phosphate and a lower production of mito- chondrial ATP.9–11 Whereas mitochondria-related ener- getic dysfunctions have been found in both the CNS and skeletal muscle of HD patients,12 it is still unclear whether the cellular energy metabolism impairment is a primary event in the cascade of pathogenic events that occurs in the brains of HD patients. To clarify this issue, previous studies used magnetic resonance spectroscopy (MRS) to analyze the brain lactate levels of symptomatic and presymptomatic HD subjects; these studies, how- ever, produced conflicting data.13–16 As skeletal muscle cells, like neurons, are postmitotic cells that are highly dependent on oxidative metabolism, we decided to inves- tigate the in vivo oxidative metabolism of exercising muscle in HD subjects. We hypothesized that during physical exercise, the lower level of ATP synthesis in HD patients would reduce the ability of muscle cells to extract O2 from blood; as a result, HD patients would reach the anaerobic threshold (AT) early and show a cor- respondingly high level of lactate production.17,18 This clinical study is designed to measure ventilatory and car- diometabolic parameters as well as lactate production in presymptomatic and symptomatic HD gene carriers dur- ing a cardiopulmonary exercise test. Patients and Methods HD Patients, Presymptomatic HD Subjects, and Control Subjects Table 1 shows the clinical and demographic data of the HD patients and control subjects. All of the HD subjects had had DNA analysis demonstrating more than 39 CAG repeats. Patients were excluded if they met any of the following criteria: (1) concomitant pres- ence of metabolic, endocrine or muscular disorders; (2) arterial hypertension that required treatment (defined as a systolic pressure >140 mm Hg and a diastolic pres- sure >90 mm Hg); (3) systolic and/or diastolic heart failure (defined as an ejection fraction <50% and/or an abnormal diastolic phase) and valvular or morphologi- cal abnormalities diagnosed through echocardiography; (4) use of drugs that affect metabolism and/or muscular functions;(5) a history of drug addiction (6) a body mass index (BMI) <18 or >25 and (7) an inability to use the bicycle ergometer. All HD patients were able to walk without assistance and able to independently carry out activities of daily living. Controls were selected from healthy volunteer sub- jects according to the same exclusion criteria. Mean age of the control group did not significantly differ from mean age of presymptomatic and symptomatic HD group. Mean age of presymptomatic group was lower as compared to symptomatic HD group (P < 0.05). Presymptomatic-HD and control groups both included subjects that had a sedentary life style and subjects that performed moderate physical activity (1– 2 hours physical exercise/week). Each subject gave his or her written consent after being fully informed of any risks and discomfort asso- ciated with participation in the study. The study was approved by the Ethics Committee of the Istituto Aux- ologico Italiano, and all study procedures followed the recommendations of the Helsinki Declaration of 1975. Exercise Protocol All subjects rested for 45 minutes before beginning the exercise portion of the study. The exercise test was performed on an electrically-braked bicycle according to a validated protocol.19 A cardiopulmonary exercise system (Sensor Medics V2900, USA) was used to mon- itor breath-by-breath measurements of VE (expired ventilation), VO2 and VCO2. Derived entities such as VE for O2 and CO2 (VE/VO2, VE/VCO2), the respira- tory quotient VCO2/VO2 and respiratory rate per mi- nute were presented on-line. The equipment was calibrated before every test. A 12-lead ECG was used to monitor for arrhythmia and ST segment changes. The test began with 2 minutes of variable sampling whereas the subject was at rest and was followed by 2 minutes of unrestricted exercise. The workload was increased by 25 Watts every 2 minutes. The exercise test was symp- tom-limited and used a Borg scale (from 0 to 10) to rate dyspnoea, fatigue and chest pain. The subjects were TABLE 1. Demographic, clinical, and genetic data of HD patients (nine males and six females), presymptomatic subjects (seven males and three females), and healthy controls (16 males and nine females) Symptomatic HD patients (N ¼ 15) Presymptomatic HD subjects (N ¼ 10) Controls (N ¼ 25) Age (yr) 48.2 6 10.2 (29–67) 37.6 6 6.7 (21–45) 43.7 6 10.6 (31–70 ) CAG triplet number 45.3 6 3.2 (41–52) 43.8 6 2.5 (42–49) – Age at onset (yr) 44.7 6 10.9 (28–65) – – Duration of illness (yr) 3.9 6 3.1 (1–10) – – UHDRS part I 31.0 6 12.2 (17–53) – – Total functional capacity 10.7 6 2.2 (6–13) – – Data are expressed as mean 6 SD (range). A N A E R O B I C T H R E S H O L D I N H U N T I N G T O N ’ S D I S E A S E Movement Disorders, Vol. 26, No. 1, 2011 131 encouraged to exercise until they were exhausted. Blood pressure and heart rate were measured every 2 minutes. All respiratory parameters were measured from plots over time, resulting in moving average values. The peak VO2, VE/VO2, and VE/VCO2 were the last of three val- ues that were recorded during the final 30 seconds of exercise. If this last value was not the highest, the mean of the last three values was calculated. The anaerobic threshold was calculated according to the V-slope method. After the test, patients rested in a supine posi- tion for at least 5 minutes. The following exercise pa- rameters were evaluated in all subjects: 1. Exercise/cardiac parameters: (a) Maximal ergometric working capacity (Wpeak), defined as the maximal work (Watts) reached for at least 1 minute (b) Peak exercise heart rate (HRpeak) and heart rate at the anaerobic threshold (HR AT) (c) Peak VO2/kg (mL/Kg/min), i.e., the maxi- mal oxygen consumption, expressed both in absolute values (ml/Kg/min) and as a percent of theoretical maximum capacity according to age, body type, and sex (peak VO2 %) (d) O2 pulse (ml/beat) both at anaerobic threshold (AT pO2) and at the exercise peak (peak pO2) (e) Aerobic threshold (AT VO2), expressed as an absolute value (ml/Kg/min), as a percent of the predicted maximum (AT%) and as Watts reached (AT Watts) 2. Ventilatory variables: (a) Respiratory quotient at the anaerobic threshold (RQ AT) and at peak exercise (RQ peak) (b) Ratio of dead space to tidal volume (VD/ Vt) (c) Ratio of ventilation to CO2 production at peak exercise (peak VE/VCO2) Blood Sampling and Lactate Concentration Assay A peripheral antecubital venous access was posi- tioned before the beginning of the test. Blood samples were drawn whereas the subject was at rest and at the beginning of each 2-minute incremental step during the exercise. The lactate concentration of the plasma was assessed using a colorimetric assay (Lactate Rea- gent, Trinity Biotech, Ireland). Muscle Biopsies Informed consent was obtained from each patient. Open muscle biopsies were obtained at rest from the biceps brachii muscle of patients through a small sur- gical incision under local anesthesia. Human Muscular Cultures Human myoblast cultures were obtained from bi- opsy specimens (supporting information Table 1) as previously described.20 Equal numbers of myoblasts were plated on 100-mm dishes in 10 ml of culture me- dium. The media and cells were collected 24 hours later. The media were assayed for lactate concentra- tion and the cells were counted using Coulter Counter cell (Beckman, CA). Morphological Studies We examined skeletal muscle biopsies from seven HD patients. For light microscopy studies, cryostat cross sections were processed according to standard histological (Gomori’s Trichrome, H&E) and histo- chemical (COX, SDH, double staining for COX, and SDH) techniques.21 A small part of each bioptic sam- ple was fixed in 2,5% glutaraldehyde (pH 7,4), post- fixed in 2% osmium tetroxide and then embedded in Spurr’s resin for ultrastructural examination. Finally, ultrathin sections were stained with lead citrate and uranyl acetate and examinated with Zeiss EM109 transmission electron microscope.21 Statistical Analysis A Kolmogorov-Smirnov test was used to test the data for normality and a Levene test was used to ver- ify the homogeneity of group variances. Cardiopulmo- nary parameters and blood lactate concentrations were compared with an analysis of variance (ANOVA) procedure using a Tukey test. Pearson or Spearman correlation coefficients were used to test for correla- tions between clinical and genetic data and the cardio- pulmonary parameters. Lactate concentrations in myoblast culture media were compared with a Krus- kal-Wallis ANOVA followed by Dunn’s test. Results Cardiorespiratory Measurements All of the subjects completed the exercise test with- out complications. In all subjects, the peak RQ was close to 1, showing a truly maximal test. No arrhyth- mias or ST changes suggestive of ischemic problems were detected in HD or control subjects during the exercise. The cardiopulmonary test parameters of HD patients, presymptomatic HD subjects and healthy controls are reported in Table 2. The peak power (Wpeak) and peak oxygen con- sumption (peak VO2) values were significantly reduced in symptomatic HD patients as compared to controls (Table 2, Fig. 1A,B). No difference in maximal C I A M M O L A E T A L . 132 Movement Disorders, Vol. 26, No. 1, 2011 exercise capacity was detected between presympto- matic HD and control subjects. Notably, there was no difference in O2 peak pulse, VD/Vt or VE/VCO2 among the groups; this data indicates a normal cardiac and ventilatory performance in all the groups.22 The anaerobic threshold values were significantly lower in the symptomatic and presymptomatic HD sub- jects than in the control subjects; this was true for all measurements, including the absolute value (ATVO2, mL/Kg/min), percent of the predicted maximum (AT%) and Watts reached (AT, Watt) (Table 2, Fig. 1C–E). We examined the data for a potential correlation between the cardiopulmonary test parameters and clinical or genetic data of the HD subjects. In the symptomatic HD patients, no significant correlation was found among clinical data (age at onset, duration of illness, UHDRS I, and TFC), genetic data (CAG repeat number), and Wpeak, ATVO2, AT%, or AT. Notably, a significant negative correlation was found between AT% and CAG repeat number in presymptomatic HD subjects (Fig. 1F; P < 0.0001; R ¼
0.873, Spearman Correlation). Blood Lactate Accumulation During a Cycloergometric Test and Lactate Production from In Vitro Muscular Cell Cultures Figure 2A shows mean values of blood lactate con- centrations at the various levels of work. The plasma lactate values did not differ between the symptomatic HD patients and the control subjects when they were at rest; however, at 50 and 75 Watts the mean plasma lactate concentration was significantly higher in symp- tomatic HD patients than in the controls (50 Watts, P ¼ 0.021 vs controls; 75 Watts, P ¼ 0.014 vs controls). Presymptomatic HD subjects had a mean lactate value at 50 Watts that was higher than that of the controls, but the difference was not statistically significant. To determine whether the increased lactate produc- tion was related to a primary defect in the mitochon- drial function of muscular cells, we measured lactate production in in vitro muscular cell cultures from five HD patients and five age-matched controls (the biopsy data are reported in supplemental Table 1). The lac- tate concentration in the media of the HD cultures was significantly higher than in the media of the con- trol cultures (Fig. 2B). Histochemistry and Ultrastructural Studies of HD Skeletal Muscle We examined six out of seven muscle biopsies (sam- ple n 3 was too small for reliable examination, Fig. 3A) and we found small groups of type II fibers in patients 1, 5, 6, and 7, patient n 1 also presenting scattered type II hypotrophic fibers. We detected no significant oxidative alterations except for the presence of 1–2 COX-negative fibers, without mitochondrial proliferation (normal SDH), in patients n 1, 6, and 7. In two patients (n 3 and 5), ultrastructural studies showed a consistent number of abnormally elongated mitochondria with derangement of cristae and vacuoles (Fig. 3B,C). Also, some mitochondria gradu- ally become swollen with progressive loss of matrix substance and disruption of residual cristae (Fig. 3D,E). Discussion Our study shows that symptomatic HD subjects have a reduced work capacity (Wpeak) during a car- diopulmonary test. This data complements the recent reported of a significant reduction in muscle strength in symptomatic HD patients.4 Presymptomatic HD subjects had normal Wpeak values during the same exercise test, which suggests that the Wpeak reduction TABLE 2. Cardiopulmonary test parameters of HD patients, presymptomatic HD subjects, and healthy controls Symptomatic HD patients Presymptomatic HD subjects Controls Wpeak (Watts) 111.7 6 37.6; (75–200) 165.0 6 39.4; (125–225) 158.7 6 45.8; (100–250) P ¼ 0.003 s-HD vs C Peak VO2/kg (mL/Kg/min) 23.4 6 6.7; (14.4–39.1) 29.5 6 7.0; (19.6–42.2) 28.8 6 6.0; (19.7–47.5) P ¼ 0.026 s-HD vs C Peak VO2/kg (% of theorethical) 75.7 6 22.3; (42–125) 78.7 6 21.1; (53–113) 83.3 6 14.7; (60–129) Heart rate peak (beats/min) 145.6 6 19.4; (106–180) 156.3 6 11.0; (139–176) 154.3 6 18.9; (112–185) RQ (peak) adimensional ratio 1.0 6 0.1; (1.0–1.1) 1.1 6 0.1; (0.9–1.3) 1.1 6 0.2; (0.9–1.7) O2 pulse peak (mL/beat) 11.3 6 4.2; (5.8–20.4) 13.5 6 3.7; (8.2–18.3) 13.4 6 3.5; (6.9–18.8) O2 pulse peak (%) 84.5 6 19.7; (49–128) 93.1 6 19.0; (73–135) 100.0 6 21.6; (62–140) VD/VT (%) 97.3 6 36.5; (57–187) 72.6 6 17.6; (40–92) 66.5 6 19.4; (30–104) VE/VCO2 adimensional ratio 32.0 6 2.9; (27–36) 30.1 6 4.3; (24–38) 29.9 6 3.2; (26–36) AT VO2 (mL/Kg/min) 13.3 6 2.5; (9.7–19.0) 13.6 6 3.3; (9.8–20.3) 19.0 6 5.0; (11.9–33.1) P ¼ 0.000125 s-HD vs C P ¼ 0.000125 preHD vs C AT (%) 38.9 6 7.3; (27–50) 35.3 6 8.0; (26–56) 54.7 6 13.1; (39–88) P ¼ 0.000251 s-HD vs C P ¼ 0.000166 preHD vs C AT (Watts) 38.3 6 12.9; (25–50) 57.5 6 16.9; (25–75) 99.0 6 43.0; (50–200) P ¼ 0.000002 s-HD vs C P ¼ 0.000002 preHD vs C Data are expressed as mean 6 SD; (range). Not reported statistical scores were P > 0.05. A N A E R O B I C T H R E S H O L D I N H U N T I N G T O N ’ S D I S E A S E Movement Disorders, Vol. 26, No. 1, 2011 133 in symptomatic patients may be related to the reduc- tion in muscle bulk that occurs as the disease pro- gresses.5,6 Our findings confirm a strength deficit in HD patients and support the idea that physical ther- apy aimed at improving muscle strength could benefit these patients, particularly during the early stages of the disease.23 This study shows that low anaerobic threshold (AT) values and an early increase of blood lactate are linked to HD. Both symptomatic and presymptomatic HD subjects had an anticipated AT during the incre- mental exercise. The AT is an index normally used to estimate exercise capacity. During the initial (aerobic) phase of cardiopulmonary exercise, expired ventilation (VE) increases linearly with VO2 and reflects aerobi- cally produced CO2 in the muscles. During the latter phase of exercise, anaerobic metabolism occurs when the oxygen supply cannot keep up with the increasing FIG. 1. (A) Scatter plot of maximal ergometric working capacity values (Wpeak) and (B) maximal oxygen consumption, expressed as absolute val- ues (Peak VO2) in HD patients (N 5 15), presymptomatic subjects (N 5 10) and control subjects (N 5 25). (C) Scatter plot of aerobic threshold val- ues expressed as absolute value (ATVO2), (D) as percent of the predicted maximum (AT%) and (E) as Watts reached (AT Watts). Mean values are indicated with horizontal bars. (F) Scatter plot graph showing that AT% correlates with CAG repeat number in presymptomatic HD subjects. The graph shows nine dots because two subjects had identical AT% and CAG repeat number. C I A M M O L A E T A L . 134 Movement Disorders, Vol. 26, No. 1, 2011 metabolic requirements of exercising muscles. At this time, there is a significant increase in lactic acid pro- duction in the muscles and in the blood lactate concentration.24 In our opinion, the low AT values and elevation of blood lactate in HD subjects reflect abnormalities in O2 utilization; this is consistent with abnormal oxida- tive metabolism in skeletal muscle. Presymptomatic subjects did not show a reduction in Wpeak values, which suggests that lower AT% values are not corre- lated with muscular atrophy. Notably, our data high- lighted an inverse correlation between AT% values and CAG repeats in HD gene carriers; this data strongly suggests that mutant htt directly results in deficits in the mitochondrial respiratory chain, even in presymptomatic HD patients. Among symptomatic HD patients, the CAG repeat number was not signifi- cantly correlated with AT% values. This data indi- cates that factors other than the CAG repeat number (such as muscular atrophy) may also contribute to AT% reduction in the more advanced stages of the disease. Several studies have suggested that the work rate corresponding to the AT could be used as an index for determining the optimal training intensity,25 therefore the information gathered in this study sug- gests that a cardiopulmonary test should be included in the physical therapy program for HD subjects. Our examination of skeletal muscle tissue from six HD subjects with an histochemical marker for mito- chondrial oxidative function (COX) did not reveal any significant abnormalities in both presymptomatic and symptomatic subjects. This data agrees with previously reported histological and histochemical examination of muscle biopsies from HD subjects.6 Given the pre- viously reported observations of structural mitochon- drial abnormalities in cortical biopsies from HD patients26–28 and in the muscle biopsy from one HD gene carrier,3 we performed electron microscopy ex- amination of HD muscle biopsies. Interestingly, we found abnormally elongated mitochondria with cristae derangement and vacuoles in two specimens (pt n 3 and 5). These findings are similar to those described.28,29 Pt n 3 is a 63-years-old woman with a disease duration of 13 years and 42 CAG repeats, whereas pt n 5 is a presymptomatic 36-years-old man with 42 repeats. Interestingly, his father (pt n 6), who has the same number of repeats and a disease duration of 22 years, does not show structural mitochondrial changes. We hypothesize that, as reported,28 the same mitochondrial alterations could be present at central nervous system level also in patients who do not show skeletal muscle abnormalities. Also, a possible expla- nation for the finding of mitochondrial alterations in few subjects is that these alterations may correlate with the lifestyle of the patients and may be more evi- dent in physically active subjects than in more seden- tary, possibly older, ones. Conflicting data have been reported about cardiac dysfunction in HD.12 Indeed, mutant htt has been blamed for cardiotoxic effects in mouse models, including heart atrophy7 and defects in contractile functions.30 Nevertheless, epidemiological studies have not found heart disease to be more common in HD patients than in controls.31 In this study, the patients’ cardiopulmonary response to exercise did not resemble the pattern that is typical of patients with heart failure. At peak exer- cise, the HD patients showed a normal O2 pulse, which suggests a normal cardiac output22; in addition, they had a normal ventilatory response, with VE/ VCO2 values below the cutoff-value of 35.32 These results do not show an increased risk for cardiac dis- ease in HD patients. Rather, the response of HD patients to cardiopulmonary testing suggests a primary defect in the muscular energetic metabolism. The increased lactate production we found in HD myo- blast cultures further highlights the inadequate mito- chondrial oxidative respiration of HD muscle and FIG. 2. (A) Lactate concentrations in blood (mean 6 SD) during car- diopulmonary test (*P 5 0.021; **P 5 0.014 vs. controls). (B) Graph representing the median and the percentiles of lactate concentrations in cell culture media. Data were expressed as mg/dL and normalized on cell number. The ends of the boxes define the 25th and 75th per- centiles, with a line at the median and error bars defining the 10th and 90th percentiles. Medians were: HD cells 3.0 mg/dL/number of cells. Control cells: 1.6 mg/dL/number of cells (P 5 0.003 vs. control cells). A N A E R O B I C T H R E S H O L D I N H U N T I N G T O N ’ S D I S E A S E Movement Disorders, Vol. 26, No. 1, 2011 135 agrees with our previous reports showing mitochon- drial dysfunction in HD myoblasts.20,33 Finally, we believe that AT measures could be useful as in vivo assays during the screening of drugs designed to improve mitochondrial function in HD patients. For example, a deficit in PGC-1a (peroxi- some proliferator-activated receptor-c coactivator 1a), a transcriptional coactivator implicated in mitochon- drial biogenesis, was recently found in both the brain34 and skeletal muscles35 of HD patients. Mole- cules that activate PGC-1a may be therapeutically use- ful,36 and in vivo AT measures in HD subjects could help to evaluate a potential drug’s benefits. Acknowledgments: The authors wish to thank the patients and their families (Associazione Mauro Emolo O.N.L.U.S.) for their precious sup- port. We thank Dr.ssa Cinzia Tiloca for her technical support. References 1. HDCRG.A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington’s disease chromosomes. Cell 1993;72:971–983. 2. Sharp AH, Love SJ, Schilling G, et al. Widespread expression of Huntington’s disease gene (IT15) protein product. Neuron 1995; 14:1065–1074. 3. Kosinski CM, Schlangen C, Gellerich FN, et al. Myopathy as a first symptom of Huntington’s disease in a Marathon runner. Mov Disord 2007;22:1637–1640. 4. Busse ME, Hughes G, Wiles CM, Rosser AE. Use of hand-held dy- namometry in the evaluation of lower limb muscle strength in peo- ple with Huntington’s disease. J Neurol 2008;255:1534–1540. 5. Hamilton JM, Wosfon T, Peavy GM, Jacobson MW, Corey-Bloom J. Rate and correlates of weight change in Huntington’s disease. J Neurol Neurosurg Psychiatr 2004;75:209–212. 6. Turner C, Cooper JM, Schapira AH. Clinical correlates of mito- chondrial function in Huntington’s disease muscle. Mov Disord 2007;22:1715–1721. FIG. 3. (A) Data of muscle biopsies analyzed for light microscopy and electronic microscopy. (B-E) Electron microscopy results (Part 3) (20000 X). Abnormally elongated mitochondria with derangement of cristae and vacuoles (B and C). Swollen mitochondria with progressive loss of matrix sub- stance (D and E) and disruption of residual cristae (E). C I A M M O L A E T A L . 136 Movement Disorders, Vol. 26, No. 1, 2011 7. Sathasivam K, Hobbs C, Turmaine M, et al. Formation of poly- glutamine inclusions in non-CNS tissue. Hum Mol Genet 1999;8: 813–822. 8. Ribchester RR, Thoson D, Wood NI, et al. Progressive abnormal- ities in skeletal muscle and neuromuscular junctions of transgenic mice expressing the Huntington’s disease mutation. Eur J Neurosci 2004;20:3092–3114. 9. Koroshetz WJ, Jenkins BG, Rosen BR, Beal MF. Energy metabo- lism defects in Huntington’s disease and effects of coenzyme Q10. Ann Neurol 1997;41:160–165. 10. Lodi R, Schapira AM, Manners D, et al. Abnormal in vivo skeletal muscle energy metabolism in Huntington’s disease and dentatorubropallidoluysian atrophy. Ann Neurol 2000;48: 72–76. 11. Saft C, Zange J, Andrich J, et al. Mitochondrial impairment in patients and asymptomatic mutation carriers of Huntington’s dis- ease. Mov Disord 2005;20:674–679. 12. Sassone J, Colciago C, Cislaghi G, Silani V, Ciammola A. Hunting- ton’s disease. The current state of research with peripheral tissues. Exp Neurol 2009;219:385–397. 13. Nicoli F, Vion-Dury J, Maloteaux JM, et al. CSF and serum meta- bolic profile of patients with Huntington’s chorea: a study by high resolution proton NMR spectroscopy and HPLC. Neurosci Lett 1993;154:47–51. 14. Ga˚rseth M, Sonnewald U, White LR, et al. Proton magnetic res- onance spectroscopy of cerebrospinal fluid in neurodegenerative disease: indication of glial energy impairment in Huntington chorea, but not Parkinson disease. J Neurosci Res 2000;60: 779–782. 15. Reynolds NC,Jr,Prost RW, Mark LP. Heterogeneity in 1H-MRS profiles of presymptomatic and early manifest Huntington’s dis- ease. Brain Res 2005;1031:82–89. 16. Martin WR, Wieler M, Hanstock CC. Is brain lactate increased in Huntington’s disease?J Neurol Sci 2007;263:70–74. 17. Morgan-Hughes JA. Mitochondrial diseases of muscle. Curr Opin Neurol 1994;7:457–462. 18. Shapira AHV, DiMauro S.Mitochondrial disorders in Neurology.- Boston, MA:Butterworth and Heinemann;1994. 19. Wasserman K. Critical capillary PO2 and the role of lactate pro- duction in oxyhemoglobin dissociation during exercise. Adv Exp Med Biol 1999;471:321–333. 20. Ciammola A, Sassone J, Alberti L, et al. Increased apoptosis, Hun- tingtin inclusions and altered differentiation in muscle cell cultures from Huntington’s disease subjects. Cell Death Differ 2006;13: 2068–2078. 21. Sciacco M., Fagiolari G., Lamperti C, et al. Lack of apoptosis in mitochondrial encephalomyopathies. Neurology 2001;56: 1070–1074. 22. Agostoni PG, Guazzi M, Bussotti M, Grazi M, Palermo P, Marenzi G. Lack of improvement of lung diffusing capacity following fluid withdrawal by ultrafiltration in chronic heart failure. J Am Coll Cardiol 2000;36:1600–1604. 23. Busse ME, Khalil H, Quinn L, Rosser AE. Physical therapy inter- vention for people with Huntington disease. Phys Ther 2008;88: 820–831. 24. Albouaini K, Egred M, Alahmar A, Wright DJ. Cardiopulmo- nary exercise testing and its application. Heart 2007;93: 1285–1292. 25. Tanaka H, Shindo M. The benefits of the low intensity training. Ann Physiol Anthropol 1992;11:365–368. 26. Goebel HH, Heipertz R, Scholz W, Iqbal K, Tellez-Nagel I. Juve- nile Huntington chorea: clinical, ultrastructural, and biochemical studies. Neurology 1978;28:23–31. 27. Tellez-Nagel I, Johnson AB, Terry RD. Studies on brain biopsies of patients with Huntington’s chorea. J Neuropathol Exp Neurol 1974;33:308–332. 28. Quintanilla RA, Johnson GV. Role of mitochondrial dysfunction in the pathogenesis of Huntington’s disease. Brain Res Bull 2009; 80:242–247. 29. Squitieri F, Falleni A, Cannella M, et al. Abnormal morphology of peripheral cell tissues from patients with Huntington disease. J Neural Transm 2010;117:77–83. 30. Mihm MJ, Amann DM, Schanbacher BL, Altschuld RA, Bauer JA, Hoyt KR. Cardiac dysfunction in the R6/2 mouse model of Hun- tington’s disease. Neurobiol Dis 2007;25:297–308. 31. Lanska DJ, Lanska MJ, Lavine L, Schoenberg BS. Conditions asso- ciated with Huntington’s disease at death. A case-control study. Arch Neurol 1988;45:878–880. 32. Piepoli MF, Corra` U, Agostoni PG, et al. Statement on cardiopul- monary exercise testing in chronic heart failure due to left ventric- ular dysfunction. recommendations for performance and interpretation. Part I: definition of cardiopulmonary exercise test- ing parameters for appropriate use in chronic heart failure. Eur J Cardiovasc Prev Rehabil 2006;13:150–164. 33. Sassone J, Colciago C, Marchi P, et al. Mutant Huntingtin induces activation of the Bcl-2/adenovirus E1B 19-kDa interacting protein (BNip3). Cell Death Dis 2010,1,e7; doi:10.1038/cddis.2009.6 34. Cui L, Jeong H, Borovecki F, Parkhurst CN, Tanese N, Krainc D. Transcriptional repression of PGC-1 alpha by mutant huntingtin leads to mitochondrial dysfunction and neurodegeneration. Cell 2006;127:59–69. 35. Chaturvedi RK, Adhihetty P, Shukla S, et al. Impaired PGC-1 {alpha}function in muscle in Huntington’s disease. Hum Mol Genet 2009;18:3048–3065. 36. Mc Gill JK, Beal MF. PGC-1 alpha, a new therapeutic target in Huntington’s disease?Cell 2006;127:465–468. A N A E R O B I C T H R E S H O L D I N H U N T I N G T O N ’ S D I S E A S E Movement Disorders, Vol. 26, No. 1, 2011 137

Low anaerobic threshold and increased skeletal muscle lactate production in subjects with Huntington's disease.

10-07-2010

Ciammola, Andrea,Sassone, Jenny,Sciacco, Monica,Mencacci, Niccolò E,Ripolone, Michela,Bizzi, Caterina,Colciago, Clarissa,Moggio, Maurizio,Parati, Gianfranco,Silani, Vincenzo,Malfatto, Gabriella

eng

PMC8523042

0.5 1.0 1.5 2.0 2.5 2 3 4 5 9:American football (M) Step length (m) Cadence (steps/s) Active athletes S3 Fig 0.5 1.0 1.5 2.0 2.5 2 3 4 5 19:Golf (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 3:Soccer (F) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 10:Softball (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 16:Weightlifting (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 5:Soccer (F) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 4:Soccer (F) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 18:Rowing (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 6:Basketball (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 11:Handball (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 15:Badminton (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 17:Weightlifting (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 8:Basketball (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 1:Soccer (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 7:Basketball (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 13:Boxing (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 14:Lacrosse (F) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 12:Volleyball (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 2:Soccer (M) 0.5 1.0 1.5 2.0 2.5 2 3 4 5 20:Judo (M)

Spatiotemporal inflection points in human running: Effects of training level and athletic modality.

10-18-2021

Goto, Yuta,Ogawa, Tetsuya,Kakehata, Gaku,Sazuka, Naoya,Okubo, Atsushi,Wakita, Yoshihiro,Iso, Shigeo,Kanosue, Kazuyuki

eng

PMC4439428

1 3 J Comp Physiol A (2015) 201:645–656 DOI 10.1007/s00359-015-0999-2 ORIGINAL PAPER Walking and running in the desert ant Cataglyphis fortis Verena Wahl · Sarah E. Pfeffer · Matthias Wittlinger Received: 25 November 2014 / Revised: 4 March 2015 / Accepted: 5 March 2015 / Published online: 1 April 2015 © The Author(s) 2015. This article is published with open access at Springerlink.com Keywords Desert ant · Cataglyphis · Stepping pattern · Inter-leg coordination · Gait Introduction If you are in a North African salt pan in the middle of the day, you would probably encounter Cataglyphis for- tis desert ants pacing around with tremendous speeds on their long legs, insects Rüdiger Wehner likes to call “race horses in the insect world” (Wehner 2009). Like race horses with their shiny and delicate bodies, they can doubtlessly exert a fascination on the observer when they attain high walking speeds while swiftly manoeuvring through their harsh environment, always on the look-out for dead insects that succumbed to the heat of the day (Wehner 1983). Indi- viduals with a prey item, one can see running along an imaginary straight line which connects the place where they had encountered the food with the nest entrance. The kind of navigation that Cataglyphis fortis ants perform on their foraging excursions is an approximate form of dead reckoning, the so-called path integration where the ants are constantly connected to the nest location via an invis- ible link (Collett and Collett 2000; Wehner and Srinivasan 2003; Wehner and Wehner 1986, 1990). Combining path integration as a navigation mode and high walking speeds, Cataglyphis ants maximize their chances of finding food and returning to the nest even in the hottest times of the day without succumbing to the hostile conditions. To perform path integration Cataglyphis ants would need two inputs: (1) information about angles steered, that is, the direction and (2) information about the distance travelled. Direc- tional information is provided by a celestial compass (Weh- ner 1982; Müller and Wehner 1988), and distance informa- tion is gained by means of a stride integrator (Ronacher and Abstract Path integration, although inherently error- prone, is a common navigation strategy in animals, par- ticularly where environmental orientation cues are rare. The desert ant Cataglyphis fortis is a prominent example, covering large distances on foraging excursions. The stride integrator is probably the major source of path integration errors. A detailed analysis of walking behaviour in Catagly- phis is thus of importance for assessing possible sources of errors and potential compensation strategies. Zollikofer (J Exp Biol 192:95–106, 1994a) demonstrated consist- ent use of the tripod gait in Cataglyphis, and suggested an unexpectedly constant stride length as a possible means of reducing navigation errors. Here, we extend these stud- ies by more detailed analyses of walking behaviour across a large range of walking speeds. Stride length increases linearly and stride amplitude of the middle legs increases slightly linearly with walking speed. An initial decrease of swing phase duration is observed at lower velocities with increasing walking speed. Then it stays constant across the behaviourally relevant range of walking speeds. Walking speed is increased by shortening of the stance phase and of the stance phase overlap. At speeds larger than 370 mms−1, the stride frequency levels off, the duty factor falls below 0.5, and Cataglyphis transitions to running with aerial phases. V. Wahl and S. E. Pfeffer equally contributed to the manuscript. Electronic supplementary material The online version of this article (doi:10.1007/s00359-015-0999-2) contains supplementary material, which is available to authorized users. V. Wahl · S. E. Pfeffer · M. Wittlinger (*) Institute of Neurobiology, University of Ulm, 89069 Ulm, Germany e-mail: [email protected] 646 J Comp Physiol A (2015) 201:645–656 1 3 Wehner 1995; Wittlinger et al. 2006, 2007) which might be a major source of navigational errors. To better understand the stride integrator, we need a detailed analysis of walk- ing behaviour and thus the inter-leg coordination across the entire range of walking speeds employed by Cataglyphis fortis. Zollikofer (1994a) demonstrated consistent use of the tripod gait in desert ants and suggested an unexpect- edly constant stride length as a possible means of reduc- ing navigation errors. During his time in Rüdiger Wehner’s lab, Christoph Zollikofer pioneered the work on walk- ing kinematics in these fast running desert ants, and since then many details have been revealed about the locomotor behaviour of Cataglyphis fortis compared to other species, namely the influence of speed and curvature, of body mor- phology and load (Zollikofer 1988, 1994a, 1994b, 1994c) or locomotion on sloped surfaces (Seidl and Wehner 2008; Weihmann and Blickhan 2009). Nevertheless, with advanced high-speed videography at hand, we are now able to get a more thorough insight into Cataglyphis’ walking behaviour. Moreover, we can extend these studies not only by more detailed analyses of inter-leg coordination but also expand the range of walking speeds to where we assume its limits. The aim of this paper is to investigate the effect of walking speed on the inter-leg coordination or gait, stride length, walking speed and stride amplitude, duty factor, as well as swing and stance phase and phase relationships of all six legs. Materials and methods Ant colonies High-speed video recordings were performed in the field near Maharès, Tunisia and in the laboratory at University of Ulm, Germany. For the laboratory recordings, several colonies of Cataglyphis fortis were kept and raised under annual temperature and daily light–dark cycles based on conditions in their natural habitat (20–35 °C, winter–sum- mer; 14 h:10 h, light:dark cycle in summer). The colonies in the laboratory consisted of several hundred ants, with an active forager force of approximately 10 % of the popula- tion size. Estimated from the number of active foragers, the field colonies and the colony size were comparable. The laboratory ants received water ad libitum and were fed with honey water and insects, five times a week. Experimental procedure Medium to large sized (2.5–3.3 mm alitrunk length) Cat- aglyphis fortis ants were individually marked and were filmed with a camera placed above the channel while they walked in a linear channel with a width of 7 cm and channel wall height of 7 cm. We video filmed the run- ning ants between 0900 and 1600 h. The highest walk- ing speeds were usually recorded around noon, when the highest air temperatures were reached in the field. Channel floors were evenly coated with a very fine layer of firmly attached white sand (0.1–0.4 mm particle size) to provide good traction and thus to facilitate slip-free natural walking and running kinematics. Film recordings were made with a high-speed camera (MotionBlitz Eosens Mini1, Mikro- tron Unterschleissheim, Germany) at 500 and 1000·frames per second (Fig. 1) and shutter times of 100–200 µs. The indoor laboratory video shoots were illuminated with two fibre optic cold light sources (Schott KL 1500LCD, 150W, Schott AG, Mainz, Germany) whereas videos filmed under open sky outdoor conditions needed no external light sources since the sun provided plenty light. To get videos of very slowly walking desert ants, the channel setup was cooled down to about 10–15 °C by means of cooling pads. Data analysis In the experiments, the ants walked through the channel at different speeds. Both inbound and outbound walking ants were considered for the walking analysis. Especially in the outdoor video sessions, the inbound walking ants sometimes carried a minute food item. Each individual was video recorded up to five times, consequently in the data of N = 388 runs up to five runs might origin from one ant. Only those individuals exhibiting regular straight and lin- ear walks without de- or acceleration or abrupt stops were used for the tests. Each analysed walk contained at least three step cycles per leg. A 5 cm long black and white scale bar was filmed after each set of videos with the same set- tings to calibrate the analysed videos. Tarsal footfall posi- tions as well as times of lift-off (or movement away from the contact point) and touch-town of the tarsal tips were digitized with ImageJ (US National Institutes of Health, Bethesda, Maryland, USA, http://imagej.nih.gov/ij/) on a frame-by-frame basis. The duration of swing phases were calculated as the difference between the time the tarsal tip lifts off the ground and subsequent touchdown of the tar- sal tip of the same leg for the swing phase or vice versa for the stance phase. In the hind legs, the tarsal tip often is dragged over the floor without being lifted off the ground. Here, we define the moment when the tarsal tip leaves the contact point on the floor as start of the swing phase (Rein- hardt and Blickhan 2014). The onset of stance was used as the reference time for the analysis of temporal coordina- tion of all legs for the phase analyses. The CircStat Toolbox in MATLAB was used for phase analyses and the corre- sponding plots (Berens 2009; Wosnitza et al. 2013). Stride frequency is defined as the walking speed divided by the stride length. The stride length was calculated for each leg 647 J Comp Physiol A (2015) 201:645–656 1 3 pair (L1, R1; L2, R2; L3, R3) as the mean of each leg pairs’ strides in one video sequence. Stride length is specified as the measure from two successive footfalls of the same leg of one body side (Alexander 2003). A stride should not be confused with a step, which is the distance the body cov- ers from the footfall of a leg pairs’ left leg to the footfall of the right leg or vice versa. A stride thus basically incor- porates two steps, the left and the right. Stride length is therefore actually double the step length (assuming the left step is more or less the same as the right step and walk- ing speed is constant). When we look at the tripod shaped gait in Fig. 1, one stride describes the relationship of two successive triangles of the same colour whereas one step describes the relationship of two differently coloured suc- cessive triangles. In this account, we only employ the term stride as mentioned above and as it is defined in Alexan- der (Alexander 2003). The stride amplitude is a measure for the swing of one leg during a stride without the addi- tional body movement during the swing phase (Wosnitza et al. 2013). We calculated the stride amplitude as the stride length minus swing phase duration multiplied by walking speed. The stride amplitude (Wosnitza et al. 2013) which is misleadingly called “stride length” in Hughes (1951) is technically a body coordinate based measure for the swing movement of a leg. We, however, calculated the mean stride amplitude of a run as an indirect measure from geo- coordinate based data, such as the means of stride length, swing phase duration and walking speed. We also assume a constant mean walking speed for all runs evaluated. There- fore, minor errors might occur. Although a certain variabil- ity of walking speed within a step cycle might be observed especially for the slow walks, we only evaluated video sequences with a constant mean walking speed over several step cycles. Mean walking speed was measured from the start of the first step cycle to the end of the last step cycle in one video sequence. We calculated the tripod coordination strength (TCS) which evaluates the quality of the tripod coordination (Wos- nitza et al. 2013; compare also Spagna et al. 2011). First, we calculated the time from the earliest swing onset to the latest swing termination. This gave us time t1, during which at least one of the three legs was in swing phase. Then we determined time t2, during which all three legs were in swing phase at the same time. The ratio t2/t1 then described the TCS. A TCS of 1 indicated perfect tripod coordina- tion; it approached 0 when the temporal relationship of swing phases shifted to other coordination patterns (Wos- nitza et al. 2013). The duty factor, a ratio of stance phase Fig. 1 Tripod gait of a fast running and a slowly walking Catagly- phis individual. Six complete strides—three of each body side—cap- tured by high-speed video are shown. Tripods formed by the right front and hind leg (R1, R3) and the left middle leg (L2) are drawn in red; tripods formed by the left front and hind legs (L1, L3) and right middle (R2) leg are drawn in blue. Stride length (s) was determined as the distance between two successive footfalls of the same leg. a Very fast running ant showing the typical tripod gait (s = 19.8 mm; v = 597.4 mm s−1). b A rather slowly walking ant also showing the typical tripod gait, however, with reduced stride length (s = 9.1 mm; v = 95.2 mm s−1). Single video frames of the ant, taken during the first and sixth captured steps, are pasted into the tripod analysis fig- ure 648 J Comp Physiol A (2015) 201:645–656 1 3 to cycle period can be used as a measure that describes the transition from walking to running (Alexander 2003). We measured the cycle period as the time between successive touchdowns of the same limbs. Thus, one gait cycle begins when the reference foot contacts the ground and ends with subsequent touchdown of the same foot. Since cycle period of very slow walks gets more variable and calculations of TCS or duty factors do not deliver appropriate, compara- ble values, we carried out a separate evaluation of walking behaviour during slow locomotion (Fig. 5). We did a frame- by-frame analysis of 76 videos within a speed range of 4.5 to 29.9 mms−1 (five different speed groups I–V) by clas- sifying each frame according to its gait pattern similar to the work of Mendes et al. (2013). Each frame was assigned a certain colour and a number representing the different leg coordination types. For the different leg combinations used for our gait analysis, see supplementary material. If none of the listed leg combinations was found, the frame was clas- sified as ‘undefined’. For each of our five speed groups, we calculated a percentage distribution of different leg combi- nations, which the ants applied during slow walks. Further, the frames’ gait index was averaged for each video and pooled according to the five speed groups to accomplish a more inter-individual comparison. Statistical analyses were performed with SigmaPlot 11.0 (Systat Software Inc., San Jose, California, USA). Pair-wise comparisons (Fig. 5) and comparisons of slope and y-intercept (Figs. 3a, 6b) were performed with a t test, since respective groups were all normally distributed. We fitted data with linear, power and polynomial functions and calculated R2 in Microsoft Office Excel 2013. Results The walking parameters of Cataglyphis fortis were evalu- ated spatially and temporally over the entire walking speed range from 4.5 to 619.2 mms−1. With increasing walking speed, the stride length increases in an almost perfectly linear fashion (Fig. 2a). The faster the ant runs, the longer the strides get. The stride length increases more than fourfold over the entire speed range from 3.5 mm (at 4.5 mms−1) to 19.8 mm (at 589.5 mms−1). Stride frequency increases as a function of walk- ing speed and levels off at a frequency plateau of around 30 Hz beginning somewhere between 300 and 400 mms−1 (Fig. 2b). In the desert ants, the start of the frequency pla- teau is a first indication that the ants attain aerial phases. Ants that are achieving longer strides, increase stride fre- quency to a maximum at which the frequency reaches the upper level while the strides are still getting larger. From this point on, walking speed is increased by increasing stride length only. To maximise stride length in spite of a stagnant stride frequency, the ants become “airborne” from footfall to footfall to cover a larger distance (Fig. 2c). The stride amplitude (Wosnitza et al. 2013), is a body coordinate based measure for the swing of a leg. The stride amplitude of the middle leg shows a good linear correlation with increasing walking speed. The amplitude of the mid- dle legs doubles, whereas the amplitudes of front and hind legs do not increase significantly and show only a weak correlation (R2 = 0.28, front legs; R2 = 0.66, middle legs; R2 = 0.20, hind legs) (compare Fig. 3a). For the middle leg, Fig. 2 General walking parameters, stride length, stride frequency and walking speed and their relationships. Only middle leg data are plotted; each data point represents one video sequence (N = 388). a Stride length as a function of walking speed for the entire walking speed range. Linear regression is indicated; y = 0.023 × x + 5.93; R2 = 0.93. b Stride frequency as a function of walking speed. Best fit regression is indicated; y = −0.0001 x2 + 0.11x + 1.63; R2 = 0.97. c Stride frequency as a function of stride length. Best fit regression is indicated; y = −0.115x2 + 4.78x − 19.77; R2 = 0.81. The grey hori- zontal bar highlights the frequency plateau (b, c) 649 J Comp Physiol A (2015) 201:645–656 1 3 this means that 66 % of the variability can be described by the linear regression model. Since Cataglyphis is known to employ tripod coordina- tion over most of the walking speed range (Seidl and Weh- ner 2008; Wittlinger et al. 2006, 2007; Zollikofer 1988, 1994a), we evaluated the quality and synchrony of the tri- pods by means of the tripod coordination strength (TCS) (Wosnitza et al. 2013; compare also Spagna et al. 2011). The variability of the TCS decreases with increasing walk- ing speed and at the same time converges towards the max- imum levels of around 0.7 to 0.85. From a walking speed of around 300 mms−1, the variability is least whereas at lower speeds, the TCS varies between 0.02 and 0.78. Above walk- ing speeds of around 300 mms−1, t2 and t1 of the TCS both remain at constant levels of 12–22 ms (t2) and 24–34 ms (t1). To further analyse the inter-leg coordination and the phase relationships of the tripods, we made footfall pat- terns or podograms that show the swing and stance phases of every leg as black (swing) and white (stance) bars along a timeline (Fig. 4a–d). The podogram in Fig. 4a shows a very slow locomotion. This walk with 6.9 mms−1 is at the lower edge of walking speed and exemplifies that calcula- tions used for the walking speed larger than 30 mms−1 (e.g. TCS and duty factor) do not provide any useful informa- tion in this case. Therefore, slow walks were analysed and quantified separately in Fig. 5. In contrast, the podograms of the higher walking speeds (Fig. 4b–d) beautifully show tripod coordination. The green bar in Fig. 4b highlights the stance phase overlap where all six legs are on the ground at the same time (hexa support phase) for a relatively slow walk. The blue bar in a very fast run (Fig. 4d), however, exemplifies the swing phase overlap (aerial phase) which is the time where the ant is airborne—all legs lost ground contact—except for some cases where the hind legs might be dragged over the substrate. We also calculated phase plots of the stance phase onset of all six legs with respect to the left hind leg (Fig. 4e, f). Each of the three leg pairs shows an antiphasic relation. The legs are more or less coordinated as a tripod of L1, L3, R2 and L2, R1, R3. Figure 4e and f show that the middle leg of one tri- pod tends to touchdown first, and then the hind leg touches the ground, followed immediately by the front leg, which is nearly in phase with the hind leg. The data points (blue) of slow walks (Fig. 4f) are more spread than in the fast walks (Fig. 4e). This also confirms what we already know from the TCS analysis. The tripods are never perfectly in phase and the TCS improves with increasing walking speed. Nev- ertheless, we can see how a tripod is temporally formed. The three legs of one tripod never touch down or lift-off the ground simultaneously but the temporal coordination improves with increasing walking speed. In a separate analysis, we focused on walking behav- iour during slow locomotion below walking speeds of 30 mms−1. A continuous gait transition from tripod to tet- rapod to wavegait coordination is proposed for hexapods with decreasing walking speeds (Schilling et al. 2013). Throughout its entire walking speed range, Cataglyphis fortis ants predominantly walk in tripod-fashion, which is also true for the runs at the lower edge of walking speeds (Fig. 5b). However, it seems evident that with decreasing speed, the tripod coordination is getting more inconsistent and the number as well as the proportion of other stepping patterns increases. We observed that ants use poorly coordi- nated or non-tripod pattern only for a short period of time. Almost all ants that show tetrapod, wavegait or other unde- fined stepping patterns during more than one step cycle, subsequently display the transition into tripod coordination within the same video sequence (Fig. 5a). To illustrate the variability of leg coordination of very slow walks, we not only used the podograms but also colour coding and indexing of stepping patterns (see examples in Fig. 5a, 6.9 mms−1 with the transition from tetrapod to tripod Fig. 3 Walking parameters of N = 388 high-speed videos. a Stride amplitude as a function of walking speed for all three leg pairs. Leg pairs are represented in green (front legs), magenta (mid- dle legs) and blue lines (hind legs); linear regression lines are indi- cated, front legs: y = 0.0032 × x + 4.54; R2 = 0.28; middle legs: y = 0.0067 × x + 4.72; R2 = 0.66; hind legs: y = 0.0026 × x + 4.33; R2 = 0.20. The slope of front and middle legs differ significantly (t test, p < 0.05) as well as that of the middle and the hind legs (t test, p < 0.05) while front and hind legs are not significantly different. For all leg pair combinations, the y-intercept is significantly different (t test, p < 0.05). b Tripod coordination strength (TCS, for definition see “Materials and methods”) as a function of walking speed 650 J Comp Physiol A (2015) 201:645–656 1 3 coordination; and Fig. 5b, 6.0 mms−1 with tripod coordina- tion). The colour coding and indexing was also applied to quantify the leg coordination in all (N = 76) videos below walking speeds of 30 mms−1. In Fig. 5c, we give a sum- mary of percentage values of different gait patterns. They show that with increasing speed, the proportion of tripod gait increases, while tetrapod coordination and wave gait decreases as well as the time where all six legs have ground contact simultaneously (hexa support phase). To further compare the individual performance, we averaged the index that was assigned to each frame in one video (Fig. 5d). This shows that with increasing speed the indices also increase, which reflects the increasing consistency of the tripod. Note that a large fraction of non-tripod combinations forms in the transitional time from one tripod group (e.g. L1, R2, L3) to the subsequent one (L2, R1, L3). When we look at Fig. 5b, we clearly notice tripod coordination in the podogram, though other coordinations are also present to a large extent (compare Fig. 5b, colour coding and indexing graph). Hence, our analysis shows that even slow walking Cataglyphis ants preferentially employ tripod coordination, but with decreasing speed, the tripod gets more variable and other leg coordination are used as well. We will now have a look at the swing and stance phase durations as a function of walking speed (Fig. 6a). Both the swing phases and the stance phase are significantly reduced at the initial part of the walking speed range. While the stance phases are longer than the swing phases at lower walking speeds, this relation reverses at higher walk- ing speeds. Interestingly, the reversal in the hind legs and front legs occur much earlier (hind legs: 95 mms−1) than in the middle legs (middle legs: 349 mms−1). The duration of swing and stance phases in Cataglyphis decrease with increasing walking speed in the fashion of a power function (compare Fig. 6a) and remains more or less constant from a walking speed of 300 mms−1 in (Fig. 6a). For a large part of the range, the walking speed is increased by reducing the stance phase while the swing phase stays rather constant. At highest walking speeds, the middle legs have the short- est swing phase and longest stance phase of all legs. Hence, the middle legs are the first to touch the ground and the last to lift-off again. We define the swing phase as the time where the leg is in motion, that is, the time from where the tarsal tip of one leg leaves the contact point on the substrate to the subsequent contact point on the ground. The hind legs displayed a peculiarity in that they often moved the Fig. 4 Analysis of inter-leg coordination. (a–d) Footfall patterns, podograms, of all six legs from four runs with different walking speeds, from minimum to almost maximum speed. White bars represent stance phases, black bars represent swing phases; L left, R right body side; 1, 2 and 3, front-, mid- and hind leg. Shaded areas highlight exem- plary tripod gait patterns with stance phase overlap (green, see b) and swing phase overlap (blue, compare d). Shaded area (grey, compare c) highlights an exemplary footfall pattern with a TCS of 0.77. Walk- ing speeds are 6.9 mms−1 (a), 18.9 mms−1 (b), 95.2 mms−1 (c) and 597.4 mms−1 (d). (e, f) Phase plots of the stance, phase onset of all legs with respect to the left hind leg; L1, L2, L3, left side front, middle and hind leg; R1, R2, R3 right side front, middle and hind leg. Two exem- plary walking speed ranges are shown, 560–620 mms−1 (e) and 90–110 mms−1 (f). Blue data from five runs; red line mean vector 651 J Comp Physiol A (2015) 201:645–656 1 3 tarsi along the floor without being lifted off the floor. This “gliding phase” is part of the swing phase, although the gliding hind legs that are basically dragged behind the ants still touched the ground. This phenomenon has recently been observed in Formica ants as well where the tarsi of the hind legs were regularly dragged over the substrate without being significantly raised off the ground (Reinhardt and Blickhan 2014). In some video sequences, we were able to observe that the tarsal claws were retracted before the gliding phase and thus the swing phase started. Another measure for the phase relationship is the duty factor. Besides, it is one measure that characterises the dynamics of when the transition from walking to running occurs. It is assumed that at values of around 0.5, this tran- sition happens (Alexander 2003). With increasing walking speed, the duty factor decreases linearly for all three leg pairs. The hind legs are the first to fall below the duty fac- tor of 0.5 at 132 mms−1, then the front legs (at 182 mms−1) followed by the middle legs (at 369 mms−1). The middle legs are the last to reach aerial phases and thus determine the walking speed threshold at which the transition from walking to running occurs. From that speed on the ants are “jumping” from step to step to further increase their strides (compare the gaps between the triangles in Fig. 1a). Fig. 5 Quantification of gait pattern during slow walking. Gait Pat- tern analysis for ants walking at a 6898 mms−1 and b 5959 mms−1. (a, b) Podogram (above), coloured coding (middle) and indexing (below). Illustration details of the podograms as in Fig. 4. For the colour coding and the indexing we used five different classifications: ‘tripod’ (dark-blue, 4), ‘tetrapod’ (light-blue, 3), ‘wavegait’ (yel- low, 2) or ‘hexa support phase’ (white, 0). If none of these possibili- ties were applicable, the frame was classified as ‘undefined’ (red, 1). For the list of exact leg combinations representing a typical gait see supplementary material. c Quantification of the N = 76 slow walk- ing speed videos were grouped into five categories: I 5–10 mms−1 (17 videos, 8950 frames; 27, 14, 29, 6, 23 %), II 10–15 mms−1 (16 videos, 7376 frames; 33, 14, 22, 10, 21 %), III 15–20 mms−1 (20 vid- eos, 7116 frames, 51, 8, 15, 6, 18 %), IV 20–25 mms−1 (14 videos, 4284 frames; V 25–30 mms−1 (9 videos, 2423 frames; 58, 7, 15, 13, 7 %). The percentage information in brackets after the semicolon is rounded and is arranged as follows: tripod, tetrapod, wavegait, unde- fined gait, hexa support phase. d The averaged index for each video provide a more individual analysis of the ants’ walk. Group I differs significantly from group II (t test; p = 0,014); the same was true for group IV and group V (t test; p = 0,004). The three intermediate speed groups (II, III, IV) do not show any statistically significant dif- ferences to their respective neighbouring groups 652 J Comp Physiol A (2015) 201:645–656 1 3 Discussion In 1850, the long legged desert ant of the genus Catagly- phis was described as a “most remarkable appearance within the insect fauna of old world desert areas” (Foerster 1850). With its long legs that characterise all Cataglyphis species, Cataglyphis fortis reaches the highest running speeds with values of up to 0.7 ms−1 in the literature (Weh- ner 1983). How are these ants able to reach such high run- ning speeds? This question was already tackled by Christop Zollikofer when he was a PhD student in Rüdiger Wehner’s lab (Zollikofer 1988, 1994a, 1994b, 1994c). His work was the beginning and basis of our data collections and analyses that we present here. With more advanced techniques, we were able to expand the range of walking speeds to its lim- its and to extend the analysed parameters. Contribution and role of the leg pairs to locomotion The variation in stride amplitude as well as in stance and swing phase duration of the front legs tends to be higher than that of the middle and hind legs. A freer and unham- pered positioning of the frontal tarsi is possible here because there are no legs in front of them with which they could interfere and thus limit their range in frontal direc- tion. One could assume that the front legs generate the smallest forces with reference to the body movement. In Acheta domestica (Harris and Chiradella 1980), Carausius morosus (Cruse 1976) and Periplaneta americana (Full and Tu 1991) force measurements confirm the front leg part in keeping the body’s stability. The longitudinal forces of the protarsi act against the moving direction. Interestingly, Zollikofer (1988) observed a higher corre- lation of the front leg stride length with walking speed than that of the middle and hind legs. Moreover, he describes that when sprinting, the front legs of Cataglyphis bomby- cina specimens would often not leave any tarsal imprints on the smoked-glass plates that he used for the stride anal- ysis. This fact made him conclude that at very high run- ning speeds, the ant’s front legs would stop touching the ground, performing a form of quadrupedalism (Zollikofer 1994b). Loss of ground contact is well known in insects (Periplaneta americana: Full and Tu 1991), in crabs (Ocy- pode quadrata: Blickhan and Full 1987) and in vertebrates (Heglund et al. 1974).We cannot confirm this observation in Cataglyphis fortis, although we analysed a large num- ber of runs from the laboratory and the field over the entire speed range. Sometimes, however, when ants got startled, they showed a short sequence where they accelerated, ris- ing the head and prosoma and lifted the forelegs off the ground. They performed a movement comparable to a “wheelie” known from motorbikes when their front wheel loses ground contact during high accelerations. However, we did not see this behaviour in fast running ants with con- stant speed. The middle legs seem to play a distinctive role in the locomotor apparatus of Cataglyphis fortis desert ants. They show the longest stance phase and the shortest swing phase of all legs. The middle leg of the tripod is thus the first leg touching down and the last lifting off the ground. Hence, the duty factor of the middle legs is the last to underscore 0.5 with increasing speed and thus determines the start of aerial phases. At high running speeds, the tarsi of the mid- dle legs show the most distal trace of swing and are posi- tioned at a great lateral distance reaching over the neigh- bouring legs without interfering with them (Zollikofer 1988). Although this overlap happens, the legs are not Fig. 6 Stance and swing phase duration and duty factor. a Durations of stance (three shades of purple) and swing phases (three shades of blue) as a function of speed of all three leg pairs. Graphical fits are represented for middle and hind legs in purple (y = 0.22x−0.38, R2 = 0.75; y = 0.26x−0.41, R2 = 0.77) and blue lines (y = 1.76x−0.83, R2 = 0.88; y = 1.58x−0.79, R2 = 0.84), respectively. Runs without tripod coordination with walking speeds below 25 mms−1 have not been considered for these graphs. b Duty factor, which is the ratio of stance phase duration to duty cycle, versus walking speed for all three leg pairs. Leg pairs are represented in green (front legs), magenta (middle legs) and blue lines (hind legs); linear regression lines are indicated, front legs: y = −0.0005x + 0.59; R2 = 0.66; middle legs: y = −0.0004x + 0.66; R2 = 0.61; hind legs: y = −0.0006x + 0.57; R2 = 0.73. The slopes of front and middle legs differ significantly (t test, p < 0.05) as well as that of the middle and the hind legs (t test, p < 0.05) while front and hind legs are not significantly different. For all leg pair combinations, the y-intercept is significantly different (t test, p < 0.05) 653 J Comp Physiol A (2015) 201:645–656 1 3 hampering each other. Further, the middle legs also per- form the largest stride amplitude. Considering all this, we may conclude that the middle legs exert the biggest influ- ence on the speed and thus on locomotion. The stance phase of the hind legs at high walking speeds is very short compared to that of the other legs. This might be due to the fact that the hind legs display something like an intermediate phase where the tarsi are moved along the floor without being lifted off the ground. This gliding phase is a part of the swing phase, although the gliding hind legs that are basically dragged behind the ants’ body prob- ably still provide support and thus stability, while they are already swung. This phenomenon has also been recently observed in spiders and Formica ants (Spagna et al. 2011; Reinhardt and Blickhan 2014). Moll et al. (2013) also pre- sent an example of a grass-cutting ant that gains static sta- bility by sliding hind legs during transport of load. Stepping pattern of slow and fast walking ants Leg coordination during locomotion is flexible and can be adapted according to environmental circumstances (Alex- ander 1989). Walking speed can be one of those factors modulating locomotor output. With changes in walking speed quadrupeds, like horses, adapt their leg coordination to achieve an energetically optimal locomotion (Hoyt and Taylor 1981). Thereby, the transition from one to the next gait occurs in a discontinuous way. In hexapods also dif- ferent gait types are known, but the question of gait tran- sition has not yet been resolved (Graham 1985; Mendes et al. 2013). After examinations in several species, the cur- rent understanding is that the different leg patterns are part of a continuum with a continuous transition from tripod to tetrapod to wavegait coordination with decreasing walking speed (Schilling et al. 2013). Stick insects (Carausius morosus) have been observed to use tetrapod coordination during slow locomotion but switch to tripod pattern with higher speeds (Wendler 1964; Graham 1972, 1985). The analysis of kinematics and walk- ing behaviour in cockroaches (Periplaneta americana and Blaberus discoidalis) revealed two different types of tri- pods for locomotion, a low-speed amble and a high-speed trot (Delcomyn 1971; Bender et al. 2011). Fruit flies (Dros- ophila melanogaster) prefer tripod gait during the entire range of walking speeds, but leg coordination also gets more variable with the decrease in walking speed (Strauss and Heisenberg 1990; Mendes et al. 2013; Wosnitza et al. 2013). Wood ants (Formica polyctena) show stable tri- pod coordination during the entire range of running speed (Reinhardt and Blickhan 2014). Our results show that the walking behaviour of desert ants (Cataglyphis fortis) is in close agreement with that described in Drosophila melanogaster and Formica polyctena. Desert ants employ tripod gait as their major coordination pattern over the entire walking speed. This was also the case for very slow walks, where tripod pattern was generally preserved. However, it also becomes appar- ent that during slow walks, synchrony of tripod coordina- tion could be reduced or other non-tripod combinations, especially tetrapod coordination could occur, as well. This variability shows that Cataglyphis fortis does not need to rely strictly on tripod coordination and is per se able to use different patterns during walking. However, the still preferred use of tripod seems to be kind of advantageous, probably it is an option to reduce errors arising from the iterative processes of path integra- tion. The preference of tripod coordination also during slow walks shows that Cataglyphis ants mostly remain at the upper end of gait continuum proposed for hexapods (see explanation above). Regarding the higher variability of leg coordination during slow locomotion, ants scale down slightly from this upper end. It is conceivable that ants might also be able to reach the lower part of the continuum, yet in our investigation this was never evident. The very slow walks rarely occur in the field. We know from observations that the walking speed employed during foraging is reached within the first two strides. To make the ants constantly walk below 30 mms−1 speed, we had to chill the environment, which in this case was a walk- ing channel in the laboratory. Very rarely did we observe ants in the field in late spring and on relatively chilly early mornings walking at very low speeds out of the nest and soon back into the nest. They have never been observed to forage under these chilly conditions. The quality of tripod coordination can be evaluated by means of a simple measure of tripod coordination strength (TCS) (Fig. 3b) (Wosnitza et al. 2013; compare also Spagna et al. 2011). With increasing walking speed, the TCS reaches values above 0.7 but never goes beyond 0.85. The legs of one tripod are at a minimum 15 % out of phase, even at highest walking speeds with maxi- mum stride frequencies. From a walking speed of around 300 mms−1 on t2 and t1 of the TCS, both remain at a con- stant level of 12–22 ms (t2) and 24–34 ms (t1). This cor- responds to the swing and stance durations that remain relatively constant for these higher speeds (see Fig. 6a). A TCS of 1.0 might increase the chance of jerky move- ments concentrating all impact forces of one tripod into one instant; especially at high speeds, there are less than 18 ms to distribute all ground reaction forces over the con- tact phase (compare Fig. 6a). As a result, a slight cutback of the TCS still assures a smooth run with maximum stability. The ants reach a TCS larger than 0.5 (an overlap of at least 50 %) from very low walking speeds on, while TCS smaller than 0.5 only occurs at walking speeds below 100 mms−1. If we compare TCS of Cataglyphis and Drosophila which 654 J Comp Physiol A (2015) 201:645–656 1 3 can be between 0.1 and 0.8 (Wosnitza et al. 2013), we find that Drosophila at top speeds displays TCS comparable to Cataglyphis. Due to the wide range of walking speeds, Cataglyphis reaches top TCS values already at one-fifth of its speed range. The ants never touch ground with the tarsi associated with one tripod at the same time but kind of unroll the tripod like a ‘functional foot’ tarsal claw after tarsal claw. Especially at high walking speeds, the legs seem to act in a specific sequence. This tendency was also observed in Drosophila (Wosnitza et al. 2013). The alter- nating tripods are comparable to the alternating footfalls of bipedal walking animals (Full and Tu 1991). The big differ- ence, however, is that tripods engage a larger area and thus provide more static stability especially for slower walking speeds whereas at higher walking speeds static stability is replaced by dynamic stability (Ting et al. 1994; Zollikofer 1994c). How do Cataglyphis ants reach high running speeds? Stride frequency increases in a nonlinear fashion with increasing walking speeds. The stride frequency levels off at around 30 Hz and shows a frequency plateau. From this point on, walking speed is increased by increasing stride length only. Heglund et al. (1974) described that a constant stride frequency can be an indicator for a change in gait. Small animals reach a certain speed with smaller strides and higher stride frequencies (Heglund et al. 1974; Zol- likofer 1988). In the desert ants, the start of the frequency plateau is a first indication that the ants attain aerial phases. Zollikofer already presumed a frequency plateau for Cat- aglyphis, although he did not observe one. With maximum frequencies of 28 Hz, the plateau was not yet evident (Zol- likofer 1988). Aerial phases during running are also known from cock- roaches (Full and Tu 1990, 1991) and vertebrates (Heglund et al. 1974). However, this is not necessarily true for all animals. For instance, ghost crabs, wood ants, ostriches, cockroaches and the American wandering spider can reach a frequency plateau without aerial phases by means of compliant legs and the employment of grounded running (compare Blickhan and Full 1987; Reinhardt and Blickhan 2014; Rubenson et al. 2004; Ting et al. 1994; Weihmann 2013). The difference is probably due to the relatively longer legs of Cataglyphis, which changes the biomechan- ics of walking. Longer legs mean larger strides in terms of stride amplitude and stride length. This characterizes the desert ants as stride length maximizers (Zollikofer 1988). The duty factor, a ratio of stance phase to cycle period, is a measure that describes the transition from walking to run- ning (Alexander 1984, 2003). At values below 0.5, swing phases are longer than stance phases, and thus aerial phases occur. Horses, dogs, ostriches and lizards reach duty factors well below 0.5 (Alexander 1984; Fieler and Jayne 1998). Cockroaches as fast-running specimens in the insect world, however, rarely reach such small duty factors (Ting et al. 1994). The middle legs of Cataglyphis fortis are the last of the three leg pairs to fall below the duty factor of 0.5 at a speed of 369 mms−1 (compare Fig. 6b). At speeds between 132 and 369 mms−1, the ants are in a kind of transitional phase where the front and middle legs are already showing aerial phases while at least one middle leg has still ground contact. The gait transition is not abrupt at all, which means that the ants probably adopt a kind of grounded running within quite a wide range of running speeds. Thus, the dynamics of Cataglyphis fortis’ locomotor apparatus seems to be quite similar to those of Formica worker ants and even similar to birds, but distinctively different from those of human beings (compare Reinhardt and Blickhan 2014; Rubenson et al. 2004). In several insect species (Wilson 1966; Graham 1972; Strauss and Heisenberg 1990), stance phase duration becomes shorter with increasing speed, while swing phase duration remains largely constant; at the fastest speeds, the durations of both swing and stance phases equalize (Mendes et al. 2013; Wosnitza et al. 2013). The duration of swing and stance phases in Cataglyphis decreases with increasing walking speed and remain more or less constant at the upper end of the range (Fig. 6a). This corresponds approximately with the observations Delcomyn made in Periplaneta americana (Delcomyn 1971). In his obser- vations, the swing and stance phases are reduced at low stride frequencies. While in Cataglyphis at lower speeds, stance phases are longer than swing phases, at high walk- ing speeds the swing phases are longer than the stance phases. This reversal occurs for the hind legs already at around 95 mms−1, and for the middle legs only at much higher speeds of 349 mms−1. The walking speed (from 200 mms−1 on) is increased by reducing stance phase while the swing phase stays rather constant. Walking speeds of up to 0.7 ms−1 have been reported for Cataglyphis fortis (Wehner 1983). Although we video filmed in the field several times at optimal conditions, we never measured higher walking speeds than 0.62 ms−1. We believe that this is the upper limit of walking speeds for Cataglyphis fortis ants in the field site near Maharès, coastal Tunisia, which admittedly never reaches such tem- perature extremes like for instance the Chott El Cherid in central Tunisia. Why is fast running important anyway? Fast running helps the ants to quickly cover large areas and thus to enhance the chance of finding food and then back home. It is probably also advantageous with regard to potential dan- ger coming from predators and enemies like robber flies, 655 J Comp Physiol A (2015) 201:645–656 1 3 spiders, fringe toe lizards and conspecific ants (Dahbi et al. 2008; Schmid-Hempel and Schmid-Hempel 1984; Weh- ner et al. 1992). Hence, the ants reduce the time they are exposed to their harsh habitat. Long legs do not only help to reach larger strides and thus high walking speeds. They can also help to minimize heat stress (Zollikofer 1994b). Even slightly above the hot desert floor, temperatures decrease to values that the ants still can tolerate (Zollikofer 1988; Wehner et al. 1992; Gehring and Wehner 1995). Outlook It seems that every pair of leg contributes in a distinc- tive way to the ants’ locomotion. The middle legs seem to play a major role in gaining speed and the hind legs contribute in supporting stability. Nevertheless, ground reaction force measurement of the legs would be desirable to further confirm our conclusions. With higher walking speed, the stride frequency levels off and Cataglyphis for- tis ants show aerial phases to expand the walking speed range. Each tripod group is used as a functional foot liter- ally jumping from footfall to footfall comparable to our human run. Consistent tripod coordination throughout the entire walking speed range may be advantageous for the stride integrator. The occurrence of very slow walk- ing speeds, where the non-tripod stepping patterns are mostly observed is usually restricted to walks inside the nest and the immediate surroundings of the nest entrance. Especially on foraging excursions, where higher walking speeds occur—never below 30 mms−1—robust and steady stepping coordination might induce errors as minimal as possible. Acknowledgments We express our gratitude to Rüdiger Wehner for sharing his outstanding knowledge of this fascinating ant. We also thank Ursula Seifert for editing the text, Nadja Eberhardt, for record- ing the very slow walks. Till Bockemühl deserves a special thanks for providing Matlab code for the phase shift analysis plots. Harald Wolf provided the high-speed camera system and supported this study in many ways. We are much indebted to two anonymous ref- erees for their many valuable suggestions on an earlier version of the manuscript. Initial Part of this investigation was supported by grants from the Volkswagen-Stiftung (project I/78580) and the Deutsche Forschungsgemeinschaft (WO466/9-1) to H. W. Financial and infra- structural support was provided by the University of Ulm. Conflict of interest The authors declare that they have no compet- ing interests. Ethical standard All experiments comply with the current laws and regulations of the University of Ulm and of the country where they have been performed. Open Access This article is distributed under the terms of the Crea- tive Commons Attribution License which permits any use, distribu- tion, and reproduction in any medium, provided the original author(s) and the source are credited. References Alexander RM (1984) Walking and running: legs and leg movements are subtly adapted to minimize the energy costs of locomotion. Am Sci 72:348–354 Alexander RM (1989) Optimization and gaits in the locomotion of vertebrates. Physiol Rev 69:1199–1227 Alexander RM (2003) Principles of animal locomotion. Princeton University Press Bender JA, Simspon EM, Tietz BR, Daltorio KA, Quinn RD, Ritz- mann RE (2011) Kinematic and behavioral evidence for a dis- tinction between trotting and ambling gaits in the cockroach Bla- berus discoidalis. J Exp Biol 214:2057–2064 Berens P (2009) CircStat: a MATLAB toolbox for circular statistics. J Stat Softw 31:1–21 Blickhan R, Full RJ (1987) Locomotion energetics of the ghost crab: II. Mechanics of the centre of mass during walking and running. J Exp Biol 130:155–174 Collett M, Collett TS (2000) How do insects use path integration for their navigation? Biol Cybern 83:245–259 Cruse H (1976) The function of the legs in the free walking stick insect, Carausius morosus. J Comp Physiol 112:235–262 Dahbi A, Retana J, Lenoir A, Cerdá X (2008) Nest-moving by the polydomous ant Cataglyphis iberica. J Ethol 26:119–126 Delcomyn F (1971) Locomotion of cockroach Periplaneta ameri- cana. J Exp Biol 54:443–452 Fieler C, Jayne BC (1998) Effects of speed on the hindlimb kinemat- ics of the lizard Dipsosaurus dorsalis. J Exp Biol 201:609–622 Foerster A (1850) Eine Centurie neuer Hymenopteren. Verh Naturh Ver Preuss Rheinlde Westphal, Bonn 7:485–518 Full RJ, Tu MS (1990) Mechanics of six-legged runners. J Exp Biol 148:129–146 Full RJ, Tu MS (1991) Mechanics of a rapid running insect: two-, four- and six-legged locomotion. J Exp Biol 156:215–231 Gehring WJ, Wehner R (1995) Heat shock protein synthesis and ther- motolerance in Cataglyphis, an ant from the Sahara desert. PNAS 92:2994–2998 Graham D (1972) A behavioural analysis of the temporal organisa- tion of walking movements in the 1st instar and adult stick insect (Carausius morosus). J Comp Physiol A 81:23–52 Graham D (1985) Pattern and control of walking in insects. Adv Insect Physiol 18:31–140 Harris J, Chiradella H (1980) The forces exerted on substrate by walk- ing and stationary crickets. J Exp Biol 85:263–279 Heglund NC, Taylor CR, McMahon TA (1974) Scaling stride frequency and gait to animal size: mice to horses. Science 186:1112–1113 Hoyt DF, Taylor CR (1981) Gait and the energetics of locomotion in horses. Nature 292:239–240 Hughes GM (1951) The co-ordination of insect movements. I. The walking movements of insects. J Exp Biol 29:267–285 Mendes CS, Bartos I, Akay T, Márka S, Mann RS (2013) Quantifi- cation of gait parameters in freely walking wild type and sen- sory deprived Drosophila melanogaster. Elife. doi:10.7554/ eLife.00231 Moll K, Roces F, Federle W (2013) How load-carrying ants avoid falling over: mechanical stability during foraging in Atta vollen- weideri grass-cutting ants. PLoS One 8(1):e52816 Müller M, Wehner R (1988) Path integration in desert ants, Catagly- phis fortis. PNAS 85:5287–5290 Reinhardt L, Blickhan R (2014) Level locomotion in wood ants: evi- dence for grounded running. J Exp Biol 217:2358–2370 Ronacher B, Wehner R (1995) Desert ants Cataglyphis fortis use self- induced optic flow to measure distances travelled. J Comp Phys- iol A 177:21–27 656 J Comp Physiol A (2015) 201:645–656 1 3 Rubenson J, Heliams DB, Llooyd DG, Fournier PA (2004) Gait selec- tion in the ostrich: mechanical and metabolic characteristics of walking and running with and without an aerial phase. Proc R Soc Lond B 271:1091–1099 Schilling M, Hoinville T, Schmitz J, Cruse H (2013) Walknet, a bio-inspired controller for hexapod walking. Biol Cybern 107:397–419 Schmid-Hempel P, Schmid-Hempel R (1984) Life duration and turno- ver of foragers in the ant Cataglyphis bicolor (Hymenoptera, For- micidae). Insectes Soc 31:345–360 Seidl T, Wehner R (2008) Walking on inclines: how do desert ants monitor slope and step length. Front Zool 5:8. doi:10.1186/1742-9994-5-8 Spagna JC, Valdivia EA, Mohan V (2011) Gait characteristics of two fast-running spider species (Hololena adnexa and Hololena curta), including an aerial phase (Araneae: Agelenidae). J Arach- nol 39:84–91 Strauss R, Heisenberg M (1990) Coordination of legs during straight walking and turning in Drosophila melanogaster. J Comp Physiol A 167:403–412 Ting LH, Blickhan R, Full RJ (1994) Dynamic and static stability in hexapedal runners. J Exp Biol 197:251–269 Wehner R (1982) Himmelsnavigation bei Insekten. Neurophysiologie und Verhalten. Neujahrsbl Naturforsch Ges Zür 184:1–132 Wehner R (1983) Taxonomie, Funktionsmorphologie und Zoogeog- raphie der saharischen Wüstenameise Cataglyphis fortis (Forel 1902) stat. nov. Senckenbergiana Biol 64:89–132 Wehner R (2009) The architecture of the desert ant’s navigational toolkit (Hymenoptera: Formicidae). Myrmecol News 12:85–96 Wehner R, Srinivasan MV (2003) Path integration in insects. In: Jef- frey KJ (ed) The neurobiology of spatial behaviour. Oxford Uni- versity Press, Oxford, pp 9–30 Wehner R, Wehner S (1986) Path integration in desert ants. Approach- ing a long-standing puzzle in insect navigation. Monit Zool Ital 20:309–331 Wehner R, Wehner S (1990) Insect navigation: use of maps or Ari- adne’s thread. Ethol Ecol Evol 2:27–48 Wehner R, Marsh AC, Wehner S (1992) Desert ants on a thermal tightrope. Nature 357:586–587 Weihmann T (2013) Crawling at high speeds: steady level locomotion in the spider Cupiennius salei—global kinematics and for centre of mass dynamics. PLoS One 8(6):e65788 Weihmann T, Blickhan R (2009) Comparing inclined locomotion in a ground-living and a climbing ant species: sagittal plane kinemat- ics. J Comp Physiol A 195:1011–1020 Wendler G (1964) Laufen und Stehen der Stabheuschrecke Carausius morosus: Sinnesborstenfelder in den Beingelenken als Glieder von Regelkreisen. Z Vergl Physiol 48:198–250 Wilson DM (1966) Insect walking. Annu Rev Entomol 11:103–122 Wittlinger M, Wehner R, Wolf H (2006) The ant odometer: stepping on stilts and stumps. Science 312:1965–1967 Wittlinger M, Wehner R, Wolf H (2007) The desert ant odometer: a stride integrator that accounts for stride length and walking speed. J Exp Biol 210:198–207 Wosnitza A, Bockemühl T, Dübbert M, Scholz H, Büschges A (2013) Inter-leg coordination in the control of walking speed in Dros- ophila. J Exp Biol 216:480–491 Zollikofer CPE (1988) Vergleichende Untersuchungen zum Laufver- halten von Ameisen. Dissertation, University of Zürich, Switzerland Zollikofer CPE (1994a) Stepping patterns in ants—influence of speed and curvature. J Exp Biol 192:95–106 Zollikofer CPE (1994b) Stepping patterns in ants—influence of body morphology. J Exp Biol 192:107–118 Zollikofer CPE (1994c) Stepping patterns in ants—influence of load. J Exp Biol 192:119–127

Walking and running in the desert ant Cataglyphis fortis.

04-01-2015

Wahl, Verena,Pfeffer, Sarah E,Wittlinger, Matthias

eng

PMC5919653

RESEARCH ARTICLE Psychophysiological responses of junior orienteers under competitive pressure Claudio Robazza1*, Pascal Izzicupo1, Maria Angela D’Amico1, Barbara Ghinassi1, Maria Chiara Crippa2, Vincenzo Di Cecco3, Montse C. Ruiz4, Laura Bortoli1, Angela Di Baldassarre1 1 Department of Medicine and Aging Sciences, “G. d’Annunzio” University of Chieti-Pescara, Chieti, Italy, 2 SPAEE, Service of Educational and Learning Psychology, “Sacro Cuore” Catholic University of Milan, Milan, Italy, 3 FISO, Italian Federation of Orienteering Sports, Trento, Italy, 4 Faculty of Sport and Health Sciences, University of Jyva¨skyla¨, Jyva¨skyla¨, Finland * [email protected] Abstract The purpose of the study was to examine psychobiosocial states, cognitive functions, endo- crine responses (i.e., salivary cortisol and chromogranin A), and performance under com- petitive pressure in orienteering athletes. The study was grounded in the individual zones of optimal functioning (IZOF) and biopsychosocial models. Fourteen junior orienteering ath- letes (7 girls and 7 boys), ranging in age from 15 to 20 years (M = 16.93, SD = 1.77) took part in a two-day competitive event. To enhance competitive pressure, emphasis was placed on the importance of the competition and race outcome. Psychophysiological and performance data were collected at several points before, during, and after the races. Results showed that an increase in cortisol levels was associated with competitive pressure and reflected in higher perceived exertion (day 1, r = .32; day 2, r = .46), higher intensity of dysfunctional states (day 1, r = .59; day 2, r = .55), lower intensity of functional states (day 1, r = -.36; day 2, r = -.33), and decay in memory (day 1, r = -.27; day 2, r = -.35), visual atten- tion (day 1, r = -.56; day 2, r = -.35), and attention/mental flexibility (day 1, r = .16; day 2, r = .26) tasks. The second day we observed better performance times, lower intensity of dys- functional states, lower cortisol levels, improved visual attention and attention/mental flexi- bility (p < .050). Across the two competition days, chromogranin A levels were higher (p < .050) on the most difficult loops of the race in terms of both physical and psychological demands. Findings suggest emotional, cognitive, psychophysiological, and performance variables to be related and to jointly change across different levels of cognitive and physical load. Overall results are discussed in light of the IZOF and biopsychosocial models. The pro- cedure adopted in the study also supports the feasibility of including additional cognitive load for possible practical applications. Introduction The interplay between emotion and cognition under pressure has recently attracted research interest [1]. A leading perspective to the study of emotions in sport is the individual zones of PLOS ONE | https://doi.org/10.1371/journal.pone.0196273 April 26, 2018 1 / 16 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Robazza C, Izzicupo P, D’Amico MA, Ghinassi B, Crippa MC, Di Cecco V, et al. (2018) Psychophysiological responses of junior orienteers under competitive pressure. PLoS ONE 13(4): e0196273. https://doi.org/10.1371/journal. pone.0196273 Editor: Luca Paolo Ardigò, Universita degli Studi di Verona, ITALY Received: July 28, 2017 Accepted: April 10, 2018 Published: April 26, 2018 Copyright: © 2018 Robazza et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Funding: This research was funded by a grant from the Italian Olympic Committee of Abruzzo Region (CONI, Comitato Regionale Abruzzo), http:// abruzzo.coni.it/abruzzo.html. Competing interests: The authors have declared that no competing interests exist. optimal functioning (IZOF) model [2]. The model provides a holistic perspective in the de- scription of subjective emotion and non-emotion performance-related states (i.e., psychobio- social states). The main dimensions that define the structure of a performance-related psychobiosocial state are form, content, and intensity. The form dimension refers to the multi- modal display of performance-experiences in a wide range of specific and interrelated psycho- biosocial states. The content dimension involves the functionality–hedonic tone interplay that leads to functional or dysfunctional states for performance perceived as pleasant or unpleasant. The intensity dimension relates to the states amount or quantity. According to the tenets of the IZOF model [2], past, ongoing, and anticipated person-environment interactions are reflected in a variety of psychobiosocial states. These functional/dysfunctional, pleasant/ unpleasant states are manifested in psychological (i.e., affective, cognitive, motivational, voli- tional), biological (i.e., bodily-somatic, motor-behavioral), and social (i.e., operational, com- municative) modalities [3, 4]. The relationship between psychobiosocial states and performance is assumed to be bi-direc- tional, implying that psychobiosocial states can influence performance and, conversely, on- going performance can influence psychobiosocial states. Prior to and during performance, one’s appraisals of anticipated and current gains or losses tend to elicit challenge states (e.g., feeling confident) or threat states (e.g., feeling worried), respectively. Performance level is pre- dicted based on the interaction of both functional (challenge) and dysfunctional (threat) states. High probability of successful performance is expected to occur when the athlete experiences high functional and low dysfunctional psychobiosocial states [2]. This multimodal view con- curs with the biopsychosocial model of challenge and threat [5], which integrates biological (i.e., autonomic and endocrine influences on the cardiovascular system), psychological (i.e., affective and cognitive influences on evaluative processes), and social (i.e., person and environ- mental interplay) modalities to explain motivational processes of individual performance. Both the IZOF and biopsychosocial models build upon Lazarus’ [6] appraisal theory of emotion. The theory draws on the notion that threatening situations involve the appraisal of potential for harm or loss, whereas challenging situations entail the appraisal of opportunities for growth, mastery, or gain. Emotional responses are also triggered by individual evaluation of available coping resources and response options. In motivated performance contexts, the interaction between appraisal of situational demands and coping resources elicits challenge and threat responses, which encompass a set of interrelated affective, cognitive, motivational, physiological, expressive or behavioral, and social components [2, 5, 7]. Challenge is experi- enced when the appraisal of personal coping resources meets or exceeds situational demands, whereas threat arises when perceived demands exceed resources. Extant research findings sup- port the hypothesis that challenge states lead to superior athletic performance compared to threat states [8–11]. Distinct patterns of neuroendocrine and cardiovascular activity are postulated to reflect challenge or threat states in athletes [12]. According to this view, a challenge state is accompa- nied by increased epinephrine level and cardiac activity, reduced total peripheral vascular resistance, and either pleasant or unpleasant emotions experienced as helpful for performance. On the other hand, a threat state is associated with increased cortisol level, smaller increases in cardiac activity, stable or enhanced peripheral vascular resistance, and unpleasant emotions perceived as harmful [12–14]. Although research on the biopsychosocial mechanisms associ- ated with performance in sport is still scant, a challenge state is suggested to determine positive consequences on performance deriving from improved decision making and cognitive func- tioning, enhanced task engagement, and less effort spent to self-regulation [12]. On the other hand, a challenge state is proposed to influence performance negatively due to decreased cog- nitive functioning and task involvement, and greater resources devoted to self-regulation. Psychophysiological responses under pressure PLOS ONE | https://doi.org/10.1371/journal.pone.0196273 April 26, 2018 2 / 16 Several interactive factors have been proposed to influence whether individuals feel that they have or not the resources to cope with a stressful situation and, therefore, to determine a challenge or threat state with the subsequent physiological and psychological responses [5, 14]. These antecedents include, among others, familiarity, required effort, skills, knowledge, and abilities. For example, high familiarity with a task, low required effort, and high skill levels are likely leading to one’s evaluation of a situation as a challenge instead of a threat. In contrast, low familiarity, high effort, and poor skills are expected to evoke evaluations of the situation in terms of a threat rather than a challenge. It should be noted that challenge and threat are not dichotomous states, but represent anchors along a bipolar continuum. Thus, researchers have often studied relative differences in challenge and threat rather than absolute differences [13, 14]. Grounded in the IZOF and biopsychosocial models, the purpose of this study was to exam- ine psychobiosocial states, cognitive (executive) functions, endocrine responses (i.e., salivary cortisol and chromogranin A), and performance under pressure in orienteering sport, which involves highly physical, cognitive, and emotional demands. Orienteers need good aerobic fit- ness to engage in a foot race in a wild environment. Navigating on an unfamiliar terrain between a number of control locations in an established order with the help of a map and com- pass in the quickest time is also a cognitive challenge. The orienteers, indeed, are provided with the orienteering map just seconds before the beginning of the race. This implies that they plan a route from the map during the race. Successful performance requires considerable visual attention to critical cues from the map, the environment, and the travel [15]. Attending simultaneously to the three sources of information and making effective decisions under time constraints and competitive stress entails complex and dynamic processes of perception, encoding, retrieval, decision making, and emotion regulation. Thus, executive functions, such as focused attention, working memory, and cognitive flexibility, are essential in orienteering. These top-down control processes underlie higher order cognitive functions involved in goal- directed behaviors, such as problem-solving, decision making, and planning [16]. Working memory, in particular, refers to the limited capacity and multicomponent cogni- tive ability to retain in mind and manipulate complex information (i.e., verbal and visuospa- tial), no longer perceptually present, over short periods of time [16, 17]. It is critical for making sense of experiences that unfold over time (i.e., remembering what happened earlier and relating it to what comes later), and for different mental processes including attentional allocation (i.e., selectively attending to environmental stimuli and tuning out irrelevant sti- muli) and switching between mental sets (i.e., cognitive flexibility). Working memory is reflec- tive of one’s ability to focus on task goals, suppress interferences, and avoid distractions. Research evidence has shown correlations between working memory, fluid intelligence [18], and reasoning ability [19]. Individuals with higher working memory capacity are able to effec- tively adjust their attention to the requirements of the task, inhibit distracting stimuli, and flex- ibly use their cognitive resources (for a review in sport, see [20]). Given its impact on several mental processes, any approach aimed at enhancing working memory and other related execu- tive functions (i.e., selective focused attention and cognitive flexibility) is particularly relevant in the athletic domain [21]. Together with psychological burdens, endocrine responses are reflective of race demands and competitive strain. Cortisol has been a widely used marker of stress. An elevation in the cortisol level, deriving from stimulation of the hypothalamic-pituitary-adrenal axis, indicates an individual’s experience of stress and/or physical effort [22]. Research in sport generally shows a negative relationship between cortisol and performance. Cortisol has also been found to influence decision making, attention, and memory by inhibiting information processing [23]. Another index of exercise intensity is chromogranin A, a soluble protein co-stored and Psychophysiological responses under pressure PLOS ONE | https://doi.org/10.1371/journal.pone.0196273 April 26, 2018 3 / 16 co-released with catecholamines, deemed an accurate marker of the sympathetic adrenal activ- ity [24–26]. Robazza et al. [27] assessed both salivary cortisol and chromogranin A of basket- ball players within an hour prior to games played at the team’s home venue across a whole season. Although the two biological markers were not related to performance, their salivary concentration was associated with perceived intensity, frequency, and functional impact of a number of psychobiosocial states. In line with the IZOF model assumptions [2], higher scores of functional states were linked to higher individual performance ratings. Most research in sport has focused, so far, on assessment of hormone levels prior to or after competition [28–30]. Lautenbach, Laborde, Kla¨mpfl, and Achtzehn [31] were the first who assessed the dynamics in cortisol levels, anxiety, affect intensity and valence, and performance parameters of two tennis players before, during, and after a match. Cortisol was negatively cor- related with some performance parameters (e.g., unforced errors and return performance) and uncorrelated with other parameters (e.g., serving performance). These results, however, cannot be generalized because of the single subject nature of the study. Moreover, executive functions were not assessed. Study purpose and hypotheses To date, no study has investigated the relationships among salivary cortisol, chromogranin A levels, psychobiosocial states, executive functions, and performance, and their fluctuations in pressurized contexts eliciting different levels of challenge and threat. Thus, drawing on the assumptions from the IZOF [2] and biopsychosocial [5] models, the purpose of this study was to examine the relationships among the variable levels and their changes over the course of meaningful competitive situations. A main contribution to the extant literature is that this study combined the IZOF [2] and biopsychosocial [5] theoretical frameworks in a single inves- tigation. Findings were expected to provide support for the joint use of the two perspectives for both theoretical and applied objectives. From a conceptual standpoint, we predicted emo- tional, cognitive, psychophysiological, and performance variables to be related and to jointly change across different levels of cognitive and physical load. From a practical point of view, we explored the feasibility of implementing cognitive tasks to enhance the cognitive load for train- ing purposes. Specific hypotheses were then formulated. Regarding the relationships among variables, according to the IZOF and biopsychosocial models we hypothesized (H1) cortisol elevation responses under competitive pressure to be: (1) reflected in higher perceived exertion, (2) negatively related to functional psychobiosocial states and executive functions, and (3) positively related to dysfunctional psychobiosocial states. We did not formulate detailed predictions regarding chromogranin A relationships with the other variables due to the novel use of this marker in sport. However, previous research results on basketball players showed salivary concentration of chromogranin A asso- ciated with perceived beneficial effects of functional psychobiosocial states toward perfor- mance [27]. Based on these findings, we expected to find higher levels of chromogranin A related to better performance. Concerning variable level fluctuations, we expected to find (H2) within-competition varia- tions of variable scores in function of the different physical and psychological demands of the race. According to the biopsychosocial model, high levels of effort and skill requirements are antecedents of threat states [5]. Thus, we hypothesized that more physically and cognitively difficult routes would engender in orienteers higher perceived exertion, enhanced salivary cor- tisol levels, and related changes of all other variable scores (as stated in H1). To further investigate the changes in the variable scores across competitive situations, we compared the orienteers’ psychophysiological responses in the usual condition of both Psychophysiological responses under pressure PLOS ONE | https://doi.org/10.1371/journal.pone.0196273 April 26, 2018 4 / 16 physical and cognitive load with a condition in which the cognitive load was considerably reduced. To this purpose, the participants were asked to complete again the same course on the following day. The second race was therefore less psychologically demanding, because par- ticipants were acquainted with the course and did not need to use the map and compass. Lower levels of effort and skill requirements are likely conducive to challenge states [5]. Thus, we expected to find (H3) in the orienteers lower cortisol levels, higher levels of functional states, enhanced executive functions, lower levels of dysfunctional states, and improved performance. Method Participants The sample consisted of 14 junior orienteering athletes, 7 boys and 7 girls, ranging in age from 15 to 20 years (M = 16.93 yrs., SD = 1.77). All participants were part of the Italian Junior National Team. Seven of them were skilled runners, with several years of practice (M = 5.85 yrs., SD = 1.35) and substantial amount of training during the week (M = 7.28 hrs., SD = 2.06). The other seven were medium level runners, with an average of four years of practice (M = 3.71 yrs., SD = 1.80) and a moderate amount of training per week (M = 5.57 hrs., SD = 1.57). Measures Perceived exertion. Perceived exertion was rated on a modified Borg’s Category Ratio scale (CR-10 [32]) using the following verbal anchors: 0 = nothing at all, 0.5 = very, very little, 1 = very little, 2 = little, 3 = moderately, 5 = much, 7 = very much, 10 = very, very much, • = max- imal possible (no verbal anchors were used for 4, 6, 8, and 9). The score of 11 is assigned to maximal possible. The CR-10 Borg scale has been shown to be closely related to various physio- logical and psychophysiological measurements in sport and exercise psychology [33, 34], and has been widely used to monitor training load [35]. Psychobiosocial states. Assessment was conducted using the psychobiosocial states scale, trait version (PBS-ST [3]). The scale is composed of 15 items, 8 functional and 7 dysfunctional, intended to assess seven modalities of a performance-related state (i.e., affective, cognitive, moti- vational, volitional, bodily-somatic, motor-behavioral, and operational). The scale derived from the original English version of the Individualized Profiling of Psychobiosocial States [4], and was validated to Italian language. Each item includes 3–4 descriptors conveying a similar experience that are categorized as functional or dysfunctional for performance. The aim is to transmit to the participants a straightforward depiction of an emotional experience. Specifically, the affective modality is assessed by three rows of synonym adjectives for: functional pleasant states, ‘enthusi- astic, confident, carefree, joyful’; dysfunctional anxiety, ‘worried, apprehensive, concerned, trou- bled’; and functional anger, ‘fighting spirit, fierce, aggressive’. For the other six modalities, two rows of adjectives assess functional (+) or dysfunctional (-) states: Cognitive (+) modality, ‘alert, focused, attentive’; Cognitive (-), ‘distracted, overloaded, doubtful, confused’; Motivational (+), ‘motivated, committed, inspired’; Motivational (-), ‘unmotivated, uninterested, uncommitted’; Volitional (+), ‘purposeful, determined, persistent, decisive’; Volitional (-), ‘unwilling, undeter- mined, indecisive’; Bodily-somatic (+), ‘vigorous, energetic, physically-charged’; Bodily-somatic (-), ‘physically-tense, jittery, tired, exhausted’; Motor-behavioral (+), ‘relaxed-, coordinated-, powerful-, effortless-movement’; Motor-behavioral (-), ‘sluggish, clumsy, uncoordinated, power- less-movement’; Operational (+), ‘effective-, skillful-, reliable-, consistent-task execution’; and Operational (-), ‘ineffective-, unskillful-, unreliable-, inconsistent-task execution’. The stem of items of the trait version was modified from ‘how do you usually feel’ to ‘how do you feel right now–at this moment’ in order to assess the current psychobiological states of Psychophysiological responses under pressure PLOS ONE | https://doi.org/10.1371/journal.pone.0196273 April 26, 2018 5 / 16 participants. For each item of the scale athletes were requested to select one or more descriptors that best reflected their current state, and to rate the intensity on a 5-point Likert scale ranging from 0 (not at all) to 4 (very, very much). Mean scores of functional and dysfunctional items were computed. Robazza et al. [3] showed a two-factor solution (i.e., functional and dysfunctional intensity subscales) to be acceptable, with CFI = .950, TLI = .942, RMSEA (90% CI) = .108 (.098 ± .118), and SRMR = .121 in a sample of male and female athletes from different sports. Memory. Three technical elements (symbols) were placed on each control point of the course, for a total of 48 elements (i.e., 12 symbols on the 4 control points comprised in a loop). The symbols were selected by three expert orienteering coaches and were customarily used in orienteering maps, such as trees, pits, marshes, springs, ponds, rock pillars, cliffs, caves, rocks, boulders, buildings, and fences. Orienteers were asked to memorize the 12 symbols they encountered on the four control points of a loop, and report them to the examiner in the main checking point within 30 sec. We deemed this ecological assignment to be a representative task in the assessment of working memory in the context of orienteering. Participants, indeed, engaged in an elaboration process of selective attention and inhibition in their effort to hold information in mind and, at the same time, to keep symbols separate from those included in the map, thereby avoiding interference during recall. The score was the number of symbols correctly reported. Visual attention. The Bells Test was used to assess visual attention [36]. The test was orig- inally developed to identify visual inattention (neglect) associated with clinical manifestation of attentional deficits in space [37]. Seven lines of 35 target figures (bells) are presented in a 21.5 × 28 cm sheet of paper interspersed with distractor figures (e.g., horse, bird, key, apple, mushroom, guitar, and car) in a pseudo-random manner. Each line contains 5 bells and 40 dis- tractors. The paper was rotated 45˚ clockwise at the end of each loop to prevent habituation. Orienteers were required to circle with a pencil as many bells as possible in 30 sec. The score was the number of bells correctly circled. A number of validity studies documented the superi- ority of the Bells Test in detecting mild and moderate neglect, likely because this was a task demanding more selective attention in comparison with other measures [37]. Attention/Mental flexibility. We used the Trail Making Test as a measure of attention, speed, and mental flexibility [38]. Using a pencil, participants were required to connect 13 encircled numbers and 12 encircled letters randomly arranged on a page. The task consisted of connecting in 30 sec all letters and numbers in alternating order (i.e., 1, A, 2, B, 3, C, and so on). A normal printed version was administered after the first and third loops, while a specular version was used after the second and fourth loops to counteract habituation. The score was expressed in terms of the time in seconds required for task completion. Substantial research evidence has indicated the Trail Making Test to be a reliable and valid measure of attentional abilities, including visual search and visual-spatial sequencing, as well as speed and mental flexibility [38]. Cortisol and chromogranin A. Salivary cortisol and chromogranin A were obtained from saliva samples. The athletes were requested to refrain from ingesting stimulating (e.g., coffee and chocolate) or dye containing substances, and from brushing their teeth during the three hours before assessment. Saliva samples were collected by chewing regular cotton saliv- ette sampling devices (Sarstedt, Nu¨mbrecht, Germany), thus without chemical stimulants. Samples were kept on ice and then stored at -20˚C until the day of analysis. At the day of the analysis, saliva samples were centrifuged 10 min at 2000 x g to remove particulate material. Hormonal determinations were obtained by using the Human Chromogranin A ELISA Kit (MyBiosource, San Diego, CA, USA) and Human Cortisol ELISA Kit (Diagnostics Biochem Canada Inc, London, Ontario, Canada) according to the manufacturers’ directions and were expressed as ng/ml. All samples were processed in duplicate during the same assay section. Psychophysiological responses under pressure PLOS ONE | https://doi.org/10.1371/journal.pone.0196273 April 26, 2018 6 / 16 Performance. The time to complete each loop was recorded in sec through Sport Ident Technology, an orienteering race timing system. This system consisted of a Sport Ident Card, an extended data memory stick fixed on an athlete’s finger, and a Sport Ident Station, an elec- tronic device placed on each control point. The running time is automatically calculated by punching the Sport Ident Card into the Sport Ident Stations at each checking point. The inter- mediate time between control points of each loop, the total time of each loop, and the total time of the orienteering performance (four loops) were obtained for each participant. Procedure The ethics committee for biomedical research of the “G. d’Annunzio” University of Chieti- Pescara, Italy, approved the study, with anonymity and confidentiality being assured for all the participants. A regional delegate of the Italian Federation of Orienteering and coaches of junior orienteers were initially contacted and informed about the study purposes. They showed interest in the investigation and agreed to organize a competition in a route located in a large natural area in the center of Italy. The athletes and their parents or guardians signed an informed consent form in accordance with the Declaration of Helsinki. Day 1: Briefing. Participants were gathered nearby the competition site one the day before the commencement of the study. Upon their arrival, the orienteers were explained the main purposes of the study and procedures during a two-hour session. To create an experience of competing under pressure, we emphasized that the two-day competition was important, the race would be objectively assessed, and the final performance ranking would be evaluated by the coaches of the junior national team. During the initial two-hour session, all psychological measures were presented to help the participants become acquainted with the assessment pro- cedures. Participants were recommended to abstain from consuming stimulating (e.g., coffee and chocolate) or dye containing substances, and to not brush their teeth three hours before collection of the saliva samples. Day 2: First competitive race. The orienteering courses are usually composed of start and finish points, and a series of control points. The proposed course was comprised of four laps of different physical and psychological demands. To facilitate data collection, the course had a single start and finish point. Each lap included four control points. The orienteer’s task was to complete all laps in the shortest time possible passing through all control points with the aid of a map and a compass. Participants started the race at 9:00 a.m., three minutes apart. The assessment schedule is depicted in Fig 1. Eight assessments were performed to measure perceived exertion and psychobiosocial states, and to collect salivary samples. Specifically, such data were collected 60 min before the race, within 3 min after each loop, 5 min, 15 min, and 60 min after the race. Four memory assessments were carried out, one after each loop. Visual attention and attention/mental flexibility tests were administered at six different times (i.e., before the race start, after each loop, and 60 min after the race). The order of administra- tion of the assessments after every loop was the following: (1) perceived exertion, (2) memory, (3) biomarkers, (4) visual attention, (5) attention/mental flexibility, and (6) psychobiosocial states. Six research assistants, with a specific measurement task each, were involved in the assessments. An additional experimenter supervised the whole procedure to ensure a correct measurement sequence and administration in a timely fashion. Two hours after the race, all participants were allowed to walk across the same course led by a coach to memorize the specific features of the terrain (e.g., ground, environment) in prepara- tion for the next race on the following day. Day 3: Second competitive race. The second race took place on the same course, the fol- lowing day. Participants started again the race at 9:00 a.m., three minutes apart, in the same Psychophysiological responses under pressure PLOS ONE | https://doi.org/10.1371/journal.pone.0196273 April 26, 2018 7 / 16 order as in the first competition. Data were collected using the same administration procedures and participants’ starting order. Considering that the participants were already familiar with the course, they did not need to use a map and a compass. Thus, the cognitive load was largely reduced compared to the previous day, while the physical load remained about the same. Data analysis Data were initially screened for missing cases, outliers, normality (using Shapiro–Wilk statis- tic), and sphericity [39]. A series of two–way repeated measures ANOVAs was then performed on the dependent variables. The independent variables were the competition day (two days) and the assessment phase (eight phases, from 0 to 7; see Fig 1). In particular, 2 × 4 (day × assessment phase) analysis was conducted on performance and memory data, 2 × 6 on visual attention and attention/mental flexibility data, and 2 × 8 on perceived exertion, cortisol, chro- mogranin A, and functional/dysfunctional psychobiosocial states. The sources of significant effects were then identified through pair–wise comparison of means. Results Descriptive statistics and bivariate correlations among the variable scores collected across the two-day competition are reported in Table 1. The complete trend over time of the variable mean scores is shown in Fig 2. Evidence of non-normality was found for chromogranin A and dysfunctional psychobiosocial states. Thus, the data of these variables were transformed using square root transformation before conducting the main analysis [40]. Across the two-day com- petition, low to moderate negative correlations were shown between performance time and chromogranin A, visual attention, and functional psychobiosocial states, while moderate posi- tive correlations were observed between performance time and dysfunctional psychobiosocial states. Furthermore, cortisol levels were negatively related to memory, visual attention, and functional psychobiosocial states, and positively related to dysfunctional psychobiosocial states. ANOVA results are contained in Table 2. Sphericity assumptions were examined through the Mauchly’s test and, in case of violation, Greenhouse–Geisser correction in the degrees of Fig 1. Timeline of assessment schedule across the investigation. https://doi.org/10.1371/journal.pone.0196273.g001 Psychophysiological responses under pressure PLOS ONE | https://doi.org/10.1371/journal.pone.0196273 April 26, 2018 8 / 16 freedom was applied. Compared to the first race, better performance time, visual attention scores, and attention/mental flexibility scores were reported on the second race. Lower levels of cortisol and dysfunctional psychobiosocial states were also found. With the exception of functional psychobiosocial states, significant differences were observed on the scores of all variables across the assessment phases. Pair–wise comparisons showed significant differences (p < .050) in a number of variable scores across assessment phases (see S1 Data set and post- hoc test results). Interestingly, performance time (p < .001), perceived exertion (p < .005), cor- tisol levels (p < .004), and chromogranin A levels (p < .050) of the two races were higher on loops 3 and 4 compared to loops 1 and 2. Dysfunctional psychobiosocial states scores (p < .009) were larger on loop 3 compared to loops 1 and 2, while memory scores were lower (p < .005). Mean performance time of the race (p < .001), and mean performance scores of visual attention (p < .001) and of attention/mental flexibility (p < .001) improved from day 1 to day 2, while mean intensity scores of dysfunctional psychobiosocial states decreased (p < .050). However, post-hoc results should be interpreted with caution because of the small sample size and the number of comparisons. Discussion The purpose of this study was to investigate the relationships among psychobiosocial states, cognitive executive functions, and endocrine responses of orienteers involved in a two-day competitive race based on the assumptions of the IZOF [2] and biopsychosocial [5] models. Table 1. Descriptive statistics and pearson correlation coefficients of mean scores of measures collected across the four loops of the orienteering course. Measures M SD 1 2 3 4 5 6 7 8 Day 1 1. Performance time (in sec) 884.82 227.59 — 2. Perceived exertion 5.90 1.54 .02 — 3. Cortisol 2.97 0.13 .15 .32† — 4. Chromogranin A 10.60 5.79 -.22† -.10 -.34† — 5. Memory 5.91 1.63 -.24† .11 -.27† -.27† — 6. Visual attention 19.55 3.21 -.39† .13 -.56†† .28† .35† — 7. Attention/mental flexibility 53.02 8.27 .45†† -.16 .16 .12 -.73††† -.49†† — 8. Functional psychobiosocial states 2.27 0.61 -.30† .07 -.36† .19 .15 .20 .13 — 9. Dysfunctional psychobiosocial states 0.91 0.37 .48†† .43†† .59†† -.04 -.14 -.19 -.04 -.56†† Day 2 1. Performance time 707.77 159.88 — 2. Perceived exertion 5.75 1.03 .19 — 3. Cortisol 2.92 0.16 -.02 .46†† — 4. Chromogranin A 11.87 6.61 -.42†† -.16 -.09 — 5. Memory 6.52 1.46 .21† -.48†† -.35† .27† — 6. Visual attention 22.96 3.15 -.38† .06 -.35† .20† .26† — 7. Attention/mental flexibility 37.95 6.50 .14 .26† .26† -.24† -.44†† -.66††† — 8. Functional psychobiosocial states 2.36 0.80 -.29† .15 -.33† -.15 -.34† .11 .45†† — 9. Dysfunctional psychobiosocial states 0.76 0.36 .53†† .38† .55†† .00 .04 -.23† -.14 -.72††† Note. Scores of chromogranin A and dysfunctional psychobiosocial states are normalized using square root transformation. †Low correlation. ††Moderate correlation. †††Moderately high correlation. https://doi.org/10.1371/journal.pone.0196273.t001 Psychophysiological responses under pressure PLOS ONE | https://doi.org/10.1371/journal.pone.0196273 April 26, 2018 9 / 16 Combining predictions and indications stemming from both views can provide a better under- standing of athletes’ psychophysiological reactions in pressurized contexts and also inform applied interventions. Our findings provided support for the first hypothesis (H1), showing that an elevation in cortisol levels due to competitive pressure, and mirrored in higher perceived exertion, was associated with higher intensity of dysfunctional psychobiosocial states, lower intensity of functional psychobiosocial states, and decay in memory, visual attention, and attention/mental flexibility. Results are consistent with previous research on the negative influence of increased Fig 2. Trend over time of mean variable scores. Solid lines represent the data on the first competitive race, while dashed lines represent the data on the second competitive race. The numbers on the horizontal axis indicate the assessment phase: 0 = 60 min before the race (salivary samples) or just before the race (visual attention and attention/mental flexibility tests); 1 to 4 = after each loop; 5 to 7 = 5 min, 15 min, and 60 min after the race. The first loop and the fourth loop are also marked by vertical-dotted lines. Cortisol and chromogranin A are expressed as ng/ml. https://doi.org/10.1371/journal.pone.0196273.g002 Psychophysiological responses under pressure PLOS ONE | https://doi.org/10.1371/journal.pone.0196273 April 26, 2018 10 / 16 cortisol levels on cognitive processes, including working memory, attention control, and deci- sion making [23]. The worsening in the athletes’ psychobiosocial states (i.e., higher intensity of dysfunctional states and lower intensity of functional states) and top-down executive functions may hamper performance. More importantly, our findings concur with a body of IZOF-based research evidence in sport generally suggesting that a high probability of optimal functioning can occur when the athlete experiences a combination of high intensity of functional psycho- biosocial states and low intensity of dysfunctional psychobiosocial states [41]. Conversely, less than optimal functioning is associated with low levels of functional states and high levels of dysfunctional states. Results are also in line with the predictions stemming from the biopsy- chosocial model of challenge and threat [5, 13] and its application to the sport and perfor- mance contexts [12]. Using a golf putting task, for example, Moore et al. [10] manipulated the instructions provided to novice golfers to create a challenge group and a threat group. The challenge group executed more accurately, and showed more efficient putting kinematics and forearm muscle activity than the threat group. Similarly, in a pressurized environment requir- ing accurate execution of a novel motor task (i.e., laparoscopic surgery), Vine et al. [42] found that evaluating the task as challenging resulted in effective attentional control and superior performance. In our study, increased levels of cortisol and dysfunctional emotions and decreased cognitive processes (i.e., memory, visual attention, and attention/mental flexibility) suggest a state typified in terms of threat rather than challenge. Table 2. Analysis of variance results. Measure Source F df p value ηp 2 Power Performance time Day 22.85 1, 13 < .001 .64 .99 Assessment 43.69 3, 39 < .001 .77 1.00 Day × Assessment 1.47 3, 39 .239 .10 .36 Perceived exertion Day 0.67 1, 13 .429 .05 .12 Assessment 70.40 2.074, 26.968 < .001 .84 1.00 Day × Assessment 0.53 2.899, 37.681 .658 .04 .15 Cortisol Day 9.43 1, 13 .009 .42 .81 Assessment 32.06 2.661, 34.588 < .001 .71 1.00 Day × Assessment 1.03 3.139, 40.801 .392 .07 .26 Chromogranin A Day 0.70 1, 13 .418 .05 .12 Assessment 3.41 3.201, 41.609 .024 .21 .75 Day × Assessment 1.07 7, 91 .390 .08 .44 Memory Day 2.67 1, 13 .126 .17 .33 Assessment 7.03 3, 39 .001 .35 .97 Day × Assessment 1.97 3, 39 .134 .13 .47 Visual attention Day 38.07 1, 13 < .001 .75 1.00 Assessment 9.57 5, 65 < .001 .42 1.00 Day × Assessment 4.84 5, 65 .001 .27 .97 Attention/mental flexibility Day 221.04 1, 13 < .001 .94 1.00 Assessment 9.55 5, 65 < .001 .42 1.00 Day × Assessment 1.64 5, 65 .163 .11 .53 Functional psychobiosocial states Day 0.78 1, 13 .392 .06 .13 Assessment 1.39 2.677, 34.806 .264 .10 .32 Day × Assessment 1.62 7, 91 .139 .11 .64 Dysfunctional psychobiosocial states Day 4.94 1, 13 .045 .28 .54 Assessment 3.85 7, 91 .001 .23 .97 Day × Assessment 0.36 3.142, 40.850 .789 .03 .12 https://doi.org/10.1371/journal.pone.0196273.t002 Psychophysiological responses under pressure PLOS ONE | https://doi.org/10.1371/journal.pone.0196273 April 26, 2018 11 / 16 According to our second hypothesis (H2), we found within-competition fluctuations of psychophysiological variables. Specifically, the third and fourth race loops seemed to be more difficult than the first and second ones in terms of both physical and psychological demands. Increased difficulty requires more effort and skills, which can induce a threat state with related emotional and cognitive consequences. Indeed, race difficulty was manifested in slower perfor- mance times and higher levels of perceived exertion, cortisol, chromogranin A, and dysfunc- tional psychobiosocial states. Lower scores of memory and attention/mental flexibility were also observed (Table 2 and Fig 2). Modifications in psychophysiological responses can be interpreted again within the tenets of the IZOF model [2] and the biopsychosocial model of challenge and threat [5]. Both models, indeed, emphasize the interplay among emotional, cognitive, biological, and social modalities for an individual’s adaptation to environmental changes. The second day we observed faster performance times, lower intensity of dysfunctional psy- chobiosocial states, and improved visual attention and attention/mental flexibility (Table 2 and Fig 2). These results highlight the impact of the cognitive burden usually associated with orienteering race that is added to the physical load [15]. Findings are also aligned with our last study hypothesis (H3) stating that a substantial reduction in the cognitive load of the race, due to the familiarity with the course, would result in enhanced psychophysical states and improved performance. As postulated in the biopsychosocial model, familiarity and low levels of effort and skill requirements can lead to a challenge state. When the cognitive load specific to the race was removed, more cognitive resources were available for other cognitive tasks. Notably, while the cortisol levels were lower across the second-day race compared to the first day, the athletes’ chromogranin A levels did not differ significantly in the two competitions. This finding, together with the correlation observed between chromogranin A and executive functions (even though small), may support the view of chromogranin A as a marker of the sympathetic adrenal activity in response to exercise intensity [24, 26]. Low and moderate correlations across the two races were also shown between chromogranin A levels and perfor- mance times suggesting that higher levels of chromogranin A were related to better perfor- mance. This is in accordance with previous IZOF-based research results in basketball players showing salivary concentration of chromogranin A to be associated with perceived beneficial effects of functional psychobiosocial states on performance [27]. Taken together, findings of the current study can be understood in light of the combined and unique contributions of the IZOF [2] and biopsychosocial [5] theoretical frameworks. Both models build upon the Lazarus’ [6] notion that the athlete’s appraisal of situational demands and personal resources determines the perception of a situation as challenging or threatening. Within this combined view, the contribution of the IZOF model is more on the description, prediction, explanation, and self-regulation of a wide range of functional or dysfunctional psychobiosocial experiences that accompany challenge or threat. In contrast, the biopsychosocial model focuses on the distinct patterns of neuroendocrine and cardiovascular activity of challenge and threat indexed objectively as well as subjectively [5, 14]. Drawing on both perspectives, results of this study highlighted the expected relationships among a number of psychological, physiological, and performance variables, as well as their fluctuations as a function of cognitive and physical demands. Variations in cognitive and physical loads within the race (different loops) and between races (day 1 and day 2) were likely conducive to changes in the individual experience along the challenge–threat continuum, also manifested in the observed psychophysiological responses. Notwithstanding the encouraging findings, we acknowledge some study limitations. First, the low power associated with the small sample size reduces the generalizability of the results. However, our purposeful sample of high-medium competitive level athletes and the within- subjects design, in which participants served as their own controls, tend to enhance the power of the analysis [43]. Second, we did not assess the fluctuations of one’s appraisal of perceived Psychophysiological responses under pressure PLOS ONE | https://doi.org/10.1371/journal.pone.0196273 April 26, 2018 12 / 16 competitive demands and personal coping resources to identify the individual’s state within the challenge–threat continuum postulated in the biopsychosocial model (e.g., [10]). Finally, the present study examined the effects of competitive pressure in high and medium level junior orienteers. Given the small sample size, potential differences by level, experience, and gender were not examined. Thus, future research should aim to investigate the psychophysiological effects of competitive pressure in larger samples, taking into account individual appraisals of situational demands and coping resources, as well as individual differences such as perfor- mance level, experience, age, and gender. Despite these issues, the current study had high eco- logical validity, and findings can be regarded as valuable preliminary evidence to promote further research for a better understanding of the athletes’ experience during competition. From a practical perspective, this study provides novel findings that may inform strategies practitioners apply to assist orienteers in dealing with performance and competitive demands. In particular, helping athletes become aware of their performance-related psychobiosocial states and their effects on performance, cognitive functions, and endocrine responses can be an important step toward self-regulation of thoughts, feelings, attention focus, and behaviors to achieve performance goals. The procedure adopted in the current study also suggests the feasibility of including additional cognitive load to usual performance, with the purpose to deplete and then replenish and increase cognitive resources through training. According to the strength model of self-control [44, 45], for example, it might be speculated that the working memory task implemented during the route (i.e., recalling the symbols placed on the control points of the course) may be used to strengthen working memory, which is a critical ability in orienteering. Future research should examine the effects of an increase in cognitive load dur- ing training on individual’s resources and performance in orienteering and other sports. In conclusion, this investigation provides a unique contribution to the literature on psycho- biosocial states, cognitive functions, endocrine responses, and performance under competitive pressure. To our knowledge, this is the first study that combines the IZOF [2] and biopsychoso- cial [5] theoretical views. Findings offer initial and substantial support for the joint use of the two perspectives for both theoretical and practical purposes. Theoretically, the pattern of results highlight the expected relationship among emotional, cognitive, psychophysiological, and per- formance variables, as well as their changes across different levels of cognitive and physical load. From a practical perspective, findings support the feasibility of implementing training tasks to increase cognitive resources, and suggest potential benefits derived from the self-regulation of psychobiosocial states to deal with performance and competitive demands. Future research combining the IZOF and biopsychosocial theoretical frameworks is warranted. Supporting information S1 Data set and post-hoc test results. (PDF) Acknowledgments The authors gratefully acknowledge the contribution of coaches and research assistants who provided their help with participant recruitment and data collection. Author Contributions Conceptualization: Claudio Robazza, Pascal Izzicupo, Maria Angela D’Amico, Barbara Ghi- nassi, Maria Chiara Crippa, Vincenzo Di Cecco, Montse C. Ruiz, Laura Bortoli, Angela Di Baldassarre. Psychophysiological responses under pressure PLOS ONE | https://doi.org/10.1371/journal.pone.0196273 April 26, 2018 13 / 16 Data curation: Claudio Robazza, Pascal Izzicupo, Maria Angela D’Amico, Barbara Ghinassi, Maria Chiara Crippa, Angela Di Baldassarre. Formal analysis: Claudio Robazza, Angela Di Baldassarre. Funding acquisition: Claudio Robazza, Vincenzo Di Cecco. Investigation: Claudio Robazza, Pascal Izzicupo, Maria Angela D’Amico, Barbara Ghinassi, Maria Chiara Crippa, Montse C. Ruiz, Laura Bortoli, Angela Di Baldassarre. Methodology: Claudio Robazza, Pascal Izzicupo, Maria Angela D’Amico, Barbara Ghinassi, Maria Chiara Crippa, Montse C. Ruiz, Laura Bortoli, Angela Di Baldassarre. Project administration: Claudio Robazza, Pascal Izzicupo, Maria Angela D’Amico, Barbara Ghinassi, Maria Chiara Crippa, Vincenzo Di Cecco, Angela Di Baldassarre. Resources: Claudio Robazza, Pascal Izzicupo, Barbara Ghinassi, Angela Di Baldassarre. Software: Claudio Robazza. Supervision: Claudio Robazza, Vincenzo Di Cecco, Montse C. Ruiz, Laura Bortoli, Angela Di Baldassarre. Validation: Claudio Robazza, Laura Bortoli, Angela Di Baldassarre. Visualization: Claudio Robazza, Angela Di Baldassarre. Writing – original draft: Claudio Robazza, Angela Di Baldassarre. Writing – review & editing: Claudio Robazza, Pascal Izzicupo, Maria Angela D’Amico, Bar- bara Ghinassi, Montse C. Ruiz, Laura Bortoli. References 1. Laborde S. Bridging the gap between emotion and cognition: An overview. In: Raab M, Lobinger B, Hoff- mann S, Pizzera A, Laborde S. editors. Performance psychology: Perception, action, cognition, and emo- tion. London, UK: Elsevier; 2016. p. 275–289. https://doi.org/10.1016/B978-0-12-803377-7.00017-X 2. Hanin YL. Emotions in sport: Current issues and perspectives. In: Tenenbaum G, Eklund R. editors. Handbook of sport psychology. 3rd ed. Hoboken, NJ: Wiley; 2007. p. 31–58. 3. Robazza C, Bertollo M, Ruiz MC, Bortoli L. Measuring psychobiosocial states in sport: Initial validation of a trait measure. Plos One. 2016; 11(12). e0167448. https://doi.org/10.1371/journal.pone.0167448 PMID: 27907111 4. Ruiz MC, Hanin Y, Robazza C. Assessment of performance-related experiences: An individualized approach. Sport Psychol. 2016; 30: 201–218. https://doi.org/10.1123/tsp.2015-0035 5. Blascovich J. Challenge and threat. In: Elliot AJ. editor. Handbook of approach and avoidance motiva- tion. New York, NY: Psychology Press; 2008. p. 431–445. 6. Lazarus RS. Stress and emotion: A new synthesis. New York: Springer; 1999. 7. Blascovich J, Tomaka J. The biopsychosocial model of arousal regulation. In: Zanna MP, editor. Adv Exp Soc Psychol. 1996; 28: 1–51. https://doi.org/10.1016/S0065-2601(08)60235-X 8. Blascovich J, Seery MD, Mugridge CA, Norris RK, Weisbuch M. Predicting athletic performance from cardiovascular indexes of challenge and threat. J Exp Soc Psychol. 2004; 40: 683–688. https://doi.org/ 10.1016/j.jesp.2003.10.007 9. Cumming SJD, Turner MJ, Jones M. Longitudinal changes in elite rowers’ challenge and threat apprais- als of pressure situations: A season-long observational study. Sport Psychol. 2017; 31: 217–226. https://doi.org/10.1123/tsp.2016-0087 10. Moore LJ, Vine SJ, Wilson MR, Freeman P. The effect of challenge and threat states on performance: An examination of potential mechanisms. Psychophysiol. 2012; 49: 1417–1425. https://doi.org/10. 1111/j.1469-8986.2012.01449.x PMID: 22913339 11. Turner MJ, Jones MV, Sheffield D, Barker JB, Coffee P. Manipulating cardiovascular indices of chal- lenge and threat using resource appraisals. Int J Psychophysiol. 2014; 94: 9–18. https://doi.org/10. 1016/j.ijpsycho.2014.07.004 PMID: 25036595 Psychophysiological responses under pressure PLOS ONE | https://doi.org/10.1371/journal.pone.0196273 April 26, 2018 14 / 16 12. Jones M, Meijen C, McCarthy PJ, Sheffield D. A theory of challenge and threat states in athletes. Int Rev Sport Exerc Psychol. 2009; 2: 161–180. https://doi.org/10.1080/17509840902829331 13. Jamieson JP. Challenge and threat appraisals. In: Elliot A J, Dweck CS, Yeager DS editors. Handbook of competence and motivation: Theory and application. 2nd ed. New York, NY: The Guilford Press. 2017. p. 175–191. 14. Seery MD. Challenge or threat?: Cardiovascular indexes of resilience and vulnerability to potential stress in humans. Neurosci Biobehav Rev. 2011; 35: 1603–1610. https://doi.org/10.1016/j.neubiorev. 2011.03.003 PMID: 21396399 15. Eccles DW, Arsal G. How do they make it look so easy? The expert orienteer’s cognitive advantage. J Sports Sci. 2015; 33:609–615. https://doi.org/10.1080/02640414.2014.951953 PMID: 25156474 16. Diamond A. Executive functions. Annu Rev Psychol. 2013; 64: 135–168. https://doi.org/10.1146/ annurev-psych-113011-143750 PMID: 23020641 17. Baddeley A. Working memory: Theories, models, and controversies. Annu Rev Psychol. 2012; 63: 1– 29. https://doi.org/10.1146/annurev-psych-120710-100422 PMID: 21961947 18. Demetriou A, Spanoudis G, Shayer M, van der Ven S, Brydges CR, Kroesbergen E, et al. Relations between speed, working memory, and intelligence from preschool to adulthood: Structural equation modeling of 14 studies. Intelligence. 2014; 46(Supplement C): 107–121. https://doi.org/10.1016/j.intell. 2014.05.013 19. Harrison TL, Shipstead Z, Engle RW. Why is working memory capacity related to matrix reasoning tasks? Mem Cogn. 2015; 43: 389–396. https://doi.org/10.3758/s13421-014-0473-3 PMID: 25331277 20. Furley P, Wood G. Working memory, attentional control, and expertise in sports: A review of current lit- erature and directions for future research. J Appl Res Mem Cogn. 2016; 5: 415–425. https://doi.org/10. 1016/j.jarmac.2016.05.001 21. Alesi M, Bianco A, Padulo J, Luppina G, Petrucci M, Paoli A., et al. 2015. Motor and cognitive growth fol- lowing a Football Training Program. Front Psychol. 2015; 6 (1627). https://doi.org/10.3389/fpsyg.2015. 01627 PMID: 26579014 22. Salvador A, Costa R. Coping with competition: Neuroendocrine responses and cognitive variables. Neurosci Biobehav Rev. 2009; 33: 160–170. https://doi.org/10.1016/j.neubiorev.2008.09.005 PMID: 18845183 23. Lautenbach F, Laborde S. The influence of hormonal stress on performance. In: Raab M, Lobinger B, Hoffmann S, Pizzera A, Laborde S. editors. Performance psychology: Perception, action, cognition, and emotion. London, UK: Elsevier; 2016. p. 315–328. https://doi.org/10.1016/B978-0-12-803377-7. 00019–3 24. Bocanegra OL, Diaz MM, Teixeira RR, Soares SS, Espindola FS. Determination of the lactate threshold by means of salivary biomarkers: Chromogranin A as novel marker of exercise intensity. Eur J Appl Physiol. 2012; 112: 3195–3203. https://doi.org/10.1007/s00421-011-2294-4 PMID: 22227853 25. D’amico MA, Ghinassi B, Izzicupo P, Manzoli L, Di Baldassarre A. Biological function and clinical rele- vance of chromogranin A and derived peptides. Endocr Connect. 2014; 3: R45–R54. https://doi.org/10. 1530/EC-14-0027 PMID: 24671122 26. Gallina S, Di Mauro M, D’Amico MA, D’Angelo E, Sablone A, Di Fonso A, et al. Salivary chromogranin A, but not alpha-amylase, correlates with cardiovascular parameters during high-intensity exercise. Clin Endocrinol. 2011; 75: 747–752. https://doi.org/10.1111/j.1365-2265.2011.04143.x PMID: 21671973 27. Robazza C, Gallina S, D’Amico MA, Izzicupo P, Bascelli A, Di Fonso A, et al. Relationship between bio- logical markers and psychological states in elite basketball players across a competitive season. Psy- chol Sport Exerc. 2012; 13: 509–517. https://doi.org/10.1016/j.psychsport.2012.02.011 28. Cunniffe B, Morgan KA, Baker JS, Cardinale M, Davies B. Home versus away competition: Effect on psychophysiological variables in elite rugby union. Int J Sports Physiol Perform. 2015; 10: 687–694. https://doi.org/10.1123/ijspp.2014-0370 PMID: 25946022 29. Di Corrado D, Agostini T, Bonifazi M, Perciavalle V. Changes in mood states and salivary cortisol levels following two months of training in elite female water polo players. Mol Med Rep. 2014; 9: 2441–2446. https://doi.org/10.3892/mmr.2014.2115 PMID: 24714848 30. Filaire E, Alix D, Ferrand C, Verger M. Psychophysiological stress in tennis players during the first single match of a tournament. Psychoneuroendocrinology. 2009; 34: 150–157. https://doi.org/10.1016/j. psyneuen.2008.08.022 PMID: 18845399 31. Lautenbach F, Laborde S, Klampfl M, Achtzehn S. A link between cortisol and performance: An explor- atory case study of a tennis match. Int J Psychophysiol. 2015; 98: 167–173. https://doi.org/10.1016/j. ijpsycho.2015.10.002 PMID: 26458284 32. Borg G. Borg’s range model and scales. Int J Sport Psychol. 2001; 32: 110–126. Psychophysiological responses under pressure PLOS ONE | https://doi.org/10.1371/journal.pone.0196273 April 26, 2018 15 / 16 33. Bertollo M, Bortoli L, Gramaccioni G, Hanin Y, Comani S, Robazza C. Behavioural and psychophysio- logical correlates of athletic performance: A test of the multi-action plan model. Appl Psychophysiol Bio- feedback. 2013; 38: 91–99. https://doi.org/10.1007/s10484-013-9211-z PMID: 23483293 34. Borg G. Borg’s perceived exertion and pain scales. Champaign, IL: Human Kinetics; 1998. 35. Bourdon PC, Cardinale M, Murray A, Gastin P, Kellmann M, Varley MC, et al. Monitoring athlete training loads: Consensus statement. Int J Sports Physiol Perform. 2017; 12: S2-161–S2-170. https://doi.org/ 10.1123/IJSPP.2017-0208 PMID: 28463642 36. Gauthier L, Dehaut F, Joanette Y. The bells test—A quantitative and qualitative test for visual neglect. J Clin Neuropsychol. 1989; 11: 49–54. 37. Strauss E, Sherman EMS, Spreen O. A compendium of neuropsychological tests: Administration, norms, and commentary. 3rd ed. New York, NY: Oxford University Press; 2006. 38. Lezak MD, Howieson DB, Bigler ED, Tranel D. Neuropsychological assessment. 5th ed. New York, NY: Oxford University Press; 2012. 39. Verma JP. Sports research with analytical solution using SPSS®. Hoboken, NJ: John Wiley & Sons; 2016. 40. Mertler CA, Reinhart RV. Advanced and multivariate statistical methods: Practical application and inter- pretation. 6th ed. New York, NY: Routledge; 2017. 41. Ruiz MC, Raglin JS, Hanin YL. The individual zones of optimal functioning (IZOF) model (1978–2014): Historical overview of its development and use. Int J Sport Exerc Psychol. 2017; 15: 41–63. https://doi. org/10.1080/1612197X.2015.1041545 42. Vine SJ, Freeman P, Moore LJ, Chandra-Ramanan R, Wilson MR. Evaluating stress as a challenge is associated with superior attentional control and motor skill performance: Testing the predictions of the biopsychosocial model of challenge and threat. J Exp Psychol-Appl. 2013; 19: 185–194. https://doi.org/ 10.1037/a0034106 PMID: 24059821 43. Hallahan M, Rosenthal R. Statistical power: Concepts, procedures, and applications. Behav Res Ther. 1996; 34: 489–499. https://doi.org/10.1016/0005-7967(95)00082-8 PMID: 8687371 44. Baumeister RF, Vohs KD, Tice DM. The strength model of self-control. Curr Dir Psychol Sci. 2007; 16: 351–355. https://doi.org/10.1111/j.1467-8721.2007.00534.x 45. Englert C. The strength model of self-control in sport and exercise psychology. Front Psychol. 2016; 7 (314). https://doi.org/10.3389/fpsyg.2016.00314 PMID: 26973590 Psychophysiological responses under pressure PLOS ONE | https://doi.org/10.1371/journal.pone.0196273 April 26, 2018 16 / 16

Psychophysiological responses of junior orienteers under competitive pressure.

04-26-2018

Robazza, Claudio,Izzicupo, Pascal,D'Amico, Maria Angela,Ghinassi, Barbara,Crippa, Maria Chiara,Di Cecco, Vincenzo,Ruiz, Montse C,Bortoli, Laura,Di Baldassarre, Angela

eng

PMC7365446

RESEARCH ARTICLE Shoe feature recommendations for different running levels: A Delphi study Eric C. HonertID1☯*, Maurice Mohr1,2☯, Wing-Kai LamID3,4,5☯, Sandro Nigg1☯ 1 Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada, 2 Institue of Sport Science, University of Innsbruck, Innsbruck, Austria, 3 Guangdong Provincial Engineering Technology Research Center for Sports Assistive Devices, Guangzhou Sport University, Guangzhou, China, 4 Department of Kinesiology, Shenyang Sport University, Shenyang, China, 5 Li Ning Sports Science Research Center, Li Ning (China) Sports Goods company, Beijing, China ☯ These authors contributed equally to this work. * [email protected] Abstract Providing runners with footwear that match their functional needs has the potential to improve footwear comfort, enhance running performance and reduce the risk of overuse injuries. It is currently not known how footwear experts make decisions about different shoe features and their properties for runners of different levels. We performed a Delphi study in order to understand: 1) definitions of different runner levels, 2) which footwear features are considered important and 3) how these features should be prescribed for runners of different levels. Experienced academics, journalists, coaches, bloggers and physicians that examine the effects of footwear on running were recruited to participate in three rounds of a Delphi study. Three runner level definitions were refined throughout this study based on expert feedback. Experts were also provided a list of 20 different footwear features. They were asked which features were important and what the properties of those features should be. Twenty-four experts, most with 10+ years of experience, completed all three rounds of this study. These experts came to a consensus for the characteristics of three different running levels. They indicated that 12 of the 20 footwear features initially proposed were important for footwear design. Of these 12 features, experts came to a consensus on how to apply five footwear feature properties for all three different running levels. These features were: upper breathability, forefoot bending stiffness, heel-to-toe drop, torsional bending stiffness and crash pad. Interestingly, the experts were not able to come to a consensus on one of the most researched footwear features, rearfoot midsole hardness. These recommendations can provide a starting point for further biomechanical studies, especially for features that are considered as important, but have not yet been examined experimentally. Introduction Matching running footwear features to the functional needs of the runner has the potential to improve footwear comfort [1,2], enhance running performance [3,4] and reduce the risk of overuse injuries [1,5]. The majority of biomechanical studies have examined the effects of PLOS ONE PLOS ONE | https://doi.org/10.1371/journal.pone.0236047 July 16, 2020 1 / 17 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Honert EC, Mohr M, Lam W-K, Nigg S (2020) Shoe feature recommendations for different running levels: A Delphi study. PLoS ONE 15(7): e0236047. https://doi.org/10.1371/journal. pone.0236047 Editor: Chris Harnish, Mary Baldwin University Murphy Deming College of Health Sciences, UNITED STATES Received: March 12, 2020 Accepted: June 26, 2020 Published: July 16, 2020 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0236047 Copyright: © 2020 Honert et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. footwear interventions for a general group of runners and/or athletes rather than specific groups of runners, stratified according to their training status and/or running experience. This is despite evidence that runners of different levels (e.g. novice, recreational, high caliber) have clear differences in functional needs and running goals that need to be addressed in the design of their footwear (e.g. through cushioning or stability features, [6–9]). As a result, there is a large gap of knowledge on how to match specific footwear features, and their properties, to runners from different levels. This gap in knowledge limits the potential beneficial effects that more individualized footwear may have on comfort, performance or injury risk. Literature has presented a variety of definitions for different running levels. Studies have suggested standard definitions for different runner levels, which have been derived from sub- jective questionnaires [6,7]. However, these definitions are often not translated to biomechani- cal studies examining footwear features for runners. For example, subjective questionnaires indicate that recreational runners run, on average, between 25 and 35 km/week [7]. Yet, bio- mechanical studies have recruited “recreationally running” subjects with an average training distance between 10 km/week [10] and 50 km/week [11]. On the other hand, literature has consistently described novice runners as having little to no running experience in the past year (see [9] for a Meta-Analysis of novice runners). Due to the wide range of definitions for run- ning levels used in literature, there is a need to reach a consensus on an operational definition for different running levels. Modern running shoes are complex systems. They incorporate many different features (e.g. crash-pads, heel counters, flares, midsole hardness) and each of these features can be included, excluded and/or tuned individually to modify the characteristics of the final running shoe sys- tem (e.g. cushioning, stability, heel-to-toe transition, energy return). Some of these shoe fea- tures have been studied more extensively than others [12,13]. A strong research focus on certain footwear features does not necessarily translate into agreement on how modifying these features may affect the running mechanics, performance, injury risk or footwear comfort in runners of different levels. For example, a recent review found inconclusive evidence regarding the biomechanical effects of different midsole hardness—one of the most studied footwear features [13]. On the other hand, there has been little scientific attention on footwear features such as outsole traction or forefoot flares. A lack of scientific attention could indicate that the prescription of these features to different runner levels is trivial, these features are not considered important by footwear professionals or little is known on how to prescribe these features. An understanding of how footwear experts make decisions about different footwear features and their properties can be obtained through gathering and summarizing opinions of experts in the field of running biomechanics and footwear using a Delphi study. The Delphi method has been utilized for gathering and summarizing opinions via survey-based responses of an expert panel in order to obtain consensus on complex topics. For example, this technique has been successfully applied to establish the now frequently reported “Minimalist Index” of running shoes [14]. Such an understanding can target future systematic investigations around the presumed optimal property of important footwear features. The purpose of this study was to utilize a Delphi technique to summarize the opinions of running footwear experts and reach consensus on 1) runner level definitions, 2) which foot- wear features are important when designing footwear for different running levels, and 3) matching the specific properties of footwear features to the respective running levels. Methods Footwear experts were asked to complete three rounds of a Delphi study, with each successive round building on the results gathered from the previous round. Three runner level definitions PLOS ONE Shoe feature recommendations for different running levels PLOS ONE | https://doi.org/10.1371/journal.pone.0236047 July 16, 2020 2 / 17 Funding: “Li-Ning provided support in the form of a salary for WKL, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific role of WKL is articulated in the ‘author contributions’ section”. Competing interests: “WKL affiliation with Li-Ning does not alter our adherence to PLOS ONE policies on sharing data and materials”. were refined throughout the three rounds of the Delphi study through expert feedback. Experts were also provided a list of 20 different footwear features. Through the three rounds of the study, experts provided opinions on which features were important and what their properties should be for the three different running levels. Delphi study In total, 142 experts from 18 countries were contacted by e-mail to participate in this Delphi study: 44 academics, 35 journalists, 25 coaches, 24 scientists in the footwear industry, seven bloggers and seven physicians. The participants for this Delphi study were compiled from: authors that appeared on multiple papers from a recent literature review [13], podium present- ers at the 2019 Footwear Biomechanics Symposium, coaches of national and/or college track and field teams with publicly available e-mail addresses, scientists working in research and development at the leading running footwear brands, running shoe bloggers and journalists identified from an online search of popular running blogs and magazines and running and/or footwear journalists that Professor Benno Nigg has compiled over the years. All potential par- ticipants were contacted via e-mail to participate in this Delphi study. Participants were excluded if they had under two years of experience related to running footwear in their respec- tive fields of expertise. Each participant was provided an implied consent form stating that returning the survey was their agreement to participate. The protocol was approved by the University of Calgary’s Conjoint Heath Research Ethics Board (REB19-0240). The footwear experts completed web-based surveys through QuestionPro (questionpro.com) and could pro- vide feedback after the completion of each round of this Delphi study. The participants that completed the first-round survey were invited to participate in the second-round. Similarly, the participants that completed the second-round survey were invited to participate in the third round. To prevent bias in the responses and feedback, all participants’ survey responses were anonymized by the QuestionPro platform. All participants were encouraged to e-mail the authors upon completion of each respective round of the Delphi study for additional feedback and/or comments, and to create a list of respondents for successive rounds of the survey. Running levels Three different running levels were initially proposed: novice, recreational and high caliber. The initial characteristics of each running level (Table 1) were defined based on running litera- ture [6,7,9–11,15–20]. The proposed characteristics provide guidelines for runner classifica- tion. As such, there were overlaps in the running distance per week between the running levels in order to accommodate runners that train less and have a better running performance. Feed- back on the running level definitions was requested from the participants during each round of the Delphi study. The feedback from rounds one and two was integrated into the running level definitions and presented to the participants in rounds two and three, respectively. In each round, the experts rated the running level definitions on a 10-point scale where “1” indi- cated that the definitions were “Not at all appropriate” and “10” indicated “Most Appropriate”. Novice runners—Initial definition. Novice or occasional runners have little running experience. These runners typically have less than six months of cumulative regular running training (i.e. at least one day per week) over the previous 12 months [9,15,17]. They run zero to three times per week with a maximum of about 20 km per week [6,7,10]. Novice runner per- formance (Table 1) was extrapolated from an average running pace [10]. These runners are typically not involved in marathons [7]. Surveys have shown that these runners run to improve general health, manage stress and weight [7]. Novice runners may choose footwear based on comfort [16], reduce injury risk and improve performance [7]. PLOS ONE Shoe feature recommendations for different running levels PLOS ONE | https://doi.org/10.1371/journal.pone.0236047 July 16, 2020 3 / 17 Recreational runners—Initial definition. The recreational group is the largest running group [7]. These runners typically have more than six months of cumulative regular running training (i.e. at least one day per week) over the previous 12 months [10,15]. They run one to five days per week for a total of 10 to 50 km per week [6,7,10,11,15]. Recreational running per- formance (Table 1) was extrapolated from running times reported in [21]. Surveys have shown that these runners run to improve general health, manage stress and be involved with a team [7]. Recreational runners may choose footwear based on comfort [16], reduce injury risk and improve performance [7]. High caliber runners—Initial definition. High caliber runners have significant distance running experience, train almost daily and regularly compete in regional to international com- petitions [18]. These runners typically have over three years of regular running experience [7,20]. They run about three times per week for at least 30 km per week [6,7]. High caliber run- ning performance (Table 1) inclusion criteria has been reported in several running studies [18–20]. Surveys have shown that these runners run to improve general health, manage stress and compete [7]. High caliber runners may choose footwear based on performance, comfort and reduced injury risk [7,16]. Footwear features Twenty running footwear features were initially assessed in this Delphi study. These features were chosen from an initial list of 31 footwear features that were identified based on a prelimi- nary literature review, market analysis and internal discussion. Two influential studies during this process were reports from [6] and [14]. This initial list was reduced to 23 features by removing or joining related features that were reflected in other features or similar in their function, respectively (e.g. remove midfoot midsole hardness and only retain forefoot and rearfoot midsole hardness). Pilot testing with four footwear experts (not included in the main study) indicated that a survey including 23 features required more than an hour to complete and could potentially lead to a high-drop out rate. Therefore, we limited the number of foot- wear features to 20, by removing features that pilot participants indicated had low relevance Table 1. Initial definitions of running levels. Level 1 Novice Level 2 Recreational Level 3 High-caliber Running experience Less than six months of regular running experience More than six months of regular running experience More than three years of regular running experience Running habits 0–3 sessions / week 1–5 sessions / week > 3 sessions / week 5–20 km / week 15–50 km / week > 30 km / week Running performance (times are for male runners) 5km time > 30 min OR 5km time > 20 min OR 5 km time 15–20 min$ OR 10km time > 60 min 10km time > 45 min OR 10 km time 30–45 min$ OR No marathon racing Marathon time 3–4.5 h Marathon time <3h$ Running motivation (ordered according to importance) Improve general health Improve general health Improve general health Stress management Stress management Stress management Weight management Team affiliation Competition Priorities for footwear design (from high to low) 1) Improve comfort 1) Improve comfort 1) Improve performance 2) Reduce injury risk 2) Reduce injury risk 2) Improve comfort 3) Improve performance 3) Improve performance 3) Reduce injury risk The () indicates regular running experience defined as running at least once per week. The ($) indicates that elite runners with faster race times than high caliber runners were not considered since they represent a small percentage of the population and may require individual running footwear recommendations. https://doi.org/10.1371/journal.pone.0236047.t001 PLOS ONE Shoe feature recommendations for different running levels PLOS ONE | https://doi.org/10.1371/journal.pone.0236047 July 16, 2020 4 / 17 (e.g. upper overlays or varus alignment). In return, the option was added for experts to suggest footwear features that should be added to the questionnaire. The final 20 footwear features assessed in this Delphi study were (see S1 Appendix for description of each feature): crash pad, forefoot flares, forefoot longitudinal bending stiffness, forefoot midsole hardness, heel counter, heel flare, heel (stack) height, heel-to-toe drop, insole shape, medial post, midfoot longitudinal bending stiffness, midsole thickness, outsole traction, rearfoot midsole hardness, rocker (heel), shoe mass, toe spring (forefoot rocker), torsional bending stiffness, upper material (breathabil- ity) and upper material (elasticity). The importance of the footwear features was assessed in the first-round and verified in the second-round. In the first-round, participants were asked if each footwear feature was impor- tant when designing footwear for different running levels. The experts could choose between the following for each footwear feature: (a) is important, (b) is not important or (c) they do not know if it is important. If over 75% (a similar threshold to [22,23]) of the first-round partici- pants selected option (a), the footwear feature was defined as important. The important fea- tures were then presented to the second-round participants. The participants were asked if they agreed with the list of the features selected as important/non important on a 10-point scale where “1” indicated that the list of important/non important features was “Not at all appropriate” and “10” indicated “Most Appropriate”. The list of important features was veri- fied if over 75% of the second-round participants answered with a seven or higher on the 10 point-scale. The second- and third-rounds of the Delphi study were then limited to the impor- tant footwear features. In each round, the experts were asked if other footwear features should be included in the Delphi study. If there were at least five suggestions to add a certain feature, this new feature was added to the subsequent round. The participants were then asked if this new feature was important. Footwear feature properties The experts were asked to recommend footwear feature properties for the different running levels in each round of the study from a multiple-choice selection (see S1 Appendix for the lists of footwear feature properties). Most footwear feature properties were defined based on the reviewed footwear literature (see S1 Appendix). If there was no related literature (e.g. upper elasticity), properties were provided based on commercially available shoes. In rounds 2 and 3, the results from the previous round were presented to the participants. If at least 51% of the participants agreed on a footwear feature property (a similar threshold to [24]) for a specific running level (e.g. high breathability for novice runners), the participants would be asked if they agreed with the consensus the next round. If at least 51% of the participants verified the consensus, the experts were not asked again to recommend a footwear feature property for that running level (see Fig 1). In comparison to the consensus for the importance of shoe fea- tures (75%), the threshold for consensus was set lower for agreement on footwear feature prop- erties (51%) because of the greater number of available response options. Additional Delphi questions In the second-round of the Delphi study, we aimed to quantify why the participants chose “I don’t know” for the footwear feature properties. The participants were prompted to choose one of the following if they selected “I don’t know”: feature is not well defined, fea- ture is dependent on foot contact pattern (e.g. heel strike), feature is dependent on bio- mechanical variables (e.g. foot inversion), feature has interplaying effects with other shoe features, feature function is not known or other. These questions were included due to a PLOS ONE Shoe feature recommendations for different running levels PLOS ONE | https://doi.org/10.1371/journal.pone.0236047 July 16, 2020 5 / 17 high frequency of “I don’t know” responses for some footwear feature properties. These questions were only included in the second-round as we received feedback that the ques- tionnaire was time consuming, which may have increased drop-out rate if included in the third-round. Fig 1. Flowchart describing the consensus and verifying consensus process for different shoe feature properties (XX) for each running level (YY). The participants were asked to provide feedback for the recommended properties for all runner levels on all 20 shoe features (XX1). In the second- and third-rounds, the participants were asked to provide feedback for the recommended properties for all runner levels on the important shoe features and any additional shoe features the participants recommended (XX2/3). https://doi.org/10.1371/journal.pone.0236047.g001 PLOS ONE Shoe feature recommendations for different running levels PLOS ONE | https://doi.org/10.1371/journal.pone.0236047 July 16, 2020 6 / 17 Statistical analysis and visualization Paired statistical analyses were performed to determine if the running level definitions improved through the three rounds of this Delphi study. A Friedman’s test was performed uti- lizing the subjective ratings from the respondents that participated in all three rounds of the study (N = 24). If the Friedman’s test revealed a significant effect, follow-up Wilcoxon signed- rank tests with a Bonferroni correction were performed to investigate pairwise differences between the individual rounds. The significance level α was set to 0.05 for all statistical tests. The median and inter-quartile ranges of the participants’ responses were also computed from the subjective ratings. These descriptive statistics were computed to demonstrate if the ratings increased and if there was less variability in the responses. All analyses were performed in MATLAB (version 2019a, MathWorks, Natick, MA, USA). Figures were created in MATLAB and Adobe Illustrator (version 22.1, San Jose, CA, USA). Results Participation Of the 142 experts initially contacted, 29 responded to the first-round of this Delphi study (Fig 2, Table 2). Twenty-five respondents participated in the second-round and 24 participated in the third-round (Fig 2, Table 2). Note that one academic moved to industry from academia between rounds one and two. Running level definitions The respondents’ rating of the running level definitions improved as the Delphi study pro- gressed, χ2 (2, N = 24) = 13.95, p = 0.0009. The median rating increased each round and the Fig 2. Participation in each round of this Delphi study. https://doi.org/10.1371/journal.pone.0236047.g002 PLOS ONE Shoe feature recommendations for different running levels PLOS ONE | https://doi.org/10.1371/journal.pone.0236047 July 16, 2020 7 / 17 interquartile range decreased. For example, 69% of respondents rated the running level defini- tions between 7 and 10 in the first-round which increased to 88% of respondents in the third- round (see Fig 3). The increase in the running level scores between the first and third rounds was statistically significant (p = 0.006). The increased running level ratings were accompanied by changes to the running level definitions. The changes to the “novice” running level defini- tion for the second-round were: increased running experience to one year and replaced “stress management” with “enjoyment” for running motivation. The changes to the “recreational” running level definition for the second-round were: increased running experience to greater than one year and replaced “stress management” with “enjoyment” for running motivation. The changes to the “high-caliber” running level definition for the second-round were: increased running habits to >4 sessions/week and >50 km/week, replaced “stress manage- ment” with “enjoyment” for running motivation, re-order the running motivation to 1) Com- petition, 2) Improve general heath, and 3) Enjoyment, and re-order the priorities for footwear design to 1) Improve performance, 2) Reduce injury risk, 3) Improve comfort. We also speci- fied the running performance as males between the ages of 18 to 34. Subsequent changes to the running level definitions were to ensure that the high caliber and recreational runner 5 km and 10 km times were indicative of the respective marathon times. These updates resulted in the final runner level definitions in Table 3. Footwear features Twelve of the 20 footwear features reached the level of consensus to be considered important. The majority (92%) of the second-round respondents rated the appropriateness of the 12 important footwear features as a 7/10 or higher. “Lacing system” was added to the second- round of this Delphi study as five first-round respondents suggested that it should be included in the list of footwear features. This feature did not reach the threshold of consensus in the sec- ond-round (68%, Table 4) to be considered important. “Toe spring” was initially not an important footwear feature as only 19/29 (66%, Table 4) first-round respondents thought it was important for footwear design. Five second-round participants suggested to add “toe spring” back into the survey (as it was removed because it was below the threshold of consen- sus) and 22/24 (92%, Table 4) third-round participants thought that it was important for foot- wear design. Table 2. Number of participants and their experience investigating/designing footwear. Experience (yrs) Round 1 Round 2 Round 3 Academic 2–5 1 0 0 5–10 6 4 4 10+ 8 7 7 Professional in the footwear industry 2–5 0 1 1 5–10 2 2 2 10+ 8 8 8 Clinician 10+ 2 1 1 Journalist 5–10 1 1 0 Coach 10+ 1 1 1 Total 29 25 24 Note that one academic moved to industry between the first and second rounds of this study. https://doi.org/10.1371/journal.pone.0236047.t002 PLOS ONE Shoe feature recommendations for different running levels PLOS ONE | https://doi.org/10.1371/journal.pone.0236047 July 16, 2020 8 / 17 Fig 3. Subjective rating of the running level definitions for the three rounds of this Delphi study. Changes in subjective ratings were accompanied by updating the running levels definition based on respondents’ feedback. The diamonds represent the median of each round and the bars indicate the interquartile range. Each shaded dot indicates one response made by a respondent. The asterisk () indicates a statistical difference in the subjective ratings (p = 0.006). https://doi.org/10.1371/journal.pone.0236047.g003 Table 3. Final running level definitions. Level 1 Novice Level 2 Recreational Level 3 High caliber Running experience Less than one year of regular running experience More than one year of regular running experience More than three years of regular running experience Running habits 0–3 sessions / week 1–5 sessions / week > 4 sessions / week 5–20 km / week 15–50 km / week > 50 km / week Running performance (example times are for male runners age 18–34) 5km time > 30 min OR 5km time > 21 min OR 5 km time 15–20 min$ OR 10km time > 60 min 10km time > 42 min OR 10 km time 30–42 min$ OR No marathon racing Marathon time 3–4.5 h Marathon time <3h$ Running motivation (ordered according to importance) Improve general health Improve general health Competition Enjoyment Enjoyment Improve general health Weight management Team affiliation Enjoyment Priorities for footwear design (from high to low) 1) Improve comfort 1) Improve comfort 1) Improve performance 2) Reduce injury risk 2) Reduce injury risk 2) Reduce injury risk 3) Improve performance 3) Improve performance 3) Improve comfort These definitions were refined by the Delphi study participants through the three rounds of feedback. The () indicates regular running experience defined as running at least once per week. The ($) indicates that elite runners with faster race times than high caliber runners were not considered since they represent a small percentage of the population and may require individual running footwear recommendations. Bolded characteristics indicate characteristics that changed from the first characteristics presented to the respondents (Table 1). https://doi.org/10.1371/journal.pone.0236047.t003 PLOS ONE Shoe feature recommendations for different running levels PLOS ONE | https://doi.org/10.1371/journal.pone.0236047 July 16, 2020 9 / 17 Footwear feature properties Twenty-three of the 36 shoe feature properties (3 running levels x 12 important shoe features) reached the 51% consensus threshold (Table 5). Consensus was obtained for upper breathabil- ity, heel-to-toe drop, forefoot bending stiffness, crash pad and torsional bending stiffness for all three running levels (Table 5). The consensus for the feature properties from the first- and second-rounds was verified in the second- and third-rounds, respectively (Table 5). There was no consensus for the properties of the toe spring as well as the rearfoot and forefoot midsole hardness for any of the running levels (Table 5). The most frequent response regarding fore- foot and rearfoot midsole hardness was “I don’t know”. In the second-round when participants were asked further about this response, the most frequent answer (4/10 participants) for the forefoot midsole hardness was “feature function is not known”. The responses for the rearfoot midsole hardness were spread across the six different responses (see Methods: Additional Del- phi Questions for full list of possible responses). Discussion This study provides a unique perspective of footwear experts, most of whom have been exam- ining this topic for 10+ years. These experts indicated that 12 of the 21 footwear features were important for footwear design with respect to different running levels. Experts came to a con- sensus on the properties for five footwear features for all three running levels. Furthermore, this study has highlighted footwear features that experts consider important but have received little scientific attention, such as: upper breathability, forefoot bending stiffness, heel-to-toe drop, torsional bending stiffness and crash pad (Fig 4). Future, novel research can be per- formed with these features to add to the collective knowledge of how footwear features can affect the running biomechanics of runners from different levels. Interestingly, participants in this Delphi study did not come to a consensus for the recom- mended footwear properties for some of the most researched shoe features: forefoot and rear- foot midsole hardness [12,13]. Previous research has shown that a softer rearfoot midsole can reduce ground reaction force loading metrics such as vertical loading rate or peak impact forces [25–27], which have been hypothesized to reduce running-related injuries [28,29]. The causal relationship between ground reaction force loading metrics and running-related inju- ries has not been established. Furthermore, examining prospective running injury studies Table 4. Percent of participants that agreed upon the importance of shoe features. Shoe Feature % Participants Shoe Feature % Participants Shoe Mass 100 Toe Spring 66/92 Upper Breathability 97 Heel Counter 72 Forefoot Midsole Hardness 93 Medial Post 72 Rearfoot Midsole Hardness 93 Midfoot Bending Stiffness 72 Heel (stack) Height 90 Upper Elasticity 72 Midsole Thickness 86 Insole Shape 69 Forefoot Bending Stiffness 83 Lacing System 68 Outsole Traction 83 Rocker 59 Heel-to-Toe Drop 79 Heel Flares 55 Torsional Bending Stiffness 79 Forefoot Flares 45 Crash Pad 76 The shoe features with a consensus above 75% were considered important (bolded). The toe spring was initially not considered important (consensus: 66%), but was considered important in the third-round (consensus: 92%). The lacing system was added in the second-round to the study, but was not considered important. https://doi.org/10.1371/journal.pone.0236047.t004 PLOS ONE Shoe feature recommendations for different running levels PLOS ONE | https://doi.org/10.1371/journal.pone.0236047 July 16, 2020 10 / 17 together demonstrates that ground reaction force loading metrics are not related to injuries [30–40]. This paradigm shift could be the reason for the high frequency of “I don’t know” responses for the recommended properties for the forefoot and rearfoot midsole hardness, with the most frequent feedback being “the feature function is not known”. Additionally, shoe midsole hardness may interplay with other shoe features such as heel (stack) height or heel-to-toe drop to affect the overall shoe cushioning. This interplay could be the reason for inconsistent findings across studies examining midsole hardness [25,41,42]. In total, further Table 5. Shoe feature properties that were most frequently chosen for each running level. Shoe Feature Running Level Recommended Property Round % Participants % Participants in agreement with consensus Shoe Mass Novice 225–275 g 3 43 - Recreational 225–275 g 3 54 - High Caliber <175 g 1 59 72 Upper Breathability Novice High Breathability 1 69 100 Recreational High Breathability 1 79 100 High Caliber High Breathability 1 86 100 Forefoot Midsole Hardness Novice I don’t know 3 50 - Recreational I don’t know 3 50 - High Caliber I don’t know 3 42 - Rearfoot Midsole Hardness Novice I don’t know 3 42 - Recreational I don’t know 3 42 - High Caliber I don’t know 2 48 - Heel (stack) Height Novice 14–32 mm 2 72 88 Recreational 14–32 mm 1 65 88 High Caliber 14–32 mm 3 42 - Midsole Thickness Novice 10–15 mm 2 60 58 Recreational 10–15 mm 2 52 71 High Caliber 10–15 mm 3 50 - Forefoot Bending Stiffness Novice Low Stiffness 1 55 64 Recreational Medium Stiffness 1 66 100 High Caliber High Stiffness 1 55 84 Outsole Traction Novice Medium Traction 1 52 76 Recreational Medium Traction 1 55 72 High Caliber Medium Traction 3 50 - Heel-to-Toe Drop Novice 8–12 mm 2 56 88 Recreational 8–12 mm 3 58 - High Caliber 4–8 mm 3 71 - Torsional Bending Stiffness Novice Medium Stiffness 2 72 92 Recreational Medium Stiffness 1 52 76 High Caliber Medium Stiffness 2 52 88 Crash Pad Novice Include Crash Pad 1 76 88 Recreational Include Crash Pad 1 72 88 High Caliber Include Crash Pad 3 58 - Toe Spring Novice Mid (16–30 deg) 1 34 - Recreational Mid (16–30 deg) 3 38 - High Caliber I don’t know 1 34 - “Round” indicates which round of the Delphi study provided the highest consensus. The footwear feature properties that were above the consensus threshold for Rounds 1 and 2 were all verified in the subsequent rounds as indicated by the percent agreed with consensus (last column). https://doi.org/10.1371/journal.pone.0236047.t005 PLOS ONE Shoe feature recommendations for different running levels PLOS ONE | https://doi.org/10.1371/journal.pone.0236047 July 16, 2020 11 / 17 investigations are warranted to determine the biomechanical function of the midsole hardness during running and its relationship with running-related injuries. To achieve this goal, future studies should focus on how footwear properties affect the internal forces (e.g. muscle, tendon, or bone forces) that act on the structures at risk of injury during running [8,43]. Though the experts did provide opinions regarding property ranges for different footwear features, there should be considerations for how these features affect runners and how these features interact. Studies have shown that subject-specific tuning of the forefoot longitudinal bending stiffness can improve running performance [4,44]. Utilizing the expert opinions for groups of runners may overlook this aspect that might be a consideration for footwear design. On the other hand, tuning of multiple features together (e.g. midsole hardness, longitudinal bending stiffness) can provide benefits across a wide range of runners as exemplified by the Nike Vaporfly [19,45]. Such interplay was not addressed in our study as it would exponentially complicate the survey provided to the participants. However, the respondents had mentioned (in feedback and in responses to the round 2 survey, see the S2 Appendix for full responses) that it is difficult to consider some of these footwear features in isolation. The footwear experts came to a consensus on the running level definitions through slight adjustments to the initial definitions proposed and derived from literature. We opted to pro- vide initial running level definitions to our expert panel rather than letting the panel formulate the definitions independently. This latter approach would have required additional Delphi Fig 4. Footwear feature importance and the number of related publications. Footwear feature importance as rated by experts in this study in comparison to the number of available publications for each footwear feature based on a recent literature review (with permission from [13]). The footwear features inside the box represent opportunities for future footwear research: while these features were deemed important by footwear experts, only few publications exist regarding how these features affect runners from different levels. https://doi.org/10.1371/journal.pone.0236047.g004 PLOS ONE Shoe feature recommendations for different running levels PLOS ONE | https://doi.org/10.1371/journal.pone.0236047 July 16, 2020 12 / 17 rounds prior to the recommendation of footwear features and their properties. Panel formu- lated definitions may have resulted in different running level definitions compared to the approach presented here. Different running level definitions could have led to altered footwear feature recommendations. However, the experts’ consensus on the running level definitions was in agreement with prior literature. This is exhibited by the novice runner level definition which is similar to a definition created based on subjective running questionnaires [7]. These definitions may be viewed more as guidelines as one footwear expert mentioned that “Even elite athletes perform training runs with different intensities, durations, on different surfaces and so on. For each of these runs they might select a different type of footwear.” This comment touches on the competing requirements for running shoes as there may be multiple “correct” shoes for a given running level, especially in the high caliber category. The Delphi methodology is a useful tool for understanding the current status of a given research area, as understood by experts in the field [46]. As such, results from this study can be leveraged to 1) determine if experts are correct in their assumptions (e.g. high forefoot bending stiffness for high caliber runners), 2) determine important areas of limited research and 3) demonstrate areas where there is a lot of research, but little consensus (e.g. rearfoot midsole hardness). The relatively low drop out rate (17%) in conjunction with the extensive feedback obtained from the respondents via open ended questions provides confidence in our methodological approach. The Delphi methodology appears to be relevant when explor- ing high level topics related to running, and identifying the areas where further research is required. There are several limitations to acknowledge with this study. Consensus on the recom- mended footwear feature properties from the third-round could not be confirmed as there was no fourth-round. We believe that the third-round consensus would have been confirmed as the consensus from the first- and second-rounds were confirmed in the second- and third- rounds, respectively. During the second- and third-rounds of the Delphi study, we aimed to reduce the time it took to complete the survey to limit the drop-out rate. To do so, we elimi- nated footwear features that were not considered important (consensus below 75%) and elimi- nated footwear feature properties once they were confirmed. Without such eliminations, a different consensus may have been obtained, but there may have also been a larger drop out rate due to the lengthy and repetitive survey. It is recommended to have a drop out rate of less than 30% [47]. We attained a drop out rate of 17%. Additionally, we did not specify whether the footwear recommendations were for male or female runners. As such, these results may not be generalizable between male and female runners as they show distinct anthropometrics and movement mechanics [48]. These results may also not be generalizable to different run- ning surfaces/terrains as we asked participants to only consider running on a hard surface. Furthermore, the final recommendations may be biased as the majority of experts were male (e.g. 22/26 of the final participants). This expert panel was otherwise diverse as nine countries were represented. The recommended footwear feature properties may have been influenced by a dynamic definition of the runner levels, which changed slightly throughout the study. These changing definitions seemed to have little effect on expert opinions on the footwear feature properties as the verifying consensus level was generally higher than the original consensus level (Table 4, last vs. second-to-last column). We also did not specify to the experts how many of the of the categories a runner must match to be considered a “novice”, “recreational” or “high caliber” runner. This may have led to minor variations in expert recommendations. Lastly, the data presented here reflect opinions of experts that have experience with footwear. As such, the findings from this study can serve as a valuable starting point for future systematic biomechanical investigations. PLOS ONE Shoe feature recommendations for different running levels PLOS ONE | https://doi.org/10.1371/journal.pone.0236047 July 16, 2020 13 / 17 Conclusion Footwear experts provided feedback on the effects of different footwear features on running biomechanics across three running levels. These experts also came to a consensus on the char- acteristics of runners in these different running levels. The footwear experts indicated that 12 of the 21 footwear features were important for footwear design. Of these 12 features, experts were able to come to a consensus for five footwear feature properties for all three running lev- els. These features were: upper breathability, forefoot bending stiffness, heel-to-toe drop, tor- sional bending stiffness and crash pad. Interestingly, the experts were not able to come to a consensus for one of the most researched footwear features, i.e. rearfoot midsole hardness. These recommendations can provide a starting point for further biomechanical studies, espe- cially for features that have not yet been examined experimentally, e.g. upper breathability. Supporting information S1 Appendix. Shoe feature descriptions and properties. (DOCX) S2 Appendix. Raw data from the Delphi study. (XLSX) Acknowledgments We would like to thank all of the participants who gave their time to complete the three rounds of this Delphi study including: Michael Asmussen, Christopher Bishop, Jason Bonacci, Nicho- las Delattre, Cedric Morio, Tim Derrick, Ned Frederick, Marlene Giandolini, Allison Gruber, Bryan Heiderscheit, Laurent Malisoux, Sabina Manz, Frank Bichel, Benno Nigg, Max Paquette, Craig Payne, Natsuki Sate, Thorsten Sterzing, Matthieu Trudeau, Steffen Willwacher, Beat Hintermann, and Helen Woo. We would also like to thank Ross Miller regarding discussions about prospective studies examining ground reaction force metrics. Author Contributions Conceptualization: Maurice Mohr, Wing-Kai Lam, Sandro Nigg. Data curation: Eric C. Honert, Sandro Nigg. Formal analysis: Eric C. Honert. Investigation: Eric C. Honert. Methodology: Maurice Mohr, Wing-Kai Lam. Software: Sandro Nigg. Supervision: Wing-Kai Lam, Sandro Nigg. Validation: Eric C. Honert. Writing – original draft: Eric C. Honert. Writing – review & editing: Eric C. Honert, Maurice Mohr, Wing-Kai Lam, Sandro Nigg. References 1. Mu¨ndermann A, Stefanyshyn DJ, Nigg BM. Relationship between footwear comfort of shoe inserts and anthropometric and sensory factors. Med Sci Sports Exerc. 2001; 33: 1939–1945. https://doi.org/10. 1097/00005768-200111000-00021 PMID: 11689747 PLOS ONE Shoe feature recommendations for different running levels PLOS ONE | https://doi.org/10.1371/journal.pone.0236047 July 16, 2020 14 / 17 2. Reinschmidt C, Nigg BM. Current Issues in the Design of Running and Court Shoes. Sportverletz
Sportschaden. 2000; 14: 72–81. https://doi.org/10.1055/s-2000-7866 PMID: 11081243 3. Roy J-PR, Stefanyshyn DJ. Shoe Midsole Longitudinal Bending Stiffness and Running Economy, Joint Energy, and EMG. Med Sci Sports Exerc. 2006; 38: 562–569. https://doi.org/10.1249/01.mss. 0000193562.22001.e8 PMID: 16540846 4. Stefanyshyn DJ, Fusco C. Increased shoe bending stiffness increases sprint performance. Sports Bio- mech. 2004; 3: 55–66. https://doi.org/10.1080/14763140408522830 PMID: 15079988 5. Trudeau MB, Willwacher S, Weir G, Rohr E, Ertel C, Bruggemann G-P, et al. A novel method for esti- mating an individual’s deviation from their habitual motion path when running. Footwear Sci. 2019; 0: 1– 11. https://doi.org/10.1080/19424280.2019.1615004 6. Clifton P, Subic A, Burton M, Bedford A, Schembri A. FUNCTIONAL CHARACTERISTICS OF RUN- NING SHOES FOR DIFFERENT USER GROUPS. Seoul, Korea; 2013. p. 10. 7. Besomi M, Leppe J, Silvestre MCD, Setchell J. SeRUN® study: Development of running profiles using a mixed methods analysis. PLOS ONE. 2018; 13: e0200389. https://doi.org/10.1371/journal.pone. 0200389 8. Nigg BM, Mohr M, Nigg SR. Muscle tuning and preferred movement path—a paradigm shift. Curr Issues Sport Sci CISS. 2017; 0. 9. Videbæk S, Bueno AM, Nielsen RO, Rasmussen S. Incidence of Running-Related Injuries Per 1000 h of running in Different Types of Runners: A Systematic Review and Meta-Analysis. Sports Med. 2015; 45: 1017–1026. https://doi.org/10.1007/s40279-015-0333-8 PMID: 25951917 10. Theisen D, Malisoux L, Genin J, Delattre N, Seil R, Urhausen A. Influence of midsole hardness of stan- dard cushioned shoes on running-related injury risk. Br J Sports Med. 2014; 48: 371–376. https://doi. org/10.1136/bjsports-2013-092613 PMID: 24043665 11. Jakobsen BW, Kro´ner K, Schmidt SA, Kjeldsen A. Prevention of injuries in long-distance runners. Knee Surg Sports Traumatol Arthrosc. 1994; 2: 245–249. https://doi.org/10.1007/BF01845597 PMID: 8536050 12. Sun X, Lam W-K, Zhang X, Wang J, Fu W. Systematic Review of the Role of Footwear Constructions in Running Biomechanics: Implications for Running-Related Injury and Performance. J Sports Sci Med. 2020; 19: 20–37. PMID: 32132824 13. Hoitz F, Mohr M, Asmussen M, Lam W-K, Nigg S, Nigg B. The effects of systematically altered foot- wear features on biomechanics, injury, performance, and preference in runners of different skill level: a systematic review. Footwear Sci. 2020; 0: 1–23. https://doi.org/10.1080/19424280.2020. 1773936 14. Esculier J-F, Dubois B, Dionne CE, Leblond J, Roy J-S. A consensus definition and rating scale for mini- malist shoes. J Foot Ankle Res. 2015; 8: 42. https://doi.org/10.1186/s13047-015-0094-5 PMID: 26300981 15. Malisoux L, Chambon N, Urhausen A, Theisen D. Influence of the Heel-to-Toe Drop of Standard Cush- ioned Running Shoes on Injury Risk in Leisure-Time Runners: A Randomized Controlled Trial With 6- Month Follow-up. Am J Sports Med. 2016; 44: 2933–2940. https://doi.org/10.1177/0363546516654690 PMID: 27501833 16. Tay CS, Sterzing T, Lim CY, Ding R, Kong PW. Overall Preference of Running Shoes Can Be Predicted by Suitable Perception Factors Using a Multiple Regression Model. Hum Factors. 2017; 59: 432–441. https://doi.org/10.1177/0018720816681147 PMID: 28430546 17. Malisoux L, Nielsen RO, Urhausen A, Theisen D. A step towards understanding the mechanisms of run- ning-related injuries. J Sci Med Sport. 2015; 18: 523–528. https://doi.org/10.1016/j.jsams.2014.07.014 PMID: 25174773 18. Cavanagh PR, Pollock ML, Landa J. A Biomechanical Comparison of Elite and Good Distance Runners. Ann N Y Acad Sci. 1977; 301: 328–345. https://doi.org/10.1111/j.1749-6632.1977.tb38211.x PMID: 270926 19. Hoogkamer W, Kipp S, Frank JH, Farina EM, Luo G, Kram R. A Comparison of the Energetic Cost of Running in Marathon Racing Shoes. Sports Med. 2018; 48: 1009–1019. https://doi.org/10.1007/ s40279-017-0811-2 PMID: 29143929 20. de Ruiter CJ, Verdijk PWL, Werker W, Zuidema MJ, de Haan A. Stride frequency in relation to oxygen consumption in experienced and novice runners. Eur J Sport Sci. 2014; 14: 251–258. https://doi.org/10. 1080/17461391.2013.783627 PMID: 23581294 21. Fuller JT, Thewlis D, Tsiros MD, Brown NAT, Buckley JD. Effects of a minimalist shoe on running econ- omy and 5-km running performance. J Sports Sci. 2016; 34: 1740–1745. https://doi.org/10.1080/ 02640414.2015.1136071 PMID: 27328725 PLOS ONE Shoe feature recommendations for different running levels PLOS ONE | https://doi.org/10.1371/journal.pone.0236047 July 16, 2020 15 / 17 22. Cook C, Brisme´e J-M, Fleming R, Sizer PS. Identifiers Suggestive of Clinical Cervical Spine Instability: A Delphi Study of Physical Therapists. Phys Ther. 2005; 85: 895–906. https://doi.org/10.1093/ptj/85.9. 895 PMID: 16117600 23. Binkley J, Finch E, Hall J, Black T, Gowland C. Diagnostic Classification of Patients with Low Back Pain: Report on a Survey of Physical Therapy Experts. Phys Ther. 1993; 73: 138–150. https://doi.org/ 10.1093/ptj/73.3.138 PMID: 8438002 24. Loughlin KG, Moore LF. Using Delphi to achieve congruent objectives and activities in a pediatrics department. J Med Educ. 1979; 54: 101–106. https://doi.org/10.1097/00001888-197902000-00006 PMID: 762686 25. Baltich J, Maurer C, Nigg BM. Increased Vertical Impact Forces and Altered Running Mechanics with Softer Midsole Shoes. PLoS ONE. 2015; 10. https://doi.org/10.1371/journal.pone.0125196 PMID: 25897963 26. Sterzing T, Schweiger V, Ding R, Cheung JT-M, Brauner T. Influence of rearfoot and forefoot midsole hardness on biomechanical and perception variables during heel-toe running. Footwear Sci. 2013; 5: 71–79. https://doi.org/10.1080/19424280.2012.757810 27. Nigg BM, Morlock M. The influence of lateral heel flare of running shoes on pronation and impact forces. Med Sci Sports Exerc. 1987; 19: 294–302. PMID: 3600244 28. Messier SP, Davis SE, Curl WW, Lowery RB, Pack RJ. Etiologic factors associated with patellofemoral pain in runners. Med Sci Sports Exerc. 1991; 23: 1008–1015. PMID: 1943620 29. van der Worp H, Vrielink JW, Bredeweg SW. Do runners who suffer injuries have higher vertical ground reaction forces than those who remain injury-free? A systematic review and meta-analysis. Br J Sports Med. 2016; 50: 450–457. https://doi.org/10.1136/bjsports-2015-094924 PMID: 26729857 30. Nigg BM, Baltich J, Hoerzer S, Enders H. Running shoes and running injuries: mythbusting and a pro- posal for two new paradigms: ‘preferred movement path’ and ‘comfort filter’. Br J Sports Med. 2015; 49: 1290–1294. https://doi.org/10.1136/bjsports-2015-095054 31. Dudley RI, Pamukoff DN, Lynn SK, Kersey RD, Noffal GJ. A prospective comparison of lower extremity kinematics and kinetics between injured and non-injured collegiate cross country runners. Hum Mov Sci. 2017; 52: 197–202. https://doi.org/10.1016/j.humov.2017.02.007 PMID: 28237655 32. Napier C, MacLean CL, Maurer J, Taunton JE, Hunt MA. Kinetic risk factors of running-related injuries in female recreational runners. Scand J Med Sci Sports. 2018; 28: 2164–2172. https://doi.org/10.1111/ sms.13228 PMID: 29846979 33. Gerlach KE, White SC, Burton HW, Dorn JM, Leddy JJ, Horvath PJ. Kinetic Changes with Fatigue and Relationship to Injury in Female Runners. Med Sci Sports Exerc. 2005; 37: 657–663. https://doi.org/10. 1249/01.mss.0000158994.29358.71 PMID: 15809566 34. Bredeweg SW, Kluitenberg B, Bessem B, Buist I. Differences in kinetic variables between injured and noninjured novice runners: A prospective cohort study. J Sci Med Sport. 2013; 16: 205–210. https://doi. org/10.1016/j.jsams.2012.08.002 PMID: 22921763 35. Messier SP, Martin DF, Mihalko SL, Ip E, DeVita P, Cannon DW, et al. A 2-Year Prospective Cohort Study of Overuse Running Injuries: The Runners and Injury Longitudinal Study (TRAILS). Am J Sports Med. 2018; 46: 2211–2221. https://doi.org/10.1177/0363546518773755 PMID: 29791183 36. Davis IS, Bowser BJ, Mullineaux DR. Greater vertical impact loading in female runners with medically diagnosed injuries: a prospective investigation. Br J Sports Med. 2016; 50: 887–892. https://doi.org/10. 1136/bjsports-2015-094579 PMID: 26644428 37. Kuhman DJ, Paquette MR, Peel SA, Melcher DA. Comparison of ankle kinematics and ground reaction forces between prospectively injured and uninjured collegiate cross country runners. Hum Mov Sci. 2016; 47: 9–15. https://doi.org/10.1016/j.humov.2016.01.013 PMID: 26827155 38. Nigg BM, Cole GK, Bru¨ggemann G-P. Impact Forces during Heel-Toe Running. J Appl Biomech. 1995; 11: 407–432. 39. Willems TM, Witvrouw E, De Cock A, De Clercq D. Gait-Related Risk Factors for Exercise-Related Lower-Leg Pain during Shod Running. Med Sci Sports Exerc. 2007; 39: 330–339. https://doi.org/10. 1249/01.mss.0000247001.94470.21 PMID: 17277598 40. Stefanyshyn DJ, Stergiou P, Lun VMY, Meeuwisse WH. Dynamic Variables and Injuries in Running. Proceedings of the 5th Symposium on Footwear Biomechanics. Zuerich, Switzerland; 2001. 41. Hamill J, Russell EM, Gruber AH, Miller R. Impact characteristics in shod and barefoot running. Foot- wear Sci. 2011; 3: 33–40. https://doi.org/10.1080/19424280.2010.542187 42. Clarke TE, Frederick EC, Cooper LB. Effects of Shoe Cushioning Upon Ground Reaction Forces in Running. Int J Sports Med. 1983; 04: 247–251. https://doi.org/10.1055/s-2008-1026043 PMID: 6654550 PLOS ONE Shoe feature recommendations for different running levels PLOS ONE | https://doi.org/10.1371/journal.pone.0236047 July 16, 2020 16 / 17 43. Tilp M. Nigg Benno M., Mohr Maurice M. & Nigg Sandro R.–New paradigms in running injury prevention. Curr Issues Sport Sci. 2019;4. https://doi.org/10.15203/CISS_2019.100 44. Oh K, Park S. The bending stiffness of shoes is beneficial to running energetics if it does not disturb the natural MTP joint flexion. J Biomech. 2017; 533: 127–135. https://doi.org/10.1016/j.jbiomech.2017.01. 014 PMID: 28168959 45. Barnes KR, Kilding AE. A Randomized Crossover Study Investigating the Running Economy of Highly- Trained Male and Female Distance Runners in Marathon Racing Shoes versus Track Spikes. Sports Med. 2019; 49: 331–342. https://doi.org/10.1007/s40279-018-1012-3 PMID: 30374945 46. Hasson F, Keeney S. Enhancing rigour in the Delphi technique research. Technol Forecast Soc Change. 2011; 78: 1695–1704. https://doi.org/10.1016/j.techfore.2011.04.005 47. Sumsion T. The Delphi Technique: An Adaptive Research Tool. Br J Occup Ther. 1998; 61: 153–156. https://doi.org/10.1177/030802269806100403 48. Ferber R, McClay Davis I, Williams DS III. Gender differences in lower extremity mechanics during run- ning. Clin Biomech. 2003; 18: 350–357. https://doi.org/10.1016/S0268-0033(03)00025-1 PLOS ONE Shoe feature recommendations for different running levels PLOS ONE | https://doi.org/10.1371/journal.pone.0236047 July 16, 2020 17 / 17

Shoe feature recommendations for different running levels: A Delphi study.

07-16-2020

Honert, Eric C,Mohr, Maurice,Lam, Wing-Kai,Nigg, Sandro

eng

PMC7827107

medicina Article The Impact of the COVID-19 Pandemic on Endurance and Ultra-Endurance Running Volker Scheer 1,2 , David Valero 1, Elias Villiger 3 , Thomas Rosemann 3 and Beat Knechtle 3,4,*



 Citation: Scheer, V.; Valero, D.; Villiger, E.; Rosemann, T.; Knechtle, B. The Impact of the COVID-19 Pandemic on Endurance and Ultra-Endurance Running. Medicina 2021, 57, 52. https://doi.org/ 10.3390/medicina57010052 Received: 9 December 2020 Accepted: 7 January 2021 Published: 9 January 2021 Publisher’s Note: MDPI stays neu- tral with regard to jurisdictional clai- ms in published maps and institutio- nal affiliations. Copyright: © 2021 by the authors. Li- censee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and con- ditions of the Creative Commons At- tribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Ultra Sports Science Foundation, 69310 Pierre-Bénite, France; [email protected] (V.S.); [email protected] (D.V.) 2 Health Science Department, Universidad a Distancia de Madrid (UDIMA), Collado Villaba, 28400 Madrid, Spain 3 Institute of Primary Care, University of Zurich, 8091 Zurich, Switzerland; [email protected] (E.V.); [email protected] (T.R.) 4 Medbase St. Gallen Am Vadianplatz, 9006 St. Gallen, Switzerland * Correspondence: [email protected] Abstract: Background and objectives: The COVID-19 outbreak has become a major health and economic crisis. The World Health Organization declared it a pandemic in March 2020, and many sporting events were canceled. Materials and Methods: We examined the effects of the COVID-19 pandemic on endurance and ultra-endurance running (UER) and analyzed finishes and events during the COVID-19 pandemic (observation period March 2020–October 2020) to the same time period pre- COVID-19 outbreak (March 2019–October 2019). Results: Endurance finishes decreased during the pandemic (459,029 to 42,656 (male: 277,493 to 25,582; female 181,536 to 17,074; all p < 0.001). Similarly, the numbers of endurance events decreased (213 vs. 61 events; p < 0.001). Average marathon finishing times decreased during the pandemic in men (5:18:03 ± 0:16:34 vs. 4:43:08 ± 0:25:08 h:min:s (p = 0.006)) and women (5:39:32 ± 0:19:29 vs. 5:14:29 ± 0:26:36 h:min:s (p = 0.02)). In UER, finishes decreased significantly (580,289 to 110,055; p < 0.001) as did events (5839 to 1791; p < 0.001). Popular event locations in United States, France, UK, and Germany decreased significantly (p < 0.05). All distance and time-limited UER events saw significant decreases (p < 0.05). Conclusions: The COVID-19 pandemic has had a significant effect on endurance and UER, and it is unlikely that running activities return to pre-pandemic levels any time soon. Mitigation strategies and safety protocols should be established. Keywords: COVID-19; endurance running; marathon; ultra-endurance; running; sport industry 1. Introduction The coronavirus (COVID-19) pandemic, due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a major health and economic crisis around the world during 2020 [1]. First reports of a new viral pneumonia appeared in December 2019 in Wuhan, China, and by January 2020, the World Health Organization (WHO) reported of the spread of a new type of Coronavirus—COVID-19 [2]. The virus rapidly expanded throughout the world, and in March 2020, the WHO declared it a pandemic [2]. Countries around the world implemented travel restrictions, closed borders, and imposed local and national lockdowns of varying degrees to reduce the spread of the virus and manage health care resources [1]. These measures also included the cancelation of mass gathering and sporting events in order to reduce and control the spread of the virus, as particularly mass gatherings and sporting events on a large scale present unique challenges to public health authorities and governments [3]. Undoubtedly, this has had adverse effects on the economy, with tourism and sport tourism being important economic sectors and likely some of the sectors most severely impacted due to lockdowns, travel restrictions, and closed borders [1,4]. The Medicina 2021, 57, 52. https://doi.org/10.3390/medicina57010052 https://www.mdpi.com/journal/medicina Medicina 2021, 57, 52 2 of 8 extent to which this has affected endurance and ultra-endurance running (UER) events and finishing numbers is not known. The most prominent cancelations or (postponements) of sporting events due to the COVID-19 pandemic are the Olympic Games in Tokyo 2020 and Union of European Football Associations (UEFA) Euro 2020 Football Championships [3]. However, similarly, all other professional and amateur sports, including football, basketball, golf, athletics, triathlon etc. were initially canceled or postponed, with some professional sports (e.g., football, basketball, golf) being allowed to resume sometime later despite ongoing lockdowns or restrictions of movements under strict public health protocols [5,6]. Endurance running events in 2020 were no exception, with cancelation or postpone- ments of big city marathons such as those in Berlin, Paris, Boston, New York, and London due to safety concerns [7]. Some events were hosted as virtual competitions, whereas others such as the Tokyo marathon 2020 were only open to elite runners [7]. This has had important economic effects on the host city, as most of these big marathons attract around 50,000 participants. For the New York City Marathon, it was estimated that, in 2014, the economic impact of the race was approximately 415 million US dollars [8]. Thus, cancelation of any of these mass gathering events will likely have an important effect on the sporting industry and the local economy. However, not only mass gathering events and prominent races were canceled due to the COVID-19 pandemic but also many smaller and local endurance events, with an impact on the local economy and the sporting community that is still not fully understood, and its effects are difficult to estimate. Similarly, cancelation of ultra-endurance running (UER) events occurred, such as the Ultra Trail du Mont-Blanc® 2020, one of the most well-known UER event worldwide that usually attracts over 7000 participants from all around the world and with race distances of up to 170 km [9]. Other iconic races, such as the Comrades marathon in South Africa, the oldest ultramarathon, as well as the oldest 100 km race (Bieler Lauftage), the Spartathlon, a 246 km race in Greece, and many more UER were canceled or held virtually in 2020 [10,11]. The Two Oceans Marathon, a prominent race in South Africa, was also canceled in 2020, a race that attracted over 34,000 participants in 2019 [4]. Many of these events were canceled at short notice with considerable loss to the stakeholders, and in the case of the Two Oceans Marathons, the estimated loss of revenue was thought to be in the region of 2 million dollars [4]. However, considering endurance and UER events as a whole, the vast majority of races are smaller events (e.g., the Al Andalus Ultra Trail in Spain or the Isle of Wight Ultra Challenge in the UK) with fewer participant and less revenue at individual races; nevertheless, they represent an integral part of the sporting community and the wider sporting tourism branch. Running is an important sporting activity and one of the most popular sports world- wide that has seen an important increase in participation in endurance and UER events over the last few decades [12,13]. Therefore, it is worth a closer examination of the effects from the COVID-19 pandemic on this sporting sector. The main aim of this study, therefore, was to explore the impact of the COVID-19 pandemic on the number of endurance and UER events and finisher numbers as well as a secondary aim to explore the age and the finishing marathon times during the first few months of the COVID-19 pandemic (March 2020–October 2020) and compare them to the same time period pre-COVID-19 pandemic (March 2019–October 2019) to evaluate the effect COVID-19 has had so far on the endurance and UER event sector. Our hypothesis was that finishes and events in endurance and UER would decrease significantly during the COVID-19 pandemic. 2. Materials and Methods Marathons (distance 42.195 km) were classified as endurance running events, while UER events included running distances over marathon distance, timed-events over 6 h duration, and multi-day and multi-stage events on all running surfaces [14]. Medicina 2021, 57, 52 3 of 8 2.1. Ethical Procedures This study was approved by the Institutional Review Board of Kanton St. Gallen, Switzerland, with a waiver of the requirement for informed consent of the participant as the study involved the analysis of publicly available data (EKSG 01-06-2010). The study was conducted in accordance with the recognized ethical standards according to the Declaration of Helsinki (2013). 2.2. Data Sampling Data on marathon results were obtained through the publicly available database, accessible through the website (http://www.marathonguide.com/results/). This database represents the largest marathon database in the world. Data on UER events were obtained from a publicly available database, accessible through the website of the Deutsche Ultra- marathon Vereinigung (DUV) at: https://statistik.d-u-v.org/geteventlist.php. The DUV contains the largest dataset on UER worldwide and has been frequently evaluated within the scientific literature [10,15,16]. Data on race events, race location, race distance, finishing numbers, race time, sex, and nationality were, when available and accessible, analyzed for the time period since the declaration of the COVID-19 pandemic by the WHO in March 2020 [2] until the end of data sampling in October 2020 COVID-19 pandemic period (March 2020–October 2020) and compared to the same time period in the prior year, called the pre-pandemic period (March 2019–October 2019). In total, 1,192,029 finishes in 7478 events were examined. 2.3. Data Analysis Kolmogorov–Smirnov test was applied to test for normality. Descriptive analysis was performed and presented as mean and relative (%) frequency and change. Mean marathon finishing times and age were also presented with standard deviations (SD). An independent t-test was used to test the differences between groups and Mann–Whitney test for not normally distributed data (pre-pandemic vs. pandemic). Statistical significance was set at 5% (p < 0.05). All analyses were carried out using the Python programming language (Python Software Foundation, https://www.python.org/), Google Colab notebook, and the Statistical Software for the Social Sciences (IBM SPSS v26. Chicago, IL, USA). 3. Results Data and results for endurance (marathon) running are available in Table 1. The number of marathon finishes according to sexes with monthly breakdowns and percentage change during the COVID-19 pandemic was compared to the pre-pandemic period and is shown in Table 1. Monthly breakdowns were used to demonstrate the evolution of the pandemic, as at different time points, different lockdown restrictions applied throughout the world. A 10.8-fold drop could be observed in total finishes pre- to pandemic times, with almost no finishes during April/May 2020. Table 1. Data on number of marathon finishes according to sexes with monthly breakdowns and percentage change during the time period of the start of the COVID pandemic (March 2020) until the end of the observation period (October 2020) and comparison to the same time period pre-COVID in 2019 (March–October 2019). March April May June July August September October Total Marathon finishes 2019 45,593 100,898 66,159 28,176 15,633 11,512 26,996 164,062 459,029 Marathon finishes 2020 32,549 8 0 199 293 1872 4676 3059 42,656 * Change (%) −28.6 −100.0 −100.0 −99.3 −98.1 −83.7 −82.7 −98.1 −90.7 Male finishes 2019 27,812 58,521 41,680 17,187 10,161 8058 17,277 96,797 277,493 Male finishes 2020 19,204 4 0 137 170 1179 2899 1988 25,582 * Change (%) −31.0 −100.0 −100.0 −99.2 −98.3 −85.4 −83.2 −97.9 −93.0 Female finishes 2019 17,781 42,377 24,479 10,989 5472 3454 9719 67,265 181,536 Female finishes 2020 13,345 4 0 62 123 693 1777 1071 17,074 * Change (%) −24.9 −100.0 −100.0 −99.4 −97.8 −79.9 −81.7 −98.4 −92.8 * p < 0.001. Medicina 2021, 57, 52 4 of 8 The number of endurance events dropped significantly pre-pandemic to the COVID- 19 pandemic period from 213 to 61 races in the database (p < 0.001), an approximately 3.5-fold drop. Most events were held in United States (pre-pandemic vs. pandemic 61.6% vs. 72.7%), United Kingdom (10.7% vs. 11.4%), and Canada (10.0% vs. 4.5%), and the majority of finishes originated from the United States (pre-pandemic vs. pandemic 72.0% vs. 93.5%). The ratio between finishes per event dropped from 2.155 pre-pandemic to 700 finishers/event during the COVID-19 pandemic (p < 0.001), suggesting that events were smaller during the pandemic, as less finishes were observed. The average age of finishers pre-pandemic was 47.8 ± 2.0 years and, during the pandemic, was 43.6 ± 4.3 years (p = 0.02), with female finishers pre-pandemic 46.0 ± 2.08 years compared to 42.6 ± 2.3 years during the pandemic (p = 0.01) and for male finishers 48.9 ± 2.1 years and 43.6 ± 4.3 years (p = 0.02), respectively. Average marathon finishing times for men pre-pandemic were 5:18:03 ± 0:16:34 h:min:s compared to 4:43:08 ± 0:25:08 h:min:s (p = 0.006) pandemic period and for women 5:39:32 ± 0:19:29 h:min:sec compared to 5:14:29 ± 0:26:36 h:min:s (p = 0.02), respectively. Data for UER finishes, event distances, and event locations are shown in Tables 2–4. Data for UER event finishes, ultra events, and finishes per event with monthly breakdowns and percentage change during the COVID pandemic compared to the pre-pandemic period are shown in Table 2. Table 2. Data for ultra-endurance event finishes, ultra events, and finishes per event with monthly breakdowns and percentage change during the time period of the start of the COVID pandemic (March 2020) until the end of the observation period (October 2020) and comparison to the same time period pre-COVID in 2019 (March–October 2019). March April May June July August September October Total Finishes Ultra 2019 56,741 96,709 74,678 107,273 58,196 54,627 62,147 69,927 580,289 Finishes Ultra 2020 21,310 680 1262 3031 10,124 20,978 27,860 24,810 110,055 * Change (%) −62.4 −99.3 −98.3 −97.2 −82.6 −61.6 −55.2 −64.5 −81.0 Ultra events 2019 577 643 775 883 648 707 799 807 5839 Ultra events 2020 205 21 49 92 175 360 447 442 1791 * Change (%) −64.5 −96.7 −93.7 −89.6 −73.0 −49.1 −44.1 −45.2 −69.3 Finishes/event 2019 98.3 150.4 96.4 121.5 89.8 77.3 77.8 86.7 99.8 Finishes/event 2020 104.0 32.4 25.8 32.9 57.9 58.3 62.3 56.1 53.7 # Change (%) 5.7 −78.5 −73.3 −72.9 −35.6 −24.6 −19.9 −35.2 −46.2 * p < 0.001, # p < 0.05. Table 3. Data for ultra-endurance event finishes in distance-limited events (50 km, 100 km, and 100 miles) and time-limited events (6 h, 12 h, and 24 h) with monthly breakdowns and percentage change during the time period of the start of the COVID pandemic (March 2020) until the end of the observation period (October 2020) and comparison to the same time period pre-COVID in 2019 (March–October 2019). Finishes March April May June July August September October Total 50 km 2019 19,289 36,907 17,831 14,433 10,126 10,206 6554 15,424 130,770 50 km 2020 5626 32 126 390 974 3588 4921 5189 20,846 † Change (%) −70.8 −99.9 −99.3 −97.3 −90.4 −64.8 −24.9 −66.4 −84.1 100 km 2019 6690 5013 2848 8915 5097 3112 7819 9059 48,553 100 km 2020 250 19 205 149 467 992 3782 1240 7104 # Change (%) −96.3 −99.6 −92.8 −98.3 −90.8 −68.1 −51.6 −86.3 −85.4 100 miles 2019 1364 604 1521 3113 1103 3092 2484 3257 16,538 100 miles 2020 129 0 41 34 604 289 1340 1048 3485 † Change (%) −90.5 −100.0 −97.3 −98.9 −45.2 −90.7 −46.1 −67.8 −78.9 6 h 2019 4902 4175 3621 2206 2587 1617 8292 3043 30,443 6 h 2020 2374 0 0 282 460 717 936 2917 7686 † Change (%) −51.6 −100.0 −100.0 −87.2 −82.2 −55.7 −88.7 −4.1 −74.8 12 h 2019 1329 3825 3477 3673 3406 1586 484 3919 21,699 12 h 2020 143 0 84 246 761 646 1334 1717 4931 † Change (%) −89.2 −100.0 −97.6 −93.3 −77.7 −59.3 175.6 −56.2 −77.3 24 h 2019 1753 905 6617 4940 466 3645 865 1910 21,101 24 h 2020 243 0 162 24 1677 1043 1540 895 5584 † Change (%) −86.1 −100.0 −97.6 −99.5 259.9 −71.4 78.0 −53.1 −73.5 # p < 0.001, † p < 0.05. Medicina 2021, 57, 52 5 of 8 Table 4. Event location (country) of ultra-endurance running events pre- COVID-19 (March 2019-Ocotber 2019) compared with monthly numbers and total numbers and percentage change to the observation period during the COVID-19 pandemic (March 2020–October 2020) with listings of the top three event locations (pre-COVD-19: USA, FRA (France), and GBR (Great Britain); COVID-19 pandemic: USA, GER (Germany), and UK (United Kingdom)), with percentage of all event locations during that particular month). Further analysis of the location with the greatest change during the observation period is included (TPE: China Tapei). Ultra Event Location March April May June July August September October Total 2019 USA (%) 183 (31.7) 214 (33.3) 210 (27.1) 199 (22.5) 161 (24.8) 216 (30.6) 217 (27.2) 266 (33.0) 1666 (28.5) 2020 USA (%) 84 (41.0) 0 (0.0) 13 (26.5) 29 (31.5) 58 (33.1) 80 (22.2) 126 (28.2) 173 (39.1) 563 (31.4) * Change (%) −54.1 −100.0 −93.8 −85.4 −64.0 −63.0 −41.9 −35.0 −66.2 2019 FRA (%) 30 (5.2) 67 (10.4) 65 (8.4) 103 (11.7) 68 (10.5) 35 (5.0) 43 (5.4) 58 (7.2) 469 (8.0) 2020 FRA (%) 15 (7.3) 0 (0.0) 0 (0.0) 0 (0.0) 4 (2.3) 33 (9.2) 25 (5.6) 21 (4.8) 98 (5.5) # Change (%) −99.8 N/A N/A N/A −99.6 −99.9 −99.9 −99.9 −100.0 2019 UK (%) 44 (7.6) 28 (4.4) 64 (8.3) 63 (7.1) 50 (7.7) 54 (7.6) 86 (10.8) 32 (4.0) 421 (7.2) 2020 UK (%) 11 (5.4) 0 (0.0) 0 (0.0) 1 (1.1) 3 (1.7) 17 (4.7) 31 (7.0) 36 (8.1) 99 (5.5) * Change (%) −99.8 N/A N/A −98.4 −99.3 −99.9 −100.0 −99.9 −100.0 2019 GER (%) 30 (5.2) 23 (3.6) 30 (3.9) 50 (5.7) 27 (4.3) 58 (8.2) 37 (4.6) 23 (2.9) 278 (4.8) 2020 GER (%) 5 (2.4) 0 (0.0) 4 (8.2) 11 (12.0) 13 (7.4) 30 (8.3) 29 (6.5) 27 (6.1) 119 (6.6) # Change (%) −83.3 −100.0 −86.7 −78.0 −51.9 −48.3 −21.6 17.4 −57.2 2019 TPE (%) 20 (3.5) 24 (3.7) 13 (1.7) 20 (2.3) 12 (1.9) 18 (2.5) 15 (1.9) 16 (2.0) 138 (2.4) 2020 TPE (%) 10 (4.9) 19 (90.5) 17 (34.7) 12 (13.0) 12 (6.9) 19 (5.3) 14 (3.1) 6 (1.4) 109 (6.1) # Change (%) −50.0 −20.8 30.8 −40.0 0.0 5.6 −6.7 −62.5 −21.0 * p < 0.001, # p < 0.05. A 5.3-fold decrease in UER finishes can be observed during the COVID-19 pandemic, and a 3.3-fold decrease in UER events. Finishes per events also decreased from 99.8 to 53.7 finishes/event, a 1.9-fold decrease, demonstrating that events were smaller with fewer finishes. The 50 km distance remained the most popular UER distance, and data for UER event finishes in distance-limited events (50 km, 100 km, and 100 miles) and time-limited events (6 h, 12 h, and 24 h) with monthly breakdowns and percentage change during the COVID pandemic compared to pre-pandemic period are shown in Table 3. UER event locations (countries) during the COVID-19 pandemic are compared monthly to the pre-pandemic period and shown in Table 4. The three top event locations pre- pandemic were USA, France, and United Kingdom and during the COVID-19 pandemic were USA, Germany, and United Kingdom. For further comparison and illustration, the UER events of China Tapei are shown, as this location showed, in contrast to others, a relative increase during the COVID-19 pandemic, especially in April 2020 with over 90% of events hosted in this location. Additionally, UER event numbers and performances from 2018 (6708 vs. 609,847) increased to 2019 (7468 vs. 671,738), increases of 11.3% and 10.1%, respectively. 4. Discussion Running is one of the most popular sports worldwide, with endurance and UER showing important increases in participation and finishes over the last few decades [13,14]. Since the onset of the COVID-19 pandemic in March 2020 [2], many sporting events were canceled or postponed, but the impact of the pandemic on endurance and UER has not been examined thus far. The aim of the study was, therefore, to explore the impact of the COVID-19 pandemic on endurance and UER events and participation and the implications of the endurance and UER sector as a whole. We hypothesized that, during the COVID-19 pandemic, finishing numbers and events would decrease significantly when compared to the same time period in the preceding year in pre-pandemic times. The main findings of our study were: (i) finishes in endurance races decreased signifi- cantly during the pandemic, with an almost 11-fold decrease; (ii) event numbers decreased significantly in endurance events during the pandemic, with almost no activity during April/May 2020; finishes/event ratio decreased as well, suggesting that events were smaller during the pandemic, as less finishes were observed; (iii) average age of endurance Medicina 2021, 57, 52 6 of 8 finishers decreased significantly during the pandemic, as did the marathon finishing times; (iv) finishes in UER decreased significantly during the pandemic, with an over 5-fold decrease, with the biggest decrease in April/May 2020; (v) event numbers and locations in UER decreased significantly, with a proportional increase in events in China Tapei during the pandemic, especially in April 2020; (vi) the 50 km event remained the most popular distance based UER event and the 6 h the most popular time-limited event, both showing a significant decline during the pandemic. As with other sports and sporting events during the global crisis, many endurances and UER were postponed, canceled, or held virtually due the continued uncertainty of the virus’s spread and the potential risk of spreading the virus through congregation of runners. From personal experience, a number of events were held virtually, however, we are unaware of any available data sets on a larger scale for analysis and comparison. Our data show the effect of the COVID-19 pandemic since it was first declared on 11 March 2020 [2] on endurance and UER running. We observed a significant decrease in finishes and event numbers in endurance and UER, especially in the first two full months after the pandemic was declared (April/May 2020) with little to no activity in endurance and UER during this time. This is something that could be expected, as with national and local lockdowns, restrictions, and bans on travel, very little movement occurred during these time periods [1,3]. Overall finishes in endurance running decreased almost 11-fold, while UER finishes decreased 5-fold during the observation period of the pandemic. One explanation may be that endurance running is generally more popular than UER and that the UER community tends to be smaller. Perhaps participation continued in smaller, more local races, as demonstrated by our data that showed that approximately 50 finishes/event in UER were observed compared to 700 finishes/endurance event, although the percentage drop in events for endurance and UER were very similar. Additionally, the number of events in the endurance running database was comparatively smaller than for UER events, which further may add to this. UER events and finishing numbers have been increasing over the last 20 years [12,13], and this can similarly be observed when comparing UER event numbers and performances from 2018 to 2019. A further increase in 2020 could have been expected if not for the COVID-19 pandemic. Another interesting observation in endurance events is that the average age of marathon finishers decreased during the pandemic, as did the average marathon finishing times. The reason for this may be, that more experienced runners kept participating in marathons during the COVID-19 pandemic, whilst more amateur runners stayed at home, similarly to older participants, that present a higher risk population of developing more severe symptoms of COVID-19. In UER, race distances of 50 km are generally the most popular UER distances [13]; this was also observed during the pandemic, however, with a significant decrease compared to pre-pandemic levels. The same could be observed in all other distance limited events (100 km and 100 miles) and time limited UER (6 h, 12 h, and 24 h). Since August 2020, numbers of finishes have increased notably, however are still lagging behind considerably compared to pre-pandemic levels. Running events can have positive economic effects with short and long term economic consequences [17], whereas cancelation may have a detrimental effect, as exemplified by the cancelation of the Two Oceans Marathon and the New York Marathon [4,8]. The cancelation of the 2020 Two Oceans Marathon in South Africa reported an approximate loss of revenue of around 2 million dollars [4]. Similarly, the New York City marathon incurred significant losses when canceled due to the effects of a devastating natural disaster (hurricane Sandy) with estimated losses of charitable donations of 36.1 million US dollars and an total estimated economic impact of the race of approximately 415 million US dollars in 2014 [8]. Additionally important to note is that running events can have positive economic effects during the COVID-19 pandemic, as sporting events can create positive publicity in sending out the signal that the city or the country is open for business and thus can create economic growth [17]. This was the case of China Tapei that was the most Medicina 2021, 57, 52 7 of 8 popular UER event location in April/May 2020. Whether this also created additional economic growth is not known. However, it is also important to note that running poses an extremely low risk of COVID-19 transmission, with only one reported case among 571,401 athletes and 98,035 officials and staff that took part in 787 races and track meetings in Japan since July 2020 [18]. All events were held without spectators, and specific safety measures were introduced, which may potentially allow running activities to resume with appropriate safety proto- cols [18]. Such tools have been developed by the WHO that provides a risk assessment tool for sporting and mass gathering events during the COVID-19 pandemic, considering spe- cific action plans and risk mitigation strategies [19]. These strategies may help in delaying the spread of an outbreak [20] and may be useful tools in the decision-making processes of hosting an event [3]. Our results show the devastating effects COVID-19 had on endurance and UER. It is necessary to examine the possibility of returning to pre-COVID-19 levels, as a whole branch of the sporting industry is dependent on this activity. With risk assessment tools, mitigation strategies, and strict safety protocols, a gradual return to endurance and UER may be possible, especially considering the extremely low risk outdoor running poses for contracting COVID-19. However, a return to pre-pandemic levels any time soon remains unlikely until the time an effective drug treatment or vaccine becomes available [20]. Further studies examining the economic effect of the COVID-19 pandemic on en- durance and UER may be useful to estimate the potential loss to the industry in addition to examining the impact on health. Similarly, examining the demographics and the per- formance times further and over a longer time period may provide additional important information on how COVID-19 has impacted running and UER. Limitations Of the two publicly available databases used for this analysis, the marathon results database (http://www.marathonguide.com/results/) is the largest database of marathon results in the world, fully searchable by name, place, or time. However, the majority of data pertain to races held in the United States, Canada, Australia, and New Zealand, and we recognize this as a limiting factor for applicability on races worldwide. The DUV database (https://statistik.d-u-v.org/geteventlist.php) is the largest database worldwide of UER events and has been widely used in the past in the scientific literature [10,15,21]. However, as with any large database, not all results may be complete, and we recognize that this as a limiting factor. Nevertheless, the aim of the study was to examine the impact of COVID-19 on endurance and UER, and both databases provide a sufficiently sizable data sample for analyses. Examining and comparing marathon finishing times pre-pandemic to pandemic provides some interesting insights, however, comparing several different events with different ambient conditions and race profiles has its limitations and needs to be interpreted with care. 5. Conclusions Endurance and UER have seen a significant decrease in the number of finishes and events during the COVID-19 pandemic with a devastating effect on the sporting industry. It is unlikely that running activities will return to pre-pandemic levels any time soon, and mitigation strategies and safety protocols should be established until the time an effective drug treatment or vaccine becomes available. Future studies might analyze the economic impact COVID-19 has had on the endurance and UER industry as a whole. Author Contributions: Conceptualization, V.S. and B.K.; methodology, V.S.; software, D.V.; valida- tion, D.V.; formal analysis, D.V.; investigation, V.S.; resources, E.V.; data curation, E.V.; writing— original draft preparation, V.S.; writing—review and editing, T.R., V.S., D.V., B.K. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Medicina 2021, 57, 52 8 of 8 Institutional Review Board Statement: This study was approved by the Institutional Review Board of Kanton St. Gallen, Switzerland, with a waiver of the requirement for informed consent of the participant as the study involved the analysis of publicly available data (EKSG 01-06-2010). The study was conducted in accordance with the recognized ethical standards according to the Declaration of Helsinki (2013). Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. References 1. Gössling, S.; Scott, D.; Hall, C.M. Pandemics, tourism and global change: A rapid assessment of COVID-19. J. Sustain. Tour. 2021, 29, 1–20. [CrossRef] 2. World Health Organisation (WHO). Available online: https://www.who.int/news/item/27-04-2020-who-timeline---covid-19 (accessed on 25 November 2020). 3. Parnell, D.; Widdop, P.; Bond, A.; Wilson, R. COVID-19, networks and sport. Manag. Sport Leis. 2020, 1–7. [CrossRef] 4. Swart, K.; Maralack, D. COVID-19 and the cancellation of the 2020 Two Oceans Marathon, Cape Town, South Africa. Sport Soc. 2020, 23, 1736–1752. [CrossRef] 5. Drewes, M.; Daumann, F.; Follert, F. Exploring the sports economic impact of COVID-19 on professional soccer. Soccer Soc. 2020, 1–13. [CrossRef] 6. Horky, T. No sports, no spectators—no media, no money? The importance of spectators and broadcasting for professional sports during COVID-19. Soccer Soc. 2020, 1–7. [CrossRef] 7. The Runner’s World Editors. Available online: https://www.runnersworld.com/news/a31353444/coronavirus-marathons- running-events-postponed-canceled/ (accessed on 30 November 2020). 8. Martin, J.; Hall, J. The Impact of the New York City Marathon on Hotel Demand. Economies 2020, 8, 89. [CrossRef] 9. Suter, D.; Sousa, C.V.; Hill, L.; Scheer, V.; Nikolaidis, P.T.; Knechtle, B. Even Pacing Is Associated with Faster Finishing Times in Ultramarathon Distance Trail Running-The “Ultra-Trail du Mont Blanc” 2008–2019. Int. J. Environ. Res. Public Health 2020, 17, 7074. [CrossRef] [PubMed] 10. Knechtle, B.; Scheer, V.; Nikolaidis, P.T.; Sousa, C.V. Participation and Performance Trends in the Oldest 100-km Ultramarathon in the World. Int. J. Environ. Res. Public Health 2020, 17, 1719. [CrossRef] [PubMed] 11. SPARTATHLON Ultra Race. Available online: http://www.spartathlon.gr/en/ (accessed on 25 February 2018). 12. Ahmadyar, B.; Rüst, C.A.; Rosemann, T.; Knechtle, B. Participation and performance trends in elderly marathoners in four of the world’s largest marathons during 2004–2011. Springerplus 2015, 4. [CrossRef] [PubMed] 13. Scheer, V. Participation Trends of Ultra Endurance Events. Sports Med. Arthrosc. Rev. 2019, 27, 3–7. [CrossRef] [PubMed] 14. Scheer, V.; Basset, P.; Giovanelli, N.; Vernillo, G.; Millet, G.P.; Costa, R.J.S. Defining off-road running: A position statement from the Ultra Sports Science Foundation. Int. J. Sports Med. 2020, 41, 275–284. [CrossRef] [PubMed] 15. Scheer, V.; Di Gangi, S.; Villiger, E.; Nikolaidis, P.T.; Rosemann, T.; Knechtle, B. Age-related participation and performance trends of children and adolescents in ultramarathon running. Res. Sports Med. 2020, 23, 1–11. [CrossRef] [PubMed] 16. Scheer, V.; Di Gangi, S.; Villiger, E.; Rosemann, T.; Nikolaidis, P.T.; Knechtle, B. Participation and Performance Analysis in Children and Adolescents Competing in Time-Limited Ultra-Endurance Running Events. Int. J. Environ. Res. Public Health 2020, 17, 1628. [CrossRef] [PubMed] 17. Papanikos, G.T. The Economic Effects of a Marathon as a Sport Tourism Event. Athens J. Sports 2015, 2, 225–239. [CrossRef] 18. Japan Association of Athletics Federation. Available online: https://japanrunningnews.blogspot.com/2020/11/jaaf-study-of-78 7-races-held-since-july.html (accessed on 27 November 2020). 19. World Health Organisation (WHO). Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019 /technical-guidance (accessed on 27 November 2020). 20. Ebrahim, S.H.; Memish, Z.A. COVID-19: Preparing for superspreader potential among Umrah pilgrims to Saudi Arabia. Lancet 2020, 395, e48. [CrossRef] 21. Cejka, N.; Rüst, C.A.; Lepers, R.; Onywera, V.; Rosemann, T.; Knechtle, B. Participation and performance trends in 100-km ultra-marathons worldwide. J. Sports Sci. 2014, 32, 354–366. [CrossRef] [PubMed]

The Impact of the COVID-19 Pandemic on Endurance and Ultra-Endurance Running.

01-09-2021

Scheer, Volker,Valero, David,Villiger, Elias,Rosemann, Thomas,Knechtle, Beat

eng

PMC9794057

1 S7 Table. Results of round 3. Factors rated in round 3; n=22. Factor Level of agreement (%) Training Endurance capacitya,b 72,2 Recovery speed† 66,7 Metabolism Angiogenesis (=formation of new blood vessels)b 55,6 Body Muscle fibres - transformation capacity (type 1 vs. type 2) 55,6 Weight / BMI 44,4 Total fat mass 50,0 Lean mass (=mass of all organs except body fat including bones, muscles, blood, skin) 44,4 Tendon stiffness 55,6 Hormones Insulin-like growth factor-1 (IGF-1) level 55,6 Growth hormone level 66,7 Nutrition Vitamin B complex vitamins (B1-12) deficiencyb 55,6 Immune system Blood pressure regulation 50,0 Healing function of soft tissue 50,0 Injuries Risk of joint injuries 66,7 Risk of upper respiratory tract infectionsb 66,7 Psychological Emotion regulation 66,7 Pain sensitivityb 50,0 Self-control 50,0 Resilience capacity 50,0 Concentration capacity 44,4 Environment Heat resistance capacity 50,0 Altitude training sensitivity 55,6 aLevel of agreement achieved 70% threshold and therefore was included in the consensus report. bLevel of agreement changed compared to round 2.

Factors associated with high-level endurance performance: An expert consensus derived via the Delphi technique.

12-27-2022

Konopka, Magdalena J,Zeegers, Maurice P,Solberg, Paul A,Delhaije, Louis,Meeusen, Romain,Ruigrok, Geert,Rietjens, Gerard,Sperlich, Billy

eng

PMC7663387

Supplementary Material Supplementary Table S1 Narrative description of findings of the 47 cross-sectional studies. Author Narrative description of findings 1 Wilson (1980) comparing the mood states of marathon runners, regular joggers and non-exercisers A Canadian cross sectional study by Wilson et al. (1980) used 30 male participants ranging from age 20-45 from the same socioeconomic area to compare the mood states of marathon runners (n=10), regular joggers (n=10) and non-exercisers (n=10) using the Profile of Mood States as measurement. The marathoners and joggers reported less depression (F(2,28) =7.51, p<0.003), less anger (F=10.11, p<0.001), less confusion (F=12.41,p<0.001) and more vigor (F=103.21,p<0.001) than the non- exercisers. The marathoners also reported less fatigue (F=10.26, p<.001) and less tension (F=7.51, p<0.003) than the non-exercisers. The marathoners and joggers did not significantly differ on reported fatigue and tension, however marathoners had significantly less depression, less anger, less confusion and more vigor than the joggers. Overall results found that the joggers reported better mood states than the non- exercisers, and the marathon runners reported even more positive mood states than the joggers1. 2 Joesting (1981) investigating the relationship between running and depression An American controlled cross sectional questionnaire by Joesting (1981) used 100 runners (21 women, mean age 16.53) and 79 men (mean age 18.36) to investigate the relationship between running and depression using the Depression Adjective Checklist as measurement. The only significant sex difference between male and female runners was their age (t=2.85, p<.01). Results found that using the t test, runners of both sexes were significantly (p< .01) less depressed than Lubin's data for non-psychiatric sample of patients. Female runners mean score on the Depression Adjective Check List was 4.33, while the normative non-psychiatric patient normative sample mean was 7.32. Male runners mean depression score was 4.59 while the normative non-psychiatric male sample mean was 8.02. Overall results suggest that running decreased depression measurements in both males and females2. 3 Jorgenson (1981) investigating the relationship between emotional wellbeing and running An American study by Jorgenson et al. (1981) used 454 regular runners (390 males and 64 females) of whom 9.9% were under 20, 25% were age 21-29, 37% were age 30-39, 23% were 40 and over, 4.8 did not respond about age. The study used a structured questionnaire consisting of 55 items designed by the author to investigate the relationship between emotional wellbeing and running. The majority of runners (92.3%, n=419) indicated an increase in emotional wellbeing (p<0.01), while 5.7% runners reported no effect and 1.8% reported a reduction in emotional wellbeing. Results did not report the scale of improvement. More than half the runners (54.8%, n=249) indicated an increase in general tolerance of others (p<.01), while 33.7% of runners indicated no effect and 6.8% had reduced tolerance of others. Results found that age and emotional well-being were significantly correlated (gamma value of 0.42, p<0.001), that is the older the runner, the greater the perception of emotional well-being resulting from running. The inverse relationship between average hours per week running and emotional wellbeing was highly significant (gamma value = -0.43, p<.001). Overall, results suggest that running increases emotional wellbeing3. 4 Valliant (1981) compare self- sufficiency and personality profiles in marathon runners vs recreational joggers A Canadian cross sectional study by Valliant et al. (1981) used 68 male participants to compare self-sufficiency and personality profiles in marathon runners (n=30, mean age 34.4) vs recreational joggers (n=38, mean age 20.6) using a one hour ‘Sixteen Personality Factor Questionnaire’ as measurement. Marathoners were on average more reserved (F=17.07, df=1,66, p<0.001), intelligent (F = 12.69, df=1,66, p<0.001), tender-minded (F = 11.79, df = 1,66, p<0.001), imaginative (F=11.09, df=1,66, p<0.005), and self-sufficient (F = 19.84, df=1,66, p<0.001) than joggers. Conversely, joggers were more happy-go-lucky (F = 10.05, df = 1,66, p<0.005), apprehensive (F = 10.51, df=1,66, p<0.005) and controlled (F=7.09, df=1,66, p<0.01). Marathoners were on average significantly older (F=99.45, df=1,66, p<0.001), ran more miles per week (F=167.6, df=1,66, p<0.001) and trained for more years (F=20.55, df=1,66, p<0.001) than the joggers. Overall results found that marathon runners had a more self-sufficient personality as compared to joggers who were less assertive, more conscientious and controlled personality types4. 5 Francis (1982) Comparing anxiety, depression and hostility in various groups of runners vs sedentary controls An American cross sectional study by Francis et al. (1982) used 44 male participants with a mean age of 32 to compare anxiety, depression and hostility in various groups of runners vs sedentary controls using the State-Trait Anxiety Inventory (STAI) and the Multiple Affect Adjective Check List (MAACL) as measurements. Participants were separated into 4 groups based on the number of miles jogged per week: non-running controls who ran 0 miles weekly (n=16), 20 miles (n=10), 30-40 miles (n=8) and 50-60 miles (n=10). There were no significant differences in psychological variables when jogging groups (ie. Miles jogged per week) were compared to each other. Spearman correlations for the psychological parameters were as follows: MAACL anxiety= -0.08, MAACL depression= -0.15, MAACL hostility= 0.11, STAI anxiety= -0.12, none of which were significant. Runners had significantly lower (p<0.01) anxiety, hostility and depression than their sedentary counterparts. Combined joggers scores vs sedentary control scores for MAACL anxiety were 4.2 vs 7.2, respectively, for MAACL hostility were 4.8 vs 6.8, for MAACL depression were 8.6 vs 12.3, and for STAI anxiety were 30.8 vs 42.8. Combined trait scores revealed that anxiety, hostility, and depression as measured by the MAACL in joggers were respectively 27%, 23% and 14% lower than normative scores (reported in other papers) and anxiety as measured by the STAI was 19% lower in joggers when compared to the reported norms. Overall results suggest that running lowers depression and anxiety measures5. 6 Hailey (1982) investigate the relationship between running and negative addiction An American cross sectional questionnaire by Hailey et al. (1982) used 60 male runners aged between 13 and 60 years old to investigate the relationship between running and negative addiction using the negative addiction scale as measurement. The subjects were split into three groups: those who had run for less than 1 year (n=12), those who had ran for 1-4 years (n=32) and those who had ran for over 4 years (n=16). Overall, the sample mean for negative addiction scores was 5.39 on a scale of 1 to 14. There was significant difference in negative addiction between the groups (F(2,58)= 3.48, p<.05), with length of running history associated with increasing negative addiction scores. Runners with a running history of less than 1 year scored a mean of 3.84, those running for 1-4 years scored 5.63, and those running for over 4 years scored 6.38. Addiction scores for runners of over 4 years was greater than the addiction score for runners of under one year (t(59)=2.72, p<.005). Likewise, the addiction score for runners of between one and four years was greater than the score for runners under one year (t(59)=2.52, p<.01). However the difference in addiction scores between the 1-4 year group and the 4+ year group was not statistically significant, which may suggest that negative addiction reaches a plateau with running and does not increase at the same rate in later stages as it does in the beginning of the development of running behaviour with significant differences between the groups. Overall, results suggest that the more years a male has been running, the greater the risk of developing negative addiction6. 7 Callen (1983) Investigating mental and emotional aspects associated with long-distance running in non- professional runners, including depression, tension, mood, happiness, self- confidence and self-image. An American cross sectional study by Callen (1983) used 424 non-professional runners (303 men and 121 women) with a mean age of 34 years old and who ran on average more than 28.8 miles per week to investigate the mental and emotional aspects associated with long-distance running, including depression, tension, mood, happiness, self-confidence and self-image. A questionnaire designed by the author was used as measurement. 96% of the subjects noticed mental or emotional benefits from running, however no details were reported of the size of these mental and emotional benefits. Benefits included relief of tension (86% of all respondents, 88% of men and 82% of women, ns for all three); improved self-image (77% of all runners, 74% of men and 82% of women, ns for all three); better mood (66% of all participants, 62% of men (p<0.05) and 82% of women(p<0.05)), improved self-confidence (64% of all participants, 63% of men and 65% of women, ns for all three), relieved depression (56% of all participants, 52% of men (p< 0.05) and 69% of women (p < 0.05)) and improved happiness (58% of all participants, 56% of men and 64% of women). However, 25% state that they had experienced emotional problems associated with running, in which almost every instance the problem is one of depression, anger, or frustration associated with not being able to run due to an injury. No further details were reported on this. 69% of runners experienced an emotional "high" associated with running7. 8 Galle (1983) Comparing psychologic profiles including anxiety and depression in runners, infertility patients, fertile An American controlled cross sectional questionnaire by Galle et al. (1983) used 391 female subjects to compare psychologic profiles including anxiety and depression in runners (n=102), infertility patients (n=103), fertile subjects (n=139) and Clomid study patients whose only infertility abnormality was ovulation dysfunction (n= 47), using the Hopkins Symptom Checklist-90 (SCL-90) as measurement. The runners were aged 15 to 50 years, 15% had amenorrhea, 70% had regular cycles, and 15% had irregular cycles. The SCL data showed that the mean scores in all groups-runners, Clomid study patients, infertility patients, and fertile control subjects were in the normal range for all factors. Mean total scores for SCL did not vary significantly amongst the 4 groups (F=1.19, ns), subjects and Clomid study patients whose only infertility abnormality was ovulation dysfunction however, there was a significant difference for the depression subscale (F=3.42, p<0.025): the depression scores of runners (were nearly identical to those of fertile control subjects but were significantly lower than the depression scores of the Clomid study patients or the infertility patients. The only significant difference between runners and fertile control subjects was that control subjects had higher hostility (p<0.05). Significant differences were noted with the factors of obsessive-compulsive behaviour (p< 0.01) and psychoticism (p<0.005). The women running more than 30 miles per week had higher mean scores for all factors, with significant differences in somatization (P<0.05) and anxiety (P<0.005). Regarding just runners, there was significant differences in depression between amenorrhoeic (n=15) and regular cycling runners (n=87), with amenorrhoeic runners scoring higher in the depression factor than regular cycling runners (F=3.0, p<0.10). Overall the emotional distress scores of runners were not significantly different from fertile control subjects, but both groups of infertility patients showed greater distress on items in the depression factor than the runners and fertile control subjects. Emotional distress factors were associated with the development of amenorrhea in these runners8. 9 Lobstein (1983) Impact of a treadmill run with increasing gradient on depression An American pre-post controlled between subject design by Lobstein et al. (1983) used 22 medically healthy men aged 40-60, to compare depression in physically active men (n=11) to sedentary men (n=11) using the Minnesota Multiphasic Personality Inventory (MMPI) as measurement. The MMPI indicated that sedentary men were more depressed (mean 61.36) than the physically active men (mean 50.73) with p < 0.01 and standardized canonical coefficients of 0.929. However, both groups of men were within clinical limits for normal, mentally healthy, middle aged men9. 10 Rudy (1983) investigating how levels of anxiety and self-esteem related to intensity of jogging An American cross sectional questionnaire by Rudy et al. (1983) used 319 female regular runners between the ages of 16 and 60 to investigate how levels of anxiety and self-esteem related to intensity of jogging using the Rosenberg Self-esteem Scale and Zuckerman's Anxiety Adjective Checklist as measurements. Results found that female runners jogging with great intensity demonstrated significantly less anxiety (x 2 = 22.83; p<.001). In addition 14% of women listed decreased tension as a result of jogging they felt others should know about. No significant relationship was drawn between self- esteem score and intensity of jogging, however the majority (89%) of women scored in the range designated as high self-esteem, and there was evidence that jogging influenced self-esteem in the open-ended answers with 29% of responses stating they feel better about themselves, 12% stating they have increased self-confidence and 6% stating they had a sense of accomplishment. Hence this paper does show evidence that jogging influences self-esteem, just not significant evidence10. 11 Goldfarb (1984) Investigating anorexia nervosa traits within distance runners An American cross sectional study by Goldfarb et al. (1984) used 200 distance runners (136 men and 64 women) to investigate anorexia nervosa traits within distance runners using the Goldfarb Fear of Fat scale and Activity Vector Analysis to measure personality characteristics. The study does not give details on the demographics of the participants. Results do not support a connection between running and fear of fat, a central component of anorexia nervosa, with only 29 (14.5%) participants reporting a high fear of fat score (i.e. between 6 and 10 on the scale). Overall, the mean fear of fat score for these runners was 2.91, indicating a low-normal fear of fat. Fear of fat scores did not correlate significantly with any of the measures of running zealousness including miles run per week (r=-.04), number of workouts per week (r=.09), number of road races (r=.05), or marathons completed (r=-.05), and degree of importance placed on running (r=-.03). The runners who demonstrated the greatest zealousness demonstrated AVA profiles that closely clustered around one particular profile (r=.64, p< .05) consisting of high ipsative scores on aggressiveness and dependence and low ipsative scores on sociability and emotional stability… indicating that these individuals are assertive, obsessive, perfectionistic, and anxious. Overall, results do not support a correlation between running and fear of fat. However, the runners most closely resembling "obligatory runners" exhibited traits characteristic of anorexia nervosa patients11. 12 Guyot (1984) comparing death anxiety in runners vs non- runners An American controlled cross sectional study by Guyot et al. (1984) used 126 participants to compare death anxiety in runners (44 males and 20 females) vs non- runners (37 males and 25 females) using the Death Concern Scale as measurement. The study did not give details on the demographics of the participants. Runners scored significantly higher (mean= 19.5) than nonrunners (mean= 17.6) on the death thoughts subscale within the Death Concern Scale (F(1,122) =4.49, p<.05), meaning that runners reported thinking more about death than nonrunners. However, nonrunners scored significantly higher (mean =12.5) than runners (mean =10.8) on the death anxiety subscale (F(1,122)=6.35, p<.05), indicating that nonrunners had more anxiety about death than runners. Sex of the subject was not significant in either analysis and there were no significant interactions. The number of years running, which averaged 5.5 years for male runners and 4.9 years for female runners did not significantly correlate with either death thoughts subtotal (r=-.04) or death anxiety subtotal (r=-.04). Overall results found runners experienced more death thoughts but less death anxiety than nonrunners12. 13 Rape (1987) Comparing depression scores in runners vs non- exercisers An American controlled cross sectional study with a matched two-group design by Rape (1987) used 42 male participants between the ages of 18 and 25 to compare depression scores in 21 runners (ran 15 or more miles weekly) vs 21 non-exercisers using the Beck Depression Inventory as measurement. Results found that the runners were significantly less depressed [M=4.38, SD = 3.88] than the non-exercisers [M=9.55, SD = 5.40]; (t40= 3.55, p<0.001). Overall results suggest that running reduces depression13. 14 Weight (1987) comparing eating attitudes and disorders in marathon runners vs cross country runners vs non-running controls A South African cross sectional controlled study by Weight et al. (1987) used 135 female participants between ages 18-56 to compare eating attitudes and disorders in marathon runners (n=85) vs cross country runners (n=25) vs non-running controls (n=25) using the Eating Attitudes Test (EAT) and the Eating Disorder Inventory (EDI) as measurements. One way ANOVA of the different groups showed no significant difference between any group on any of the EAT sub-scores (P<0.05), with mean EAT scores for the marathoners, cross country runners and non-running control at 8.4, 14.3 and 11.8, respectively. The EDI scores did not follow a definite pattern, with all groups showing a gradual, if erratic downward trend. Mean EDI scores for the marathoners, cross country runners and non-running controls were 24.8, 27.1 and 32.0, respectively. All subjects with high EAT scores (>20) also had high EDI scores (>30) but there was no relationship between high EDI scores and the EAT scores. Overall results found that abnormal eating attitudes and the incidence of anorexia was no more common among competitive female runners than it is among the general population, with a low incidence of anorexia in the total group (2 out of 135 participants)14. 15 Chan (1988) Comparing depression, self esteem and mood in prevented runners vs continuing runners An American cross sectional questionnaire by Chan et al. (1988) used 60 runners (32 women and 28 men) aged between 15 & 50 who had ran consistently (at least 3x per week) for a minimum of a year and more than 20 miles per week when not injured. The study compared depression, self-esteem and mood in 30 prevented runners (unable to run for 4 weeks due to a running-related injury) to 30 continuing runners (ran without interruption) using the Zung depression Scale, Rosenberg Self-esteem Scale and Profile of Mood States as measurement. Prevented runners reported significantly greater over-all psychological distress than the continuing runners group (Wilks's =0.63, p<.01: X92 = 24.38, p<.01). Regarding Zung Depression Scale scores, the prevented runners were significantly more depressed than the continuing runners (F(1,58)= 11.57, p<0.01). Based on POMS total score, prevented runners reported a significantly greater over-all mood disturbance than the continuing runners group (F(1,58) =11.03, p<.01). vs 42.60). On the Rosenberg Self-esteem scale, the prevented runners reported significantly lower self-esteem than the continuing runners (F(1,58) =3.17, p<.05). Prevented runners reported that they were less satisfied with the way their bodies presently look (F(1,58)=4.17, p<.05) and had a greater desire to change something about the way their bodies presently look (F(1,58) =4.54, p<.05) compared to continuing runners. Overall, results suggest that preventing running in regular runners increases depression, overall mood disturbance, as well as decreasing self-esteem and body confidence15. 16 Frazier (1988) Investigating the relationship between running and mood in regular distance runners An American post only, non-randomised, long term observational study that is unlikely to have made any controls for confounding, by Frazier (1988) used 86 regular, distance runners who had all completed a marathon (68 males with mean age of 33.7 and 18 females with a mean age of 32.2) to investigate the relationship between running and mood using the Profile of Mood States as measurement. The running subjects had lower mean scores on tension, depression, anger, fatigue and confusion, and a higher mean of vigor compared to scores for test norms, however statistical significance between the runners and the norm values was not reported. female subjects recorded higher mean scores on all sex states (tension, depression, anger, vigor, fatigue, confusion), however, only a significant difference was noted on confusion between females (mean =7.8) and males (5.5) (F(1,84) = 5.33, p<.05). Overall results suggest that regular, distance running improves mood in both males and females16. 17 Lobstein, Ismail (1989) Comparing anxiety and depression levels in runners vs sedentary controls An American controlled cross sectional study by Lobstein, Ismail et al. (1989) used 36 male participants aged between 40 & 60 years old to compare anxiety and depression levels in runners (n=21) vs sedentary controls (n=15) using the Minnesota Multiphasic Personality Inventory (MMPI) and Eysenck Personality Inventory as measurements. In MMPI scores, both groups appeared to be within psychologically normal limits, however, physically active men exhibited significantly less anxiety than sedentary men (mean = 48.95 vs 61.48 respectively, p<0.05, standardised canonical coefficient = -1.07) and less depression compared to the sedentary men (mean = 50.76 vs 57.93, respectively, p<0.05, standardised canonical coefficient = 0.00). Discriminant function analysis showed that anxiety index was the most powerful discriminator between the physically active and sedentary men (standardised canonical coefficient =-1.07). Neurotocism score (Eysenck) was not significant between the physically active group and the sedentary group (4.95 vs 6.20) (standardised canonical coefficient = -0.72). High physical fitness scores were correlated with low depression (r=-0.40, p<0.05). Overall results indicate that running reduces anxiety and depression compared to being sedentary17. 18 Lobstein, Rasmussen (1989) Comparing depression and stress in sedentary men to physically active joggers An American cross sectional study by Lobstein, Rasmussen et al. (1989) used 20 psychologically normal, medically healthy men, aged between 40 & 60, to compare depression and stress in sedentary men (n=10) compared to physically active joggers who had been running about 20 miles per week for at least 3 years (n=10) using the Eysenck Personality Inventory (EPI) and Minnesota Multiphasic Personality Inventory (MMPI) as measurements. EPI scores demonstrated that the joggers (mean=2.80) exhibited significantly more emotional stability than the sedentary group (mean=7.10) (t=-2.84, p<0.01). Regarding the MMPI profile, both physically active and sedentary group profiles were within clinical limits for psychologically normal middle-aged men. The MMPI subscales of depression and Wiggins depression were both significantly lower in the joggers (t=3.70,p<0.01; t=2.40, p< 0.05; respectively) indicating that the physically active men were less depressed than the sedentary me. The magnitude and direction of the canonical coefficients (0.98) indicated that the subjective depression subscale appeared to be the most powerful discriminator between the two groups. Overall the findings suggest that regular jogging decreases subjective depression and increases emotional stability18. 19 Nouri (1989) investigating the relationship between various levels of jogging vs non- exercising on anxiety and addiction/ commitment An American cross sectional study by Nouri et al. (1989) used 100 male participants aged between 18 and 62 to investigate the relationship between various levels of jogging vs non-exercising on anxiety and addiction/commitment using the Commitment to Running Scale, The Buss-Dutkee Inventory measuring hostility and aggression and the Spielberger State-Trait Anxiety Inventory as measurements. Participants were divided into 5 groups: non-exercisers (n=28), drop-out joggers (n=21), beginning joggers (n=15), intermediate joggers (n=16), 20 advanced joggers (n=20). Commitment to Running gave a main effect for level of jogging (F(4,89) = 14.30, p<.01). Advanced, Intermediate and Begging joggers all scored higher than drop-outs or non-exercisers on the Commitment to Running Scale. However, there was no statistically significant difference between non-exercisers and drop-out joggers or among the other jogging groups. ANOCA for trait anxiety scores was significant (F(4,89) = 4.43, p<.01). Non- exercisers had higher mean scores on trait anxiety than advanced, intermediate, bigger and drop-out joggers (2.00, 1.42, 1.69, 1.77 and 1.68, respectively). Advanced joggers had the lowest mean trait-anxiety score (1.42) and were significantly lower than the other groups p<.01). Overall results suggest that running reduces anxiety levels compared to physical inactivity, with advanced joggers having even less anxiety than beginner and intermediate joggers19. 20 Chan (1990) Investigating a relationship between running and depression, stress, tension and personality profiles A Hong Kong based cross sectional study by Chan et al. (1990) used 44 male, Chinese runners with a mean age of 27.8, who all except 1 belonged to a single track club, who ran for a mean of 4.66 years and ran a mean of 57.2km per week. The study investigated a relationship between running and depression, stress, tension and personality profiles, using a Chinese version of the Personality Research Form and a questionnaire designed by the authors to assess running history and experience. 36.4% of participants reported ‘improving mental health’ as a reason to starting running. Emotional benefits from running reported were: more self-confident (59.1% of respondents), happier (56.8%), better mood (50.0%), relieved tension (45.5%), better self-image (36.4%), relieved depression (36.4%), more aggression (36.4%), improved outlook (34.1%), more content (31.8%) and better family relationship (15.9%). However, when participants stopped running 38.6% experienced low mood and 25.0% experienced anxiousness. Significance was largely not reported on throughout the results. Results inferred that the typical male runner was more controlled and less oriented intellectually and aesthetically. More experienced runners, compared to less experienced runners, were less aggressive or easily angered (t=2.92, df=42, p<0.01), less guarded or defensive (t=2.13, df=42, p<0.005), and more likely to present themselves favourably (t=2.68, df=35, p<0.05). Overall, results suggested that running increased mood, happiness and outlook, while relieving depression, aggression and anger, however there was no reporting of the size of these changes or their significance20. 21 Chapman (1990) investigating the relationship between running addiction, psychological An American cross sectional study by Chapman et al. (1990) used 47 runners (32 males aged 34-57, and 15 females aged 35 to 59) to investigate the relationship between running addiction, psychological characteristics and running using the Running Addiction Scale (RAS), Commitment to Running Scale (CR), Symptom Checklist (SCL-90- R) and Levenson's Locus of Control Scale as measurements. RAS correlated for both sexes of runners strongly with self-rated addiction (p<0.05) and moderately with discomfort (p<.05). However, CR did not significantly correlate with self-rated addiction characteristics and running in females (.246, ns) while the RAS did (.753, p<.05) (z=2.00, p<.05). Running addiction (RAS) was found to be associated with high frequency of running (p<.05) and longer duration of running (males=p<.05; females= ns). The CR score correlated significantly with run frequency for the male (.59, p<0.05) but not the female runners (.14, ns), while CR and run duration did not correlate significantly for either sex (males=.16, females=.28, ns for both). Male runners were above the norm for obsessive compulsive tendencies (SCL-90 score) and significantly higher than female runners (p<.05). The female runners were above the norm in hostility (p<.05) and interpersonal sensitivity (p<.05) and significantly higher than males (p<.05). For males, correlations indicated a significant relationship between positive personality characteristics and addiction, high frequency and long duration running and psychological health (p<0.05). There were no significant correlations with personality traits for females. Overall, results indicates that for female runners commitment to running can occur without addiction and that there is a sex difference in the relationship between addiction and commitment. Running addiction was found to be associated with male positive personality characteristics but not with mood enhancement. While the duration of running was found to be associated with mood enhancement implying that the benefits of running to mood may be obtained without addiction21. 22 Guyot (1991) Investigating the relationship between addiction and death anxiety between pain runners and non-pain runners An American cross-sectional questionnaire by Guyot (1991) used 370 runners to investigate the relationship between addiction and death anxiety between pain runners and non-pain runners using the Dickstein Death Concern Scale and author created questionnaires for pain running, running motives, risk taking and medical symptoms as measurements. Participants consisted of 78% males, who had a mean age of 38, ran 33.3 miles per week and had been running for an average of 7.9 years, and 22% females, who had a mean age was 35, ran 21.3miles per week and had been running for an average of 6.3 years. 56% of the 370 runners pushed themselves during running until they felt pain: 60% of the males and 43% of female runners were classified as pain runners. This difference between genders was significant (p=0.008). Compared to non- pain runners, pain runners were more likely to be male, taller (F(1,366) =11.45, p<0.05), heavier (F(1,366) =9.19, p<0.05), and younger (F(1,366) =5.75, p<0.05). Pain runners were more likely to be running for competition (58% of PR vs 42% of NPR) and less likely for improved health (75% of PR vs 84% of NPR) (p<0.05 for both). Pain runners were significantly higher on 17 of 23 (74%) medical symptoms than non-pain runners (p<0.05). Pain runners reported significantly more death thoughts Dickstein Death Concern Scale (Mean = 16.77) than non-pain runners (Mean = 15.78) F(1,364)= 5.04, p<0.05, as well significantly more death anxiety (Mean = 10.95) than non-pain runners (Mean = 9.66), F(1,364)= 8.86, p<0.05. Overall, results suggest that runners classified as pain runners were experienced significantly more death thoughts and death anxiety than non-pain runners22. 23 Maresh (1991) Investigating psychological characteristics including anxiety, depression and stress in distance runners An American cross sectional study by Maresh et al. (1991) used 29 male, distance runners with a mean age of 40.1 who had been running for an average length of 11.8 years prior to the study, to investigate psychological characteristics including anxiety, depression and stress using the Myers-Briggs Type Indicator Form to determine personality characteristics and the Multidimensional Anger-Inventory as measurements. Results suggested that long term involvement in running is associated with low levels of self-reported anxiety (m=2.5 on a 6 point scale), depression (M=1.8) and stress (m=2.5). 82% of the male runners reported suffering from withdrawal symptoms when forced to be inactive, with the level of self-reported addiction average 4.4 ('moderately' to 'very') on a 6 point scale. Withdrawal symptoms were experienced 5.0+/-6.2 days after exercise ceased. A majority (55%) of those experiencing withdrawal did so within 3 days. Compared to a normative sample of male control students, the runners were less angry overall and were less frequently angry across fewer situations. Runners also reported very low scores on hostile outlook, however there were no differences between the two samples on brooding, guilt over anger, or tendencies to turn anger inward against the self. The subjects personality profiles differed markedly from the normative sample, with men in the general population tending to be more extraverted (75%) than introverted (25%), more sensate (57% than intuitive (25%), more thinking (60%) than feeling (40%), and equally split between judging (50%) and perception (50%). Overall results suggest that running is associated with a positive sense of self; reduced anxiety, depression and stress; and more introverted personalities. However, many runners experienced withdrawal symptoms if forced to be inactive23. 24 Gleaves (1992) Comparing depression, body image disturbance and An American controlled cross sectional study by Gleaves et al. (1992) used 60 female participants to compare depression, body image disturbance and bulimia nervosa symptomology in runners (n=20), bulimia patients (n=20) and a non-exercising, non- dieting control group (n=20) using the Beck's depression inventory (BDI) Automatic bulimia nervosa symptomology in runners, bulimia patients and a non- exercising, non- dieting control group thoughts Questionnaire (ATQ), subscales from the Eating Disorder Inventory (EDI) (Ineffectiveness scale, Drive for Thinness scale, Bulimia scale, and Body dissatisfaction scale), a Bulimia test, Body Image Assessment Procedure, and a dieting/weight loss questionnaire as measurements. For depression scores the overall MANOVA was significant, F(6110.25) = 14.76, P < 0.0001. Bulimics scored significantly higher for BDI depression than the runners and controls (20.65, 3.30 and 4.80 respectively, F=56.95, p<0.0001), but runners and controls did not differ from each other. The same pattern of results was found for the ATQ and the EDII with bulimics differing from the two other groups and no significant difference between runners and controls: ATQ (F=45.87, p<0.0001), means for runners =41.10, controls=41.50, bulimics = 85.40; EDII (F=34.95, p<0.0001), means for runners =0.80, controls=1.60, bulimics = 12.80. There were significant group effects on all four dependent variables of bulimia (p<0.001), with bulimics scoring higher compared to the other groups who did not differ among themselves. There were significant group effects for all three variables of body image (p<0.01), again, bulimics differed from runners and controls. Overall, results did not indicate that running leads to development of disordered eating or problems with body image, but instead that runners are generally indistinguishable from control subjects with no differences found between runners and controls throughout the study. Bulimics were significantly more disturbed than runners or controls24. 25 Coen (1993) investigate the relationship between obligatory running vs non- obligatory running on anxiety, anorexia and self-identity An American cross sectional study by Coen et al. (1993) used 142 male marathon runners with a mean age of 44.07 to investigate the relationship between obligatory running (n=65) vs non-obligatory running (n=77) on anxiety, anorexia and self-identity using the Obligatory Exercise Questionnaire (OEQ), State-Trait Personality Inventory and The Ego Identity Scale (EIS) as measurements. The obligatory runners had a mean total OEQ score of 56.64; the non-obligatory group had a mean score of 47.60. There was a statistical difference between (OEQ) scores of the obligatory and the non- obligatory runners (t(140) = 13.19, p <0.001), with the obligatory group running more miles per week and spending more time running each week. The obligatory group had significantly higher (p<0.01) mean levels of anxiety than the non-obligatory group (18.85 vs 6.45, respectively), indicating that obligatory runners appear to be more perfectionistic and to have higher levels of trait anxiety than non-obligatory runners. Although the non-obligatory runners had an average Ego Identity Scale score that was higher than the obligatory group (means 8.68 vs 8.34, respectively), the difference was not statistically significant (p>0.05) indicating that neither group showed a higher developed sense of identity. Overall results suggest that running represents a successful coping mechanism to reduce anxiety and becomes problematic only when the obligatory individual is unable to run because of injury or other circumstances25. 26 Furst (1993) Comparing negative addiction in runners vs gym exercisers An American controlled cross sectional study by Furst et al. (1993) used 188 subjects to compare negative addiction in runners (72 male & 26 female runners, 82% white, 42% were aged between 20-29 y/o, 36% between 30 and 39 y/o) to gym exercisers (60 male & 30 female, 95% were white, 42% were aged between 20 & 29, 36% between 30 & 39) using the Negative Addiction Scale as measurements. Subjects were divided into 6 groups by years of participation and a significant difference was found between years of physical activity and addiction scores (F(5,182) = 6.39, p<0.01) indicating that longer involvement in physical activity was associated with higher addiction scores. When runners were compared with gym exercisers, there were no significant differences in mean addiction scores. Only 5 people scored 9 or above on the negative addiction scale (ranges from 0 to 14), indicating that none of the 188 participants were extremely addicted to the activity. Overall results suggest that the longer people had been exercising, both runners and gym goers, the more addicted they were to exercise 26. 27 Masters (1993) Investigating self-esteem and psychological coping of marathon runners An American cross sectional study by Masters et al. (1993) used 712 participants in a marathon (601 men and 111 women) all aged between 16 and 79 to assess self-esteem and psychological coping of runners using the Motivation of Marathoners Scales (MOMS), Sport Orientation Questionnaire, Marlowe-Crowne Social Desirability Scale, Attentional Focusing Questionnaire (AFQ) and 3 body satisfaction and composition questions. There were significant positive correlations between the AFQ dissociation and the MOMS psychological coping [r(66)=.54, p<.001], self-esteem [r(66)=.31, p<.01] and life meaning scales [r(66)=.36, p<.01]. Marathon runners reporting higher anxiety levels were more likely to endorse psychological motives for marathon running, indicating that their running helps them avoid or dampen negative emotional experiences: psychological coping, [r(62)= .38, p<.01] and self-esteem, [r(62)=.36, p<.01]. A t-test comparing average rating on the weight concern scale for the two genders was calculated. Women had a higher mean score than men, and women more strongly endorsed weight concern as a reason for involvement in marathons [t(588)= - 3.52, p<.001]. No significant relationship was found indicating that social desirability played a major role in subjects responses to the MOMS. Personal goal achievement and competition were both positively related to training miles per week [r(575)=.22, p<.001 and r(576) =.30, p<.001, respectively]. Overall, results suggest that participation in marathon running and training was used as a way of problem solving, providing self- distraction and improving mood and self-esteem27. 28 Pierce (1993) Comparing exercise dependence in recreational (non- competitive) runners vs 5km runners vs marathoner runners An American cross sectional questionnaire by Pierce et al. (1993) used 89 male runners to compare exercise dependence in recreational (non-competitive) runners (n=33) vs 5km runners (n=24) vs marathoner runners (n=32) using the negative addiction scale as measurement. Marathoners showed significantly higher (p<0.05) mean exercise dependence scores (3.78) compared to 5K (2.9) and recreational runners (2.16). There was no significant difference in exercise dependence scores found between recreational and 5K runners. Comparisons between competitive groups and variables of exercise addiction and miles per week yielded correlational coefficients of +0.68 and +0.81, respectively. Overall results show that training mileage was significantly correlated with exercise dependence and competitive orientation28. 29 Klock (1995) Comparing depression, anorexia nervosa, excessive exercise and eating disorders in amenorrhoeic runners, eumenorrheic runners and eumenorrheic sedentary women as controls An American controlled cross sectional study by Klock et al. (1995) used 22 females who were not currently pregnant or taking oral contraceptives to compare depression, anorexia nervosa, excessive exercise and eating disorders in amenorrhoeic runners (n=7, mean age 28.0), eumenorrheic runners (n = 9, mean age 32.1) and eumenorrheic sedentary women as controls (n=6, mean age = 27.5) using the modified Body Image Questionnaire (BIQ), the Beck Depression Inventory (BDI), the Symptom Checklist-90 (SCL-90) and the Eating Disorders Inventory (EDI) as measurements. All 3 groups had overall satisfaction with general body appearance and there were no significant differences regarding body satisfaction. No differences were found among groups on the BDI, however the amenorrhoeic runners' mean score was double that of the eumenorrheic runners and sedentary controls (8.3 versus 3.8 and 3.0, respectively) but it was below 11 which is the lowest score indicative of mild depression. No significant differences found between groups on the SCL-90 scores or total EDI scores, although the amenorrhoeic runners' mean EDI score was at the level indicative of a clinically significant eating disorder. 3 of the 9 amenorrhoeic runners scored in the clinically depressed range on the BDI, indicating that they were mild to moderately depressed, and also had the highest scores in their group on the SCL-90 and the EDI. Overall results found no significant differences between amenorrhoeic runners, eumenorrheic runners, and eumenorrheic sedentary controls on any of the psychological measures, hence these results do not suggest that there are psychological similarities between obligatory runners and anorexics. However, there was a subgroup of amenorrhoeic runners who scored in the extreme range on the depression and eating disorder measures29. 30 Thornton (1995) Investigating a relationship between habitual running and addiction A UK based cross sectional questionnaire by Thornton et al. (1995) used 40 long- standing, habitual male runners with a mean age of 38 and who ran on average 4 times a week with a weekly mileage of 42.5miles, to investigate a relationship between habitual running and addiction using the Rudy and Estok Running Addiction Scale (RE/RAS), the Hailey and Bailey Running Addiction Scale (HB/RAS) and the Personal Incentives for Exercise questionnaire (PIE) as measurements. The majority (77%) of subjects were committed to levels of running which would be classified as moderately (scores 13-20) or highly 'addictive' (scores +20) (55% and 22%, respectively). The correlation between the two addiction scales revealed a strong positive relationship (rs = 0.81; p<0.001). The primary motivation for running was mastery (mean PIE score of 4.2) followed by competition (3.93), weight regulation (3.9), health benefits (3.89), fitness (3.87) and social recognition (3.01). For RE/RAS scores, only two variables, mastery (F(1,38) = 12.1, p < 0.001) and social recognition (F(2,37) = 9.4, p < 0.001) contributed to the predictive equation. In a second regression analysis for the HB/RAS scores, mastery was again entered as the first step (F(1,38) = 16.5,p < 0.001), with both social recognition (F(2,37) = 11.8, p< 0.001) and distance (F(3,36) = 11.6, p < 0.001) providing significant contributions. A final regression analysis was performed to predict 'distance run' according to both addiction scales and PIE subscale scores. The model entered two variables with the HB/RAS addition scores as the initial variable (F(1,38) = 8.1, p< 0.01; R2 = 0.18), and social recognition in the second step (F(2,37) = 11.3, p< 0.01; R2=0.06). There was no relationship between years of running and either of the addiction scales. This contrasts with the significant correlations between both the RE/ RAS and the frequency of running (rs = 0.38; p < 0.05) and the HB/RAS scale and the number of runs per week (rs = 0.55; p < 0.01). The effect of mileage run was related only to the HB/RAS (rs =0.39; p <0.05). Overall results found a high level of commitment in the sample of runners, but there was no relationship between years of running and addiction measured by the regression analysis30. 31 Powers (1998) Comparing psychological An American controlled cross sectional study by Powers et al. (1998) used 57 participants to compare psychological profiles of habitual male runners (n=20), habitual profiles of habitual male runners, habitual female runners and female anorexia nervosa patients female runners (n=20) and female anorexia nervosa patients (n=17) using the Minnesota Multiphasic Personality Inventory (MMPI), Leyton Obsessional Inventory, Obligate Running Questionnaire, Becks Depression Inventory and three body image tests (open door test, body parts satisfaction test and colour-a-person body dissatisfaction) as measurements. In the open door test there were significant differences between the groups (F=7.969, p<.001) but no significant differences between the female groups. On the Body Parts Satisfaction Questionnaire, male runners were significantly more satisfied with their bodies than female runners, who were significantly more satisfied than anorexics. In the ORQ item “I worry almost constantly that I will get fat" anorexics were more likely to answer true, male runners more likely to answer false and female runners answered true as often as they answered false (p=0.001). In the MMPI subscale scores anorexics scored significantly higher than either group of runners (p<.001) for the nine subscales except scale 9 (p=.0001). Mean T scores were above 70 (considered clinically significant) for subscales of depression, hysteria and psychopathic deviate in the anorexic group, while none of the mean scores for either set of runners were considered clinically significant. There were significant differences in depression scores (F=68,645, p=.0001) with anorexics scoring significantly higher (p<.0001) than both male and female runners (mean scores were 23, 2.4 and 3.45, respectively) but there was no significant differences between the runners. While there were suggestive similarities between female runners and anorexics on body image, the overall results found few psychological similarities between anorexia patients and habitual runners with evidence of significant psychopathology on all psychological measures in the anorexia group, while both groups of runners were consistently in the normal range31. 32 Slay (1998) Comparing eating pathology traits between obligatory and non-obligatory runners An American cross sectional questionnaire by Slay et al. (1998) used 324 regular runners (240 males and 84 females) between the ages of 15 and 71 to compare eating pathology traits between obligatory and non-obligatory runners using the Eat Attitudes Test (EAT) and Obligatory Running and Motivations for Running Questionnaire as measurements. 21 women (25%) and 63 men (26.2%) were classified as obligatory runners. There was a significant effect for miles run per week [F(1,164) =8.31, p<0.001], with men running more than women [p<0.05] and obligatory runners with higher mileage than non-obligatory runners [p<0.001]. Obligatory runners scored significantly higher on the EAT test, with female obligatory runners having the highest mean EAT score. A partial correlation, controlling for miles run per week, between the EAT and obligatory running scores for men was slightly weaker (r=.28, p<.0001) than for women (r=.40, p<.0002), showing a stronger relationship of obligatory running with eating pathology in women than in men. Results found women and men scored similarly on the EAT with no significant differences at low levels of obligatory running [F(1,164) =2.78, p>.05]; however at higher levels women demonstrated significantly higher EAT scores than did men [F(1,164) =29.50, p<.001]. Independent of miles run per week, there was still significant overall effect on EAT scores, [F(1,164) =9.65, p<.0001] and a sex/obligatory running interaction, [F(1,164) =8.02, p<.05]. Overall results suggest that obligatory runners, particularly females are most at risk of eating pathophysiology32. 33 Ryujin (1999) Comparing eating disorder symptomology in collegiate distance runners to non- running undergraduate student controls An American controlled cross-sectional study by Ryujin et al. (1999) used 55 female participants to compare eating disorder symptomology in collegiate distance runners (n=20) to non-running undergraduate student controls (n=35) using the Eating Disorders Inventory 2 as measurement. Differences were significant in the following subscales: Drive for Thinness (t(107) = 3.34, p < .005), Bulimia (t(107) = 2.48, p < .05) and Body Dissatisfaction (t(107) = 4.23, p < .001). Significance was approached for Interpersonal Distrust (t(107) = 1.70, p < .10) and Impulse Regulation (t(107) = 1.65, p = .10). Results found that distance runners showed no enhanced symptomatology of eating disorders, instead the female distance runners exhibited fewer symptoms of eating disorders on all subscales of the EDI-2 except Perfectionism33. 34 Leedy (2000) Comparing depression and anxiety in runners to non- runners An American controlled cross sectional study by Leedy (2000) used 276 participants to compare depression and anxiety in runners with an average of 11.5 years of running experience (n=239, 56.1% men, mean age 37.9) to non-runners (n=37, 62% women, mean age 40.5) using an author created questionnaire designed to measure anxiety and depression based on the Diagnostic and Statistical Manual -IV, and an author adapted scaled based on the Running Addiction Scale as measurement. 16.2% of non- runners and 4.6% of runners indicated that they had been diagnosed with an anxiety disorder or prescribed an anxiolytic medication at some point in their life. These participants had significantly higher anxiety trait scores than those without a diagnosis, F(1,274)= 18.87, p<.0001. 27% of non-runners and 11.8% of runners reported a diagnosis of depression or being prescribed an antidepressant. These participants had significantly higher measures of depression traits: F(1,274)=22.46, p<.0001. Runners who were classified as highly committed (n=31) had significantly lower anxiety (F(2,113)= 5.73, p<.01) and depression scores (F(2,113)= 8.00, p<.001) than those classified as recreational runners (n=46) and non-runners (n=39). Women's Stress Relief scores were significantly higher than the men's (F(1,229)= 20.51, p<.001). Stress relief scores also varied across race length, F(2,229) = 6.47, p<.005, indicating that the runners entered in the 5K - 10K runs had lower scores than those running the half or full marathon. Overall, the strongest motivator for running was Health/Fitness, (F(2,229)=135.3, p<.001), for both men and women, and for all three race distances. The Committed Runners had significantly higher scores across motivation factor scores compared to the Recreational Runners, (F(1,156)= 7.00, p<.01), with again, the most strongly endorsed motivation factor being Health/Fitness (F(2,156)=39.13, p<.001). Overall, results indicate that highly committed runners had significantly lower anxiety and depression than recreational runners and non-runners34. 35 Edwards (2005) Comparing psychological wellbeing and physical self- perception in hockey players, runners and health club gym members vs a control group of non-exercisers A South African cross sectional study by Edwards et al. (2005) used 277 participants (183 women and 94 men) with a mean age of 25.2 to compare psychological wellbeing and physical self-perception in regular exercisers including hockey players (n=60), runners (n=40) and health club gym members (n=69) vs a control group of non- exercisers (n=108), using Ryff's Short Standardized 18 item scale of Objective Psychological Wellbeing and Fox's Physical Self-Perception Profile (PSPP) and the Physical Self-Perception Profile as measurements. Regular exercisers scored significantly higher (p<0.01) than controls on 11 out of the 15 dimensions of psychological-well-being and physical self-perception: autonomy (F=11.3), personal growth (F=35.4), environmental mastery (F=9.6), purpose in life (F=149.2), positive relations with others (F=81.6), self-acceptance (F=50.4), sport competence (F=41.3), conditioning (F=28.1), sport importance (F=11.7), conditioning importance (F=28.1), body importance (F=31.0). Runners scored significantly higher than controls on autonomy, personal growth, environmental mastery, purpose in life, positive relations, self-acceptance, sport competence, conditioning, sport importance and conditioning importance. However, results don’t give details of significance. Runners had the lowest physical self-worth average score out all groups. Hockey players reported more positive relations with others and sport competence compared to health club members or runners, but no report of significance for either. Men scored higher on sport (F=27.2, p<0.01), conditioning (F=20.1, p<0.01), body (F=13.3, p<0.01), sport importance (F=7.2, p<0.01) and conditioning importance (F=6.3, p<0.01). No significant influences of age or language, but gender was related to body attractiveness (F=13.5, p<0.01). Overall results show that all three forms of physical activity were associated with higher scores on the psychological well-being and physical self-perception scales compared to the control group35. 36 Schnohr (2005) Comparing stress levels between jogging and various levels of physical (in)/activity in leisure time A large Danish observational cohort study by Schnohr et al. (2005) used 12,028 participants (5479 men and 6549 women) aged 20-79 to compare stress levels between jogging and various levels of physical (in)/activity in leisure time using an author created questionnaire as measurement. In both males and females, those who were vigorously physically active (joggers) had the lowest level of stress compared to those with low activity levels (males 3.1% vs 12.8%, respectively; females= 3.3% vs 19.3%, respectively). With increasing physical activity in leisure time, there was a decrease in high level of stress, between sedentary persons and joggers (OR= 0.30). With increasing physical activity there was also a decrease in life dissatisfaction, between sedentary persons and joggers (OR= 0.30). Highest levels of stress and dissatisfaction was seen in the sedentary persons who remained inactive at follow-up. In contrast, the group that changed from sedentary to active had an adjusted OR of <0.50. The physically active who remained active through follow-up reported the lowest level of both stress and dissatisfaction. Associations between physical activity and stress/life dissatisfaction were similar in men and women, showing that if either gender changed from sedentary to more physical activity in leisure time, the was decreased stress and lower life dissatisfaction. In contrast if they become sedentary, the opposite is true. Overall results showed a clear trend of higher level of stress and of life dissatisfaction in the sedentary group compared with the more active running groups36. 37 Strachan (2005) Investigating the relationship between running and self-efficacy and self-identity A Canadian prospective longitudinal study by Strachan et al. (2005) used 67 regular runners with an average age of 40.6 (52% were female and had been running on average for 8.69 years) to investigate the relationship between running and self- efficacy and self-identity using author created measures of task self-efficacy, self- regulatory efficacy and a 10-item, validated athletic identity measurement scale. Participants filled out a questionnaire and four weeks following this initial assessment, were contacted over the phone in order to obtain a measure of their running behaviour over the last week. There was significant comparisons between extreme self-identity groups (high vs low) on social cognitive and behavioural variables (F(5,37)=4.72, p<.002). Results found that those higher in self-identity showed significantly higher scores on task self-efficacy (p<.001), scheduling self-efficacy (p<.03), ran more frequently (p<.001) and for longer durations (p<.005), than those who scored lowest on self-identity. Both scheduling self-efficacy (R2change =.16, p< .001) and barriers self-efficacy (R2change = .22, p<.001), were significantly correlated with self-identity to prospectively predict running frequency (F(2,64) =9.98, p< .001; F(2, 63) = 12.89, p<.001, respectively). Both task self-efficacy (R2change=.06, p< .05) and self-identity (R2change =.06, p< .04) were significant predictors of maintenance duration. Overall both types of self-regulatory efficacy were related to prospectively were predictive of maintenance running frequency37. 38 Galper (2006) Assessing retrospectively if level of walking/running impacted depression and emotional wellbeing An American retrospective cross sectional study by Galper et al. (2006) used 6728 participants (5451 men with a mean age of 49.5, and 1277 women with a mean age of 48.1) to assess retrospectively if level of walking/running impacted depression and emotional wellbeing using the Center for Epidemiological Studies Scale for Depression and the General Well-Being Schedule as measurements. The participants were classified into four categories: inactive (walking/jogging/running <1 mile per week); insufficiently active (1–10 miles per week); sufficiently active (11–19 miles per week); (highly active (>20 miles per week). 27% (n=1454) of the men were classified as inactive, 35% (n=1892) as insufficiently active, 26% (n=1396) as sufficiently active, and 13% (n=709) as highly active. Likewise, 33% (n=422) of the women were classified as inactive, 35% (n=443) as insufficiently active, 22% (n=283) as sufficiently active, and 10% (n=129) as highly active. Results found that among men and women in the study, relative increases in habitual physical activity are cross-sectionally associated with significantly lower depressive symptomatology (P < 0.0001) and greater emotional well-being (P< 0.0001). This peaked at 11–19 miles per week. There was an inverse association between physical activity and estimated mean depression scores for both men (F(6, 5306)=20.93, P<0.0001) and women (F(6, 1247) = 11.80, P< 0.0001). Inactive men had greater depressive symptom severity than insufficiently active men (P < 0.0001) and highly active men (P<0.0001). Inactive women had greater depressive symptom severity than insufficiently active women (P<0.0001), sufficiently active women (P< 0.0001) and highly active women (P<0.0001). ANCOVA demonstrated a positive association between physical activity and estimated mean wellbeing scores in men (F(6, 5306) = 78.65, P<0.0001) and women (F(6, 1247) = 24.82, P<0.0001). Inactive men had lower emotional wellbeing than insufficiently active men (P<0.0001), sufficiently active men (P<0.0001), highly active men (P<0.0001). Inactive women had lower emotional well- being than insufficiently active women (P<0.0001), sufficiently active women (P <0.0001), and highly active women (P<0.0001). Overall results suggest that increased habitual physical activity reduces depression and increases emotional wellbeing38. 39 Luszcynska (2007) Investigate the relationship between self- efficacy and running behaviour A UK based longitudinal prospective cohort study by Luszcynska et al. (2007) used 139 runners (111 men and 29 women) with a mean age of 29.5 to investigate the relationship between self-efficacy and running behaviour using an author created questionnaire as measurement to collect data twice with a time gap of 2 years. Participants were divided into subgroups with strong (n=72) and weak (n=66) maintenance self-efficacy, into strong (n=72) and weak (n= 61) recovery self-efficacy, and into strong (n=87) and weak (n=45) intentions. Participants reduced the number of running or jogging sessions over the 2 years, regardless of their strong, or weak intentions at baseline (F(1,130)=34.55, p<.001). Again, participants declined in frequency of running/jogging over 2 years, regardless of their strong or weak baseline maintenance self-efficacy (F(1,130)= 42.12, p<.001). Overall, all participants reduced the number of jogging or running sessions over two years (F(1,131)=43.43, p<.001), however, those with strong baseline recovery self-efficacy ran/jogged more often at 2 year measurement than those who had weak recovery self-efficacy at baseline (F(1,131)=6.12, p<.05). Recovery self-efficacy and intention jointly predicted running/jogging behaviour 2 years later ([F(1,131)= 43.43, p<.001] and [F(1,130) =34.55, p<.001], respectively), whereas running/jogging behaviour did not predict recovery self-efficacy and intention. No effects of maintenance self-efficacy were found. Recovery self-efficacy at T1 predicted recovery self-efficacy (p<.05), maintenance self-efficacy (p<.05) and jogging or running behaviour (p<.05) assessed 2yr later. Overall, social–cognitive variables predicted behaviour, whereas behaviour did not predict social–cognitive variables. The majority of participants (n =120) experienced at least one 2-week period of decline in running or jogging behaviour. Among those who experienced lapses, recovery self-efficacy remained the only significant social-cognitive predictor of behaviour, accounting for 30% of the variance of behaviour measured 2 years later (B=.19, p<.05). Overall, results found that participants decreased the frequency of running sessions after 2 years, regardless of baseline intensions or self-efficacy, however those with stronger recovery self-efficacy jogged more than those with weaker recovery self-efficacy 2 years later39. 40 Smith (2010) Comparing exercise dependence, running addiction and social physique anxiety in male vs female runners A UK based cross sectional questionnaire by Smith et al. (2010) used 93 non- competitive, regular runners with a mean age of 28.05 to compare exercise dependence, running addiction and social physique anxiety in male (n=47) vs female (n=46) runners using the Exercise Dependence Scale, Running Addiction Scale and Social Physique Anxiety Scale as measurements. Results found that a significant proportion of runners displayed symptoms of exercise dependence, however there were no significant differences were found between the males and females (p>.05 in all cases). There was no significant difference between males and females for running addiction scale (22.64 and 20.91, respectively), social physique anxiety scale (22.30 and 22.61, respectively) or total exercise dependence scale scores (72.56 and 66.86, respectively). Overall results did not find that exercise dependence was linked to social physique anxiety (F(3.179) = 1.21, p>.05), nor that there was a difference between men and women40. 41 Gapin (2011) Comparing disordered eating in obligatory and non-obligatory runners An American cross sectional study by Gapin et al. (2011) used 179 regular runners (88 male and 91 female) with a mean age of 36.0 to compare disordered eating in obligatory (91) and non- obligatory runners (n=82) using the Eating Disorder Inventory (EDI), Athletic Identity Measurement Scale (AIMS) and Obligatory Exercise Questionnaire (OEQ) as measurements. Obligatory runners scored significantly higher (P<0.002) on all of the EDI eating attitudes/disorder measures: (Obligatory mean = 8.07, non-obligatory mean =4.42), F(1,166)=9.75, P=0.002; Drive for Thinness: (Obligatory mean = 6.42, non-obligatory mean =3.01), F(1,166) =28.91, P<0.001; Perfectionism: (Obligatory mean = 6.77, non-obligatory mean =3.73), F(1,166) = 21.59, P<0.001; Bulimia (Obligatory mean =1.37, non-obligatory mean =0.17), F(1,166) =10.43, P= 0.001. Obligatory runners also scored significantly higher on the AIMS (Hotelling’s T2= 0.440, F(8,161) = 8.85, P<0.001). Results from the OEQ indicated that runners in the obligatory group demonstrated greater concern with dieting, preoccupation with weight, and pursuit of thinness. Overall the findings suggest that obligated running (exercising to maintain identification with the running role) may be associated with pathological eating and training practices41. 42 Wadas (2014) Investigating any relationship between male runners with disordered eating behaviours and eating attitudes An American cross sectional study by Wadas (2014) used 68 male high school cross country runners with a mean age of 15.9 (70.6% white race) to investigate any relationship between male runners with disordered eating behaviours and eating attitudes using a questionnaire consisting of The Exercise Motivation Inventory 2, the Eating Attitudes Test 26 and the ATHLETE questionnaire as measurements. Factors that had a significant relationship with disordered eating are weight management (r =.31, p =.011), drive for thinness and performance (r = 0.36: p < 0.05), and the Feelings about Performance subscale (or Performance Perfectionism) (r = 0.26: p < 0.05). No significant relationships were found between disordered eating behaviors in male cross country athletes and personal body feelings (r =.19, p = .109), feelings about eating (r =.18, p =.137), and feelings about being an athlete (r =.12, p =.345). The mean EAT-26 score for all participants was 6.0, with 4.41% (n=3) male high school cross country runners scoring 20 or higher on the EAT-26, indicating at risk for disordered eating and displays symptoms. An additional 13.2% (n = 9) met the cut-off score of 14 for disordered eating behaviours, one standard deviation above the mean for population norms. Overall results found that risk factors associated with eating disorders existed within high school male cross country runners42. 43 Samson (2015) Investigating the relationship between self- esteem and psychological coping with marathon running An American cross sectional questionnaire by Samson et al. (2015) used 308 marathon runners (117 males and 191 females) with a mean age of 41 to investigate the relationship between self-esteem and psychological coping with marathon running, using the Motivation for Marathons Scale, The Perceived control questionnaire and The Sport Mental Toughness Questionnaire as measurements. Self-esteem was positively associated with perceived control (r=.40) (x27= 47.08, p<.001, CFI=.85; RMSEA=.14), but negatively associated with mental toughness. There was also a positive relationship between perceived control and psychological coping (r=.42) (x28 =45.65, p<.001; CFI=.85; RMSEA=.12), suggesting that runners who ran for those reasons also reported higher levels of perceived control regarding the outcome of the race, however, it was not directly related to perceptions of mental toughness. The mean MOMS scores for psychological coping and self-esteem suggested than females were more likely to run for these reasons than males: 4.8 & 4.42 respectively for psychological coping, and 5.22 & 4.62 for self-esteem. Overall results suggest that females were motivated to run to improve self-esteem and psychological coping than men43. 44 Lucidi (2016) Investigating the relationship between running and stress in An Italian cross sectional prospective field study by Lucidi et al. (2016) used 669 runners training for a marathon (85% male) with a mean age of 42.07 to investigate the relationship between running and stress using the Perceived Stress Scale, the Passion Scale and The Italian version of the Locomotion and Assessment Scales as measurements. Runners filled out the survey 15 days prior to the marathon to evaluate runners training for a marathon stress. Running positively predicted harmonious passion (β = 0.37; P < 0.001), which in turn reduced athletes’ experience of stress, whereas assessment positively predicted obsessive passion (β = 0.26; P < 0.001). Harmonious passion negatively predicted athletes’ experience of anticipatory stress (β = −0.28; P< 0.001), whereas obsessive passion positively predicted it (β = 0.45; P< 0.001). These effects were estimated controlling for athletes’ training frequency, which was not significantly related to athletes’ stress. The indirect effect of running on anticipatory stress perception through harmonious passion was statistically significant (αβ = −0.10; 95% confidence interval: from −0.15 to −0.05). Similarly, the indirect effect of assessment on stress through obsessive passion was statistically significant (αβ = 0.12; 95% confidence interval: from 0.07 to 0.17). Results also indicated a significant direct effect of assessment on the athletes’ experience of stress (β = 0.22; P < 0.001). The direct effect of running on stress was not significant (β = −0.01; P = 0.75). Overall results suggest that running does not directly impact stress, however running increases harmonious passion which improves stress44. 45 Batmyagmar (2019) Comparing self- reported health & wellbeing & quality of life over 4 years in elderly marathon runners to non- exercising controls An Austrian prospective longitudinal study by Batmyagmar et al. (2019) used 99 participants to compare self-reported health and wellbeing and quality of life over 4 years in elderly marathon runners (n=50, mean age of 66, 46 men and 4 women) to non-exercising controls (n=49, mean age of 66, 44 men and 5 women) using the Short Form Health Survey (SF-36) as measurement. SF-35 scores in all domains remained stable over time and, in nearly all of them, marathon runners showed higher self- reported health than non-athlete controls. Athletes evaluated their health as better than non-athletes in the following categories: general health perceptions (mean control= 81 vs athletes=81; between subjects F= 14.21, p<0.001); vitality (mean control= 69 vs athlete= 80; between subjects F= 13.37, p<0.001); social functioning (mean control= 87 vs athlete=97; between subjects F= 11.30, p<0.001); emotional role functioning (mean control= 84 vs athlete=98; between subjects F=1.42, p<0.002); mental health (mean control= 78 vs athletes=84; between subjects F=6.07, p<0.0016). Overall, findings suggest that extensive high intensity endurance exercise is associated with improved subjective health and wellbeing in elderly persons45. 46 Cleland (2019) Investigating enjoyment, self- efficacy and factors of participation in Parkrun event participants An Australian cross sectional study by Cleland et al. (2019) used 372 participants of ‘Parkrun’ events with a mean age of 43.8 to investigate enjoyment, self-efficacy and factors of participation using author-created questionnaires to assess psychological/cognitive measures, social support and environmental level factors. These Parkrun subjects were divided into three groups: regular walker/runner (n=175, 55% female, mean age 45.0), occasional walker/runner (n=142, 57.8% female, mean age 42.5) and non-walker/runner (n=54, 68.5% female, mean age 43.3). Results were often reduced when adjusted for length of time since registration, ie. absolute parkrun participation (total number of parkrun events) compared to adjusted parkrun participation (absolute park-run participation adjusted for the number of weeks registered). Perceived benefits of parkrun including enjoyment (absolute participation: B coefficients= 0.32; and adjusted participation: B coefficients = 0.22) and social factors (absolute: B= 0.70; and adjusted: B= 0.35) were positively associated with participation as was overall enjoyment (absolute: B=0.30; and adjusted: B= 0.30), self-efficacy for parkrun (absolute: B=0.46; and adjusted: B= 0.33), social support for parkrun from family (absolute: B =0.05; and adjusted: B =0.03) and social support from friends (absolute: B= 0.04; and adjusted: B= 0.02) related to parkrun. Perceived social benefits (absolute: B= 0.43; and adjusted: B= 0.17) and self-efficacy for parkrun (absolute: B= 0.18; and adjusted: B= 0.13) were positively associated with absolute and adjusted parkrun participation. Overall results suggested that higher participation levels of park- run events correlated with greater self-efficacy and perceived social benefits46. 47 Lukacs (2019) Investigating the prevalence of exercise addiction and psychological features in amateur runners, including; perceived health, life satisfaction, loneliness, stress, anxiety, depression, A Hungarian cross sectional questionnaire study by Lukacs et al. (2019) used 257 amateur runners (126 females and 131 males) with a mean age of 40.49 and at least 2 years of running experience. The study investigated the prevalence of exercise addiction and psychological features including; perceived health, life satisfaction, loneliness, stress, anxiety, depression, body shape and eating disorders; using the Exercise Dependence Scale, a Cantril ladder for Overall life satisfaction, SCOFF eating disorder questionnaire, the UCLA 3-item Loneliness Scale, Body Image Subscale from the Body Investment scale and the ‘Depression, Anxiety and Stress Scale-21’. About 53.6% (n=137) of respondents were characterized as non-dependent symptomatic, 37.8% (n=97) as non-dependent asymptomatic and 8.6% (n=23) were at risk of exercise addiction. The logistic regression model indicated that five variables significantly predicted the risk of exercise addiction: weekly time spent running [B=1.42, 95% CI for odds ratio=4.17, p<.001], childhood physical activity [B=2.06, 95% CI for odds ratio=7.86, p=.008], lower educational attainment [B=1.97, 95% CI for odds ratio=7.17, p=.006], anxiety [B=0.47, 95% CI for odds ratio=1.61, p=.023], and loneliness [B=0.79, body shape and eating disorders 95% CI for odds ratio=2.21, p=.004]). Subscale results of the exercise dependence scale suggested that to deal with both anxiety and loneliness, as runners from all groups found it important to spend a significant amount of time engaging in exercise [Time subscale (3.09, SD = 1.11, 95% CI = 2.96–3.23]) and continually increase exercise intensity, frequency, and duration [Tolerance subscale (3.71, SD = 1.28, 95% CI = 3.55– 3.87)] to achieve joyfulness and happiness. The at risk group for exercise addiction scored higher on the Lack of Control subscale (4.90, SD = 0.76, 95% CI = 4.57–5.23) and therefore these runners were less able to control the urge to exercise or to stop exercising for a significant time. All investigated groups showed fewer problems on the Intention subscale (exercising longer than intended, expected, or planned; (2.39, SD = 1.10, 95% CI = 2.25–2.52) and the Reduction in Other Activities subscale (choosing or thinking about exercise rather than spending time with family, friends, or concentrating on school or work; (1.90, SD = 0.82, 95% CI = 1.80–2.00). ANOVA post hoc test results showed that all three groups significantly differed from each other in all subscales (all p<.001): tolerance (F=63.053, np2 = .365), Time (F=68.147, np2 = .371), Continuance (F=41.578, np2=.304), Lack of control (F= 171.509, np2 = .587), Withdrawal (F=32.757, np2 = .222), Intention Effect (F= 61.963, np2 = .360), Reduction (F=65.249, np2 = .386). The study results did not comment on the other psychological features (perceived health, life satisfaction, stress, depression, body shape and eating disorders)47. Supplementary Table S2 Narrative description of findings of the 23 studies with a single bout of running. Author Narrative description of findings 1 Nowlis (1979) Impact of a 12.5 mile jog on mood and anxiety A Canadian pre-post non-controlled study by Nowlis et al. (1979) used 18 experienced joggers (5 females and 13 males) who ranged in age from 17 to 55, to investigate how a 12.5 mile jog impacted mood and anxiety using the Mood Adjective Checklist and State Trait Anxiety Inventory as measurements. Following the 12.5 mile run there was significant improvement from pre- to post- measures of pleasantness (2.00 to 2.67;p< 0.01) and a significant decrease in Trait anxiety (34.81 to 33.31; p<0.10). There was an increase in activation, a reduction in state-anxiety, and a reduction of sadness, anxiety, depression and relaxation subscales… but no significance was reached in any of these 48. 2 Wilson (1981) Impact of a solo indoor track run on anxiety A Canadian pre-post controlled study by Wilson et al. (1981) used 42 participants consisting of 20 runners, 12 participants of a 40 minute aerobic exercise class and 10 lunchers, all aged between 21 and 28 (23 women and 19 men) to compare the impact of solo indoor track running (n=20), an aerobics class (n=12) and lunching (n=10) on anxiety using the State-Trait Anxiety Inventory as measurement. Each group showed significant decreases in anxiety after the activity (F(1,39)=15.63, p<0.003) but no differences between groups (F2,39= 1.27, p > 0.05) and no interaction (F2,39 = 1.57, p > 0.005) were observed. Results suggest that frequency of runs per week is the most important factor for decrements in anxiety during running sessions (r = -0.58, p < 0.01) and that initial level of anxiety was positively related to decreased anxiety following activity (r = .63, p < .005) for both men and women49. 3 Markoff (1982) Impact of a 1 hour run on mood A Hawaiian pre-post non-controlled study by Markoff et al. (1982) used 15 participants (11 men & 4 women) aged 23-45 who had all ran at least 1 marathon to examine the impact of 1 hour of running on mood using the Profile of Mood States as measurement. There was a significant reduction of anxiety pre- to post-run (5.2 to 3.27 in men and 3.08 to 2.15 in women). The t-test for anxiety was 2.72 and thus p<0.01. For depression, there was a non-significant decrease in scores pre- to post- run (4.93 to 1.73 in men and 7.39 to 1.83 in women). The t-test for depression was 1.80 which was not significant50. 4 Thaxton (1982) Impact of 30 minutes outdoor running on mood An American non-randomised controlled trial by Thaxton et al. (1982) used 33 regular runners with a mean age of 36 (24 males and 9 females) who were divided into 4 groups to compare pre-test 30 minute outdoor running test (n=6), pre-test no running test (n=9) no pre-test 30 minute running test (n=11) and no pre-test no running test (n=7) on mood using the Profile of Mood States as measurement. Significant differences in the depression scores between the running and abstaining (non- pretested) groups, F(1,29) = 4.8,p<.05, however no significant differences between anxiety, vigour, and fatigue scores51. 5 McGowan (1991) Impact of 75 minutes of jogging on an outdoor track on mood An American non-randomised controlled trial by McGowan et al. (1991) used 72 college students to compare the effect of 75 minutes of jogging on an outdoor track (n=25) vs 75 minutes of karate (n=11), weight training (n=26) and a stationary science lecture class (n=10) on mood using the Profile of Mood States as measurement. The running group exhibited significant changes in total mood disturbance from pre-(35.68) to post (24.16) test… t24 = 2.84, p<0.009. The weight lifting group had changes of F6,20 = 2.60, p = 0.05, but there were no significant changes observed for the karate group or control52. 6 Goode (1993) Impact of own training run on mood An American pre-post non-controlled study by Goode et al. (1993) used 150 regular runners with a mean age of 31.7 (69% male, 31% female) to investigate mood states before and after subjects own training run using the Profile of Mood States as measurement. All but one (vigor) of the POMS scales showed significant (p<0.1) changes following the run. Tension/anxiety (mean change of -3.1, p<0.1), depression (mean change of -1.5, p<0.1), confusion (mean change of -1.1, p<0.1) and anger mean change of -1.8, p<0.1) all reduced significantly post run, while fatigue significantly increased post run (mean change of +1.8, p<0.1)53. 7 Morris (1994) Impact of a 3 mile ‘fun-run’ on mood A British pre-post non-controlled study by Morris et al. (1994) used 165 members of a road runners club (98 males and 67 females) with a mean age of 34 to examine how a 3 mile ‘fun-run’ impacted mood using an author devised adjective checklist based on POMS as measurement. Positive mood was increased after running (F( 1,163)=68.18, p<0.001), negative mood decreased after running (F(1.163) = 47.62, p<0.001) and improvements in mood were greater in women than men but was not significant (p>0.1)54. 8 Rudolph (1996) Impact of various timings of treadmill running on self-efficacy (10, 15 and 20 minutes) An American randomised non-controlled trial by Rudolph et al. (1996) used 36 moderately-active female university students with a mean age of 20.6 to compare the impact of 10 (n=12), 15 (n=12) and 20 (n=12) minutes of treadmill running on self- efficacy using the Exercise-Efficacy Scale as measurement. Mean scores of self-efficacy increased significantly in all 3 groups, from pre to post exercise (F(1, 33)= 74.57, p< .001): the 10 minute (43.2 to 55.6), 15 minute (34.7 to 45.6) and 20 minute (37.3 to 53.4) exercise conditions. The within-group effect sizes for self-efficacy were calculated. Although the largest effect size (ES) occurred in the 20 minute condition (ES= .68), the effect sizes in the 15 (ES= .36) and 10 (ES= .49) minute conditions are also moderate in magnitude55. 9 Cox (2001) Impact of 30 minutes of treadmill jogging at either 50% or 75% predicted VO2 max on psychological affect and wellbeing An American randomised controlled trial by Cox et al. (2001) used 24 physically active male university students with a mean age of 28.3 to compare the impact of 30 minutes of treadmill jogging at either 50% or 75% predicted VO2 max vs a stationary stepper on psychological affect and wellbeing using the Subjective Exercise Experiences Scale. Results showed that following an acute bout of aerobic exercise a significant linear trend for time was observed for psychological distress (p=0.001, η2p=0.17) and a significant linear trend for time for positive well-being (p=0.037, η2p =0.07) but there was no significant difference between the wellbeing for the stepper vs the treadmill running group56. 10 O’Halloran (2002) Impact of a 60 minute treadmill run on mood An Australian pre-post non-controlled study by O’Halloran et al. (2002) used 50 regular runners (25 men and 25 women) with a mean age of 26.6 to examine how a 60 minute treadmill run impacted mood using the Profile of Mood States and Beliefs Concerning Mood Improvements Associated With Running Scale as measurements. The pre vs post exercise scores from POMS was as follows: Agreeable-Hostile = 27.3 to 27.58; Composed-Anxious = 25.6 to 29.12; Clearheaded-Confused = 27.20 to 28.62; Confident- Unsure = 25.22 to 25.10; Elated-Depressed = 24.56 to 27.10; Energetic-Tired = 22.42 to 23.48. There were significant reductions in anxiety (p<0.05), depression (p<0.01) and confusion (p<0.05, however there was not a significant change of confidence. The largest correlation (r =.44) was between the beliefs scale and changes on the Elated- Depressed subscale (p<0.01)57. 11 Szabo (2003) Impact of 20 minutes of track running on anxiety and feelings A UK based pre-post non-controlled time series quasi-experimental study by Szabo et al. (2003) used 39 sports science university students (22 male and 17 female) aged between 20 and 23 who all had a British-Caucasian cultural background to compare the impact of 20 minutes of track running, a comedy video and a nature documentary on anxiety, positive wellbeing and psychological distress using the Spielberger State Anxiety Inventory and Exercise induced Feeling Inventory as measurements. Both exercise and humour had an equally positive effect on psychological distress and positive wellbeing. State anxiety significantly reduced with exercise (F(1.5, 58.3) = 5.32, p<0.01), however, exercise had a statistically lower reduction on anxiety than humour (t(38) = 3.36, p<0.002)58. 12 O’Halloran (2004) Impact of a 60 minute treadmill run on mood An Australian randomised controlled study by O’Halloran et al. (2004) used 160 regular runners (80 males and 80 females) between age 18 and 40 to compare how a 60 minute treadmill run (n=80) vs no running (n=80) impacted mood using the Profile of Mood States and Beliefs Concerning Mood Improvements Associated With Running Scale as measurements. There were improvements in composure, energy, elation and mental clarity during the run relative to the control condition and pre-exercise assessment. Other than the Energetic-tired subscale where improvements were evident at 25 minutes (F(1,156) =10.09, p=.002), most subscales didn’t have mood improvements until the 40 minute assessment during the run. Runners became more composed (less anxious) F(1,156) =9.47, p=.002), more clear headed (less confused) (F(1,156) =5.57, p=.02) and more elated (less depressed) (F(1,156) =10.18, p=0.002) by 40 minutes into a 60 minute treadmill run. Although there was a trend for differences on the beliefs concerning mood improvements after running scale, mean scores on the beliefs concerning mood during running scale were strikingly similar for the running (3.69) and the control groups (3.67)59. 13 Robbins (2004) Impact of 20 minute treadmill run on self- efficacy in children and adolescents An American pre-post non-controlled study by Robbins et al. (2004) used 168 inactive African American and European American children & adolescents with a mean age of 12.6 (49% female) to investigate how 20 minutes of treadmill exercise impacted self- esteem using the Walking Efficacy Scale as measurement. There was an increase in self- efficacy post-exercise F(1, 158) = 84.31, p < .001, however girls reported significantly lower pre-activity self-efficacy (M = 41.02, SD = 24.37) than boys (M = 52.46, SD= 23.99) with t(166) = 3.07, p < .01, and subsequently reported greater perceived exertion. African American girls reported significantly lower pre-activity self-efficacy than the other three race-gender groups F(3,164) = 5.55, p < .0160. 14 Pretty (2005) Impact of a 20 minute treadmill run with rural vs urban stimuli on mood and self-esteem A UK based randomised controlled trial by Pretty et al. (2005) used 100 participants (55 female and 45 male) with a mean age of 24.6 to investigate how pleasant and unpleasant urban vs rural stimuli whilst running on a treadmill for 20 minutes impacted mood and self-esteem, using the Profile of Mood States and Rosenberg Self-Esteem Questionnaire as measurements. There were 20 participants in each of the 5 different stimuli groups: rural pleasant, rural unpleasant, urban pleasant, urban unpleasant and the control group who exercised with blank white screens. There was a significant increase in self-esteem (from 19.4 (+/-0.4) to 18.1 (+/-0.4), p < 0.001) following exercise, however both rural and urban pleasant scenes produced a significantly greater positive effect on self-esteem than exercise alone, while both rural and urban unpleasant scenes reduced the positive effects of exercise on self-esteem. There were significant reductions in confusion, p < 0.01; and tension-anxiety, p < 0.001, while a significant improvement vigour, p < 0.001) following exercise61. 15 Hoffman (2008) Impact of a 30 minute treadmill run on mood An American pre-post pre-experimental study by Hoffman et al. (2008) used POMS to examine how a 30 minute treadmill run altered mood using 32 subjects (16 regular exercisers and 16 non exercisers, consisting of 8 women and 8 men in each group). Post exercise total mood disturbance was decreased 16 +/- 10 (95% CI, 7–24) among the moderate exercisers, and 9+/- 13 points (95% CI, 1–18) among the non-exercisers. TMD improves post-exercise in both the exercisers and non-exercisers, but the exercisers experience almost double the effect. A “nearly significant group-by-time interaction” (P.08) is suggestive of a trend toward less of an effect among the non- exercisers than the other groups62. 16 Kwan (2010) Impact of a 30 minute treadmill run on general affective response An American pre-post non-controlled study by Kwan et al. (2010) used 129 participants (67 women and 62 men, 80% white ethnicity) with a mean age of 22 to show the positive impact of a 30 minute treadmill run on general affective response using the Physical Activity Affect scale (PAAS) as measurement at 6 time points before, during and after the exercise. There was a positive effect during exercise (b = .52, SE = .09, p < .0001) and between baseline and 15 minutes post-exercise (b= .73, CI.95 = .56, .89, t(126)= 8.63, p < .0001)63. 17 Weinstein (2010) Impact of 25 minutes of increasing graded treadmill running on mood and depression An American pre-post controlled study by Weinstein et al. (2010) used 30 participants with a mean age of 39.8 (50% women); 14 of whom were diagnosed with minor (n=2) or major (n=12) depressive disorder and 16 of whom were controls; to examine how 25 minutes of increasing graded treadmill exercise impacts mood and depression using the Becks depression Inventory scale and Profile of Mood States as measurements. Immediately following exercise, depressed individuals displayed improvements in depressed mood from baseline (p=0.02) but subsequently exhibited increased depressed mood from baseline at 30 mins post exercise F interaction(1,27)= 3.98; p=0.05; ηp2 =0.13. The severity of depression (as assessed by BDI-II) was significantly related to increases in depressed mood (r = 0.60; p = 0.001) at 30 min post-exercise64. 18 Anderson (2011) Impact of a light 10 minute outdoor jog on mood A British randomised controlled trial 2x2 mixed design by Anderson et al (2011) used 40 participants aged 18-25 from various sports clubs to compare the impact of a light 10 minute jog outside on a grass playing field vs a 10 minute cognitive task on mood using the ‘Incredibly Short Profile of Mood States’ as measurement. The between persons design found a significant mood enhancement (F(1,38) = 24.18, p <.001, n2p = .39) within the exercise group, compared with the non-exercise control group65. 19 Kane (2013) Impact of the running pacer challenge (20m sprints within increasing pace inside a gymnasium) on self- efficacy in children An American pre-post non-controlled study by Kane et al. (2013) used 34 school children aged 11 to 14 (18 female and 16 male) to examine how the PACER challenge (20m sprints within increasing pace inside a gymnasium) effected self-efficacy using the self-efficacy questionnaire adapted for children. The study found a decrease in self- efficacy following participation in the PACER (mean score decreased from 2.7 to 2.3 following exercise, t=4.6, p<.001, large effect size of d = 0.79), however there was a positive correlation between PACER laps and pre- and post- measures of exercise self- efficacy (mean score increased from .58 to .70 following exercise)66. 20 Szabo (2013) Impact of a 5km self- paced run along a public running path on states of affect A Hungarian pre-post non-controlled study by Szabo et al. (2013) used 50 recreational runners (37 males and 13 females) with a mean age of 29.02 to investigate how a 5km self-paced run on a public running path impacted states of affect using the Exercise Induced Feeling Inventory as measurement. Significant positive changes were seen in all 4 measures of affect following the run: revitalisation (F(1,48) = 145.93, p < .001, partial n2 = .75 with an effect size of 2.0), positive engagement (F(1,48) = 97.11, p< .001, partial = n2 =.67 with an effect size of 1.6), tranquillity (F(1,48) = 85.02, p < .001, partial n2 = .64 with an effect size of 1.5) and exhaustion (F(1,48) = 32.25, p < .001, partial n2 = .40 with an effect size of 1.0)67. 21 McDowell (2016) Impact of a 30 minute treadmill run on mood and anxiety An Irish randomised controlled trial by McDowell et al. (2016) used 53 participants (27 males and 26 females) with a mean age of 21.2 to compare the effects of 30 minutes of vigorous treadmill running vs 30 minutes of seated quiet rest on mood and anxiety using the State-Trait Anxiety Inventory and Profile of Mood States as measurements. Compared with the control, 30 minutes of acute aerobic exercise significantly improved state anxiety (F1,92 = 12.52, P < 0.001), feelings of depression (F1,86 = 5.05, P < 0.027), and total mood disturbance F = 36.91, P < 0.00168. 22 Rogerson (2016) Impact of a 5km park run on psychological wellbeing A British pre-post non-controlled mixed between-within study by Rogerson et al. (2016) used 331 Park Run attendees (180 males and 151 females) with a mean age of 40.8 to investigate how a 5km Park Run impacted affective outcomes of psychological wellbeing using a questionnaire containing parts of the Profile of Mood States, Rosenberg Self-esteem scale and Perceived Stress Scale as measurement. There were significant (p<.001) improvements from pre- to post-run for self-esteem (7.7% improvement; F(1, 324) = 100.58, η2 = .24), stress (18.4% improvement; F(1, 315) =50.78, η2 p = .139) and total mood disturbance (14.2% improvement; F(1, 278) =22.15, η2p = .07)69. 23 Edwards (2017) Impact of a 15 minute treadmill jog on stress and anxiety An American randomised controlled trial by Edwards et al. (2017) used 27 participants aged between 18 and 35 to compare the effects of a 15 minute treadmill jog (n = 8) to the equivalent amount of time walking (n = 9) or stretching (n=10) on stress and anxiety after exposure to a film clip which was intended to elicit a negative emotional response using the Exercise Induced Feeling Inventory and Affective Circumplex Scale and the Strait-Trait Anxiety Inventory as measurements. It found a protective emotional effect from jogging, with reduced anxiousness (28.8 vs 13.1, p = 0.06) and stress (11.3 vs 9.4, p = 0.11) within the runners after being shown the emotive film. When comparing anxiousness scores from baseline to post-film clip, the p-values for the stretching, walking, and jogging groups were .21, .21, and .06, respectively, suggesting that anxiousness was more significantly different between baseline and post-film clip in the jogging group versus the walking or stretching groups. Unlike the walking (p = .11) and jogging (11.3 to 9.4, p = .19) groups, only the stretching group (1.2 to 26.0, p = .048) had an increased anger score from baseline to post-film clip70. Supplementary Table S3 Narrative description of findings of the 9 studies with a double bout of running. Author Narrative description of findings 1 Krotee (1980) Impact of 50m group vs solo run on anxiety A pre-post pre-experimental non-controlled design by Krotee (1980) in the USA used 78 children (31 females and 47 males) between the ages of 7 and 12 to compare how a 50 metre run in individual vs small group settings impacted anxiety using the State-Trait Inventory for Children as measurement. In the individual setting, the level of pre initial (31.56) to pre termination (31.07) anxiety levels decreased while in the small group setting a slight but not significant gain in pre initial (30.54)to pre termination (31.40) anxiety level was realised. In the individual setting, the level of post initial anxiety (31.56) was higher at post termination (32.72)> This also happened in the small group setting, the level of post initial anxiety (30.67) increased at post termination level (31.83). There was Significant pre to pre (individual r = .9050 and small group r = .8667) and post to post (individual r=.8684 and small group r=.7335) correlations concerning the A-State level exists at the 0.001 level of confidence. It appears that there is relative stability between the various measures of pre and post A-State anxiety level and perhaps the physical activity and sport situational setting does not create as much anxiety for the participant as popularly purported. Results indicate that the children did not significantly increase in anxiety level (A-STATE) when participating in various physical activity and sport situational settings (ie. individual or group), however females) exhibited a significantly higher competitive anxiety level (A-TRAIT) than males prior to participation in the physical activity and sport situational setting (20.90 & 18.40, respectively)71. 2 Wildmann (1986) Impact of 2 identical 10km runs (1 week apart) on feelings of pleasantness & change of mood A German based pre-post non-controlled study by Wildmann et al. (1986) used 21 male long-distance runners with a mean age of 29.8 to investigate how two 10km runs (1 week apart) under equal conditions on a 400m running track impacted ‘feelings of pleasantness’ and ‘changes of mood’ using the Eigenschaftsworterliste scale (an adjective check list) as measurement. Following running bouts there was a change in mood with good mood scoring higher after running. The mood elevation had considerable individual variability but there was a significant correlation in the mean values of the 2 runs between ratings in feelings of pleasantness. General feeling of pleasantness, which combines items of the EWL checklist related to self-confidence and elevated mood, scored higher post-run as compared to pre-run. The mean increase of the two runs for all subjects tested was 2.79 + 5.54 from a total of 19 items. However, again considerable individual differences were striking, therefore the increase did not reach significance72. 3 O'Connor (1991) Impact of 5 mile outdoor group vs solo run on anxiety An American pre-post non-controlled study by O'Connor et al. (1991) used 17 members of local running clubs (10 males and 7 females) with a mean age of 25, to compare how a group vs solo 5 mile outdoor run impacted anxiety and body awareness using the State-Trait Anxiety Inventory and Body Awareness Scale as measurements. Both cognitive (STAI) and somatic (BAS) anxiety were reduced following intense running, performed either in the absence or in the presence of interpersonal competition, and that the magnitude of these anxiety reductions were equal in the two conditions. When interpersonal competition was present, post-exercise state anxiety levels (m=27.5) were significantly (p <0.05) below the pre-exercise (m=42.5) and the baseline (m=34) anxiety levels. When interpersonal competition was absent, post- exercise state anxiety levels (m=30) were also significantly below (p<0.05) pre-exercise (m=40) and baseline (m=34) anxiety levels. While body awareness levels were significantly (p <0.05) below both the pre-exercise but not reduced below the baseline value. When interpersonal competition was present, post-exercise body awareness (m=27.5) were lower than pre- exercise (32.5), but not below baseline levels (m=24). When interpersonal competition was absent, post-exercise body awareness (m=26) were again, below pre-exercise levels (m=31), but not below baseline (m=24). No significant effect for gender was found73. 4 Nabetani (2001) Impact of a 10 minute vs a 15 minute treadmill run on mood A Japan based pre-post non-controlled study by Nabetani et al. (2001) used 15 healthy, moderately active male graduate students with a mean age of 23.4 to compare how two self-selected intensity runs on a treadmill (one for 10 minutes vs the other for 15 minutes) impacted mood using the Mood Checklist Short-form 1 containing three subscales: pleasantness, relaxation and anxiety as measurement. The results found that exercise of 10 and 15 minutes produced similar psychological benefits. Following the 10 minute trial: anxiety (ES = 0.61) significantly decreased (p<0.01), whilst there was no significant difference of pleasantness (ES = 0.86) and relaxation (ES = 0.33). Following the 15 minute trial, anxiety (ES = 0.51) and pleasantness (ES = 0.62) significantly decreased (p<0.01), but relaxation (ES = 0.07) had no significant pre-post difference74. 5 Bodin (2003) Impact of 1 hour park vs urban run on depression and anxiety A Swedish pre-post non-controlled within-subjects study by Bodin et al. (2003) used 12 regular runners (6 female and 6 males) with a mean age of 39.7 to compare how a 1 hour run in a park vs a 1 hour run in an urban environment impacted emotional restoration (depression/ anxiety) using the Exercise-Induced Feeling Inventory and the Negative Mood Scale as measurements. In both men and women, in park and urban settings, running caused a significant decline in anxiety/depression between pre- and post-test measures with F(1,10) = 16.2, p <0.002, r=0.78 and a moderate effect size of rs = 0.30. The runners preferred the park to the urban environment in a global sense (F(1, 10) = 133.07; punadjusted < 0.0001; padjusted < 0.002) and perceived it as more psychologically restorative, however, results did not indicate any greater emotional benefit from running in the park versus the urban environment, nor that men and women differed75. 6 Butryn (2003) Impact of 4 mile park vs urban run on mood An American pre-post non-controlled within-subjects study by Butryn et al. (2003) used 30 non-elite female distance runners with a mean age of 31 to compare how a 4 mile run in a natural setting vs a 4 mile run in an urban setting impacted mood, feeling states and cognition states using the Profile of Mood States, Exercise Induced Feeling Inventory and Thoughts During Running Scale as measurements. Despite 93% of participants preferring running in the park setting, following a 4-mile run regardless of whether the run was completed in a park or urban setting there was a decrease in negative mood and increase in positive mood. Following the park run, total mood disturbance scores decreased 8.97 (p < 0.001), while positive engagement, revitalisation and tranquillity all significantly increased (p < 0.05, p < 0.001 and p < 0.01 respectively). A similar effect was found following the urban run: total mood disturbance scores decreased 9.13 (p < 0.001), whilst positive engagement and revitalisation significantly increased (p < 0.05 and p < 0.01 respectively), and tranquillity increased but not significantly76. 7 Kerr (2006) Impact of indoor vs outdoor 5km run on stress and emotions A Japanese pre-post non-controlled study by Kerr et al. (2006) used 22 recreational runners with a mean age of 22.7 years to compare how a 5km indoor run on a treadmill vs a 5km outdoor run in a natural environment impacted stress and emotions using the Tension and Effort Stress Inventory as measurement. There were significant pre/post effects for total pleasant somatic emotions [F(1, 21) = 16.35, p< 0.01], and total unpleasant somatic emotions [F(1, 21) =7.08, p <0.05]. Post hoc tests indicated that total pleasant somatic emotions increased from pre- (M=12.55), to post-running (M= 14.66), while total unpleasant somatic emotions decreased from pre (M=9.39), to post-session (M=7.77), while irrespective of running condition. There were significant pre/post effects, irrespective of running condition, for relaxation [F(1, 21) =5.60, p< 0.05], anxiety [F(1, 21) =9.90, p< 0.01], and excitement [F(1, 21) =24.65, p< 0.001]. Relaxation and excitement increased, and anxiety decreased from pre- (M=4.30; M=2.50; M=3.14 respectively), to post-session (M=4.86; M=3.77; M=2.36 respectively)77. 8 Rose (2012) Impact of self- paced vs prescribed pace 30 minute treadmill run on self-efficacy A New Zealand based pre-post controlled study by Rose et al. (2012) used 32 females (17 sedentary and 15 active) with a mean age of 45, to compare how a 30 minute self-paced bout of treadmill exercise vs a 30 minute prescribed- paced bout of treadmill exercise (1 week apart) impacted self-efficacy using the Self-Efficacy for Exercise Scale as measurement. There was a significant main effect for group (F 1,28 = 4.74; P = 0.038; n2 = 0.14), with significantly higher self-efficacy in the active women (M=70.9) than the sedentary women (M=57.7). There was also a significant main effect for condition (F1,28 = 5.81; P<0.023; n2 = 0.17), with higher self-efficacy before the prescribed condition (M=66.1) compared with the self-selected condition (M=62.6). There was also a significant condition by order interaction (F1, 28 = 18.8; P <0.001; n2 = 0.39) that showed when the prescribed session was completed first, self- efficacy was equal for the self-selected (M=65.7) and prescribed (M=63.1) conditions, however, when the self-selected condition was completed first, self-efficacy was greater for the prescribed (M=69.0) compared with the self- selected (M=59.4) condition78. 9 Reed (2013) Impact of rural vs urban 1.5 mile run on self- esteem A UK based pre-post non-controlled study by Reed et al. (2013) used 75 children aged 11 & 12 to compare how a 1.5 mile run in an urban vs a rural environment impacted self-esteem using the Rosenberg Self Esteem Scale as measurement. Following exercise there was a significant increase in self- esteem (F(1,74), = 12.2, p <0.001), however there was no significant difference between the urban or green exercise condition (F(1,74) =0.13, p = 0.72), or any significant difference between boys and girls79. Supplementary Table S4 Narrative description of findings of the 3 studies with a triple bout of running. Author Narrative description of findings 1 Harte (1995) Impact of 12km outdoor run vs indoor treadmill run with external vs indoor treadmill run with internal stimuli on mood An Australian pre-post non randomised controlled-repeated measure design by Harte et al. (1995) used 10 male amateur triathletes or marathon runners with a mean age of 27.1 to investigate how an outdoor 12km run, 1 indoor treadmill run with external stimuli, an indoor run with internal stimuli vs a sedentary control impacted mood using the Profile of Mood States as measurement. Following the outdoor run, subjects felt less anxious F(3,35) = 14.12 (p <0.005); less depressed F(3,35) = 4.16 (p <0.01); less hostile F(3,35) = 13.13 (p < 0.005); less fatigued F(3,35) = 15.09 (p < 0.005); and more invigorated F(3,35) = 13.01 (p < 0.005) than at pre-test; while the two indoor runs had less positive effects on mood80. 2 Berger, Owen + Motl (1998) Impact of three 15 minute runs of varying intensities (50, 65 or 80% age- adjusted HR max) on mood Berger, Owen + Motl (1998)81 Study 1 …. A pre-post non controlled study by Berger, Owen + Motl (1998) used 71 USA college students (32 male and 39 female) with a mean age of 21.39 to investigate how three 15 minute runs at intensities of 50, 65 or 80% age-adjusted HR max impacted mood using the Profile of Mood States as measurement. There were significant overall mood benefits for women (p<0.001) and for men (p<0.03) post-exercise, with all subscales except vigor and fatigue showing significant pre-post changes. No results were provided differentiating the three different intensities of running81. Study 2 ….. A pre-post non controlled study by Berger, Owen + Motl (1998) used 68 USA college students (28 male and 40 female) with a mean age of 22.22 to investigate how three 15 minute runs at intensities of 50, 65 or 80% age-adjusted HR max impacted mood using the Profile of Mood States as measurement. There was significant mood benefits following exercise (F(6.57) = 6.43, p< 0.0001) and all POMS subscales, apart from fatigue, had significant pre-post improvements reported following running (p<0.05). Again, there were no results provided comparing the three different intensities of running81. 3 Markowitz (2010) Impact of three 20 minute treadmill runs of varying intensities (5% below, 5% above and directly at lactate threshold) on anxiety An American pre-post controlled trial by Markowitz et al. (2010) used 28 college- aged students with a mean age of 21, to compare anxiety using the State-Trait Anxiety Inventory in 14 active vs 14 sedentary college students following 20 minutes of treadmill exercise at 5% below, 5% above and directly at their lactate threshold. State anxiety improved post-exercise at 5% below (F(1,21) = 22.781, p< 0.001 and effect size -0.38) and at lactate threshold (F(1, 21) = 16.223, p < 0.001 and effect size -0.20) but increased at 5% above lactate threshold (F(1)= 10.891, p = 0.003 and effect size = +0.1382. Supplementary Table S5 Narrative description of findings of the 34 studies with longer term intervention of running. Author Narrative description of findings 1 Lion (1978) Impact of running a mile 3 times per week for 2 months on anxiety and body image in chronic psychiatric patients An American randomised controlled trial by Lion (1978) used 6 middle aged, chronic psychiatric patients (4 females, 2 male) to compare how running a mile 3 times per week for 2 months (n=3) vs a control group (n=3), impacted anxiety and body image using the State-Trait Anxiety Inventory (STAI) and Rorschach Inkblot Test for body- boundary image as measurements. Post-test anxiety scores on the STAI were significantly reduced in the jogging group compared to the control group (t=3.2, df = 4, p<0.05), with the joggers showing an average drop of 9 points on the STAI (39.3 to 30.3) between pre and post-test, while the control group showed an average rise of 4 points (32.6 to 36.6, SD=12). However there was no statistical difference found between the groups for post-test body image scores on the Inkblot test for barrier (t=0.81, df=4. p<0.05) or penetration responses (t=0.23, df = 4, p<0.05)83. 2 Blue (1979) Impact of 3 runs per week for 9 weeks on depression An American pre-post non-controlled study by Blue (1979) used 2 former in- patients of a psychiatric hospital (1 male aged 37 and 1 female aged 32) to examine how 3 runs per week for 9 weeks impacted depression using the Zung depression scale as measurement. Following the running intervention, both patients’ depression scores reduced from the category of "moderately depressed" to "mildly depressed", with the male patient reducing his score by 18 points, while the female patient reduced her score by 15 points84. 3 Young (1979) Impact of a 10 week walking/ jogging programme consisting of 1 hour 3x per week on anxiety and depression An American pre-post non-controlled intervention study by Young (1979) used 32 adult participants separated into 4 groups by age and sex: young males (n=8, mean age 30.13), middle aged males (n=8, mean age 53.00), young females (n=8, mean age 28.25) and middle aged females (n=8, mean age 50.25). The study investigated how a walking/jogging programme consisting of one hour 3x per week for 10 weeks, impacted anxiety and depression using the Multiple Affect Adjective Checklist as measurement. Results showed significant reductions in pre- to post- test anxiety scores within subject (ANOVA =6.01, p<0.05) and also a significant age difference on anxiety in favour of older subjects (ANOVA = 5.37, p<0.05, d.f.(1,28)). Results for depression also showed significant age differences in favour of older subjects (ANOVA =5.21, p<0.05, d.f.(1,28)), however there was no significant improvement within subject depression scores (ANOVA = 0.25, n.s.) 85. 4 Blumenthal (1982) Impact of 3 times weekly walking-jogging programme for 10 weeks vs 10 weeks of sedentary controls on anxiety and mood An American non-randomised controlled cohort study by Blumenthal et al. (1982) used 16 healthy adults (11 women and 5 men) with a mean age of 45.1 to compare how a 3-times weekly walking-jogging programme for 10 weeks vs 10 weeks of sedentary controls, impacts anxiety and mood using the Profile of Mood States and the State-Trait Anxiety Inventory as measurements. Results did not detail the number of participants in each group. There were no differences between the exercise and control groups POMS scores at pretesting, but after 10 weeks of training the exercise group exhibited less tension (F(1,30) = 4.49, p <0.04), less depression (F(1,15) = 4.82, p <0.04), less fatigue (F(1,30)= 3.88, p <0.05), less confusion (F(1,15) = 4.40, p <0.05) and more vigor (F(1,15)= 3.28, p <0.09) than the sedentary controls. There was no change for either group on the POMS anger subscale. Similarly for State-trait anxiety: there was no difference between the two groups at the time of pretesting, but after the 10 week programme exercisers also exhibited less state anxiety (F(1,26) = 4.15, p <0.05), and less trait anxiety (F(1,26) = 6.05, p <0.02)86. 5 Trujillo (1983) Impact of a 16 week running programme vs weight training vs a control on self-esteem An American randomised controlled trial by Trujillo (1983) used 35 female college students to compare the impact of a 16 week programme of weight training (n=13) vs running (n=12) vs a physical activity control such as swimming (n=10) control on self-esteem using the Tennessee Self-concept Scale and the Bem Sex Role Inventory as measurements. Results found that both the running and weight training group showed a significant increase in self-esteem from pre- to post-programme ([t,(11)=2.11, p<0.05] and [t,(12)= 1.82, p<0.05], respectively), however the control group showed a nonsignificant loss in self-esteem [t,(9) =0.55, p>0.05]. Although both the weight training and running groups reported significant change in the level of self-esteem, the amount of actual change when compared with between groups was significantly higher for only the weight training group: with the gain scores in weight training as compared to the control group at tD(31)=2.83, p<0.05, while the gain scores comparing weight training to running [tD(31)=1.00, p >0.05] and running to the control were both non-significant [tD(31) = 1.75, p>0.05]. With regards to the Bem Sex Role Inventory, the majority of participants in all 3 groups were androgynous in nature at pre-test measurement (n=7, N=11, n=7 for the weight training, running and control groups respectively), with no change occurring at post-test measurement in either of the groups87. 6 Tuckman (1986) Impact of three 30 minute runs per week on an outdoor running track for 12 weeks on psychological affects in children (creativity, perceptual function, behaviour & self- concept An American randomised non-controlled trial by Tuckman et al. (1986) used 154 children aged 9-11 to compare how three 30 minute running sessions on an outdoor running track per week for 12 weeks, vs 12 weeks of the school’s regular physical education schedule, effected psychological affects such as creativity, perceptual function, behaviour and self-concept, using the Alternate Uses Test, Bender-Gestalt Test, Devereaux Elementary School Behaviour Rating Scale and Piers-Harris Children’s Self-Concept Scale, respectively, as measurements. Running significantly improved creativity of school children compared to regular physical education participants (F ratio = 17.00, p<0.001), with running treatment children averaging 3 to 5 more creative responses than controls. However running had no significant difference on classroom behaviour (F = 0.91), self-concept (F = 1.02), or perceptual functioning (F = 2.17)88. 7 Doyne (1987) Impact of 3 runs on an indoor track per week for 8 weeks on depression in women with a diagnosis of major or minor depression An American randomised controlled trial by Doyne et al. (1987) used 40 women all with a diagnosis of major or minor depression and a mean age of 28.52 to compare the impact of 8 weeks of 3 sessions per week of running on an indoor track vs 8 weeks of weight lifting vs a wait-list control on depression using the Beck's Depression Inventory, Hamilton Rating Scale for Depression and Depression Adjective Checklists as measurements. Results found statistically and clinically significant decreases (F(4, 138) = 14.98,p < .01) in mean depression scores from baseline to post-measurements in both running (22.27 vs 8.18) and weight lifting (22.07 to 5.93) relative to the wait-list control group (20.17 to 15.25), with improvements reasonably well maintained at 1 year follow-up, however no significant overall differences found between the two exercise groups89. 8 Fremont (1987) Impact of 3 runs per week for 10 week on depression, anxiety and mood An American randomised non-controlled trial by Fremont et al. (1987) used 49 participants (13 male and 36 female) aged between 19 and 62) to compare how 10 weeks of running (3 runs per week) vs 10 weeks of counselling vs 10 weeks of a combination of running and counselling impacted depression, anxiety and mood state using the Beck's Depression inventory, State-Trait Anxiety Inventory and The Profile of Mood States as measurements. There were no significant differences between the three programmes, they all produced similar improvements in depression, anxiety and mood states; with improvement maintained at 4 months follow-up. Depression (BDI), trait anxiety and state anxiety scores all decreased significantly over the 10 weeks ([F(4,184) =50.3, p < 0.0001]; [F(1, 46) = 27.1, p < 0.0001]; [F(1,46) = 21.9, p < 0.0001] respectively). Mood improved over the 10 weeks (F(18,378) = 4.5, p < 0.001), with significant decreases over time for depression (F(3, 138) = 23.6, p < 0.0001), confusion (F(3, 138) = 15.6,p < 0.0001), anger (F(3, 138) = 12.4, p < 0.0001), fatigue (F(3, 138) = 17.9, p < 0.0001), and tension (F(3, 138) =16.1, p < 0.0001), whilst there was significant increase in vigor over time (F(3,138) = 14.6,p < 0.001)90. 9 Hannaford (1988) Impact of three 30 minute runs per week for 8 weeks on depression and anxiety in psychiatric patients with major psychiatric disorders An American randomised controlled trial by Hannaford et al. (1988) used 27 male psychiatric patients with major psychiatric disorders and an age range of 25 to 60, to compare the impact of three 30 minute runs per week for 8 weeks (n=9) vs corrective therapy 3 days a week for 8 weeks (n=9) vs waiting list controls (n=9) on depression and anxiety using the Zung Self Rating Depression Scale and State Trait Anxiety Index as measurements. Results found significant reductions in depression scores (F(2,23) = 3.61, p= 0.043) for the running treatment group compared to the waiting list controls (adjusted means = 45.99 and 51.67, respectively), while the corrective therapy group was intermediate between (adjusted mean = 47.12), but not significantly different from either of the other two groups. Results regarding anxiety scores were in the hypothesized direction, but were not significant (F(2,23) = 1.085, p=0.354) with the running group not significantly lower than either the corrective therapy group or the waiting list control group (adjusted means = 38.92, 42.76 and 38.98, respectively)91. 10 Long (1988) Impact of an 8 week running programme consisting of a weekly group session plus twice weekly solo jogs on A 14 month follow-up from a Canadian randomised non-controlled trial by Long et al. (1988) used 39 chronically stressed, sedentary working women with a mean age of 40 to compare how an 8 week running programme of a weekly group session plus twice weekly solo jogs (n=18) vs 8 weeks of progressive relaxation intervention (n=21) impacted stress, anxiety and self-efficacy using the Trait Anxiety Inventory, Sherer et al.'s inventory for self-efficacy and a modified version of the Ways of Coping Checklist. stress, anxiety and self-efficacy At follow-up, considerably more subjects in the exercise group compared to the relaxation group self-reported program maintenance (67% vs. 14%, respectively). At follow-up, both intervention groups reported significantly less anxiety and greater self-efficacy. In addition, subjects tended to increase their use of problem-focused coping as compared to emotion-focused coping, and 64% of them were still regularly using some structured form of relaxation or exercise. The proportion of subjects reaching clinically significant improvements was 24% at the end of treatment and 36% at the 14-month follow-up. Regarding trait anxiety and self-efficacy results showed a significant group main effect (F(2, 36)=3.16, p<.05), however, only the univariate for self-efficacy was significant (p<.02). Overall, the exercise group exhibited higher self-efficacy. The time effect for the pre to the post/follow-up average was significant (F(2, 36)= 15.38, p<.001) with significant univariate Fs for both measures (both ps<.001). Furthermore, the time effect for post to follow-up approached significance at p<.07, with only the univariate F for trait anxiety significant, F(1, 37)=5.01, p<.03. These analyses indicated that both the exercise and relaxation groups maintained treatment effects on self-efficacy, with even further reductions on trait anxiety from post to follow-up. However, despite the exercise group's higher self-efficacy scores, there were no significant interaction effects (Fs<1), suggesting that the exercise and relaxation groups did not change differentially over time. Regarding coping, there was a significant group main effect on the two coping dependent measures (F(2, 35) =4.97, p<0.01), with both the exercise and relaxation groups decreased emotion- focused coping and increased problem-focused coping, while total coping scores did not change (F(2, 35) =2.88, p<.07) for the pre to the post/follow-up average contrast. The time effect for post to follow-up was not significant (F(2, 35) = 1.30, p=0.28) indicating that posttreatment changes were maintained at follow-up. Finally, there were no significant interaction effects, indicating that the coping within both groups changed similarly over time (both Fs<1)92. 11 Simons (1988) Impact of two 30 minute walk/runs per week for 8 weeks on mood An American non-randomised controlled trial by Simons et al. (1988) used 128 participants consisting of 53 experimental subjects (24 male, 30 female and mean age 44.9) and 75 control subjects (28 male, 47 female and mean age of 42.0) to compare how two 30 minute walk/run per week for 8 weeks vs a weekly 30 minute fitness lecture for 8 weeks effected mood using the Profile of Mood States (POMS), Nowicki-Strickland Internal-External Control Scale for Adults (ANSIE) and Marlowe- Crowne Social Desirability Scale for measurements. Exercise class subjects had significant improvement in mood compared to non-treatment controls, with mean pre- to post-test summed mood change scores improving significantly for experimental (28.8 to 15.6) in comparison with control subjects (23.5 to 20.9), F(1,126) = 4.46, p < 0.05. There was also significant improvement in pre- to follow-up mood change scores, F(1,98) = 7.63, p < 0.01. Mood improvement was predicted by initial mood, with improvement limited to the most mood-disturbed subjects93. 12 Moses (1989) Impact of varying intensity 10 week walk- jog programmes on mood and mental wellbeing A British randomised controlled trial by Moses et al. (1989) had 75 sedentary adult volunteers with an average age of 38.8 years who were assigned to one of four 10 week conditions: high intensity aerobic walk-jog programme (n=18), moderate intensity walk-jog programme (n=19), attention-placebo including strength, mobility and flexibility exercises (n=18) or a waiting list control (n=20). The study compared the 4 conditions effects on mood and mental wellbeing using the Profile of Mood States and the Hospital Anxiety and Depression Scale as measurements. There were no significant differences before training between groups on any of the POMS, coping or self-efficacy measures. There was a significant group by time interaction for ratings on the tension/anxiety scale of the POMS [F(3,71) = 2.94, p<0.05], with reductions in tension/anxiety reported only by subjects in the moderate exercise condition. There were also significant differences in the POMS subscale of confusion, were there were differences over time [F(1,71) = 3.70, p<0.06] and group by time [F(3,71) = 2.61, p<0.06], with greater decreases in the moderate exercise group (mean change - 0.193) than in the high exercise (-0.039), attention- placebo (-0.0003) or waiting list (+0.008) conditions. No significant effects were found on the perceived coping scales, but there was significant effects on the physical well-being scale [F(3,71) = 3.82, p<0.01], with all three active treatment groups showed improvements after the 10 week programmes, while the waiting list group ratings decreased. (+0.046, + 0.046 and +0.146, in the high intensity, moderate intensity and attention-placebo condition, respectively). At follow-up there was a significant group by time interaction on the coping deficits scale [F(2,55) = 3.45, p<0.05] and ratings of depression/ dejection [F(2,55) = 3.00, p<0.06] with decreases reported in the moderate exercise group, but not in the high exercise or attention-placebo conditions. Also, the group by time interaction approached significance for the perceived coping assets scale [F(2,55) = 2.56, p<0.08] where again, positive changes were confined to subjects in the moderate exercise condition94. 13 Ossip-Klein (1989) Impact of running on an indoor track 4 times per week for 8 weeks on self-concept in clinically depressed women An American randomised controlled trial by Ossip-Klein et al. (1989) used 32 clinically depressed women with an average age of 28.52 to compare the effects of 8 weeks of running 4 times weekly on an indoor track vs weight lighting 4 times weekly vs a delayed treatment (assessment only) control on self-concept using the Beck Self-Concept Test as measurement. Results did not detail the number of participants in each group. No significant differences between exercise groups were found, with results showing that both running (F( 3,99) =7.62, p<0.0001) and weight lifting (F(3,99) = 11.92,p <0.0001) exercise programs significantly improved self- concept in the clinically depressed women compared to wait-list controls. Scores for the track and universal conditions were significantly higher than those for the wait-list condition at post treatment for the Beck Self-Concept Test (F(2, 33)= 4.69, p<0.05). Improvements were also reasonably well-maintained over time. In general, no significant differences were found between exercise groups; but where differences did occur, they slightly favoured the weightlifting group95. 14 Morris (1990) Impact of stopping running for 2 weeks on anxiety and depression A UK based pre-post study with randomised comparison by Morris et al. (1990) used 40 male regular runners with a mean age of 37 years to compare how stopping running for 2 weeks (n=20) vs continuing to run as normal (n=20) over a 6 week timeframe impacted anxiety and depression using the General Health Questionnaire and short forms of the Zung Anxiety and Zung Depression scales as measurements. The groups did not differ at baseline on any scale (all Fs for group main effects and interactions). Scores on the GHQ subscales, Somatic Symptoms, Anxiety/Insomnia and Social Dysfunction, were all significantly greater in deprived than in continuing runners after both the first and second week of deprivation, and significantly more deprived (11 & 9 subjects in weeks 3&4 respectively) than non- deprived subjects (3 & 2 subjects in weeks 3&4 respectively) exceeded the suggested cut-off score for a psychiatric case after both the first and second weeks of deprivation (x2 = 5.38, 4.51, respectively, df = 1, p <0.05). Symptoms of depression were greater in the withdrawn than in the control group at the end of the second week of withdrawal, the effect reached significance by a randomization test (t = 2.33, df = 38, p < 0.05, l-tailed). There was a tendency for a similar, although reduced, effect after the first week of resumed running (Table IV) but this did not reach significance (t = 1.60, p = 0.05 at 1.68). A significant difference between the groups arose only after the second week of deprivation, with scores in the Zung depression and anxiety scales only reaching significance after week 2 of deprivation (F(1,37) = 22.64, p<0.001 for depression and F(1,37) =11.51, p<0.01 for anxiety). Despite the tendency for the deprived group to continue to decline from weeks 5-6 in anxiety and depression scores, there was no statistical difference between the groups once the deprived group resumed running96. 15 Friedman (1991) Impact of 12 weeks of jogging on stress and mood An American randomised controlled trial by Friedman et al. (1991) used 387 students (188 female and 117 male) with an average age of 20.0 to compare how 12 weeks of either jogging (n= 84), relaxation (n= 96), group interaction (n= 100), and lecture-control (n= 107) impacted stress and mood using the Profile of Mood States and Bem Sex Role Inventory as measurements. In initial measures the relaxation response, jogging, group interaction, and lecture-control groups did not differ on psychological masculinity [F(3, 367)= .38], femininity [F(3, 367)= .38] or the six POMS subscales. High masculinity male and female joggers reported significantly more mood improvement than those who were low, indicating that psychological masculinity, rather than gender, was associated with joggers’ short-term mood improvement. Although all women reported significant mood benefits, high masculinity women benefitted more than low masculinity women, with greater reductions in tension, depression, and anger. For men, psychological masculinity was related to benefits on tension and vigor, but not on the other subscales. Although all women reported significant reductions in depression after the relaxation and jogging sessions, women joggers who were high in psychological masculinity experienced significantly greater reductions than low masculinity joggers (p<0.04). The interaction between technique, gender, moderating variable, and pre-post session was significant for masculinity [F(18,843.4) = 2.14, p < .004], but not for femininity [F(18,843.4) =.62]. Femininity had a significant effect on combined POMS scores [F(6, 297)= 2.79, p< .02], with higher psychological femininity associated with higher tension, depression, and fatigue and lower vigor and confusion scores compared to those low in femininity. There were significant pre-post session x technique interactions for both high and low masculinity women [F(18, 843.36)= 2.47, p<0.0007; F(18, 843.36)= 2.49, p<0.0006, respectively]. In both the jogging and group interaction techniques, the masculinity x pre-post session interaction was significant [Fs(6,298)= 3.32,3.53;p’s < .004,.003,respectively]. Short- term improvements in POMS scores depended upon masculinity for women joggers and participants in group interaction. For the relaxation response and lecture-control groups, the hypothesized interactions between masculinity and pre-post session were not significant [Fs(6,298) = 1.48, 1.15;p’s < .19, .38]. Short-term improvements in mood did not depend on masculinity in these groups; women reported significant improvements in mood from pre- to post session [Fs(6,298) = 6.01, 4.36; p’s< .0001, .0003]97. 16 Williams (1991) Impact of 4 weeks of treadmill running 5 times per week at set paces reflecting 50, 60 & 70% VO2 max on mood An American pre-post non-controlled within-subject design by Williams et al. (1991) used 10 moderately trained male runners with a mean age of 25.6 to assess the impact of 4 weeks of treadmill running 5 times a week at set paces reflecting 50, 60 & 70% VO2 max, on mood using the Profile of Mood States as measurement. The within-subject data indicated a positive correlation, showing that an increase in mean VO2 (decrease in RE) is associated with an increase in mood disturbance, as reflected by the total mood disturbance score (r = 0.88; p<0.01) as well as 5 of the 6 POMS subscales: tension (r = 0.81; p<0.01), depression (r = 0.73; p<0.01), anger (r =0.58; p<0.01), vigor (r = -0.60; p<0.01), fatigue (r = 0.18; not significant) and confusion (r = 0.60; p<0.01). This positive correlation indicates that, when the focus of attention was on within-subject variation, weeks featuring more economical values were associated with more positive mental health profiles. However, in moderately trained male runners considered as a group, there is no relationship between running efficiency and total mood disturbance98. 17 Kerr (1993) Impact of a weekly 40 minute fixed distance run (5km for females, 6.6km for males) through a wooded area for 7 weeks on mood A Netherlands based pre-post non-controlled study by Kerr et al. (1993) used 32 regularly exercising university students (18 male and 14 female) aged between 18 & 22 to investigate the effect of a weekly 40 minute fixed distance, running session in a wooded area (5.0km for females, 6.6km for males) for 7 weeks on mood using the Stress-Arousal Checklist and Telic State Measure as measurements. Over the running programme, subjects’ mood experience was generally pleasant, characterized by high arousal and low stress. In males, from pre- to post-running there were significant increases in TSM felt arousal scores (F(1,16)=52.37, p=0.0001), SACL arousal scores (F(1,16)= 15.34, p=0.001) and TSM preferred arousal scores (F(1,16) = 4.49, p=0.05). In contrast, TSM arousal discrepancy scores were found to decrease significantly for males (F(1,16)= 6.74 , p=0.02) pre- to post-running. Similar significant effects were observed pre-post running for females, with strongly significant increases in TSM felt arousal scores (F(1,12)=16.16 ,p=0.002), SACL arousal scores (F(1,12)=25.19, p=0.0001) and TSM preferred arousal scores (F(1,12)= 11.82, p=0.005). Female runners’ TSM arousal discrepancy scores also decreased significantly (F(1,12)= 11.86, p=0.005) pre- to post-running. When comparing mood responses of fast runners to slow runners, both female and male fast runners scored higher on TSM felt arousal than slow runners ([F(1,12)= 6.50, p=0.03] and [F(1,16)= 4.97, p=0.04], respectively)99. 18 Long (1993) Impact of 3 runs per week for 10 weeks on anxiety and stress A Canadian randomised controlled trial by Long (1993) used 35 participants (14 males and 21 females) with a mean age of 35.6 to compare the effects of running 3 times per week for 10 weeks (n = 12) vs stress inoculation for 10 weeks (n = 9) vs waiting list controls (n = 14), on anxiety and stress using the Cornell Medical Symptom Checklist as measurement. Although the exercise group was more likely to report using exercise to cope with stress, compared to the stress inoculation group, there was no significant differences found between groups on stress or coping classifications. There were also no significant difference of scores of the Cornell Medical Symptom Checklist between the aerobic conditioning and the stress inoculation treatment groups (F<1; M = 87.4, SD = 16.7; Ms = 86.2, SD = 13.5, respectively)100. 19 Berger & Friedman (1998) Impact of three jogs per week for a minimum of 20 minutes over 12 weeks on stress and mood An American randomised controlled trial by Berger & Friedman (1998) used 387 undergraduate college students (188 women and 117 men) with an average age of 20.0 to compare how: jogging three times per week for a minimum of 20 minutes per session over 12 weeks (n=84) vs 12 weeks of relation response (n=96), 12 weeks of discussion groups (n=100) and a control group (n=107), impacted stress and mood using the Profile of Mood States as measurement. All three techniques were significantly more effective in reducing stress than the control activity (p<0.03): with joggers, students practicing the relaxation response, and discussion group members collectively reporting significantly greater stress reduction than the control group during October F(18, 280) =1.79, p<0.03, and November, F(18, 280) =1.85,  p<0.03. However, jogging and practice of the relaxation response were significantly more beneficial in helping students reduce short-term stress than group support (p<.04), with joggers and members of the relaxation response group reporting larger and more numerous reductions in tension, depression, and anger than members of the discussion and control groups. Changes in vigor, fatigue, and confusion were sporadic. There were no long-term benefits observed101. 20 Berger & Owen (1998) Impact of twice weekly walking/ jogging for 14 weeks on mood and anxiety An American pre-post with comparison study by Berger & Owen (1998) used 91 college students to compare how 14 weeks of twice weekly walking/jogging (n=67, 35 female and 32 male) vs a weekly health science class (n =24, 15 female and 9 men) impacted mood and anxiety using the Profile of Mood States and State-Trait Anxiety Inventory as measurements. The interaction between exercise intensity and pre-post mood benefits was not significant (F(12,50) = 1.27, ns), however, joggers reported short-term mood benefits on the combined subscales of the Profile of Mood States, and each subscale contributed to the benefits. Regardless of their exercise intensities, the pre-post-test exercise effect was significant (F(6,56) = 4.87, p < ,0005), with joggers reporting significant pre-post exercise mood changes on each of the six subscales of POMS: tension (F=15.67, p <.0002), depression (F=15.64, p< .0002), anger (F=12.77, p< .0007), vigor (F= 22.29, p<.00005), fatigue (F=20.14,p< .00005), and confusion (F=26.34, p<.00005). Regarding sex differences, the largest interaction was on the fatigue subscale with women's scores decreasing more after jogging than the men's (Fl,6,=9.85)', while the F ratios (1 and 61 df) for the other subscales were for tension 0.60, depression 1.17, anger 0.33, vigor 1.96, and confusion 1.50102. 21 Szabo (1998) Impact of running vs non- running days on anxiety and mood over 21 consecutive days A UK based pre-post non-controlled observational cohort study by Szabo et al. (1998) used 40 members of an amateur running club (30 males with a mean age of 40.5, and 10 females with a mean age of 37) to assess how anxiety and mood (exhaustion, tranquillity, positive engagement and revitalization) varied on running vs non-running days over 21 consecutive days, using daily night time recording of the their own individual running time/distance on running days and the Commitment to running scale, Spielberger State Anxiety Inventory and Exercise induced Feeling Inventory as measurements. There were statistical differences, but small effect sizes, between average values for anxiety and mood on running and non-running days, with runners reporting lesser anxiety and better mood on running days in contrast to non-running days. Mean state anxiety on running days was 35.7 (SD=7.1) compared to 37.2 (SD=7.9) on non-running days, with a period main effect (F(1,38)=5.22, p<0.03). All subscales for mood (exhaustion, tranquillity, revitalisation and positive engagement) were significantly different (p<0.05) on running days as compared to non-running days, with (F(1,38)=4.34,p<0.04) for exhaustion; (F(1,38)=5.56, p<0.02) for tranquillity; (F(1,38)=18.32, p<0.001) for revitalisation and (F(1,38)=11.79, p<0.001) for positive engagement. There were gender differences in the commitment to running (F(1,36)=10.5, p<0.03) with males having a higher value than females103. 22 Broman-Fulks (2004) Impact of six 20 minute treadmill sessions of either high or low intensity aerobic exercise across 2 weeks on anxiety sensitivity in participants with elevated anxiety sensitivity scores An American randomised non-controlled trial by Broman-Fulks et al. (2004) used 54 participants (41 women) with elevated anxiety sensitivity scores with a mean age of 21.17 to compare how six 20 minute treadmill exercise sessions across 2 weeks of either high intensity aerobic exercise (n=29) vs low intensity aerobic exercise (n=25) impacts anxiety sensitivity using the Anxiety Sensitivity Index, State-trait Anxiety Inventory and Body Sensations Questionnaire as measurements. Results indicated that both high- (34.17 to 25.03) and low-intensity (31.44 to 28.56), exercise reduced anxiety sensitivity. However, high-intensity exercise caused more rapid reductions in a global measure of anxiety sensitivity and produced more treatment responders than low-intensity exercise [x2(1, N = 54) = 6.27, p = 0.01]. A significant simple effect for assessment session emerged for the high-intensity exercise group, F(2, 56) = 42.50, p <0.001, n2 = 0.60. An assessment session effect was also found for the low- intensity comparison group, (F(2, 48) = 13.72, p < 0.001, n2 = 0.36). State anxiety mean scores from pre and post-intervention decreased for high intensity (35.10 to 32.03) but increased from low intensity running (35.12 to 38.24), while trait anxiety decreased for both high (41.67 to 38.79) and low intensity running (42.72 to 42.32), however there were no significant effects for either state or trait anxiety. Only high- intensity exercise reduced fear of anxiety-related bodily sensations (F(1, 52) = 9.44, p <0.01, n2 = 0.15) with mean BSQ scores for the high-intensity exercise group (M = 2.12, SD = 0.10) significantly lower on average compared to the low-intensity comparison group (M = 2.56, SD = 0.11)104. 23 Haffmans (2006) Impact of running therapy for 3 days per A randomised controlled trial from the Netherlands by Haffmans et al. (2006) used 60 psychiatric patients (19 men, 41 women) in a day treatment programme for affective disorders who had a mean age of 39 and were all suffering from a week for 12 weeks on depression and self-efficacy in psychiatric patients all suffering from depression depressive disorder. They compared the impact of running therapy 3 days per week for 12 weeks (n=20) vs physio training therapy (n=21) and a control (n=19) on depression and self-efficacy using the Hamilton Rating Scale for Depression (HRSD), Becks Depression Inventory (BDI), Self-Efficacy Scale and Physical Self-Efficacy Scale (PSES) as measurements. Although both groups were positive about the training programme, participants in the PT group gave a significantly higher evaluation than participants in the running group (p<0.05). After 6 weeks, no significant differences were found between both the training groups and the control group; however, after 12 weeks, the physio training group showed significant improvement on scores for blind-rated HRSD and BDI scores (p= 0.004 and p=0.002, respectively). The running group had no significant difference in depression scores from baseline (26.7) to 12 weeks(25.5). Regarding self-efficacy, the RT group scored significantly higher in PSES after 6 weeks (p=0.03), feelings of self-efficacy did not change significantly in either the running or the physio groups after 12 weeks. Running group feelings of self-efficacy was 46.6 at baseline and 49.1 at 12 weeks105. 24 Thornton (2008) Investigating the relationship between anxiety and marathon An American cross sectional survey by Thornton et al. (2008) used 50 runners over age 18 to investigate the relationship between anxiety and marathon training using the Beck Anxiety Inventory as measurement. Mean anxiety scores decreased from baseline pre-training levels (0.9) compared to 2 months prior to marathon day (to 0.7, respectively, with 72% had no change from baseline pre-training levels on the Beck Anxiety Inventory (0.9) compared to 2 months prior to marathon day (0.7; 72% had no change from baseline, 22% were less anxious and 6% were more anxious than baseline). However, anxiety scores increased as race day approached: at 1 month prior to race day (1.4; 46% had no change from baseline, 19% were less anxious and 35% were more anxious than baseline) and 1 week prior to race (2.6; 22% had no change from baseline, 14% were less anxious and 64% were more anxious than baseline, respectively). Overall results found that marathon training decreased anxiety initially, but overall anxiety increased as race day approached106. 25 Scholz (2008) Impact of a 1 year marathon training programme on self-efficacy A pre-post non-controlled non-experimental longitudinal study based in Switzerland by Scholz et al. (2008) used 30 untrained participants (26 women, 4 men) with a mean age of 41.2 to investigate how a 1 year marathon training running programme impacted self-efficacy using a 4 part author-created measurement. There were no statically significant differences in baseline level, trend or fluctuation of self-efficacy between the participants who successfully completed the marathon, and those who did not. Self-efficacy had a baseline level mean of 3.45 (p<0.01), a linear trend mean of -0.05 (p<0.05), a fluctuation mean of 0.33 (p<0.01) and an intra-class correlations of 0.46. Baseline level of self-efficacy was positively associated with baseline level in running (correlation analyses =.27;p<0.05 (95% confidence intervals = .00; .53) and fluctuation in self-efficacy correlated positively with fluctuation in running .39;p<0.05 (95% confidence interval .03;.74). A substantial correlation between the trend in running and self-efficacy emerged as well (.39, not significant but was associated with a very wide confidence band (95% Confidence interval -.10; .87) and was not significant. As this was a non- experimental longitudinal study, no causal statements can be drawn, hence cannot conclude that, self-efficacy leads to increased levels of exercise and vice versa107. 26 Kalak (2012) Impact of daily 30 minute morning runs on weekdays for 3 weeks (ie. 3x5 runs) on stress and mood A Swiss randomised controlled trial by Kalak et al. (2012) used 51 adolescents (27 female and 24 male) with a mean age of 18.3 to compare the effects of a daily 30 minute morning run on weekdays for 3 weeks (ie. 3 x 5 runs) (n=27) vs a control group (n=24) on stress and mood using the Perceived Stress Scale, a daily mood log and a questionnaire assessing positive and negative comping strategies as measurements. Perceived stress and positive/ negative coping strategies did not differ significantly between the running and the control groups (F(1,49) =1.71, n2=0.034, not significant) nor was there a statistically significant group x time interaction (F(1,49) =2.97, n2=0.057, not significant). Mood in the morning was significantly higher in the running group than the control group (F(5,245) =4.42, n2=0.083, p<0.001); the group x time interaction was also significant (F(5,245) = 6.32, n2 = 0.114, p<0.01); mood in the morning also increased significantly over time in the RG compared with the CG (F(5,245) =16.08, n2 = 0.247, p<0.05). Over time, irrespective of group, mood in the evening improved, but there was no difference of mood in the evening between groups and the group x time interaction was not significant108. 27 Inoue (2013) Impact of 10 organised runs on self- sufficiency in homeless people An American pre-post non-controlled study by Inoue et al. (2013) used 148 homeless people involved in the “Back on my feet” programme who had an average age of 29.9 and 90.5% of whom were male. They examined the impact of 10 organised runs on self-sufficiency using an author-created scale to measure the psychological benefits of the program. Results suggested that increases in running involvement had a significant positive correlation with perceived self-sufficiency (r = .30, p < 0.01). The mean value of perceived self- sufficiency (M = 5.95) exceeded the midpoint (4.0), indicating on average participants agreed the program provided increased psychological benefits associated with self-sufficiency. Results suggested that the participants gained higher levels of perceived self-sufficiency as they became more involved with running during the program, with the regression model showing a significant proportion of the variation in perceived self-sufficiency (F = 3.39, p < .01, Adjusted R2 = .08), and increases in running involvement was the sole significant predictor of the outcome (β = .29, t = 3.57, p < .01)109. 28 Samson (2013) Impact of a 15 week marathon training program consisting of 3 group training days per week plus one run of 8-20 miles on the weekend, on general affect & self-efficacy An American pre-post non-controlled study by Samson et al. (2013) used 39 Caucasian university students (11 males and 28 females) who all had running experience and a mean age of 20.5. They examined how a 15 week marathon training program, consisting of 3 group training days per week plus one training run of 8-20 miles during the weekend, impacted general affect and self-efficacy using the Positive and Negative Affect Scale (PANAS) and author-created measurements for self-efficacy. Results showed a significant increase in self-efficacy over the training programme (F(12,444)=5.81, p<0.01, partial eta2 =0.136). While there was a significant decrease of positive affect over time (F(12,444)=8.35, p<0.01, partial eta2=0.184), there was no significant change found for negative affect over the programme110. 29 Doose (2015) Impact of group walking/running 3 times per week for 8 weeks on depression A German randomised controlled trial by Doose et al. (2015) used 46 outpatients aged 18-65 diagnosed with mild to severe depression to compare the impact of an 8 week, 3-times weekly, group walking/running exercise programme (n=30) vs wait list (n=16) on depression using the Hamilton Rating Scale and Beck Depression Inventory as measurements. Out of forty-six participants, 11 participants (24%) dropped out: 7 (23%) from the intervention group and 4 (25%) from the control group. While both the exercise intervention and control group had reductions in scores for the Hamilton Rating Scale for Depression (-9.48 and -1.2, respectively), results attributed a large and clinically significant change to the exercise intervention (Cohen’s d= 1.8; mean change = 8.24; p = <0.0001). There were moderate changes in Becks Depression Inventory scores without clinical significance (Cohen’s d: 0.50; mean change = 4.66; p=0.09), with the intervention group BDI scores reducing a mean of -8.20, while the control had a mean reduction of -3.54111. 30 Von Haaren (2015) Impact of a 20 week running training course on stress and mood during academic examinations A German randomised controlled trial, within subject design by Von Haaren et al. (2015) used 61 inactive male university students with a mean age of 21.4 to compare how a 20 week aerobic running training course vs a waiting list control, impacted stress and mood during academic examinations using a shorten mood scale based on the Multidimensional Mood Questionnaire and a one item test for perceived control and stress as measurements. Results did not detail the number of participants in each group. Significant emotional stress reactivity was evident in both groups during both academic assessment episodes, with an increase of mean perceived stress of 0.23 in control group, and 0.21 in aerobic group in the first academic assessment. However, participants in the aerobic training group showed lower emotional stress reactivity compared with the control participants after the 20-week training programme, with perceived stress of the aerobic group remaining similar during both exam periods(2.27 to 2.24), however it increased further in the control group (2.43 to 2.51). After both academic assessment periods, the coefficient for the group by perceived stress interaction was higher (B= -0.18, p<0.001) compared with just the first academic assessment (B= -0.11, p<0.05), descriptively indicating a larger effect of the group x PS interaction at the end of the 20 week exercise programme112. 31 Kahan (2018) Impact of 20 running sessions alternating between game vs lap running on self-esteem and self-efficacy in children An American pre-post with comparison study by Kahan et al. (2018) used 11 children (9 males and 2 females) aged 9 & 10 to compare the impact of 20 running sessions alternating between game vs lap running on self-esteem and self-efficacy using a 50-item, author-created questionnaire as measurement. High inherent interest participants (ie, higher MVPA% in the running laps condition) had statistically significant higher scores than low inherent interest participants on Recognition (p=0.01), Ego Orientation (p=0.03), and Expectancy Beliefs (p=0.03) subscales. No differences were detected between high and low response to treatment groups. Results for self-esteem: Cronbach’s alpha score was 0.69 and self-esteem mean was 3.63 on a 5 point scale; while mean for task-efficacy was 4.16 on a 5 point scale. PA% (62.2% vs 76.1%, effect size [ES] = −0.65) was lower and moderate-vigorous physical activity (MVPA%) (33.3% vs 15.8%, ES = 0.75) and MVPA% of PA (53.6% vs 20.2%, ES = 0.91) were higher during game vs lap running conditions113. 32 Keating (2018) Impact of 12 weeks of twice weekly running in a group setting that offers social support supervised by clinical professionals, on stress, anxiety and depression A Canadian pre-post non-controlled study by Keating et al. (2018) used 46 participants with complex mood disorders (11 males and 35 females) consisting of 29 youths (mean age 22.1) and 17 adults (mean age 45.2), to examine how 12 weeks of twice weekly running groups impacted stress, anxiety and depression using the Cohen’s Perceived Stress Scale, Becks Depression Inventory, Becks Anxiety Inventory and Short Form Survey as measurements. Adults and youths with complex mood disorders benefited from the running therapy programme supervised by clinical professionals in a group setting that offers social support. There were significant decreases in depression (F=4.5, df=11, 201, p<0.0001), anxiety (F=4.8, df=11, 186, p<0.0001) and stress (F=2.3, df=11, 186, p=0.01) scores from baseline to the end of the study. Mean BDI scores from baseline to post- exercise intervention, decreased by 39% in adults from high (30.8) to low levels (18.8) and by 27% in youths from moderate (26.9) to reduced moderate levels (19.5). Younger participant age, younger age at onset of illness and higher perceived levels of friendship with other running group members (ps ≤0.04) were associated with lower end-of-study depression, anxiety and stress scores, while higher attendance was associated with decreasing depression and anxiety (ps ≤0.01) scores over time114. 33 Nezlek (2018) Impact of 3 months of self- prescribed running on psychological wellbeing, self- esteem, self- efficacy and affect A Polish pre-post observational cohort study with no control by Nezlek et al. (2018) used 244 recreational runners with a mean age of 32.5 and 48% of whom were women, to investigate how the volume of recreational running could impact psychological wellbeing, self-esteem, self-efficacy and affect. Over 3 months the participants recorded weekly how far they had run and the psychological outcomes were measured using the Rosenberg Self-esteem Scale, the Satisfaction With Life Scale and a circumplex model that distinguishes the valence (positive or negative) and arousal (activated or deactivated) of affect. Results found a positive within- person relationships between how much people ran each week and self-reports of well-being. The more often and farther people ran during a week, the better they thought about themselves and their lives and the better they felt affectively. Results found that sex and age did not significantly moderate any of the relationships reported above (all ps > .13), however, how long people had been running significantly moderated the slope between number of days run each week PA (γ11 = −.033, p < .05). Self-efficacy was related to distance run, but not to frequency. When analyzed separately, both measures of running were significantly related to all measures of well-being, such that well-being was higher during weeks when individuals ran more often and further than it was during weeks when they ran less often and less far. By contrast, the average kilometers people ran each week moderated most relationships between running and well-being such that relationships between well-being and running were weaker for people who ran more than they were for people who ran less. For the kilometers people ran each week, significant moderation was found for weekly Satisfaction with Life Scale (γ11 = −.0002, p = .013), self-esteem (γ11 = −.0002, p = .015), positive activated affect (γ11=−.0003, p<.001), positive deactivated affect (γ11=−.0008, p<.01), negative activated affect (γ11= .0002, p = .046), and negative deactivated affect (γ11 = .0003, p = .01). Satisfaction with progress fully mediated relationships between all measures of well-being and number of days run each week and between all measures of well-being and kilometers run each week. In all of these analyses including self-efficacy and self-esteem, the direct effect of the predictor (running) on the outcome (well-being) was not significant (p>0.12), whereas the direct effect of the mediator (satisfaction with progress) was (p<0.001), and the indirect effect of running was significant (the critical path for demonstrating mediation) (p<0.001) 115. 34 Kruisdijk (2019) Impact of 6 months of running-walking for one hour twice a week on depression in subjects with major depressive disorder A randomised controlled trial from The Netherlands by Kruisdijk et al. (2019) used 48 participants with major depressive disorder with a mean age of 41.6 to compare how 6 months of running-walking for one hour twice a week (n=25) vs a control group (n=23) impacted depression using the Hamilton Depression Scale as measurement. Depression on the HDS decreased in both the intervention and the control group on average by 2–3 points after 3 months, but there was no significant difference or effect on depression in favour of the intervention group (Cohen’s d < 0.2, F = .13, p = 0.73). Conclusions about the anti-depressive effect of this exercise intervention were not possible due to only 9 participants (19%) completing the study, low statistical power and lack of follow-up at six and 12 months. An integrated lifestyle intervention may be more effective than a single add-on exercise intervention for patients with major depressive disorder, as these results found that the exercise intervention and study design weren’t feasible in terms of motivation and compliance116.  References 1. Wilson VE, Morley NC, Bird EI. Mood profiles of marathon runners, joggers and non-exercisers. Percept Mot Skills. 1980;50(1):117-118. doi:10.2466/pms.1980.50.1.117 2. Joesting J. Running and Depression. Percept Mot Skills. 1981;52(2):442-442. doi:10.2466/pms.1981.52.2.442 3. Jorgenson DE, Jorgenson CB. Perceived Effects of Running/Jogging: A Social Survey of Three Clubs. Int Rev Sport Sociol. 1981;16(3):75-85. doi:10.1177/101269028101600305 4. Valliant PM, Bennie FA, Valiant JJ. Do marathoners differ from joggers in personality profile: a sports psychology approach. J Sports Med Phys Fitness. 1981;21(1):62-67. 5. Francis KT, Carter R. Psychological characteristic of joggers. J Sports Med Phys Fitness. 1982;22(3):386-391. 6. Hailey BJ, Bailey LA. Negative addiction in runners: A quantitative approach. J Sport Behav. 1982;5(3):150- 154. 7. Callen KE. Mental and emotional aspects of long-distance running. Psychosomatics. 1983;24(2):133-134, 139, 141, passim. doi:10.1016/S0033-3182(83)73239-1 8. Galle PC, Freeman EW, Galle MG, Huggins GR, Sondheimer SJ. Physiologic and psychologic profiles in a survey of women runners. Fertil Steril. 1983;39(5):633-639. doi:10.1016/s0015-0282(16)47058-5 9. Lobstein DD, Mosbacher BJ, Ismail AH. Depression as a powerful discriminator between physically active and sedentary middle-aged men. J Psychosom Res. 1983;27(1):69-76. doi:10.1016/0022-3999(83)90111-3 10. Rudy EB, Estok PJ. Intensity of jogging: its relationship to selected physical and psychosocial variables in women. West J Nurs Res. 1983;5(4):325-336. doi:10.1177/019394598300500406 11. Goldfarb LA, Plante TG. Fear of fat in runners: an examination of the connection between anorexia nervosa and distance running. Psychol Rep. 1984;55(1):296. doi:10.2466/pr0.1984.55.1.296 12. Guyot GW, Fairchild L, Nickens J. Death concerns of runners and nonrunners. J Sports Med Phys Fitness. 1984;24(2):139-143. 13. Rape RN. Running and Depression. Percept Mot Skills. 1987;64(3_suppl):1303-1310. doi:10.2466/pms.1987.64.3c.1303 14. Weight LM, Noakes TD. Is running an analog of anorexia?: A survey of the incidence of eating disorders in female distance runners. Med Sci Sports Exerc. 1987;19(3):213-217. 15. Chan CS, Grossman HY. Psychological effects of running loss on consistent runners. Percept Mot Skills. 1988;66(3):875-883. doi:10.2466/pms.1988.66.3.875 16. Frazier SE. Mood state profiles of chronic exercisers with differing abilities. Int J Sport Psychol. 1988;19(1):65-71. 17. Lobstein DD, Ismail AH, Rasmussen CL. Beta-endorphin and components of emotionality discriminate between physically active and sedentary men. Biol Psychiatry. 1989;26(1):3-14. doi:10.1016/0006- 3223(89)90003-6 18. Lobstein DD, Rasmussen CL, Dunphy GE, Dunphy MJ. Beta-endorphin and components of depression as powerful discriminators between joggers and sedentary middle-aged men. J Psychosom Res. 1989;33(3):293-305. doi:10.1016/0022-3999(89)90020-2 19. Nouri S, Beer J. Relations of moderate physical exercise to scores on hostility, aggression, and aggression, and trait-anxiety. Percept Mot Skills. 1989;68(3 Pt 2):1191-1194. doi:10.2466/pms.1989.68.3c.1191 20. Chan DW, Lai B. Psychological aspects of long-distance running among Chinese male runners in Hong Kong. Int J Psychosom Off Publ Int Psychosom Inst. 1990;37(1-4):30-34. 21. Chapman CL, De Castro JM. Running addiction: measurement and associated psychological characteristics. J Sports Med Phys Fitness. 1990;30(3):283-290. 22. Guyot WG. Psychological and medical factors associated with pain running. J Sports Med Phys Fitness. 1991;31(3):452-460. 23. Maresh CM, Sheckley BG, Allen GJ, Camaione DN, Sinatra ST. Middle age male distance runners: physiological and psychological profiles. J Sports Med Phys Fitness. 1991;31(3):461-469. 24. Gleaves DH, Williamson DA, Fuller RD. Bulimia nervosa symptomatology and body image disturbance associated with distance running and weight loss. Br J Sports Med. 1992;26(3):157-160. doi:10.1136/bjsm.26.3.157 25. Coen SP, Ogles BM. Psychological characteristics of the obligatory runner: A critical examination of the anorexia analogue hypothesis. J Sport Exerc Psychol. 1993;15(3):338-354. 26. Furst DM, Germone K. Negative addiction in male and female runners and exercisers. Percept Mot Skills. 1993;77(1):192-194. doi:10.2466/pms.1993.77.1.192 27. Masters KS, Ogles BM, Jolton JA. The development of an instrument to measure motivation for marathon running: the Motivations of Marathoners Scales (MOMS). Res Q Exerc Sport. 1993;64(2):134-143. doi:10.1080/02701367.1993.10608790 28. Pierce EF, McGowan RW, Lynn TD. Exercise dependence in relation to competitive orientation of runners. J Sports Med Phys Fitness. 1993;33(2):189-193. 29. Klock SC, DeSouza MJ. Eating disorder characteristics and psychiatric symptomatology of eumenorrheic and amenorrheic runners. Int J Eat Disord. 1995;17(2):161-166. doi:10.1002/1098-108x(199503)17:2<161::aid- eat2260170209>3.0.co;2-r 30. Thornton EW, Scott SE. Motivation in the committed runner: correlations between self-report scales and behaviour. Health Promot Int. 1995;10(3):177-184. doi:10.1093/heapro/10.3.177 31. Powers PS, Schocken DD, Boyd FR. Comparison of habitual runners and anorexia nervosa patients. Int J Eat Disord. 1998;23(2):133-143. doi:10.1002/(sici)1098-108x(199803)23:2<133::aid-eat3>3.0.co;2-l 32. Slay HA, Hayaki J, Napolitano MA, Brownell KD. Motivations for running and eating attitudes in obligatory versus nonobligatory runners. Int J Eat Disord. 1998;23(3):267-275. doi:10.1002/(sici)1098- 108x(199804)23:3<267::aid-eat4>3.0.co;2-h 33. Ryujin DH, Breaux C, Marks AD. Symptoms of eating disorders among female distance runners: can the inconsistencies be unraveled? Women Health. 1999;30(1):71-83. doi:10.1300/j013v30n01_05 34. Leedy G. Commitment to Distance Running: Coping Mechanisms or Addiction. J Sport Behav Mob Ala. 2000;23(3):255–270. 35. Edwards SD, Ngcobo HS, Edwards DJ, Palavar K. Exploring the relationship between physical activity, psychological well-being and physical self- perception in different exercise groups. South Afr J Res Sport Phys Educ Recreat. 2005;27(1):59-74-74. 36. Schnohr P, Kristensen TS, Prescott E, Scharling H. Stress and life dissatisfaction are inversely associated with jogging and other types of physical activity in leisure time--The Copenhagen City Heart Study. Scand J Med Sci Sports. 2005;15(2):107-112. doi:10.1111/j.1600-0838.2004.00394.x 37. Strachan SM, Woodgate J, Brawley LR, Tse A. The Relationship of Self-Efficacy and Self-Identity to Long-Term Maintenance of Vigorous Physical Activity. J Appl Biobehav Res. 2005;10(2):98-112. doi:10.1111/j.1751- 9861.2005.tb00006.x 38. Galper DI, Trivedi MH, Barlow CE, Dunn AL, Kampert JB. Inverse association between physical inactivity and mental health in men and women. Med Sci Sports Exerc. 2006;38(1):173-178. doi:10.1249/01.mss.0000180883.32116.28 39. Luszczynska A, Mazurkiewicz M, Ziegelmann JP, Schwarzer R. Recovery self-efficacy and intention as predictors of running or jogging behavior: A cross-lagged panel analysis over a two-year period. Psychol Sport Exerc. 2007;8(2):247-260. doi:10.1016/j.psychsport.2006.03.010 40. Smith D, Wright C, Winrow D. Exercise dependence and social physique anxiety in competitive and non‐ competitive runners. Int J Sport Exerc Psychol. 2010;8(1):61-69. doi:10.1080/1612197X.2010.9671934 41. Gapin JI, Petruzzello SJ. Athletic identity and disordered eating in obligatory and non-obligatory runners. J Sports Sci. 2011;29(10):1001-1010. doi:10.1080/02640414.2011.571275 42. Wadas G, DeBeliso M. Disordered eating, eating attitudes, and reasons for exercise among male high school cross country runners. Sport J. 2014;17. Accessed July 15, 2020. https://www.cabdirect.org/cabdirect/abstract/20153072464 43. Download citation of The relationship between motivations, perceived control, and mental toughness among marathon runners. ResearchGate. Accessed July 15, 2020. https://www.researchgate.net/publication/325793259_The_relationship_between_motivations_perceived _control_and_mental_toughness_among_marathon_runners 44. Lucidi F, Pica G, Mallia L, et al. Running away from stress: How regulatory modes prospectively affect athletes’ stress through passion. Scand J Med Sci Sports. 2016;26(6):703-711. doi:10.1111/sms.12496 45. Batmyagmar D, Kundi M, Ponocny-Seliger E, et al. High intensity endurance training is associated with better quality of life, but not with improved cognitive functions in elderly marathon runners. Sci Rep. 2019;9(1):4629. doi:10.1038/s41598-019-41010-w 46. Cleland V, Nash M, Sharman MJ, Claflin S. Exploring the Health-Promoting Potential of the “parkrun” Phenomenon: What Factors are Associated With Higher Levels of Participation? Am J Health Promot AJHP. 2019;33(1):13-23. doi:10.1177/0890117118770106 47. Lukács A, Sasvári P, Varga B, Mayer K. Exercise addiction and its related factors in amateur runners. J Behav Addict. 2019;8(2):343-349. doi:10.1556/2006.8.2019.28 48. Nowlis DP, Greenberg N. Empirical description of effects of exercise on mood. Percept Mot Skills. 1979;49(3):1001-1002. doi:10.2466/pms.1979.49.3.1001 49. Wilson VE, Berger BG, Bird EI. Effects of running and of an exercise class on anxiety. Percept Mot Skills. 1981;53(2):472-474. doi:10.2466/pms.1981.53.2.472 50. Markoff RA, Ryan P, Young T. Endorphins and mood changes in long-distance running. Med Sci Sports Exerc. 1982;14(1):11-15. doi:10.1249/00005768-198201000-00002 51. Thaxton L. Physiological and Psychological Effects of Short-term Exercise Addiction on Habitual Runners. J Sport Exerc Psychol. 1982;4(1):73-80. doi:10.1123/jsp.4.1.73 52. McGowan RW, Pierce EF, Jordan D. Mood alterations with a single bout of physical activity. Percept Mot Skills. 1991;72(3 Pt 2):1203-1209. doi:10.2466/pms.1991.72.3c.1203 53. Goode KT, Roth DL. Factor Analysis of Cognitions during Running: Association with Mood Change. J Sport Exerc Psychol. 1993;15(4):375-389. doi:10.1123/jsep.15.4.375 54. Morris M, Salmon P. Qualitative and quantitative effects of running on mood. J Sports Med Phys Fitness. 1994;34(3):284-291. 55. Rudolph DL, Butki BD. Self-efficacy and affective responses to short bouts of exercise. J Appl Sport Psychol. 1998;10(2):268-280. doi:10.1080/10413209808406393 56. Cox RH, Thomas TR, Davis JE. Positive and negative affect associated with an acute bout of aerobic exercise. J Exerc Physiol Online. 2001;4:13-20. 57. O’Halloran PD, Murphy GC, Webster KE. Measure of beliefs about improvements in mood associated with exercise. Psychol Rep. 2002;90(3 Pt 1):834-840. doi:10.2466/pr0.2002.90.3.834 58. Szabo A. The Acute Effects of Humor and Exercise on Mood and Anxiety. J Leis Res. 2003;35(2):152-162. doi:10.1080/00222216.2003.11949988 59. O’Halloran PD, Murphy GC, Webster K. Mood during a 60-minute treadmill run: Timing and type of mood change. Int J Sport Psychol. 2004;35:309-327. 60. Robbins LB, Pender NJ, Ronis DL, Kazanis AS, Pis MB. Physical activity, self-efficacy, and perceived exertion among adolescents. Res Nurs Health. 2004;27(6):435-446. doi:10.1002/nur.20042 61. Pretty J, Peacock J, Sellens M, Griffin M. The mental and physical health outcomes of green exercise. Int J Environ Health Res. 2005;15(5):319-337. doi:10.1080/09603120500155963 62. Hoffman MD, Hoffman DR. Exercisers achieve greater acute exercise-induced mood enhancement than nonexercisers. Arch Phys Med Rehabil. 2008;89(2):358-363. doi:10.1016/j.apmr.2007.09.026 63. Kwan BM, Bryan AD. Affective response to exercise as a component of exercise motivation: Attitudes, norms, self-efficacy, and temporal stability of intentions. Psychol Sport Exerc. 2010;11(1):71-79. doi:10.1016/j.psychsport.2009.05.010 64. Weinstein AA, Deuster PA, Francis JL, Beadling C, Kop WJ. The Role of Depression in Short-Term Mood and Fatigue Responses to Acute Exercise. Int J Behav Med. 2010;17(1):51-57. doi:10.1007/s12529-009-9046-4 65. Anderson RJ, Brice S. The mood-enhancing benefits of exercise: Memory biases augment the effect. Psychol Sport Exerc. 2011;12(2):79-82. doi:10.1016/j.psychsport.2010.08.003 66. Kane I, Robertson R, Fertman C, Nagle E, McConnaha W, Rabin B. Self-efficacy and enjoyment of middle school children performing the Progressive Aerobic Cardiovascular Endurance Run (PACER). Percept Mot Skills. 2013;117:470-483. doi:10.2466/29.25.PMS.117x23z3 67. Szabo A, Abrahám J. The psychological benefits of recreational running: a field study. Psychol Health Med. 2013;18(3):251-261. doi:10.1080/13548506.2012.701755 68. McDowell CP, Campbell MJ, Herring MP. Sex-Related Differences in Mood Responses to Acute Aerobic Exercise. Med Sci Sports Exerc. 2016;48(9):1798-1802. doi:10.1249/MSS.0000000000000969 69. Rogerson M, Brown DK, Sandercock G, Wooller J-J, Barton J. A comparison of four typical green exercise environments and prediction of psychological health outcomes. Perspect Public Health. 2016;136(3):171- 180. doi:10.1177/1757913915589845 70. Edwards MK, Rhodes RE, Loprinzi PD. A Randomized Control Intervention Investigating the Effects of Acute Exercise on Emotional Regulation. Am J Health Behav. 2017;41(5):534-543. doi:10.5993/AJHB.41.5.2 71. Krotee ML. The Effects of Various Physical Activity Situational Settings on the Anxiety Level of Children. J Sport Behav Mob Ala. 1980;3(4):158–164. 72. Wildmann J, Krüger A, Schmole M, Niemann J, Matthaei H. Increase of circulating beta-endorphin-like immunoreactivity correlates with the change in feeling of pleasantness after running. Life Sci. 1986;38(11):997-1003. doi:10.1016/0024-3205(86)90233-x 73. O’Connor PJ, Carda RD, Graf BK. Anxiety and intense running exercise in the presence and absence of interpersonal competition. Int J Sports Med. 1991;12(4):423-426. doi:10.1055/s-2007-1024706 74. Nabetani T, Tokunaga M. The effect of short-term (10- and 15-min) running at self-selected intensity on mood alteration. J Physiol Anthropol Appl Human Sci. 2001;20(4):231-239. doi:10.2114/jpa.20.233 75. Bodin M, Hartig T. Does the outdoor environment matter for psychological restoration gained through running? Psychol Sport Exerc. 2003;4(2):141-153. doi:10.1016/S1469-0292(01)00038-3 76. Butryn TM, Furst DM. The effects of park and urban settings on the moods and cognitive strategies of female runners. J Sport Behav. 2003;26(4):335-355. 77. Kerr JH, Fujiyama H, Sugano A, Okamura T, Chang M, Onouha F. Psychological responses to exercising in laboratory and natural environments. Psychol Sport Exerc. 2006;7(4):345-359. doi:10.1016/j.psychsport.2005.09.002 78. Rose EA, Parfitt G. Exercise experience influences affective and motivational outcomes of prescribed and self-selected intensity exercise. Scand J Med Sci Sports. 2012;22(2):265-277. doi:10.1111/j.1600- 0838.2010.01161.x 79. Reed K, Wood C, Barton J, Pretty JN, Cohen D, Sandercock GRH. A repeated measures experiment of green exercise to improve self-esteem in UK school children. PloS One. 2013;8(7):e69176. doi:10.1371/journal.pone.0069176 80. Harte JL, Eifert GH. The effects of running, environment, and attentional focus on athletes’ catecholamine and cortisol levels and mood. Psychophysiology. 1995;32(1):49-54. doi:10.1111/j.1469-8986.1995.tb03405.x 81. Berger B, Owen D, Motl R, Parks L. Relationship between expectancy of psychological benefits and mood alteration in joggers. Int J Sport Psychol. 1998;29:1-16. 82. Markowitz SM, Arent SM. The exercise and affect relationship: evidence for the dual-mode model and a modified opponent process theory. J Sport Exerc Psychol. 2010;32(5):711-730. doi:10.1123/jsep.32.5.711 83. Lion LS. Psychological effects of jogging: a preliminary study. Percept Mot Skills. 1978;47(3 Pt 2):1215-1218. doi:10.2466/pms.1978.47.3f.1215 84. Blue FR. Aerobic running as a treatment for moderate depression. Percept Mot Skills. 1979;48(1):228. doi:10.2466/pms.1979.48.1.228 85. Young RJ. The effect of regular exercise on cognitive functioning and personality. Br J Sports Med. 1979;13(3):110-117. doi:10.1136/bjsm.13.3.110 86. Blumenthal JA, Williams RS, Needels TL, Wallace AG. Psychological changes accompany aerobic exercise in healthy middle-aged adults. Psychosom Med. 1982;44(6):529-536. doi:10.1097/00006842-198212000- 00004 87. Trujillo CM. The effect of weight training and running exercise intervention programs on the self-esteem of college women. Int J Sport Psychol. 1983;14(3):162-173. 88. Tuckman BW, Hinkle JS. An experimental study of the physical and psychological effects of aerobic exercise on schoolchildren. Health Psychol Off J Div Health Psychol Am Psychol Assoc. 1986;5(3):197-207. doi:10.1037//0278-6133.5.3.197 89. Doyne EJ, Ossip-Klein DJ, Bowman ED, Osborn KM, McDougall-Wilson IB, Neimeyer RA. Running versus weight lifting in the treatment of depression. J Consult Clin Psychol. 1987;55(5):748-754. doi:10.1037//0022- 006x.55.5.748 90. Fremont J, Craighead LW. Aerobic exercise and cognitive therapy in the treatment of dysphoric moods. Cogn Ther Res. 1987;11(2):241-251. doi:10.1007/BF01183268 91. Hannaford CP, Harrell EH, Cox K. Psychophysiological Effects of A Running Program on Depression and Anxiety in A Psychiatric Population. Psychol Rec. 1988;38(1):37-48. doi:10.1007/BF03395005 92. Long BC, Haney CJ. Long-Term Follow-up of Stressed Working Women: A Comparison of Aerobic Exercise and Progressive Relaxation. J Sport Exerc Psychol. 1988;10(4):461-470. doi:10.1123/jsep.10.4.461 93. Simons CW, Birkimer JC. An exploration of factors predicting the effects of aerobic conditioning on mood state. J Psychosom Res. 1988;32(1):63-75. doi:10.1016/0022-3999(88)90089-X 94. Moses J, Steptoe A, Mathews A, Edwards S. The effects of exercise training on mental well-being in the normal population: a controlled trial. J Psychosom Res. 1989;33(1):47-61. doi:10.1016/0022-3999(89)90105- 0 95. Ossip-Klein DJ, Doyne EJ, Bowman ED, Osborn KM, McDougall-Wilson IB, Neimeyer RA. Effects of running or weight lifting on self-concept in clinically depressed women. J Consult Clin Psychol. 1989;57(1):158-161. doi:10.1037//0022-006x.57.1.158 96. Morris M, Steinberg H, Sykes EA, Salmon P. Effects of temporary withdrawal from regular running. J Psychosom Res. 1990;34(5):493-500. doi:10.1016/0022-3999(90)90023-w 97. Friedman E, Berger BG. Influence of gender, masculinity, and femininity on the effectiveness of three stress reduction techniques: Jogging, relaxation response, and group interaction. J Appl Sport Psychol. 1991;3(1):61-86. doi:10.1080/10413209108406435 98. Williams TJ, Krahenbuhl GS, Morgan DW. Mood state and running economy in moderately trained male runners. Med Sci Sports Exerc. 1991;23(6):727-731. 99. Kerr JH, Vlaswinkel EH. Self-reported mood and running under natural conditions. Work Stress. 1993;7(2):161-177. doi:10.1080/02678379308257058 100. Long BC. Aerobic conditioning (jogging) and stress inoculation interventions: an exploratory study of coping. Int J Sport Psychol. 1993;24(2):94-109. 101. Berger BG, Friedman E. Comparison of Jogging, the Relaxation Response, and Group Interaction for Stress Reduction. J Sport Exerc Psychol. 1988;10(4):431-447. doi:10.1123/jsep.10.4.431 102. Berger BG, Owen DR. Relation of low and moderate intensity exercise with acute mood change in college joggers. Percept Mot Skills. 1998;87(2):611-621. doi:10.2466/pms.1998.87.2.611 103. Szabo A, Frenkl R, Janek G, Kálmán L, Lászay D. Runners’ anxiety and mood on running and non-running days: An in situ daily monitoring study. Psychol Health Med. 1998;3(2):193-199. doi:10.1080/13548509808402235 104. Broman-Fulks JJ, Berman ME, Rabian BA, Webster MJ. Effects of aerobic exercise on anxiety sensitivity. Behav Res Ther. 2004;42(2):125-136. doi:10.1016/S0005-7967(03)00103-7 105. Judith Haffmans PM, Kleinsman ACM, van Weelden C, Huijbrechts IPAM, Hoencamp E. Comparing running therapy with physiotraining therapy in the treatment of mood disorders. Acta Neuropsychiatr. 2006;18(3-4):173-176. doi:10.1111/j.1601-5215.2006.00115.x 106. Thornton E, Cronholm P, McCray L, Webner D. Does Marathon Training Adversely Affect Baseline Anxiety Levels? AMAA J. 2008;21(3):5-9. 107. Scholz U, Nagy G, Schüz B, Ziegelmann JP. The role of motivational and volitional factors for self- regulated running training: associations on the between- and within- person level. Br J Soc Psychol. 2008;47(Pt 3):421-439. doi:10.1348/014466607X266606 108. Kalak N, Gerber M, Kirov R, et al. Daily morning running for 3 weeks improved sleep and psychological functioning in healthy adolescents compared with controls. J Adolesc Health Off Publ Soc Adolesc Med. 2012;51(6):615-622. doi:10.1016/j.jadohealth.2012.02.020 109. Inoue Y, Funk D, Jordan JS. The role of running involvement in creating self-sufficiency for homeless individuals through a community-based running program. J Sport Manag. 2013;27(6):439-452. doi:10.1123/jsm.27.6.439 110. Samson A, Solmon M, Stewart L. Changes in self-efficacy and affect during a 15-week marathon training program. Int J Sport Psychol. 2013;44(1):55-68. 111. Doose M, Ziegenbein M, Hoos O, et al. Self-selected intensity exercise in the treatment of major depression: A pragmatic RCT. Int J Psychiatry Clin Pract. 2015;19(4):266-275. doi:10.3109/13651501.2015.1082599 112. von Haaren B, Haertel S, Stumpp J, Hey S, Ebner-Priemer U. Reduced emotional stress reactivity to a real-life academic examination stressor in students participating in a 20-week aerobic exercise training: A randomised controlled trial using Ambulatory Assessment. Psychol Sport Exerc. 2015;20:67-75. doi:10.1016/j.psychsport.2015.04.004 113. Kahan D, McKenzie TL. Physical Activity and Psychological Correlates During an After-School Running Club. Am J Health Educ. 2018;49(2):113-123. doi:10.1080/19325037.2017.1414646 114. Keating LE, Becker S, McCabe K, et al. Effects of a 12-week running programme in youth and adults with complex mood disorders. BMJ Open Sport Exerc Med. 2018;4(1). doi:10.1136/bmjsem-2017-000314 115. Nezlek JB, Cypryańska M, Cypryański P, et al. Within-Person Relationships Between Recreational Running and Psychological Well-Being. J Sport Exerc Psychol. 2018;40(3):146-152. doi:10.1123/jsep.2017- 0244 116. Kruisdijk F, Hopman-Rock M, Beekman ATF, Hendriksen I. EFFORT-D: results of a randomised controlled trial testing the EFFect of running therapy on depression. BMC Psychiatry. 2019;19(1):170. doi:10.1186/s12888-019-2156-x

A Scoping Review of the Relationship between Running and Mental Health.

11-01-2020

Oswald, Freya,Campbell, Jennifer,Williamson, Chloë,Richards, Justin,Kelly, Paul

eng

PMC6143236

RESEARCH ARTICLE Using wearable sensors to classify subject- specific running biomechanical gait patterns based on changes in environmental weather conditions Nizam Uddin AhamedID1*, Dylan Kobsar1☯, Lauren Benson1☯, Christian ClermontID1☯, Russell KohrsID1☯, Sean T. Osis1,2☯, Reed Ferber1,2,3☯ 1 Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada, 2 Running Injury Clinic, University of Calgary, Calgary, Alberta, Canada, 3 Faculty of Nursing, University of Calgary, Calgary, Alberta, Canada ☯ These authors contributed equally to this work. * [email protected] Abstract Running-related overuse injuries can result from a combination of various intrinsic (e.g., gait biomechanics) and extrinsic (e.g., running surface) risk factors. However, it is unknown how changes in environmental weather conditions affect running gait biomechanical patterns since these data cannot be collected in a laboratory setting. Therefore, the purpose of this study was to develop a classification model based on subject-specific changes in bio- mechanical running patterns across two different environmental weather conditions using data obtained from wearable sensors in real-world environments. Running gait data were recorded during winter and spring sessions, with recorded average air temperatures of -10˚ C and +6˚ C, respectively. Classification was performed based on measurements of pelvic drop, ground contact time, braking, vertical oscillation of pelvis, pelvic rotation, and cadence obtained from 66,370 strides (~11,000/runner) from a group of recreational runners. A non- linear and ensemble machine learning algorithm, random forest (RF), was used to classify and compute a heuristic for determining the importance of each variable in the prediction model. To validate the developed subject-specific model, two cross-validation methods (one-against-another and partitioning datasets) were used to obtain experimental mean classification accuracies of 87.18% and 95.42%, respectively, indicating an excellent dis- criminatory ability of the RF-based model. Additionally, the ranked order of variable impor- tance differed across the individual runners. The results from the RF-based machine- learning algorithm demonstrates that processing gait biomechanical signals from a single wearable sensor can successfully detect changes to an individual’s running patterns based on data obtained in real-world environments. PLOS ONE | https://doi.org/10.1371/journal.pone.0203839 September 18, 2018 1 / 15 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Ahamed NU, Kobsar D, Benson L, Clermont C, Kohrs R, Osis ST, et al. (2018) Using wearable sensors to classify subject-specific running biomechanical gait patterns based on changes in environmental weather conditions. PLoS ONE 13(9): e0203839. https://doi.org/ 10.1371/journal.pone.0203839 Editor: Yih-Kuen Jan, University of Illinois at Urbana-Champaign, UNITED STATES Received: May 8, 2018 Accepted: August 28, 2018 Published: September 18, 2018 Copyright: © 2018 Ahamed et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Relevant data are included with the submission, any additional data (e.g., remaining 5 runners data) will be available upon request from the Running Injury Clinic and University of Calgary Institutional Data Access / Ethics Committee (CHREB) by contacting Dr. Stacey A. Page, chair of CHREB at omb@ucalgary. ca. Funding: This study was partially funded by the Natural Sciences and Engineering Research Introduction Running is one of the most common recreational activities around the world but despite its popularity, each year approximately 50% of runners experience a running-related musculo- skeletal injury [1–3]. The etiology of overuse running injuries is multifactorial, and can result from the interaction of many extrinsic factors, such as environmental conditions, running sur- face, footwear, and weekly training mileage, as well as intrinsic risk factors such as age, foot strike pattern, and gait biomechanics [1–4]. Prolonged exposure to these intrinsic and extrinsic risk factors may lead to overuse running injury [5]. One risk factor that has received very little attention in the literature is whether gait biomechanical patterns change as a result of environ- mental weather conditions. Previous investigations of injury risk, based on ambient temperature, have suggested that tissue damage may occur due to a lack of proper warm up. For example, Milgrom et al. reported an increased risk of Achilles paratendinitis among infantry recruits in winter condi- tions, as compared to summer [6]. On the other hand, cold weather has been shown to reduce shoe-surface traction, resulting in a reduced risk of acute knee and ankle injuries among foot- ball players [7, 8]. Only a handful of studies have investigated the effect of environmental weather conditions on running performance, but none have investigated whether gait biome- chanics change as a result of environmental weather. For example, Ely et al., [9] reported a progressive reduction in marathon performance as temperatures increased from 5 to 25 degrees C, for both males and females and across competitive and recreational runners, but performance was more negatively affected for slower runners. These studies suggest that weather can affect both physiological and mechanical aspects of running gait. Thus, it is possi- ble that different weather conditions may be associated with concomitant changes in gait bio- mechanical running patterns, however, to our knowledge no study has directly investigated this hypothesis. The main reason the inter-relationship between environmental weather conditions and gait biomechanics has not been investigated is most likely due to the inability to collect such data in a laboratory setting. However, due to the availability and utility of modern portable inertial measurement units (IMUs) and global positioning system (GPS), it is now possible to collect data outside of the laboratory setting [10–12]. Since large quantities of data can be collected using wearable devices, machine learning (ML) techniques are also needed to better under- stand the complexities of gait biomechanics and how concomitant changes in biomechanical patterns may be related to injury or performance [13, 14]. Furthermore, traditional biome- chanics research generally investigates potential differences between two groups using group- based analyses. For example, several researchers have identified differences in running patterns based on different age groups, gender and/or injury status [15–17]. In contrast, more recent research has shown that group-based comparisons are not efficacious due to the existence of sub-groups [18, 19], and other studies have shown that subject-specific models are necessary to understand individual differences and risk factors [20–23]. Several authors have also used different ML algorithms to develop these sub-group-based models, including principal compo- nent analysis, support vector machine and hierarchical cluster analysis [17–19]. However, to our knowledge no study has directly investigated whether a subject-specific model provide deeper insight into emerging IMU-based biomechanical investigations based on changes in environmental weather conditions. Therefore, the purpose of this study was to develop a classification model based on subject- specific changes in biomechanical running patterns across two different environmental weather conditions using data obtained from wearable sensors in out-of-laboratory environ- ments. We hypothesized that we could classify changes in subject-specific running patterns Running biomechanical gait patterns identification based on environmental weather conditions PLOS ONE | https://doi.org/10.1371/journal.pone.0203839 September 18, 2018 2 / 15 Council of Canada (NSERC: Discovery Grant 1028495, Accelerator Award 1030390, and Idea-2 Innovation Awared I2IPJ 493875-16), a University of Calgary Eyes High Postdoctoral Research award, and a Strategic Research Grant from the Vice- President (Research) at the University of Calgary. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. based on weather conditions with a classification accuracy greater than 80% and that the ranked order of variable importance would be based on subject-specific ML models. A second- ary objective was to determine the ranking of the biomechanical variables, based on their importance in the classification margin, in order to better understand changes in subject-spe- cific running patterns. Methods Participants Six recreational runners (Five females: age = 47.5±9.69 years, height = 169.17±6.56 cm, weight = 67.42±11.5 kg; and one male: age = 29 years, height = 170 cm, weight = 75 kg) volun- teered to participate in the study. The runners were free of any neuromuscular diseases or musculoskeletal injuries and they were registered for a half-marathon training program man- aged by a local running group. This protocol was approved by the University of Calgary Con- joint Health Research Ethics Board (REB 16–2035) and all runners provided their written informed consent. Instrumentation Biomechanical gait variables from each runner were recorded using the Lumo Run1 (Lumo Bodytech Inc., Mountain View, CA) wearable inertial measurement unit (IMU), consisting of a 3-dimensional (3D) accelerometer, magnetometer, and gyroscope. (dimension: 4.98cm x 2.84cm x 0.99cm). The Lumo Run IMU was attached to the posterior aspect of either the run- ner’s waistband or running belt as per the manufacturer’s instructions [24] (Fig 1). This wear- able sensor device measured and recorded data for six different biomechanical variables [24] and averaged these data for each ten-strides (Table 1) and a complete description of these vari- ables can be found on the manufacturer’s website [24]. A GPS watch (Garmin vı´voactive1 HR; Garmin International Inc., KS, USA) was attached to each runner’s preferred wrist (Fig 1) and recorded running speed (m/s), distance (kilometers (km)), and global positioning data, including latitude, longitude and altitude, every second. Data collection Gait variables from winter runs were recorded from mid-February to mid-March, while spring runs were recorded from late April to mid-May. Each runner performed two training runs during each weather condition for a total of four runs used in this analysis. Each run began at 8:30 AM on a Sunday, and was completed outdoors on pavement, and along a similar route. Data corresponding to the temperature (degrees Celsius), snow depth (cm), precipitation (mm), and humidity (%) for each run were derived from three different International Air Transport Association-affiliated weather stations in Calgary, AB: Canada Olympic Park (WDU), Calgary International Airport (YYC), and Calgary INT’L CS Alberta (PCI). For each run, data from km 0 to 1 were discarded, as this was considered a warmup period, and any data following 6-km was also not used in the analysis in order to minimize the effects of fatigue, if any. Therefore, only 5-km of data (i.e., from km 1 to 6) were analyzed from each run and in total, the input data consisted of 66,370 strides (~11,000/runner) across the four runs. Altitude, latitude and longitude data from the Garmin watch were used to ensure the ele- vation profile for each of the four runs were similar, and that the data from each run were col- lected from a route with minimal changes in elevation, in order to minimize the effect of running on uphill and/or downhill. Running biomechanical gait patterns identification based on environmental weather conditions PLOS ONE | https://doi.org/10.1371/journal.pone.0203839 September 18, 2018 3 / 15 Data analysis A robust, and non-linear machine learning classifier, called Random Forest (RF), was used to develop the classification model which measured the accuracy and importance of gait bio- mechanical variables in classifying runs of differing environmental weather conditions. The RF classifier has been shown to provide a higher classification accuracy than other existing ML classifiers with a faster computation speed, while facilitating complex interactions among pre- dictor variables and providing information about the importance of each predictor variable [25–27]. In other word, RF provides variable importance measures to rank predictors accord- ing to their predictive power [28]. Two validation methods (Method 1: one-against-another and Method 2: partitioning datasets) were used to ensure that the proposed RF-based subject- specific classification approach was robust and that the data were not overfit [29]. With Method 1 (one-against-another) data combining one winter run and one spring run were con- sidered the training dataset, and the testing dataset consisted of the remaining winter and spring runs. With Method 2 (partitioning datasets), 70% of each runner’s total strides per- formed in both weather condition were randomly selected for training, and the remaining Fig 1. The two wearable sensors devices (Lumo Run and Garmin) used to record the data during running. https://doi.org/10.1371/journal.pone.0203839.g001 Running biomechanical gait patterns identification based on environmental weather conditions PLOS ONE | https://doi.org/10.1371/journal.pone.0203839 September 18, 2018 4 / 15 30% were used for testing purposes. Individual training and test sets were generated for each subject. Each classification method was applied using the standalone Python programming language (version 3.6, www.python.org) [30]. The developed RF model was trained and cross- validated using the built-in Anaconda distribution of Python with notable packages including matplotlib, numpy, scipy, and scikit-learn (“sklearn.ensemble.RandomForestClassifier”) [31, 32]. The number of trees in the RF was set to 100, as previous research has shown this is a suffi- cient number for obtaining high accuracy solutions to similar classification problems [33, 34]. Additionally, the RF used a Gini index to calculate the impurity of a node from the CART (classification and regression tree) learning system in order to construct the decision trees [26]. The RF trees compute a heuristic for determining how significant a variable (6 Lumo Run gait variables) is in predicting a target (weather). Statistical analyses were performed using repeated measures ANOVA (P<0.05) and Cohen’s d effects size estimates were calculated for each difference on the outcome measures between each weather condition. Results Fig 2 presents an overview of the RF-based classification accuracy obtained with test data gen- erated using the two validation methods. Using Method 2 (partitioning datasets), the RF-based model demonstrated an excellent overall mean classification accuracy of 95.42%. In fact, all runners yielded a classification accuracy higher than 90% with the exception of Runner 5, who exhibited a classification accuracy of 89.06%. In contrast, the overall mean classification accu- racy obtained with Method 1 (one-against-another) was 87.18%, and all the runners yielded a classification accuracy higher than 85% except for Runner 5, who exhibited an accuracy of 70.47%. Significant differences (P<0.05) in the overall classification accuracies were also found between the methods. Overall, for all runners, Method 2 yielded a higher classification accuracy than Method 1. Moderate differences in classification accuracy were also observed between Methods 1 and 2 for Runner 5 (18.59%) and Runner 6 (14.37%), but the differences in classification accuracy between the methods were slight for Runner 3 (8.0%) and Runner 4 (6.14%), and non-existent for Runner 1 (2.16%), and Runner 2 (0.45%). Table 1. Features recorded from the wearable devices. Device Features Unit Frequency Lumo Run Pelvic drop (PD) (frontal plane motion of the runner’s pelvis) Degree (deg) 100- Hz Vertical oscillation of pelvis (VOP) (measurement of vertical displacement) Centimeter (cm) Ground contact time (GCT) (time of total foot contact with the ground) Millisecond (ms) Braking (reduction in forward velocity following foot strike) Meter/sec (m/s) Pelvic rotation (PR) (transverse plane motion of the runner’s pelvis) Degree (deg) Cadence (number of bilateral steps per minute) Steps per minute (SPM) Garmin Vı´voactive HR Heart rate (HR) Beats per minute (BPM) 1- Hz Altitude Meter (m) Distance Kilometer (km) Global position-latitude Degree (deg) Global position- longitude Degree (deg) Running speed Meter/sec (m/s) https://doi.org/10.1371/journal.pone.0203839.t001 Running biomechanical gait patterns identification based on environmental weather conditions PLOS ONE | https://doi.org/10.1371/journal.pone.0203839 September 18, 2018 5 / 15 Overall, the ranking of the variables, based on their importance in the classification margin, differed across all runners and classification methods (Table 2 and Fig 3). For example, although vertical oscillation of pelvis was the most important variable, using both methods, for Runners 2 and 5, it ranked lower for Runner 1, wherein pelvic drop was the most important variable across both methods. Similarly, pelvic rotation was the second-ranked variable for both methods for Runners 2 and 4 but was less significant for the other runners. Overall, cadence was less important for all runners, with the exception of for Runner 3, wherein it was the second most important variable using Method 2. Another notable difference was found for braking where for Runner 4 it was the most important variable using Method 1 but only the third most important variable with Method 2. A similar inconsistency was found for pelvic rotation, which was identified as the most important variable with Method 1 but was ranked fourth with Method 2. The remaining three variables, braking, ground contact time, and cadence, were not found to be important for the classification task and were consistently ranked third, fifth and sixth across both methods, respectively (Fig 4). Table 2 also presents the results of the statistical analyses of the individual and overall results from both weather conditions. All runners, except Runner 4, demonstrated lower verti- cal oscillation of the pelvis in winter than in spring. The pelvic drop of two runners (Runner 2 and Runner 3) and the pelvic rotation of three runners (Runner 3, 4 and 6) were higher in win- ter than in spring. There was no clear difference in braking between winter and spring because three runners (Runners 1, 3 and 4) exhibited the same values during both conditions, two run- ners (Runners 4 and 6) had lower braking values in winter, and one runner (Runner 2) Fig 2. Classification accuracies obtained with Method 1 (black) and Method 2 (white); : P<0.05. https://doi.org/10.1371/journal.pone.0203839.g002 Running biomechanical gait patterns identification based on environmental weather conditions PLOS ONE | https://doi.org/10.1371/journal.pone.0203839 September 18, 2018 6 / 15 presented a higher braking value in winter. Two runners (Runners 1 and 2) had lower ground contact time values in winter, whereas two runners (Runners 3 and 4) had a higher ground contact time in winter, and the remaining two runners (Runners 5 and 6) had a similar value during both weather conditions. Finally, with the exception of Runner 4, all runners demon- strated a higher cadence during winter. Overall, five biomechanical variables (excluding cadence) demonstrated lower values during winter runs as compared to spring runs. However, no significant differences were found between the two weather conditions for any of the six variables (P>0.05). Cohen’s d effect size and 95% confidence intervals [95%CI] are presented in Table 2 and reveal the effect sizes between winter and spring runs were small (i.e., d<0.5), except for vertical oscillation of the pelvis, pelvic drop, and cadence, which were moderate (i. e., 0.5<d<0.8). The results of the environmental weather conditions are presented in Table 3 and show the average temperature, humidity and snow depth were significantly different between winter and spring runs, along with no differences in precipitation. Table 2. RF-based variable importance and descriptive statistics obtained with both methods and for all individual runners. Gait Variable Analyzed parameters Subject-specific results Overall results R-1 R-2 R-3 R-4 R-5 R-6 Mean±SD 95%CI P ES Vertical oscillation of pelvis (cm) M1-VI (%) 5.84 46.24 8.87 6.59 32.39 15.15 19.18±16.53 (-17.55, 7.07) 0.32 -0.45 M2-VI (%) 2.18 40.84 33.64 12.87 44.63 12.37 24.42±17.53 Win (mean) 6.08 4.53 6.34 7.03 11.38 7.90 7.21±2.33 (-1.38, 0.21) 0.12 -0.78 Spr (mean) 6.22 5.03 7.97 6.68 12.72 8.17 7.81±2.68 Pelvic drop (deg) M1-VI (%) 39.62 6.25 21.59 14.03 9.67 15.63 17.80±11.91 (-16.98, 7.62) 0.37 -0.41 M2-VI (%) 58.23 7.78 9.57 31.46 7.91 19.93 22.48±19.8 Win (mean) 8.8 9.16 11.2 8.26 10.24 7.59 9.21±1.32 (-0.57, 2.89) 0.14 -0.71 Spr (mean) 11.61 7.81 10.92 10.5 11.53 9.88 10.37±1.41 Pelvic rotation (deg) M1-VI (%) 15.57 23.41 10.74 27.62 26.55 26.15 21.67±6.91 (-4.35, 16.9) 0.19 0.62 M2-VI (%) 12.9 27.17 4.85 30.38 3.66 13.17 15.36±11.16 Win (mean) 14.27 11.52 11.31 19.39 15.51 11.74 13.96±3.16 (-3.70, 3.89) 0.95 -0.03 Spr (mean) 15.98 16.36 10.49 13.69 17.48 10.29 14.05±3.09 Braking (m/s) M1-VI (%) 13.85 9.1 12.91 38.42 6.1 27.33 17.95±12.39 (-12.50, 12.29) 0.98 -0.01 M2-VI (%) 9.5 10.35 14.3 19.88 9.02 45.29 18.06±13.95 Win (mean) 0.27 0.25 0.36 0.34 0.3 0.31 0.31±0.04 (-0.03, 0.06) 0.34 -0.43 Spr (mean) 0.27 0.22 0.36 0.37 0.31 0.4 0.32±0.07 Ground contact time (ms) M1-VI (%) 19.87 11.05 41.98 8.68 13.03 6.42 16.84±13.15 (-6.59, 17.15) 0.31 0.47 M2-VI (%) 15.01 11.06 15.73 3.36 20.63 3.56 11.56±6.97 Win (mean) 258.33 254.47 298.37 247.04 290.8 272.88 270.32±20.7 (-7.8, 3.93) 0.43 -0.35 Spr (mean) 267.47 263.26 297.04 243.13 290.03 272.59 272.25±19.4 Cadence (steps/min) M1-VI (%) 5.25 3.95 3.91 4.66 12.26 9.32 6.56±3.44 (-10.27, 7.13) 0.66 -0.19 M2-VI (%) 2.18 2.8 21.91 2.05 14.17 5.66 8.13±8.16 Win (mean) 173.81 183.63 161.67 173.73 151.64 166.21 168.45±11.1 (-1.62, 7.21) 0.16 0.66 Spr (mean) 172.53 181.29 150.68 174.48 149.05 165.89 165.65±13.2 VI: variable importance; M1: Method 1; M2: Method 2; R: Runner. Win: Winter; Spr: Spring.  P: significantly different (P<0.05) ES: effect size (Cohen’s d). 95%CI: 95% confidence intervals https://doi.org/10.1371/journal.pone.0203839.t002 Running biomechanical gait patterns identification based on environmental weather conditions PLOS ONE | https://doi.org/10.1371/journal.pone.0203839 September 18, 2018 7 / 15 The speed and overall route were similar between sessions, as presented in Table 4. In addi- tion, the speed, heart rate, altitude, latitude and longitude showed no significant differences between the two weather conditions (Table 4). Discussion The objective of this study was to classify changes in subject-specific running gait patterns based on the environmental weather (winter vs. spring) conditions using an RF classifier. The findings of the current study support our hypotheses and demonstrate that an RF-approach was a robust method for accurately classifying large datasets collected using wearable sensors in real-world settings. Interestingly, each subject’s classification method had different impor- tant predictor variables based on the RF evaluation. Therefore, each individual runner exhib- ited different changes in overall gait biomechanics, and changes in the weather conditions affected the mechanics of individual runners differently. To our knowledge, this study consti- tutes the first examination of changes in subject-specific gait biomechanics based on environ- mental weather conditions. These findings also support the efficacy of wearable technology, and subsequent data science approaches for understanding the complexities of running gait patterns based on collecting data in out-of-laboratory environments [29, 35]. Overall, the results of this investigation demonstrate that the presence of snow and colder temperatures results in runner-specific changes in biomechanical gait patterns, possibly in an effort to reduce the risk of falling due to the slippery surface [36]. These assumptions are sup- ported by previous studies that also indicated injury rates were higher in colder weather Fig 3. Importance of the different variables for each runner identified using two validation methods. All the variables in this stacked bar graph are shown in the same vertical order for both methods (VOP, PD, PR, braking, GCT and cadence). https://doi.org/10.1371/journal.pone.0203839.g003 Running biomechanical gait patterns identification based on environmental weather conditions PLOS ONE | https://doi.org/10.1371/journal.pone.0203839 September 18, 2018 8 / 15 conditions compared to warmer weather due to running on icy and slippery running paths [37–39]. Moreover, the results of the current study also indicate that the changes in running biomechanical patterns between weather conditions may contribute to overuse running- related injuries [5]. For example, when pelvic drop was important for classification (e.g. Run- ner 1), there was greater pelvic drop in spring than winter, but when it was not important (e.g. Runners 2 and 3), it was lower in spring than winter. A similar pattern was observed in vertical oscillation of the pelvis: when it was important (e.g. Runners 2 and 5), there was greater amounts of oscillation in spring than winter, but when it was less important (e.g. Runner 4), there was greater oscillation in winter than spring. These results suggest that the runners involved in the current study adjusted to different weather conditions by reducing vertical or frontal plane motion accompanied by slight increases in running cadence and shorter stride Fig 4. Graphical representation of the three most important variables (braking, PD and PR) for Runner 4 with Method 2. Each point is equivalent to five strides. Data from both the training and testing sets are shown. https://doi.org/10.1371/journal.pone.0203839.g004 Table 3. Environmental weather conditions experienced during running. Weather Temperature (˚C) Snow depth (cm) Humidity (%) Precipitation (mm) Winter -9.74±4.85 P = 0.001  2.97±2.83 P = 0.001  75.41% P = 0.000  1.35±0.89 P = 0.46 Spring +5.33±2.65 0.31±0.21 63.32% 1.73±0.62 : Significantly different (P <0.05) https://doi.org/10.1371/journal.pone.0203839.t003 Running biomechanical gait patterns identification based on environmental weather conditions PLOS ONE | https://doi.org/10.1371/journal.pone.0203839 September 18, 2018 9 / 15 length. However, it is important to note that all of the participants were injury-free and these aforementioned gait changes were not necessary to mitigate symptoms of injury. On the other hand, adopting a more constrained running pattern may, over time, may contribute to an overuse running injury [40]. Future prospective research is therefore necessary to help under- stand the inter-relationship between environmental weather conditions, concomitant and sub- ject-specific changes in gait patterns, and the etiology of injury. The RF classifier has received increasing attention within the gait-related research commu- nity due to its ability to yield excellent classification results and its fast-computational process- ing speed [41, 42]. In addition, this classifier provides consistent classifications using predictions derived from an ensemble of decision trees as well as a ranking of the variables according to their ability to differentiate between the target classes [41, 43]. The results of the current study are largely consistent with previous RF-based gait biomechanics studies involv- ing wearable sensors (40,41). However, while research has investigated how IMUs systems can be used for the assessment of running biomechanics in laboratory and clinical settings [44], very few studies have been conducted in real-world settings [45, 46]. Therefore, to provide insights into this knowledge gap and open new research directions, the current study devel- oped and evaluated subject-specific methods, using an RF classifier using data from a single IMU, and achieved excellent classification accuracy results. Interestingly, the slight differences in classification accuracy obtained between the two tested RF-methods suggest that the inclu- sion of information from multiple runs is beneficial for building a successful model. In addi- tion, the current study demonstrates that the RF algorithm was able to accurately classify and determine the relative importance of each input variable for an individual runner [47, 48]. While it is important to note that the combination of multiple variables was needed to achieve a high classification accuracy and fully understand the multidimensional characteris- tics of the subject-specific running biomechanics associated with different weather conditions, the current findings can be compared to previous studies that have either addressed the effects of temperature on running performance [9, 49, 50] or injury rates [51]. For example, our find- ings are consistent with previous work demonstrating the usefulness of multidimensional anal- yses to better understand the complex patterns and inter-relationships between multiple biomechanical variables when classifying runners based on subtle differences in gait patterns that may be indicative of performance and/or injury [52–55]. Moreover, in the current study, regardless of the classification method, all runners exhibited slightly lower values for all bio- mechanical gait variables, except cadence, during winter as compared to spring. These findings support previous research indicating a more economical running technique with a lower risk Table 4. Specific running measurements of the different runners recorded using a wearable GPS (Garmin Vı´voactive HR). Runners Speed (m/s) Heart rate (BPM) Altitude (m) Latitude (deg.) Longitude (deg.) Winter Spring Winter Spring Winter Spring Winter Spring Winter Spring R-1 2.40 2.39 161.13 154.01 1050.32 1067.18 51.05 51.05 -114.07 -114.05 R-2 2.36 2.36 112.45 117.97 1049.44 1071.58 50.84 51.06 -113.61 -114.06 R-3 2.18 2.27 146.65 143.02 1045.34 1066.30 51.05 51.06 -114.08 -114.07 R-4 2.39 2.36 140.68 126.82 1036.76 1061.51 51.05 51.05 -114.05 -114.05 R-5 2.54 2.32 154.94 155.96 1046.59 1064.64 51.05 51.06 -114.08 -114.06 R-6 2.36 2.40 141.99 151.14 1072.51 1051.28 51.05 51.05 -114.07 -114.05 Overall 2.37 2.35 142.97 141.49 1050.16 1063.75 51.02 51.06 -113.99 -114.06 P = 0.54 P = 0.57 P = 0.19 P = 0.27 P = 0.42 R: Runner. https://doi.org/10.1371/journal.pone.0203839.t004 Running biomechanical gait patterns identification based on environmental weather conditions PLOS ONE | https://doi.org/10.1371/journal.pone.0203839 September 18, 2018 10 / 15 of overuse injury during winter (colder) weather conditions [56–58]. Reduced pelvic drop has also been considered a protective factor for patellofemoral pain [59, 60], as well as a gait retraining strategy to reduce pain associated with this common running-related injury [61]. Future research is therefore necessary using wearable sensors in real-world situations to help better elucidate these inter-relationships. To our knowledge, this is the first study to quantify subject-specific changes in real-world running gait biomechanics as a result of changes in environmental weather conditions. More- over, the current study also represents one of the first investigations to analyse data from a runner’s actual training run. Specifically, a recent systematic review [62] suggested that future studies should involve long-term data collections, across multiple running bouts, and in a run- ner’s natural environment, thus enabling prospective studies and the development of subject- specific models of gait. Considering that the etiology of overuse running injuries is multifacto- rial, and can result from the interaction of many extrinsic factors such as environmental condi- tions, the results of the current study are an important contribution to help to better understand injury etiology. Limitation and future directions The stated findings should be considered with respect to limitations. First, although there was a small number of runners (n = 6), the method employed is generalizable considering that we used subject-specific models to measure changes in gait parameters across 66,370 strides. Regardless, further investigation using a larger sample size is necessary to determine if homog- enous sub-groups, or clusters, will form as a result of consistent within-group biomechanical changes (18,58). Second, we did not include any non-weather-related factors such as changes in runner’s clothing, footwear, nutrition, sleep, or daily mood state profile. Future research should consider these factors in order to gain a more complete understanding of how external factors can influence running gait biomechanics. Third, although the present study examined two different weather conditions, these results of the present study may only be applicable to these weather conditions and temperatures. As well, the temperatures in the present study (i.e., -10˚C to +6˚C) were lower than those of Ely et al., [9] (i.e., +5˚C to +25˚C) and Knapik et al., [51] (i.e., +15˚C to +35˚C). Lastly, a limited number of spatiotemporal and biomechanical var- iables obtained from a commercially available wearable sensor device were used for the current study. While it is likely that additional or more complex variables from one or more wearable sensors could improve the classification accuracy of the current study, we posit that the sim- plicity and translatability to the current market of wearable sensors is a significant advantage that should not be overlooked. Regardless, future research should include a broader range of variables, and possibly more wearable sensor devices, in order to gain a deeper understanding for subject-specific changes in gait patterns during out-of-laboratory data collections. Conclusion In summary, our developed RF-based subject-specific classification model demonstrated excellent mean classification accuracies (87.18% and 95.42%) based on a large set of running gait data from a small group of runners. These novel results support the use of a robust machine learning approach for determining subject-specific changes in running gait patterns based on differences in external weather conditions using a single IMU device. We believe that our RF-based method may provide a more in-depth understanding of changes in gait biome- chanics in response to extrinsic injury-risk factors and therefore conclude that the relationship between environmental weather conditions and gait biomechanics is subject-specific and mul- tifactorial and involves unique interactions between intrinsic and extrinsic factors. Running biomechanical gait patterns identification based on environmental weather conditions PLOS ONE | https://doi.org/10.1371/journal.pone.0203839 September 18, 2018 11 / 15 Supporting information S1 File. Validation for Runner 4 (R-4) using Method 1 (one-against-another); classification accuracy: 91.42%. (XLSX) S2 File. Validation for Runner 4 (R-4) using Method 2 (partitioning datasets in two sets at a ratio of 7:3); classification accuracy: 97.56%. (XLSX) Acknowledgments This study was partially funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) Idea-2-Innovation Award (grant #I2IPJ 493875–16), a University of Calgary Eyes High Postdoctoral Research award, and a Strategic Research Grant from the Vice-Presi- dent (Research) at the University of Calgary. Author Contributions Conceptualization: Dylan Kobsar, Reed Ferber. Data curation: Nizam Uddin Ahamed. Formal analysis: Lauren Benson, Sean T. Osis. Funding acquisition: Reed Ferber. Investigation: Christian Clermont. Methodology: Nizam Uddin Ahamed. Resources: Russell Kohrs, Reed Ferber. Software: Christian Clermont, Russell Kohrs. Supervision: Reed Ferber. Validation: Lauren Benson. Visualization: Christian Clermont. Writing – original draft: Nizam Uddin Ahamed, Dylan Kobsar. Writing – review & editing: Nizam Uddin Ahamed, Lauren Benson, Sean T. Osis, Reed Ferber. References 1. Saragiotto BT, Di Pierro C, Lopes AD. Risk factors and injury prevention in elite athletes: a descriptive study of the opinions of physical therapists, doctors and trainers. Brazilian journal of physical therapy. 2014; 18(2):137–43. https://doi.org/10.1590/S1413-35552012005000147 PMID: 24845023 2. Saragiotto BT, Yamato TP, Junior LCH, Rainbow MJ, Davis IS, Lopes AD. What are the main risk fac- tors for running-related injuries? Sports medicine. 2014; 44(8):1153–63. https://doi.org/10.1007/ s40279-014-0194-6 PMID: 24809248 3. van Gent BR, Siem DD, van Middelkoop M, van Os TA, Bierma-Zeinstra SS, Koes BB. Incidence and determinants of lower extremity running injuries in long distance runners: a systematic review. British journal of sports medicine. 2007; 41:469–80. https://doi.org/10.1136/bjsm.2006.033548 PMID: 17473005 4. Meeuwisse WH. Assessing causation in sport injury: A multifactorial model. LWW; 1994. 5. Hreljac A. Impact and overuse injuries in runners. Medicine and science in sports and exercise. 2004; 36(5):845–9. PMID: 15126720 Running biomechanical gait patterns identification based on environmental weather conditions PLOS ONE | https://doi.org/10.1371/journal.pone.0203839 September 18, 2018 12 / 15 6. Milgrom C, Finestone A, Zin D, Mandel D, Novack V. Cold weather training: a risk factor for Achilles paratendinitis among recruits. Foot & ankle international. 2003; 24(5):398–401. 7. Orchard JW, Powell JW. Risk of knee and ankle sprains under various weather conditions in American football. Medicine and science in sports and exercise. 2003; 35(7):1118–23. https://doi.org/10.1249/01. MSS.0000074563.61975.9B PMID: 12840631 8. Orchard J, Seward H, McGivern J, Hood S. Intrinsic and extrinsic risk factors for anterior cruciate liga- ment injury in Australian footballers. The American journal of sports medicine. 2001; 29(2):196–200. https://doi.org/10.1177/03635465010290021301 PMID: 11292045 9. Ely MR, Cheuvront SN, Roberts WO, Montain SJ. Impact of weather on marathon-running perfor- mance. Medicine and Science in Sports and Exercise. 2007; 39(3):487–93. https://doi.org/10.1249/ mss.0b013e31802d3aba PMID: 17473775 10. Giandolini M, Pavailler S, Samozino P, Morin J-B, Horvais N. Foot strike pattern and impact continuous measurements during a trail running race: proof of concept in a world-class athlete. Footwear Science. 2015; 7(2):127–37. 11. Giandolini M, Horvais N, Rossi J, Millet GY, Samozino P, Morin J-B. Foot strike pattern differently affects the axial and transverse components of shock acceleration and attenuation in downhill trail run- ning. Journal of biomechanics. 2016; 49(9):1765–71. https://doi.org/10.1016/j.jbiomech.2016.04.001 PMID: 27087676 12. Meardon SA, Hamill J, Derrick TR. Running injury and stride time variability over a prolonged run. Gait & posture. 2011; 33(1):36–40. 13. Scho¨llhorn W, Nigg B, Stefanyshyn D, Liu W. Identification of individual walking patterns using time dis- crete and time continuous data sets. Gait & Posture. 2002; 15(2):180–6. 14. Kiernan D, Hawkins DA, Manoukian MA, McKallip M, Oelsner L, Caskey CF, et al. Accelerometer- based prediction of running injury in National Collegiate Athletic Association track athletes. Journal of Biomechanics. 2018. 15. Bus SA. Ground reaction forces and kinematics in distance running in older-aged men. Medicine & Sci- ence in Sports & Exercise. 2003; 35(7):1167–75. 16. Ferber R, Davis IM, Williams Iii DS. Gender differences in lower extremity mechanics during running. Clinical biomechanics. 2003; 18(4):350–7. PMID: 12689785 17. Phinyomark A, Hettinga BA, Osis ST, Ferber R. Gender and age-related differences in bilateral lower extremity mechanics during treadmill running. PLoS One. 2014; 9(8):e105246. https://doi.org/10.1371/ journal.pone.0105246 PMID: 25137240 18. Phinyomark A, Osis S, Hettinga BA, Ferber R. Kinematic gait patterns in healthy runners: A hierarchical cluster analysis. Journal of biomechanics. 2015; 48(14):3897–904. https://doi.org/10.1016/j.jbiomech. 2015.09.025 PMID: 26456422 19. Watari R, Osis ST, Phinyomark A, Ferber R. Runners with patellofemoral pain demonstrate sub-groups of pelvic acceleration profiles using hierarchical cluster analysis: an exploratory cross-sectional study. BMC musculoskeletal disorders. 2018; 19(1):120. https://doi.org/10.1186/s12891-018-2045-3 PMID: 29673341 20. Bates BT, Stergiou N. Performance Accomodation to Midsole Hardness During Running. Journal of Human Movement Studies. 1996; 31(4):189–210. Epub 210. 21. Stacoff A, Nigg BM, Reinschmidt C, Van Den Bogert AJ, Lundberg A. Tibiocalcaneal kinematics of bare- foot versus shod running. Journal of biomechanics. 2000; 33(11):1387–95. PMID: 10940397 22. Stacoff A, Reinschmidt C, Nigg B, van den Bogert AJ, Lundberg A, Denoth J, et al. Effects of foot ortho- ses on skeletal motion during running. Clinical Biomechanics. 2000; 15(1):54–64. PMID: 10590345 23. Bates B, James C, Dufek J. Single-subject analysis. In Stergiou N (Ed), Innovative analyses of human movement. 2004:3–28. 24. LumoBack. Available from: https://www.lumobodytech.com/science-of-lumo-run/. 2018. 25. Cutler DR, Edwards TC, Beard KH, Cutler A, Hess KT, Gibson J, et al. Random forests for classification in ecology. Ecology. 2007; 88(11):2783–92. PMID: 18051647 26. Strobl C, Boulesteix A-L, Kneib T, Augustin T, Zeileis A. Conditional variable importance for random for- ests. BMC bioinformatics. 2008; 9(1):307. 27. Ziegler A, Ko¨nig IR. Mining data with random forests: current options for real-world applications. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2014; 4(1):55–63. 28. Degenhardt F, Seifert S, Szymczak S. Evaluation of variable selection methods for random forests and omics data sets. Briefings in bioinformatics. 2017. 29. Ferber R, Osis ST, Hicks JL, Delp SL. Gait biomechanics in the era of data science. Journal of biome- chanics. 2016; 49(16):3759–61. https://doi.org/10.1016/j.jbiomech.2016.10.033 PMID: 27814971 Running biomechanical gait patterns identification based on environmental weather conditions PLOS ONE | https://doi.org/10.1371/journal.pone.0203839 September 18, 2018 13 / 15 30. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine learn- ing in Python. Journal of Machine Learning Research. 2011; 12(Oct):2825–30. 31. Gupta VP, Garton AL, Sisti JA, Christophe BR, Lord AS, Lewis AK, et al. Prognosticating Functional Outcome After Intracerebral Hemorrhage: The ICHOP Score. World Neurosurgery. 2017; 101:577–83. https://doi.org/10.1016/j.wneu.2017.02.082 PMID: 28242488 32. Rendeiro AF, Schmidl C, Strefford JC, Walewska R, Davis Z, Farlik M, et al. Chromatin accessibility maps of chronic lymphocytic leukemia identify subtypespecific epigenome signatures and associated transcription regulatory networks. Nature Communications. 2016:1–25. 33. Pavey TG, Gilson ND, Gomersall SR, Clark B, Trost SG. Field evaluation of a random forest activity classifier for wrist-worn accelerometer data. Journal of Science and Medicine in Sport. 2017; 20(1):75– 80. https://doi.org/10.1016/j.jsams.2016.06.003 PMID: 27372275 34. Singh K, Guntuku SC, Thakur A, Hota C. Big data analytics framework for peer-to-peer botnet detection using random forests. Information Sciences. 2014; 278:488–97. 35. Phinyomark A, Petri G, Iba´ñez-Marcelo E, Osis ST, Ferber R. Analysis of Big Data in Gait Biomechan- ics: Current Trends and Future Directions. Journal of Medical and Biological Engineering. 2017:1–17. https://doi.org/10.1007/s40846-017-0217-5 36. Cappellini G, Ivanenko YP, Dominici N, Poppele RE, Lacquaniti F. Motor patterns during walking on a slippery walkway. Journal of Neurophysiology. 2010; 103(2):746–60. https://doi.org/10.1152/jn.00499. 2009 PMID: 19955283 37. Powell KE, Kohl HW, Caspersen CJ, Blair SN. An epidemiological perspective on the causes of running injuries. The physician and Sportsmedicine. 1986; 14(6):100–14. https://doi.org/10.1080/00913847. 1986.11709103 PMID: 27463440 38. van Mechelen W. Running Injuries. Sports Medicine. 1992; 14(5):320–35. https://doi.org/10.2165/ 00007256-199214050-00004 39. Renstro¨m P, Peterson L. Groin injuries in athletes. British Journal of Sports Medicine. 1980; 14(1):30. PMID: 7378668 40. Hamill J, van Emmerik RE, Heiderscheit BC, Li L. A dynamical systems approach to lower extremity running injuries. Clinical biomechanics. 1999; 14(5):297–308. PMID: 10521606 41. Reynard F, Terrier P. Determinants of gait stability while walking on a treadmill: a machine learning approach. arXiv preprint arXiv:170505191. 2017. 42. Samà A, Rodrı´guez-Martı´n D, Pe´rez-Lo´pez C, Català A, Alcaine S, Mestre B, et al. Determining the optimal features in freezing of gait detection through a single waist accelerometer in home environ- ments. Pattern Recognition Letters. 2017. https://doi.org/10.1016/j.patrec.2016.12.012 43. Breiman L. Random forests. Machine learning. 2001; 45(1):5–32. 44. Wundersitz DW, Josman C, Gupta R, Netto KJ, Gastin PB, Robertson S. Classification of team sport activities using a single wearable tracking device. Journal of biomechanics. 2015; 48(15):3975–81. https://doi.org/10.1016/j.jbiomech.2015.09.015 PMID: 26472301 45. Buckley C, O’Reilly M, Whelan D, Farrell AV, Clark L, Longo V, et al., editors. Binary classification of running fatigue using a single inertial measurement unit. Wearable and Implantable Body Sensor Net- works (BSN), 2017 IEEE 14th International Conference on; 2017: IEEE. 46. Ahmadi A, Mitchell E, Destelle F, Gowing M, O’Connor NE, Richter C, et al., editors. Automatic activity classification and movement assessment during a sports training session using wearable inertial sen- sors. Wearable and Implantable Body Sensor Networks (BSN), 2014 11th International Conference on; 2014: IEEE. 47. Nandy A, Chakraborty R, Chakraborty P. Cloth invariant gait recognition using pooled segmented statis- tical features. Neurocomputing. 2016; 191(Supplement C):117–40. https://doi.org/10.1016/j.neucom. 2016.01.002. 48. Langs G, Menze BH, Lashkari D, Golland P. Detecting stable distributed patterns of brain activation using gini contrast. NeuroImage. 2011; 56(2):497–507. https://doi.org/10.1016/j.neuroimage.2010.07. 074 PMID: 20709176 49. Vihma T. Effects of weather on the performance of marathon runners. International journal of biometeo- rology. 2010; 54(3):297–306. https://doi.org/10.1007/s00484-009-0280-x PMID: 19937453 50. Suping Z, Guanglin M, Yanwen W, Ji L. Study of the relationships between weather conditions and the marathon race, and of meteorotropic effects on distance runners. International journal of biometeorol- ogy. 1992; 36(2):63–8. PMID: 1634282 51. Knapik JJ, Canham-chervak M, Hauret K, Laurin MJ, Hoedebecke E, Craig S, et al. Seasonal variations in injury rates during US Army Basic Combat Training. Annals of occupational hygiene. 2002; 46(1):15– 23. PMID: 12005127 Running biomechanical gait patterns identification based on environmental weather conditions PLOS ONE | https://doi.org/10.1371/journal.pone.0203839 September 18, 2018 14 / 15 52. Clermont CA, Osis ST, Phinyomark A, Ferber R. Kinematic Gait Patterns in Competitive and Recrea- tional Runners. Journal of Applied Biomechanics. 2017:1–26. https://doi.org/10.1123/jab.2017-0010 53. Clermont CA, Phinyomark A, Osis ST, Ferber R. Classification of higher-and lower-mileage runners based on running kinematics. Journal of Sport and Health Science. 2017. https://doi.org/10.1016/j.jshs. 2016.11.005 54. Phinyomark A, Osis S, Hettinga B, Leigh R, Ferber R. Gender differences in gait kinematics in runners with iliotibial band syndrome. Scandinavian journal of medicine & science in sports. 2015; 25(6):744– 53. 55. Ahamed NU, Benson L, Clermont C, Osis ST, Ferber R. Fuzzy Inference System-based Recognition of Slow, Medium and Fast Running Conditions using a Triaxial Accelerometer. Procedia Computer Sci- ence. 2017; 114:401–7. 56. Wellenkotter J, Kernozek T, Meardon S, Suchomel T. The effects of running cadence manipulation on plantar loading in healthy runners. International journal of sports medicine. 2014; 35(09):779–84. 57. Hobara H, Sato T, Sakaguchi M, Nakazawa K. Step frequency and lower extremity loading during run- ning. International journal of sports medicine. 2012; 33(04):310–3. 58. Schubert AG, Kempf J, Heiderscheit BC. Influence of stride frequency and length on running mechan- ics: a systematic review. Sports health. 2014; 6(3):210–7. https://doi.org/10.1177/1941738113508544 PMID: 24790690 59. Dierks TA, Manal KT, Hamill J, Davis IS. Proximal and distal influences on hip and knee kinematics in runners with patellofemoral pain during a prolonged run. Journal of Orthopaedic & Sports Physical Ther- apy. 2008; 38(8):448–56. 60. Willson JD, Davis IS. Lower extremity mechanics of females with and without patellofemoral pain across activities with progressively greater task demands. Clinical biomechanics. 2008; 23(2):203–11. https:// doi.org/10.1016/j.clinbiomech.2007.08.025 PMID: 17942202 61. Willy RW, Scholz JP, Davis IS. Mirror gait retraining for the treatment of patellofemoral pain in female runners. Clinical Biomechanics. 2012; 27(10):1045–51. https://doi.org/10.1016/j.clinbiomech.2012.07. 011 PMID: 22917625 62. Benson LC, Clermont CA, Bosˇnjak E, Ferber R. The Use of Wearable Devices for Walking and Running Gait Analysis Outside of The Lab: A Systematic Review. Gait & Posture. 2018; 63:124–38. Running biomechanical gait patterns identification based on environmental weather conditions PLOS ONE | https://doi.org/10.1371/journal.pone.0203839 September 18, 2018 15 / 15

Using wearable sensors to classify subject-specific running biomechanical gait patterns based on changes in environmental weather conditions.

09-18-2018

Ahamed, Nizam Uddin,Kobsar, Dylan,Benson, Lauren,Clermont, Christian,Kohrs, Russell,Osis, Sean T,Ferber, Reed

eng

PMC5649866

Effects of a 4-week high-intensity interval training on pacing during 5-km running trial R. Silva1, M. Damasceno1, R. Cruz1, M.D. Silva-Cavalcante1,2, A.E. Lima-Silva2,3, D.J. Bishop4,5 and R. Bertuzzi1 1Grupo de Estudos do Desempenho Aeróbio (GEADE-USP), Departamento de Esportes Escola de Educac¸ão Física e Esportes, Universidade de São Paulo, São Paulo, Brasil 2Grupo de Pesquisa em Ciência do Esporte, Faculdade de Nutric¸ão, Universidade Federal de Pernambuco, Pernambuco, Brasil 3Grupo de Pesquisa em Desempenho Humano, Universidade Tecnológica Federal do Paraná, Paraná, Brasil 4Institute of Sport, Exercise and Active Living, Victoria University, Melbourne, Victoria, Australia 5School of Medical and Health Sciences, Edith Cowan University, Perth, Australia Abstract This study analyzed the influence of a 4-week high-intensity interval training on the pacing strategy adopted by runners during a 5-km running trial. Sixteen male recreational long-distance runners were randomly assigned to a control group (CON, n=8) or a high-intensity interval training group (HIIT, n=8). The HIIT group performed high-intensity interval-training twice per week, while the CON group maintained their regular training program. Before and after the training period, the runners performed an incremental exercise test to exhaustion to measure the onset of blood lactate accumulation, maximal oxygen uptake (VO2max), and peak treadmill speed (PTS). A submaximal constant-speed test to measure the running economy (RE) and a 5-km running trial on an outdoor track to establish pacing strategy and performance were also done. During the 5-km running trial, the rating of perceived exertion (RPE) and time to cover the 5-km trial (T5) were registered. After the training period, there were significant improvements in the HIIT group of B7 and 5% for RE (P=0.012) and PTS (P=0.019), respectively. There was no significant difference between the groups for VO2max (P=0.495) or onset of blood lactate accumulation (P=0.101). No difference was found in the parameters measured during the 5-km trial before the training period between HIIT and CON (P40.05). These findings suggest that 4 weeks of HIIT can improve some traditional physiological variables related to endurance performance (RE and PTS), but it does not alter the perception of effort, pacing strategy, or overall performance during a 5-km running trial. Key words: Rating of perceived exertion; Running economy; Peak treadmill speed; Maximal oxygen uptake Introduction It has been widely recognized that during recreational and official athletic events the running intensity is always self-selected by athletes (1–3). The manner by which runners self-select their running speed during a given competition has been defined as pacing strategy (4). Specifically, during a 5-km running race, athletes usually adopt a pacing strategy characterized by a fast start (first 400 m), followed by a period of slower speed during the middle part (400–4600 m), and a significant increase in running speed during the last part (final 400 m) of the race (2). These variations in running speed seem to occur to optimize the use of the available energy resources (5). Based on the linear increase in the rating of perceived exertion (RPE) during time-trials, some studies have sug- gested that this triphasic pacing strategy profile (so-called ‘‘U-shaped’’) could reflect a centrally-regulated control system (1,2). It is believed that athletes might consciously monitor their RPE based on internal (physiological) signals and change their running speed in order to prevent a premature exercise termination (6,7). Previous studies have observed a significant relation- ship between traditional physiological predictors of endur- ance performance and running pacing strategy (7–9). Lima-Silva et al. (9) reported that runners with a higher running economy (RE), peak treadmill speed (PTS), and a faster speed corresponding to onset of blood lactate accumulation (OBLA) presented a more aggressive U-shaped speed curve during a 10-km running race com- pared with their counterparts. In addition, high-performance athletes ran the first 1200 m of a 10-km race at a speed faster than the average speed of the entire race and above their PTS, while a low-performance group started Correspondence: R. Bertuzzi: <[email protected]> Received January 11, 2017 | Accepted July 10, 2017 Braz J Med Biol Res | doi: 10.1590/1414-431X20176335 Brazilian Journal of Medical and Biological Research (2017) 50(12): e6335, http://dx.doi.org/10.1590/1414-431X20176335 ISSN 1414-431X Research Article 1/7 the race with a less aggressive pacing strategy and slightly below the OBLA speed (9). These results suggest that athletes with higher PTS, OBLA, and RE may be able to improve their performance by increasing mainly their speed during the first part of a running race. According to the theory suggesting that the exercise intensity is regu- lated by the central nervous system (CNS), an improve- ment in these physiological variables could enable athletes to begin the race with a higher starting speed without provoking critical changes in homeostasis that otherwise could lead to premature fatigue. It is also well recognized that physical training pro- duces a number of changes in the metabolic function in different physiological systems (10,11). Specifically, the addition of a short-term, high-intensity interval training (HIIT) program performed for 3 to 6 weeks is able to promote significant improvements in RE, PTS, and OBLA in trained participants (5,12–16). For instance, Smith et al. (15) applied a 4-week HIIT program to well-trained runners and observed a significant increase in PTS. In addi- tion, Smith et al. (16) reported significant improvements in VO2max and RE in a group of well-trained runners after a 4-week HIIT. Based on these findings, one could hypothesize that the inclusion of a HIIT program can improve physiological variables related to endurance performance and, therefore, alter pacing strategy. How- ever, to the best of our knowledge, no previous study has investigated the effects of a HIIT program on self-selected pacing during a 5-km running trial. Therefore, the main purpose of this study was to analyze the influence of a 4-week HIIT program on pacing strategy during a 5-km running trial. Our hypothesis was that the HIIT program might improve physiological vari- ables related to running pacing strategy (e.g., in RE, PTS, and OBLA), resulting in an altered U-shaped speed curve (i.e., a more intense and faster start). Material and Methods Participants The sample size required was estimated using 5-km running performance as the main outcome from the equa- tion n=8e2/d2, as proposed by Hopkins (17), where n, e, and d denote predicted sample size, coefficient of varia- tion, and the magnitude of the treatment effect, respec- tively. The coefficient of variation was assumed to be 1.7% (18). Expecting a 2.8% magnitude of effect for the treatment (16), the detection of a very conservative 2% difference as statistically significant would require at least 5 participants for each group. However, to allow for any possible sample dropout, we targeted 8 participants per group. Thus, sixteen male long-distance runners were invited to participate in the present study. All participants were recreational runners from local clubs. The participants were included if they had participated in 5-km running races during the last two years, their best performance in the 5-km running races had been under 25 min, and if they had not participated in any HIIT program 6 months before the start of this study. They performed only low-intensity, continuous aerobic training (50–70% VO2max) before the beginning of the study and were instructed to maintain this aerobic training schedule during the experimental period. The participants’ running training volume was reported as the mean distance covered per week (19,20), which was assessed through a training log recorded for two weeks prior to the beginning of the study and for the last two weeks before the study completion. The participants were assigned to the HIIT group (n=8, age 35±6 years, body mass 70.5±4.6 kg, height 172.5±4.1 cm) or a control group (CON, n=8, age 32±9 years, body mass 70.2±11.3 kg, height 172.8±9.0 cm). The groups were matched for pre- training 5-km running overall performance. All of the partici- pants were medication-free, non-smokers, and were free of neuromuscular disorders and cardiovascular dysfunctions. The participants received a verbal explanation about the pos- sible benefits, risks, and discomfort associated with the study and signed a written informed consent before enrollment. The procedures adopted in this study were approved by the Ethics Committee for Human Studies from the School of Physical Education and Sport, University of São Paulo. Experimental design Before and after the training intervention, the runners were required to visit the laboratory on three separate occasions, at least 72 h apart, over a 2-week period. During the first session, anthropometric measurements and a 5-km running trial on an outdoor track to establish pacing strategy were performed. The 5-km running trial was repeated 48 h after the first training session. The runners were familiar with long-distance running since they regularly competed in 5-km running events. During the second session, an incremental exercise test to exhaustion on a treadmill was conducted to determine the OBLA and VO2max. During the third session, the participants performed a submaximal constant-speed test on a treadmill to measure the RE. During the pre-training period, only the HIIT group performed a constant-speed run- ning test at the speed corresponding to VO2max (vVO2max) to determine time to exhaustion at this speed (TLim), which was used for individualizing the HIIT program (13). All tests were performed at the same period of day, and the first and second sessions were established randomly. All the parti- cipants were instructed to refrain from any exhaustive or unusual exercise 48 h before the test and to refrain from taking nutritional supplements during the training period. During training period, the HIIT program was added to the regular training schedule of HIIT group, while the participants of the CON group were instructed to maintain their regular training. Maximal incremental treadmill test Participants performed a maximal incremental test on a motor-driven treadmill (model TK35, Cefise, Brazil). Braz J Med Biol Res | doi: 10.1590/1414-431X20176335 High-intensity interval training and pacing 2/7 After a 3-min warm-up at 8 km/h, the speed was increased by 1 km/h every three minutes until exhaustion. The tread- mill was set at a gradient of 1% to simulate physiological demand during outdoor running (21). Each stage was separated by a 30-s rest in which blood samples (25 mL) were collected from the ear lobe to determine blood lactate accumulation. The participants received strong verbal encouragement to ensure the attainment of maximal effort. Gas exchange was measured breath-by-breath using a gas analyzer (Cortex Metalyzer 3B, Cortex Biophysik, Germany) and subsequently averaged over 30-s intervals throughout the test. Before each test, the gas analyzer was calibrated according to the recommen- dations of the manufacturer. The VO2max was determined as the highest 20-s value reached during the last stage of the incremental test (13). The vVO2max was defined as the speed at which VO2max was achieved. The OBLA was defined as the running speed associated with 3.5 mmol/L of lactate concentration (22). PTS was established as the highest speed obtained in the last stage maintained for at least 3 min. Running economy The RE was determined on a motor-driven treadmill (model TK35, Cefise). Participants performed a standard- ized warm-up, consisting of 5 min of running at 8 km/h followed by a 5-min passive recovery. Thereafter, they performed a constant-speed running test at 12 km/h for 10 min in order to measure the RE. During the entire test the oxygen uptake was obtained breath-by-breath. RE was defined by averaging the oxygen uptake values during the last 30 s. Time to exhaustion at the speed corresponding to VO2max The participants in the HIIT group performed the same warm-up routine adopted during the RE test. The vVO2max was immediately adjusted after the warm-up and the participants ran until they could no longer maintain the required speed. The test began with the participant’s feet on the moving belt and hands on the handrail. The TLim was measured using a manual stopwatch and defined as the moment that the participant released the handrail (about 2 s) until he grasped it again (i.e., exhaustion). The participants received strong verbal encouragement to continue as long as possible. 5-km running trial Participants individually performed a 5-km running trial on an outdoor 400-m track. They were instructed to maintain regular water consumption within the six hours prior to testing and water was provided ad libitum during the entire event. The runners performed a 10-min, warm- up consisting of a free-paced run, followed by 5 min of light stretching. The RPE was reported by participants every 1000 m using the Borg 15-point scale (23). Copies of this scale were reduced to 10 by 5 cm and laminated, and affixed to the wrist of the dominant arm of the individuals. The participants were instructed to finish the race as quickly as possible, as in a competitive event. Verbal encourage- ment was provided during the entire event. However, runners were not advised of their lap splits. Time to cover the 5-km (T5) and heart rate (HRT5) were registered by a GPS every 400 m (GPS Forerunners 410, USA). The pattern of data collecting for RPE (at 1000 m intervals), T5, and HRT5 (both at 400 m intervals) was according with a previous study carried out by Lima-Silva et al. (9). All tests were performed at the same time of the day and the mean values of the ambient temperature and air relative humidity were 19±4°C and 59±5%, respectively. Training program The HIIT group performed a high-intensity interval training program twice weekly (separated by 48 h) for 4 weeks in addition to their normal endurance training. The athletes were instructed to perform their regular endurance training on different days to those of the HIIT sessions. In order to equal the training load between the training regimes, there was a reduction of B10% of the total endurance training volume (i.e., km/week) in the HIIT group. A standardized warm-up consisting of a 5-min run at 9 km/h followed by light lower-limb stretching exercises was performed before each training session. Because TLim is assumed to be a useful tool for intermittent training prescription (13), in the present study athletes completed five intervals at the vVO2max for a duration equal to 50% of the TLim, interspersed with an active recovery at 60% of the speed corresponding to vVO2max for a duration equal to the time of effort (i.e., 1:1 work:recovery ratio). The running speed during the HIIT was monitored by a GPS (GPS Forerunners 305). The training sessions were individually supervised to control the training loads. Over this 4-week training period, the CON group was instructed to maintain their previous endurance training routine. Statistical analysis Data normality was confirmed using the Shapiro-Wilk test. Two-way analysis of variance (group
time) was used to compare the physiological and performance variables. In order to mitigate the impact of inter-individual data variability, physiological variables are also reported as percentage of change from pre-training period (i.e., Post-Pre). Comparison between groups for percent of changes (%) after the experimental period was performed using unpaired t-test. Significance was accepted at Po0.05. All statistical analyses were performed using the software package Statistica 8 (StataSoft Inc., USA). Results Training All of the participants in the HIIT group completed over 85% of the scheduled training sessions. The mean value Braz J Med Biol Res | doi: 10.1590/1414-431X20176335 High-intensity interval training and pacing 3/7 of TLim used for prescription of HIIT was 265±67 s. No statistical difference was observed in the endurance training volume (reported as the mean weekly covered distance) between before (HIIT: 28.7±2.3 km/week, CON: 30.2±1.3 km/week) and after (HIIT: 29.3±9.8 km/week, CON: 32.1±2.3 km/week) the completion of the study (P40.05), indicating that training load was equal between training regimes. Physiological variables Figure 1 shows the relative changes in the physio- logical parameters measured during the maximal incre- mental and constant-speed running. After the experimental period, the HIIT program produced significant improve- ments in RE (P=0.012) and PTS (P=0.019) when compared with the CON group. There was no significant difference between the groups for VO2max (P=0.495) and OBLA (P=0.101). Table 1 presents the absolute values of the physiological parameters measured during the maxi- mal incremental and constant-speed running. There were no main effects for time (P40.05), group (P40.05), or interaction (P40.05) for all measured variables. 5-km running trial Table 2 presents the main variables measured during the 5-km running trial. All parameters measured during the 5-km trial before the training period were the same between HIIT and CON groups (P40.05). There were no significant main effects for time, group, nor interaction effects for T5, HRT5, and RPET5 (P40.05). Figure 2 shows the pacing strategy and RPE during the time trial before and after training. No significant main effect was observed for either variable (P40.05). Discussion The main objective of the present study was to investi- gate the effects of the addition of a 4-week HIIT program on the pacing strategy adopted by long-distance runners during a 5-km running trial. The main findings were that the HIIT program improved physiological variables related to endurance performance (i.e., RE and PTS), but these changes were not accompanied by modifications in pacing strategy or overall performance. Previous findings have showed that a similar HIIT protocol was able to improve some physiological variables related to endurance performance (13). Although there was no significant difference between the groups for the absolute values of the physiological variables after the training period, our findings revealed that the addition of 4-week HIIT program produced significant improvements in percentage of change in PTS and RE, corresponding to a mean improvement of 5.6 and 4.1%, respectively. These data are in agreement with several studies that have reported similar improvements of B4.4% in the vVO2max (13,15) and B5% in the RE (13,14,16) after a 4-week HIIT program. Specifically, it has been proposed that PTS is influenced not only by maximal aerobic power, but also by Table 1. Parameters related to endurance performance before and after the 4-week high-intensity interval training period. HIIT (n=8) CON (n=8) Pre Post Pre Post .VO2max (mL  kg-1  min-1) 54.5±8.1 57.1±6.4 56.6±7.3 56.9±7.6 PTS (km/h) 16.5±1.8 17.2±1.8 17.9±1.0 17.7±1.6 OBLA (km/h) 14.1±2.3 15.0±2.4 15.1±2.2 15.3±1.8 RE (mL  kg-1  min-1) 43.1±3.5 40.7±4.3 40.9±4.7 41.2±4.4 Data are reported as means±SD. HIIT: high-intensity interval training group; CON: control group; .VO2max: maximal oxygen uptake; PTS: peak treadmill speed; OBLA: running speed correspond- ing to onset of blood lactate accumulation; RE: running economy. Figure 1. Percentage of changes of the physiological variables after the training period. Data are reported as means±SD. HIIT: high-intensity interval training group; CON: control group; VO2max: maximal oxygen uptake; PTS: peak treadmill speed; OBLA: running speed associated with onset of blood lactate accumula- tion; RE: running economy measured at 12 km/h. *Po0.05 (unpaired t-test). Braz J Med Biol Res | doi: 10.1590/1414-431X20176335 High-intensity interval training and pacing 4/7 RE (24,25). An improvement in RE after the HIIT program could lead to a lower energy cost during submaximal running bouts, which might allow the athletes to achieve higher speeds at the end of the maximal incremental treadmill test. Therefore, it seems that the main beneficial effects of the HIIT program are mediated by a reduction in the energetic cost of running. Taken together, these find- ings reinforce the suggestion that a HIIT program per- formed during 4 weeks is an effective short-term strategy to alter some physiological variables related to endurance performance. In the present study, we have provided the first data analyzing the effectiveness of a HIIT program on the pacing strategy adopted by endurance runners during a long-distance event. It was found that although the percentage of changes of the PTS and RE were improved after the HIIT program, the pacing strategy was main- tained after the experimental intervention. Previous studies have proposed that pacing strategy can be controlled by a centrally-regulated system that monitors the RPE in order to minimize physiological strain and to prevent a pre- mature exercise termination (26,27). It has been proposed that the CNS interprets afferent feedback from physio- logical systems in order to adjust the work performed by skeletal muscles and avoid premature fatigue (28). Thus, the RPE is the integration of alterations in physiological systems used during dynamic exercise and is considered a primary regulator of pacing strategy (27). This has led some researchers to hypothesize that interventions (i.e., physical training and dietary manipulation) that change Table 2. Running performance, heart rate, and rate of perceived exertion during a 5-km running trial pre- and post-training. HIIT CON Pre Post Pre Post T5 (s) 1196±173 1168±135 1149±153 1165±164 HRT5 (bpm) 178±4 176±5 174±8 172±9 RPET5 (score) 17±1 17±2 17±1 16±1 Data are reported as means±SD. HIIT: high-intensity interval training group; CON: control group; T5: time to cover; HRT5: mean heart rate at T5; RPET5: mean rate of perceived exertion during the 5-km running trial. Figure 2. Running pacing strategy (panels A and B) and rating of perceived exertion (RPE; panels C and D) during a 5-km running trial, pre- and post-training. Data are reported as means±SD. HIIT: high-intensity interval training group; CON: control group. Braz J Med Biol Res | doi: 10.1590/1414-431X20176335 High-intensity interval training and pacing 5/7 these physiological variables could influence the RPE, resulting in an altered pacing strategy (28). However, the data of the present study revealed that the HIIT program improved the RE and PTS (Figure 1), but without changes in RPE (Figure 2). These findings suggest that improve- ments in physiological variables would produce only a small reduction in metabolic disturbance during exercise of self-paced intensity. This could result in a similar afferent feedback from physiological systems when compared with pre-training. Thus, the interpretation of the afferent feedback during running was not altered after the HIIT program, as revealed by RPE, and athletes adopted a similar pacing strategy to that used before the training period. These findings are in agreement with a previous suggestion that athletes adjust their pacing strategy by comparing actual and expected RPE during the course of a race for a given distance (29). The improvement of only B2.5% in 5-km running performance detected in the present study is in agreement with others that reported small enhancement in over- all running performance after a 4-week HIIT program (13,15,16). For instance, Smith et al. (15) found a 2.7% improvement on 3000-m running performance after a HIIT program, while both VO2max and vVO2max showed a significant increase (B4.9%). Smith et al. (16) verified that a similar HIIT program was able to promote improvements of around 6.0% in VO2max, 5.2% in vVO2max, and a non- significant improvement in 5-km running performance. In addition, Billat et al. (13) found non-significant changes in 3000-m running performance after a 4-week HIIT program. Taken together, these findings suggest that the improve- ments in physiological variables (i.e., 3–6%) produced by a short-term HIIT program were not translated to improved endurance performance. The reasons for this stable running endurance performance after HIIT programs are not clear, but it is possible that moderate improvements on physiological variables are not enough to reduce afferent feedback from physiological systems when compared with pre-training. This could explain the non-significant change in perception of effort found in the present study, producing only a small improvement in overall running performance. It is important to acknowledge some of the limita- tions of the present study. First, our participants were recreational long-distance runners who had only low- intensity continuous aerobic training experience. Thus, caution should be taken in extrapolating these findings to highly-trained athletes who frequently perform HIIT train- ing sessions. Second, the athletes individually performed the 5-km running trial, while during official competitive running races, they compete in a head-to-head manner. Previous findings have suggested that the presence of other competitors would alter the pacing strategy, induc- ing to a more aggressive and faster start and improving overall performance (2). This could limit the extrapolation of the findings of the current study to a more realistic scenario of endurance competition. Thus, future studies are encouraged to verify the impact of the HIIT on running pacing strategy determined in a head-to-head manner. In conclusion, the results of the present study showed that the addition of 4 weeks of HIIT produced relevant gains on the PTS and RE, but without changes in RPE, pacing strategy, and overall performance. These findings suggest that improved aerobic power and lower energy cost during submaximal running were not sufficient to alter the perceived effort behavior during a 5-km running trial, resulting in a similar pacing strategy to that used before the training period. References 1. Faulkner J, Parfitt G, Eston R. The rating of perceived exertion during competitive running scales with time. Psycho- physiology 2008; 45: 977–985, doi: 10.1111/j.1469-8986.2008. 00712.x. 2. Tomazini F, Pasqua LA, Damasceno MV, Silva-Cavalcante MD, de Oliveira FR, Lima-Silva AE, et al. Head-to-head running race simulation alters pacing strategy, performance, and mood state. Physiol Behav 2015; 149: 39–44, doi: 10.1016/ j.physbeh.2015.05.021. 3. Tucker R, Lambert MI, Noakes TD. An analysis of pacing strategies during men’s world-record performances in track athletics. Int J Sports Physiol Perform 2006; 1: 233, doi: 10.1123/ijspp.1.3.233. 4. Abbiss CR, Laursen PB. Describing and understanding pacing strategies during athletic competition. Sport Med 2008; 38: 239–252, doi: 10.2165/00007256-200838030-00004. 5. Billat VL, Wesfreid E, Kapfer C, Koralsztein JP, Meyer Y. Non- linear dynamics of heart rate and oxygen uptake in exhaustive 10,000 m runs: influence of constant vs. freely paced. J Physiol Sci 2006; 56: 103–111, doi: 10.2170/physiolsci.R2028. 6. Ulmer H-V. Concept of an extracellular regulation of muscular metabolic rate during heavy exercise in humans by psycho- physiological feedback. Experientia 1996; 52: 416–420, doi: 10.1007/BF01919309. 7. Zamparo P, Perini R, Peano C, di Prampero PE. The self selected speed of running in recreational long distance runners. Int J Sports Med 2001; 22: 598–604, doi: 10.1055/ s-2001-18559. 8. Bertuzzi R, Lima-Silva AE, Pires FO, Damasceno MV, Bueno S, Pasqua LA, et al. Pacing strategy determinants during a 10-km running time trial: contributions of perceived effort, physiological, and muscular parameters. J Strength Cond Res 2014; 28: 1688–1696, doi: 10.1519/JSC.0000000000000314. 9. Lima-Silva AE, Bertuzzi RCM, Pires FO, Barros RV, Gagliardi JF, Hammond J, et al. Effect of performance level on pacing strategy during a 10-km running race. Eur J Appl Physiol 2010; 108: 1045–1053, doi: 10.1007/s00421-009-1300-6. 10. De Feo P, Di Loreto C, Lucidi P, Murdolo G, Parlanti N, De Cicco A, et al. Metabolic response to exercise. J Endocrinol Inves 2003; 26: 851–854, doi: 10.1007/BF03345235. Braz J Med Biol Res | doi: 10.1590/1414-431X20176335 High-intensity interval training and pacing 6/7 11. Jones AM, Carter H. The effect of endurance training on parameters of aerobic fitness. Sport Med 2000; 29: 373–386, doi: 10.2165/00007256-200029060-00001. 12. Billat LV. Interval training for performance: a scientific and empirical practice. Sport Med 2001; 31: 13–31, doi: 10.2165/ 00007256-200131010-00002. 13. Billat VL, Flechet B, Petit B, Muriaux G, Koralsztein J-P. Interval training at VO2max: effects on aerobic performance and overtraining markers. Med Sci Sports Exerc 1999; 31: 156–163, doi: 10.1097/00005768-199901000-00024. 14. Burgomaster KA, Howarth KR, Phillips SM, Rakobowchuk M, MacDonald MJ, McGee SL, et al. Similar metabolic adaptations during exercise after low volume sprint interval and traditional endurance training in humans. J Physiol 2008; 586: 151–160, doi: 10.1113/jphysiol.2007.142109. 15. Smith TP, McNaughton LR, Marshall KJ. Effects of 4-week training using Vmax/Tmax on VO2max and performance in athletes. Med Sci Sports Exerc 1999; 31: 892–896, doi: 10.1097/00005768-199906000-00019. 16. Smith TP, Coombes JS, Geraghty DP. Optimising high- intensity treadmill training using the running speed at maximal O2 uptake and the time for which this can be maintained. Eur J Appl Physiol 2003; 89: 337–343, doi: 10.1007/s00421-003- 0806-6. 17. Hopkins WG. Measures of reliability in sports medicine and science. Sport Med 2000; 30: 1–15, doi: 10.2165/00007256- 200030010-00001. 18. Laursen PB, Francis GT, Abbiss CR, Newton MJ, Nosaka K. Reliability of time-to-exhaustion versus time-trial running tests in runners. Med Sci Sports Exerc 2007; 39: 1374–1379, doi: 10.1249/mss.0b013e31806010f5. 19. Taipale RS, Mikkola J, Nummela A, Vesterinen V, Capostagno B, Walker S, et al. Strength training in endurance runners. Int J Sports Med 2010; 31: 468–476, doi: 10.1055/s-0029-1243639. 20. Sedano S, Marín PJ, Cuadrado G, Redondo JC. Concurrent training in elite male runners: the influence of strength versus muscular endurance training on performance outcomes. J Strength Cond Res 2013; 27: 2433–2443, doi: 10.1519/ JSC.0b013e318280cc26. 21. Jones AM, Doust JH. A 1% treadmill grade most accurately reflects the energetic cost of outdoor running. J Sports Sci 1996; 14: 321–327, doi: 10.1080/02640419608727717. 22. Heck H, Mader A, Hess G, Mücke S, Müller R, Hollmann W. Justification of the 4-mmol/l lactate threshold. Int J Sports Med 1985; 6: 117–130, doi: 10.1055/s-2008-1025824. 23. Borg G. Psychophysical scaling with applications in physical work and the perception of exertion. Scand J Work Environ Health 1990; 16 (Suppl 1): 55–58, doi: 10.5271/sjweh.1815. 24. Noakes TD. Implications of exercise testing for prediction of athletic performance: a contemporary perspective. Med Sci Sports Exerc 1988; 20: 319–330, doi: 10.1249/00005768- 198808000-00001. 25. Mikkola J, Rusko H, Nummela A, Pollari T, Häkkinen K. Concurrent endurance and explosive type strength train- ing improves neuromuscular and anaerobic characteristics in young distance runners. Int J Sports Med 2007; 28: 602–611, doi: 10.1055/s-2007-964849. 26. Tucker R. The anticipatory regulation of performance: the physiological basis for pacing strategies and the develop- ment of a perception-based model for exercise performance. Br J Sports Med 2009; 43: 392–400, doi: 10.1136/bjsm. 2008.050799. 27. Tucker R, Noakes TD. The physiological regulation of pacing strategy during exercise: a critical review. Br J Sports Med 2009; 43: e1, doi: 10.1136/bjsm.2009.057562. 28. Lambert EV, Gibson ASC, Noakes TD. Complex systems model of fatigue: integrative homoeostatic control of peri- pheral physiological systems during exercise in humans. Br J Sports Med 2005; 39: 52–62, doi: 10.1136/bjsm.2003. 011247. 29. De Koning JJ, Foster C, Bakkum A, Kloppenburg S, Thiel C, Joseph T, et al. Regulation of pacing strategy during athletic competition. PLoS One 2011; 6: e15863, doi: 10.1371/ journal.pone.0015863. Braz J Med Biol Res | doi: 10.1590/1414-431X20176335 High-intensity interval training and pacing 7/7

Effects of a 4-week high-intensity interval training on pacing during 5-km running trial.

10-19-2017

Silva, R,Damasceno, M,Cruz, R,Silva-Cavalcante, M D,Lima-Silva, A E,Bishop, D J,Bertuzzi, R

eng

PMC9736486

Citation: Olaizola, A.; Errekagorri, I.; Lopez-de-Ipina, K.; María Calvo, P.; Castellano, J. Comparison of the External Load in Training Sessions and Official Matches in Female Football: A Case Report. Int. J. Environ. Res. Public Health 2022, 19, 15820. https://doi.org/10.3390/ ijerph192315820 Academic Editors: José Alberto Frade Martins Parraca, Bernardino Javier Sánchez-Alcaraz Martínez and Diego Muñoz Marín Received: 29 October 2022 Accepted: 25 November 2022 Published: 28 November 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). International Journal of Environmental Research and Public Health Article Comparison of the External Load in Training Sessions and Official Matches in Female Football: A Case Report Aratz Olaizola 1,* , Ibai Errekagorri 1,2 , Karmele Lopez-de-Ipina 3,4 , Pilar María Calvo 4 and Julen Castellano 1,2 1 Department of Physical Education and Sport, Faculty of Education and Sport, University of the Basque Country (UPV/EHU), 01007 Vitoria-Gasteiz, Spain 2 Society, Sports and Physical Exercise Research Group (GIKAFIT), Department of Physical Education and Sport, Faculty of Education and Sport, University of the Basque Country (UPV/EHU), 01007 Vitoria-Gasteiz, Spain 3 Department of Psychiatry, Cambridge Neuroscience, University of Cambridge, Cambridge 01223, UK 4 Department of Computers’ Arquitecture and Technology, University of the Basque Country (UPV/EHU), Paseo M. Lardizabal, 1, 20018 San Sebastian, Spain * Correspondence: [email protected] Abstract: The objective of this study was to compare the external load of training sessions using as a reference an official competition match in women’s football in order to find if the training sessions replicate the competition demands. Twenty-two semi-professional football players were analyzed during 17 weeks in the first phase of the competitive period of the 2020–2021 season of Spanish women’s football. In addition to the competition (Official Matches, OM), four types of sessions were distinguished: strength or intensity (INT), endurance or extensity (EXT), velocity (VEL), and activation or pre-competitive (PREOM). The external load variables recorded were total distance (TD), high-speed running (HSR), sprint (Sprint), accelerations (ACC2), decelerations (DEC2), player load (PL), distance covered per minute (TDmin), high metabolic load distance (HMLD), and total impacts. The main results were that the external load demanded was different according to the type of session, being, in all cases, much lower than OM. The variables referring to the neuromuscular demands (ACC2 and DEC2) were higher in the INT sessions, the TD variable in the EXT sessions and the velocity variables (HSR and Sprint) in the VEL sessions. We can conclude that there was an alternating horizontal distribution of training loads within the competitive micro-cycle in women’s football, although the order was not the usual one for tactical periodization. Keywords: team sport; women; external load; periodization; electronic performance; tracking systems 1. Introduction In recent decades, there has been exponential global development of women’s football, both in its practice and in the entities and institutions that promote and manage it [1,2]. This has been accompanied by a greater professionalization in elite game standards, in addition to an increase in audiences, leading to the creation of professional leagues and clubs, generating greater professionalization [3]. The scientific interest in women’s football has not been left out of this reality, which is why competition analysis is now a focus with the aim of deepening its knowledge [4–7]. It is essential to know the demands of women’s competition [8] in order to have reference values to guide training content. In a complementary way, the evaluation of the training process is essential to verify the effectiveness of the intervention and to search for the best strategy to stimulate the athlete, that is, to distribute training and recovery [9] in order to optimize physical condition [10]. It is also important to pay attention to load management in order to reduce the probability that players could suffer over-training or even injury [11–13]. Int. J. Environ. Res. Public Health 2022, 19, 15820. https://doi.org/10.3390/ijerph192315820 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2022, 19, 15820 2 of 10 In men’s football, the two usual planning strategies are the structured micro-cycle together with tactical periodization [10,14]. Elite football teams use the latter because the proposed content prioritizes technical/tactical objectives over conditional and psy- chological capabilities simultaneously [15]. Previous studies [4,14,16–19] agree that the planning strategy of a competitive micro-cycle with a single competitive game is three main acquisition sessions during the week in order to prepare and sustain physical abilities, such as strength, endurance, and velocity. This first part of acquisition is followed by a load reduction phase at the end of the week (tapering phase) to ensure greater freshness and, therefore, a greater willingness to compete [18,20]. Both phases result in a horizontal alternation in the distribution of conditional demands during the micro-cycle [16,17]. In order to improve the knowledge of types of load management strategies in women’s football in a novel way, the objective of the present study is to describe the external load in the different types of training and competitive matches in women’s football during a competitive micro-cycle. The starting hypothesis is that there is a horizontal alternation in the conditional demand between the training sessions in the competitive micro-cycle, but none of the sessions replicates the competition demand. In this sense, the results of this study will allow increasing the information on the type of periodization that is proposed in an elite women’s football team, which could help to facilitate load management in women’s football, which is still little known and under research. 2. Materials and Methods 2.1. Participants A total of 22 semi-professional female football players (age: 24.6 ± 4.0 years; height: 163.9 ± 5.0 cm; weight: 58.5 ± 4.2 kg; skinfolds (i.e., the sum of 6 skinfolds: triceps, subscapular, supraspinal, abdominal, front thigh, and medial calf): 65.4 ± 17.9 mm) took part in the study. The data recording was carried out during 17 weeks of the competitive period of the Second Women’s Football Division (Reto Iberdrola) during the 2020–2021 season. Usually, the team completed four days of training and one day of competition per week. 2.2. Procedures In order to obtain position data, the players were monitored with WIMU PRO devices (RealTrack Systems, Almeria, Spain) using the global positioning system (GPS). The GPS de- vice used in this study can operate at 10 Hz, and it is compatible with the Galileo and Ameri- can satellite constellation, which seems to provide more precision [21]. For the analysis, data were collected on outdoor football fields without any possibility of infrastructure interfering with the data collection. During the sessions, a mean of 12 satellites were connected with each device. The value of DDOP was 0.95. This equipment and its measurements are valid and reliable using the GNSS for time-motion analysis in football (distance covered variable: accuracy = 0.69–6.05%, test–retest reliability = 1.47, inter-unit reliability = 0.25; mean veloc- ity variable: accuracy = 0.18, intra-class correlation = 0.95, inter-unit reliability = 0.03) [22], and has been awarded with the FIFA Quality Performance certificate. Each WIMU PRO device was placed in a vertical position between the players’ shoulder blades, in a pocket of a specific chest vest (dimensions of the devices = 81 × 45 × 16 mm). The GPS devices were activated 15 min before the start of each session or match in accordance with the manufacturer’s instructions. All the players were familiar with the use of GPS. Only the players that completed official matches or training sessions were included in the analy- sis. To avoid possible differences between devices, during the entire registration period, each player wore the same device [23,24]. The records were downloaded using the SPRO software (RealTrack Systems, Almeria, Spain) after the end of each session. Once the data were filtered through the software, they were imported into a Microsoft Excel spreadsheet (Microsoft Corporation, Washington, DC, USA) to configure a matrix. Int. J. Environ. Res. Public Health 2022, 19, 15820 3 of 10 2.3. Physical Variables The duration of the session was recorded considering only the effective time, that is, the time in which the players were active, excluding times of inactivity (e.g., stoppages between tasks). The external load variables were: total distance (TD, in m); high speed running (HSR, in m), established as 60% of the maximum individual velocity of the participants [25]; sprint (Sprint, in m), defined as the distance accumulated above 85% of the maximum individual velocity [26]; accelerations over 2 m/s2 (ACC2, in n); decelerations of less than −2 m/s2 (DEC2, in n); player load (PL, in au), distance traveled per minute (TDmin, in m/min1); high metabolic load distance (HMLD, in m), defined as the distance covered by a player when their metabolic power is above 25.5 W/kg1, ratios per kilogram [14]; and, total impacts (Total Impacts, in n) using an Earth sensor to calculate the three axes value of the module. As for the choice of the maximum individual velocity, the highest value recorded during the 17-week period of the study was chosen, considering both the training sessions and the competition. 2.4. Type of Training Sessions and Official Matches Apart from the official matches (OM, n = 12), the types of sessions were differentiated based on the priority conditional objective that was developed: strength or intensity session (INT, n = 10); endurance or extensity session (EXT, n = 15); velocity session (VEL, n = 7); and activation session (PREOM, n= 14). In total, 805 recordings were collected from official matches (n = 49) and 756 training sessions, distributed as follows: VEL = 114, INT = 173, EXT = 239, and PREOM = 230. The number of records per player was 36.8 ± 10.6. The order of the sessions in the training micro-cycle was conditioned to the number of days after (OM+) and before (OM−) of the OM, following the proposal of previous studies [10,14]. The VEL session was usually the first acquisition session, located 2 days after the OM (OM + 2) and 5 or 6 days before the next official match (OM − 6 or OM − 5), characterized by velocity work, locomotive-oriented and based on tasks of intermittent nature or in waves (with breaks of 1 or 2 min between drills). The tasks were mostly carried out with goalposts and goalkeepers and were made up of a large relative space per player (e.g., >250 m2). The INT session was the second acquisition session of the week, usually located 3 or 4 days after the OM (OM + 3 or OM + 4) and 3 or 4 days before the next competition (OM − 3 or OM − 4), characterized by neuromuscular and mechanically-oriented work, based on reduced and positional games, with a relative space per player of less than 100 m2 and with a number of participants ranging from one to six per team. The EXT session was usually the third acquisition session of the micro-cycle, located 4 or 5 days after the OM (OM + 4 or OM + 5) and 2 or 3 days before the next competition (OM − 2 or OM − 3), characterized by cardiovascular work, based on large-format tasks, that is, with a moderate to high number of participants per team (>6), in a relative space equal to or greater than 250 m2 per player, and with goalposts and goalkeepers. Pre-match day (PREOM) was the fine-tuning session. It was always conducted the day before match day (OM − 1). The contents that were developed in it were activation tasks based on activities with a large number of participants (>6) in a medium relative space (equal to or less than 250 m2 per player) or small (equal to or less than 100 m2 per player) with a polarized orientation and a tactical and strategic approach. The last day of the week was match day (OM). To calculate the load on the day of the competition, the previous warm-up carried out by the players was taken into account in addition to the two parts of the match, which had the following approximate load: duration was 20.1 ± 2.1 min, and the physical demands were as follows: TD: 1280 ± 266 m, HSR: 64.3 ± 37.7 m, Sprint: 5 ± 10.7 m, ACC2: 27.9 ± 9.6 n, DEC2: 28.4 ± 9.6 n, PL 20.6 ± 4.3 au and TDmin 64 ± 12.3 (m/min1). Int. J. Environ. Res. Public Health 2022, 19, 15820 4 of 10 2.5. Statistical Analysis Descriptive statistics data from variables were presented using mean and standard deviation. Tests for normality (Shapiro–Wilk) and equality of variances (Levene) were applied. The null hypothesis was accepted because the distribution of the data met the normality criterion. Furthermore, the variances were homogeneous. Therefore, a one-way ANOVA analysis of variance for independent samples was used to test for differences in the variables between the different sessions (INT, EXT, VEL, PREOM, and OM). Significant results were then analyzed using post hoc Tukey’s test. Effect size (ES) was also calculated to determine meaningful differences with magnitudes classified as [27] trivial (<0.2), small (>0.2–0.6), moderate (>0.6–1.2), large (>1.2–2.0), and very large (>2.0–4.0). The level of significance was set at p < 0.05. The statistical analysis was conducted using the software JASP 0.14.1 (University of Amsterdam, Amsterdam, The Netherlands) and a customized Microsoft Excel spreadsheet (Microsoft Corporation, Washington, DC, USA) for Windows. 3. Results Figure 1 shows the total time (min), effective time (min), and density (%) of the training and competition sessions. There were significant differences (p < 0.05) between training sessions for all variables. The PREOM session had the lowest (p < 0.05) volume (total and effective time) but with higher density than the VEL and INT sessions. The INT session had the highest volume (total and effective time) but with a lower density than the EXT and PREOM sessions and higher than VEL. TDmin 64 ± 12.3 (m/min ). 2.5. Statistical Analysis Descriptive statistics data from variables were presented using mean and standa deviation. Tests for normality (Shapiro–Wilk) and equality of variances (Levene) were a plied. The null hypothesis was accepted because the distribution of the data met the n mality criterion. Furthermore, the variances were homogeneous. Therefore, a one-w ANOVA analysis of variance for independent samples was used to test for differences the variables between the different sessions (INT, EXT, VEL, PREOM, and OM). Sign cant results were then analyzed using post hoc Tukey’s test. Effect size (ES) was also c culated to determine meaningful differences with magnitudes classified as [27] triv (<0.2), small (>0.2–0.6), moderate (>0.6–1.2), large (>1.2–2.0), and very large (>2.0–4.0). T level of significance was set at p < 0.05. The statistical analysis was conducted using t software JASP 0.14.1 (University of Amsterdam, Amsterdam, Kingdom of the Neth lands) and a customized Microsoft Excel spreadsheet (Microsoft Corporation, Washin ton, DC, USA) for Windows. 3. Results Figure 1 shows the total time (min), effective time (min), and density (%) of the tra ing and competition sessions. There were significant differences (p < 0.05) between tra ing sessions for all variables. The PREOM session had the lowest (p < 0.05) volume (to and effective time) but with higher density than the VEL and INT sessions. The INT s sion had the highest volume (total and effective time) but with a lower density than t EXT and PREOM sessions and higher than VEL. Figure 1. Total duration (min, in dark blue), effective duration (min, in light blue), and density fective/total, %, in green) of the sessions. Note: VEL is velocity day, INT is intensity or strength d EXT is extensity or endurance day, PREOM is previous day to match day, OM is official match d Significant differences (p < 0.05) are represented: 1 is higher than VEL, 2 is higher than INT, 3 higher than EXT, and 4 is higher than PREOM. As shown in Table 1, all external load variables were higher in OM compared to t rest of the training sessions, with a difference ranging from moderate to extremely lar (ES = 0.7–6.6). On the contrary, the session with the lowest load in all the variables w Figure 1. Total duration (min, in dark blue), effective duration (min, in light blue), and density (effective/total, %, in green) of the sessions. Note: VEL is velocity day, INT is intensity or strength day, EXT is extensity or endurance day, PREOM is previous day to match day, OM is official match day. Significant differences (p < 0.05) are represented: 1 is higher than VEL, 2 is higher than INT, 3 is higher than EXT, and 4 is higher than PREOM. As shown in Table 1, all external load variables were higher in OM compared to the rest of the training sessions, with a difference ranging from moderate to extremely large (ES = 0.7–6.6). On the contrary, the session with the lowest load in all the variables was the PREOM. The variables ACC2 and DEC2 oriented towards neuromuscular work were higher in INT, the variable TD oriented towards cardiovascular work was higher in EXT, Int. J. Environ. Res. Public Health 2022, 19, 15820 5 of 10 and variables of velocity (HSR and Sprint) oriented towards locomotor work were higher in VEL. The values of the intensity variable (TDmin) were highest in OM with respect to all training sessions. Table 1. Mean and standard deviation (SD) of the external load variables in different training and competition sessions. Type of Sessions VEL INT EXT PREOM OM Variables Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD) TD 5058.3 (712.3) 2,3 4840.0 (1237.8) 4 5418.5 (957.6) 1,2,4 3546.8 (1007.8) 10,576.33 (602.77) 1,2,3,4 (m) HSR 614.5 (318.1) 2,3,4 184.0 (187.1) 424.3 (247.7) 2,4 245.3 (126.0) 2 906.7 (208.0) 1,2,3,4 (m) Sprint 35.1 (49.6) 2,3,4 1.5 (5.5) 28.2 (42.4) 2,4 7.8 (15.2) 2 69.2 (51.2) 1,2,3,4 (m) ACC2 115.3 (25.2) 3,4 145.0 (40.7) 1,3,4 99.7 (24.7) 4 71.1 (24.2) 181.8 (35.1) 1,2,3,4 (n) DEC2 118.5 (29.2) 3,4 138.1 (39.0) 1,3,4 99.0 (24.4) 4 70.1 (23.9) 190.2 (33.7) 1,2,3,4 (n) PL 66.9 (11.0) 2,4 66.4 (18.1) 4 69.0 (14.8) 1,2,4 46.7 (15.2) 142.1 (15.6) 1,2,3,4 (au) TDmin 85.1 (9.2) 2,4 72.7 (10.5) 4 85.4 (11.3) 1,2,4 65.1 (13.2) 90.8 (5.3) 1,2,3,4 (m/min1) Note: VEL is velocity day, INT is intensity or strength day, EXT is extensity or endurance day, PREOM is previous day to match day, OM is official match day. TD is total distance expressed in meters (m), HSR is high-intensity running distance (m), Sprint is sprint running distance (m), ACC2 is the number of accelerations at >2 m/s2 (n), DEC2 is the number of decelerations at <−2 m/s2 (n), PL (au) is player load, and TDmin is distance per min (m/min1). Significant differences (p < 0.05) are represented: 1 is higher than VEL, 2 is higher than INT, 3 is higher than EXT, and 4 is higher than PREOM. Figure 2 shows the mean and standard deviations of two external load variables: High Metabolic Load Distance (HMLD) and Total Impacts. Significant differences were found for both variables in the different training and competition sessions. In line with the rest of the variables, the highest values were obtained in OM and the lowest in PREOM. The VEL session obtained the highest HMLD values compared to the other training sessions. Likewise, the EXT session accumulated the highest values in Total Impacts compared to the other training sessions. Table 2 shows the effect size (ES) of the external load variables obtained from the comparison between training sessions and official matches. As can be seen, the magnitude of the differences ranged from a very large decrease (ES = −2.17) to an extremely large increase (ES = 7.4). Int. J. Environ. Res. Public Health 2022, 19, 15820 6 of 10 Int. J. Environ. Res. Public Health 2022, 19, x 6 of 10 Figure 2. Means and deviations of the high metabolic load distance (HMLD, m, in blue) and total impacts (Total impacts, n, in green) of the players on the different training days. Note: VEL is veloc- ity day, INT is intensity or strength day, EXT is extensity or endurance day, PREOM is previous day to match day, OM is official match day. Significant differences (p < 0.05) are represented: 1 is higher than VEL, 2 is higher than INT, 3 is higher than EXT, and 4 is higher than PREOM. Table 2 shows the effect size (ES) of the external load variables obtained from the comparison between training sessions and official matches. As can be seen, the magnitude of the differences ranged from a very large decrease (ES = −2.17) to an extremely large increase (ES = 7.4). Table 2. Effect size of external load variables in different training and competition sessions. Comparison of Type of Sessions Variables TD HSR Sprint ACC2 DEC2 PL TDmin VEL vs. OM 8.4 (ELI) 1.1 (MI) 0.7 (MI) 2.2 (VLI) 2.3 (VLI) 5.6 (ELI) 0.8 (MI) VEL vs. PREOM −1.8 (LD) −1.7 (LD) −0.8 (MD) −1.8 (LD) −1.8 (LD) −1.5 (LD) −1.8 (LD) VEL vs. INT −0.2 (SD) −1.7 (LD) −1.2 (LD) 0.9 (MI) 0.6 (MI) 0.0 (T) −1.2 (LD) VEL vs. EXT 0.4 (SI) −0.7 (MD) −0.2 (SD) −0.6 (MD) −0.7 (MD) 0.2 (SI) 0.1 (T) EXT vs. OM 6.6 (ELI) 2.1 (VLI) 0.9 (MI) 2.7 (VLI) 3.1 (VLI) 4.8 (ELI) 0.8 (MI) EXT vs. PREOM −1.9 (LD) −1.0 (MD) −0.7 (MD) −1.2 (LD) −1.2 (LD) −1.5 (LD) −1.7 (LD) EXT vs. INT −0.5 (MD) −1.1 (MD) −1.1 (MD) 1.4 (LI) 1.2 (LI) −0.2 (SD) −1.2 (LD) INT vs. OM 6.2 (ELI) 3.7 (VLI) 2.4 (VLI) 1.0 (MI) 1.4 (LI) 4.5 (ELI) 2.3 (VLI) INT vs. PREOM −1.2 (LD) 0.4 (SI) 0.6 (MI) −2.3 (VLD) −2.2 (VLD) −1.2 (LD) −0.6 (MD) PREOM vs. OM 8.7 (ELI) 4.0 (ELI) 1.8 (LI) 3.7 (VLI) 4.2 (ELI) 6.2 (ELI) 2.8 (VLI) Note: ELD is extremely large decrease, VLD is very large decrease, LD is large decrease, MD is moderate decrease, SD is small decrease, T is trivial, SI is small increase, MI is moderate increase, LI is large increase, VLI is very large increase, ELI is extremely large increase. VEL is velocity day, INT is intensity or strength day, EXT is extensity or endurance day, PREOM is previous day to match day, OM is official match day. TD is total distance expressed in meters (m), HSR is high-intensity running distance (m), Sprint is sprint running distance (m), ACC2 is the number of accelerations at >2 m/s2 (n), DEC2 is the number of decelerations at <−2 m/s2 (n), PL (au) is player load, and TDmin is distance per min (m/min1). Figure 2. Means and deviations of the high metabolic load distance (HMLD, m, in blue) and total impacts (Total impacts, n, in green) of the players on the different training days. Note: VEL is velocity day, INT is intensity or strength day, EXT is extensity or endurance day, PREOM is previous day to match day, OM is official match day. Significant differences (p < 0.05) are represented: 1 is higher than VEL, 2 is higher than INT, 3 is higher than EXT, and 4 is higher than PREOM. Table 2. Effect size of external load variables in different training and competition sessions. Comparison of Type of Sessions Variables TD HSR Sprint ACC2 DEC2 PL TDmin VEL vs. OM 8.4 (ELI) 1.1 (MI) 0.7 (MI) 2.2 (VLI) 2.3 (VLI) 5.6 (ELI) 0.8 (MI) VEL vs. PREOM −1.8 (LD) −1.7 (LD) −0.8 (MD) −1.8 (LD) −1.8 (LD) −1.5 (LD) −1.8 (LD) VEL vs. INT −0.2 (SD) −1.7 (LD) −1.2 (LD) 0.9 (MI) 0.6 (MI) 0.0 (T) −1.2 (LD) VEL vs. EXT 0.4 (SI) −0.7 (MD) −0.2 (SD) −0.6 (MD) −0.7 (MD) 0.2 (SI) 0.1 (T) EXT vs. OM 6.6 (ELI) 2.1 (VLI) 0.9 (MI) 2.7 (VLI) 3.1 (VLI) 4.8 (ELI) 0.8 (MI) EXT vs. PREOM −1.9 (LD) −1.0 (MD) −0.7 (MD) −1.2 (LD) −1.2 (LD) −1.5 (LD) −1.7 (LD) EXT vs. INT −0.5 (MD) −1.1 (MD) −1.1 (MD) 1.4 (LI) 1.2 (LI) −0.2 (SD) −1.2 (LD) INT vs. OM 6.2 (ELI) 3.7 (VLI) 2.4 (VLI) 1.0 (MI) 1.4 (LI) 4.5 (ELI) 2.3 (VLI) INT vs. PREOM −1.2 (LD) 0.4 (SI) 0.6 (MI) −2.3 (VLD) −2.2 (VLD) −1.2 (LD) −0.6 (MD) PREOM vs. OM 8.7 (ELI) 4.0 (ELI) 1.8 (LI) 3.7 (VLI) 4.2 (ELI) 6.2 (ELI) 2.8 (VLI) Note: ELD is extremely large decrease, VLD is very large decrease, LD is large decrease, MD is moderate decrease, SD is small decrease, T is trivial, SI is small increase, MI is moderate increase, LI is large increase, VLI is very large increase, ELI is extremely large increase. VEL is velocity day, INT is intensity or strength day, EXT is extensity or endurance day, PREOM is previous day to match day, OM is official match day. TD is total distance expressed in meters (m), HSR is high-intensity running distance (m), Sprint is sprint running distance (m), ACC2 is the number of accelerations at >2 m/s2 (n), DEC2 is the number of decelerations at <−2 m/s2 (n), PL (au) is player load, and TDmin is distance per min (m/min1). 4. Discussion The aim of this study is to describe the distribution of external load in different training sessions and competitions in competitive micro-cycles in women’s football. According to the current state of the art, this is the first study carried out on the description of tactical periodization in women’s football. The results confirm the starting hypotheses because despite describing a distribution of the conditional demands (for example, strength, Int. J. Environ. Res. Public Health 2022, 19, 15820 7 of 10 resistance, and speed) based on the horizontal alternation during the competitive micro- cycle sessions, none of them replicated the demands of the competition. In line with the results obtained in previous studies carried out in men’s football [14,28], the total and effective duration was significantly shorter in the training sessions compared to those obtained in official matches (Figure 1). Among the three acquisition sessions (VEL, INT, and EXT), the VEL session was the one with the lowest density, probably due to the intermittent or dividing nature of the tasks of this session. It should be highlighted as well that the lowest values in all the variables are obtained in the PREOM session, confirming the existence of tapering in the training load in the days prior to competition in order to favor the recovery of the players and, consequently, to guarantee greater freshness and willingness to compete [10,16,17,19]. The results of this study concur with these previous contributions in men’s football, where the weekly periodization was described, accumulating the highest loads in the middle of the week, that is, 2 or 3 days before the match (OM − 2 or OM − 3), coinciding with the EXT day, and lower loads at the end of the week, the 2 days before the competition. On the other hand, unlike previous studies [10,18], the location of the VEL session within the weekly distribution was novel. This was probably motivated by the distribution of the training sessions that the team arranged, trying to distance the VEL session from the EXT session, thereby stimulating the distance covered at high-speed ranges (e.g., HSR and Sprint) and that of the following match. The reason could lie in the need to emphasize this conditional capacity given its low stimulation both in the competition itself (e.g., players accumulated little distance in high-speed ranges) and throughout the competitive micro-cycle. The VEL session was located on the second day after the match played in the previous micro-cycle (OM + 2), that is, 5 or 6 days prior to the following match (OM − 5 or OM − 6). Significantly more HSR and Sprint were accumulated in these sessions, with a volume load (TD) similar to the EXT session. Regarding the INT session, which was usually carried out on the central days of the week (OM − 5 or OM − 4) and prior to the EXT session, a predominance of the force variables (accelerations and decelerations) was described compared to the other sessions, as described in men’s football in previous works [10,29]. However, although Stevens et al. (2017) described that medium (1.5–3 m/s2 and −1.5–3 m/s2) and high (>3 m/s2 and <−3 m/s2) accelerations and decelerations during training were similar to the competition values in this type of session, in this research work, the competition values were significantly higher than the rest of the types of training sessions. With regard to the EXT session, which was usually carried out in the middle of the week (OM − 4 or OM − 3), high values were described in TD and PL compared to the other sessions, showing a similar trend as in previous studies [18,30]. In elite male football, these studies described greater distances covered in OM − 3 sessions compared to OM − 4 sessions, which could be related to the EXT and INT sessions described in this study. Despite the global coincidence of this approach, not all the works [17,31–33] concurred in the same weekly profile of the loads. While some showed a downward progression or from more to less [31], others described two peaks, Monday and Thursday [33], with a remarkable peak in the main session of the week [32] or with high values in the middle of the week without showing great differences between sessions [17]. Likewise, the present work shows three load sessions (VEL, INT, AND EXT), of which two obtain higher values (VEL and EXT), located at the beginning and middle of the week, as represented in the study by [33]. This study describes a variant of tactical periodization, which, while respecting hori- zontal alternation, proposes a novel distribution in the orientation of content throughout the competitive micro-cycle. The possible practical application of this study is that the content of the training sessions with different conditional orientations could present a different ordering than the one proposed by the tactical periodization. In this way, instead of respecting the usual order of the sessions proposed by the tactical periodization (e.g., INT+EXT+VEL), it could be altered by proposing VEL+INT+EXT when the contextual Int. J. Environ. Res. Public Health 2022, 19, 15820 8 of 10 needs and/or the characteristics of the type of population to which it is directed requires adapting content to optimize their preparation process. It has been verified that none of the training sessions obtained higher values than those of the competition in all the variables (e.g., oriented towards neuromuscular, cardiovascular, and locomotor works). Therefore, coaches should be careful in the return-to-play process and should prepare players adequately because there could be an excessive gap between the demands of training sessions and those of competition, putting players at risk [11–13]. This work has some context limitations. In the first place, since just one team was analyzed, the periodization results should not be interpreted as something generalized in women’s football; more case studies would be required to approach an extrapolation. In ad- dition, the inclusion of internal load variables [34] could have allowed knowing in greater detail the internal response generated in the athlete [35]. Likewise, it could be interesting to carry out a battery of physical tests or to pass wellness and readiness questionnaires to assess the physical condition of the players or their willingness to compete [10,26]. How- ever, it is worth mentioning that the team achieved promotion to the highest category of women’s football at the national level; therefore, it could support the idea that the proposed periodization strategy not only had no negative effects on the performance of the players but also had possibly a great impact on the competitive performance. 5. Conclusions In conclusion, this research work provides a description of the profile of training loads throughout a competitive week in women’s football. A horizontal alternation in the stimulation of physical capacities in female football was described, although the order of the training contents varied with respect to the original proposal of tactical periodization. This study could open the possibility of proposing a variant that can better adapt to a particular reality conditioned by the schedules and pitches established by the sports club of the players. In this sense, in ongoing works, a larger sample and other contexts will be included. Author Contributions: Investigation, A.O., I.E., K.L.-d.-I., P.M.C. and J.C. All authors have read and agreed to the published version of the manuscript. Funding: This study was supported by the Universidad del País Vasco/Euskal Herriko Unibertsitatea, EUSK22/17, PES22/30, COLAB22/15, COST Actions CA18106 supported by COST (European Cooperation in Science and Technology). Institutional Review Board Statement: The Ethics Committee of research with humans (CEISH) of the University of the Basque Country (UPV/EHU) gave its institutional approval of the study (code M10-2019-099). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The datasets generated by and/or analyzed during the current study are not publicly available due to ethics and privacy requirements, but they are available from the corresponding author upon reasonable request. Acknowledgments: This study was supported by the Spanish government subproject Mixed method approach on performance analysis (in training and competition) in elite and academy sport [PGC2018- 098742-B-C33] (2019-2021) [Ministerio de Ciencia, Innovación y Universidades (MCIU), la Agencia Estatal de Investigación (AEI) y el Fondo Europeo de Desarrollo Regional (FEDER)], that is part of the coordinated project New approach of research in physical activity and sport from mixed methods perspective (NARPAS_MM) [SPGC201800 × 098742CV0]. The University of Cambridge, the Basque Government, Engineering and Society and Bioengineering Research Groups, IT1489-22, and ELKARTEK (KK-2020/00092, KK-2021/00033), “Ministerio de Ciencia e Innovación” (SAF2016 77758 R), FEDER funds. Conflicts of Interest: No potential conflict of interest was reported by the authors. Int. J. Environ. Res. Public Health 2022, 19, 15820 9 of 10 References 1. Malina, R.M.; Martinho, D.V.; Valente-dos-Santos, J.; Coelho-e-Silva, M.J.; Kozieł, S.M. Growth and Maturity Status of Female Soccer Players: A Narrative Review. Int. J. Environ. Res. Public Health 2021, 18, 1448. [CrossRef] [PubMed] 2. Martínez-Lagunas, V.; Niessen, M.; Hartmann, U. Women’s Football: Player Characteristics and Demands of the Game. J. Sport Health Sci. 2014, 3, 258–272. [CrossRef] 3. Harkness-Armstrong, A.; Till, K.; Datson, N.; Emmonds, S. Whole and Peak Physical Characteristics of Elite Youth Female Soccer Match-Play. J. Sports Sci. 2021, 39, 1320–1329. [CrossRef] [PubMed] 4. Andersson, H.Å.; Randers, M.B.; Heiner-Møller, A.; Krustrup, P.; Mohr, M. Elite Female Soccer Players Perform More High- Intensity Running When Playing in International Games Compared With Domestic League Games. J. Strength Cond. Res. 2010, 24, 912–919. [CrossRef] 5. Datson, N.; Drust, B.; Weston, M.; Jarman, I.H.; Lisboa, P.J.; Gregson, W. Match Physical Performance of Elite Female Soccer Players During International Competition. J. Strength Cond. Res. 2017, 31, 2379–2387. [CrossRef] 6. Griffin, J.; Newans, T.; Horan, S.; Keogh, J.; Andreatta, M.; Minahan, C. Acceleration and High-Speed Running Profiles of Women’s International and Domestic Football Matches. Front. Sports Act. Living 2021, 3, 604605. [CrossRef] 7. Hewitt, A.; Norton, K.; Lyons, K. Movement Profiles of Elite Women Soccer Players during International Matches and the Effect of Opposition’s Team Ranking. J. Sports Sci. 2014, 32, 1874–1880. [CrossRef] 8. Vescovi, J.D.; Fernandes, E.; Klas, A. Physical Demands of Women’s Soccer Matches: A Perspective across the Developmental Spectrum. Front. Sports Act. Living 2021, 3, 634696. [CrossRef] 9. Campos-Vazquez, M.A.; Mendez-Villanueva, A.; Gonzalez-Jurado, J.A.; León-Prados, J.A.; Santalla, A.; Suarez-Arrones, L. Relationships between Rating-of-Perceived-Exertion- and Heart- Rate-Derived Internal Training Load in Professional Soccer Players: A Comparison of On-Field Integrated Training Sessions. Int. J. Sports Physiol. Perform. 2015, 10, 587–592. [CrossRef] 10. Guridi, I.; Castellano, J.; Echeazarra, I. Physical Demands and Internal Response in Football Sessions according to Tactical Periodization. Int. J. Sports Physiol. Perform. 2021, 16, 858–864. [CrossRef] 11. Gabbett, T.J. The Training—Injury Prevention Paradox: Should Athletes Be Training Smarter and Harder? Br. J. Sports Med. 2016, 50, 273–280. [CrossRef] [PubMed] 12. Hulin, B.T.; Gabbett, T.J.; Lawson, D.W.; Caputi, P.; Sampson, J.A. The Acute: Chronic Workload Ratio Predicts Injury: High Chronic Workload May Decrease Injury Risk in Elite Rugby League Players. Br. J. Sports Med. 2016, 50, 231–236. [CrossRef] [PubMed] 13. Soligard, T.; Schwellnus, M.; Alonso, J.-M.; Bahr, R.; Clarsen, B.; Dijkstra, H.P.; Gabbett, T.; Gleeson, M.; Hägglund, M.; Hutchinson, M.R.; et al. How Much Is Too Much? (Part 1) International Olympic Committee Consensus Statement on Load in Sport and Risk of Injury. Br. J. Sports Med. 2016, 50, 1030–1041. [CrossRef] [PubMed] 14. Martín-García, A.; Gómez Díaz, A.; Bradley, P.S.; Morera, F.; Casamichana, D. Quantification of a Professional Football Team’s External Load Using a Microcycle Structure. J. Strength Cond. Res. 2018, 32, 3511–3518. [CrossRef] 15. Buchheit, M.; Lacome, M.; Cholley, Y.; Simpson, B.M. Neuromuscular Responses to Conditioned Soccer Sessions Assessed via GPS-Embedded Accelerometers: Insights Into Tactical Periodization. Int. J. Sports Physiol. Perform. 2018, 13, 577–583. [CrossRef] 16. Akenhead, R.; Harley, J.A.; Tweddle, S.P. Examining the External Training Load of an English Premier League Football Team With Special Reference to Acceleration. J. Strength Cond. Res. 2016, 30, 2424–2432. [CrossRef] 17. Malone, J.J.; Di Michele, R.; Morgans, R.; Burgess, D.; Morton, J.P.; Drust, B. Seasonal Training-Load Quantification in Elite English Premier League Soccer Players. Int. J. Sports Physiol. Perform. 2015, 10, 489–497. [CrossRef] 18. Owen, A.L.; Djaoui, L.; Newton, M.; Malone, S.; Mendes, B. A Contemporary Multi-Modal Mechanical Approach to Training Monitoring in Elite Professional Soccer. Sci. Med. Footb. 2017, 1, 216–221. [CrossRef] 19. Hannon, M.P.; Coleman, N.M.; Parker, L.J.F.; McKeown, J.; Unnithan, V.B.; Close, G.L.; Drust, B.; Morton, J.P. Seasonal Training and Match Load and Micro-Cycle Periodization in Male Premier League Academy Soccer Players. J. Sports Sci. 2021, 39, 1838–1849. [CrossRef] 20. Fessi, M.S.; Zarrouk, N.; Di Salvo, V.; Filetti, C.; Barker, A.R.; Moalla, W. Effects of Tapering on Physical Match Activities in Professional Soccer Players. J. Sports Sci. 2016, 34, 2189–2194. [CrossRef] 21. Jackson, B.M.; Polglaze, T.; Dawson, B.; King, T.; Peeling, P. Comparing Global Positioning System and Global Navigation Satellite System Measures of Team-Sport Movements. Int. J. Sports Physiol. Perform. 2018, 13, 1005–1010. [CrossRef] [PubMed] 22. BastidaCastillo, A.; Carmona, C.D.G.; Belmonte, A.H.; Ortega, J.P. Validez y fiabilidad de un dispositivo inercial (WIMU PROTM) para el análisis del posicionamiento en balonmano. J. Sports Sci. 2018, 14, 9–16. 23. Bastida Castillo, A.; Gómez Carmona, C.D.; De la cruz sánchez, E.; Pino Ortega, J. Accuracy, Intra- and Inter-Unit Reliability, and Comparison between GPS and UWB-Based Position-Tracking Systems Used for Time–Motion Analyses in Soccer. Eur. J. Sport Sci. 2018, 18, 450–457. [CrossRef] [PubMed] 24. Bastida-Castillo, A.; Gómez-Carmona, C.; De la Cruz-Sánchez, E.; Reche-Royo, X.; Ibáñez, S.; Pino Ortega, J. Accuracy and Inter-Unit Reliability of Ultra-Wide-Band Tracking System in Indoor Exercise. Appl. Sci. 2019, 9, 939. [CrossRef] 25. Gabbett, T.J. Use of Relative Speed Zones Increases the High-Speed Running Performed in Team Sport Match Play. J. Strength Cond. Res. 2015, 29, 3353–3359. [CrossRef] 26. Zurutuza, U.; Castellano, J.; Echeazarra, I.; Casamichana, D. Absolute and Relative Training Load and Its Relation to Fatigue in Football. Front. Psychol. 2017, 8, 878. [CrossRef] Int. J. Environ. Res. Public Health 2022, 19, 15820 10 of 10 27. Batterham, A.M.; Hopkins, W.G. Making Meaningful Inferences About Magnitudes. Int. J. Sports Physiol. Perform. 2006, 1, 50–57. [CrossRef] 28. Castillo, D.; Raya-González, J.; Weston, M.; Yanci, J. Distribution of External Load During Acquisition Training Sessions and Match Play of a Professional Soccer Team. J. Strength Cond. Res. 2021, 35, 3453–3458. [CrossRef] 29. Stevens, T.G.A.; de Ruiter, C.J.; Twisk, J.W.R.; Savelsbergh, G.J.P.; Beek, P.J. Quantification of In-Season Training Load Relative to Match Load in Professional Dutch Eredivisie Football Players. Sci. Med. Footb. 2017, 1, 117–125. [CrossRef] 30. Anderson, L.; Orme, P.; Di Michele, R.; Close, G.L.; Morgans, R.; Drust, B.; Morton, J.P. Quantification of Training Load during One-, Two- and Three-Game Week Schedules in Professional Soccer Players from the English Premier League: Implications for Carbohydrate Periodisation. J. Sports Sci. 2016, 34, 1250–1259. [CrossRef] 31. Impellizzeri, F.M.; Rampinini, E.; Coutts, A.J.; Sassi, A.; Marcora, S.M. Use of RPE-Based Training Load in Soccer. Med. Sci. Sports Exerc. 2004, 36, 1042–1047. [CrossRef] [PubMed] 32. Jeong, T.-S.; Reilly, T.; Morton, J.; Bae, S.-W.; Drust, B. Quantification of the Physiological Loading of One Week of “Pre-Season” and One Week of “in-Season” Training in Professional Soccer Players. J. Sports Sci. 2011, 29, 1161–1166. [CrossRef] [PubMed] 33. Wrigley, R.; Drust, B.; Stratton, G.; Scott, M.; Gregson, W. Quantification of the Typical Weekly In-Season Training Load in Elite Junior Soccer Players. J. Sports Sci. 2012, 30, 1573–1580. [CrossRef] [PubMed] 34. Akubat, I.; Barrett, S.; Abt, G. Integrating the Internal and External Training Loads in Soccer. Int. J. Sports Physiol. Perform. 2014, 9, 457–462. [CrossRef] [PubMed] 35. Delaney, J.A.; Duthie, G.M.; Thornton, H.R.; Pyne, D.B. Quantifying the Relationship between Internal and External Work in Team Sports: Development of a Novel Training Efficiency Index. Sci. Med. Footb. 2018, 2, 149–156. [CrossRef]

Comparison of the External Load in Training Sessions and Official Matches in Female Football: A Case Report.

11-28-2022

Olaizola, Aratz,Errekagorri, Ibai,Lopez-de-Ipina, Karmele,María Calvo, Pilar,Castellano, Julen

eng

PMC4501764

RESEARCH ARTICLE It’s a Matter of Mind! Cognitive Functioning Predicts the Athletic Performance in Ultra- Marathon Runners Giorgia Cona1,2*, Annachiara Cavazzana1, Antonio Paoli3, Giuseppe Marcolin3, Alessandro Grainer3, Patrizia Silvia Bisiacchi1,4 1 Department of General Psychology, University of Padua, Padua, Italy, 2 Department of Neuroscience, University of Padua, Padua, Italy, 3 Department of Biomedical Science, University of Padua, Padua, Italy, 4 Center for Cognitive Neuroscience, University of Padua, Padua, Italy * [email protected] Abstract The present study was aimed at exploring the influence of cognitive processes on perfor- mance in ultra-marathon runners, providing an overview of the cognitive aspects that char- acterize outstanding runners. Thirty runners were administered a battery of computerized tests right before their participation in an ultra-marathon. Then, they were split according to the race rank into two groups (i.e., faster runners and slower runners) and their cognitive performance was compared. Faster runners outperformed slower runners in trials requiring motor inhibition and were more effective at performing two tasks together, successfully suppressing the activation of the information for one of the tasks when was not relevant. Furthermore, slower runners took longer to remember to execute pre-defined actions asso- ciated with emotional stimuli when such stimuli were presented. These findings suggest that cognitive factors play a key role in running an ultra-marathon. Indeed, if compared with slower runners, faster runners seem to have a better inhibitory control, showing superior ability not only to inhibit motor response but also to suppress processing of irrelevant infor- mation. Their cognitive performance also appears to be less influenced by emotional stimuli. This research opens new directions towards understanding which kinds of cognitive and emotional factors can discriminate talented runners from less outstanding runners. Introduction “I just run. I run in a void. Or maybe I should put it the other way: I run in order to acquire a void. But as you might expect, an occasional thought will slip into this void. People’s minds can’t be a complete blank. Human beings’ emotions are not strong or consistent enough to sustain a vacuum. What I mean is, the kinds of thoughts and ideas that invade my emotions as I run remain subordinate to that void.” PLOS ONE | DOI:10.1371/journal.pone.0132943 July 14, 2015 1 / 12 OPEN ACCESS Citation: Cona G, Cavazzana A, Paoli A, Marcolin G, Grainer A, Bisiacchi PS (2015) It’s a Matter of Mind! Cognitive Functioning Predicts the Athletic Performance in Ultra-Marathon Runners. PLoS ONE 10(7): e0132943. doi:10.1371/journal.pone.0132943 Editor: Giuseppe di Pellegrino, University of Bologna, ITALY Received: March 3, 2015 Accepted: June 21, 2015 Published: July 14, 2015 Copyright: © 2015 Cona et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Data of our experiment are available upon request because of ethical restriction in order to protect the privacy of research participants. Readers can contact giorgia. [email protected] to request the data, which would be given anonymously. Race ranking is freely available on the web. Funding: This study was supported by a grant from the University of Padova (Strategic Grant NEURAT; STPD11B8HM_004) to P.B. The funder did not have any role in study, data collection or analysis, decision to publish, or preparation of the manuscript. Haruki Murakami—What I Talk About When I Talk About Running All kinds of sports imply, to different extents, the application of cognitive, perceptual and motor skill [1, 2]. Nevertheless, although superior performance is clearly evident on observa- tion, the cognitive mechanisms that contribute to a successful performance are less clear. For several decades researchers have sought to better understand the cognitive factors that are able to discriminate between talents and less outstanding athletes [3]. Outstanding athletes were shown to have enhanced declarative and procedural knowledge, and to be more able at making decisions and at extrapolating relevant information from the environment to anticipate future events and outcomes [4–6]. Experts seem also to have a more effective visuo-spatial processing and greater selective attention [7–9]. In particular, the effect of focus of attention on athletic skills has been extensively explored across sporting domains. Attentional focus is typically clas- sified as internal or external, where the internal focus is meant to be directed toward the perfor- mance of movements, whereas the external one is meant to direct attention toward the effects of a movement [10] and/or to external environmental stimuli [11]. Overall, an external focus of attention appears to be more beneficial for a successful sporting performance [12, 13]. Furthermore, motor response selection and inhibition processes were shown to be crucial, for example, in fencing, baseball, tennis and soccer [14–17]. Despite the increasing evidence of the key role of cognitive factors across a wide range of sporting domains, the contribution of such factors to endurance sports and, more specifically, to running performance, is still poorly understood. The few studies that have addressed this issue focused on the influence of cognitive strategies and focus of attention on quality and per- formance of the run. Cognitive strategies are typically subdivided into associative strategies, which imply directing of attention towards task-relevant stimuli and physiological sensations experienced during exercise, and dissociative strategies, consisting in directing attention toward distracting thoughts, as work, relationships, and other kinds of thoughts unrelated to the experience of running [18]. Generally, these studies showed that runners adopting an asso- ciative strategy ran faster than runners adopting a dissociative strategy [19; 18]. A recent study also highlighted that having an external focus of attention increases running economy (mea- sured as oxygen consumption at a set running speed), leading to a better performance as com- pared with an internal focus of attention [20]. Since the contribution of the other cognitive aspects has been almost neglected, the present study aimed to provide, for the first time, an overview of the impact of various cognitive func- tions upon running performance. More specifically, the starting point questions were: Could cognitive functioning contribute to running performance? And, if so, which cognitive processes are the best mediators of running performance? To answer these questions we asked a group of ultra-runners to execute a series of cognitive tasks immediately before the running race. Then, we analysed the cognitive performance on these tasks comparing the runners who obtained a batter rank in the race (i.e., faster runners) with those who obtained a worse rank (i.e., slower runners). We explored cognitive functioning by means of the modified versions of two computerized tasks: The Inhibitory Control Task (ICT) and a dual-task paradigm with emotional stimuli, which have been already utilized in our lab [21–25]. The ICT is composed of multiple types of trials, thus allowed us to test distinct cognitive processes, including response speed, selective attention, working memory updating and response inhibition [24] To better explore the impact of executive functions on running, we utilized a dual-task para- digm, in which two distinct tasks, heavily dependent on frontal executive processes, needed to be executed simultaneously (Fig 1). The dual-task paradigm consists of an ongoing activity, namely a working memory 2-back task, and a Prospective Memory (PM) task [25]. For the Cognitive Factors in Running Performance PLOS ONE | DOI:10.1371/journal.pone.0132943 July 14, 2015 2 / 12 Competing Interests: The authors have declared that no competing interests exist. 2-back task, participants were instructed to decide whether the picture occurring on the screen was same or different from the picture presented two trials before by pressing one of two possi- ble response keys (i.e., 2-back task). While executing the ongoing task, they had to remember to complete a pre-specified intention (i.e., pressing a third key) when a pre-memorized picture, Fig 1. Schematic illustration of the Dual-Task paradigm. The figure illustrates the pleasant PM session, in which five pleasant PM cues needed to be encoded for later execution of the intention. The same tasks and procedure were run for both unpleasant and neutral sessions. Although not displayed, a blank screen with a fixation cross (lasting 1200, 1400, or 1600 ms) always occurred between two distinct stimuli. For the ongoing task, participants had to press one of two keys with the right hand to decide whether the picture was same or different from the picture presented two trials before. For the PM task, participants were required to remember to press an additional key, with their left index finger, when they saw a picture presented during the encoding phase. Note: The pictures displayed in the figure are not those used in the study, but are taken from Internet only for illustration purposes. doi:10.1371/journal.pone.0132943.g001 Cognitive Factors in Running Performance PLOS ONE | DOI:10.1371/journal.pone.0132943 July 14, 2015 3 / 12 namely the PM cue, occurred on the screen amid the ongoing trials (i.e., PM task). Therefore, by using such paradigm we were able to test the runners’ ability: (i) to manage two tasks simul- taneously; (ii) to monitor the external, ongoing, stimuli driven by an internal goal (i.e., the identification of the PM cue), providing information about the runners’ attitude to adopt an internal versus external focus of attention, as postulated in the PM context by the Attention to Delayed Intention (AtoDI) model [26]; (iii) to remember to execute delayed intentions when the appropriate cues occur; (iv) to process and react to emotional stimuli. In order to address the fourth issue, we included pictures that were characterized by a specific emotional valence (i.e., pleasant, unpleasant, or neutral) in both the ongoing and PM tasks. Cognitive-evaluative reactions to emotional stimuli and situations were shown, indeed, to be pivotal for athletic per- formance [27–29]. Thus, the inclusion of emotional stimuli in this paradigm was important to illuminate the relationship between processing and reacting to emotional stimuli and the sub- sequent running behaviour. Materials and Methods Running race and Participants Data were collected on July 25th 2014, in occasion of the Trans d’Havet race. This competition took place in the northeast of Italy and was part of the Ultra race of the European Skyrunning championships. The track consisted of 80 km with a total elevation of 5500 mt and a maximum altitude of 2238 mt. The race started on Saturday at 12.00 pm. The organizations guaranteed medical stations and rest stops with drinks and food along the whole race. Each participant had to pass pre-defined gate not exceeding a certain time to continue the race. Unexpectedly, the race was interrupted because of weather conditions, and the race rank was obtained from the order of arrival recorded at the last pre-defined gate passed before the interruption of the race, which corresponded to the 30th km for all the participants. Thirty ultra-marathon runners (M = 43 years, S.D. = 8.6) took part in this study. The deter- mination of the sample size to detect a medium size effect (ηp² = .25) was based on a previous study that used the same task (i.e., the ICT; [23]). All participants were males, had normal, or corrected-to-normal, vision and no neurologi- cal, psychiatric or psychological (including phobias) pathologies. Participants were in good physical health, as proven by the medical certificate. All the runners were tested on cognitive tasks right before their participation to the ultra-marathon (between the 7 pm and the 9 pm). Then, a median split based on the ranking recorded at the last pre-defined gate before the inter- ruption of the race was performed. This allowed us to create two groups, distinguishing between faster runners and slower runners. The two groups did not differ either in age or edu- cational level (Faster runners: Age 42.8 ± 9.6 yrs, Education 14.0 ± 4.1 yrs; Slower runners: 42.1 ± 7.7 yrs, Education 16.3 ± 2.5 yrs; all ps > .05). Ethics Statement The study was approved by the ethical committee of the Department of Biomedical Sciences (University of Padua) and was conducted according to the principles of the Declaration of Hel- sinki. All the participants were informed about the experimental procedure and signed a writ- ten consent form. Inhibitory Control Task The ICT was adapted from the version used in our previous studies [23, 24]. Black letters were presented, one after the other, for 500 ms without inter-stimulus interval, in the center of a Cognitive Factors in Running Performance PLOS ONE | DOI:10.1371/journal.pone.0132943 July 14, 2015 4 / 12 white background computer screen. Interspersed within the other letters, the target letters X and Y were presented. During the first session of the task, the participants were instructed to respond by pressing the spacebar for every X and Y (detect trials). During the second session of the task, participants were instructed to press the spacebar only when X and Y were alternating (go trials) and to inhibit their response when X and Y were repeated (nogo trials). Two target letters never occurred consecutively. The first session comprised 122 distracting letters and 30 target letters (detect trials). The second session was composed of 3 blocks, for a total of 567 dis- tracting letters, 90 go trials and 18 nogo trials. Dual-Task paradigm Following our previous study [25], the dual-task paradigm consisted of an ongoing working memory task and a PM task, simultaneously executed (Fig 1). The ongoing task was a 2-back task comprising pleasant, neutral and unpleasant pictures. Pictures were selected from the International Affective Picture System ([30]; see [25], for more details on the features of the sti- muli selected). Participants were instructed to decide whether the picture occurring on the screen was same or different from the picture occurring two trials before by pressing one of two possible response keys on the keyboard with the index or middle finger of their right hand (‘N’ or ‘M’ keys). On each trial, the stimulus remained on the screen for 2000 ms or until a response was made, and was followed by a black screen with a fixation cross that pseudo-ran- domly lasted 1200, 1400, or 1600 ms. Simultaneously with the ongoing task, individuals were instructed to remember to accomplish a PM task, which consisted in pressing the ‘Z’ key, with their left index finger, when particular pictures (i.e., PM cues) occurred on the screen. The par- adigm was composed of three PM sessions, which differ for the emotional valence of the PM cue (pleasant, unpleasant, neutral). Each session was preceded by an encoding phase, during which the PM cues were presented, one after the other, in the center of the screen and partici- pants were required to memorize them. Within a PM session, pleasant, neutral and unpleasant ongoing pictures were pseudo-randomly presented, whereas the valence of the PM cues was constant. The order of the PM sessions was counterbalanced across participants. Each of the PM sessions comprised 55 ongoing stimuli and 5 PM cues each. A PM cue was never also a ‘same’ 2-back trial. Before the PM sessions, a practice block comprising 39 ongoing trials was given. Data Analysis We compared the ICT performance between faster runners versus slower runners by analyzing the mean accuracy for the three ICT types of trials (detect, go, nogo trials) and the RTs for the detect and go trials by means of two separate ANOVAs. In order to investigate the effect of monitoring for emotional PM cues on the ongoing per- formance, the mean RTs and the proportion of correct responses to the 2-back task were ana- lyzed in two separate ANOVAs including one between-subject factor (i.e., runners group: faster versus slower runners) and three within-subject factors: Stimulus type (same 2-back trial, different 2-back trial), PM cue valence (unpleasant, neutral, pleasant) and Ongoing stimulus valence (unpleasant, neutral, pleasant). Indeed, previous studies showed that the allocation of attentional resources towards ongoing stimuli to monitor for PM cue were reflected in an increase of RTs and were greater when there was a match between the valence of the PM cues and the valence of the ongoing stimuli, revealing the Stimulus Specific Interference Effect (SSIE; [25]). The mean RTs and accuracy in the PM task were entered into two ANOVAs with the Run- ners group and the Valence of the PM cues as factors. Cognitive Factors in Running Performance PLOS ONE | DOI:10.1371/journal.pone.0132943 July 14, 2015 5 / 12 For all the analyses, post hoc comparisons were conduced applying the Fisher's LSD (Least Significant Difference) correction. We estimated effect sizes using partial eta squared (ηp²). Results Inhibitory Control Task The analysis of mean accuracy in the ICT revealed a significant main effect of the Runners group [F(1,28) = 4.81, p < .05, ηp² = .15], of the Type of trial [F(2,56) = 40.99, p < .001, ηp² = .54], as well as a significant interaction between the two variables [F(2,56) = 8.57, p < .001, ηp² = .23]. As can be also seen in Fig 2, post hoc comparisons revealed that faster runners outperformed slower runners selectively in the nogo trials (p < .001), whereas did not differ from slower run- ners in the detect and go trials (p > .05). The same analysis performed on RTs showed no significant difference between the two groups of runners [F(1,28) = 0.80, p > .05, ηp² = .02, Faster runners: M = 455 ms, standard error, SE = 16.97; Slower runners: M = 438 ms, SE = 8.90]. It however revealed a main effect of the Type of trial, revealing that the RTs were slower in go trials (M = 476 ms, SE = 12.47) than in the detect trials (M = 417 ms, SE = 8.46) [F(1,28) = 4.81, p < .001, ηp² = .57], for both groups. Dual-Task Paradigm The analysis of the RTs in the ongoing task revealed a significant Runners group × PM cue valence interaction [F(2,56) = 3.48, p < .05, ηp² = .11]. Post hoc comparisons showed that, as compared with faster runners, slower runners tended to have increased RTs in ongoing trials when they had to monitor for pleasant and unpleasant PM cues (both ps = .05), whereas they did not differ from faster runners when they had to monitor for neutral PM cues. Fig 2. Mean Accuracy in the Inhibitory Control Task (ICT) trials for the faster and slower runners. Faster runners outperform slower runners selectively in the nogo trials, whereas they did not differ from slower runners in the detect and go trials. Vertical bars represent standard error. doi:10.1371/journal.pone.0132943.g002 Cognitive Factors in Running Performance PLOS ONE | DOI:10.1371/journal.pone.0132943 July 14, 2015 6 / 12 The three-way and the four-way interactions were both significant. To better investigate the pattern of results in ongoing performance, the highest-level interaction [F(4,112) = 3.31, p < .01, ηp² = .10] was split in two ANOVAs, separately for the ‘same’ and the ‘different’ 2-back stimuli (Fig 3). Indeed, while there were not significant group differences in ‘different’ 2-back sti- muli (all ps > .05), the effect of the Runners group was shown to be significant in the ANOVA performed on the ‘same’ stimuli (i.e., pictures that were also presented two stimuli before). More specifically, this analysis revealed a significant Runners group × PM cue valence × Ongoing stim- ulus valence interaction [F(4,112) = 4.23, p < .01, ηp² = .13]. As can be seen in Fig 3, if compared with the faster runners, the slower runners showed an increase in the RTs especially when the valence of the ongoing trials matched the valence of the PM cue to be monitored for in that ses- sion, thus revealing a higher SSIE. Indeed, the slower runners had slower RTs for pleasant ongo- ing pictures when monitoring for pleasant PM cues (p < .05), and for neutral ongoing pictures when monitoring for neutral PM cues (p < .01). The pattern of results in the unpleasant session was instead less clear, as slower runners showed increased RTs especially for pleasant ongoing sti- muli (p < .01). The analysis of the accuracy in the ongoing task did not reveal any significant effect (all ps > .05). The analysis of the RTs in the PM task showed a significant interaction between Runners group and PM cue valence factors [F(2,56) = 3.37, p < .05, ηp² = .11]. Faster runners responded more quickly than slower runners to both pleasant PM cues (Faster runners: mean = 308 ms, SE = 32.97; Slower runners: M = 463 ms, SE = 49.49; p < .05) and unpleasant PM cues (Faster runners: M = 359 ms, SE = 39.25; Slower runners: mean = 496 ms, SE = 51.99; p < .05), whereas they did not differ between each other in responding to neutral PM cues (Faster runners: M = 414 ms, SE = 55.30; Slower runners: M = 422 ms, SE = 46.68; p > .05). The analysis of the accuracy in the PM task did not show any significant effect (all ps > .05). Discussion What are the factors that make an outstanding athlete? In the last decades it appeared clear that there is a combination of multiple factors, many of these are not strictly related to physical skills but concern other individual aspects, such as cognitive abilities. The present study cor- roborates this view, showing that some of the cognitive measures seem to be predictive of the quality of running performance. More specifically, the findings indicate that, as compared with slower runners, faster run- ners had a better accuracy in nogo trials of the ICT, in which it was required to promptly inhibit a dominant, but inappropriate, response. Thus, our study showed enhanced motor inhi- bition in faster runners, suggesting that such cognitive function might be essential for success- ful running performance. Importantly, it extends the results of previous studies, which found comparable results on motor inhibition in soccer, baseball, tennis and volleyball players [15– 17] so revealing that motor inhibition is crucial not only in team sports but also in endurance sports. As hypothesized by Verburgh et al. [31], motor inhibition might have a key role in some physical skills, as agility. Agility has been indeed defined as ‘‘a rapid whole-body move- ment with change of velocity or direction in response to a stimulus” [32]. We can speculate that the ability to re-direct a movement in response to a stimulus might be particularly impor- tant in a mountain ultra-marathon consisting in running and walking uphill and downhill in pebbly and stony terrains. Future studies might be useful to investigate whether motor inhibi- tion has the same influence also on marathons that are performed on flat city roads. Con- versely, no group difference was found for selective attention and working memory, which were evaluated in detect and go trials of the ICT. These abilities might be more required in Cognitive Factors in Running Performance PLOS ONE | DOI:10.1371/journal.pone.0132943 July 14, 2015 7 / 12 Fig 3. Mean reaction times (RTs) in the ongoing 2-back task, separately for each type of PM cue valence and ongoing stimulus valence. Runners group differences were observed in the ‘same’ trials, especially when participants had to monitor for unpleasant and pleasant PM cues and when the valence of the PM cue matched the valence of the ongoing stimuli. doi:10.1371/journal.pone.0132943.g003 Cognitive Factors in Running Performance PLOS ONE | DOI:10.1371/journal.pone.0132943 July 14, 2015 8 / 12 other kinds of sports, as in soccer, baseball, or volley, which rely more upon strategic abilities as well as upon the execution of rapid actions towards stimuli. Finally, the executive functions were evaluated in more depth by exploring the results on a particular kind of dual-task paradigm. In general, the performance to the 2-back working memory task did not vary according to the runners group. This would corroborate the findings obtained with the ICT in indicating that working memory has not a great importance on run- ning. However, investigating the RTs in the 2-back trials depending on the valence of the PM cue to monitor for provided information about the interference derived from checking the presence of an emotional PM cue on ongoing task. To this regard, we observed that the inter- ference on RTs due to the addition of the PM task was greater for the slower runners than the faster runners. More specifically, such greater interference shown by slower runners was observed in the RTs for the 2-back ‘same’ stimuli (i.e., stimuli that were same to those pre- sented two trials before) and it was displayed in particular when individuals had to monitor for emotional PM cues. Notably, slower runners tended to have a greater Stimulus Specific Inter- ference Effect (SSIE), which consisted of the increase in RTs when the valence of the PM cue matched the valence of the ongoing stimulus [25]. Therefore, a possible explanation is that slower runners were less able to suppress/inhibit the interfering representation of the PM cue, especially when such cue was emotional and when the task was more demanding (as in the ‘same’ trials). The increased SSIE for slower runners also supports this view, indicating that the group difference was observed mainly when the PM cue valence matched the ongoing stimulus valence, thus when the degree of interference between the internal representation of the PM cue and the external ongoing stimulus was higher given their similar valence. Following the Attention to Delayed Intention (AtoDI) model [26], our hypothesis is that faster runners tended to be more focused on external, ongoing stimuli, and were more effective at inhibiting the internal interfering PM cue representation. In this sense, outperforming runners seem to have a better inhibitory control not only over motor responses, but also over interfering dis- tracting information. By contrast, slower runners tended to be more focused on the internal representation of the PM cue, which was less effectively inhibited. This finding is in agreement with the literature on the focus of attention, which highlighted that adopting an external focus of attention was associated with a better sporting performance and an increase in running economy [11–13; 20]. This was probably the experience that the writer Haruki Murakami meant to describe in the sentences that we reported at the beginning of the present manuscript. When he wrote “. . .the kinds of thoughts and ideas that invade my emotions as I run remain subordinate to that void” he might indeed refer to the tendency to focus on the ongoing activity as, in this case, on running, without being distracted by internal, momentaneously not relevant, thoughts. An alternative hypothesis is that slower runners were more motivated to perform success- fully the PM task, thus they were more engaged in monitoring for the presence of the PM cue amid ongoing stimuli. However, this would not explain why group difference was observed only in the 2-back ‘same’ stimuli and not in the 2-back ‘different’ stimuli. Furthermore, as com- pared with the faster runner, the slower runners took more time in executing the intention when the PM cue was emotional (i.e., pleasant and unpleasant). This seems to indicate that the emotional content of information had a greater impact on the motor responses in the slower runners, leading to the suggestion that the different way to process and react to emotional sti- muli might contribute to account for differences in running performance [28–29;33]. A limitation of this research is that the race has been interrupted, thus one might wonder whether the rank at the intermediate gate would have been confirmed by the final rank. Basi- cally, would this ranking have been somehow the same also at the end of the competition? Although we cannot answer this question, we sought to clarify this issue by analyzing the rank Cognitive Factors in Running Performance PLOS ONE | DOI:10.1371/journal.pone.0132943 July 14, 2015 9 / 12 obtained by each runner in the last race that was characterized by a similar running distance, which was freely available on the web. Then, we compared the mean rank between faster run- ners and slower runners. We found that also for that race, the faster runners had a better run- ning performance compared to the slower runners (Mean ranking score: Faster runners = 47.2 ± 40.5; Slower runners = 116.9 ± 87.5; p < .01). This suggests that the interme- diate ranking of the Trans d’Havet race was likely to reflect the final ranking, so it was a good index to differentiate runners. Finally, another question that can arise from this research concerns the role of cardiorespi- ratory fitness in modulating the cognitive performance of ultra-runners. It might be possible that faster runners had a better cognitive performance compared to slower runners since they were characterized by higher cardiorespiratory fitness. The relationship between cardiorespira- tory fitness and cognitive efficiency has been indeed increasingly explored over the past decades, especially in relation to age-related cognitive differences [34–36]. Higher cardiorespi- ratory fitness was found to be associated with increases in white and grey matter volume in the prefrontal, parietal, anterior cingulate and temporal cortices and in the hippocampus, leading to improvements in multiple cognitive functions, such as attention, control and memory [34– 38]. In the present experiment, we could not collect and assess physiological parameters, as maximal oxygen uptake (VO2max) and Running Economy (RE) because of logistic reasons (participants came from all over Italy and stayed at the experiment’s location only for the dura- tion of the race). Nevertheless, we suppose that cardiorespiratory fitness played a minor role in accounting for the cognitive differences highlighted by the present research, for two main rea- sons. First, several studies showed that the VO2max is not a good performance predictor in homogeneous groups [39]—as our sample is—since it does not vary with great extent within such kind of groups. Second, we found that the cognitive differences between faster and slower runners involved selectively some functions (e.g., inhibition but not working memory and selective attention) rather than consisting in a global difference in cognitive functioning, which would be instead the expected result of variations in cardiorespiratory fitness. However, our hypotheses are still speculative, hence they need to be tested in future studies. Summarizing, this is the first study to highlight that cognitive functioning seems to be pre- dictive of the quality of running performance in ultra-trail. Indeed, as compared with slower runners, outperforming runners have a better inhibitory control, showing superior ability not only to inhibit motor responses but also to suppress processing of irrelevant distracting infor- mation. Their cognitive performance also seems to be less influenced by emotional stimuli. This research might open new directions toward understanding what cognitive and emotional factors characterize talented runners. Acknowledgments The authors wish to thank Vincenza Tarantino, Davide Cappon, Claudia Pellegrino and Ric- cardo Tronca for their help in data collection. The authors wish also to thank the organisers and the participants of the “Trans d’Havet” and the municipality of Valdagno for the logistic support. Author Contributions Conceived and designed the experiments: GC PB AP GM AG. Performed the experiments: GC AC. Analyzed the data: GC. Contributed reagents/materials/analysis tools: GC AC. Wrote the paper: GC AC GM PB. Cognitive Factors in Running Performance PLOS ONE | DOI:10.1371/journal.pone.0132943 July 14, 2015 10 / 12 References 1. Ali A. Measuring soccer skill performance: a review. Scand J Med Sci Sports. 2011; 21(2):170–183. doi: 10.1111/j.1600-0838.2010.01256.x PMID: 21210855 2. Bate D. Soccer skills practice. In: Reilly T, editor. Science and soccer. London: E & FN Spon; 1996. p. 227–241. 3. Starkes JL, Ericsson KA. Expert performance in sports: Advances in research on sport expertise. In: Human Kinetics Publishers. Champaign, IL; 2003. p. 371–402. 4. Holyoak K. Symbolic connectionism: Toward third-generation theories of expertise. In: Ericsson KA, Smith J, editors. Towards a general theory of expertise. Cambridge: Cambridge University Press; 1991. p. 301–336. 5. Starkes JL, Allard F. Cognitive issues in motor expertise. Amsterdam: North-Holland; 1993. p. 365. 6. Williams AM, Davids K, Williams JG. Visual Perception and Action in Sport. London: E & FN Spon; 1999. 7. Alves H, Voss MW, Boot WR, Deslandes A, Cossich V, Salles JI et al. Perceptual-cognitive expertise in elite volleyball players. Front. Psychol. 2013; 4. 8. Jansen P, Lehmann J, Van Doren J. Mental rotation performance in male soccer players. PLOS One. 2012; 7(10):e48620. doi: 10.1371/journal.pone.0048620 PMID: 23119073 9. Voss MW, Kramer AF, Basak C, Prakash RS, Roberts B. Are expert athletes ‘expert’in the cognitive laboratory? A meta‐analytic review of cognition and sport expertise. Appl Cogn Psychol. 2010; 24 (6):812–826. 10. Wulf G. Attentional focus and motor learning: A review of 10 years of research. E-journal Bewegung und Training. 2007; 1(2–3):1–11. 11. Beilock SL, Carr TH, MacMahon C, Starkes JL. When paying attention become counterproductive: impact of divided versus skill-focused attention on novice and experienced performance of sensorimo- tor skills. J Exp Psychol Appl. 2002; 8(1):6. PMID: 12009178 12. Castaneda B, Gray R. Effects of focus of attention on baseball batting performance in players of differ- ing skill levels. J Sport Exerc Psychol. 2007; 29(1):60. PMID: 17556776 13. Jackson RC, Ashford K, Norsworthy G. Attentional focus, dispositional reinvestment, and skilled motor performance under pressure. J Sport Exerc Psychol. 2006; 28:49–68. 14. Di Russo F, Taddei F, Apnile T, Spinelli D. Neural correlates of fast stimulus discrimination and response selection in top-level fencers. Neurosci Lett. 2006; 408(2):113–118. PMID: 17018246 15. Nakamoto H, Mori S. Sport-specific decision-making in a go/nogo reaction task: difference among non- athletes and baseball and basketball players. Percep Mot Skills. 2008; 106(1):163–170. 16. Vestberg T, Gustafson R, Maurex L, Ingvar M, Petrovic P. Executive functions predict the success of top-soccer players. PLOS One. 2012; 7(4): e34731. doi: 10.1371/journal.pone.0034731 PMID: 22496850 17. Wang CH, Chang CC, Liang YM, Shih CM, Chiu WS, Tseng P et al. Open vs. closed skill sports and the modulation of inhibitory control. PLOS One. 2013; 8(2): e55773. doi: 10.1371/journal.pone.0055773 PMID: 23418458 18. Masters KS, Ogles BM. Associative and dissociative cognitive strategies in exercise and running: 20 years later, what do we know? Sport Psychol. 1998; 253–270. 19. Morgan WP, Pollock ML. Psychological characteristics of the elite distance runner. Annuals of the New York Academy of Sciences. 1997; 301(1):382–403. 20. Schücker L, Hagemann N, Strauss B, Völker K. The effect of attentional focus on running economy. J Sports Sci. 2009; 27(12):1241–1248. doi: 10.1080/02640410903150467 PMID: 19787539 21. Amodio P, Ridola L, Schiff S, Montagnese S, Pasquale C, Nardelli S, et al. Improving the inhibitory con- trol task to detect minimal hepatic encephalopathy. Gastroenterology. 2010; 139(2): 510–518. doi: 10. 1053/j.gastro.2010.04.057 PMID: 20470775 22. Bisiacchi P, Cona G, Tarantino V, Schiff S, Montagnese S, Amodio P, et al. Assessing inter-and intra- individual cognitive variability in patients at risk for cognitive impairment: the case of minimal hepatic encephalopathy. Metab Brain Dis. 2014; 29(4):945–953. doi: 10.1007/s11011-014-9529-0 PMID: 24682793 23. Cona G, Arcara G, Amodio P, Schiff S, Bisiacchi PS. Does executive control really play a crucial role in explaining age-related cognitive and neural differences? Neuropsychology. 2013; 27(3):378–89. doi: 10.1037/a0032708 PMID: 23688219 Cognitive Factors in Running Performance PLOS ONE | DOI:10.1371/journal.pone.0132943 July 14, 2015 11 / 12 24. Cona G, Montagnese S, Bisiacchi PS, Gatta A, Cillo U, Angeli P, et al. Early markers of neural dysfunc- tion and compensation: a model from minimal hepatic encephalopathy. Clin Neurophysiol. 2014; 125 (6):1138–44. doi: 10.1016/j.clinph.2013.10.048 PMID: 24333166 25. Cona G, Kliegel M, Bisiacchi PS. Differential effects of emotional cues on components of prospective memory: an ERP study. Front Hum Neurosci. 2015; 9(10): 1–15. 26. Cona G, Scarpazza C, Sartori G, Moscovitch M, Bisiacchi PS. Neural bases of Prospective Memory: A meta-analysis and the “Attention to Delayed Intention” (AtoDI) model. Neurosci Biobehav Rev. 2015; 52: 21–37. doi: 10.1016/j.neubiorev.2015.02.007 PMID: 25704073 27. Lazarus RS. How emotions influence performance in competitive sports. Sport Psychol. 2000. 28. Robazza C, Bortoli L, Hanin Y. Precompetition emotions, bodily symptoms, and task-specific qualities as predictors of performance in high-level karate athletes. J Appl Sport Psychol. 2004; 16(2):151–165. 29. Neil R, Hanton S, Mellalieu SD, Fletcher D. Competition stress and emotions in sport performers: The role of further appraisals. Psychol Sport Exerc. 2011; 12(4):460–470. 30. Lang PJ, Bradley MM, Cuthbert BN. Technical Report A-6. Gainesville, FL: University of Florida; 2005. International Affective Picture System (IAPS): Affective ratings of pictures and instruction manual. 31. Verburgh L, Scherder EJ, van Lange PA, Oosterlaan J. Executive functioning in highly talented soccer players. PLOS One. 2014; 9(3):e91254. doi: 10.1371/journal.pone.0091254 PMID: 24632735 32. Sheppard JM, Young W. Agility literature review: classifications, training and testing. J Sport Sci. 2006; 24(9):919–932. 33. Robazza C, Pellizzari M, Hanin Y. Emotion self-regulation and athletic performance: An application of the IZOF model. Psychol Sport Exerc. 2004; 5(4): 379–404. 34. Colcombe SJ, Kramer AF, Erickson KI, Scalf P, McAuley E, Cohen NJ, et al. Cardiovascular fitness, cortical plasticity, and aging. Proc Natl Acad Sci U.S.A. 2004; 101: 3316–3321. PMID: 14978288 35. Hillman CH, Erickson KI, Kramer AF. Be smart, exercise your heart: exercise effects on brain and cog- nition. Nat Rev Neurosci. 2008; 9(1):58–65. PMID: 18094706 36. Pontifex MB, Raine LB, Johnson CR, Chaddock L, Voss MW, Cohen NJ, et al. Cardiorespiratory fitness and the flexible modulation of cognitive control in preadolescent children. J Cogn Neurosci. 2011; 23:1332–1345. doi: 10.1162/jocn.2010.21528 PMID: 20521857 37. Erickson KI, Voss MW, Prakash RS, Basak C,Szabo A, Chaddock L, et al. Exercise training increases size of hippocampus and improves memory. 2011. Proc Natl Acad Sci U.S.A. 2011; 108: 3017–3022. doi: 10.1073/pnas.1015950108 PMID: 21282661 38. Hayes SM, Hayes JP, Cadden M, Verfaellie M. A review of cardiorespiratory fitness-related neuroplas- ticity in the aging brain. Front Aging Neurosci. 2013; 5:31. doi: 10.3389/fnagi.2013.00031 PMID: 23874299 39. Noakes TD. Implications of exercise testing for prediction of athletic performance: a contemporary per- spective. Med Sci Sports and Exerc. 1988; 20(4): 319–330. Cognitive Factors in Running Performance PLOS ONE | DOI:10.1371/journal.pone.0132943 July 14, 2015 12 / 12

It's a Matter of Mind! Cognitive Functioning Predicts the Athletic Performance in Ultra-Marathon Runners.

07-14-2015

Cona, Giorgia,Cavazzana, Annachiara,Paoli, Antonio,Marcolin, Giuseppe,Grainer, Alessandro,Bisiacchi, Patrizia Silvia

eng

PMC6939913

Supplementary information on methods “The force-length-velocity potential of the human soleus muscle is related to the energetic cost of running” by Sebastian Bohm, Falk Mersmann, Alessandro Santuz & Adamantios Arampatzis Journal: Proceedings of the Royal Society B DOI: http://dx.doi.org/10.1098/rspb.2019.2560 Determination of the ankle joint moments during MVC The resultant moments at the ankle joint were calculated by means of an established inverse dynamics approach [1], which takes the effects of gravitational and passive moments and any misalignment between ankle joint axis and dynamometer axis into account. The required kinematic data were recorded during the MVCs on the basis of anatomically referenced reflective markers (medial and lateral malleoli and epicondyle, calcaneal tuberosity, 2nd metatarsal and greater trochanter) using a Vicon motion capture system (Version 1.8, Vicon Motion Systems, Oxford, UK). The ankle joint angle-specific moments due to gravity and passive moments were measured during an additional ankle joint rotation driven by the dynamometer at 5 °/s with the participants completely relaxed. Thus, moments due to gravity and passive moments in a certain joint angle were attributed to the measured moment during the MVCs in the same joint angle configuration [1]. Furthermore, the contribution of the antagonistic muscles to the different measured ankle joint moments [2] was considered by establishing an individual relationship of EMG amplitude of the tibialis anterior muscle, agonistic moment as well as ankle joint angle. For this reason, EMG activity was measured at rest and during two submaximal isometric dorsiflexion contractions that displayed slightly lower and higher EMG magnitude as during the maximum plantar flexions [2] in three different joint angles (i.e. dorsiflexion, neutral position and plantar flexion) within the assessed range of motion. The relationship was described by the following regression equation: 𝑀𝑀𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 = 𝐸𝐸𝑀𝑀𝐸𝐸𝑐𝑐𝑡𝑡𝑡𝑡. 𝑐𝑐𝑎𝑎𝑐𝑐. ∙ (𝑎𝑎 + 𝑏𝑏 ∙ 𝛼𝛼𝑐𝑐𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 + 𝑐𝑐 ∙ 𝛼𝛼𝑐𝑐𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎²) (eq. 1) where Mcoact is the antagonistic joint moment during the maximum plantar flexion, EMGtib. ant. is the respective tibialis anterior EMG activity during the MVCs, αankle the ankle joint angle measured via the Vicon system and a, b and c the individual regression coefficients. This means that for each joint angle the relationship between moment and EMG activity was assumed to be linear because of the small differences of the EMG magnitude of the two submaximal isometric dorsal flexion contractions [2]. The joint angle-moment relationship presented by the three different measured angles was than formulated by a quadratic function to account for the force-length dependence of the muscle. EMG activity of the tibialis anterior and soleus muscle was measured using a wireless EMG system (Myon m320RX, Myon AG, Baar, Switzerland) and two bipolar surface electrodes (2 cm inter-electrode distance) that were placed on the muscle at an acquisition frequency of 1000 Hz, synchronized with the kinematic data. Determination of the Achilles tendon lever arm The Achilles tendon lever arm was individually determined by means of the tendon excursion method [3,4]. In this method, the lever arm of the Achilles tendon is calculated as the ratio of the m. gastrocnemius medialis myotendinous junction displacement obtained by ultrasonography to the corresponding angular excursion of the ankle joint during a passive joint rotation by the dynamometer (5 °/s). The lever arm values were further corrected for the alignment of the tendon occurring during contractions using the factor provided by Maganaris et al. (1998) [5]. Fascicle length determination from the ultrasound images The procedure included an approximation of the deeper and upper aponeurosis by a best linear fit through three manually placed and frame-by-frame adjusted marks. By means of the bwtraceboundary function of the Matlab Image Processing toolbox the algorithm then identified the shape and orientation of image brightness features between both aponeuroses in each frame, which are indicative for the hyperechoic perimysial connective tissue parts aligned with the muscle fascicles (fig. 1A in main manuscript). The feature identification criteria were set to: minimal length of 23 pixels (i.e. 0.4 cm, from the bottom left to the top right), area to length ratio of 8.5, angle between feature and deeper aponeurosis between 10° and 80° and 80% of the pixels on a line between the start and end point of a feature had to be white [6]. Every frame was visually controlled for adequate feature placement and manually corrected if necessary. Based on the identified features, a linear averaged reference fascicle was calculated (fig. 1A in main manuscript). Reliability of the tracking approach was confirmed and reported in two previous studies [6,7]. EMG processing Raw EMG signals from the running and MVC trials were processed by a fourth-order high-pass Butterworth zero-phase filter with a 50 Hz cut-off frequency then a full-wave rectification and a low- pass zero-phase filter with a 20 Hz cut-off frequency for creating a linear envelope of the signal [8,9]. References 1. Arampatzis A, Morey-Klapsing G, Karamanidis K, DeMonte G, Stafilidis S, Brüggemann G-P. 2005 Differences between measured and resultant joint moments during isometric contractions at the ankle joint. J. Biomech. 38, 885–892. (doi:10.1016/j.jbiomech.2004.04.027) 2. Mademli L, Arampatzis A, Morey-Klapsing G, Brüggemann G-P. 2004 Effect of ankle joint position and electrode placement on the estimation of the antagonistic moment during maximal plantarflexion. J. Electromyogr. Kinesiol. 14, 591–597. (doi:10.1016/j.jelekin.2004.03.006) 3. An KN, Takahashi K, Harrigan TP, Chao EY. 1984 Determination of muscle orientations and moment arms. J. Biomech. Eng. 106, 280–282. 4. Fath F, Blazevich AJ, Waugh CM, Miller SC, Korff T. 2010 Direct comparison of in vivo Achilles tendon moment arms obtained from ultrasound and MR scans. J. Appl. Physiol. Bethesda Md 1985 109, 1644–1652. (doi:10.1152/japplphysiol.00656.2010) 5. Maganaris CN, Baltzopoulos V, Sargeant AJ. 1998 Changes in Achilles tendon moment arm from rest to maximum isometric plantarflexion: in vivo observations in man. J. Physiol. 510, 977–985. (doi:10.1111/j.1469-7793.1998.977bj.x) 6. Marzilger R, Legerlotz K, Panteli C, Bohm S, Arampatzis A. 2018 Reliability of a semi-automated algorithm for the vastus lateralis muscle architecture measurement based on ultrasound images. Eur. J. Appl. Physiol. 118, 291–301. (doi:10.1007/s00421-017-3769-8) 7. Bohm S, Marzilger R, Mersmann F, Santuz A, Arampatzis A. 2018 Operating length and velocity of human vastus lateralis muscle during walking and running. Sci. Rep. 8, 5066. (doi:10.1038/s41598- 018-23376-5) 8. Nikolaidou ME, Marzilger R, Bohm S, Mersmann F, Arampatzis A. 2017 Operating length and velocity of human M. vastus lateralis fascicles during vertical jumping. R. Soc. Open Sci. 4, 170185. (doi:10.1098/rsos.170185) 9. Santuz A, Ekizos A, Janshen L, Baltzopoulos V, Arampatzis A. 2017 On the Methodological Implications of Extracting Muscle Synergies from Human Locomotion. Int. J. Neural Syst. 27, 1750007. (doi:10.1142/S0129065717500071)

The force-length-velocity potential of the human soleus muscle is related to the energetic cost of running.

12-18-2019

Bohm, Sebastian,Mersmann, Falk,Santuz, Alessandro,Arampatzis, Adamantios

eng

PMC7893283

Reports © 2021 The Reviewers; Decision Letters © 2021 The Reviewers and Editors; Responses © 2021 The Reviewers, Editors and Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited Review History RSPB-2020-2784.R0 (Original submission) Review form: Reviewer 1 (Richard Blagrove) Recommendation Accept with minor revision (please list in comments) Scientific importance: Is the manuscript an original and important contribution to its field? Excellent General interest: Is the paper of sufficient general interest? Good Quality of the paper: Is the overall quality of the paper suitable? Excellent Is the length of the paper justified? Yes Should the paper be seen by a specialist statistical reviewer? No Enthalpy efficiency of the soleus muscle contributes to improvements in running economy Sebastian Bohm, Falk Mersmann, Alessandro Santuz and Adamantios Arampatzis Article citation details Proc. R. Soc. B 288: 20202784. http://dx.doi.org/10.1098/rspb.2020.2784 Review timeline Original submission: 6 November 2020 Revised submission: 30 December 2020 Final acceptance: 5 January 2021 Note: Reports are unedited and appear as submitted by the referee. The review history appears in chronological order.  2 Do you have any concerns about statistical analyses in this paper? If so, please specify them explicitly in your report. No It is a condition of publication that authors make their supporting data, code and materials available - either as supplementary material or hosted in an external repository. Please rate, if applicable, the supporting data on the following criteria. Is it accessible? N/A Is it clear? N/A Is it adequate? N/A Do you have any ethical concerns with this paper? No Comments to the Author General comments: Many thanks for the invitation to review this paper, it was a thoroughly enjoyable and fascinating read. The paper describes the results of a 14-week muscle-tendon strength training intervention that found enhancements in running economy, plantar flexion strength, and Achilles stiffness compared to a control group. An improvement in enthalpy efficiency of the soleus muscle reveals novel insight into the mechanism by which strength training may have a positive effect of the metabolic cost of running. In my opinion, this study is much needed in this area of research. Papers have speculated in the past around the mechanisms of change associated with improved running economy following a strength training intervention (e.g. Fletcher and Macintosh, 2017, doi: 10.3389/fphys.2017.00433; Blagrove et al., 2018, doi: 10.1007/s40279-017-0835-7), however measuring changes to the intrinsic behaviour of muscles is difficult. This study makes a very good attempt at providing that insight for the soleus. I have some minor comments that I hope will improve clarity and readability of the paper, but overall, I feel that this paper will be of considerable interest to both scientists and applied practitioners. Specific comments: Keywords: These should be different to the terms in the study title to enable wider search returns. Please amend. Can I suggest ‘calf’ ‘triceps surae’ ‘endurance running’ ‘strength training’? Line 44: ‘for’ should read ‘in’ Line 105: Was allocation to groups completely random or were participants matched for running economy and randomised by matched pairs (or similar) to ensure minimal differences existed between groups at baseline? Line 106: Was the participants only sport/exercise running? It would be useful for others (particularly those undertaking reviews and meta-analyses in this area) to be able to accurately determine if participants were trained ‘runners’ or simply people that ran as a small part of a wider exercise/sport training routine.  3 Line 106: Please define ‘severe’ in brackets here (i.e. days/weeks away from running with injury) Line 108: Why were only rear-foot striking runners considered? In female participants, was the menstrual cycle accounted for or hormonal contraceptive use during recruitment and testing? A criticism often levelled at studies in the area of strength training for endurance athletes is that studies rarely equate the total amount of physical exercise done between groups, i.e. the control group do not have ‘placebo’ exercise(s) or add running training to match the duration of strength work performed by the intervention group (e.g. Dankel et al., 2017, doi: 10.1080/02640414.2017.1398884). Although a performance measure was not taken in this study, how do the authors know that the change in running economy they observed is not due to differences in the amount of physical training performed? An alternative, in practice, for runners could be to add running training instead of strength training to their routine, which may produce even larger improvements in economy. The changes in soleus fascicle behaviour were not quantified in the control group. I am slightly puzzled why not. Would the authors consider this a limitation of the study? Exercise protocol: Given that a single strength training exercise was used in the intervention I would strongly recommend that authors include an image of the exercise apparatus and set-up. I appreciate there are currently a high number of figures included but I would contend this is important for both scientific replication and applied practice. Line 147: Why was 2.5 m/s used as the speed for all participants? Was this sufficiently slow enough to ensure a plateau in oxygen consumption and RER value of <1 during the collection period? Line 149: The citation here is a paper comparing methods of quantifying energy cost of running. It is not clear which method was used without referring to the supplementary material. Line 205: Which post-hoc adjustment was used? Line 210: How were the effect sizes interpreted? Line 229: There appears to be a word missing in this sentence. ‘an altered lengthening-shortening behaviour’ or similar Line 272: It would be more accurate to discuss the change in economy in the context of within- participant variability (measurement error), rather than between-participant variability, which depends on the sample. A subtle tweak to wording and the reference (eg Blagrove et al., 2017, doi; 10.1080/17461391.2017.1364301; Shaw et al., 2013, doi: 10.1139/apnm-2013-0055 ) here would provide a more compelling that the 4% improvement is indeed real. Line 304-305: Why does the higher maximum plantar flexion moment indicate hypertrophy has occurred? It would be unusual to expect substantial hypertrophy with short-duration isometric contractions. Why can the improvements in strength not be explained as neural adaptation? If so, the discussion below this statement will need to be amended. Line 346: ‘a’ seems to be a typographical error here. Line 346: The ref. 16 study (Fletcher et al., 2010) did not find a significant change in running economy following a calf strengthening intervention.  4 Line 350: There appears to be a word missing between ‘training’ and ‘may’ Line 355: ‘endurance performance’ should read ‘running economy’ here as no performance measures were taken. It has long been recognised that the soleus possesses a high proportion of slow twitch muscle fibres compared to other muscle groups (eg Gollnick et al., 1974, doi: 10.1007/BF00587415). Clearly it is possible to make the soleus stronger and given its role in locomotion and energy cost during exercise, it would certainly make sense for runners to strengthen the muscle. However, do authors think that the soleus has a limited capacity to improve its maximal force output due to its morphological characteristics? The intervention applied here would certainly be novel for the participants, thus beneficial, but would long-term engagement with this type of training for soleus continue to yield benefits in running economy? Review form: Reviewer 2 Recommendation Major revision is needed (please make suggestions in comments) Scientific importance: Is the manuscript an original and important contribution to its field? Good General interest: Is the paper of sufficient general interest? Good Quality of the paper: Is the overall quality of the paper suitable? Good Is the length of the paper justified? Yes Should the paper be seen by a specialist statistical reviewer? No Do you have any concerns about statistical analyses in this paper? If so, please specify them explicitly in your report. No It is a condition of publication that authors make their supporting data, code and materials available - either as supplementary material or hosted in an external repository. Please rate, if applicable, the supporting data on the following criteria. Is it accessible? Yes Is it clear? Yes Is it adequate? Yes Do you have any ethical concerns with this paper? No  5 Comments to the Author In this study, the authors examined the effects of a resistance training program on running economy, and additionally examined how changes in running economy were associated with changes in estimated soleus muscle strength, Achilles tendon stiffness, and operating soleus muscle efficiency, force-length, and force-velocity behaviour. This study provides insight into the mechanisms that may underly improvements in running economy with resistance training. The majority of our understanding of the role of series elasticity on efficiency is from controlled in situ or simulation studies. Thus, this study also provides novel insight into the implications of in vivo muscle and tendon properties during real-world conditions. This manuscript is well-written and interesting to read, and the methods appear sound and appropriate for addressing the research questions. I only have a few comments below that aim to clarify details of the methodology and interpretation of the results. Comments: 1. Lines 137-144: Given that increased plantar flexor strength and tendon stiffness are identified as a possible mechanism underlying the main results of this paper, it would be helpful to provide further details of how these variables were measured rather than referring readers to other papers. For example, in Supplementary material 1, section 2: “Furthermore, the contribution of the antagonistic muscles to the ankle joint moment was considered by means of an EMG-based method [4].” What specific method was this? “which was determined using the tendon-excursion method [5,6] and corrected for tendon alignment during the contraction [7].” How were the moment arms corrected for tendon alignment? “The MTJ displacement artefacts due to an unavoidable change in the ankle joint angle during the MVCs was corrected [8] and the five contractions were averaged to give a reliable measure of the elongation [9]. The AT stiffness was calculated between 50% and 100% of the maximum tendon force using linear regression [10]” How were the changes in ankle joint angle corrected? Currently the reader would have to consult a range of other papers to fully understand the methods and their justification. More details of these methods and less reliance on previous works would be beneficial. 2. Similar to 1., given that running economy is an important variable in this paper, further details in the main text would be helpful. Since the section “Energetic cost of running” in supplementary material 1 is only one paragraph long, could this not be included in the methods section of the main text? I realize the authors may be limited in terms of length; however, these details are important for interpreting the results of this paper. Similarly, at least the first paragraph of the section “Statistics” in supplementary material 1 could be included in the main text. Important methods that could affect interpretation of results and conclusions should be easy for readers to access in the main text. 3. Line 194: Why did the authors use an efficiency-velocity function rather than a more established metabolic power function (e.g. Minetti & Alexander, 1997 or Umberger, 2010, etc.)? Mechanical work and metabolic cost depend on factors other than just velocity, so why is an efficiency function that depends only on velocity, instead of separately estimating mechanical work and metabolic cost that depend on muscle velocity, length, activation, etc., appropriate for this study? Further explanation/justification in the text would be helpful. Also, the fitted values in Table 1 of Hill (1967) are for frog muscle at 0 degrees C. Since frogs are ectotherms, the muscle temperature would be near that of the external environment, far below physiological temperature for human muscle. This could affect both muscle force and velocity (see James, 2013 for review) and therefore the fitted function. Additionally, amphibian muscle contains larger concentrations of parvalbumin compared to terrestrial muscles, which can alter the heat rate and estimated metabolic cost (Woledge et al., 1985, pp. 257-260). What are the implications of these  6 considerations on the results of this study? James, R. S. (2013). A review of the thermal sensitivity of the mechanics of vertebrate skeletal muscle. Journal of Comparative Physiology B, 183(6), 723-733. Woledge, R. C., Curtin, N. A., & Homsher, E. (1985). Energetic aspects of muscle contraction. Monographs of the Physiological Society. 4. Line 268: “… the results provide additional evidence that a combination of greater plantar flexor muscle strength and Achilles tendon stiffness decrease the energy cost of running [14,15] and indicate that the soleus enthalpy efficiency is a contributive determinant.” It’s alluded to with “a combination” but consider an additional sentence here noting that an increase in stiffness by itself may not increase efficiency. Later in line 349 the authors state “strength increases without concomitant stiffening of the AT after a period of training may increase levels of operating and maximum AT strain [24], which have been associated with pathologies [53] but also possible functional decline [54].” Function may also decline with increases in stiffness without concomitant increases in muscle strength. For example, see Figure 5 in Lichtwark and Wilson (2005) in which muscle efficiency during running decreased with increases in AT stiffness beyond the optimal stiffness. Lichtwark, G. A., & Wilson, A. M. (2007). Is Achilles tendon compliance optimised for maximum muscle efficiency during locomotion? Journal of Biomechanics, 40(8), 1768-1775. 5. Line 291: “The exercise-induced increase in muscle strength and AT stiffness resulted in an alteration of the operating fascicle velocity profile that led to a significant increase of the enthalpy efficiency of the operating soleus […], improving the efficiency of muscular work production.” Because the only factor that was manipulated in this study was the exercise intervention, changes in muscle strength, AT stiffness, fascicle velocities, and enthalpy efficiency are only associated with one another rather than there being any causal relationship between them. Decision letter (RSPB-2020-2784.R0) 07-Dec-2020 Dear Dr Bohm: Your manuscript has now been peer reviewed and the reviews have been assessed by an Associate Editor. The reviewers’ comments (not including confidential comments to the Editor) and the comments from the Associate Editor are included at the end of this email for your reference. As you will see, the reviewers and the Editors have raised some concerns with your manuscript and we would like to invite you to revise your manuscript to address them. We do not allow multiple rounds of revision so we urge you to make every effort to fully address all of the comments at this stage. If deemed necessary by the Associate Editor, your manuscript will be sent back to one or more of the original reviewers for assessment. If the original reviewers are not available we may invite new reviewers. Please note that we cannot guarantee eventual acceptance of your manuscript at this stage. To submit your revision please log into http://mc.manuscriptcentral.com/prsb and enter your Author Centre, where you will find your manuscript title listed under "Manuscripts with Decisions." Under "Actions”, click on "Create a Revision”. Your manuscript number has been appended to denote a revision.  7 When submitting your revision please upload a file under "Response to Referees" - in the "File Upload" section. This should document, point by point, how you have responded to the reviewers’ and Editors’ comments, and the adjustments you have made to the manuscript. We require a copy of the manuscript with revisions made since the previous version marked as ‘tracked changes’ to be included in the ‘response to referees’ document. Your main manuscript should be submitted as a text file (doc, txt, rtf or tex), not a PDF. Your figures should be submitted as separate files and not included within the main manuscript file. When revising your manuscript you should also ensure that it adheres to our editorial policies (https://royalsociety.org/journals/ethics-policies/). You should pay particular attention to the following: Research ethics: If your study contains research on humans please ensure that you detail in the methods section whether you obtained ethical approval from your local research ethics committee and gained informed consent to participate from each of the participants. Use of animals and field studies: If your study uses animals please include details in the methods section of any approval and licences given to carry out the study and include full details of how animal welfare standards were ensured. Field studies should be conducted in accordance with local legislation; please include details of the appropriate permission and licences that you obtained to carry out the field work. Data accessibility and data citation: It is a condition of publication that you make available the data and research materials supporting the results in the article. Please see our Data Sharing Policies (https://royalsociety.org/journals/authors/author-guidelines/#data). Datasets should be deposited in an appropriate publicly available repository and details of the associated accession number, link or DOI to the datasets must be included in the Data Accessibility section of the article (https://royalsociety.org/journals/ethics-policies/data-sharing-mining/). Reference(s) to datasets should also be included in the reference list of the article with DOIs (where available). In order to ensure effective and robust dissemination and appropriate credit to authors the dataset(s) used should also be fully cited and listed in the references. If you wish to submit your data to Dryad (http://datadryad.org/) and have not already done so you can submit your data via this link http://datadryad.org/submit?journalID=RSPB&manu=(Document not available), which will take you to your unique entry in the Dryad repository. If you have already submitted your data to dryad you can make any necessary revisions to your dataset by following the above link. Please note: your data accessibility states "Analyses of experimental data were performed using MATLAB (R2016b, MathWorks) and the code is available from the corresponding author on reasonable request."-- this is not acceptable via our policy. All data and code must be provided with your revised MS or we may delay or reject your study unless this is amended. For more information please see our open data policy http://royalsocietypublishing.org/data- sharing. Electronic supplementary material: All supplementary materials accompanying an accepted article will be treated as in their final form. They will be published alongside the paper on the journal website and posted on the online figshare repository. Files on figshare will be made available approximately one week before the  8 accompanying article so that the supplementary material can be attributed a unique DOI. Please try to submit all supplementary material as a single file. Online supplementary material will also carry the title and description provided during submission, so please ensure these are accurate and informative. Note that the Royal Society will not edit or typeset supplementary material and it will be hosted as provided. Please ensure that the supplementary material includes the paper details (authors, title, journal name, article DOI). Your article DOI will be 10.1098/rspb.[paper ID in form xxxx.xxxx e.g. 10.1098/rspb.2016.0049]. Please submit a copy of your revised paper within three weeks. If we do not hear from you within this time your manuscript will be rejected. If you are unable to meet this deadline please let us know as soon as possible, as we may be able to grant a short extension. Thank you for submitting your manuscript to Proceedings B; we look forward to receiving your revision. If you have any questions at all, please do not hesitate to get in touch. Best wishes, Dr John Hutchinson, Editor mailto: [email protected] Associate Editor Board Member: 1 Comments to Author: Dear Dr. Bohm, Thank you for submitting your manuscript entitled “Enthalpy efficiency of the soleus muscle contributes to improvements in running economy” to the Proceedings of the Royal Society. I have received two peer reviews, and both are highly supportive of your manuscript but also have a several suggestions, which I hope you will find useful when revising your manuscript. Proceedings B aims to publish studies that significantly increase or alter our current understandings in a way that is relevant to a broad readership beyond the disciplinary area of the manuscript. Both reviewers find your study of high scientific importance and broad interest, and many of their comments aim mainly at improving the clarity of the manuscript. The reviewers furthermore ask for additional information on the methodology and share their thoughts concerning the findings, cautioning against overstatements and arguing for nuance. Reviewer(s)' Comments to Author: Referee: 1 Comments to the Author(s) General comments: Many thanks for the invitation to review this paper, it was a thoroughly enjoyable and fascinating read. The paper describes the results of a 14-week muscle-tendon strength training intervention that found enhancements in running economy, plantar flexion strength, and Achilles stiffness compared to a control group. An improvement in enthalpy efficiency of the soleus muscle reveals novel insight into the mechanism by which strength training may have a positive effect of the metabolic cost of running. In my opinion, this study is much needed in this area of research. Papers have speculated in the past around the mechanisms of change associated with improved running economy following a strength training intervention (e.g. Fletcher and Macintosh, 2017, doi: 10.3389/fphys.2017.00433; Blagrove et al., 2018, doi: 10.1007/s40279-017-0835-7), however measuring changes to the intrinsic behaviour of muscles is difficult. This study makes a very good attempt at providing that insight for the soleus. I have some minor comments that I hope will improve clarity and readability of  9 the paper, but overall, I feel that this paper will be of considerable interest to both scientists and applied practitioners. Specific comments: Keywords: These should be different to the terms in the study title to enable wider search returns. Please amend. Can I suggest ‘calf’ ‘triceps surae’ ‘endurance running’ ‘strength training’? Line 44: ‘for’ should read ‘in’ Line 105: Was allocation to groups completely random or were participants matched for running economy and randomised by matched pairs (or similar) to ensure minimal differences existed between groups at baseline? Line 106: Was the participants only sport/exercise running? It would be useful for others (particularly those undertaking reviews and meta-analyses in this area) to be able to accurately determine if participants were trained ‘runners’ or simply people that ran as a small part of a wider exercise/sport training routine. Line 106: Please define ‘severe’ in brackets here (i.e. days/weeks away from running with injury) Line 108: Why were only rear-foot striking runners considered? In female participants, was the menstrual cycle accounted for or hormonal contraceptive use during recruitment and testing? A criticism often levelled at studies in the area of strength training for endurance athletes is that studies rarely equate the total amount of physical exercise done between groups, i.e. the control group do not have ‘placebo’ exercise(s) or add running training to match the duration of strength work performed by the intervention group (e.g. Dankel et al., 2017, doi: 10.1080/02640414.2017.1398884). Although a performance measure was not taken in this study, how do the authors know that the change in running economy they observed is not due to differences in the amount of physical training performed? An alternative, in practice, for runners could be to add running training instead of strength training to their routine, which may produce even larger improvements in economy. The changes in soleus fascicle behaviour were not quantified in the control group. I am slightly puzzled why not. Would the authors consider this a limitation of the study? Exercise protocol: Given that a single strength training exercise was used in the intervention I would strongly recommend that authors include an image of the exercise apparatus and set-up. I appreciate there are currently a high number of figures included but I would contend this is important for both scientific replication and applied practice. Line 147: Why was 2.5 m/s used as the speed for all participants? Was this sufficiently slow enough to ensure a plateau in oxygen consumption and RER value of <1 during the collection period? Line 149: The citation here is a paper comparing methods of quantifying energy cost of running. It is not clear which method was used without referring to the supplementary material. Line 205: Which post-hoc adjustment was used? Line 210: How were the effect sizes interpreted?  10 Line 229: There appears to be a word missing in this sentence. ‘an altered lengthening-shortening behaviour’ or similar Line 272: It would be more accurate to discuss the change in economy in the context of within- participant variability (measurement error), rather than between-participant variability, which depends on the sample. A subtle tweak to wording and the reference (eg Blagrove et al., 2017, doi; 10.1080/17461391.2017.1364301; Shaw et al., 2013, doi: 10.1139/apnm-2013-0055 ) here would provide a more compelling that the 4% improvement is indeed real. Line 304-305: Why does the higher maximum plantar flexion moment indicate hypertrophy has occurred? It would be unusual to expect substantial hypertrophy with short-duration isometric contractions. Why can the improvements in strength not be explained as neural adaptation? If so, the discussion below this statement will need to be amended. Line 346: ‘a’ seems to be a typographical error here. Line 346: The ref. 16 study (Fletcher et al., 2010) did not find a significant change in running economy following a calf strengthening intervention. Line 350: There appears to be a word missing between ‘training’ and ‘may’ Line 355: ‘endurance performance’ should read ‘running economy’ here as no performance measures were taken. It has long been recognised that the soleus possesses a high proportion of slow twitch muscle fibres compared to other muscle groups (eg Gollnick et al., 1974, doi: 10.1007/BF00587415). Clearly it is possible to make the soleus stronger and given its role in locomotion and energy cost during exercise, it would certainly make sense for runners to strengthen the muscle. However, do authors think that the soleus has a limited capacity to improve its maximal force output due to its morphological characteristics? The intervention applied here would certainly be novel for the participants, thus beneficial, but would long-term engagement with this type of training for soleus continue to yield benefits in running economy? Referee: 2 Comments to the Author(s) In this study, the authors examined the effects of a resistance training program on running economy, and additionally examined how changes in running economy were associated with changes in estimated soleus muscle strength, Achilles tendon stiffness, and operating soleus muscle efficiency, force-length, and force-velocity behaviour. This study provides insight into the mechanisms that may underly improvements in running economy with resistance training. The majority of our understanding of the role of series elasticity on efficiency is from controlled in situ or simulation studies. Thus, this study also provides novel insight into the implications of in vivo muscle and tendon properties during real-world conditions. This manuscript is well-written and interesting to read, and the methods appear sound and appropriate for addressing the research questions. I only have a few comments below that aim to clarify details of the methodology and interpretation of the results. Comments: 1. Lines 137-144: Given that increased plantar flexor strength and tendon stiffness are identified as a possible mechanism underlying the main results of this paper, it would be helpful to provide further details of how these variables were measured rather than referring readers to other papers. For example, in Supplementary material 1, section 2:  11 “Furthermore, the contribution of the antagonistic muscles to the ankle joint moment was considered by means of an EMG-based method [4].” What specific method was this? “which was determined using the tendon-excursion method [5,6] and corrected for tendon alignment during the contraction [7].” How were the moment arms corrected for tendon alignment? “The MTJ displacement artefacts due to an unavoidable change in the ankle joint angle during the MVCs was corrected [8] and the five contractions were averaged to give a reliable measure of the elongation [9]. The AT stiffness was calculated between 50% and 100% of the maximum tendon force using linear regression [10]” How were the changes in ankle joint angle corrected? Currently the reader would have to consult a range of other papers to fully understand the methods and their justification. More details of these methods and less reliance on previous works would be beneficial. 2. Similar to 1., given that running economy is an important variable in this paper, further details in the main text would be helpful. Since the section “Energetic cost of running” in supplementary material 1 is only one paragraph long, could this not be included in the methods section of the main text? I realize the authors may be limited in terms of length; however, these details are important for interpreting the results of this paper. Similarly, at least the first paragraph of the section “Statistics” in supplementary material 1 could be included in the main text. Important methods that could affect interpretation of results and conclusions should be easy for readers to access in the main text. 3. Line 194: Why did the authors use an efficiency-velocity function rather than a more established metabolic power function (e.g. Minetti & Alexander, 1997 or Umberger, 2010, etc.)? Mechanical work and metabolic cost depend on factors other than just velocity, so why is an efficiency function that depends only on velocity, instead of separately estimating mechanical work and metabolic cost that depend on muscle velocity, length, activation, etc., appropriate for this study? Further explanation/justification in the text would be helpful. Also, the fitted values in Table 1 of Hill (1967) are for frog muscle at 0 degrees C. Since frogs are ectotherms, the muscle temperature would be near that of the external environment, far below physiological temperature for human muscle. This could affect both muscle force and velocity (see James, 2013 for review) and therefore the fitted function. Additionally, amphibian muscle contains larger concentrations of parvalbumin compared to terrestrial muscles, which can alter the heat rate and estimated metabolic cost (Woledge et al., 1985, pp. 257-260). What are the implications of these considerations on the results of this study? James, R. S. (2013). A review of the thermal sensitivity of the mechanics of vertebrate skeletal muscle. Journal of Comparative Physiology B, 183(6), 723-733. Woledge, R. C., Curtin, N. A., & Homsher, E. (1985). Energetic aspects of muscle contraction. Monographs of the Physiological Society. 4. Line 268: “… the results provide additional evidence that a combination of greater plantar flexor muscle strength and Achilles tendon stiffness decrease the energy cost of running [14,15] and indicate that the soleus enthalpy efficiency is a contributive determinant.” It’s alluded to with “a combination” but consider an additional sentence here noting that an increase in stiffness by itself may not increase efficiency. Later in line 349 the authors state “strength increases without concomitant stiffening of the AT after a period of training may increase levels of operating and maximum AT strain [24], which have been associated with pathologies [53] but also possible functional decline [54].” Function may also decline with increases in stiffness without concomitant increases in muscle strength. For example, see Figure 5 in Lichtwark and Wilson (2005) in which muscle efficiency during running decreased with increases in AT stiffness beyond the optimal stiffness. Lichtwark, G. A., & Wilson, A. M. (2007). Is Achilles tendon compliance optimised for maximum muscle efficiency during locomotion? Journal of Biomechanics, 40(8), 1768-1775.  12 5. Line 291: “The exercise-induced increase in muscle strength and AT stiffness resulted in an alteration of the operating fascicle velocity profile that led to a significant increase of the enthalpy efficiency of the operating soleus […], improving the efficiency of muscular work production.” Because the only factor that was manipulated in this study was the exercise intervention, changes in muscle strength, AT stiffness, fascicle velocities, and enthalpy efficiency are only associated with one another rather than there being any causal relationship between them. Author's Response to Decision Letter for (RSPB-2020-2784.R0) See Appendix A. RSPB-2020-2784.R1 (Revision) Review form: Reviewer 1 (Richard Blagrove) Recommendation Accept as is Scientific importance: Is the manuscript an original and important contribution to its field? Excellent General interest: Is the paper of sufficient general interest? Excellent Quality of the paper: Is the overall quality of the paper suitable? Excellent Is the length of the paper justified? Yes Should the paper be seen by a specialist statistical reviewer? No Do you have any concerns about statistical analyses in this paper? If so, please specify them explicitly in your report. No It is a condition of publication that authors make their supporting data, code and materials available - either as supplementary material or hosted in an external repository. Please rate, if applicable, the supporting data on the following criteria. Is it accessible? Yes Is it clear? Yes Is it adequate? Yes  13 Do you have any ethical concerns with this paper? No Comments to the Author Many thanks for taking the time to provide clear and comprehensive responses to my comments and questions. I am satisfied they have been appropriately addressed. I look forward to seeing this paper published and will circulate it to my networks. I'm sure i'll refer to it regularly. Decision letter (RSPB-2020-2784.R1) 05-Jan-2021 Dear Dr Bohm I am pleased to inform you that your manuscript entitled "Enthalpy efficiency of the soleus muscle contributes to improvements in running economy" has been accepted for publication in Proceedings B. Congratulations!! You can expect to receive a proof of your article from our Production office in due course, please check your spam filter if you do not receive it. PLEASE NOTE: you will be given the exact page length of your paper which may be different from the estimation from Editorial and you may be asked to reduce your paper if it goes over the 10 page limit. If you are likely to be away from e-mail contact please let us know. Due to rapid publication and an extremely tight schedule, if comments are not received, we may publish the paper as it stands. If you have any queries regarding the production of your final article or the publication date please contact [email protected] Open Access You are invited to opt for Open Access, making your freely available to all as soon as it is ready for publication under a CCBY licence. Our article processing charge for Open Access is £1700. Corresponding authors from member institutions (http://royalsocietypublishing.org/site/librarians/allmembers.xhtml) receive a 25% discount to these charges. For more information please visit http://royalsocietypublishing.org/open-access. Your article has been estimated as being 10 pages long. Our Production Office will be able to confirm the exact length at proof stage. Paper charges An e-mail request for payment of any related charges will be sent out after proof stage (within approximately 2-6 weeks). The preferred payment method is by credit card; however, other payment options are available Electronic supplementary material: All supplementary materials accompanying an accepted article will be treated as in their final form. They will be published alongside the paper on the journal website and posted on the online figshare repository. Files on figshare will be made available approximately one week before the accompanying article so that the supplementary material can be attributed a unique DOI.  14 Thank you for your fine contribution. On behalf of the Editors of the Proceedings B, we look forward to your continued contributions to the Journal. Sincerely, Dr John Hutchinson Editor, Proceedings B mailto: [email protected] Associate Editor: Board Member: 1 Comments to Author: (There are no comments.) Referee: 1 General comments: Comment: Many thanks for the invitation to review this paper, it was a thoroughly enjoyable and fascinating read. The paper describes the results of a 14-week muscle-tendon strength training intervention that found enhancements in running economy, plantar flexion strength, and Achilles stiffness compared to a control group. An improvement in enthalpy efficiency of the soleus muscle reveals novel insight into the mechanism by which strength training may have a positive effect of the metabolic cost of running. In my opinion, this study is much needed in this area of research. Papers have speculated in the past around the mechanisms of change associated with improved running economy following a strength training intervention (e.g. Fletcher and Macintosh, 2017, doi: 10.3389/fphys.2017.00433; Blagrove et al., 2018, doi: 10.1007/s40279-017-0835-7), however measuring changes to the intrinsic behaviour of muscles is difficult. This study makes a very good attempt at providing that insight for the soleus. I have some minor comments that I hope will improve clarity and readability of the paper, but overall, I feel that this paper will be of considerable interest to both scientists and applied practitioners. Response: Thank you for your thorough and valuable comments. Specific comments: Comment: Keywords: These should be different to the terms in the study title to enable wider search returns. Please amend. Can I suggest ‘calf’ ‘triceps surae’ ‘endurance running’ ‘strength training’? Response: Thanks for this comment. We replaced four of the keywords to: “Force-length and force- velocity relationship, enthalpy-velocity relationship, triceps surae, endurance running, strength training, tendon stiffness” Comment: Line 44: ‘for’ should read ‘in’ Response: Corrected. Thank you. Comment: Line 105: Was allocation to groups completely random or were participants matched for running economy and randomised by matched pairs (or similar) to ensure minimal differences existed between groups at baseline? Response: It was completely random and the outcome parameters for the group comparison were not significantly different at the baseline level as reported. Comment: Line 106: Was the participants only sport/exercise running? It would be useful for others (particularly those undertaking reviews and meta-analyses in this area) to be able to accurately determine if participants were trained ‘runners’ or simply people that ran as a small part of a wider exercise/sport training routine. Appendix A Response: Participants were running on a regular, yet recreational basis with a minimum of twice a week set as an inclusion criterion. None of the participants was involved in professional competitive running. We added to the following information to be more clear (page: 3, line: 103): “Inclusion criteria were age 20 to 40 years, at least two running sessions weekly on a recreational basis and no muscular-tendinous injuries in the previous year.” Comment: Line 106: Please define ‘severe’ in brackets here (i.e. days/weeks away from running with injury) Response: ‘Severe’ in the context meant potential injuries that affected the running habits of the participants in the past year. We deleted the word to avoid confusion. Comment: Line 108: Why were only rear-foot striking runners considered? Response: There is an ongoing debate on the effect on foot strike pattern on running economy in the scientific community. Thus, to avoid any potential confounding effects on our study outcomes we excluded this factor by recruiting a homogenous group of rear foot runners, which is also by far the most common strike pattern (1,2,3,4). We added to the following information to be more clear (page: 3, line: 108): “Only habitual rearfoot-striking runners were considered because it is the most common foot strike pattern (4) and also to avoid potential confounding effects of the strike pattern on our study outcomes. To quantify the foot strike pattern […]” References: 1. Kasmer ME, Liu X-C, Roberts KG, Valadao JM. 2013 Foot-strike pattern and performance in a marathon. Int J Sports Physiol Perform 8, 286–292. 2. Patoz A, Lussiana T, Gindre C, Hébert-Losier K. 2019 Recognition of Foot Strike Pattern in Asian Recreational Runners. Sports (Basel) 7. 3. Cheung RTH, Wong RYL, Chung TKW, Choi RT, Leung WWY, Shek DHY. 2017 Relationship between foot strike pattern, running speed, and footwear condition in recreational distance runners. Sports Biomech 16, 238–247. 4. Santuz A, Ekizos A, Arampatzis A. 2016 A Pressure Plate-Based Method for the Automatic Assessment of Foot Strike Patterns During Running. Ann Biomed Eng 44, 1646–1655. Comment: In female participants, was the menstrual cycle accounted for or hormonal contraceptive use during recruitment and testing? Response: Menstrual cycle status was not considered systematically because of the difficulty to align pre/post measurements sessions and menstrual cycle time points. However, three of the four females of the 13 participants of the intervention group self-reported their cycle status; female 1: pre: early follicular phase (day 3 of cycle) and post: late luteal phase (day 26), female 2: pre: late follicular phase (day 9) and post: early luteal phase (day 16) and female 3: pre: late luteal (day 26) and post: late follicular phase (day 9). Over the time course of the menstrual cycle only the mid-luteal phase has been reported to impair running economy (2,3). Note that none of the three females reported this particular phase of the menstrual cycle during the test sessions. With regard to contraceptives, from the four females only two used a hormone spiral as contraceptive. Such low-dose contraceptives have been suggested to not interfere significantly with running economy (1). However, the specific application of hormone spirals in the context of running economy seems not investigated well to the best of our knowledge. Furthermore, when performing a sensitivity analysis of our reported intervention effect by changing the post energy cost values of the four females, we found that the intervention effect on the energetic cost would remain significant (p < 0.05) in case of up to a ~4% higher post value in all of the four females at the same time. Therefore, considering a) the low number of females in the intervention group, b) that none of the females reported critical mid-luteal phase during testing, c) only two used low-dose contraceptives and d) a quite robust intervention effect against the unlikely case that all the four females would have a higher energetic cost during the post test, we can argue that our found improvement in running economy following training is likely not affected by these factors. References: 1. Rebelo ACS, Zuttin RS, Verlengia R, Cesar M de C, de Sá MFS, da Silva E. 2010 Effect of low-dose combined oral contraceptive on aerobic capacity and anaerobic threshold level in active and sedentary young women. Contraception 81, 309–315. 2. Goldsmith E, Glaister M. 2020 The effect of the menstrual cycle on running economy. J Sports Med Phys Fitness 60, 610–617. 3. Williams TJ, Krahenbuhl GS. 1997 Menstrual cycle phase and running economy. Med Sci Sports Exerc 29, 1609–1618. Comment: A criticism often levelled at studies in the area of strength training for endurance athletes is that studies rarely equate the total amount of physical exercise done between groups, i.e. the control group do not have ‘placebo’ exercise(s) or add running training to match the duration of strength work performed by the intervention group (e.g. Dankel et al., 2017, doi: 10.1080/02640414.2017.1398884). Although a performance measure was not taken in this study, how do the authors know that the change in running economy they observed is not due to differences in the amount of physical training performed? An alternative, in practice, for runners could be to add running training instead of strength training to their routine, which may produce even larger improvements in economy. Response: Thank you for this important comment. We did not include an additional group that performed a time-matched running training in our experimental design. The reason was that in an earlier study (1) we applied specifically running training (i.e. focusing on the alteration of the running technique) in a group of experienced runners and we did not find any effects on running economy after the 14 weeks of running training (3x/week 30min). Furthermore, several studies in the past have shown that running training itself does not improve running economy (ref. 2: 6 weeks, ~160 km,) particularly in trained runners as in our study (ref. 3: 8 weeks, 210 km added). Therefore, we are confident that the specific strength training, which improved muscle strength of the plantar flexors and Achilles tendon stiffness, was the reason for the improved running economy and that running training in experienced runners cannot cause any additional improvements in running economy. References: 1. Ekizos A, Santuz A, Arampatzis A. 2018 Short- and long-term effects of altered point of ground reaction force application on human running energetics. Journal of Experimental Biology 221, jeb176719. 2. Daniels JT, Yarbrough RA, Foster C. 1978 Changes in VO2 max and running performance with training. Eur J Appl Physiol Occup Physiol 39, 249–254. 3. Lake MJ, Cavanagh PR. 1996 Six weeks of training does not change running mechanics or improve running economy. Medicine & Science in Sports & Exercise 28, 860–869. Comment: The changes in soleus fascicle behavior were not quantified in the control group. I am slightly puzzled why not. Would the authors consider this a limitation of the study? Response: Thank you for the comment. The participants of the control group did not change their regular training habits and therefore changes in the fascicle dynamics were not expected. In this regard, Werkhausen et al. (2019) did not find any alterations in the fascicle behavior of the soleus and gastrocnemius medialis after 10 weeks. Furthermore, our control group did not show any changes in maximal ankle joint moment and tendon stiffness, running kinematics, temporal gait characteristics, foot strike pattern and energetic cost after the intervention period. Together, this gives strong support for an unchanged fascicle behavior of soleus after our intervention period in the controls and consequently we would not see this as a limitation. According to the reviewer’s suggestion, we noted this issue in the limitation part of the revised manuscript as follows (page: 9, line: 360): “The soleus fascicle dynamics were not assessed in the control group because alterations were not expected with continued training habits as previously evidenced (1). Furthermore, the controls did not show alterations in any of the assessed parameters, giving strong support for unchanged soleus fascicle behavior after our intervention period.” References: 1. Werkhausen A, Cronin NJ, Albracht K, Paulsen G, Larsen AV, Bojsen-Møller J, Seynnes OR. 2019 Training-induced increase in Achilles tendon stiffness affects tendon strain pattern during running. PeerJ 7. Comment: Exercise protocol: Given that a single strength training exercise was used in the intervention I would strongly recommend that authors include an image of the exercise apparatus and set-up. I appreciate there are currently a high number of figures included but I would contend this is important for both scientific replication and applied practice. Response: Thanks for this comment. We added the following figure including a descriptive caption in the revised version of the manuscript. Please note that it has been placed in the supplementary material due to the limited space available. Figure 1: A conventional leg press was used for the muscle-tendon training of the m. triceps surae. The isometric plantar flexion contractions were performed at 5° dorsiflexion with the knee extended in a seating position (A). The leg press was instrumented with a force sensor in order to control the training stimulus by providing the participant with a visual feedback of the actual contraction intensity. The feedback curve was displayed together with the evidence-based loading profile defined by a sequence of 4 repetitions of 3 s loading and relaxation at 90% of the weekly-adjusted maximum voluntary plantar flexor strength in each of the 5 sets per session, 4 times a week (B). Comment: Line 147: Why was 2.5 m/s used as the speed for all participants? Was this sufficiently slow enough to ensure a plateau in oxygen consumption and RER value of <1 during the collection period? Response: A running velocity of 2.5 m/s was used in order to ensure that all participants ran at steady-state, which is a key aspect for the assessment of running economy. The plateau of the oxygen consumption was visually confirmed for each individual and trial and a representative example curve is given in the supplementary material. The average RER for the control group was pre 0.89 ± 0.05 and post 0.87 ± 0.13 and for the intervention group pre 0.94 ± 0.04 and post 0.95 ± 0.05, respectively. We added to the following sentence to be more clear in the revised manuscript (page: 4, line: 145): “During an 8-minute running trial on a treadmill at 2.5 m/s, expired gas analysis was conducted and rate of oxygen consumption (V̇ O2 ) and carbon dioxide production (V̇ CO2) was calculated as average of the last three minutes [15]. Running economy was then expressed in units of energy [4,30] as 𝐸𝑛𝑒𝑟𝑔𝑒𝑡𝑖𝑐 𝑐𝑜𝑠𝑡 = 16.89 ∙ 𝑉̇𝑂2 + 4.84 ∙ 𝑉̇𝐶𝑂2 where the energetic cost is presented in [W/kg] and V̇ O2 and V̇ CO2 in [ml/s/kg] [14,15]. Steady state was visually confirmed by the rate of (V̇ O2) during each trial and a RER of <1.0 was controlled for during the post analysis (ESM for details).” Comment: Line 149: The citation here is a paper comparing methods of quantifying energy cost of running. It is not clear which method was used without referring to the supplementary material. Response: Many apologies. The cited study investigated the appropriateness of the used formula and this was the reason for inserting only this reference. In the revised manuscript, we added the original study and also presented the formula in the main text (see previous comment). Please note that several detailed information is presented in the supplementary material because of the limited space in the main text. Comment: Line 205: Which post-hoc adjustment was used? Response: A Benjamini-Hochberg correction was applied and adjusted p-values are reported. This information can be found in the supplementary material (section statistics). Comment: Line 210: How were the effect sizes interpreted? Response: Effect sizes were interpreted according to Cohen 1988, were 0.2 ≤ g < 0.5 indicates a small, 0.5 ≤ g < 0.8 indicates a medium, and g ≥ 0.8 indicates a large effect size. This information can be found in the supplementary material (section statistics). Comment: Line 229: There appears to be a word missing in this sentence. ‘an altered lengthening- shortening behaviour’ or similar Response: Thank you for this comment. What we intended to say here is a general description of the MTU behavior that does not refer to intervention effects. Therefore, the behavior is not “altered”. We think that this solves a language issue. Comment: Line 272: It would be more accurate to discuss the change in economy in the context of within-participant variability (measurement error), rather than between-participant variability, which depends on the sample. A subtle tweak to wording and the reference (eg Blagrove et al., 2017, doi; 10.1080/17461391.2017.1364301; Shaw et al., 2013, doi: 10.1139/apnm-2013-0055) here would provide a more compelling that the 4% improvement is indeed real. Response: Thank you for this valuable comment. The sentence was changed accordingly in the revised manuscript and one mentioned references were added (page: 7, line: 262): “Following the intervention, the energetic cost of running was significantly reduced by about 4%, a quantity reported to be above test-retest typical errors [38] and to substantially enhance endurance running performance [39].” Comment: Line 304-305: Why does the higher maximum plantar flexion moment indicate hypertrophy has occurred? It would be unusual to expect substantial hypertrophy with short-duration isometric contractions. Why can the improvements in strength not be explained as neural adaptation? If so, the discussion below this statement will need to be amended. Response: We agree with the reviewer that neural adaptation could have contributed to the obtained strength gains following training besides hypertrophy. While there is evidence that neural adaptations may precede morphological responses during the early weeks of strength training onset (1), the intervention duration of our study was quite long at 14 weeks. Several studies have shown an increasing contribution of morphological changes (hypertrophy) following the first 5-6 weeks of training (2,3) beyond neural adaptations (2, 5). Moreover, strength training using explicitly isometric contractions have been shown to provide a sufficient stimulus to induce muscle hypertrophy (6,7). In our study, EMGmax obtained during the maximum voluntary plantar flexions was not changed following the training (pre 0.409 ± 0.114 mV and post 0.410 ± 0.092 mV, p = 0.300). Similarly, the training had no effect on the antagonistic co-activation (tibialis anterior EMG 0.034 ± 0.016 mV and post 0.034 ± 0.013 mV, p = 0.923). Taken together, the absence of changes in these parameters may not exclude it on other structural levels but strongly indicate that neural aspects may not the primary course of the found strength gains after the 14 weeks of training. According to the reviewers comment we changes our formulation in the revised manuscript (page: 8, line: 293): “However, the higher maximum plantar flexion moment along with no significant changes in EMGmax during the MVCs (pre 0.409 ± 0.114 mV and post 0.410 ± 0.092 mV, p = 0.300) and antagonistic co- activation (tibialis anterior EMG 0.034 ± 0.016 mV and post 0.034 ± 0.013 mV, p = 0.923) as measures for neural adaption after training strongly indicate muscle hypertrophy, resulting in a 13% increase of Fmax (pre 2903 ± 750 N, post 3285 ± 831 N).” References 1. Folland DJP, Williams AG. 2007 Morphological and Neurological Contributions to Increased Strength. Sports Med 37, 145–168. 2. Narici MV, Hoppeler H, Kayser B, Landoni L, Claassen H, Gavardi C, Conti M, Cerretelli P. 1996 Human quadriceps cross-sectional area, torque and neural activation during 6 months strength training. Acta Physiologica Scandinavica 157, 175–186. 3. Häkkinen K, Komi PV. 1983 Electromyographic changes during strength training and detraining. Med Sci Sports Exerc 15, 455–460. 4. Arampatzis A, Karamanidis K, Albracht K. 2007 Adaptational responses of the human Achilles tendon by modulation of the applied cyclic strain magnitude. J. Exp. Biol 210, 2743–2753. 5. Erskine RM, Jones DA, Williams AG, Stewart CE, Degens H. 2010 Resistance training increases in vivo quadriceps femoris muscle specific tension in young men. Acta Physiol (Oxf) 199, 83–89. 6. Davies J, Parker DF, Rutherford OM, Jones DA. 1988 Changes in strength and cross sectional area of the elbow flexors as a result of isometric strength training. Eur J Appl Physiol Occup Physiol 57, 667– 670. 7. Jones DA, Rutherford OM. 1987 Human muscle strength training: the effects of three different regimens and the nature of the resultant changes. J Physiol 391, 1–11.  Comment: Line 346: ‘a’ seems to be a typographical error here. Response: ‘a’ was deleted. Thanks for the hint. Comment: Line 346: The ref. 16 study (Fletcher et al., 2010) did not find a significant change in running economy following a calf strengthening intervention. Response: The respective citation was deleted. Comment: Line 350: There appears to be a word missing between ‘training’ and ‘may’ Response: Thank you for the comment. We corrected this in the revised manuscript. Comment: Line 355: ‘endurance performance’ should read ‘running economy’ here as no performance measures were taken. Response: We changed the term in the revised manuscript, thank you. Comment: It has long been recognised that the soleus possesses a high proportion of slow twitch muscle fibres compared to other muscle groups (eg Gollnick et al., 1974, doi: 10.1007/BF00587415). Clearly it is possible to make the soleus stronger and given its role in locomotion and energy cost during exercise, it would certainly make sense for runners to strengthen the muscle. However, do authors think that the soleus has a limited capacity to improve its maximal force output due to its morphological characteristics? The intervention applied here would certainly be novel for the participants, thus beneficial, but would long-term engagement with this type of training for soleus continue to yield benefits in running economy? Response: Thank you for this important comment. There are reports that fast-twitch fibres feature a greater hypertrophic response to resistance training compared to slow-twitch fibres (1,2,3). Therefore, one might suggest that the soleus muscle is limited in its capacity to improve its maximal force following training. However, the findings are inconsistent and there are studies reporting similar training-induced hypertrophy in slow and fast-twitch fibres (4,5). Therefore, we can argue that the morphological characteristics of the soleus muscle might not be the limiting factor. However, based on the present study we can conclude that two mechanisms contribute to the advantageous work generation by soleus, i.e. the operating enthalpy efficiency and operating force-length potential. The force-length potential was already high throughout the entire stance phase both before and after the training intervention (pre 0.89%, post 0.88%). The enthalpy efficiency throughout the stance was influenced by the intervention and increased by 7% to 92% of the maximum efficiency. Thus, the potential available adaptation range of the enthalpy efficiency for further improvements due to prolonged training seems to be the limiting factor. References: 1. Hortobágyi T, Hill JP, Houmard JA, Fraser DD, Lambert NJ, Israel RG. 1996 Adaptive responses to muscle lengthening and shortening in humans. J Appl Physiol (1985) 80, 765–772. 2. Andersen JL, Aagaard P. 2000 Myosin heavy chain IIX overshoot in human skeletal muscle. Muscle Nerve 23, 1095–1104. 3. Aagaard P, Andersen JL, Dyhre-Poulsen P, Leffers AM, Wagner A, Magnusson SP, Halkjaer-Kristensen J, Simonsen EB. 2001 A mechanism for increased contractile strength of human pennate muscle in response to strength training: changes in muscle architecture. J Physiol 534, 613–623. 4. Mero AA et al. 2013 Resistance training induced increase in muscle fiber size in young and older men. Eur J Appl Physiol 113, 641–650. 5. Bogdanis GC, Tsoukos A, Brown LE, Selima E, Veligekas P, Spengos K, Terzis G. 2018 Muscle Fiber and Performance Changes after Fast Eccentric Complex Training. Med Sci Sports Exerc 50, 729–738. Referee: 2 General comments: Comment: In this study, the authors examined the effects of a resistance training program on running economy, and additionally examined how changes in running economy were associated with changes in estimated soleus muscle strength, Achilles tendon stiffness, and operating soleus muscle efficiency, force-length, and force-velocity behaviour. This study provides insight into the mechanisms that may underly improvements in running economy with resistance training. The majority of our understanding of the role of series elasticity on efficiency is from controlled in situ or simulation studies. Thus, this study also provides novel insight into the implications of in vivo muscle and tendon properties during real-world conditions. This manuscript is well-written and interesting to read, and the methods appear sound and appropriate for addressing the research questions. I only have a few comments below that aim to clarify details of the methodology and interpretation of the results. Response: Thank you for your thorough and valuable comments. Specific Comments: Comment: 1. Lines 137-144: Given that increased plantar flexor strength and tendon stiffness are identified as a possible mechanism underlying the main results of this paper, it would be helpful to provide further details of how these variables were measured rather than referring readers to other papers. For example, in Supplementary material 1, section 2: “Furthermore, the contribution of the antagonistic muscles to the ankle joint moment was considered by means of an EMG-based method [4].” What specific method was this? “which was determined using the tendon-excursion method [5,6] and corrected for tendon alignment during the contraction [7].” How were the moment arms corrected for tendon alignment? “The MTJ displacement artefacts due to an unavoidable change in the ankle joint angle during the MVCs was corrected [8] and the five contractions were averaged to give a reliable measure of the elongation [9]. The AT stiffness was calculated between 50% and 100% of the maximum tendon force using linear regression [10]” How were the changes in ankle joint angle corrected? Currently the reader would have to consult a range of other papers to fully understand the methods and their justification. More details of these methods and less reliance on previous works would be beneficial. Response: Thanks for this comment. Please find below the more detailed descriptions of the respective methods that were also included in the revised version of the supplementary material: a. Consideration of the contribution of the antagonistic muscles: The contribution of the antagonistic muscles to the measured ankle joint moments in the different joint angles was considered by an previously reported EMG-based approach [27]. For this reason, the EMG activity of the antagonistic tibialis anterior muscle during the maximum plantar flexions (MVC) was recorded. In separate trials, an individual relationship of EMG amplitude of the tibialis anterior muscle, agonistic moment as well as ankle joint angle was then established. Thereto, the EMG activity of tibialis anterior was measured at rest and during two submaximal isometric dorsal flexion contractions that displayed slightly lower and higher EMG magnitudes as during the maximum plantar flexions [27] in three different joint angles (i.e. dorsi flexion, neutral position and plantar flexion) within the assessed range of motion. The relationship was described by the regression equation 𝑀𝑐𝑜𝑎𝑐𝑡 = 𝐸𝑀𝐺𝑡𝑖𝑏. 𝑎𝑛𝑡. ∙ (𝑎 + 𝑏 ∙ 𝛼𝑎𝑛𝑘𝑙𝑒 + 𝑐 ∙ 𝛼𝑎𝑛𝑘𝑙𝑒²), where Mcoact is the antagonistic joint moment during the maximum plantar flexion, EMGtib. ant. is the respective tibialis anterior EMG activity during the MVCs, αankle the ankle joint angle measured via the Vicon system and a, b and c the individual regression coefficients. Thus, for each joint angle the relationship between moment and EMG activity was assumed to be linear because of the small differences of the EMG magnitude of the two submaximal isometric dorsal flexion contractions [27]. Further, the ankle joint angle-moment relationship presented by the three different measured angles was formulated by a quadratic function to account for the force-length dependence of the muscle. The EMG activity of the tibialis anterior and soleus muscle was measured using a wireless EMG system (Myon m320RX, Myon AG, Baar, Switzerland) and two bipolar surface electrodes (2 cm inter-electrode distance) that were placed on the muscle at an acquisition frequency of 1000 Hz, synchronized with the kinematic data. b. Tendon excursion method and alignment correction: The Achilles tendon lever arm was determined for each participant by using the tendon excursion method [3,4]. In this method, the lever arm of the Achilles tendon is calculated as the ratio of the m. gastrocnemius medialis myotendinous junction displacement obtained by ultrasonography at 25 Hz to the corresponding angular excursion of the ankle joint during a passive joint rotation by the dynamometer (5°/s). The ratio was calculated over the interval of 5° dorsiflexion to 10° plantar flexion, where tendon deformation is negligible [8] and five passive rotation trials were averaged to ensure high reliability [29]. The lever arm values were further corrected for the alignment of the tendon occurring during contractions using the factor provided by Maganaris et al. (1998) [5]. c. MTJ displacement artefacts: The corresponding AT elongation during the ramp MVCs was analyzed based on the displacement of the gastrocnemius medialis-myotendinous junction (MTJ) visualized by B-mode ultrasonography captures (My Lab 60, Esaote, Genova, Italy, 25 Hz). The MTJ displacement artefacts due to an unavoidable increase in the plantar flexion angle during the MVCs were taken into account as they significantly affect the tendon elongation measurement [8]. For this reason, the MTJ displacement as a function of the ankle joint angle was analyzed in an additional trial where the ankle joint was passively rotated by the Biodex over the full range of motion at 5°/s and then used to correct the angle-dependent displacements obtained during the MVCs. The force and elongation data of five ramp MVCs were averaged to give a reliable measure of the AT elongation [9]. Comment: 2. Similar to 1., given that running economy is an important variable in this paper, further details in the main text would be helpful. Since the section “Energetic cost of running” in supplementary material 1 is only one paragraph long, could this not be included in the methods section of the main text? I realize the authors may be limited in terms of length; however, these details are important for interpreting the results of this paper. Similarly, at least the first paragraph of the section “Statistics” in supplementary material 1 could be included in the main text. Important methods that could affect interpretation of results and conclusions should be easy for readers to access in the main text. Response: According to the reviewer’s comment we added several information of the energetic cost assessment to the main manuscript (page: 4, line: 145, see below) and provided an extended description in the supplementary material. However, we needed to keep the details of the calculation of the required sample size (power analysis) in the supplementary material and only presented the results of this analysis in the main text because of the very limited space available. “During an 8-minute running trial on a treadmill at 2.5 m/s, expired gas analysis was conducted and rate of oxygen consumption (V̇ O2 ) and carbon dioxide production (V̇ CO2) was calculated as average of the last three minutes [15]. Running economy was then expressed in units of energy [4,30] as 𝐸𝑛𝑒𝑟𝑔𝑒𝑡𝑖𝑐 𝑐𝑜𝑠𝑡 = 16.89 ∙ 𝑉̇𝑂2 + 4.84 ∙ 𝑉̇𝐶𝑂2 where the energetic cost is presented in [W/kg] and V̇ O2 and V̇ CO2 in [ml/s/kg] [14,15]. Steady state was visually confirmed by the rate of (V̇ O2) during each trial and a RER of <1.0 was controlled for during the post analysis (ESM for details).” Comment: 3. Line 194: Why did the authors use an efficiency-velocity function rather than a more established metabolic power function (e.g. Minetti & Alexander, 1997 or Umberger, 2010, etc.)? Mechanical work and metabolic cost depend on factors other than just velocity, so why is an efficiency function that depends only on velocity, instead of separately estimating mechanical work and metabolic cost that depend on muscle velocity, length, activation, etc., appropriate for this study? Further explanation/justification in the text would be helpful. Response: Thank you for this important comment. In our opinion, the accurate assessment of muscular work in vivo is currently an unresolved problem because the required muscle force for the calculation cannot be measured. Of course, there are several studies in the literature that estimate muscle forces and muscular work by means of inverse dynamics approaches and musculoskeletal models. With all the respect towards these studies, we believe that the assumptions taken in such approaches are very strong and may dramatically affect the calculated muscular work values. Taking into consideration the relevant methodological limitations, we decided not to include calculations of force/mechanical work in our study. Rather, we tried to develop a methodological design that allows us to consider experimentally-assessed basic mechanisms for muscle force and work production (i.e. muscle force potential due to the force-length and force-velocity relationship, muscle activity and enthalpy efficiency-velocity relationship) and to investigate these mechanisms during running. Muscle length and activation can affect the heat rate, most likely due to actomyosin interaction and sarcoplasmic reticular ion transport (1), which can in turn influence the enthalpy efficiency-velocity relationship and thus might be considered as additional scale factors in our approach. However, we found a continuous shortening of the soleus fascicles very close to the optimal length and mainly in the ascending part of the force-length curve. Heat rate is nearly maximal at the optimum muscle length and there are only small changes in lengths shorter than the optimum (1,2). Furthermore, taken into consideration that the soleus fascicles operated at the same length in the pre and post condition (similar force-length potential) and the activated muscle volume based on our calculations using the Hill-type model did not show relevant pre-post differences, we can argue that both muscle operating length and muscle activation did not affect our outcomes regarding the enthalpy-efficiency. References: 1. Woledge RC, Curtin NA, Homsher E. 1985 Energetic aspects of muscle contraction. Monogr Physiol Soc 41, 1–357. 2. Hilber K, Sun Y-B, Irving M. 2001 Effects of sarcomere length and temperature on the rate of ATP utilisation by rabbit psoas muscle fibres. The Journal of Physiology 531, 771–780. Comment: Also, the fitted values in Table 1 of Hill (1967) are for frog muscle at 0 degrees C. Since frogs are ectotherms, the muscle temperature would be near that of the external environment, far below physiological temperature for human muscle. This could affect both muscle force and velocity (see James, 2013 for review) and therefore the fitted function. Additionally, amphibian muscle contains larger concentrations of parvalbumin compared to terrestrial muscles, which can alter the heat rate and estimated metabolic cost (Woledge et al., 1985, pp. 257-260). What are the implications of these considerations on the results of this study? James, R. S. (2013). A review of the thermal sensitivity of the mechanics of vertebrate skeletal muscle. Journal of Comparative Physiology B, 183(6), 723-733. Woledge, R. C., Curtin, N. A., & Homsher, E. (1985). Energetic aspects of muscle contraction. Monographs of the Physiological Society. Response: We agree with the reviewer that there is evidence for an effect of temperature on efficiency measures in both amphibian and mammalian muscles (1,2,3). Thus, it is possible that the maximum enthalpy (mechanical) efficiency of 0.44 of the frog muscle from the Hill (1964) paper (4), that we used for our analysis, is higher under more physiological temperatures. As a reference for the human soleus muscle, a maximum efficiency value of 0.48 could be taken from the murine soleus muscle under almost physiological conditions (30°C) and comparable fiber type composition (2). Please note that this value of the maximum efficiency is close to the value reported for the frog muscle (0.44) by Hill (1964) (4). Besides, since we calculated efficiency as a function of the soleus muscle shortening velocity (adjusted for physiological temperature) and only discussed our findings in terms of percentage change of the enthalpy efficiency, any discrepancies regarding the magnitude of the enthalpy efficiency would not significantly affect our results. Methodologically more important for our results would be a significant difference of the shape of the efficiency-velocity curve with great shifts of the velocity at maximum efficiency. Again the study by Barclay et al. (2010) (2) on the soleus mouse muscle showed that temperature had no effect on the velocity on the maximum efficiency in the investigated range of 20 to 30°C (between 0.19 and 0.20 V/Vmax) and shape. The reported velocity for the maximum efficiency value at 30° for the mouse soleus muscle (0.19 V/Vmax, table 1 in (2)) was very close to the value of the frog muscle provided by the paper of Hill (0.18 V/Vmax), which suggests similarity between efficiency-velocity curves in further support of our analysis. Moreover, in our reported sensitivity analysis we tried to examine the effect of changes in the shape of the curve and changes of Vmax by a) changing Vmax in 10%-intervals and b) replacing the curve from the frog muscle of the Hill paper (4) by the data presented by Barclay et al. (1993) (5) for the soleus mouse muscle. The findings showed that the significant pre to post enthalpy efficiency increase for the MTU lengthening phase and entire stance phase persisted for values between Vmax-30% and Vmax+10% both using the data of Hill or Barclay et al. (p<0.05), which confirms and strengthens the observed intervention effect (detailed descriptive values and p-values see suppl. material 2). The following changes were made in the revised limitation section to be more clear (starting page: 9, line: 358): “[..] We evaluated the effect of a) decreasing Vmax by 10% intervals and b) replacing the underlying enthalpy efficiency values measured at the frog sartorius at 0°C from Hill (1964) [20] by the data presented by Barclay et al. (1993) [22] for the predominantly slow fiber type soleus mouse muscle at 21°C, comparable to the human soleus muscle. [...] Furthermore, since we calculated the efficiency as a function of the soleus muscle shortening velocity (adjusted for physiological temperature) and only discussed our findings in terms of percentage change, any uncertainties about the magnitude of the enthalpy efficiency would not affect our results.” References: 1. James RS. 2013 A review of the thermal sensitivity of the mechanics of vertebrate skeletal muscle. J Comp Physiol B 183, 723–733. 2. Barclay CJ, Woledge RC, Curtin NA. 2010 Is the efficiency of mammalian (mouse) skeletal muscle temperature dependent? The Journal of Physiology 588, 3819–3831. 3. He ZH, Bottinelli R, Pellegrino MA, Ferenczi MA, Reggiani C. 2000 ATP consumption and efficiency of human single muscle fibers with different myosin isoform composition. Biophys J 79, 945–961. 4. Hill AV. 1964 The efficiency of mechanical power development during muscular shortening and its relation to load. Proceedings of the Royal Society of London. Series B. Biological Sciences 159, 319–324. 5. Barclay CJ, Constable JK, Gibbs CL. 1993 Energetics of fast- and slow-twitch muscles of the mouse. The Journal of Physiology 472, 61–80. 6. Barclay CJ. 2015 Energetics of contraction. Compr Physiol 5, 961–995. 7. Nelson FE, Ortega JD, Jubrias SA, Conley KE, Kushmerick MJ. 2011 High efficiency in human muscle: an anomaly and an opportunity? J Exp Biol 214, 2649–2653. Comment: 4. Line 268: “… the results provide additional evidence that a combination of greater plantar flexor muscle strength and Achilles tendon stiffness decrease the energy cost of running [14,15] and indicate that the soleus enthalpy efficiency is a contributive determinant.” It’s alluded to with “a combination” but consider an additional sentence here noting that an increase in stiffness by itself may not increase efficiency. Later in line 349 the authors state “strength increases without concomitant stiffening of the AT after a period of training may increase levels of operating and maximum AT strain [24], which have been associated with pathologies [53] but also possible functional decline [54].” Function may also decline with increases in stiffness without concomitant increases in muscle strength. For example, see Figure 5 in Lichtwark and Wilson (2005) in which muscle efficiency during running decreased with increases in AT stiffness beyond the optimal stiffness. Lichtwark, G. A., & Wilson, A. M. (2007). Is Achilles tendon compliance optimised for maximum muscle efficiency during locomotion? Journal of Biomechanics, 40(8), 1768-1775 Response: In agreement with the reviewer, we tried to make clear that it is in fact the combination of a higher muscle strength and increased tendon stiffness that potentially improved the efficiency of the operating soleus muscle and not tendon stiffness or muscle strength alone. The found increases in muscle strength and tendon stiffness were always reported alongside each other throughout the entire manuscript. Such a “balanced” adaptation seem required to facilitate the functional interplay of muscle and tendon in a way that tendon compliance can be optimally used for movement performance and efficiency but also tendon health (1,2,3,4). According to the reviewer’s comment we now added a sentence in the respective section as follows (page: 9, line: 343): “Strength increases without concomitant stiffening of the AT after a period of training can increase levels of operating and maximum AT strain [24], which have been associated with pathologies [53] but also possible functional decline [54]. On the other hand, increased stiffness without higher muscle strength may limit function by reducing relevant operating tendon strains as well (2). In our study, the maximum AT strain during the MVCs was not affected by the …” References: 1. Arampatzis A, Mersmann F, Bohm S. 2020 Individualized Muscle-Tendon Assessment and Training. Front. Physiol. 11. 2. Lichtwark GA, Wilson AM. 2007 Is Achilles tendon compliance optimised for maximum muscle efficiency during locomotion? J Biomech 40, 1768–1775. 3. Orselli MIV, Franz JR, Thelen DG. 2017 The effects of Achilles tendon compliance on triceps surae mechanics and energetics in walking. J Biomech 60, 227–231. 4. Uchida TK, Hicks JL, Dembia CL, Delp SL. 2016 Stretching Your Energetic Budget: How Tendon Compliance Affects the Metabolic Cost of Running. PLoS ONE 11, e0150378. Comment: 5. Line 291: “The exercise-induced increase in muscle strength and AT stiffness resulted in an alteration of the operating fascicle velocity profile that led to a significant increase of the enthalpy efficiency of the operating soleus […], improving the efficiency of muscular work production.” Because the only factor that was manipulated in this study was the exercise intervention, changes in muscle strength, AT stiffness, fascicle velocities, and enthalpy efficiency are only associated with one another rather than there being any causal relationship between them. Response: Thanks for this comment. We softened our formulation accordingly (page 8:, line: 281): “The exercise-induced increase in muscle strength and AT stiffness was associated with an alteration of the operating fascicle velocity profile and a significant increase of the enthalpy efficiency of the operating soleus in the phase of MTU lengthening (88% of the maximum efficiency), potentially improving the efficiency of muscular work production.”

Enthalpy efficiency of the soleus muscle contributes to improvements in running economy.

01-27-2021

Bohm, Sebastian,Mersmann, Falk,Santuz, Alessandro,Arampatzis, Adamantios

eng

PMC7037891

International Journal of Environmental Research and Public Health Article Mental Recovery and Running-Related Injuries in Recreational Runners: The Moderating Role of Passion for Running Jan de Jonge 1,2,3,* , Yannick A. Balk 4 and Toon W. Taris 2 1 Human Performance Management Group, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands 2 Department of Social, Health and Organisational Psychology, Utrecht University, P.O. Box 80140, 3508 TC Utrecht, The Netherlands; [email protected] 3 School of Psychology, Asia Pacific Centre for Work Health and Safety, University of South Australia, P.O. Box 2471, Adelaide 5001, Australia 4 Department of Work and Organizational Psychology, University of Amsterdam, P.O. Box 19268, 1000 GG Amsterdam, The Netherlands; [email protected] * Correspondence: [email protected] or [email protected]; Tel.: +31-40-247-2243 Received: 10 December 2019; Accepted: 5 February 2020; Published: 6 February 2020



 Abstract: This pilot study investigates the moderating role of passion for running in the relation between mental recovery from running and running-related injuries (RRIs). We predict that the relation between recovery and injuries is dependent on the level of passion. A cross-sectional survey study was conducted among 246 Dutch recreational runners. Multivariate logistic regression analyses revealed that the negative association between mental recovery after running and RRIs is moderated (i.e., strengthened) by harmonious passion. Put differently, runners who are able to mentally recover well after running were less likely to report RRIs in the case of harmonious passion. Additionally, findings demonstrated that obsessively passionate runners were more likely to report RRIs. Passionate runners may benefit from education programs to help them integrate running more harmoniously with other aspects of life, and to prevent injuries. In addition, they should be educated about the crucial role of appropriate mental recovery from running. Considering mental aspects in running such as mental recovery from running and passion for running seems to be worthwhile to gain a better understanding of the incidence and/or prevalence of RRIs. Future (quasi-experimental) studies should investigate the issues raised here more profoundly. Keywords:mentalrecovery; mentaldetachment; harmoniouspassion; obsessivepassion; running-related injury; recreational running 1. Introduction Running is becoming an increasingly popular activity among participants of recreational sports activities [1]. Although recreational running is in general considered a health-promoting activity with associated benefits such as social participation (e.g., through an increase in running groups and running events [1,2]) and stress reduction [3], running-related injuries (RRIs) occur quite often (e.g., [4]). Incidence and prevalence rates of RRIs reported in the literature are rather high (i.e., up to 80%; e.g., [5,6]). In The Netherlands, injury incidence is 6.1 injuries per 1000 sporting hours, which is about three times higher than the national sports average (i.e., 2.1 injuries per 1000 sporting hours). Specifically, Dutch runners suffer from 710,000 RRIs yearly, of which 220,000 are medically treated [7]. Next to soccer, running is the Dutch sport with the highest number of injuries. Forty percent of RRIs are overtraining/overuse injuries, and approximately one-third concerns a recurrence. Male Int. J. Environ. Res. Public Health 2020, 17, 1044; doi:10.3390/ijerph17031044 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2020, 17, 1044 2 of 14 runners are more often injured but the injury risk is higher among female runners. Runners between 20 and 34 years old are more prone to RRIs, especially female runners. Main injury locations are knees (29%), lower legs (25%), and ankles (17%) [7]. From a societal point of view, RRIs cost Dutch society approximately 10 million euros a year expressed in medical costs and costs due to work-related sickness absence and reduced work productivity [8]. Jungmalm and associates [9] concluded that RRIs can be viewed as recreational runners’ primary enemy, and that the public health gains of keeping runners active and healthy should not be underestimated. Most researchers agree that the majority of RRIs are sustained as a consequence of structural overtraining/overuse (e.g., [10]), or as a consequence of underrecovery (e.g., [11]). Yet, most existing empirical research on injury prediction and prevention focuses heavily upon the physical aspects of overtraining/overuse and underrecovery (e.g., [12]), and focuses less on their mental aspects, despite the potential role of mental aspects in injury prediction and prevention mentioned in the literature (e.g., [3,10,13,14]). As a result, evidence-based knowledge on the role of mental aspects in RRIs is still in its infancy. For that reason, the aim of the present pilot study is to investigate the role of two particular mental aspects in RRIs, namely mental recovery from running and passion for running. Specifically, using the Dualistic Model of Passion [15,16] as a heuristic framework, this study explores and tests the moderating role of passion in the mental recovery-injury relation. 1.1. Mental Recovery and Injuries Runners are exposed to all kinds of running-related efforts. Next to the plausible and logical physical effort, they have to face mental effort as well [3]. For instance, runners often have to run focused and concentrated during races. Research has shown that it is important to compensate running-related efforts with adequate recovery to prevent RRIs (e.g., [11]). Recovery can generally be defined as a dynamic process of restoration and unwinding in which a person’s functioning and efforts return to their initial levels before the efforts took place [17]. From a physical and physiological perspective, recovery reduces and prevents the accumulation of physical fatigue that leads to poor health. From a psychological perspective, it allows the individual to prepare for current or new efforts [18]. A large body of research has investigated the role of a variety of strategies aimed at promoting physical and physiological recovery from training and race efforts (e.g., [19]). In contrast, studies investigating the role of mental recovery in preventing RRIs are scarce [3,20]. In general, there are different perspectives on recovery. It can be considered as an outcome and a process [21]. Recovery as an outcome refers to a person’s physical, physiological and mental state after a recovery or relaxation period. Recovery as a process refers to the activities and experiences that may lead to a change in functioning and health status. As far as the latter is concerned, several authors have argued that it is not the actual recovery activity which helps recovery (such as going for a walk, watching TV, or taking a nap), but rather the psychological processes and mechanisms behind it (e.g., [17,21,22]). In other words, persons may differ with regard to preferred recovery activities while the underlying psychological processes crucial for recovery may be uniform across persons [22]. These psychological processes and mechanisms are called ‘recovery experiences’ (e.g., [22]). One experience that seems to be very important for recovery to occur is mental detachment from running [3,20]. Mental detachment refers to the personal experience of leaving running behind, to mentally switch off completely, and to forget about running immediately after the run training or race [3,17,20,22]. Mental detachment goes beyond the pure physical absence from running and abstaining from running-related efforts. It implies leaving running behind oneself in psychological terms. We are aware of one recent study among 161 recreational athletes showing that mental detachment was related to reporting less sport injuries [20]. To conclude, a completely recovered runner is not only physically recovered, but is also able to mentally detach from and mentally recover after running. If recovery through effective energy management is successful, health and performance will improve, and runners may report less RRIs accordingly [3]. Int. J. Environ. Res. Public Health 2020, 17, 1044 3 of 14 1.2. Passion and Injuries A mental aspect that has gained more and more attention in sport research is passion [15,16,23]. The Dualistic Model of Passion (DMP; [15,16,24]) defines passion as a strong inclination toward a self-defining activity that people like, value, and consider important, and in which they invest considerable time and energy. The DMP suggests that different individuals can be highly committed to the same extent toward an activity such as running, and yet pursue it in qualitatively different ways. Accordingly, the DMP posits the existence of two specific types of passion: harmonious and obsessive. Harmonious passion (HP) results from an autonomous internalization of an activity into one’s identity, and is characterized by a strong desire to freely engage in the passionate activity [25]. With HP, the passionate activity occupies a meaningful—but not overwhelming—place in one’s life and remains in harmony with other aspects of a person’s life [26]. HP is assumed to lead to flexible persistence: one is in full control of the passionate activity, so that when conditions become harmful, involvement in the activity should decline or even stop [27]. The second type of passion, obsessive passion (OP), also refers to a strong desire to engage in the passionate activity [25]. However, OP overwhelms one’s attention, and is postulated to result from an overcontrolled internalization of an activity into one’s identity. OP is also assumed to lead to rigid persistence: one comes to be fully controlled by the passionate activity at the expense of other activities [25,27]. OP leads the person to value the passionate activity over and above all other important activities. This often leads to conflicts either between the passionate activity and other activities, or with one’s partner and relatives [26]. Empirical findings have been consistent with this conceptualization of passion (e.g., [16,24]). Where both types of passion predict similar commitment to an activity and are part of someone’s identity, they have been found to be differentially associated with various outcomes (e.g., [15,16,24]). There is considerable evidence that HP is positively related to psychological outcomes (e.g., positive affect, flow, self-esteem), whereas OP is either unrelated or negatively related to these (e.g., [16,28,29]). In addition, HP has been shown to be positively associated, whereas OP is negatively associated, with experiences of conflict between one’s passion and other life domains [15,30,31]. With regard to performance as an outcome, both types of passion seem to be important. However, OP may at times lead to higher performance levels than HP [16,25]. Furthermore, research on the DMP lends support for the model in sports and sport-related injuries as well. For instance, a study among 80 student dancers showed that harmoniously passionate dancers reported less acute injuries [27]. In addition, OP was associated with prolonged suffering from chronic injuries as well as more rigid involvement in dance activities when injured, whereas HP was unrelated to chronic injuries. Another study of Vallerand and colleagues [15] found that cyclists with OP were still cycling in winter on icy roads, and thus engaged in risky (i.e., injury-promoting) activities while they may be better abstain from such activities. Similar findings regarding the OP-risky behavior relation were also found in a sample of swing dancers [16] and in a study with professional dancers [32]. Finally, in their study of 170 competitive runners, Stephan and his team [33] found that OP was positively associated with perceived susceptibility to sport-related injury. 1.3. Mental Recovery, Passion, and Injuries The current pilot study investigates the moderating role of passion for running in the relation between mental recovery and RRIs in a sample of recreational runners. First, in the case of HP, runners feel engaged with running but remain in harmony with other important activities of life. They are in full control of the passionate activity and are able to stop it whenever necessary. This implies that runners with HP are able to cease running activities at any time, are able to engage in recovery from running, mentally detach from running after a run, and feeling mentally recovered at the end. So, we expect that mental detachment from running and mental recovery after running will be negatively related to RRIs and that these relations are moderated (i.e., strengthened) by HP (Hypothesis 1). Put differently, harmoniously passionate runners who are able to mentally detach from running and/or recover well after running are less likely to report RRIs. Second, runners with OP have an uncontrollable urge to engage in running, and they highly value it over all other important activities of life. They are Int. J. Environ. Res. Public Health 2020, 17, 1044 4 of 14 fully controlled by the passionate activity (rather than that they are in full control of this activity, as in HP) and will persist in running despite the body and mind signals that recovery is necessary. Thus, obsessively passionate runners will disregard their need for recovery and, hence, will be less able to mentally detach from running as well as to mentally recover after running. Consequently, they may negate minor RRIs and overtrain/overuse, or underrecover, themselves, leading to more serious RRIs in the long run (cf. [10,11]). We expect that mental detachment from running and mental recovery after running will be negatively related to RRIs, and that these relations are moderated (i.e., buffered) by OP in such a way that the associations will be less negative (Hypothesis 2). In other words, the association between (1) mental detachment from running and mental recovery after running and (2) RRIs will be weaker in the case of obsessively passionate runners. 2. Materials and Methods 2.1. Study Design, Data, and Procedure A cross-sectional survey study was conducted in the Summer of 2017. Recreational runners were recruited via all Dutch running associations (N = 371) that were mentioned on the website of the Dutch Athletics Foundation (AU). The AU is the national umbrella organization of all Dutch athletics and running clubs, and closely linked to the International Association of Athletics Federations (IAAF) and European Athletics (EA). Both novice and more experienced runners received a unique, secured link to an online survey, where they had to fill out their email address. All participants gave their informed consent for inclusion before they participated in the study. They received information about the aim of the study and voluntary participation, and were told that their data would be handled confidentially. This pilot study was conducted in accordance with the Declaration of Helsinki and the American Psychological Association, and received institutional approval. Moreover, the Medical Ethics Committee of the University Medical Center Utrecht in the Netherlands has exempted our series of survey research studies in runners from further ethical approval (reference number: NL64342.041.17). Initially 254 recreational runners who ran at least once a week returned the questionnaire. The ultimate sample consisted of 246 runners due to some missing data. More than half of the participants were male (53.7%) and 46.3% was female. Mean age was 47.2 years (SD = 13.4; range 19–77). Average running experience was 14.4 years (SD = 12.0). On average, participants engaged in running activities 2.8 times a week (SD = 1.0). The average running distance was 26.5 kilometers per week (SD = 16.6), whereas the average running time was 3.2 hours per week (SD = 1.8). Overall, the average running speed was 10.1 km/h (SD = 18.8). Forty-two participants (17.1%) ran at least four times a week with an average running distance of 47.6 kilometers per week and an average running speed of 9.3 km/h. Ten people (4.1%) ran at least five times a week with an average running distance of 62.2 kilometers per week and an average running speed of 9.5 km/h. Two-thirds of the runners ran in groups (68.0%), and approximately half of the runners (45.5%) used an individualized training schedule for their training activities. Of all participants, 51.2% self-reported RRIs over the past 12 months, such as knee, Achilles tendon and foot injuries. These training and injury figures were comparable to other Dutch studies among recreational runners (e.g., [3,5,34]). 2.2. Variables and Instruments 2.2.1. Mental Recovery We used two scales for mental recovery reflecting the two different perspectives mentioned earlier; that is, mental detachment from running (‘recovery process’) and mental recovery after running (‘recovery outcome’; cf. [21,22]). Scales are available from the first author upon request. Mental detachment from running was measured with a slightly adapted scale developed by De Jonge and colleagues [35]. This scale had been used and well-validated in sports before (e.g., [3,36]). Participants were asked if they could mentally switch off from running immediately after a run training Int. J. Environ. Res. Public Health 2020, 17, 1044 5 of 14 or race. The scale was measured with three items, e.g., “I could mentally distance myself from running directly after a run”. Items were scored on a 5-point Likert scale, ranging from 1 (never) to 5 (always). Internal consistency of the scale expressed in Cronbach’s alpha was 0.90. Mental recovery after running was assessed with an adaptation for running of the well-validated recovery measure developed by Sonnentag and Kruel [37] to running. Participants were asked if they feel mentally recovered a couple of hours after a run training or race. The scale consisted of three items, scored on a 7-point Likert scale, ranging from 1 (totally disagree) to 7 (totally agree). An example item is: “A couple of hours after my running activities, I usually feel recovered mentally”. Cronbach’s alpha was 0.90. The factor structure of mental detachment and mental recovery was investigated with a factor analysis (PAF) with oblimin rotation. This factor analysis resulted in an obvious two-factor solution with all detachment items loading on one factor and all recovery items loading on the other. Eigenvalues were 3.50 and 2.15 respectively, explaining 80.7% of the variance. Pearson zero-order correlation for the two scales was r = 0.22 (p = 0.001), showing that mental detachment after running was positively but moderately related to mental recovery from running. 2.2.2. Harmonious and Obsessive Passion Harmonious and obsessive passion were measured by the respective scales developed by Vallerand and colleagues [15,16]. The scales were slightly adapted as the passionate activity used here is ‘running’. Harmonious passion emphasized a strong inclination where the runner feels engaged and has full control over running, and the activity is in harmony with the person’s other activities. An example item is: “Running is well integrated in my life”. As one item of the original scale did not pass psychometric scrutiny, our scale consisted of five items, with an internal consistency (Cronbach’s alpha) of 0.79. Obsessive passion reflected a strong inclination where the runner feels compelled to engage in running, running takes a lot of space, the runner loses control over running, and experiences conflict with other life activities. This scale consisted of six items, for instance: “I have almost an obsessive feeling for running” (Cronbach’s alpha = 0.90). Both scales were scored on a 7-point Likert scale, ranging from 1 (do not agree at all) to 7 (completely agree). We tested the factor structure of both passion scales with a factor analysis (PAF) with oblimin rotation. Results revealed a clear two-factor solution with eigenvalues of 4.92 and 1.41, explaining 63.3% of the variance. All OP items loaded on the first factor and all HP items loaded on the second factor. The two scales were not significantly related to each other (r = 0.08, p = 0.222). 2.2.3. Running-Related Injuries Running-related injuries were self-reported by runners, and consisted of a time frame of the past 12 months. Based on a consensus definition [38,39], RRIs were defined as: “injuries, impairments or wounds, whether or not associated with pain, caused by or developed during a running training, that causes a restriction on running (in terms of duration, speed, frequency, distance, or intensity) or stoppage of running for at least seven days or three consecutive scheduled training sessions”. In line with other large-scale research studies in RRIs (e.g., [6,34]), we assessed RRIs by means of a well-validated single question with a dichotomous response scale (0 = no; 1 = yes): “In the past 12 months, have you suffered one (or more) sport injuries following the above definition as a result of your running?”. Injuries were overall injuries; no difference was made in acute injuries or overtraining/overuse injuries. 2.2.4. Control Variables We controlled for gender (0 = female; 1 = male), age (years), use of an individualized training schedule (0 = no; 1 = yes), running distance per week (kilometers), and running time per week (hours). Past studies have shown that these characteristics could have a significant influence on runners’ injuries (e.g., [3,5,34]). In addition, a recent meta-analysis showed that the remaining running-related characteristics are less relevant as control variables [40]. Int. J. Environ. Res. Public Health 2020, 17, 1044 6 of 14 2.3. Statistical Analysis Firstly, means, standard deviations, and Pearson zero-order correlations were calculated using IBM SPSS Statistics 25 (SPSS Inc., Chicago, IL, USA). Secondly, multivariate logistic regression analyses were used to determine the associations between mental detachment, mental recovery, passion, and RRIs. Multivariate odds ratios (ORs) and 95% confidence intervals (CIs) were derived from the logistic regression models. In all analyses, gender, age, training schedule use, running distance and running time were controlled for. Postulated moderating effects of passion (i.e., HP and OP) with recovery (i.e., mental detachment and mental recovery) were tested by adding multiplicative interaction terms (recovery × passion) of standardized recovery and passion scales into the regression model. Since we expected differential effects for the two passion scales, we performed two regression analyses accordingly: one for HP and one for OP. Nagelkerke R2 was used as an approximation of the explained variance of the logistic regression model. 3. Results Means (M), standard deviations (SD), and Pearson zero-order correlations for the different variables are displayed in Table 1. A first inspection of the Pearson zero-order correlations shows that our control variables were moderately but significantly related to several predictor variables and outcome variables. For instance, age was significantly related to mental detachment from running (r = 0.24, p = 0.000), mental recovery after running (r = 0.21, p = 0.001), HP (r = 0.21, p = 0.001), and RRIs (r = −0.13, p = 0.046). Next, gender was significantly associated with both running distance (r = 0.23, p = 0.000) and running time (r = 0.20, p = 0.002), while age was significantly related to running time (r = 0.19, p = 0.003). Interestingly, mental detachment from running was significantly and negatively linked to running distance (r = −0.24, p = 0.000) and running time (r = −0.21, p = 0.001) as well. Finally, both HP (r = −0.15, p = 0.022) and OP (r = 0.14, p = 0.026) as well as the interaction between mental recovery after running and HP (r = −0.13, p = 0.048) were moderately associated with RRIs. Table 2 depicts the logistic regression results for RRIs, which showed support for an interaction model in the case of HP and, hence, a moderating effect of mental recovery after running and HP. Specifically, the negative association between mental recovery after running and RRIs is moderated (i.e., strengthened) by HP. Put differently, harmoniously passionate runners who are able to mentally recover well after running were 0.72 times (or 28%) less likely to report RRIs (OR = 0.72; 95% CI = 0.54–0.96; p = 0.031). However, findings did not show an interaction effect of mental detachment from running and HP, and did not show direct negative associations between mental recovery after running, mental detachment from running and RRIs as well. Overall, the predictor variables were able to explain 10.4% of the variance in RRIs. At the end, the classification accuracy shows that this prediction was correct 62.2% of the time. With respect to OP, logistic regression results showed a main effect-only model rather than an interaction model. Findings demonstrated that obsessively passionate runners were 1.36 times (or 36%) more likely to report RRIs (OR = 1.36; 95% CI = 1.03–1.85; p = 0.047) than others. Again, findings did not show direct negative associations between mental recovery after running, mental detachment from running and RRIs. Nagelkerke R2 shows that the predictor variables together were able to explain 7.9% of the variance in RRIs. Finally, the classification accuracy shows that this prediction was correct 59.2% of the time. Int. J. Environ. Res. Public Health 2020, 17, 1044 7 of 14 Table 1. Descriptive statistics and Pearson zero-order correlations among study variables (n = 246). Variables M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 1. Gender (0 = female; 1 = male) 0.54 # 0.50 2. Age (years) 47.15 13.41 0.26 ** 3. Training schedule (0 = no; 1 = yes) 0.46 # 0.50 −0.07 −0.01 4. Running distance (km) 26.53 16.56 0.23 ** 0.08 0.14 * 5. Running time (hours) 3.20 1.82 0.20 ** 0.19 ** 0.08 0.69 ** 6. Mental detachment from running 2.84 1.19 0.01 0.24 ** −0.11 −0.24 ** −0.21 ** 7. Mental recovery after running 5.67 1.16 0.11 0.21 ** 0.04 0.02 0.05 0.22 ** 8. Harmonious passion (HP) 2.60 1.35 −0.01 0.21 ** −0.10 −0.34 ** −0.26 ** 0.32 ** 0.17 ** 9. Obsessive passion (OP) 3.41 1.50 −0.05 −0.10 0.13 * 0.37 ** 0.29 ** −0.43 ** −0.17 ** 0.08 10. Mental detachment × HP 0.32 1.10 0.06 −0.14 * 0.12 0.23 ** 0.09 −0.06 −0.04 −0.25 ** 0.05 11. Mental recovery × HP 0.17 1.00 0.05 −0.01 0.07 0.11 0.09 −0.05 −0.17 ** −0.09 0.05 0.13 * 12. Mental detachment × OP −0.43 0.99 0.05 0.11 −0.06 −0.17 ** −0.09 −0.07 0.02 0.05 −0.04 −0.43 ** −0.11 13. Mental recovery × OP −0.17 0.96 0.07 0.06 −0.04 0.04 0.02 0.02 0.29 ** 0.05 0.05 −0.12 −0.55 ** 0.20 ** 14. RRIs (0 = no; 1 = yes) 0.51 # 0.50 0.07 −0.13* −0.07 0.05 0.10 −0.11 −0.04 −0.15 * 0.14 * 0.05 −0.13 * −0.06 0.04 * Significant at p < 0.05; ** significant at p < 0.01 (two-tailed); # these are dichotomous variables, their means can thus be interpreted as a percentage. Int. J. Environ. Res. Public Health 2020, 17, 1044 8 of 14 Table 2. Logistic regression models of running-related injuries with detachment, recovery and passion as predictor variables (n = 246). Running-Related Injuries Harmonious Passion Obsessive Passion B SE OR (95% CI) B SE OR (95% CI) Control variables Gender 0.46 0.29 1.59 (0.90, 2.81) 0.49 0.28 1.63 (0.92, 2.88) Age −0.02 0.01 0.98 * (0.96, 0.99) −0.03 0.01 0.96 * (0.94, 0.98) Training schedule use −0.40 0.28 0.67 (0.39, 1.15) −0.37 0.27 0.69 (0.41, 1.17) Running distance −0.01 0.01 0.99 (0.96, 1.02) −0.01 0.01 0.99 (0.96, 1.02) Running time 0.17 0.13 1.18 (0.92, 1.51) 0.16 0.12 1.17 (0.92, 1.48) Predictor variables Mental detachment from running −0.03 0.15 0.97 (0.72, 1.32) 0.02 0.16 1.02 (0.75, 1.39) Mental recovery after running −0.02 0.14 0.98 (0.74, 1.30) 0.03 0.14 1.02 (0.78, 1.35) Harmonious passion (HP) −0.31 0.17 0.73 (0.53, 1.02) Obsessive passion (OP) 0.32 0.15 1.36 * (1.03, 1.85) Moderating variables Mental detachment × Passion (HP) 0.02 0.16 1.02 (0.81, 1.36) Mental recovery × Passion (HP) −0.32 0.14 0.72 * (0.54, 0.96) Model test χ2 = 19.37, df = 10, p = 0.036 χ2 = 15.89, df = 8, p = 0.044 Nagelkerke R2 10.4% 7.9% Classification accuracy 62.2% 59.2% * Significant at p < 0.05 (two-tailed). Int. J. Environ. Res. Public Health 2020, 17, 1044 9 of 14 4. Discussion The general purpose of this pilot study was to investigate the moderating role of passion for running in the relation between mental detachment from running, mental recovery after running and running-related injuries (RRIs). Based upon scientific literature and preliminary evidence, we formulated and tested two hypotheses accordingly. First, we hypothesized that both mental recovery components (i.e., mental detachment from running and mental recovery after running) will be negatively related to RRIs, and that this relation is moderated (i.e., strengthened) by HP. Findings do show the expected interaction effect between mental recovery after running and HP in the prediction of RRIs. In other words, there is only a negative association between mental recovery after running and RRIs in the case of being a harmoniously passionate runner. This supports Hypothesis 1. However, findings did not show the expected interaction effect of mental detachment from running and HP in the prediction of RRIs, which is not in support of Hypothesis 1. Second, we hypothesized that both mental recovery components will be negatively related to RRIs, and that this relation is moderated (i.e., buffered) by OP in such a way that the associations will be weaker. Results do not show the proposed interaction effect between mental recovery and OP in the prediction of RRIs. Instead, they only demonstrate a main effect of OP in the prediction of RRIs. In other words, obsessively passionate runners are more likely to report RRIs. These findings do not support Hypothesis 2. Finally, the predictor variables were able to explain about 8% to 10% of the variance in RRIs, and the predictions were correct in approximately 60% of the time. Although these effects are not very strong, they are interesting and promising. 4.1. Theoretical Implications Findings with regard to the two mental recovery components (i.e., mental detachment from running and mental recovery after running) and RRIs are interesting. Although mental recovery after running is in general considered as being beneficial for injury prevention (e.g., [3,20]), this study shows that this may particularly be the case with harmoniously passionate runners. HP is characterized by a more flexible psychological state that should lead the runner to focus and to concentrate better, to experience less pressure, and to relax better accordingly (cf. [15,16]). In addition, mental recovery research showed that mental recovery potential is highest in cases where the need for recovery is intrinsically motivated [41]. HP could be such an intrinsic motivator. The present study extends the work of Balk and associates [20] who showed that mental detachment from running was related to athletes’ report of less injuries. In our study, however, it is not mental detachment from running but mental recovery after running in harmoniously passionate runners which seems to be negatively associated with RRIs. As both recovery measures are self-report instruments, an explanation for this finding is that recovery outcome measures seem to be more sensitive and concrete mental recovery measures than recovery process measures [21]. While the recovery outcome is related to the recovery process, it is the concrete mental recovery state directly after running which matters most for harmoniously passionate runners in the prediction of their RRIs. Moreover, if one moves beyond self-report ratings in the direction of more objective data (e.g., psycho-physiological data), disentangling the recovery process from the recovery outcome will be more difficult, or even not possible at all [21]. To conclude, this study shows that, for harmoniously passionate runners, mental recovery after running as an outcome of a successful recovery process is more important than mental detachment from running as part of the process itself to predict less RRIs. The findings for passion for running are consistent with previous research on the concept of passion and the Dualistic Model of Passion [15,16]. The two types of passion demonstrate the way running has been internalized into a runner’s identity: HP in which the person controls the activity, and OP where the activity controls the person [15]. In line with earlier passion research [16,27,33], it might be that harmoniously passionate runners are being able to detect early warning signals related to injuries and to adopt precautionary behavior such as taking a mental break in time. Conversely, obsessively passionate runners cannot stop running even when positive returns are no longer forthcoming and Int. J. Environ. Res. Public Health 2020, 17, 1044 10 of 14 running has become harmful to them [15]. The non-existence of interaction effects between mental detachment, mental recovery and OP could be explained by the fact that obsessively passionate runners are not capable of detecting early warning signals related to injuries as well as adopting precautionary behavior such as taking a mental break in time. In other words, they will disregard their need for recovery and, hence, will not be able to mentally detach from running as well as to mentally recover after running. Thus, while such rigid persistence to running may initially lead to benefits such as improved performance, it may also come at personal costs such as RRIs in the end. Finally, since obsessive passion is considered to be one of the key predictors for exercise addiction [42], our results are also consistent with research on exercise and running addiction (e.g., [42–45]). Exercise addiction can be defined based on the same criteria used to define other addictive behaviors including tolerance, withdrawal, lack of control, time, reduction in other rewarding activities, and continuance despite negative outcomes [46]. Of all the types of sports studied, endurance sports such as long-distance running are those showing the greatest risk of addiction [42,43]. For example, runners who find they need to run more to experience the same positive neuropsychological effects (e.g., runners high), experience irritability or even depression when unable to run, find that running time interferes with responsibilities in other domains (e.g., work or family), or exercise despite RRIs may have an addictive-like relation with exercise [47]. In a literature review of 25 empirical studies, Nogueira and associates [42] concluded that excessive practice may indeed cause the appearance of addictive behaviors and serious health problems. A recent study of Martin and her team [47] has highlighted the fact that endurance runners with high levels of exercise addiction pressed on in spite of the negative consequences brought about by not running, because the compensation they derive is greater than any rewards from not doing so. 4.2. Limitations and Future Research Directions Besides its valuable insights, this study has several limitations. A first limitation concerns its cross-sectional design which does not permit any causal conclusions for the variables under study. However, this was due to the pilot character of this study. A two-wave cross-lagged panel study by Carbonneau and associates [48] in a non-sports sample showed that passion leads to changes in outcomes, but not the other way around. Further research using longitudinal study designs is needed to replicate and corroborate current findings (cf. [3]). Such studies would also contribute to the understanding of sports-related, social and psychological factors that promote or hinder the development of one type of passion over the other [27]. A second limitation is that common method variance due to using self-report data may have played a role, although recent research studies have shown that this influence is not as strong as sometimes believed (e.g., [49]). This risk was minimized by measuring our self-report scales as objective as possible (‘facts’) with clear instructions to fill out, accompanied with concrete and different response rates as well as profound tests on validity and reliability. The risk was further reduced by assessing the outcome measure with a different response format and anchors compared to the predictor variables, as suggested by Podsakoff, MacKenzie, and Podsakoff [50]. A third limitation is that self-reported RRIs were used. This implies that the runners had to judge the injuries themselves, without a formal diagnosis from a medical practitioner. This is partly solved by providing the runners with a clear consensus definition of RRIs as well as using the same survey question as used in other, large-scale, empirical research (e.g., [6,34]). Furthermore, the quality of RRIs was not taken into account. For instance, RRIs due to overuse or overtraining might be qualitatively different in their genesis than RRIs due to trauma. Similarly, the seriousness of RRIs might vary greatly and could have an impact on recovery schemes. It is also recommended to add more formal and comprehensive diagnostic information of RRIs by practitioners, which could enhance a study’s validity in future research. Fourth, although we found direct associations between passion and RRIs, we do not know if some runners in our sample were physically predisposed to RRIs. A physical screening program at forehand would be recommended in this respect. Fifth, our logistic regression models have been adjusted for various control variables. Nevertheless, the question remains which Int. J. Environ. Res. Public Health 2020, 17, 1044 11 of 14 other control variables such as participation in competition or extent performance level are associated with HP and OP. Future research might therefore consider assessing similar questions in different groups of runners. Sixth, current findings are likely to be valid for all types of recreational runners. However, it is plausible that the associations are underestimated due to the absence of elite runners (i.e., restriction of range effect). Finally, given the current sample of recreational runners, its sample size and pilot character, future research is needed whether or not the current results will hold in other samples of recreational and elite athletes as well. An example of such research is a randomized controlled trial with a 12-month follow-up [3]. After completing a web-based baseline survey, 425 half and full marathon runners were randomly assigned to either an intervention group or a control group. Participants of the intervention group obtained access to an online injury prevention programme, consisting of a running-related smartphone application and activity trackers. The smartphone application provided the participants of the intervention group with information on how to prevent overtraining/overuse and RRIs with special attention to mental aspects such as mental recovery, passion and mental fatigue. Due to a wait list control group design, participants in the control group got access to the application and related preventive information after the first follow-up measurement as well. Data collection and analysis is in progress, and will be published elsewhere (cf. [3]). 4.3. Practical Implications The present study demonstrates the important role of passion in the relation between mental recovery and RRIs. Because many runners are devoted to and passionate about their sports, it is important to help them understand that there are two different types of passion: harmonious passion and obsessive passion. HP entails control over running and a harmonious co-existence of running with other activities in life, such as adequate mental and physical recovery. In contrast, OP entails little or even lack of control over running, rigid persistence, and conflict with other activities in a runner’s life. So, HP seems to be a more desirable type of passion than OP in the case of RRIs, and runners should be encouraged to develop a more harmonious passion in this respect (cf. [26,27]). However, this does not mean that OP is negative. It may not lead to outcomes as adaptive as those derived from HP, but OP is still more adaptive than being amotivated [15]. For instance, benefits from OP are reflected by the immediate positive consequences associated with increased performance (e.g., [16,25]). Further, OP may lead to long-term commitment and persistence in running, despite its potential countereffects on RRIs. Passionate runners may benefit from education programs in order to help them integrate running more harmoniously with other aspects of life. In addition, they should be educated about the crucial role of appropriate recovery from and after running. Moreover, run coaches and trainers should be aware of the two types of passion as well, and how they characterize different ways running has been internalized into a runner’s identity. Periodized training schemes and smartphone applications could then be adapted to the individual runner (cf. [3]), and ideally should take into account how to take mental breaks next to regular physical breaks (cf. [20]). Our study shows that this is particularly relevant for obsessively passionate runners. 5. Conclusions This pilot study in recreational runners suggests that particularly the combination of harmonious passion for running and mental recovery after running is important to predict and prevent RRIs. Moreover, it suggests that obsessive passion for running is a mental risk factor for RRIs itself. So, considering mental aspects in running seems to be valuable to gain a better understanding of the incidence and/or prevalence of RRIs. Preventing and/or reducing RRIs will facilitate runners to remain active, which in turn may contribute to their health, vitality and sustainable performance—not only in sports but also in work and private life activities [51]. This can reduce medical costs and costs due to absence from work as well. Further research on the issues raised here would be rather promising. Author Contributions: J.d.J. designed and carried out this particular study. He also conducted the logistic regression analysis. Y.A.B. and T.W.T. contributed to interpreting the findings, and collaborated on the different Int. J. Environ. Res. Public Health 2020, 17, 1044 12 of 14 drafts of the manuscript. All authors approved the final manuscript’s submission for publication. All authors have read and agreed to the published version of the manuscript. Funding: The work of all authors has not been funded by outside partners but was part of their ordinary activities at their respective institutes. Conflicts of Interest: The authors declare no conflict of interest. References 1. Scheerder, J.; Breedveld, K.; Borgers, J. Running across Europe: The Rise and Size of One of the Largest Sport Markets; Palgrave Macmillan: London, UK, 2015. 2. Hulteen, R.M.; Smith, J.J.; Morgan, P.J.; Barnett, L.M.; Hallal, P.C.; Colyvas, K.; Lubans, D.R. Global participation in sport and leisure-time physical activities: A systematic review and meta-analysis. Prev. Med. 2017, 95, 14–25. [CrossRef] 3. De Jonge, J.; Van Iperen, L.; Gevers, J.; Vos, S. Take a mental break study: Role of mental aspects in running-related injuries using a randomised-controlled trial. BMJ Open Sport Exerc. Med. 2018, 4, e000427. [CrossRef] [PubMed] 4. Videbæk, S.; Bueno, A.M.; Nielsen, R.O.; Rasmussen, S. Incidence of running-related injuries per 1000 h of running in different types of runners: A systematic review and meta-analysis. Sports Med. 2015, 45, 1017–1026. [CrossRef] 5. Van Gent, R.N.; Siem, D.; Van Middelkoop, M.; Van Os, A.G.; Bierma-Zeinstra, S.M.A.; Koes, B.W. Incidence and determinants of lower extremity running injuries in long distance runners: A systematic review. Br. J. Sports Med. 2007, 41, 469–480. [CrossRef] [PubMed] 6. Van Poppel, D.; Scholten-Peeters, G.G.M.; Van Middelkoop, M.; Verhagen, A.P. Prevalence, incidence and course of lower extremity injuries in runners during a 12-month follow-up period. Scand. J. Med. Sci. Sports 2014, 24, 943–949. [CrossRef] [PubMed] 7. Stam, C. Hardloopblessures: Blessurecijfers 2014; VeiligheidNL: Amsterdam, The Netherlands, 2016. 8. Valkenberg, H.; Stam, C. Sportblessures 2016: SEH Bezoeken; VeiligheidNL: Amsterdam, The Netherlands, 2017. 9. Jungmalm, J.; Grau, S.; Desai, P.; Karlsson, J.L.; Nielsen, R.O. Study protocol of a 52-week Prospective Running INjury study in Gothenburg (SPRING). BMJ Open Sport Exerc. Med. 2018, 4, e000394. [CrossRef] 10. Soligard, T.; Schwellnus, M.; Alonso, J.M.; Bahr, R.; Clarsen, B.; Dijkstra, H.P.; Gabbett, T.; Gleeson, M.; Hägglund, M.; Hutchinson, M.R.; et al. How much is too much? (Part 1) International olympic committee consensus statement on load in sport and risk of injury. Br. J. Sports Med. 2016, 50, 1030–1041. [CrossRef] 11. Kellmann, M.; Bertollo, M.; Bosquet, L.; Brink, M.; Coutts, A.J.; Duffield, R.; Erlacher, D.; Halson, S.L.; Hecksteden, A.; Heidari, J.; et al. Recovery and performance in sport: Consensus statement. Int. J. Sports Physiol. Perf. 2018, 13, 240–245. [CrossRef] 12. Kluitenberg, B.; Van Middelkoop, M.; Diercks, R.; Van der Worp, H. What are the differences in injury proportions between different populations of runners? A systematic review and meta-analysis. Sports Med. 2015, 45, 1143–1161. [CrossRef] 13. Ivarsson, A.; Johnson, U.; Andersen, M.B.; Tranaeus, U.; Stenling, A.; Lindwall, M. Psychosocial factors and sport injuries: Meta-analyses for prediction and prevention. Sports Med. 2017, 47, 353–365. [CrossRef] 14. Johnson, U.; Ivarsson, A. Psychosocial factors and sport injuries: Prediction, prevention and future research directions. Curr. Opin. Psychol. 2017, 16, 89–92. [CrossRef] [PubMed] 15. Vallerand, R.J.; Blanchard, C.; Mageau, G.A.; Koestner, R.; Ratelle, C.; Léonard, M.; Gagné, M.; Marsolais, J. Les passions de l’âme: On obsessive and harmonious passion. J. Pers. Soc. Psychol. 2003, 85, 756–767. [CrossRef] [PubMed] 16. Vallerand, R.J. The Psychology of Passion: A Dualistic Model; Oxford University Press: New York, NY, USA, 2015. 17. Sonnentag, S.; Venz, L.; Casper, A. Advances in recovery research: What have we learned? What should be done next? J. Occup. Health Psychol. 2017, 22, 365–380. [CrossRef] 18. Colombo, V.; Cifre Gallego, E. The importance of recovery from work: A review of where, how and why. Pap. Psicólogo 2012, 33, 129–137. 19. Hausswirth, C.; Mujika, I. Recovery for Performance in Sport; Human Kinetics: Leeds, UK, 2013. 20. Balk, Y.A.; De Jonge, J.; Oerlemans, W.G.M.; Geurts, S.A.E. Physical recovery, mental detachment, and sleep as predictors of injury and mental energy. J. Health Psychol. 2019, 24, 1828–1838. [CrossRef] Int. J. Environ. Res. Public Health 2020, 17, 1044 13 of 14 21. Sonnentag, S.; Geurts, S.A.E. Methodological issues in recovery research. In Current Perspectives on Job-Stress Recovery; Sonnentag, S., Perrewé, P.L., Ganster, D.C., Eds.; Emerald: Bingley, UK, 2009; pp. 1–36. 22. Sonnentag, S.; Fritz, C. The recovery experience questionnaire: Development and validation of a measure for assessing recuperation and unwinding from work. J. Occup. Health Psychol. 2007, 12, 204–221. [CrossRef] 23. Martin, E.M.; Horn, T.S. The role of athletic identity and passion in predicting burnout in adolescent female athletes. Sport Psychol. 2013, 27, 338–348. [CrossRef] 24. Vallerand, R.J. On passion for life activities: The Dualistic Model of Passion. In Advances in Experimental Social Psychology; Zanna, M.P., Ed.; Academic Press: New York, NY, USA, 2010; Volume 42, pp. 97–193. 25. Bélanger, J.J.; Lafrenière, M.K.; Vallerand, R.J.; Kruglanski, A.W. Driven by fear: The effect of success and failure information on passionate individuals’ performance. J. Pers. Soc. Psychol. 2012, 104, 126–147. [CrossRef] 26. Lalande, D.; Vallerand, R.J.; Lafrenière, M.A.K.; Verner-Filion, J.; Laurent, F.A.; Forest, J.; Paquet, Y. Obsessive passion: A compensatory response to unsatisfied needs. J. Personal. 2017, 85, 163–178. [CrossRef] 27. Rip, B.; Fortin, S.; Vallerand, R.J. The relationship between passion and injury in dance students. J. Danc. Med. Sci. 2006, 10, 14–20. 28. Schellenberg, B.J.I.; Bailis, D.S. Can passion be polyamorous? The impact of having multiple passions on subjective well-being and momentary emotions. J. Happiness Stud. 2015, 16, 1365–1381. [CrossRef] 29. Stenseng, F.; Forest, J.; Curran, T. Positive emotions in recreational sport activities: The role of passion and belongingness. J. Happiness Stud. 2015, 16, 1117–1129. [CrossRef] 30. Vallerand, R.J.; Paquet, Y.; Philippe, F.L.; Charest, J. On the role of passion in burnout: A process model. J. Personal. 2010, 78, 289–312. [CrossRef] [PubMed] 31. Caudroit, J.; Boiché, J.; Stephan, Y.; Le Scanff, C.; Trouilloud, D. Predictors of work/family interference and leisure-time physical activity among teachers: The role of passion towards work. Eur. J. Work Organ. Psychol. 2011, 20, 326–344. [CrossRef] 32. Akehurst, S.; Oliver, E.J. Obsessive passion: A dependency associated with injury-related risk behavior in dancers. J. Sport Sci. 2013, 32, 259–267. [CrossRef] 33. Stephan, Y.; Deroche, T.; Brewer, B.W.; Caudroit, J.; Le Scanff, C. Predictors of perceived susceptibility to sport-related injury among competitive runners: The role of previous experience, neuroticism, and passion for running. Appl. Psychol. Int. Rev. 2009, 58, 672–687. [CrossRef] 34. Van Poppel, D.; De Koning, J.; Verhagen, A.P.; Scholten-Peeters, G.G.M. Risk factors for lower extremity injuries among half marathon and marathon runners of the Lage Landen Marathon Eindhoven 2012: A prospective cohort study in the Netherlands. Scand. J. Med. Sci. Sports 2016, 26, 226–234. [CrossRef] 35. De Jonge, J.; Spoor, E.; Sonnentag, S.; Dormann, C.; Van den Tooren, M. “Take a break?!”: Off-job recovery, job demands and job resources as predictors of health, active learning, and creativity. Eur. J. Work Organ. Psychol. 2012, 21, 321–348. [CrossRef] 36. Balk, Y.A.; De Jonge, J.; Geurts, S.A.E.; Oerlemans, W.G.M. Antecedents and consequences of perceived autonomy support in elite sport: A diary study linking coaches’ recovery and athletes’ performance satisfaction. Psychol. Sport Exerc. 2019, 44, 26–34. [CrossRef] 37. Sonnentag, S.; Kruel, U. Psychological detachment from work during off-job time: The role of job stressors, job involvement, and recovery-related self-efficacy. Eur. J. Work Organ. Psychol. 2006, 15, 197–217. [CrossRef] 38. Yamato, T.P.; Saragiotto, B.T.; Lopes, A.D. A consensus definition of running-related injury in recreational runners: A modified Delphi approach. J. Orthop. Sports Phys. Ther. 2015, 45, 375–380. [CrossRef] [PubMed] 39. Theisen, D.; Malisoux, L.; Gette, P.; Nührenbörger, C.; Urhausen, A. Footwear and running-related injuries—Running on faith? Sports Orthop. Traumatol. 2016, 32, 169–176. [CrossRef] 40. Van Poppel, D.; Scholten-Peeters, G.G.M.; Van Middelkoop, M.; Koes, B.W.; Verhagen, A.P. Risk models for lower extremity injuries among short- and long distance runners: A prospective cohort study. Musculoskelet. Sci. Pract. 2018, 36, 48–53. [CrossRef] [PubMed] 41. Ten Brummelhuis, L.L.; Trougakos, J.P. The recovery potential of intrinsically versus extrinsically motivated off-job activities. J. Occup. Organ. Psychol. 2014, 87, 177–199. [CrossRef] 42. Nogueira, A.; Molinero, O.; Salguero, A.; Márquez, S. Exercise addiction in practitioners of endurance sports: A literature review. Front. Psychol. 2018, 9, 1484. [CrossRef] 43. Sachs, M.L.; Pargman, D. Running addiction. In Running as Therapy: An Integrated Approach; Sachs, M.L., Buffone, G.W., Eds.; University of Nebraska Press: Lincoln, NE, USA, 1984; pp. 231–252. Int. J. Environ. Res. Public Health 2020, 17, 1044 14 of 14 44. Hausenblas, H.A.; Downs, D.S. Exercise dependence: A systematic review. Psychol. Sport Exerc. 2002, 3, 89–123. [CrossRef] 45. Freimuth, M.; Moniz, S.; Kim, S.R. Clarifying exercise addiction: Differential diagnosis, co-occurring disorders, and phases of addiction. Int. J. Environ. Res. Public Health 2011, 8, 4069–4081. [CrossRef] 46. Landolfi, E. Exercise addiction. Sports Med. 2013, 43, 111–119. [CrossRef] 47. Martin, L.E.; Sisante, J.F.; Wilson, D.R.; Moody, A.A.; Savage, C.R.; Billinger, S.A. Pilot study of endurance runners and brain responses associated with delay discounting. Int. J. Exerc. Sci. 2017, 10, 690–701. 48. Carbonneau, N.; Vallerand, R.J.; Fernet, C.; Guay, F. The role of passion for teaching in intrapersonal and interpersonal outcomes. J. Educ. Psychol. 2008, 100, 977–987. [CrossRef] 49. Spector, P.E.; Rosen, C.C.; Richardson, H.A.; Williams, L.J.; Johnson, R.E. A new perspective on method variance: A measure-centric approach. J. Manag. 2019, 45, 855–880. [CrossRef] 50. Podsakoff, P.M.; MacKenzie, S.B.; Podsakoff, N.P. Sources of method bias in social science research and recommendations on how to control it. Ann. Rev. Psychol. 2012, 63, 539–569. [CrossRef] [PubMed] 51. De Jonge, J.; Peeters, M.C.W. The vital worker: Towards sustainable performance at work. Int. J. Environ. Res. Public Health 2019, 16, 910. [CrossRef] [PubMed] © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Mental Recovery and Running-Related Injuries in Recreational Runners: The Moderating Role of Passion for Running.

02-06-2020

de Jonge, Jan,Balk, Yannick A,Taris, Toon W

eng

PMC6195805

SDC Figure 2. Schematic representation of Study design Phase 1: Week 0-8 Phase 3: Week 11-12 3 x/week 3 x/week Overreaching Week vastus lateralis Biopsy Blood draw DXA Ultrasound Bioelectrical impedance 1-RM Strength test Wingate Vertical Jump Undulating periodized training Taper Phase 2: Week 9-10 5 x/week

Wrist-worn Accelerometry for Runners: Objective Quantification of Training Load.

[]

Stiles, Victoria H,Pearce, Matthew,Moore, Isabel S,Langford, Joss,Rowlands, Alex V

eng

PMC10002259

Citation: Drum, S.N.; Rappelt, L.; Held, S.; Donath, L. Effects of Trail Running versus Road Running—Effects on Neuromuscular and Endurance Performance—A Two Arm Randomized Controlled Study. Int. J. Environ. Res. Public Health 2023, 20, 4501. https://doi.org/10.3390/ ijerph20054501 Academic Editors: Joanna Baran and Justyna Leszczak Received: 27 December 2022 Revised: 22 February 2023 Accepted: 28 February 2023 Published: 3 March 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). International Journal of Environmental Research and Public Health Article Effects of Trail Running versus Road Running—Effects on Neuromuscular and Endurance Performance—A Two Arm Randomized Controlled Study Scott Nolan Drum 1,* , Ludwig Rappelt 2, Steffen Held 2 and Lars Donath 2 1 Department of Health Sciences—Fitness Wellness, College of Health and Human Services, Northern Arizona University, Flagstaff, AZ 86001, USA 2 Department of Intervention Research in Exercise Training, German Sport University Cologne, 50933 Cologne, Germany * Correspondence: [email protected]; Tel.: +1-970-371-2620 Abstract: Running on less predictable terrain has the potential to increase the stimulation of the neuromuscular system and can boost aerobic performance. Hence, the purpose of this study was to analyze the effects of trail versus road running on neuromuscular and endurance performance parameters in running novices. Twenty sedentary participants were randomly assigned to either a trail (TRAIL; n = 10) or road running (ROAD; n = 10) group. A supervised and progressive, moderate intensity, and work-load-matched 8 wk endurance running program on TRAIL or ROAD was prescribed (i.e., randomized). Static balance (BESS test), dynamic balance (Y-balance test), gait analysis (RehaGait test, with regard to stride time single task, stride length dual task, velocity single task), agility performance (t-test), isokinetic leg strength (BIODEX), and predicted VO2max were assessed in pre- and post-tests. rANOVA analysis revealed no significant time–group interactions. Large effect sizes (Cohen’s d) for pairwise comparison were found for TRAIL in the BESS test (d = 1.2) and predicted (pred) VO2max (d = 0.95). Moderate effects were evident for ROAD in BESS (d = 0.5), stride time single task (d = 0.52), and VO2max predicted (d = 0.53). Possible moderate to large effect sizes for stride length dual task (72%), velocity single task (64%), BESS test (60%), and the Y-balance test left stance (51%) in favor of TRAIL occurred. Collectively, the results suggested slightly more beneficial tendencies in favor of TRAIL. Additional research is needed to clearly elucidate differences between TRAIL and ROAD, not only in novices but also in experienced exercisers. Keywords: postural balance; gait; agility; muscle strength; long distance running; endurance training; running surface 1. Introduction Regular physical activity, such as running, enhances cardiorespiratory and neuromuscu- lar performance and is associated with a delay in all causes of mortality and morbidity [1–4]. Lee et al. [5] found that minimal running training volumes of 30–59 min a week, or 5–10 min a day are associated with lower risks of all-cause and cardiovascular mortality. Despite proven health benefits of physical exercise, the number of sedentary people worldwide is large and steadily growing [6–8] in both sexes and with increasing age [7,9]. Physical inactivity acceler- ates aging-induced functional decrements and compromises physical performance which can lead to impairments in activities of daily living [3,10,11]. At approximately 30 years of age, muscle mass and muscle strength begin to decrease gradually by 10–15% each decade [3]. Progressive skeletal muscle atrophy is accompanied by a loss in muscle coordination and a decline in balance [11], which can already be evident in individuals of ≥40 years of age [12]. Balance impairments and related spatiotemporal gait deficits both represent crucial risk factors for falls and fall-related injuries [13–15]. Falls and fall-related injuries as well as general health impairments not only occur in Int. J. Environ. Res. Public Health 2023, 20, 4501. https://doi.org/10.3390/ijerph20054501 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2023, 20, 4501 2 of 14 the elderly but are a frequent problem in middle-aged and young people [16,17]. Few studies have investigated falls and the frequencies of falls in young and middle-aged individuals [16]. In a longitudinal study by Niino et al. [17], the prevalence of falls among middle-aged individuals (40–59 years) was 12.9%, compared to 16.5% among the elderly group (60–79 years). Talbot et al. [16] observed a prevalence of one or more reported falls within a two-year period in 18.5% of young adults, 21% of middle-aged adults and 35% of older adults. In addition to the direct consequences of falls, many people develop a fear of falling after such an event which often leads to a vicious cycle of reduced physical activity, decreased mobility and muscle strength, and a subsequent higher risk for future falls [14,18,19]. To refute the natural decline in neuromuscular properties with aging and augment spontaneous balance and maintenance of strength, our main study objective was to de- termine the effectiveness of exercising on uneven surfaces (i.e., dirt trails) vs. familiar (or predictably even road) surfaces in a younger adult population on the prior mentioned variables (e.g., neuromuscular or gait training, balance, strength). For instance, running has been shown to improve or amplify several task-specific, metabolic, and neuromuscular factors [20]. However, few studies have focused on neuromuscular variables (e.g., gait parameters via a wearable gait analysis system) resulting from endurance training on distinctly different surfaces [20]. As a suggestion, future researchers should theoretically look at the protective effects of frequent running on uneven surfaces related to unexpected falls, especially in the elderly. Ultimately, the impact of trail running, which is attracting an increasing number of recreational and competitive runners [21,22], compared to road running, has not been extensively compared. In the present project, we hypothesized trail running would lead to more pronounced improvements in neuromuscular and endurance performance than road running. These assumptions are based on the different characteristics of surface type and gradients between the two conditions. Trail running tends to invoke higher challenges for the neuromus- cular system, especially regarding involved muscle coordination, proprioception, and activation [23–26] compared to road running. Furthermore, since uphill running is an effective stimulus for improving endurance running performance [27,28] and submaximal running economy [27,29] we expected a more pronounced performance at posttest in the submaximal incremental treadmill test for TRAIL. 2. Materials and Methods 2.1. Participants and Experimental Setting This pilot study adheres to CONSORT guidelines [30]. Participants were recruited via flyers, posters, word-of-mouth, and local advertisement as well as via “batch” emails among faculty and staff at the university where the project was conducted. Inclusion criteria [31] for participation were: (i) 18–59 years of age; (ii) currently sedentary or not exercising more than twice a week for the last three months; (iii) free from any injury or illness and currently no intake of any medication; (iv) and non-smoker. Importantly, according to ACSM, sedentary, healthy (e.g., free of disease, non-smoker, uninjured) individuals will showcase a greater physiological change from pre to post exercise intervention. To ensure that participants met the inclusion criteria, all subjects were asked to complete several physical activity questionnaires. The questionnaires included: (a) International Physi- cal Activity Questionnaire—Short Form (IPAQ-SF) [32], (b) the Physical Activity Readiness Questionnaire (PAR-Q&YOU) [33], and the (c) American College of Sports Medicine (ACSM) Risk Stratification [31] to assess individual current health and activity levels. If a participant reported two risk factors related to cardiovascular diseases, he/she had to consult a physician for medical clearance to participate in moderate to vigorous exercise. The study was conducted according to the Code of Ethics for Human Experimentation of the World Medical Association and the Declaration of Helsinki [34]. Participants were informed in detail about the design of the study, including the potential risks and benefits of included procedures, before providing their informed written consent to participate. The study protocol was approved by the Institutional Review Board of the Northern Michigan Int. J. Environ. Res. Public Health 2023, 20, 4501 3 of 14 University (Trial registration number: ID Proposal Number HS16-786; Date of registration: September, 2017). Participants were anonymously assigned by the researcher via simple randomization using a random number generator to either TRAIL (n = 20) or ROAD (n = 19) and entered into an endurance exercise program. The program consisted of 8 weeks of gradually increasing running workouts with a total amount of 29 training sessions. This randomized controlled pilot trial compared two training groups (i.e., TRAIL vs. ROAD) in terms of balance, gait, agility, along with strength and endurance performance measures in a pre- and post-intervention testing format. Participants in the TRAIL group ran outdoors on uneven and soft trails with varying gradients and under-foot terrain (e.g., rocks, roots, more consistent undulating routes). Participants from the ROAD group ran on predictable terrain or roads with asphalt, concrete or paved surfaces exhibiting no or infrequent gradients. An adherence rate of a minimum of 80% (24 runs) was required for inclusion in the final analysis. To confirm, a total of 39 healthy adults were initially assigned, whereof 6 subjects did not start the program; 5 participants dropped out during the intervention due to injuries; 3 participants did not meet the required 80% adherence rate and 1 participant was not available for post testing. Additionally, 2 participants (i.e., “4” total) from each group were excluded from analysis due to other exclusion criteria—not following the prescribed training load and for participating in additional training during the period of the study. Then end total of analyzed participants equaled 20. Demographic data at baseline for all participants who received the allocated interven- tion are depicted in Table 1. Table 1. Demographic data at baseline. TRAIL 1 (n = 10, 6 fem) ROAD 1 (n = 10, 7 fem) Total (n = 20) Female/male (n) 6/4 7/3 13/7 Age (years) 33.2 ± 6.8 29 ± 10.5 31.3 ± 8.8 Height (cm) 171.1 ± 8.0 170.9 ± 6.6 171 ± 7.3 Weight (kg) 77.4 ± 17.6 74.5 ± 15.6 76.1 ± 16.5 BMI (kg/m2) 26.2 ± 4.1 25.4 ± 4.5 25.8 ± 4.3 Physical Activity (min/week) 1904.8 ± 957.5 2105.3 ± 1679.5 2000.5 ± 1445.6 1 Values are mean (±SD). TRAIL = trail running group. ROAD = road running group. 2.2. Experimental Design Qualifying participants were asked to report to an Exercise Science Laboratory for pre- and post-intervention testing. Post-testing sessions were scheduled at a similar time of the day as pre-testing and within a week upon completion of the training program in November and December 2017, depending on pre-testing dates. Testing order, as well as the examiner were kept constant for each participant. Finally, ten participants in each group were included in the statistical analysis. The study flow is depicted following the CONSORT criteria, which is easily referenced [30]. Notably, 10 participants in each group provided significant differences (alpha error proba- bility: 0.05) and notable study power (i.e., 1-beta error probability: 0.9) when moderate to large effects size differences between group were presumed for balance performance as the primary outcome. Lastly, mandatory running meetings were held twice a week and coaching appoint- ments were scheduled as required. Furthermore, participants were contacted by email or phone once a week for feedback. As an additional motivation, a final joint 5k running event was held upon completion of the intervention. Int. J. Environ. Res. Public Health 2023, 20, 4501 4 of 14 2.3. Heart Rate and Blood Pressure Prior to baseline testing, a blood pressure cuff (Adcuff™, Hauppauge, NY, USA) and stethoscope (Littmann, St. Paul, MN, USA) were employed for blood pressure measures; then, pre-exercise resting heart rate (Polar monitor and watch, Lake Success, NY, USA), as well as body height (Seca stadiometer, Chino, CA, USA) and weight (Health O Meter scale, Mccook, IL, USA) were measured. Maximal heart rate (HRmax) in beats per minute (bpm) was predicted using the following formula according to Tanaka et al. [35]: 207—(age × 0.7) for men and 206—(age × 0.88) for women. The lateral preference inventory for measurements of footedness [36] was used to evaluate leg dominance. Limb length was measured from the umbilicus to the medial malleolus of the right leg using a tape measure [37]. Blood pressure, pre-exercise resting heart rate, as well as body height and weight measurements were repeated before post-testing as well. 2.4. Warm-Up Warm-up consisted of walking on a treadmill for 5 min at a rate of perceived exertion (RPE) of 3 on the Borg CR-10 scale [38], followed by dynamic stretching and muscle activation (Knee Hug to Forward Lunge–Elbow to Instep, Heel to Butt Moving Forward with Arm Reach, Handwalk, Lateral Squat Low). 2.5. Static Balance Testing Static balance was tested with the Balance Error Scoring System (BESS) [39], which evaluates 3 stance variations in the following order: (1) double leg, (2) single leg, and (3) tandem or feet in line with one another. The test takes place on 2 different surfaces, starting on firm for all “3” conditions and ending on foam for all “3” conditions while wearing no shoes. Each trial lasts 20 s, during which the number of deviations from the proper testing position were counted. Deviations from the proper testing position in the BESS test are: (a) moving hands off the hips; (b) opening the eyes; (c) step, stumble or fall; (d) abduction or flexion of the hip beyond 30◦; (e) lifting the forefoot or the heel off of the testing surface; and (f) remaining out of proper testing position for more than 5 s. Proper position consists of the hands on the iliac crest, eyes closed, and consistent foot position. For the double leg stance, feet need to touch and remain flat on the testing surface. For the single leg stance, the participant stands on the non-dominant leg with the other leg held in approximately 20◦ of hip flexion, 45◦ of knee flexion, and neutral position in the frontal plane. For the tandem stance, one foot is placed in front of the other with the heel of the anterior foot touching the toes of the posterior foot, and the non-dominant leg in the posterior position. The maximum amount of errors for any single condition was set at 10. If multiple errors were committed simultaneously, only one was recorded. To improve reliability, the test was repeated 3 times by the same examiner [39] and the mean score of the three trials was calculated for final analysis. 2.6. Dynamic Balance Testing The Y Balance test (YBT) was performed to evaluate dynamic postural stability and functional symmetry during single leg stance in three (anterior, posteromedial, posterolateral) directions [40]. In a Y pattern, each posterior line was marked with tape 135◦ from the anterior line and 90◦ apart from one another. Subjects performed a practice trial followed by three test trials for each direction and each leg and were instructed to reach as far as possible, thereby pushing a pen held by the examiner to mark the reaching distance. The testing order started with standing on the left foot and reaching in the anterior direction followed by the trials standing on the right foot for the same direction. This procedure was repeated for all directions. Trials were considered invalid and were repeated if the participant either made a heavy touch or rested the reaching foot on the ground, could not return in a controlled way to the starting position, raised or moved the stance foot, or kicked the marker with the Int. J. Environ. Res. Public Health 2023, 20, 4501 5 of 14 reaching foot to gain more distance [40]. Results were calculated as a composite score with the help of following formula: (((anterior length + posteromedial length + posterolateral length)/3 × leg length) × 100). (1) 2.7. Gait Analysis Spatiotemporal gait parameters (stride time [s], stride length [m], and stride velocity [m/s]) were measured during 20 m (65.6 feet) of level walking at self-selected habitual walking speed by using the portable gait analysis system RehaGait® (Hasomed GmbH, Magdeburg, Germany). The RehaGait® system consists of two mobile sensors which are attached to the lateral part of each shoe to measure linear acceleration, angular velocity, and the magnetic field of the foot at a sampling rate of 500 Hz [41]. Each participant performed a familiarization trial followed by 2 trials with single task condition and 2 trials with dual task condition. For dual task trials, participants were asked to perform a double-digit subtraction task while walking. The combination of gait analysis with cognitive interference tasks was applied to distract participants and limit the cognitive resources for gait control. The mean score for each condition was included in further analysis. For all trials, the phases of gait initiation and deceleration at the end of the walkway were excluded from analysis. For both pre- and post-testing, participants were wearing their running shoes. 2.8. Agility Testing The t-test evaluates the subjects’ agility, leg power and leg speed [42]. Four cones are set out in a T pattern. The test starts at the first cone with a forward sprint of 9.14 m to the second cone, continues with shuffling sideways for 4.57 m to another cone on the right, then 9.14 m to the one on the left, and again 4.57 m back to the middle, before ultimately running backwards 9.14 m to return to the starting point. The base of the cone always has to be touched with the hand further away from the cone when performing the test. The fastest out of 3 trials was used for analysis. 2.9. Strength Testing Unilateral isokinetic concentric leg strength was assessed for the dominant leg using the BIODEX Multi-Joint System 4 Pro (Biodex Medical Systems, New York, NY, USA). Knee extension and flexion as well as ankle plantar- and dorsi-flexion were tested for peak torque (PT) and total work (TW). Subjects were seated with chair and dynamometer position at 90◦ and the dynamometer positioned outside the testing leg. The anatomical axis rotation (lateral femoral condyle on a sagittal plane for the knee and through the body of talus, fibular malleolus, and tibial malleolus for the ankle) was in alignment with the dynamometer shaft for both knee and ankle attachment, ensuring that the testing pattern was consistent with the proper biomechanics of the joint. Body parts on either side of the tested joint were firmly secured with straps, in order to restrict motion as much as possible to the area of interest. Range of motion was set for each subject and joint individually. After a 12-repetition warm-up trial at 180◦/s and low effort, participants performed two sets of 5 repetitions at 60◦/s and maximal effort with a 60 s break between sets. 2.10. Aerobic Endurance Testing Oxygen consumption was measured by indirect calorimetry on a treadmill during the walking-based Pepper protocol [43] with the Parvo Medics TrueOne 2400 automated gas analysis system (Sandy, UT, USA). The Pepper protocol is an incremental submaximal test starting at an inclination of 0% and a velocity of 2.5 mi (4 km) per hour. Intensity increases each minute by elevating either inclination or velocity. The test is ended at 85% of predicted HRmax [35]. Gas exchange variables (VO2 and VCO2, RER), RPE on the Borg CR-10 scale [38] and HR were monitored and averaged to 15s time-intervals. Finally, maximal oxygen consumption (VO2max) was predicted from the highest value recorded at HR85% using the formula VO2max pred = VO2max at HR 85%/85 × 100. Prediction was used to minimize cardiovascular risk of pushing to maximum in this mixed age group (2). Int. J. Environ. Res. Public Health 2023, 20, 4501 6 of 14 2.11. Training Program The training program started with 3 training sessions per week in weeks 1–3. Each training session had a duration of 22–36 min (which was the standard range throughout most of the 8 wk intervention) of running interspersed with 2 min walk rest periods. Novice participants progressed to 4 running (with prescribed intermittent walking) sessions per week in weeks 4–6 with 2-min walk breaks before gradually omitting the walk breaks in week 7 and finishing the program at the end of week 8 with a 45 min continuous run (i.e., their 4th run of week 8). Exercise training started for each participant after the pretest and was performed individually on self-selected outdoor trails (i.e., TRAIL) and roads (i.e., ROAD) at a perceived exertion of 3–4 on Borg CR-10 (although the average RPE approached “5” for both groups upon end analysis). Each participant was provided with a running log in which they recorded training duration, perceived exertion levels, location, and estimated percentage of each session on TRAIL or ROAD. Actual training loads for both groups are summarized in Table 2. Table 2. Training load of trail and road physical activity interventions. Values are mean ± SD. Training Load TRAIL (n = 10, 6 fem) ROAD (n = 10, 7 fem) Total (n = 20, 13 fem) Weeks (n) 9.0 (0.7) 9.6 (0.8) 9.3 (0.8) Trainings (n) 26.5 (1.7) 27.9 (2.6) 27.2 (2.3) Sessions/week (n) 3.0 (0.3) 2.9 (0.4) 2.9 (0.3) Time/session (min) 35.4 (1.7) 34 (1.6) 34.7 (1.8) Intensity/session (RPE) 4.9 (1.1) 4.6 (0.8) 4.8 (1) Total Training Time (min) 938.4 (68.3) 946.7 (82.9) 942.5 (74.8) 2.12. Statistical Analysis Group means of all variables for all pre- and post-test data were calculated based on individual test scores in order to compare changes between groups. All data are presented as means with standard deviations (SD). Data analysis was computed using the statistical software program SPSS for Windows V.14.0 (SPSS Inc., Chicago, IL, USA). After adjustment for baseline scores (note, baseline values were added as covariate in order to adjust for potential baseline differences), repeated-measures ANOVA procedures were conducted to determine significant between-group differences. Group (TRAIL and ROAD) served as the between-subject factor, and time (pre- and post-test) as the within-subject factor. Statistical significance level was set at p < 0.05. Because of the small sample size, partial eta squared (ηp2) and Cohen’s d (d < 0.2 = trivial effect; d ≥ 0.2 = small effect; d ≥ 0.5 = moderate effect; d ≥ 0.8 = large effect), as the standardized mean difference, were calculated to estimate effect sizes from pre- to post-testing for all ANOVAs. The probability for an effect being practically worthwhile in favor of either TRAIL or ROAD was calculated accord- ing to the magnitude-based inference (MBI) approach (25–75%, possibly; 75–95%, likely; 95–99.5%, very likely; >99.5%, most likely) using the Hopkins [44] spreadsheet for analysis of controlled trials with adjustments for a predictor in Microsoft® excel. 3. Results In review, of the 33 subjects that received the allocated intervention, 5 people (4 in TRAIL; 1 in ROAD) ended the program prematurely due to injuries and/or pain. A total of 3 people (2 in TRAIL; 1 in ROAD) did not meet the required attendance rate and 1 person from ROAD never reported to the post-testing. Two more subjects of each group were excluded from further evaluation based on exclusion criteria (age, amount of previous physical activity, adherence rate, ≤2 risk factors according to the ACSM Risk Stratification). A total of 10 participants from each group were included in the final analysis. Higher baseline test scores and differences between the two groups were seen for leg strength in knee flexion PT (19.9% higher in TRAIL) and ankle plantar flexion PT (18.5% higher in TRAIL), and for VO2max pred (24.3% higher in ROAD). Int. J. Environ. Res. Public Health 2023, 20, 4501 7 of 14 The mean overall attendance rate for the intervention was 93.8% or 27.2 ± 2.3 out of 29 total trainings; 91.4% (26.5 ± 1.7) for TRAIL and 96.2% (27.9 ± 2.6) for ROAD. 3.1. Static and Dynamic Balance The repeated-measures ANOVA revealed no statistically significant differences be- tween groups for any balance measures. However, for the BESS test, a significant time-effect between pre-and post-testing was noted (p = 0.001, ηp2 = 0.46) and large and moderate effect sizes according to Cohen’s d for TRAIL (d = 1.2) and ROAD (d = 0.5), respectively. Results for static and dynamic balance testing are presented in Table 3. Table 3. Effect on balance of an 8-week trail and road running training intervention. TRAIL ROAD rANOVA TEST Pre-Test Post-Test Cohen’s D Pre-Test Post-Test Cohen’s d Time ηp2 Group × Time ηp2 BESS 12.4 (2.7) 9.5 (2.3) 1.2 11.6 (3.9) 10.0 (2.8) 0.5 p = 0.001 0.46 p = 0.38 0.05 YBT left 94 (8.2) 95.9 (7.7) 0.25 94.4 (7.3) 94.7 (7.2) 0.04 p = 0.19 0.10 p = 0.31 0.06 YBT right 93.8 (8.5) 96.4 (8.2) 0.31 92.4 (8.1) 93.8 (7.4) 0.18 p = 0.15 0.12 p = 0.3 0.06 Values are mean (±SD); statistical significance level is set at p < 0.05. 3.2. Gait The spatiotemporal gait analysis rANOVA showed no notable improvements over time in any parameter for either TRAIL or ROAD, as displayed in Table 4. According to Cohen’s d, a moderate effect size for stride time ST in ROAD (d = 0.52) as well as small effects for velocity DT in TRAIL (d = 0.32), velocity ST in ROAD (d = 0.23), and for stride time DT in both groups (d = 0.43 in TRAIL; d = 0.45 in ROAD) were calculated. Table 4. Effect on spatio-temporal gait characteristics. TRAIL ROAD rANOVA Pre-Test Post-Test Cohen’s d Pre-Test Post-Test Cohen’s d Time ηp2 Group × Time ηp2 Stride time [s] ST 1.1 (0.1) 1.1 (0.1) 0.01 1.1 (0.1) 1.1 (0.1) 0.52 p = 0.7 0.009 p = 0.37 0.05 DT 1.2 (0.1) 1.2 (0.1) 0.43 1.2 (0.1) 1.2 (0.1) 0.45 p = 0.89 0.001 p = 0.35 0.05 Stride length [m] ST 1.4 (0.1) 1.4 (0.1) −0.09 1.4 (0.1) 1.4 (0.1) 0.09 p = 0.65 0.01 p = 0.35 0.05 DT 1.3 (0.1) 1.3 (0.1) 0.19 1.3 (0.1) 1.3 (0.1) −0.17 p = 0.84 0.002 p = 0.37 0.05 Velocity [m/s] ST 1.3 (0.2) 1.3 (0.2) −0.06 1.3 (0.2) 1.4 (0.2) 0.23 p = 0.8 0.006 p = 0.34 0.05 DT 1.1 (0.2) 1.2 (0.2) 0.32 1.2 (0.2) 1.2 (0.2) 0.06 p = 0.45 0.03 p = 0.3 0.06 Values are mean (±SD); ST, single task condition; DT, dual task condition; statistical significance level is set at p < 0.05. 3.3. Agility Both groups improved their t-test performance by 4.6% (TRAIL) and 6.8% (ROAD), respectively. Yet, no significant change over time or between groups was observed. Effects from the intervention on agility are shown in Table 5. Table 5. Effect on agility. TRAIL ROAD rANOVA Agility Pre-test Post-test Cohen’s d Pre-test Post-test Cohen’s d time ηp2 group × time ηp2 t-test [s] 15.6 (3.2) 14.9 (2.4) 0.26 15.1 (3.2) 14.1 (2.4) 0.36 p = 0 0.69 p = 0.15 0.12 Values are mean (±SD); significance level is set at p < 0.05. 3.4. Strength Gains in isokinetic concentric leg strength were only recorded in knee extension TW (8.2%) and knee flexion TW (11.8%) for TRAIL, and knee extension TW (1.6%) as well as ankle dorsi flexion TW (1.9%) for ROAD. Thereof, only knee flexion TW in favor of TRAIL resulted in a close to significant between-group difference over time (p = 0.06; ηp2 = 0.19; d = 0.25). This finding was reinforced by a 76% likely probability of a substantial worthwhile effect according to the MBI approach. A significant negative time-effect in ankle plantar Int. J. Environ. Res. Public Health 2023, 20, 4501 8 of 14 flexion PT (p = 0.02; ηp2 = 0.29) was recorded for ROAD. All other strength measures showed small declines between pre- and post-testing, as shown in Table 6. Table 6. Effect on strength. TRAIL ROAD rANOVA Pre-Test Post-Test Cohen’s d Pre-Test Post-Test Cohen’s d Time ηp2 Group × Time ηp2 Knee KE PT (Nm) 175.5 (74.6) 172.7 (68) −0.04 163.9 (55.5) 154.3 (49.1) −0.18 p = 0.06 0.2 p = 0.37 0.05 KF PT (Nm) 96.3 (41) 91.6 (37.6) −0.12 80.3 (25.7) 77.0 (21.9) −0.14 p = 0.06 0.2 p = 0.77 0.005 KE TW (J) 870.9 (419.4) 945.3 (406.3) 0.18 833.9 (283.8) 847.7 (303.6) 0.05 p = 0.56 0.02 p = 0.21 0.09 KF TW (J) 487.2 (245.4) 548.3 (244.8) 0.25 447.8 (151.3) 446 (164.5) −0.01 p = 0.67 0.01 p = 0.06 0.19 Ankle PF PT (Nm) 60.2 (32.3) 55.8 (26.5) −0.15 50.8 (18.1) 45 (16.5) −0.33 p = 0.02 0.29 p = 0.49 0.03 DF PT (Nm) 25.5 (7.4) 24.1 (7.7) −0.18 24.2 (6) 23.7 (5.8) −0.09 p = 0.5 0.03 p = 0.5 0.03 PF TW (J) 167 (93.1) 166.6 (86.5) −0.004 140.6 (54.1) 134.7 (55.8) −0.106 p = 0.14 0.12 p = 0.57 0.02 DF TW (J) 104.6 (28.3) 97.6 (31.6) −0.24 94.8 (20.3) 96.6 (26.4) 0.07 p = 0.9 0.001 p = 0.28 0.07 Values are mean (±SD); KE, knee extension; KF, knee flexion; PF, plantar flexion; DF, dorsi flexion; PT, peak torque; TW, total work; i.f.o., in favor of; statistical significance level is set at p < 0.05. 3.5. VO2max The results of the aerobic endurance testing (VO2max pred) show the greatest proba- bility for a substantial beneficial effect between pre- and post-testing with 97% in favor of TRAIL. These findings are supported by the calculated Cohen’s d effect sizes (d = 0.95 in TRAIL; d = 0.53 in ROAD). Time-effect (p = 0.14) and between-group differences (p = 0.13) did not reach statistical significance. Results for VO2max pred are depicted in Table 7. Table 7. Effect on VO2max of an 8-week trail and road running training intervention. TRAIL ROAD rANOVA Pre-test Post-test Cohen’s d Pre-test Post-test Cohen’s d time ηp2 group × time ηp2 pred. VO2max 28.4 (6) 35.8 (9.2) 0.95 35.3 (8.8) 40.5 (10.4) 0.53 p = 0.14 0.12 p = 0.13 0.13 Values are mean (±SD); i.f.o., in favor of; statistical significance level is set at p < 0.05. 4. Discussion This is the first study that comparatively investigated the impact of trail running versus road running on neuromuscular performance parameters in healthy adults. We hypothesized that running on natural trails would lead to more pronounced improvements in static and dynamic balance, gait patterns, agility, and leg strength between pre- and post-testing compared to road running. This assumption was based on previous findings which have shown that the navigation of the body on varying surface densities, inclines and speeds evoked higher muscle activation and coordination as opposed to moving on more firm and flat terrain [23–26,45,46]. Greater physiological strain on softer terrain is associated with a greater degree of energy absorption by the training surface that results in a loss of elastic energy, followed by greater concentric work and overload stimulus in the lower-limb muscles [26,45]. Against this background, we expected gains in concentric quadriceps and hamstring muscle strength as well as in ankle strength and stability in favor of TRAIL from navigating in uneven terrain. However, according to the BIODEX isokinetic concentric leg strength testing, knee flexion TW was the only parameter that resulted in close to significant improvements. On the other hand, for ankle dorsi flexion PT, a significant negative time-effect was recorded. A possible explanation for this decrease could be found in a reduction in ankle work and range of motion that has been seen when running on uneven and unpredictable terrain in order to stabilize the joint [26]. The fact that all other strength measures showed small declines between pre- and post-testing might be attributed to fatigue as a result of the newly increased exercise routine. It is also possible that the reduced strength outcomes especially for PT values are a consequence of endurance training-specific adaptations. When interpreting leg strength results, baseline differences Int. J. Environ. Res. Public Health 2023, 20, 4501 9 of 14 and high standard deviations in both groups should be taken into account. Especially in TRAIL, large discrepancies in strength scores among subjects in pre- and post-testing were observed. Another factor that added to these inconsistencies is the fact that most participants from both groups had no experience in resistance training, much less with the applied strength-testing device. The lack of experience might have influenced the test performances. We found no statistically significant differences in the rANOVA analysis between TRAIL and ROAD for static and dynamic balance measures. But a significant time-effect between pre-and posttest was calculated (p = 0.001, ηp2 = 0.46) for the BESS test. In addition, large (d = 1.2) and moderate (d = 0.5) effect sizes for Cohen’s d for TRAIL and ROAD respectively indicate potential balance improvements from running, especially on trails. In a review on sports participation and balance performance, Hrysomallis et al. [47] stated that athletes generally have a superior balance ability compared to control subjects as a result of repetitive experience and improved motor responses to proprioceptive and visual cues. Additionally, the same authors observed improved coordination, strength and range of motion. However, it remains unclear whether proprioception can actually be improved by exercise or if athletes just become more skilled at reacting to sensory cues. In a study on functional fitness gains through various types of exercise in older adults, Takeshima et al. [48] reported improvements in dynamic balance (functional reach test) in all intervention groups (balance, aerobic, and resistance training). They also predicted that training on unstable surfaces not only leads to improvements in balance but also in lower-body strength due to greater muscle activation when counteracting increased sway following unexpected perturbations. A few other studies report improvements in locomotion in older adults after aerobic training interventions involving walking, treadmill walking, jogging, and step aerobics [19]. The results of the BESS test in this pilot study support previous findings that physical exercise, specifically running, may have a positive influence on balance. Nevertheless, benefits from running for dynamic and functional balance could not be proven with the administered tests for the lack of significant results in the Y-Balance test and gait analysis. Despite the close relationship between balance and gait performance in regards to fall- and injury-risk factors [14,19,26,47–49], the spatiotemporal gait analysis in this study showed no notable characteristics or changes in any parameter for either TRAIL or ROAD. rANOVA, Cohen’s d, as well as MBI calculations show inconsistent results and no conclu- sions can be drawn about the influence of trail or road running on gait stability. Likewise, no statistically significant differences for time or between groups were recorded in agility performances. Nevertheless, most participants achieved faster T-test times after the in- tervention and demonstrated noticeably increased confidence and security levels in their sprint performances. Increased confidence levels and sprint ability might result in an over- all increased gait stability and thus reduce fall risk. When discussing the lack of evidence for gait and agility in this study, testing devices and procedures need to be considered. More task-specific trials might elicit more pronounced changes. Aerobic endurance testing showed the highest probability for a substantial worthwhile effect in favor of TRAIL (97%, very likely) together with a large Cohen’s d effect size (d = 0.95). Relative VO2max outcomes from the gas analysis test improved by 23.1% and 13.7% from pre- to post-testing for TRAIL and ROAD, respectively. Still, time-effect (p = 0.14; ηp2 = 0.12) and between-group differences (p = 0.13; ηp2 = 0.13) did not reach statistical significance. Moreover, big baseline differences (24.3% higher in ROAD) need to be considered when interpreting predicted maximal oxygen consumption. Lower baseline values in TRAIL might have facilitated the larger responses to the training intervention in that group. Even so, it is probable that trail running may elicit greater benefits for cardiovascular fitness. Several studies [26,50–53] documented that running on natural surfaces such as irregular trails required a higher energy expenditure and metabolic cost, which translated to a higher training intensity and higher aerobic training adaptations. However, recorded RPE from the running logs revealed no group differences (4.6 ± 1.1 for Int. J. Environ. Res. Public Health 2023, 20, 4501 10 of 14 TRAIL; 4.9 ± 0.8 for ROAD), an interesting finding if greater energy expenditure is realized on TRAIL versus ROAD without a concurrent rise in RPE. Therefore, TRAIL could be a strategy or modality for advanced energy output and weight loss, leading to better motor control at a lower perceived exertion. To date, a lot of research regarding neuromuscular adaptations from running has focused on different types of footwear or foot strike patterns and related kinematic, metabolic, and biomechanical parameters of the lower limb, as well as running-related injuries [20,24,54]. Various research groups examined the effects of training on different outdoor terrain, mainly focused on grass or sand surface [23,45,55–57], or defined trail running as an ultra-endurance activity. In this understanding, Easthope et al. [58] analyzed performance levels between young and older master runners in a 55-km ultra-endurance trail run. They observed equal performances in both groups despite structural and func- tional age-related alterations and confirmed that the decline in physical performance can be prevented with regular endurance training such as running. In a study that compared the different effects of concrete road, synthetic track, and woodchip trail on dynamic stability and loading in runners, Schütte et al. [22] revealed significant performance differences from a biomechanical perspective. Running on woodchip trail altered measures of dynamic stability and lower-limb musculature compared to running on concrete road due to com- pression and displacement of the woodchips under the foot causing destabilization and directional shift with each stride. Similarly, Boey et al. [59] looked at running on concrete, synthetic running track, grass, and woodchip trail at two different speeds and the different vertical impacts on the lower leg. Their results showed that running on woodchip trails and at a slower speed, reduced the injury risk at the tibia. Running related injuries (RRI) of the lower extremities are a common negative side effect in runners [60,61]. The prevalence is usually higher for overuse musculoskeletal injuries than for acute injuries [21,60,62]. There is a large heterogeneity of injuries that originates from different methods and definitions when evaluating RRI [21,60]. Among the most commonly reported RRIs in the literature are to the Achilles tendon, plantar fascia, calf muscle, knee, meniscus, shin, foot, ankle, hip/pelvis, lower back, hamstring, and thigh [21,60,63,64]. Risk factors for RRI appear to be previous injuries to the same anatomical area, high training loads and little running experience [64,65]. In the current study, 5 out of 33 people reported an injury during the 8-week interven- tion that prevented them from completing the training program. Affected body sites and type of injuries are all in line with the formerly reported common injury types and risk factors. Two participants from TRAIL developed reoccurring overuse injuries (i.e., knee and lower back) that had probably not been fully and appropriately cured. The other par- ticipants suffered from tibial stress syndrome (1 in TRAIL) and ankle sprains (1 in TRAIL; 1 in ROAD). The recorded amount and type of injuries in this study seem to reinforce the fact that previous injuries, little running experience, and an increase in training load within a relatively short amount of time may be risk factors for RRI. Meanwhile, as stated by Taunton et al. [64], previous activity, cross-training and running surface appear to be non-significant injury risk factors for either gender. Interestingly, 4 of the 5 injured subjects in this study were part of the trail running group, which contrarily seems to imply a connection between surface and injury prevalence. Trail running might be more strenuous for physiological parameters due to its specific surface characteristics and the resulting challenges for involved muscle groups and the metabolic system. Therefore, running on natural and more compliant trails may be more likely to cause overuse injuries in an untrained population. Despite the mentioned risk factors, authors agree that health benefits from running outweigh the related risks and costs of RRI [21]. Limitations to this study are the small group sizes and baseline differences between groups in VO2max pred and certain strength parameters, as well as the fact that the running intervention itself was not supervised and subjects performed most of the training units individually. Consequently, even though participants were instructed to exercise at a com- fortable, moderate to somewhat hard intensity (3–4 on the Borg CR-10 scale), it is possible Int. J. Environ. Res. Public Health 2023, 20, 4501 11 of 14 that some trained at intensities that were too high for their level of fitness. Additionally, the program was based on running time and not distance, which may have resulted in a differ- ent training volume dependent on different training pace among individuals. The training log was a way of controlling for these interferences. Regarding adherence, a slightly lower attendance rate in the trail running group was expected since trails require more effort and planning to access and may become impassable in bad weather or darkness. As a final point, MBI’s should be interpreted carefully, especially implications drawn from them, and one should be mindful of how the performed tests may have related to the intervention. 5. Conclusions The results of this training intervention show no statistically significant between-group differences. This suggests that benefits derived from running on uneven and soft natural terrain as opposed to a more flat and concrete road surface in respect to static and dynamic balance, gait, agility, and lower limb strength should not be overrated. Based on current knowledge and the outcomes of this study, no well-founded recommendations for an integrative training approach in regard to trail running and the prevention of falls and fall-related injuries can be given. More research is needed on the influence of running on trails or similar natural surfaces on different neuromuscular performance parameters. Nevertheless, the findings of this intervention indicate slightly more beneficial ten- dencies for balance and leg strength improvements when running on trails as opposed to road; and, therefore, potential benefits for the prevention of falls and fall-related injuries. While a significant time-effect between pre- and post-testing in static balance was recorded for both groups (p = 0.001, ηp2 = 0.46), the trail running group also showed large effect sizes (d = 1.2) for static balance, compared to only moderate effect sizes (d = 0.5) in the road running group. Trail running also seems to have positive impacts on upper leg strength performance, which is indicated by gains in knee extension (8.2%) and flexion (11.8%) total work and a close to significant between-group difference over time (p = 0.06; ηp2 = 0.19; d = 0.25) in knee flexion TW. For more detailed and specific results, future studies should target larger group sizes of recreational runners within smaller age ranges and in a longitudinal approach over a longer time period. Moreover, the scope of the intervention should be limited to one particular neuromuscular parameter. Thereby, the combined effects for cardiovascular and neuromuscular performance factors from running on different surfaces might be disen- tangled more clearly. Finally, repeating this study in an older, untrained population and tracking at-home falls throughout a pre-determined follow-up period (e.g., over 5-years) post intervention could yield more precise commentary regarding TRAIL’s effectiveness in or lack of promoting better neuromuscular coordination. Author Contributions: Conceptualization, S.N.D. and L.D.; methodology, S.N.D. and L.D.; software, L.D.; validation, L.D.; formal analysis, L.D.; investigation, S.N.D. and L.D.; resources, S.N.D., L.R., S.H. and L.D.; data curation, S.N.D., L.R., S.H. and L.D.; writing—original draft preparation, S.N.D., L.R., S.H. and L.D.; writing—review and editing, S.N.D., L.R., S.H. and L.D.; visualization, S.N.D. and L.D.; supervision, S.N.D.; project administration, S.N.D.; funding acquisition, N/A. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: The project was approved by Northern Michigan University Institutional Review Board for human participants, Proposal Number HS16-786 (December, 2017). Further, the study was conducted in accordance with the Declaration of Helsinki. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Data for this project are maintained by the primary authors—S.N.D. and L.D.—on personal and password protected laptops. Int. J. Environ. Res. Public Health 2023, 20, 4501 12 of 14 Acknowledgments: We wish to acknowledge the efforts of the graduate students involved in training the participants for their energy with participant recruitment and the implementation of the training protocols. The authors would also like to thank the volunteer participants for their valuable time and effort during participation. Conflicts of Interest: The authors declare no conflict of interest. References 1. Blair, S.N.; Bouchard, C. Physical activity in the prevention and treatment of obesity and its comorbidities. Med. Sci. Sport. Exerc. 1999, 31, S497. [CrossRef] 2. Chakravarty, E.F. Reduced disability and mortality among aging runners: A 21-year longitudinal study. Arch. Intern. Med. 2008, 168, 1638. [CrossRef] [PubMed] 3. Paterson, D.H.; Jones, G.R.; Rice, C.L. Ageing and physical activity: Evidence to develop exercise recommendations for older adults. Appl. Physiol. Nutr. Metab. 2007, 32 (Suppl. S2E), 69–108. [CrossRef] 4. Wen, C.P.; Wai, J.P.M.; Tsai, M.K.; Yang, Y.C.; Cheng, T.Y.D.; Lee, M.-C.; Chan, H.T.; Tsao, C.K.; Tsai, S.P.; Wu, X. Minimum amount of physical activity for reduced mortality and extended life expectancy: A prospective cohort study. Lancet 2011, 378, 1244–1253. [CrossRef] 5. Lee, D.; Pate, R.R.; Lavie, C.J.; Sui, X.; Church, T.S.; Blair, S.N. Leisure-time running reduces all-cause and cardiovascular mortality risk. J. Am. Coll. Cardiol. 2014, 64, 472–481. [CrossRef] 6. Hespanhol Junior, L.C.; Pillay, J.D.; van Mechelen, W.; Verhagen, E. Meta-analyses of the effects of habitual running on indices of health in physically inactive adults. Sport. Med. 2015, 45, 1455–1468. [CrossRef] 7. Wurm, S.; Tomasik, M.J.; Tesch-Römer, C. On the importance of a positive view on ageing for physical exercise among middle-aged and older adults: Cross-sectional and longitudinal findings. Psychol. Health 2010, 25, 25–42. [CrossRef] 8. Singh, M.A.F. Exercise comes of age: Rationale and recommendations for a geriatric exercise prescription. J. Gerontol. A Biol. Sci. Med. Sci. 2002, 57, M262–M282. [CrossRef] 9. Talbot, L.A.; Morrell, C.H.; Fleg, J.L.; Metter, E.J. Changes in leisure time physical activity and risk of all-cause mortality in men and women: The Baltimore Longitudinal Study of Aging. Prev. Med. 2007, 45, 169–176. [CrossRef] 10. Fleg, J.L. Accelerated longitudinal decline of aerobic capacity in healthy older adults. Circulation 2005, 112, 674–682. [CrossRef] 11. Ruiz, R.; Richardson, M.T. Functional balance training using a domed device. Strength Cond. J. 2005, 27, 50–55. [CrossRef] 12. Era, P.; Sainio, P.; Koskinen, S.; Haavisto, P.; Vaara, M.; Aromaa, A. Postural balance in a random sample of 7979 subjects aged 30 years and over. Gerontology 2006, 52, 204–213. [CrossRef] 13. Aslan, U.B.; Cavlak, U.; Yagci, N.; Akdag, B. Balance performance, aging and falling: A comparative study based on a Turkish sample. Arch. Gerontol. Geriatr. 2008, 46, 283–292. [CrossRef] 14. Hollman, J.H.; Kovash, F.M.; Kubik, J.J.; Linbo, R.A. Age-related differences in spatiotemporal markers of gait stability during dual task walking. Gait Posture 2007, 26, 113–119. [CrossRef] 15. Lockhart, T.E.; Liu, J. Differentiating fall-prone and healthy adults using local dynamic stability. Ergonomics 2008, 51, 1860–1872. [CrossRef] 16. Talbot, L.A.; Musiol, R.J.; Witham, E.K.; Metter, E.J. Falls in young, middle-aged and older community dwelling adults: Perceived cause, environmental factors and injury. BMC Public Health 2005, 5, 86. [CrossRef] 17. Niino, N.; Tsuzuku, S.; Ando, F.; Shimokata, H. Frequencies and circumstances of falls in the National Institute for Longevity Sciences Longitudinal Study of Aging (NILS-LSA). J. Epidemiol. 2000, 10 (Suppl. S1), 90–94. [CrossRef] 18. Thralls, K.J.; Levy, S.S.; Carmody, K.; Smith, K. Balance is associated with lower body strength and mobility in older adults: 178 Board #15 June 1, 9. Med. Sci. Sport. Exerc. 2016, 48, 33. 19. Mian, O.S.; Baltzopoulos, V.; Minetti, A.E.; Narici, M.V. The impact of physical training on locomotor function in older people. Sports Med. 2007, 37, 683–701. [CrossRef] 20. Braun, W.A. Neuromuscular adaptations in running. In Routledge Handbook of Ergonomics in Sport and Exercise; Hong, Y., Ed.; Routledge: London, UK, 2014; pp. 26–36. 21. Hespanhol Junior, L.C.; van Mechelen, W.; Verhagen, E. Health and economic burden of running-related injuries in dutch trailrunners: A prospective cohort study. Sport. Med. 2017, 47, 367–377. [CrossRef] 22. Schütte, K.H.; Aeles, J.; De Beéck, T.O.; van der Zwaard, B.C.; Venter, R.; Vanwanseele, B. Surface effects on dynamic stability and loading during outdoor running using wireless trunk accelerometry. Gait Posture 2016, 48, 220–225. [CrossRef] [PubMed] 23. Binnie, M.J.; Peeling, P.; Pinnington, H.; Landers, G.; Dawson, B. Effect of surface-specific training on 20-m sprint performance on sand and grass surfaces. J. Strength Cond. Res. 2013, 27, 3515–3520. [CrossRef] [PubMed] 24. Lohman, E.B.; Balan Sackiriyas, K.S.; Swen, R.W. A comparison of the spatiotemporal parameters, kinematics, and biomechanics between shod, unshod, and minimally supported running as compared to walking. Phys. Ther. Sport 2011, 12, 151–163. [CrossRef] [PubMed] 25. Mohammadi, R.; Phadke, C.P. The impact of incline and speed of treadmill on ankle muscle activity in middle-aged adults. J. Bodyw. Mov. Ther. 2017, 21, 306–313. [CrossRef] 26. Voloshina, A.S.; Ferris, D.P. Biomechanics and energetics of running on uneven terrain. J. Exp. Biol. 2015, 218, 711–719. [CrossRef] Int. J. Environ. Res. Public Health 2023, 20, 4501 13 of 14 27. Barnes, K.R.; Kilding, A.E. Strategies to improve running economy. Sports Med. 2015, 45, 37–56. [CrossRef] 28. Kurz, M.J.; Berg, K.; Latin, R.; Degraw, W. The Relationship of Training Methods in NCAA Division I Cross-Country Runners and 10,000-Meter Performance. J. Strength Cond. Res. 2000, 14, 185–192. 29. Barnes, K.R.; Hopkins, W.G.; McGuigan, M.R.; Kilding, A.E. Effects of different uphill interval-training programs on running economy and performance. Int. J. Sports Physiol. Perform. 2013, 8, 639–647. [CrossRef] 30. Bennett, J.A. The Consolidated Standards of Reporting Trials (CONSORT) Guidelines for Reporting Randomized Trials. Nurs. Res. 2005, 54, 128–132. [CrossRef] 31. American College of Sports Medicine. ACSM’s Guidelines for Exercise Testing and Prescription, 11th ed.; Lippincott Williams & Wilkins: Philadelphia, PA, USA, 2021. 32. Lee, P.H.; Macfarlane, D.J.; Lam, T.H.; Stewart, S.M. Validity of the international physical activity questionnaire short form (IPAQ-SF): A systematic review. Int. J. Behav. Nutr. Phys. Act. 2011, 8, 115. [CrossRef] 33. Warburton, D.E.R.; Jamnik, V.K.; Bredin, S.S.D.; Gledhill, N. The Physical Activity Readiness Questionnaire for Everyone (PAR-Q+) and Electronic Physical Activity Readiness Medical Examination (ePARmed-X+). Health Fit. J. Can. 2011, 4, 3–23. 34. World Medical Association. Declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA 2013, 310, 2191–2194. [CrossRef] 35. Tanaka, H.; Monahan, K.D.; Seals, D.R. Age-predicted maximal heart rate revisited. J. Am. Coll. Cardiol. 2001, 37, 153–156. [CrossRef] 36. Coren, S. The lateral preference inventory for measurement of handedness, footedness, eyedness, and earedness: Norms for young adults. Bull. Psychon. Soc. 1993, 31, 1–3. [CrossRef] 37. Sabharwal, S.; Kumar, A. Methods for assessing leg length discrepancy. Clin. Orthop. Relat. Res. 2008, 466, 2910–2922. [CrossRef] 38. Borg, G. Psychophysical scaling with applications in physical work and the perception of exertion. Scand. J. Work. Environ. Health 1990, 16, 55–58. [CrossRef] 39. Bell, D.R.; Guskiewicz, K.M.; Clark, M.A.; Padua, D.A. Systematic review of the balance error scoring system. Sport. Health A Multidiscip. Approach 2011, 3, 287–295. [CrossRef] 40. Coughlan, G.F.; Fullam, K.; Delahunt, E.; Gissane, C.; Caulfield, B.M.; Sci, M. A comparison between performance on selected directions of the Star Excursion Balance Test and the Y Balance Test. J. Athl. Train. 2012, 47, 366–371. [CrossRef] 41. Donath, L.; Faude, O.; Lichtenstein, E.; Nüesch, C.; Mündermann, A. Validity and reliability of a portable gait analysis system for measuring spatiotemporal gait characteristics: Comparison to an instrumented treadmill. J. Neuro Eng. Rehabil. 2016, 13, 6. [CrossRef] 42. Pauole, K.; Madole, K.; Garhammer, J.; Lacourse, M.; Rozenek, R. Reliability and validity of the T-test as a measure of agility, leg power, and leg speed in college-aged men and women. J. Strength Cond. Res. 2000, 14, 443–450. 43. Peterson, M.J.; Pieper, C.F.; Morey, M.C. Accuracy of VO2(max) prediction equations in older adults. Med. Sci. Sports Exerc. 2003, 35, 145–149. [CrossRef] [PubMed] 44. Hopkins, W.G. Probabilities of Clinical or Practical Significance. 2002. Available online: http://sportsci.org/resource/stats/ generalize.html#calculate (accessed on 21 November 2016). 45. Pinnington, H.C.; Lloyd, D.G.; Besier, T.F.; Dawson, B. Kinematic and electromyography analysis of submaximal differences running on a firm surface compared with soft, dry sand. Eur. J. Appl. Physiol. 2005, 94, 242–253. [CrossRef] [PubMed] 46. Cohen, R.; Mitchell, C.; Dotan, R.; Gabriel, D.; Klentrou, P.; Falk, B. Do neuromuscular adaptations occur in endurance-trained boys and men? Appl. Physiol. Nutr. Metab. 2010, 35, 471–479. [CrossRef] [PubMed] 47. Hrysomallis, C. Balance ability and athletic performance. Sport. Med. 2011, 41, 221–232. [CrossRef] 48. Takeshima, N.; Rogers, N.L.; Rogers, M.E.; Islam, M.M.; Koizumi, D.; Lee, S. Functional fitness gain varies in older adults depending on exercise mode. Med. Sci. Sport. Exerc. 2007, 39, 2036–2043. [CrossRef] 49. Muehlbauer, T.; Gollhofer, A.; Granacher, U. Associations between measures of balance and lower-extremity muscle strength/power in healthy individuals across the lifespan: A systematic review and meta-analysis. Sport. Med. 2015, 45, 1671–1692. [CrossRef] 50. Jensen, K.; Johansen, L.; Karkkainen, O.-P. Economy in track runners and orienteers during path and terrain running. J. Sport. Sci. 1999, 17, 945–950. [CrossRef] 51. Lejeune, T.M.; Willems, P.A.; Heglund, N.C. Mechanics and energetics of human locomotion on sand. J. Exp. Biol. 1998, 201, 2071–2080. [CrossRef] 52. Pinnington, H.C.; Dawson, B. The energy cost of running on grass compared to soft dry beach sand. J. Sci. Med. Sport 2001, 4, 416–430. [CrossRef] 53. Zamparo, P.; Perini, R.; Orizio, C.; Sacher, M.; Ferretti, G. The energy cost of walking or running on sand. Eur. J. Appl. Physiol. Occup. Physiol. 1992, 65, 183–187. [CrossRef] 54. Ferris, D.P.; Liang, K.; Farley, C.T. Runners adjust leg stiffness for their first step on a new running surface. J. Biomech. 1999, 32, 787–794. [CrossRef] 55. Binnie, M.J.; Dawson, B.; Arnot, M.A.; Pinnington, H.; Landers, G.; Peeling, P. Effect of sand versus grass training surfaces during an 8-week pre-season conditioning programme in team sport athletes. J. Sport. Sci. 2014, 32, 1001–1012. [CrossRef] 56. Gortsila, E. Effect of training surface on agility and passing skills of prepubescent female volleyball players. J. Sport. Med. Doping Stud. 2013, 3, 1000128. [CrossRef] Int. J. Environ. Res. Public Health 2023, 20, 4501 14 of 14 57. Yiˇgit, S.S.; Tuncel, F. A comparison of the endurance training responses to road and sand running in high school and college students. J. Strength Cond. Res. 1998, 12, 79–81. [CrossRef] 58. Easthope, C.S.; Hausswirth, C.; Louis, J.; Lepers, R.; Vercruyssen, F.; Brisswalter, J. Effects of a trail running competition on muscular performance and efficiency in well-trained young and master athletes. Eur. J. Appl. Physiol. 2010, 110, 1107–1116. [CrossRef] 59. Boey, H.; Aeles, J.; Schütte, K.; Vanwanseele, B. The effect of three surface conditions, speed and running experience on vertical acceleration of the tibia during running. Sport. Biomech. 2017, 16, 166–176. [CrossRef] 60. Lopes, A.D.; Hespanhol, L.C.; Yeung, S.S.; Costa, L.O.P. What are the main running-related musculoskeletal injuries?: A systematic review. Sport. Med. 2012, 42, 891–905. [CrossRef] 61. Nielsen, R.O.; Buist, I.; Sørensen, H.; Lind, M.; Rasmussen, S. Training errors and running related injuries: A systematic review. Int. J. Sports Phys. Ther. 2012, 7, 58–75. 62. Bredeweg, S.W.; Zijlstra, S.; Bessem, B.; Buist, I. The effectiveness of a preconditioning programme on preventing running-related injuries in novice runners: A randomised controlled trial. Br. J. Sport. Med. 2012, 46, 865–870. [CrossRef] 63. Taunton, J.E.; Ryan, M.B.; Clement, D.B.; McKenzie, D.C.; Lloyd-Smith, D.R.; Zumbo, B.D. A retrospective case-control analysis of 2002 running injuries. Br. J. Sports Med. 2002, 36, 95–101. [CrossRef] 64. Taunton, J.E.; Ryan, M.; Clement, D.; McKenzie, D.; Lloyd-Smith, D.; Zumbo, B. A prospective study of running injuries: The Vancouver Sun Run “In Training” clinics. Br. J. Sport. Med. 2003, 37, 239–244. [CrossRef] [PubMed] 65. Hespanhol Junior, L.C.; Pena Costa, L.O.; Lopes, A.D. Previous injuries and some training characteristics predict running-related injuries in recreational runners: A prospective cohort study. J. Physiother. 2013, 59, 263–269. [CrossRef] [PubMed] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Effects of Trail Running versus Road Running-Effects on Neuromuscular and Endurance Performance-A Two Arm Randomized Controlled Study.

03-03-2023

Drum, Scott Nolan,Rappelt, Ludwig,Held, Steffen,Donath, Lars

eng

PMC9245565

Effect of plyometric training and neuromuscular electrical stimulation assisted strength training on muscular, sprint, and functional performances in collegiate male football players Shahnaz Hasan1, Gokulakannan Kandasamy2, Danah Alyahya1, Asma Alonazi1, Azfar Jamal3,4, Amir Iqbal5, Radhakrishnan Unnikrishnan1 and Hariraja Muthusamy1 1 Physical Therapy Department, College of Applied Medical Sciences, Majmaah University, Al Majmaah, Saudi Arabia 2 School of Health and Life Sciences, Teesside University, Middlesbrough, United Kingdom 3 Department of Biology, College of Science, Al-Zulfi-, Majmaah University, Al Majmaah, Riyadh Region, Saudi Arabia 4 Health and Basic Science Research Centre, Majmaah University, Al Majmaah, Saudi Arabia 5 Rehabilitation Research Chair, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia ABSTRACT Background: The study’s objective was to analyze the influence of an 8-week neuromuscular electrical stimulation (NMES) with a plyometric (PT) and strength training (ST) program on muscular, sprint, and functional performances in collegiate male football players. Methods: Sixty collegiate male football players participated in this randomized controlled trial single-blind study. All the participants were randomly divided into two groups: (1) NMES group (Experimental, n = 30) who received NMES assisted ST and (2) sham NMES group (Control, n = 30) who received sham NMES assisted ST. In addition, participants from both groups received a PT program; both groups received intervention on three days a week for 8-weeks. The study’s outcomes, such as muscular, sprint, and functional performances, were assessed using a strength test (STN) for quadriceps muscle, sprint test (ST), and single-leg triple hop test (SLTHT), respectively, at baseline pre-intervention and 8-week post-intervention. The interaction between group and time was identified using a mixed design (2 × 2) ANOVA. Results: Significant difference found across the two time points for the scores of STN: F (1.58) = 5,479.70, p < 0.05; SLTHT: F (1.58) = 118.17, p < 0.05; and ST: F (1.58) = 201.63, p < 0.05. Similarly, the significant differences were found between groups averaged across time for the scores of STN: F (1.58) = 759.62, p < 0.05 and ST: F (1.58) = 10.08, p < 0.05. In addition, after 8-week of training, Cohen’s d observed between two groups a large to medium treatment’s effect size for the outcome STN (d = 10.84) and ST (d = 1.31). However, a small effect size was observed only for the SLTHT (d = 0.613). Conclusions: Findings suggest that the effect of PT and ST with either NMES or sham NMES are equally capable of enhancing muscular, sprint, and functional performances in collegiate male football players. However, PT and ST with NMES How to cite this article Hasan S, Kandasamy G, Alyahya D, Alonazi A, Jamal A, Iqbal A, Unnikrishnan R, Muthusamy H. 2022. Effect of plyometric training and neuromuscular electrical stimulation assisted strength training on muscular, sprint, and functional performances in collegiate male football players. PeerJ 10:e13588 DOI 10.7717/peerj.13588 Submitted 10 January 2022 Accepted 24 May 2022 Published 16 June 2022 Corresponding author Shahnaz Hasan, [email protected] Academic editor Tiago Barbosa Additional Information and Declarations can be found on page 14 DOI 10.7717/peerj.13588 Copyright 2022 Hasan et al. Distributed under Creative Commons CC-BY 4.0 have shown an advantage over PT and ST with sham NMES in improving muscular performance and sprint performance among the same participants. Subjects Anatomy and Physiology, Rehabilitation, Sports Medicine Keywords Strength, Functional performance, Sprint, Collegiate male football players, Plyometric training, NMES INTRODUCTION Strength and conditioning play an essential role in injury prevention and improving muscle performance (Wilson et al., 2013). In most sports teams or individual sports such as netball, football, and volleyball, muscle strength of the quadriceps is crucial for athletes sporting abilities such as running, sprinting, and jumping (Magalhães et al., 2004). Movements included in this type of training are powerful and fast concentric contractions followed by high-intensity eccentric contractions throughout a high-impact reaction force is proven to enhance performance (da Cunha et al., 2020). Although an athlete’s performance can be influenced by multiple factors (i.e., technical, tactical, and physical) (Aán et al., 2021), the main focus of this study was aimed at muscular, physical, and functional performance. There is a vast literature on different strengthening exercises methods to improve performance and prevent injury in sports (Myer et al., 2011; LaStayo et al., 2003). Neuromuscular electrical stimulation (NMES) is one method that involves the utilization of electrical stimuli to trigger contractions of the muscles. This technique is widely used for strengthening interventions and restoring or preserving the functioning and mass of muscles in sports (Gobbo et al., 2014). Research shows that NMES is more appropriate in improving muscle strength and performance when combined with other training such as plyometric or strength training (Bansal, Zutshi & Munjal, 2020). The quadriceps are the primary muscle group in the lower limb that controls knee movement and increases stability during any dynamic or functional movement (Markovic & Mikulic, 2010). Strength training the quadriceps muscle plays a vital role in improving functional performance in most sports (Antrich & Brewster, 1986). It is essential to keep the quadriceps strong to prevent knee injuries by reducing the shear force in the tibiofemoral joint (Augustsson & Thomee, 2000). Plyometric training is also known as dynamic or jump training, involves exercises where muscles are required to exert maximum force throughout short intervals with an overall goal of increasing power (Markovic & Mikulic, 2010). Plyometric is seen as a popular training modality, either alone or when combined with other types of training (Markovic & Mikulic, 2010; Antrich & Brewster, 1986; Augustsson & Thomee, 2000; Hasan et al., 2021). The available evidence shows that this type of training has several positive changes, whether this is in athletic performance and muscle functioning abilities (Markovic & Mikulic, 2010). Strength training, also known as resistance training, is distinguished by the deliberate action of muscular contractions against extraneous loads. It is acknowledged as the most convenient approach for strengthening muscles (Wang & Zhang, 2016). Hasan et al. (2022), PeerJ, DOI 10.7717/peerj.13588 2/18 However, there is previous literature on the effects of NMES and strength training on improving muscle strength and physical and functional performance (Gomes da Silva et al., 2018; Paillard, 2018; Basas et al., 2018). Based on our knowledge, there are limited studies and data looking at NMES and strength training effects with more extended intervention (exceeding 4 weeks). In addition, there is a scarcity of data on the effects of sport specific intervention and gender specific sample size. Previous research was more focused on the effect of NMES in post-operative rehabilitation and not on injury prevention or strength training in football players (Gatewood, Tran & Dragoo, 2017). Therefore, the study’s objective was to analyze the influence of an 8-week neuromuscular electrical stimulation (NMES) with a plyometric and strength training program on muscular strength, sprint performance, and functional performance in collegiate male football players. The study hypothesized that the effect of PT and ST with NMES would be more beneficial than PT and ST with sham NMES on muscular, sprint, and functional performances in male collegiate football players. MATERIALS AND METHODS Study design A single-blind two-arm parallel group randomized controlled trial study design was used to determine the beneficial effects of 8-week NMES training program in collegiate male football players. Ethical considerations The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Chair of Majmaah University for Research Ethics Committee, Saudi Arabia (Ethics number: MUREC-Dec. 15/COM-2020/13-2 dated 15 December 2020). Study population Two hundred colligate male football players were assessed via a telephone interview to participate. The team physiotherapist examined one hundred 10 participants who met the inclusion criteria. Young male participants aged 18 and 25 years who participated in football training were included in the study. The participants were excluded with a history of any lower limb surgery, a current injury that affected the lower limb function, cardio-respiratory disease. A total of sixty participants were divided randomly between the NMES group (experimental group): NMES aided strength training and the sham NMES group (control group): sham NMES aided strength training. Procedures The study was performed between 30 December 2020 and 28 July 2021 at the Rehabilitation center, Majmaah University, Saudi Arabia. Collegiate male football players were recruited from Majmaah and Riyadh sporting clubs and universities. The NMES experimental procedures and potential risks were explained to the participant before signing their informed consent under the Declaration of Helsinki. The College of Applied Hasan et al. (2022), PeerJ, DOI 10.7717/peerj.13588 3/18 Medical Science, Ethical Sub-Committee of the Majmaah, Saudi Arabia (Ethics Number: MUREC-Dec. 15/COM-2020/13-2) approved all procedures of this study. Conclusively, 60 participants included in this study were randomly divided into two groups: NMES Group (experimental group, n = 30) and sham NMES Group (control group, n = 30). In addition to plyometric training, both the NMES and sham NMES groups undertook NMES and sham NMES guided strength training, respectively, three sessions a week for 8-weeks. Pre- and post-test readings were taken at baseline and post 8-week of intervention, respectively. The outcome measures for this study were muscular performance i.e., maximal voluntary isometric contraction (MVIC) of quadriceps muscle strength measured by ISOMOVE dynamometer (ISO-MANSW-IT Tecno body, Italy; https://www.tecnobody.com/en/products/detail/isomove), the sprint performance test, and the single-leg triple hope test. All the participants who completed the study trial were included for the statistical analysis. A CONSORT flow diagram of the participants is illustrated in Fig. 1 (Schulz, Altman & Moher, 2010). Interventions After familiarizing two training and testing sessions, the NMES and sham NMES groups underwent an 8-week training program with three sessions per week. Both groups received the same plyometric training (Tomlinson et al., 2020). In addition, NMES group received the NMES guided strength training while the sham NMES group received the sham NMES guided strength training. Strength training includes terminal knee extension exercises. Plyometric training, including bounding, hurdling, and drop jumping. Before starting any strengthening or plyometric pieces of training, each participant underwent a standardized warm-up session for 10–15 min, which included 7–8 min of jogging and running and stretching exercises for 5–6 min (Silvers-Granelli et al., 2015). Furthermore, Fig. 2 is depicting the details of groups, interventions including types of exercises, and outcomes measures. Neuromuscular Electrical Nerve Stimulation (Martimbianco et al., 2017; Snyder- Mackler et al., 1995). A NMES guided strength training program was carried out using an electrotherapeutic device (Endomed 982, Enraf Nonius, Rotterdam, The Netherlands), a two-channel medium frequency NME stimulator. In this study, it was applied to stimulate the targeted nerve and muscles, such as the femoral nerve and quadriceps femoris muscles of both limbs. Participants from both groups were instructed to shave the part and wash thoroughly with ethanol to clean the area and reduce skin resistance before applying electrodes over the skin. For the NMES group, a standard carbon rubber electrode in moistened sponge pads was placed over the femoral triangle and transversely over the quadriceps muscle motor points of vastus medialis and vastus lateralis muscles (Fig. 3). Motor points were pointed out as the area that produced the most significant visible muscle contraction when applied electrical stimulation. The electrodes were securely fastened using Velcro straps. The participants were seated on an Isomove device during the stimulation, used for quadriceps strength training with knee fixed at 60 -0-degree angles (0 correspondence to the full extension of the leg) stimulator with a frequency of 2,500 Hz @ of 75 burst per second delivered with AMF 50 HZ with 5 s, time interval and holding Hasan et al. (2022), PeerJ, DOI 10.7717/peerj.13588 4/18 time 8 s, ramp up and down 2 s. The intensity was set maximally according to the participant’s tolerance. It was given for 25 min (Snyder-Mackler et al., 1995; Taradaj et al., 2013). For the sham NMES group, the participants followed the NMES parameters and quadriceps strength training (with knee fixed at 60-0-degree angles) similar to the NMES group. However, in contrast, the placement of electrodes was positioned away from the course of the femoral nerve (e.g., VMO and RF), and the intensity was set to very mild just for the participants’ feelings. Each training session lasted for 25 min (Snyder-Mackler et al., 1995; Taradaj et al., 2013). Terminal knee extension exercises: participants sitting with the knee flexed from 60
to 0
angles on the Isomove device and instructed for maximum voluntary isometric contraction of their quadriceps muscle for three sets of 10 repetitions, three times a week for 8-weeks. Bounding: This is plyometric training where enormous strides are used in the running action and extra time is spent in the air. The participant performed bounding for 30 m, two Figure 1 Consolidated Standards of Reporting Trials (CONSORT) diagram showing flow of participants through each stage of a randomized trial. Full-size  DOI: 10.7717/peerj.13588/fig-1 Hasan et al. (2022), PeerJ, DOI 10.7717/peerj.13588 5/18 sets for the initial 3 weeks, and after three sets of 30 m bounding with a rest period of 3–4 min. Hurdling: The participant was instructed to jump with both legs over the eight consecutive cones height of 40 cm, kept in a straight line, 1 m apart for plyometric training as hurdling. The participant performed two sets of hurdling over eight cones for the initial 2 weeks. Three sets of hurdling were completed over eight cones for the next 6 weeks. The rest period was 3–4 min between each set of hurdling. Figure 2 Depicting the details of groups, warm-up activities, interventions including types of exercises, and outcomes measures. Full-size  DOI: 10.7717/peerj.13588/fig-2 Hasan et al. (2022), PeerJ, DOI 10.7717/peerj.13588 6/18 Drop jumping: The participant drops to the ground from a stepper (height 40 cm) and immediately jumps maximally forward. The participant performed two sets of eight repetitions of drop jumping for 2 weeks, and for the next 6 weeks, three sets of eight repetitions of drop jumping with a rest time of 2–3 min between each set were completed (Tomlinson et al., 2020). For both the NMES and sham NMES groups, plyometric training (bounding, hurdling, and drop jumping) was performed three sessions weekly for 8-weeks (Tomlinson et al., 2020). Outcome measures Maximal Voluntary Isometric Contraction Strength (STN) Test: We used an ISOMOVE dynamometer, software version 0.0.1 (ISO-MANSW-IT; Tecnobody, Dalmine (BG), Italy), to assess the maximum peak torque of quadriceps muscles strength dominant leg before strength training and after 8-weeks of training. The reliability of quadriceps strength measurements of the ISOMOVE dynamometer was previously validated (Hasan et al., 2021). Participants completed the warm-up session and were familiarized with the equipment before data collection. Participants were seated with the hip at a 90-degree angle to minimize hip and thigh motion, and straps were applied across the chest, Figure 3 Electrode placement during NMES guided strength training. Full-size  DOI: 10.7717/peerj.13588/fig-3 Hasan et al. (2022), PeerJ, DOI 10.7717/peerj.13588 7/18 midthighs, and pelvis to avoid displacements during contraction. The shin pad was fixed at 5.1 cm (2 inches), superior to the medial malleolus. The knee angles are set at 60 degrees of flexion, producing the most significant torque output (Alonazi et al., 2021). Verbal instruction was given to keep his/her arm crossed over his/her chest, and verbal encouragement was given to motivate them to attain maximum effort during the 5 s contractions. Each test included three MVICs at 60-degree angles with a 3-min rest between the trials series to eliminate fatigue. The peak torque was directly measured by ISOMOVE software (Fig. 4). Sprint Test (ST): The sprint test is a reliable (Interclass coefficient correlation = 0.95–0.98) and valid to measure speed performance (Barr et al., 2014; Zabaloy et al., 2021). Participants were instructed to stand with their forward legs placed closer to the starting line, and then on verbal command, they started sprinting with a maximal speed over a 50 m distance. All the performances were recorded by a handheld stopwatch (XINJIE, SW8-2008) times (in seconds) (Hasan et al., 2021; Alonazi et al., 2021; Zafeiridis et al., 2005) when the participant’s foot touched the finishing line. The subsequent two sprint test trials were performed after 5 min recovery period, and the lowest timing of the two scores was considered the pre-test (baseline) scores. Single-Leg Triple Hop Test (SLTHT): The SLTH test scores were measured from the participant performance, as covered the distance in three hops using a measuring tape. The participants stood on the dominant limb with the toes just behind the starting line and then completed the three consecutive hops on the same limb. The single-leg triple hop test performance measured the distance covered from the starting point to where the back of the participant’s heel hit the ground (please refer to Fig. 5) (Hasan et al., 2021; Alonazi et al., 2021; Kale & Gurol, 2019; Hamilton et al., 2008). They performed three trials with Figure 4 Illustration of maximal voluntary isometric contraction 600 strength (STN) test. Full-size  DOI: 10.7717/peerj.13588/fig-4 Hasan et al. (2022), PeerJ, DOI 10.7717/peerj.13588 8/18 a 3 min’ recovery period. The best of the three scores (i.e., the maximum distance covered) was taken as the pre-test (baseline) score. All the outcomes including MVIC strength (STN), sprint performance (ST), and functional performance (SLTHT) were assessed by only one assessor who was blind to the study. The intra-observer reliability was found to be excellent (95% CI [0.91–0.97]). Statistical analysis A Statistical Programming for Social Studies SPSS software (IBM SPSS Statistics v.26, IBM Corp., Armonk, NY, USA) was used to analyze the outcomes measures. A Shapiro-Wilk test of normality was used for the homogenous distribution of collected data. The main effect of an intervention on the outcome measures across the baseline and 8-week post-intervention (2-time points), between-group (NMES vs sham NMES groups), and the interaction between group and time were identified using a mixed design (2 × 2) two-way analysis of variance (ANOVA). Further, the comparison of an intervention effect on the outcome measures within-group across the time points and between-groups at 8-week post-intervention using a Bonferroni’s multiple comparison test. Additionally, the size of an intervention effect on outcomes measures was observed within-group across the time points and between-groups at 8-week post-intervention using a Cohen’s d test. The magnitude of effect sizes in strength training research for untrained participants (who received consistently strength training less than 1-year) is as follows: d value <0.50=trivial Figure 5 Illustration of single leg triple hop test (SLTHT). Full-size  DOI: 10.7717/peerj.13588/fig-5 Hasan et al. (2022), PeerJ, DOI 10.7717/peerj.13588 9/18 effect size, 0.50–1.25=small effect size, 1.26–2.00=medium effect size, and >2.00=large effect size (Rhea, 2004). A relationship among outcomes measures were established using Pearson’s coefficient test. The magnitude of Pearson’s coefficient test i.e., r value between 0.00–0.10, 0.10–0.39, 0.40–0.69, 0.70–0.89, and 0.90–1.00 corresponds to negligible, weak, moderate, strong, and very strong correlation between the variables, respectively (Schober, Boer & Schwarte, 2018). The confidence interval (CI) level was set at 95% for mean, i.e., significant level p < 0.05. RESULTS Out of 200 telephonic conversations, 110 participants were ready to be examined; out of 110, 25 participants were excluded due to lower limb injury, 12 did not agree to take NMES, and 13 did not agree due to their availability for 8-weeks’ protocol. The mean scores (95% CI) obtained for the age, height, weight, and BMI of all the participants (n = 60) was 22.13 (95% CI [19–25] years), 1.66 (95% CI [1.62–1.70] m), 64.27 (95% CI [55–70] kg), and 23.43 (95% CI [24.40–25.70] kgm−2), respectively. A Shapiro-Wilk test reported a homogenous distribution (p > 0.05) of descriptive characteristics and outcomes measures among both the groups, except for age (NMES group, p = 0.024; sham NMES group, p = 0.010), BMI (NMES, p = 0.044), and SLTHT (NMES, p = 0.013). A group-wise (n = 30/group) mean scores for the descriptive characteristic, including age, height, body mass, and BMI of all the participants and outcomes measures, are presented in Table 1. Table 1 Depicting descriptive characteristics of the participants, baseline scores of outcomes measures, and normality test using the Shapiro-Wilk test (95% CI for mean). Variables Groups (n = 30/group) Baseline scores (Mean ± SD) Shapiro-Wilk test of normality Min. Max. Statistics df p-value Age (years) NMES 22.20 ± 1.83 19 25 0.918 30 0.024 Control 22.07 ± 1.80 19 25 0.903 30 0.010 Height (m) NMES 1.65 ± 0.01 1.63 1.68 0.938 30 0.082 Control 1.66 ± 0.02 1.62 1.70 0.958 30 0.282 Body mass (Kg) NMES 63.33 ± 2.99 55 69 0.954 30 0.214 Control 65.20 ± 2.30 59 70 0.956 30 0.242 BMI (Kg/m2) NMES 23.23 ± 1.09 20.4 24.9 0.928 30 0.044 Control 23.63 ± 0.75 21.7 25.7 0.942 30 0.102 STN (Nm-2) NMES 145.20 ± 3.68 136 151 0.942 30 0.101 Control 144.93 ± 3.98 135 153 0.975 30 0.685 SLTHT NMES 501.30 ± 54.50 390 575 0.907 30 0.013 Control 499.90 ± 51.14 375 586 0.970 30 0.541 ST NMES 9.19 ± 0.57 7.78 10.37 0.984 30 0.923 Control 9.23 ± 0.40 8.41 9.96 0.981 30 0.856 Note: Values are mean scores ± standard deviations (SD); BMI, body mass index; NMES, Neuromuscular electrical stimulation group; Control, representing the sham NMES group; Statistics, t-value of t-test; df, Degree of freedom; p-value, level of significance; p insignificant at >0.05. Hasan et al. (2022), PeerJ, DOI 10.7717/peerj.13588 10/18 Table 2 represents the main effect of the interventions on the outcome measures across the two time points (pre- and post), between the groups, and the interaction between time and group along with the effect size (η). There was a significant difference found across the two time points for the scores of the outcomes STN: F (1.58) = 5,479.70, p < 0.05; SLTHT: F (1.58) = 118.17, p < 0.05; and ST: F (1.58) = 201.63, p < 0.05. Similarly, the significant differences were found between groups averaged across time for the scores of the outcomes STN: F (1.58) = 759.62, p < 0.05 and ST: F (1.58) = 10.08, p < 0.05. However, a non-significant difference was observed between groups for the scores of the outcomes SLTHT: F (1.58) = 1.53, p > 0.05. There was also a significant interaction was observed between time and group for the scores of the outcomes STN: F Table 2 The main effect of treatment on the outcomes, within-subject factors across the time (pre and post), between-subject factors between the groups (NMES vs Control), and the interaction between groups (2) and time (2) using a mixed design 2 × 2 ANOVA test. Variables Outcomes df1 df2 F-value p-value η2 Time (2) STN 1 58 5,479.70 0.001* 0.990 SLTHT 1 58 118.17 0.001* 0.671 ST 1 58 201.63 0.001* 0.777 Time * Groups (2 × 2) STN 1 58 1,576.10 0.001* 0.965 SLTHT 1 58 44.38 0.001* 0.433 ST 1 58 24.33 0.001* 0.296 Groups (2) STN 1 58 759.62 0.001* 0.929 SLTHT 1 58 1.53 0.221 0.026 ST 1 58 10.08 0.002* 0.148 Notes: * Significant value if p < 0.05. df, Degree of freedom; η2, Eta Squared where η2 = 0.01 indicates a small effect; η2 = 0.06 indicates a medium effect; η2 = 0.14 indicates a large effect. Table 3 Pairwise comparison for the scores of outcomes muscular performance (STN), functional performance (SLTHT), and sprit performance (ST) across two-time points (pre & post) within each group using Bonferroni’s multiple comparison test. Cohen’s d test was applied for measuring effect size between two-time points. Outcomes Groups Pre- intervention Post- intervention Time (Pre-Post) p-value Cohen’s d STN (ΔMD ± SD) NMES 145.20 ± 3.68 214.67 ± 4.18 −69.47 ± 0.50 0.001* 17.64^ Control 144.93 ± 3.98 165.90 ± 4.80 −20.97 ± 0.82 0.001* 4.76^ SLTHT (ΔMD ± SD) NMES 501.30 ± 54.50 540.73 ± 51.78 −39.43 ± 2.72 0.001* 0.74^ Control 499.90 ± 51.14 509.37 ± 50.41 −9.47 ± 0.73 0.004* 0.19 ST (ΔMD ± SD) NMES 9.19 ± 0.58 7.91 ± 0.57 1.28 ± 0.01 0.001* 2.23^ Control 9.23 ± 0.40 8.61 ± 0.50 0.62 ± 0.10 0.001* 1.36^ Notes: * Significant value if p < 0.05. ^ Large and medium effect size if Cohen’s d value >2.00 and between 1.26–2.00, respectively (Rhea, 2004). ΔMD, Mean differences; SD, Standard Deviation; NMES, Neuromuscular electric stimulation; ΔMD, Mean differences; STN, Strength; SLTHT, Single leg triple hop test; ST, Resisted stride. Hasan et al. (2022), PeerJ, DOI 10.7717/peerj.13588 11/18 (1.58) = 1,576.10, p < 0.05; SLTHT: F (1.58) = 44.38, p < 0.05; and ST: F (1.58) = 24.33, p < 0.05. Tables 3 and 4 depict pairwise comparisons using Bonferroni multiple comparisons test for the scores of the outcomes within-groups across the two-time points and between-groups at 8 weeks post-intervention, respectively. The findings within-group showed significant differences (p < 0.05) for the scores of all outcome measures including STN, ST, and SLTHT across the two-time points of the study (Table 3). However, the between-group analysis demonstrated a significant difference (p < 0.05) for the outcomes STN (p < 0.001, d = 10.84) and ST (p < 0.002, d = 1.31) except for a non-significant difference for the outcome measure SLTHT (p > 0.05, d = 0.613) (Table 4). In addition, after 8-week of training, Cohen’s d observed between two groups a large to medium treatment’s effect size for the outcome STN (d = 10.84) and ST (d = 1.31). However, a small effect size was observed only for the SLTHT (d = 0.613). In addition, Pearson’s coefficient test revealed a significant (95% CI, p < 0.05) but weak to moderate correlation between: STN and SLTHT (r = −0.252, p = 0.052), STN and ST (r = −0.540, p = 0.001), and ST and SLTHT (r = −0.358, p = 0.005) at 8-week post-intervention (Table 5). DISCUSSION The present study used NMES and a plyometric training program to assess muscular strength, sprint ability, and functional performance in collegiate male football players. Findings from the 8-week combined program showed improvements in all the above three Table 4 Pairwise comparison of post-test scores (at 8-weeks) for the outcomes muscular performance (STN), functional performance (SLTHT), and sprint test (ST) between groups using Bonferroni multiple comparison test. Cohen’s d test was applied for measuring effect size between two groups. Outcomes NMES (Mean ± SD) Control (Mean ± SD) NMES vs Control (ΔMD ± SD) p-value Cohen’s d STN 214.67 ± 4.18 165.90 ± 4.80 48.77 ± −0.62 0.001* 10.84^ SLTHT 540.73 ± 51.76 509.37 ± 50.41 30.93 ± 0.37 0.221 0.613 ST 7.91 ± 0.57 8.61 ± 0.50 −0.70 ± 0.08 0.002* 1.31^ Notes: * Significant value if p < 0.05. ^ Large and medium effect size if Cohen’s d value >2.00 and between 1.26-2.00, respectively (Rhea, 2004). DMD, Mean differences; SD, Standard Deviation; NMES, Neuromuscular electric stimulation; STN, Strength test; SLTHT, Single leg triple hop test; ST, sprint test. Table 5 Correlation between strength test (STN), single-leg triple hop test (SLTHT), and sprint test (ST) at post-intervention. Variables SLTHT Po (r & p-value) ST Po (r & p-value) STN Po −0.252 (0.052)* −0.540 (0.001)* ST Po −0.358 (0.005)* 1 Note: * Significant value (2-tailed), if p < 0.05. Hasan et al. (2022), PeerJ, DOI 10.7717/peerj.13588 12/18 outcomes. A positive correlation is shown for both experimental and controlled groups in post-intervention variables. Therefore, the results suggest that NMES assisted strength training combined with plyometric training enhances strength and athletic performance in adult male college footballers. The current study adds to existing research, proving that the combination of plyometric training with NMES assisted strength training is an adequate method to improve muscular strength (Alonazi et al., 2021). Similar to the present study, previous studies have shown the effect of NMES on strengthening lower limb muscles in a post-operative population through rehabilitation of knee injuries (Gatewood, Tran & Dragoo, 2017). For example, in total knee arthroplasty patients, Walls et al. (2010) studied over a similar time frame demonstrated significant improvement in quadriceps strength and functional movements with NMES. In support of our results, a review by Kim et al. (2010) recommended using NMES and exercises together to improve quadriceps strength rather than exercises alone in anterior cruciate ligament (ACL) reconstruction post-operative rehabilitation. One of the most important reasons for the increased strength is muscle activation potentiation. NMES seems to increase the actin-myosin cross-bridges to calcium, thereby increasing the muscle’s force-generating capacity (da Cunha et al., 2020; Gomes da Silva et al., 2018; Bouguetoch, Martin & Grosprêtre, 2021). Our main findings mainly support the hypothesis in which we found the use of NMES in addition to plyometric training significantly improved strength and physical performance not just immediately but also after 8-weeks of intervention. The muscle fiber type also influences the force-generating capacity of the muscles. Stimulating type II muscle fibers produce a higher specific force than type I fibers. This type of stimulation associated with greater expression of fast-twitch myosin heavy chain isoform through plyometric has proven to increase a muscle’s overall strength and performance (Taradaj et al., 2013; Smith, Hotze & Tate, 2021; Rahmati, Gondin & Malakoutinia, 2021). Although the sham NMES group showed marginal improvements in strength, the magnitude is not the same as the NMES group. Despite the ever-growing scientific research around placebo effects, researchers have continued to present sham procedures with little benefit within clinical research. Brim & Miller (2013) states that the importance of recognizing the extent of the placebo effect from any specific sham-controlled trial is unclear. The placebo effect is well defined for specific sham procedures as it produces a more significant placebo response in more pharmacological research, such as effects on pain rather than enhancing muscular strength, fiber size, and physical performance (Hróbjartsson & Gøtzsche, 2004). In addition, the perception among researchers and the sports staff is that the NMES to improve strength and performance is more appropriate for the clinical population (who struggle to contract muscles actively) rather than the physically active or athletic population (Veldman et al., 2016). Whereas Thomé et al. (2021) and Gondin, Cozzone & Bendahan (2011) supports the current results as the research states that improvements of up to 40% of athletes sporting performance have been concluded through observation with the use of NMES and the cumulative effect of strong, plyometric training and strengthening protocol (Teixeira et al., 2021a). Hasan et al. (2022), PeerJ, DOI 10.7717/peerj.13588 13/18 The magnitude of effect size in strength training research has been considered relatively higher than in social and psychological research (Rhea, 2004). A very large effect size suggests the practical significance of that particular intervention over the study outcomes. The current study revealed a very high effect size in NMES group (d = 10.84) over sham NMES group for the outcome STN. It advocates the practical significance of NMES, application of NMES as add-on modality is very important in improving the muscle strength, thus, enough reason to consider its application in clinical settings. Besides many benefits, this study exhibits few limitations for generalizing its finding to some extent. The study was relatively limited to a short duration of the training period (only 8-weeks) in the context for improving strength and physical performance, limited to a very specific study population (i.e., collegiate male football players), and did not monitor any external factors, such as additional exercises or physical activities of the collegiate male football players other than their actual intervention that could affect the validity of the study findings (Teixeira et al., 2021b). Therefore, this study cannot be generalized to other populations. CONCLUSIONS The research findings suggest that the Plyometric and strength trainings in addition to either NMES or sham NMES for a short-duration training period are equally capable of enhancing the muscular performance, sprint performance, and functional performance of collegiate male football players. NMES has been proven as a training modality to enhance muscular performance, sprint performance, and functional performance in collegiate male football player; therefore, it might be applied to other similar competitive endurance sports, such as football and netball. Future studies will require more than 8-weeks of training that includes a wider range of endurance athletes, with strict monitoring of external factors that might affect the validity of the findings. ACKNOWLEDGEMENTS The authors extend their appreciation to the faculty members of Majmaah University, especially Mr. Raad Ibrahim Alraidan, for their sincere support and assistance in this research study. ADDITIONAL INFORMATION AND DECLARATIONS Funding This work was funded by the deputyship for Research and Innovation, Ministry of Education, Saudi Arabia, Project Number No IFP-2020-25. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Hasan et al. (2022), PeerJ, DOI 10.7717/peerj.13588 14/18 Grant Disclosures The following grant information was disclosed by the authors: Research and Innovation, Ministry of Education, Saudi Arabia: IFP-2020-25. Competing Interests The authors declare that they have no competing interests. Author Contributions  Shahnaz Hasan conceived and designed the experiments, performed the experiments, analyzed the data, authored or reviewed drafts of the article, and approved the final draft.  Gokulakannan Kandasamy conceived and designed the experiments, analyzed the data, authored or reviewed drafts of the article, and approved the final draft.  Danah Alyahya conceived and designed the experiments, authored or reviewed drafts of the article, and approved the final draft.  Asma Alonazi conceived and designed the experiments, authored or reviewed drafts of the article, and approved the final draft.  Azfar Jamal performed the experiments, analyzed the data, prepared figures and/or tables, and approved the final draft.  Amir Iqbal conceived and designed the experiments, analyzed the data, prepared figures and/or tables, and approved the final draft.  Radhakrishnan Unnikrishnan performed the experiments, prepared figures and/or tables, and approved the final draft.  Hariraja Muthusamy performed the experiments, prepared figures and/or tables, and approved the final draft. Human Ethics The following information was supplied relating to ethical approvals (i.e., approving body and any reference numbers): The Chair of Majmaah University for Research Ethics Committee, Saudi Arabia, granted Ethical approval to carry out the study (Ethics number: MUREC-Dec./COM- 2020/13-2 dated 15 December 2020). Data Availability The following information was supplied regarding data availability: The raw measurements are available in the Supplemental Files. Supplemental Information Supplemental information for this article can be found online at http://dx.doi.org/10.7717/ peerj.13588#supplemental-information. REFERENCES Aán D, Lozano D, Arjol-Serrano JL, Mainer-Pardos E, Castillo D, Torrontegui-Duarte M, Nobari H, Jaén-Carrillo D, Lampre M. 2021. Influence of contextual factors on physical demands and technical-tactical actions regarding playing position in professional soccer players. BMC Sports Science, Medicine and Rehabilitation 13:157 DOI 10.1186/s13102-021-00386-x. Hasan et al. (2022), PeerJ, DOI 10.7717/peerj.13588 15/18 Alonazi A, Hasan S, Anwer S, Jamal A, Parvez S, Alfaiz FAS, Li H. 2021. Efficacy of electromyographic-biofeedback supplementation training with patellar taping on quadriceps strengthening in patellofemoral pain syndrome among young adult male athletes. International Journal of Environmental Research and Public Health 18(9):4514 DOI 10.3390/ijerph18094514. Antrich TJ, Brewster CE. 1986. Modification of quadriceps femoris muscle exercise during knee rehabilitation. Physical Therapy 668:1246–1251 DOI 10.1093/ptj/66.8.1246. Augustsson J, Thomee R. 2000. Ability of closed and open kinetic chain tests of muscular strength to assess functional performance. Scandinavian Journal of Medicine & Science in Sports 10(3):164–168 DOI 10.1034/j.1600-0838.2000.010003164.x. Bansal S, Zutshi K, Munjal J. 2020. Effect of neuromuscular electrical stimulation integrated with closed kinetic chain exercises on strength of quadriceps muscle. Indian Journal of Physiotherapy and Occupational Therapy 14(3):49–55 DOI 10.37506/ijpot.v14i3.9666. Barr MJ, Sheppard JM, Gabbett TJ, Newton RU. 2014. Long-term training-induced changes in sprinting speed and sprint momentum in elite rugby union players. Journal of Strength and Conditioning Research 28(10):2724–2731 DOI 10.1519/JSC.0000000000000364. Basas Á, Cook J, Gómez MA, Rafael MA, Ramirez C, Medeiros B, Lorenzo A. 2018. Effects of a strength protocol combined with electrical stimulation on patellar tendinopathy: 42 months retrospective follow-up on 6 high-level jumping athletes. Physical Therapy in Sport 34:105–112 DOI 10.1016/j.ptsp.2018.09.005. Bouguetoch A, Martin A, Grosprêtre S. 2021. Insights into the combination of neuromuscular electrical stimulation and motor imagery in a training-based approach. European Journal of Applied Physiology 121(3):941–955 DOI 10.1007/s00421-020-04582-4. Brim RL, Miller FG. 2013. The potential benefit of the placebo effect in sham-controlled trials: implications for risk-benefit assessments and informed consent. Journal of Medical Ethics 39(11):703–707 DOI 10.1136/medethics-2012-101045. da Cunha RA, Pinfildi CE, de Castro Pochini A, Cohen M. 2020. Photobiomodulation therapy and NMES improve muscle strength and jumping performance in young volleyball athletes: a randomized controlled trial study in Brazil. Lasers in Medical Science 35(3):621–631 DOI 10.1007/s10103-019-02858-6. Gatewood CT, Tran AA, Dragoo JL. 2017. The efficacy of post-operative devices following knee arthroscopic surgery: a systematic review. Knee Surgery, Sports Traumatology, Arthroscopy 25(2):501–516 DOI 10.1007/s00167-016-4326-4. Gobbo M, Maffiuletti NA, Orizio C, Minetto MA. 2014. Muscle motor point identification is essential for optimizing neuromuscular electrical stimulation use. Journal of NeuroEngineering and Rehabilitation 11(1):17 DOI 10.1186/1743-0003-11-17. Gomes da Silva CF, Lima e Silva FX, Vianna KB, Oliveira Gdos S, Vaz MA, Baroni BM. 2018. Eccentric training combined to neuromuscular electrical stimulation is not superior to eccentric training alone for quadriceps strengthening in healthy subjects: a randomized controlled trial. Brazilian Journal of Physical Therapy 22:502–511 DOI 10.1016/j.bjpt.2018.03.006. Gondin J, Cozzone PJ, Bendahan D. 2011. Is high-frequency neuromuscular electrical stimulation a suitable tool for muscle performance improvement in both healthy humans and athletes? European Journal of Applied Physiology 111(10):2473–2487 DOI 10.1007/s00421-011-2101-2. Hamilton RT, Shultz SJ, Schmitz RJ, Perrin DH. 2008. Triple-hop distance as a valid predictor of lower limb strength and power. Journal of Athletic Training 43(2):144–151 DOI 10.4085/1062-6050-43.2.144. Hasan S, Kandasamy G, Alyahya D, Alonazi A, Jamal A, Unnikrishnan R, Muthusamy H, Iqbal A. 2021. Effect of resisted sprint and plyometric training on lower limb functional Hasan et al. (2022), PeerJ, DOI 10.7717/peerj.13588 16/18 performance in collegiate male football players: a randomised control trial. International Journal of Environmental Research and Public Health 18:6702 DOI 10.3390/ijerph18136702. Hróbjartsson A, Gøtzsche PC. 2004. Is the placebo powerless? Update of a systematic review with 52 new randomized trials comparing placebo with no treatment. Journal of Internal Medicine 256(2):91–100 DOI 10.1111/j.1365-2796.2004.01355.x. Kale M, Gurol B. 2019. Effects of electromyostimulation training on jumping and muscle strength in football players. Physical Education of Students 23(5):242–248 DOI 10.15561/20755279.2019.0505. Kim K-M, Croy T, Hertel J, Saliba S. 2010. Effects of neuromuscular electrical stimulation after anterior cruciate ligament reconstruction on quadriceps strength, function, and patient-oriented outcomes: a systematic review. Journal of Orthopaedic & Sports Physical Therapy 40(7):383–391 DOI 10.2519/jospt.2010.3184. LaStayo PC, Woolf JM, Lewek MD, Snyder-Mackler L, Reich T, Lindstedt SL. 2003. Eccentric muscle contractions: their contribution to injury, prevention, rehabilitation, and sport. Journal of Orthopaedic & Sports Physical Therapy 33(10):557–571 DOI 10.2519/jospt.2003.33.10.557. Magalhães J, Oliveira J, Ascensão A, Soares J. 2004. Concentric quadriceps and hamstrings isokinetic strength in volleyball and soccer players. The Journal of Sports Medicine and Physical Fitness 44(2):119–125. Markovic G, Mikulic P. 2010. Neuro-musculoskeletal and performance adaptations to lower-extremity plyometric training. Sports Medicine 40(10):859–895 DOI 10.2165/11318370-000000000-00000. Martimbianco ALC, Torloni MR, Andriolo BN, Porfírio GJ, Riera R. 2017. Neuromuscular electrical stimulation (NMES) for patellofemoral pain syndrome. Cochrane Database of Systematic Reviews 12(12):CD011289 DOI 10.1002/14651858.CD011289.pub2. Myer GD, Faigenbaum AD, Chu DA, Falkel J, Ford K, Best T, Hewett T. 2011. Integrative training for children and adolescents: techniques and practices for reducing sports-related injuries and enhancing athletic performance. The Physician and Sportsmedicine 39(1):74–84 DOI 10.3810/psm.2011.02.1864. Paillard T. 2018. Training based on electrical stimulation superimposed onto voluntary contraction would be relevant only as part of submaximal contractions in healthy subjects. Frontiers in Physiology 9:1428 DOI 10.3389/fphys.2018.01428. Rahmati M, Gondin J, Malakoutinia F. 2021. Effects of neuromuscular electrical stimulation on quadriceps muscle strength and mass in healthy young and older adults: a scoping review. Physical Therapy 101(9):pzab144 DOI 10.1093/ptj/pzab144. Rhea MR. 2004. Determining the magnitude of treatment effects in strength training research through the use of the effect size. The Journal of Strength & Conditioning Research 18(4):918–920 DOI 10.1519/14403.1. Schober P, Boer C, Schwarte LA. 2018. Correlation coefficients: appropriate use and interpretation. Anesthesia & Analgesia 126(5):1763–1768 DOI 10.1213/ANE.0000000000002864. Schulz KF, Altman DG, Moher D, For the CONSORT Group. 2010. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. Journal of Clinical Epidemiology 63(8):834–840 DOI 10.1016/j.jclinepi.2010.02.005. Silvers-Granelli H, Mandelbaum B, Adeniji O, Insler S, Bizzini M, Pohlig R, Junge A, Snyder-Mackler L, Dvorak J. 2015. Efficacy of the FIFA 11+ injury prevention program in the collegiate male soccer player. The American Journal of Sports Medicine 43(11):2628–2637 DOI 10.1177/0363546515602009. Hasan et al. (2022), PeerJ, DOI 10.7717/peerj.13588 17/18 Smith N, Hotze R, Tate AR. 2021. A novel rehabilitation program using neuromuscular electrical stimulation (NMES) and taping for shoulder pain in swimmers: a protocol and case example. International Journal of Sports Physical Therapy 16(2):579 DOI 10.26603/001c.21234. Snyder-Mackler L, Delitto A, Bailey SL, Stralka SW. 1995. Strength of the quadriceps femoris muscle and functional recovery after reconstruction of the anterior cruciate ligament. A prospective, randomized clinical trial of electrical stimulation. The Journal of Bone & Joint Surgery 77(8):1166–1173 DOI 10.2106/00004623-199508000-00004. Taradaj J, Halski T, Kucharzewski M, Walewicz K, Smykla A, Ozon M, Slupska L, Dymarek R, Ptaszkowski K, Rajfur J, Pasternok M. 2013. The effect of neuromuscular electrical stimulation on quadriceps strength and knee function in professional soccer players: return to sport after ACL reconstruction. BioMed Research International 2013(7):1–9 DOI 10.1155/2013/802534. Teixeira JE, Forte P, Ferraz R, Leal M, Ribeiro J, Silva AJ, Barbosa TM, Monteiro AM. 2021a. Monitoring accumulated training and match load in football: a systematic review. International Journal of Environmental Research and Public Health 18(8):3906 DOI 10.3390/ijerph18083906. Teixeira JE, Leal M, Ferraz R, Ribeiro J, Cachada JM, Barbosa TM, Monteiro Aónio M, Forte P. 2021b. Effects of match location, quality of opposition and match outcome on match running performance in a Portuguese professional football team. Entropy 23(8):973 DOI 10.3390/e23080973. Thomé GR, Costa RA, Marquezi ML, Aparecido JML, Durigan Jão LQ, Amorim César F, Liebano RE. 2021. Effects of neuromuscular electrical stimulation on torque and performance in recreational distance runners: a randomized controlled trial. Journal of Bodywork and Movement Therapies 28:369–375 DOI 10.1016/j.jbmt.2021.07.035. Tomlinson KA, Hansen K, Helzer D, Lewis ZH, Leyva WD, McCauley M, Pritchard W, Silvestri E, Quila M, Yi M, Jo E. 2020. The effects of loaded plyometric exercise during warm-up on subsequent sprint performance in collegiate track athletes: a randomized trial. Sports 8(7):101 DOI 10.3390/sports8070101. Veldman MP, Gondin J, Place N, Maffiuletti NA. 2016. Effects of neuromuscular electrical stimulation training on endurance performance. Frontiers in Physiology 7:544 DOI 10.3389/fphys.2016.00544. Walls RJ, McHugh G, O’Gorman DJ, Moyna NM, O’Byrne JM. 2010. Effects of preoperative neuromuscular electrical stimulation on quadriceps strength and functional recovery in total knee arthroplasty. A pilot study. BMC Musculoskeletal Disorders 11(1):1–9 DOI 10.1186/1471-2474-11-119. Wang Y-C, Zhang N. 2016. Effects of plyometric training on soccer players. Experimental and Therapeutic Medicine 12(2):550–554 DOI 10.3892/etm.2016.3419. Wilson JM, Duncan NM, Marin PJ, Brown LE, Loenneke JP, Wilson SMC, Jo E, Lowery RP, Ugrinowitsch C. 2013. Meta-analysis of post activation potentiation and power: effects of conditioning activity, volume, gender, rest periods, and training status. Journal of Strength and Conditioning Research 27:854–859 DOI 10.1519/JSC.0b013e31825c2bdb. Zabaloy S, Giráldez J, Gazzo F, Villaseca-Vicuña R, González JG. 2021. In-season assessment of sprint speed and sprint momentum in rugby players according to the age category and playing position. Journal of Human Kinetics 77(1):274–286 DOI 10.2478/hukin-2021-0025. Zafeiridis A, Saraslanidis P, Manou V, Ioakimidis P, Dipla K, Kellis S. 2005. The effects of resisted sled-pulling sprint training on acceleration and maximum speed performance. The Journal of Sports Medicine and Physical Fitness 45(3):284–290. Hasan et al. (2022), PeerJ, DOI 10.7717/peerj.13588 18/18

Effect of plyometric training and neuromuscular electrical stimulation assisted strength training on muscular, sprint, and functional performances in collegiate male football players.

06-16-2022

Hasan, Shahnaz,Kandasamy, Gokulakannan,Alyahya, Danah,Alonazi, Asma,Jamal, Azfar,Iqbal, Amir,Unnikrishnan, Radhakrishnan,Muthusamy, Hariraja

eng

PMC7379642

Supplement Table 8. Change in VO2max (L·min-1 and ml·min-1·kg-1) from 1995-1997 to 2016-2017 in relation to length of education and age-group. L·min-1 ml·min-1·kg-1 L·min-1 ml·min-1·kg-1 L·min-1 ml·min-1·kg-1 Year n Mean (SD) Change Mean (SD) Change n Mean (SD) Change Mean (SD) Change n Mean (SD) Change Mean (SD) Change 95-97 102 3.05 (0.31) Ref 41.3 (1.18) Ref 378 2.75 (0.33) Ref 35.6 (0.88) Ref 251 2.41 (0.31) Ref 31.4 (1.31) Ref 98-99 111 3.14 (0.33) 3,0% 42.8 (0.69) 3,6% 358 2.71 (0.25) -1,5% 35.2 (0.26) -1,1% 411 2.37 (0.27) -1,7% 30.5 (0.67) -2,9% 00-01 162 2.99 (0.30) -2,0% 40.3 (0.83) -2,4% 567 2.69 (0.31) -2,2% 34.8 (0.14) -2,2% 814 2.34 (0.24) -2,9% 30.2 (0.51) -3,8% 02-03 438 2.94 (0.34) -3,6% 40.3 (1.15) -2,4% 853 2.63 (0.28) -4,4% 33.7 (0.43) -5,3% 1 281 2.28 (0.27) -5,4% 29.6 (0.73) -5,7% 04-05 509 3.04 (0.35) -0,3% 41.2 (0.23) -0,2% 1 189 2.65 (0.27) -3,6% 33.7 (0.12) -5,3% 1 927 2.30 (0.27) -4,6% 29.7 (0.56) -5,4% 06-07 536 3.05 (0.33) 0,0% 40.5 (0.09) -1,9% 1 282 2.66 (0.28) -3,3% 33.5 (0.14) -5,9% 2 091 2.30 (0.28) -4,6% 29.5 (0.86) -6,1% 08-09 659 2.99 (0.28) -2,0% 40.1 (0.25) -2,9% 1 262 2.65 (0.32) -3,6% 33.2 (0.16) -6,7% 2 250 2.30 (0.26) -4,6% 29.2 (0.60) -7,0% 10-11 706 2.93 (0.32) -3,9% 39.1 (0.70) -5,3% 1 037 2.71 (0.29) -1,5% 34.0 (0.24) -4,5% 1 883 2.34 (0.28) -2,9% 29.6 (0.52) -5,7% 12-13 929 2.90 (0.30) -4,9% 38.2 (0.41) -7,5% 1 289 2.62 (0.23) -4,7% 32.9 (0.60) -7,6% 2 166 2.29 (0.24) -5,0% 29.0 (0.30) -7,6% 14-15 982 2.89 (0.28) -5,2% 38.2 (0.04) -7,5% 1 100 2.62 (0.31) -4,7% 32.6 (0.38) -8,4% 1 971 2.25 (0.26) -6,6% 28.4 (0.52) -9,6% 16-17 746 2.85 (0.34) -6,6% 37.0 (0.77) -10,4% 738 2.59 (0.25) -5,8% 32.4 (0.16) -9,0% 962 2.26 (0.28) -6,2% 28.5 (0.51) -9,2% L·min-1 ml·min-1·kg-1 L·min-1 ml·min-1·kg-1 L·min-1 ml·min-1·kg-1 Year n Mean (SD) Change Mean (SD) Change n Mean (SD) Change Mean (SD) Change n Mean (SD) Change Mean (SD) Change 95-97 1 136 3.21 (0.38) Ref 43.9 (0.83) Ref 1 513 2.85 (0.36) Ref 37.8 (0.82) Ref 567 2.42 (0.31) Ref 32.3 (1.31) Ref 98-99 1 434 3.18 (0.38) -0,9% 43.2 (0.89) -1,6% 1 925 2.83 (0.31) -0,7% 37.4 (0.03) -1,1% 1 057 2.42 (0.27) 0,0% 32.4 (0.31) 0,3% 00-01 2 446 3.18 (0.36) -0,9% 42.9 (0.48) -2,3% 3 642 2.82 (0.30) -1,1% 36.8 (0.01) -2,6% 2 310 2.39 (0.27) -1,2% 31.8 (0.20) -1,5% 02-03 4 726 3.11 (0.36) -3,1% 42.0 (0.40) -4,3% 6 734 2.79 (0.33) -2,1% 36.4 (0.42) -3,7% 4 091 2.35 (0.30) -2,9% 31.2 (0.88) -3,4% 04-05 6 059 3.09 (0.36) -3,7% 41.6 (0.45) -5,2% 11 248 2.79 (0.33) -2,1% 36.2 (0.36) -4,2% 7 005 2.35 (0.28) -2,9% 31.1 (0.63) -3,7% 06-07 5 855 3.07 (0.32) -4,4% 41.2 (0.09) -6,2% 11 715 2.80 (0.31) -1,8% 36.0 (0.20) -4,8% 7 597 2.37 (0.29) -2,1% 31.2 (0.65) -3,4% 08-09 6 659 3.07 (0.34) -4,4% 41.0 (0.34) -6,6% 12 806 2.81 (0.31) -1,4% 35.8 (0.10) -5,3% 8 592 2.40 (0.27) -0,8% 31.2 (0.29) -3,4% 10-11 6 058 3.07 (0.32) -4,4% 40.9 (0.07) -6,8% 11 612 2.81 (0.31) -1,4% 35.5 (0.18) -6,1% 7 167 2.40 (0.28) -0,8% 31.0 (0.42) -4,0% 12-13 9 154 3.06 (0.31) -4,7% 40.6 (0.03) -7,5% 15 713 2.78 (0.29) -2,5% 35.2 (0.08) -6,9% 9 971 2.39 (0.26) -1,2% 30.8 (0.29) -4,6% 14-15 10 158 2.99 (0.30) -6,9% 39.8 (0.20) -9,3% 14 665 2.74 (0.28) -3,9% 34.6 (0.18) -8,5% 10 224 2.38 (0.26) -1,7% 30.4 (0.22) -5,9% 16-17 7 732 3.00 (0.30) -6,5% 39.7 (0.40) -9,6% 8 856 2.72 (0.28) -4,6% 34.4 (0.28) -9,0% 6 753 2.38 (0.27) -1,7% 30.3 (0.10) -6,2% Educational level ≤9 years Educational level 10-12 years 18-34 years 35-49 years 50-74 years 18-34 years 35-49 years 50-74 years L·min-1 ml·min-1·kg-1 L·min-1 ml·min-1·kg-1 L·min-1 ml·min-1·kg-1 Year n Mean (SD) Change Mean (SD) Change n Mean (SD) Change Mean (SD) Change n Mean (SD) Change Mean (SD) Change 95-97 116 3.22 (0.36) Ref 45.9 (0.72) Ref 304 2.78 (0.32) Ref 39.0 (0.34) Ref 207 2.53 (0.27) Ref 34.4 (0.11) Ref 98-99 295 3.24 (0.36) 0,6% 46.1 (0.21) 0,4% 566 2.79 (0.31) 0,4% 38.3 (0.13) -1,8% 386 2.49 (0.28) -1,6% 33.9 (0.46) -1,5% 00-01 861 3.22 (0.38) 0,0% 45.6 (0.58) -0,7% 1 039 2.86 (0.32) 2,9% 39.7 (0.18) 1,8% 704 2.48 (0.34) -2,0% 33.8 (1.07) -1,7% 02-03 1 399 3.12 (0.43) -3,1% 44.6 (1.12) -2,8% 1 842 2.80 (0.36) 0,7% 38.7 (0.68) -0,8% 1 265 2.42 (0.30) -4,3% 33.1 (0.72) -3,8% 04-05 3 049 3.13 (0.38) -2,8% 44.6 (0.40) -2,8% 3 857 2.81 (0.35) 1,1% 38.6 (0.60) -1,0% 2 577 2.40 (0.30) -5,1% 32.6 (0.78) -5,2% 06-07 3 352 3.13 (0.38) -2,8% 44.3 (0.64) -3,5% 3 870 2.83 (0.35) 1,8% 38.5 (0.61) -1,3% 2 221 2.44 (0.27) -3,6% 32.9 (0.19) -4,4% 08-09 3 950 3.14 (0.37) -2,5% 44.6 (0.25) -2,8% 4 584 2.89 (0.34) 4,0% 39.2 (0.44) 0,5% 2 717 2.48 (0.29) -2,0% 33.3 (0.46) -3,2% 10-11 3 576 3.14 (0.34) -2,5% 44.5 (0.23) -3,1% 4 969 2.89 (0.35) 4,0% 39.3 (0.56) 0,8% 2 169 2.51 (0.30) -0,8% 33.4 (0.74) -2,9% 12-13 5 654 3.10 (0.34) -3,7% 44.2 (0.02) -3,7% 8 649 2.86 (0.33) 2,9% 39.2 (0.34) 0,5% 3 721 2.47 (0.27) -2,4% 32.9 (0.13) -4,4% 14-15 5 288 3.03 (0.35) -5,9% 42.9 (0.36) -6,5% 7 960 2.81 (0.31) 1,1% 38.4 (0.14) -1,5% 3 236 2.48 (0.27) -2,0% 32.9 (0.19) -4,4% 16-17 3 466 3.04 (0.34) -5,6% 42.6 (0.08) -7,2% 5 099 2.79 (0.31) 0,4% 38.0 (0.16) -2,6% 2 209 2.53 (0.29) 0,0% 33.4 (0.38) -2,9% Educational level >12 years 18-34 years 35-49 years 50-74 years

Decline in cardiorespiratory fitness in the Swedish working force between 1995 and 2017.

11-15-2018

Ekblom-Bak, Elin,Ekblom, Örjan,Andersson, Gunnar,Wallin, Peter,Söderling, Jonas,Hemmingsson, Erik,Ekblom, Björn

eng

PMC3459930

Limited Transfer of Newly Acquired Movement Patterns across Walking and Running in Humans Tetsuya Ogawa1*, Noritaka Kawashima1, Toru Ogata1, Kimitaka Nakazawa2 1 Department of Rehabilitation for the Movement Functions, Research Institute, National Rehabilitation Center for Persons with Disabilities, Namiki, Tokorozawa, Saitama, Japan, 2 Graduate School of Arts and Sciences, The University of Tokyo, Komaba, Meguro, Tokyo, Japan Abstract The two major modes of locomotion in humans, walking and running, may be regarded as a function of different speed (walking as slower and running as faster). Recent results using motor learning tasks in humans, as well as more direct evidence from animal models, advocate for independence in the neural control mechanisms underlying different locomotion tasks. In the current study, we investigated the possible independence of the neural mechanisms underlying human walking and running. Subjects were tested on a split-belt treadmill and adapted to walking or running on an asymmetrically driven treadmill surface. Despite the acquisition of asymmetrical movement patterns in the respective modes, the emergence of asymmetrical movement patterns in the subsequent trials was evident only within the same modes (walking after learning to walk and running after learning to run) and only partial in the opposite modes (walking after learning to run and running after learning to walk) (thus transferred only limitedly across the modes). Further, the storage of the acquired movement pattern in each mode was maintained independently of the opposite mode. Combined, these results provide indirect evidence for independence in the neural control mechanisms underlying the two locomotive modes. Citation: Ogawa T, Kawashima N, Ogata T, Nakazawa K (2012) Limited Transfer of Newly Acquired Movement Patterns across Walking and Running in Humans. PLoS ONE 7(9): e46349. doi:10.1371/journal.pone.0046349 Editor: Alejandro Lucia, Universidad Europea de Madrid, Spain Received May 23, 2012; Accepted August 31, 2012; Published September 27, 2012 Copyright:
2012 Ogawa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by a Grand-in-Aid for Young Scientists (B) from the Japan Society for the Promotion of Science to T. Ogawa. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: [email protected] Introduction In everyday life, humans use two major modes of locomotion: walking and running. By definition, walking is known as a movement in which at least one foot is always in contact with the ground, whereas running involves aerial phases where both feet are off the ground. Both similarities and dissimilarities between the modes have been demonstrated from the perspectives of energetics [1], limb movements [2,3], and muscle functions [2,4,5]. Because of the spontaneous behavior to transit into the opposite modes in accordance with changing speed (walk-run or run-walk transition) [2,6–8], these two movement modes seem dependent on the demand for different locomotion speeds. On the other hand, by referring to earlier studies focusing on the behavioral aspect of human motion in simple upper-limb movements [9,10] and gait [11,12], neural control mechanisms underlying human movement are considered as very specific to given tasks or contexts. Combined with direct evidence obtained in animal models [13,14], there would be a possible independency in the neural mechanisms specific to different modes of locomotion. Walking and running in humans therefore, may not only be dependent on different speeds but also have discrete control mechanisms capable of the respective modes. The present study addressed the possibility by utilizing motor adaptation paradigms that have been well established in the field of motor control, especially in the last decade [9–12]. Based on the hypothesis that independent neural control mechanisms underlie walking and running, we established working hypotheses as follows. 1) After the acquisition of a novel movement pattern (adaptation) in one of the modes, the emergence of the novel pattern in the subsequent trials is evident only within the same mode and limited in the opposite mode (thus, limited transfer across walking and running). In addition, 2) storage of the novel movement pattern in the respective mode is maintained in- dependently of the opposite mode. The acceptance of these working hypotheses will provide indirect evidence of independent neural mechanisms underlying human walking and running. A section of the results in the present study have been presented in abstract form [15]. Methods Subjects Twenty-four healthy male volunteers (age range, 22 to 49 years old) with no known history of neurological or orthopedic disorders participated in the study. Each subject was tested in two of four experimental protocols (Figure 1). Twelve of them participated in experiments 1 and 2, while the other 12 participated in experiments 3 and 4. The order of participation was randomized across subjects. PLOS ONE | www.plosone.org 1 September 2012 | Volume 7 | Issue 9 | e46349 Ethics Statement Each subject gave written informed consent for his participation in the study. The experimental procedures were approved by the local ethics committee of the National Rehabilitation Center for Persons with Disabilities, Japan, and were conducted in accor- dance with the Declaration of Helsinki. Experiment In the present study, the subjects walked and ran on a split-belt treadmill (Bertec, Columbus, OH, USA), having two belts (one underneath each foot), each driven by an independent motor. The treadmill was operated either symmetrically (both belts moving at the same velocity) or asymmetrically (at different velocities). During the baseline period, the treadmill was operated symmet- rically and the velocity was adjusted to 1.5 m s21. This was the speed where all the subjects could both walk and run comfortably in our pilot experiment. Subsequently, the subjects learned to walk (experiments 1 and 2) or run (experiments 3 and 4) on an asymmetrically driven treadmill for 10 minutes. The speed of one belt was increased by one third from the baseline (0.5 m s21), whereas that of the other was decreased by one third; thus, the belt speeds were 2.0 and 1.0 m s21, respectively. The direction of speed change (either faster or slower) was randomized across subjects and the experimental protocol. After the 10-minute adaptation period, the belt speed was returned to symmetry (for the washout periods) as in the baseline periods. Here, the subjects were instructed to walk and run (experiments 1 and 4) or run and walk (experiments 2 and 3) in order for 1 minute each in duration depending on the experimental protocols (Figure 1). Between all testing periods (baseline walk, run, adaptation, washout walk (run), and run (walk)), the treadmill was stopped once and restarted immediately by the experimenter with an acceleration (decelera- tion) of 0.5 m s22. The subjects were instructed to walk or run normally as they looked at a wall approximately 5 meters in front of them and were instructed to refrain from looking down at the treadmill belts in order to avoid any visual biases on the speed. The subjects were also instructed to always start their task by either walking or running from the first step depending on the testing sessions. For safety, one experimenter always stood by the treadmill during the experiment, and the subjects could hold onto handrails mounted on both side of the treadmill in case of risk of falling. However, all the subjects satisfactorily completed the testing sessions without using the handrails. Recordings and Analysis Three orthogonal ground reaction force (GRF) components (mediolateral (Fx), anteroposterior (Fy), and vertical (Fz)) were detected by two force plates mounted underneath each treadmill belt. The force data were low-pass filtered at 5 Hz and were digitized at a sampling frequency of 1 kHz (Power Lab, AD Instruments, Sydney, Australia). From the Fz component of the GRF, the moments of ground contact and toe-off were detected on a stride-to-stride basis using a custom-written program (VEE pro 9.0, Agilent Technologies, Santa Clara, CA, USA). Data on the first stride cycle of each testing session were removed for later analysis in order to minimize the influences of perturbation induced by the initiation of the treadmill movements. The aspects of walking and running were investigated by addressing the peak anterior braking force upon foot contact for every stride cycle. In our pilot study, we demonstrated that, among all of the orthogonal ground reaction force (GRF) components, only this component showed clear aspects of adaptation and aftereffects with the return to symmetrical belt condition in both walking and running. A series of previous studies focused on temporal and spatial gait parameters such as stride and step length, stance and swing time, double support time, and the relationship in the gait phase between the two legs to address adaptive behavior of the split-belt treadmill walking [11,12,16,17]. However, given that gait speed is a quotient of length (spatial) and the time (temporal factors), subjects could potentially employ different strategies across individuals (either walking or running with spatially symmetrical with temporally asymmetrical move- ment patters, temporally symmetrical with spatially asymmetrical movement patterns, or changing the both parameters) with exposure to belt conditions with changing speed. Since the stride cycles taken during the testing sessions varied across subjects and tasks (walk or run), the obtained data were averaged over stride cycles in 3-second bins and were normalized to the mean during the baseline of each movement task (walk or run) to allow intersubject comparisons. For statistical comparisons, two-way analysis of variance (ANOVA) with repeated measures was used to test for statistically Figure 1. Experimental protocols (1 through 4) adopted in the present study. Subjects underwent adaptation tasks of either walking (1 and 2) or running (3 and 4) on an asymmetrically driven treadmill (one belt was set at 1.0 and the other at 2.0 m s21) for 10 minutes. Walking and running patterns on a normally operated treadmill (at 1.5 m s21 bilaterally and 1 minute each in duration) before and after the adaptation were compared on the basis of the modes of adaptation. doi:10.1371/journal.pone.0046349.g001 Neural Control of Human Walking and Running PLOS ONE | www.plosone.org 2 September 2012 | Volume 7 | Issue 9 | e46349 significant differences in the aftereffects, with factors of movement modes (walk or run) or the previously imposed adaptation tasks and the time in the respective 60-second washout period. Data are presented as the mean and standard error of the mean (mean6- SEM). Significance was accepted when P,0.05. Results The number of stride cycles taken under the identical speed differed depending on the movement mode and among subjects. Regardless of the belt condition (symmetric at 1.5 m s21 or asymmetric at 1.0 m s21 and 2.0 m s21), subjects on average took approximately 60 stride cycles for walking and 80 strides for running every minute. All of the subjects reported that their movement patterns were disturbed when returning to the symmetrical belt conditions after walking on the asymmetrically driven treadmill, as described in previous studies [11,16]. For running after adapting to run on asymmetrical belts, subjects also reported their movement patterns as perturbed. Figures 2 and 3, respectively, show typical examples of antero-posterior (braking and propulsion, respectively) ground reaction force waveforms under different time points (A), time series changes in the peak anterior force for both fast and slow sides (B), and the differences in the peak force between the sides (C) on a stride-to-stride basis for walking (Figure 2) and running (Figure 3). During the baseline where the belt conditions were symmetrical, the waveforms were very similar in shape and the amplitude (both anterior and posterior components) between the sides for both walking (Figure 2 (A)) and running (Figure 3 (A)). With exposure to the asymmetrical belt condition, the shapes resulted in prominent differences, an indication of different movement patterns between the fast and the slow sides. For both walking and running, modification in the amplitude of peak anterior braking force took place in the 10-minutes learning periods, including both rapid changes in the earlier phase (up to around 1 minute) followed by slower gradual changes (Figure 2 (B) and Figure 3 (B)). The modification in the amplitude was an increment for the fast side and a decrement for the slow side, respectively. It is especially noticeable here that the braking force in the slow side almost disappeared at the fully adapted state in running (Final of Learning period in Figure 3 (A) and near 10 minutes in the Learning period in Figure 3 (B)). As a consequence, there were large differences between the sides (asymmetry) (Figure 2 (C) and Figure 3 (C)). With return to the symmetrical belt condition (washout), the amplitudes of the force differed to a great extent between the sides despite the identical belt speed to that during the baseline. In detail, there were initially an overshoot in the amplitude for the fast side and an undershoot in the slow side for both walking and running (in comparison to the baseline). In the 1-minute washout period, the amplitudes of both sides decayed toward those found in the baseline (into the opposite direction to the changes during the learning periods). An important fact here is that the movements were initially disturbed upon walking on symmetrical belt after adapting to walk, and running after adapting to run, on the asymmetrically driven treadmill surface. The disturbance in the Figure 2. Descriptions of adaptation on the asymmetrically driven treadmill and the emergence of the aftereffect with release from the novel environment in walking in a single subject (showing only the walking periods from Experiment 1). (A) Waveforms of the antero-posterior ground reaction force under different time points in the experiment. Each waveform represents an ensemble average of five consecutive stride cycles (from heel contact to the subsequent heel contact) in the respective time points. The solid lines represent the fast-moving side and the dotted lines are those of the slow side during the adaptation period. (B) Stride-to-stride profile of the peak anterior braking force for both fast and slow sides. Filled circles and open circles represent the fast and slow sides, respectively. (C) Stride-to-stride profile of the differences in peak anterior braking force between the fast and slow sides. doi:10.1371/journal.pone.0046349.g002 Neural Control of Human Walking and Running PLOS ONE | www.plosone.org 3 September 2012 | Volume 7 | Issue 9 | e46349 movements were then, followed by gradual decay (restoring normal movements) in the following 1 minute. It should be noted that modification in the force occurred in the posterior (propulsive) component as well. In the representative waveform (Figure 3 (A)), for example, the posterior force in the fast side showed a sudden increase with exposure to the asymmetrical belt but subsequently disappeared at the end of the learning period. Combined with that in the slow side which showed a modification into the opposite direction (increase), there was large asymmetry at the initial state of the washout period. The asymmetry, however, was prominent only in running and not in walking. We therefore used anterior braking force (disturbed both in walking and running) as parameter in the present study. Given the initial disturbance in the movement patterns (asymmetry in the braking force) in both movement modes after adapting in each mode, the primary interest in the present study was whether the movement pattern acquired through each mode transferred to (or shared with) the other mode. Figure 4 (A) compares the extent of asymmetry in walking on identical belt conditions after adapting to walk (blue line) and after adapting to run (light blue line) as differences in the peak force between the sides. In contrast to the large asymmetry after learning to walk, the emergence of aftereffect was only partial (only reactively present in the first few seconds). ANOVA comparison revealed a significant difference between walking with different history (learned to walk or run) in previously imposed adaptation modes (F1, 22 = 7.285, P,0.05). On the other hand, the degree of aftereffect during running with a different adaptation history is described in Figure 4 (B). In comparison to the prominent asymmetry in the running patterns after adapting to run, individuals who adapted to walk showed far less asymmetry (F1, 22 = 15.914, P,0.01). Secondly, to further consider the independence or commonality of each movement mode in relation to the other, we investigated the extent of a possible washout in the acquired movement patterns in one mode by the other (Figures 5 and 6). As partially described in the results above, the subjects could both walk and run as normal at the end of the first washout period after adapting in the opposite modes (shown in the left columns in Figures 5 and 6). The subsequent attempts to run (right column, Figure 5) and walk (Figure 6) resulted in prominent asymmetry in the movement patterns, demonstrating little or no washout by the execution of the opposite mode. That is, the acquired movement patterns (asymmetry) were maintained independently of the subsequent trials in the opposite modes. ANOVA showed significant differences in the degree of asymmetry in the movement patterns between the first and second washout periods (F1, 11 = 6.109, P,0.05, for 1) walking, and 2) running after adapting to run (F1, 11 = 6.914, P,0.05, for 1) run and 2) walk after adapting to walk). Discussion The present results strongly confirmed our working hypotheses and demonstrated that 1) transfer of the novel movement patterns learned on an asymmetrically driven treadmill from one mode to another took place only partially for both directions (walk to run and run to walk), and 2) the learned movement patterns in the Figure 3. Descriptions of adaptation on the asymmetrically driven treadmill and the emergence of an aftereffect with release from the novel environment in running in a single subject (only the running periods from Experiment 3 are shown). (A) Waveforms of the antero-posterior ground reaction force under different time points in the experiment. Each waveform represents an ensemble average of five consecutive stride cycles (from heel contact to the subsequent heel contact) in the respective time points. The solid lines represent the fast-moving side and the dotted lines are those of the slow side during the adaptation period. (B) Stride-to-stride profile of the peak anterior braking force for both fast and slow sides. Filled circles and open circles represent the fast and slow sides, respectively. (C) Stride-to-stride profile of the differences in peak anterior braking force between the fast and the slow sides. doi:10.1371/journal.pone.0046349.g003 Neural Control of Human Walking and Running PLOS ONE | www.plosone.org 4 September 2012 | Volume 7 | Issue 9 | e46349 respective modes were rarely washed out by the subsequent execution in the opposite modes, again, for both directions. That is, the storage of a learned movement patterns were maintained independently of the opposite mode. Combined, these results demonstrated only partially overlapped elements between these two movement modes and thus support the notion of mostly independent functional networks within the CNS for the respective locomotive modes. Walking and running, therefore, reflect not only functions of different speeds of locomotion, but are different forms from the perspective of neural control mechanisms. The notion of task-specific or context-specific neural mechan- isms has been well established by using simple reaching move- ments in the upper extremities [9,10]. Locomotive movements that are more complex and autonomic have also been found as under the specificity, such as the direction (forward-backward) [11], the limb (right-left) [11], and the speed of walking [12]. Limitations in the transfer or washout in newly acquired movement patterns under certain physical constraints in one movement tasks to or by another have been accepted as indirect evidence demonstrating the specificity [9–12]. By adopting the well-established experimental paradigms in the earlier studies, the present study is the first to address the mode-specificity, comprising an important aspect of locomotion. Because of the well-known spontaneous behavior to transit into the opposite mode (walk-run or run-walk transition) in accordance with changing speed [2,6–8], walking and running may only be considered as a function of demands for different speeds. The use of split-treadmill walking to modify gait symmetry has been studied extensively in the last decade [11,12,6]. After walking on an asymmetrically-driven treadmill for a certain period of time, the movement pattern after release from the novel environment resulted in prominent asymmetry [11,12,16]. The current study, for the first time, demonstrated that movement patterns in running also could be modified as in the earlier studies focusing on walking. Detailed explanations on how the gait patterns could be adapted with exposure to the asymmetrically driven treadmill and resulted in the subsequent aftereffect have been provided previously both behaviorally and mathematically on the basis of locomotion in decerebrate cat [18]. In the present study, the modification in the gait patterns was most evident in the anterior braking component of the ground reaction force both in walking and running and we therefore focused on this parameter (detailed description in the Methods). As subjects adapted to walk or run comfortably on the asymmetrically driven treadmill, the patterns of modification in the anterior braking force showed gradual increment in the fast side and decrement in the slow side, both including brief and more rapid changes in the early phases of exposure. As a consequence, with return to the symmetrical belt in the washout period, there was initially an overshoot in the force in the fast side and an undershoot for the slow side, both followed by gradual decay into the opposite direction to those during the adaptation periods (towards baseline). Combined with results in a previous study in which novel motor pattern could be stored intralimb and independently for each leg [11], these phenomena occurring for the each limb may reflect the well-established notion of motor adaptation or learning where motor output is recalibrated to meet new task demands [19]. It is reasonable to consider that the asymmetry in the anterior braking force took place based on the recalibration of motor output in each leg under different velocity on an asymmetrically driven treadmill. The motor output acquired through the above mentioned recalibration processes, however, were only partially shared across the movement modes. Given the results, with the possibility of specificity in the neural mechanisms underlying walking and running, the discussion will now focus on the possible neural mechanisms comprising the different modes. Based on the results of animal studies and of humans, the neural mechanisms underlying the present results could be attributed to possible contribution of supraspinal structures in the brain and the Figure 4. Degree of transfer in the acquired movement pattern across walking and running, shown as differences in the peak braking force between the sides. The extent of asymmetry in (A) walking after adaptation to walk (first washout period in Experiment 1, darker line) and after adaptation to run (first washout period in Experiment 4, lighter line), and (B) running after adaptation to run (first washout period in Experiment 3, darker line) and after adaptation to run (first washout period in Experiment 2, lighter line). Data are normalized to the mean of those during the baseline on a subject-to-subject basis and are presented as the mean (thick line) and the standard errors of the mean (dotted lines). doi:10.1371/journal.pone.0046349.g004 Neural Control of Human Walking and Running PLOS ONE | www.plosone.org 5 September 2012 | Volume 7 | Issue 9 | e46349 specificity in the locomotor center in the spinal cord, known as the central pattern generator (CPG). First, in the emergence of the adaptive phenomena, the cerebellum is considered to play a significant role by recalibrating motor output that satisfies the task or environmental demand [20]. Given its function, any aspect of an aftereffect following adaptation is abolished in humans [17] and in cats [21] with cerebellar lesions. Morton et al. (2006) [17] reported that a predictive feedforward motor adaptation in splitbelt treadmill walking that is demonstrated to occur in healthy subjects [11,12,16] does not in patients with cerebellar damage. More direct evidence showed that plasticity of synaptic transmission efficacy in the cerebellum that was modified by concentration of nitric oxide (NO) played a significant role in locomotive adaptation in decerebrate cat [21]. Interestingly, regarding movement specificity, various aspects of limb movement such as direction, velocity, acceleration and force have been demonstrated to be represented in the cerebellum, as shown by discharge rate in single unit recording in the cerebellum [22]. In the present study, since the subjects performed both walking and running under identical belt speed, in which the limb movements do not simply depend on locomotion speed but are demonstrated to differ across the modes [3], it is possible that there were different representation for each locomotive mode. Along with the cerebellar function, the contribution of the descending neural drive from the supraspinal centers, especially those from the mesencephalic locomotor region (MLR) in the brainstem, provides an additional explanation for the mode- specificity. For example, in decerebrate salamander, electrical microstimulation at a particular site in the MLR resulted in a phase-dependent electromyographic (EMG) burst and conse- Figure 5. Degree of washout in the stored motor pattern in running by walking (first and second washout periods shown consecutively from Experiment 4). The asymmetrical movement pattern was evident with the initiation of running (red lines) despite a symmetrical walking pattern at the end of the first washout period in walking (blue lines), an indication of only partial washout (also described in the schematic figure). Data are presented as means (thick lines) and their standard errors of the mean (dotted lines). doi:10.1371/journal.pone.0046349.g005 Neural Control of Human Walking and Running PLOS ONE | www.plosone.org 6 September 2012 | Volume 7 | Issue 9 | e46349 quently locomotor-like movements of the body [23]. In the emergence of these behaviors, two different locomotor modes (stepping and swimming) were exhibited with different current intensities [23]. Or, more classically, an increase in stimulus intensity to the mid-brain in decerebrate cats walking on a treadmill caused them to gallop [24]. From these results, the intensities in the descending drive may significantly affect the decision of different locomotive modes. In the current study, although speculative, the gait pattern upon the initiation of walking after adapting to run was reactively disturbed (the prominent asymmetry in the first few seconds, shown by the light blue line in Figure 4). This reaction may reflect the component of running. That is, to accelerate the center of body mass upon acceleration of the treadmill by increasing the descending drive from the locomotor centers. Consequently, this could result in the partial emergence of the asymmetrical movement pattern pre- viously acquired in running. Regarding the specificity in the locomotor center in the spinal cord, on the other hand, it was recently demonstrated that specific sets of spinal interneurons are activated depending on locomotion (swimming) frequency in larval zebrafish [14]. Locomotion behavior in larval zebrafish was previously characterized as having two different modes [25]. One is the mode used to move routinely in water with lower movement frequencies and small yaw amplitudes, while the other is the escape movement with higher frequencies with larger yaws [25]. On the execution of these locomotor behaviors by zebrafish, McLean et al. (2008) [14] showed that, in contrast to motoneurons that are additionally recruited with increasing swimming frequencies following classic size principle, the activities in some sets of interneurons evident Figure 6. Degree of washout in the stored motor pattern in walking by running (first and second washout periods shown consecutively from Experiment 2). The asymmetrical movement pattern was evident with the initiation of walking (blue lines) despite the symmetrical walking pattern at the end of the first washout period in running (red lines), an indication of only partial washout (also described in the schematic figure). Data are presented as means (thick lines) and their standard errors of the mean (dotted lines). doi:10.1371/journal.pone.0046349.g006 Neural Control of Human Walking and Running PLOS ONE | www.plosone.org 7 September 2012 | Volume 7 | Issue 9 | e46349 under lower swimming frequency were inhibited during swimming at higher frequencies [14]. In other animal models, such as in a fictive scratching movement in the turtle hindlimb, it was found that different populations of propriospinal neurons were identified with respect to two different modes of scratching movements [13]. Based on these previous results in animal models, it is speculated that the specific structures to be selected in the spinal cord depending on the modes might explain the underlying differences in the neural mechanisms between walking and running in humans. Regarding adaptation as observed in the present study and in previous studies [11 12,16], the spinal cord itself is known to be capable of adapting locomotor patterns, as predominantly demonstrated in the stepping movement of human infants [26] or in cats that underwent complete spinal cord transection [27]. The relationship between mode specificity and adaptation remains unclear. It is however, reasonable to consider that the acquisition of the novel movement patterns took place in particular sites in the spinal cord or in combination with the higher structures depending on the mode, at least before motoneuron, which is the final common pathway to muscles. The acquired movement patterns were therefore only partially transferred to the opposite modes, which have different responsible sites and were rarely washed out by the execution of the opposite ones. In summary, the two major modes of human locomotion, walking and running, are not only functions of different speed but have fundamentally different neural control mechanisms. The present results provide extremely important implications for the construction of training regimens in locomotive movements in both athletic training and rehabilitation processes. Further considerations should be made among other locomotive tasks or those under different physical constraints. Acknowledgments The authors thank Dr. Bimal Lakhani for editing the English in the manuscript. Author Contributions Conceived and designed the experiments: T. Ogawa NK T. Ogata KN. Performed the experiments: T. Ogawa. Analyzed the data: T. Ogawa. Contributed reagents/materials/analysis tools: T. Ogawa NK. Wrote the paper: T. Ogawa NK T. Ogata KN. References 1. Farley CT, McMahon TA (1992) Energetics of walking and running: insights from simulated reduced-gravity experiments. J Appl Physiol 73: 2709–2712. 2. Prilutsky BI, Gregor RJ (2001) Swing- and support-related muscle actions differently trigger human walk-run and run walk transitions. J Exp Biol 204: 2277–2287. 3. Ivanenko YP, Cappellini G, Dominici N, Poppele RE, Lacquaniti F (2007) Modular control of limb movements during human locomotion. J Neurosci 27: 11149–11161. 4. Sasaki K, Neptune RR (2006) Differences in muscle function during walking and running at the same speed. J Biomech 39: 2005–2013. 5. Cappellini G, Ivanenko YP, Poppele RE, Lacquaniti F (2006) Motor patterns in human walking and running. J Neurophysiol 95: 3426–3437. 6. Diedrich FJ, Warren WH Jr (1995) Why change gaits? Dynamics of the walk-run transition. J Exp Psychol 21: 183–202. 7. Kram R, Domingo A, Ferris DP (1997) Effect of reduced gravity on the preferred walk-run transition speed. J Exp Biol 200: 821–826. 8. Bartlett JL, Kram R (2008) Changing the demand on specific muscle groups affects the walk-run transition speed. J Exp Biol 211: 1281–1288. 9. Nozaki D, Kurtzer I, Scott SH (2006) Limited transfer of learning between unimanual and bimanual skills within the same limb. Nat Neurosci 9: 1364– 1366. 10. Ikegami T, Hirashima M, Taga G, Nozaki D (2010) Asymmetric transfer of visuomotor learning between discrete and rhythmic movements. J Neurosci 30: 4515–4521. 11. Choi JT, Bastian AJ (2007) Adaptation reveals independent control networks for human walking. Nat Neurosci 10: 1055–1062. 12. Vasudevan EV, Bastian AJ (2010) Split-belt treadmill adaptation shows different functional networks for fast and slow human walking. J Neurophysiol 103(1): 183–191. 13. Berkowitz A, Stein PSG (1994) Activity of descending propriospinal axons in the turtle hindlimb enlargement during two forms of fictive scratching: phase analysis. J Neurosci 14: 5105–5119. 14. McLean DL, Masino MA, Koh IYY, Lindquist WB, Fetcho JR (2008) Contimuous shifts in the active set of spinal interneurons during changes in locomotor speed. Nat Neurosci 11: 1419–1429. 15. Ogawa T, Kawashima N, Nakazawa K, Ogata T (2011) Limited transfer of novel movement pattern between walking and running under same velocity. Soc Neurosci Abstract. 16. Reisman DS, Block HJ, Bastian AJ (2005) Interlimb coordination during locomotion: what can be adapted and stored? J Neurophysiol 94: 2403–2415. 17. Morton SM, Bastian AJ (2006) Cerebellar contributions to locomotor adaptations during splitbelt treadmill walking. J Neurosci 26: 9107–9116. 18. Ito S, Yuasa H, Luo ZW, Ito M, Yanagihara D (1998) A mathematical model of adaptive behavior in quadruped locomotion. Biol Cybern 78: 337–347. 19. Shadmehr R, Mussa-Ivaldi FA (1994) Adaptive representation of dynamics during learning of a motor task. J Neurosci. 14: 3208–3224. 20. Bastian AJ (2006) Learning to predict the future: the cerebellum adapts feedforward movement control. Curr Opin Neurobiol 16: 645–649. 21. Yanagihara D, Kondo I (1996) Nitric oxide plays a key role in adaptive control of locomotion in cat. Proc Natl Acad Sci U S A. 93: 13292–7. 22. Thach WT (1978) Correlation of neural discharge with pattern and force of muscular activity, joint position, and direction of intended next movement in motor cortex and cerebellum. J Neurophysiol 41: 654–676. 23. Cabelguen JM, Bourcier-Lucas C, Dubuc R (2003) Bimodal locomotion elicited by electrical stimulation of the midbrain in the salamander notophathalmus viridescens. J Neurosci 23: 2434–2439. 24. Shik ML, Severin FV, Orlovsky GN (1966) Control of walking and running by means of electrical stimulation of the mid-brain. Biophys 11: 756–765. 25. Budick SA, O’Malley DM (2000) Locomotor repertoire of the larval zebrafish: swimming, turning and prey capture. J Exp Biol 203: 2565–2579. 26. Lam T, Wolstenholme C, Yang JF (2003) How do infants adapt to loading of the limb during the swing phase of stepping? J Neurophysiol 89: 1920–1928. 27. Hodgson JA, Roy RR, De Leon R, Dobkin B, Edgerton VR (1994) Can the mammalian spinal cord learn a motor task? Med Sci Sports Exerc 26: 1491– 1497. Neural Control of Human Walking and Running PLOS ONE | www.plosone.org 8 September 2012 | Volume 7 | Issue 9 | e46349

Limited transfer of newly acquired movement patterns across walking and running in humans.

09-27-2012

Ogawa, Tetsuya,Kawashima, Noritaka,Ogata, Toru,Nakazawa, Kimitaka

eng

PMC6466240

Table S1. Overall times in the Ironman World Championship from 1983 to 2018 for women and men. MEN WOMEN YEAR POSITION Total Race Time (s) Total Race Time (hr:min:sec) Total Race Time (s) Total Race Time (hr:min:sec) 1983 1º 32640 09:04:00 38520 10:42:00 2º 32700 09:05:00 38880 10:48:00 3º 33600 09:20:00 39600 11:00:00 1984 1º 32060 08:54:20 32060 08:54:20 2º 33525 09:18:45 33523 09:18:43 3º 33835 09:23:55 33835 09:23:55 1985 1º 31854 08:50:54 37551 10:25:51 2º 33400 09:16:40 37614 10:26:54 3º 33992 09:26:32 37063 10:17:43 1986 1º 30480 08:28:00 35340 09:49:00 2º 30960 08:36:00 35460 09:51:00 3º 32400 09:00:00 35940 09:59:00 1987 1º 30675 08:31:15 33944 09:25:44 2º 31519 08:45:19 34260 09:31:00 3º 32333 08:58:53 33978 09:26:18 1988 1º 30660 08:31:00 32460 09:01:00 2º 30791 08:33:11 33133 09:12:13 3º 31117 08:38:37 34644 09:37:24 1989 1º 29355 08:09:15 32456 09:00:56 2º 29413 08:10:13 33714 09:21:54 3º 30736 08:32:16 33871 09:24:31 1990 1º 30497 08:28:17 33222 09:13:42 2º 31060 08:37:40 33600 09:20:00 3º 31164 08:39:24 36033 10:00:33 1991 1º 29912 08:18:32 32872 09:07:52 2º 30274 08:24:34 33818 09:23:38 3º 30475 08:27:55 34400 09:33:20 1992 1º 29348 08:09:08 32128 08:55:28 2º 29789 08:16:29 33700 09:21:40 3º 29849 08:17:29 34017 09:26:57 1993 1º 29265 08:07:45 32303 08:58:23 2º 29667 08:14:27 32884 09:08:04 3º 30013 08:20:13 33640 09:20:40 1994 1º 30027 08:20:27 33614 09:20:14 2º 30272 08:24:32 34088 09:28:08 3º 30716 08:31:56 35010 09:43:30 1995 1º 30034 08:20:34 33406 09:16:46 2º 30179 08:22:59 33913 09:25:13 3º 30323 08:25:23 34668 09:37:48 1996 1º 29048 08:04:08 32809 09:06:49 2º 29167 08:06:07 33079 09:11:19 3º 29937 08:18:57 33553 09:19:13 1997 1º 30781 08:33:01 34168 09:29:28 2º 31158 08:39:18 34302 09:31:42 3º 31119 08:38:39 34958 09:42:38 1998 1º 30260 08:24:20 33652 09:20:52 2º 30717 08:31:57 33880 09:24:40 3º 30777 08:32:57 33974 09:26:14 1999 1º 29837 08:17:17 33009 09:10:09 2º 30174 08:22:54 33457 09:17:37 3º 30342 08:25:42 33727 09:22:07 2000 1º 30060 08:21:00 33828 09:23:48 2º 30189 08:23:09 33978 09:26:18 3º 30404 08:26:44 34171 09:29:31 2001 1º 30678 08:31:18 33935 09:25:35 2º 31570 08:46:10 34208 09:30:08 3º 31660 08:47:40 34677 09:37:57 2002 1º 30596 08:29:56 32704 09:05:04 2º 30786 08:33:06 33105 09:11:45 3º 30934 08:35:34 33446 09:17:26 2003 1º 30155 08:22:35 32881 09:08:01 2º 30767 08:32:47 33272 09:14:32 3º 30951 08:35:51 33527 09:18:47 2004 1º 30809 08:33:29 35098 09:44:58 2º 31420 08:43:40 35494 09:51:34 3º 31514 08:45:14 35855 09:57:35 2005 1º 29657 08:14:17 32455 09:00:55 2º 29976 08:19:36 32841 09:07:21 3º 30004 08:20:04 32919 09:08:39 2006 1º 29516 08:11:56 33226 09:13:46 2º 29587 08:13:07 33522 09:18:42 3º 29944 08:19:04 33657 09:20:57 2007 1º 29734 08:15:34 32662 09:04:22 2º 29944 08:19:04 32970 09:09:30 3º 30090 08:21:30 33299 09:14:59 2008 1º 29865 08:17:45 32540 09:02:20 2º 30050 08:20:50 33410 09:16:50 3º 30083 08:21:23 33414 09:16:54 2009 1º 30021 08:20:21 31784 08:49:44 2º 30176 08:22:56 32994 09:09:54 3º 30272 08:24:32 33077 09:11:17 2010 1º 29437 08:10:37 32064 08:54:24 2º 29537 08:12:17 32467 09:01:07 3º 29594 08:13:14 32730 09:05:30 2011 1º 29036 08:03:56 31837 08:50:37 2º 29351 08:09:11 32023 08:53:43 3º 29467 08:11:07 32351 08:59:11 2012 1º 29917 08:18:37 33082 09:15:54 2º 30220 08:23:40 33154 09:16:58 3º 30249 08:24:09 33448 09:21:41 2013 1º 29549 08:12:29 31668 08:52:14 2º 29719 08:15:19 31991 08:57:28 3º 29964 08:19:24 32110 09:03:35 2014 1º 29658 08:14:18 32188 09:00:55 2º 29963 08:19:23 32308 09:02:57 3º 30032 08:20:32 32403 09:04:23 2015 1º 29680 08:14:40 32017 08:57:57 2º 29863 08:17:43 32782 09:10:59 3º 29930 08:18:50 33003 09:14:52 2016 1º 29190 08:06:30 31327 08:46:46 2º 29402 08:10:02 32758 09:10:30 3º 29474 08:11:14 32835 09:11:32 2017 1º 28900 08:01:40 31582 08:50:47 2º 29047 08:04:07 32116 08:59:38 3º 29231 08:07:11 32194 09:01:38 2018 1º 28359 07:52:39 30039 08:26:18 2º 28601 07:56:41 30734 08:36:34 3º 28869 08:01:09 31046 08:41:58

Celebrating 40 Years of Ironman: How the Champions Perform.

03-20-2019

Barbosa, Lucas Pinheiro,Sousa, Caio Victor,Sales, Marcelo Magalhães,Olher, Rafael Dos Reis,Aguiar, Samuel Silva,Santos, Patrick Anderson,Tiozzo, Eduard,Simões, Herbert Gustavo,Nikolaidis, Pantelis Theodoros,Knechtle, Beat

eng

PMC8874289

Physiological Reports. 2022;10:e15158. | 1 of 14 https://doi.org/10.14814/phy2.15158 wileyonlinelibrary.com/journal/phy2 Received: 2 December 2021 | Accepted: 7 December 2021 DOI: 10.14814/phy2.15158 O R I G I N A L A R T I C L E Aerobic exercise training in older men and women— Cerebrovascular responses to submaximal exercise: Results from the Brain in Motion study Sonja L. Lake1,2,3 | Veronica Guadagni1,2,4,5 | Karen D. Kendall1,3 | Michaela Chadder1,3 | Todd J. Anderson6,7 | Richard Leigh8 | Jean M. Rawling9 | David B. Hogan2,4,5,8,10,11 | Michael D. Hill2,4,7,10 | Marc J. Poulin1,2,4,5,7,12,13 1Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada 2Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada 3Clinical & Translational Exercise Physiology Lab, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada 4Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada 5O’Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada 6Department of Cardiac Sciences, Libin Cardiovascular Institute of Alberta, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada 7Libin Cardiovascular Institute of Alberta, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada 8Department of Medicine, University of Calgary, Calgary, Alberta, Canada 9Department of Family Medicine, University of Calgary, Calgary, Alberta, Canada 10Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada 11Division of Geriatric Medicine, Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada 12Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada 13Brenda Strafford Foundation Chair in Alzheimer Research, Calgary, Alberta, Canada This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2022 The Authors. Physiological Reports published by Wiley Periodicals LLC on behalf of The Physiological Society and the American Physiological Society. Sonja L. Lake and Veronica Guadagni are equally contributing first authors. Correspondence Marc J. Poulin, Laboratory of Human Cerebrovascular Physiology, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Room 210 - Heritage Medical Research Building, 3310 Hospital Drive NW, Calgary, AB T2N 4N1, Canada. Email: [email protected] Funding information The Brain in Motion Study is funded by Canadian Institutes of Health Research (CIHR, MOP142470) and The Brenda Strafford Foundation Chair in Alzheimer Research (BSFCAR). S.L. was supported by an Alberta Innovates Summer Studentship. V.G. Abstract Physical inactivity is a leading modifiable risk factor for cardiovascular and cer- ebrovascular disease, cognitive dysfunction, and global mortality. Regular exer- cise might mitigate age- related declines in cardiovascular and cerebrovascular function. In this study, we hypothesize that a 6- month aerobic exercise interven- tion will lead to a decrease in cerebrovascular resistance index (CVRi) and to an increase in cerebral blood flow (CBF) and cerebrovascular conductance index (CVCi) during two submaximal exercise workloads (40% VO2max and 65 W), in- tensities that have been shown to be comparable to activities of daily life. Two hundred three low- active healthy men and women enrolled in the Brain in Motion study, completed a 6- month exercise intervention and underwent sub- maximal and maximal tests pre- /post- intervention. The intervention improved 2 of 14 | LAKE et al. 1 | INTRODUCTION Declines in cerebral blood flow (CBF) often observed with advancing age are considered to be an important contrib- utor to cognitive decline, as well as cardiovascular and cerebrovascular diseases (Leeuwis et al., 2018). These declines in CBF are observed at rest and in response to various challenges (Tarumi & Zhang, 2018) and they are thought to reflect a decline in cerebrovascular reserve, a term used to describe “the ability of cerebral blood vessels to respond to increased metabolic demand and chemical, mechanical, or neural stimuli” (Davenport et al., 2012). Accordingly, an increase in blood flow to the brain is war- ranted when demand is increased, with relatively little en- suing change in cerebral blood pressure (Ogoh & Ainslie, 2009; Paulson et al., 1990; Silverman and Petersen, 2021). Cerebrovascular regulation is a multi- factorial process influenced by factors such as the arterial partial pressure of CO2 (PaCO2), cerebral metabolism and neurogenic activity, cardiac output and mean arterial pressure (MAP) (Ainslie & Duffin, 2009; Hoiland et al., 2019; Ogoh & Ainslie, 2009; Phillips et al., 2016). MAP gradually increases with usual aging partially due to arterial stiffening and fibro- sis (Fontana, 2018; Franklin et al., 1997). Arterial stiffen- ing leads to a reduced ability of the blood vessels to dilate, which can contribute to a further reduction in brain perfu- sion (i.e., and oxygen delivery). Cerebrovascular resistance index (CVRi) and cerebrovascular conductance index (CVCi) are traditional indices used to measure the vascular tone and the ability of the cerebrovasculature to react to stimuli (i.e., increases in arterial PCO2 or blood pressure; Joyce et al., 2019; Lautt, 1989). Both indices are metrics of vascular tone, but CVRi is normally used when changes in tone are primarily driven by changes in pressure, while CVCi is used when changes in tone are primarily driven by changes in flow (Joyce et al., 2019; Lautt, 1989). Resting CVRi increases with advancing age as shown in a study that compared young control participants to older ones (Tarumi & Zhang, 2018). Conversely, CVCi has been shown to be decreased in post- menopausal women when measured at rest and during moderate- intensity submaximal exercise (Brown et al., 2010). Regular exercise has been shown to mitigate age- related declines in cardiovascular and cerebrovascular capacity (Kirk- Sanchez & McGough, 2014; Ngandu et al., 2015; Pentikäinen et al., 2019; Stensvold et al., 2020). Higher car- diorespiratory fitness benefits both systemic and cerebral circulations, and reduces the adverse neurobiological and cognitive consequences of aging, suggesting that regular exercise may be protective for the brain and may attenu- ate the age- related reduction in CBF (Brown et al., 2010; Chapman et al., 2013; Franklin et al., 1997; Gajewski & Falkenstein, 2016; Guadagni et al., 2020; Huggett et al., 2005; Kirk- Sanchez & McGough, 2014; Tarumi & Zhang, 2018). In a previous study, Murrell et al. (2013) analyzed changes in CBF and cerebrovascular reactivity (i.e., cerebral vascula- ture response to 5% inspired CO2 [PiCO2]), during both rest and submaximal exercise (30% and 70% HRR) before and after a 12- week aerobic exercise intervention in both young and middle- aged adults. They reported no change in resting middle cerebral artery (MCAv) but an increase in cerebro- vascular reactivity (after correcting for a post- intervention decrease in resting end- tidal PCO2). These results suggest a beneficial role of exercise training on cerebrovascular func- tion in middle- aged adults. was supported by a BRAIN CREATE Postdoctoral Fellowship supported by The Brenda Strafford Centre on Aging, within the O'Brien Institute for Public Health, by an Alzheimer Society of Canada Postdoctoral Fellowship, and a Canadian Institutes of Health Research (CIHR) Postdoctoral Fellowship. The funders played no role in the concept and design of this study, analysis or interpretation of the data, or drafting and critical revision of the manuscript. the gas exchange threshold and maximal oxygen consumption (VO2max), with no change in heart rate at VO2max, during the treadmill VO2max test. Heart rate and CVRi decreased from pre- intervention values during both relative (40% VO2max) and absolute (65  W) submaximal exercise tests. Blood flow velocity in the middle cerebral artery and CVCi increased post- intervention during 40% VO2max and 65 W. Changes in mean arterial pressure were found only during the absolute component (65 W). Our study demonstrates that aerobic exercise improves not only cardiorespiratory indices but also cerebrovascular function at submaximal workloads which may help to mitigate age- related declines in eve- ryday life. Investigation of the mechanisms underlying the decline in cardiovas- cular and cerebrovascular capacity with aging has important implications for the maintenance of health and continued independence of older adults. K E Y W O R D S aerobic exercise intervention, aging, cardiorespiratory fitness, cerebral blood flow, cerebrovascular function  | 3 of 14 LAKE et al. Previous literature has shown associations between declines in CBF and objective parameters of cardiorespi- ratory fitness such as maximal oxygen uptake (VO2max) (Huggett et al., 2005), and increased risk of neurodegener- ative diseases (Ainslie et al., 2008). The reduction in cardio- respiratory function (VO2max) and muscular performance associated with advancing age and/or inactivity can contrib- ute to diminished functional capacity (Huggett et al., 2005). In low active or sedentary older adults, functional capacity can drop to levels lower than the critical functional fitness thresholds, which may result in the inability to perform daily life activities and reduce independence (Huggett et al., 2005; Paterson & Warburton, 2010; Taylor, 2014). Indeed, studies investigating the VO2 values associated with activi- ties of daily living have identified cut- off points to predict an individual's ability to perform those activities independently (Huggett et al., 2005; Morey et al., 1998; Paterson et al., 1999). For instance, Paterson and colleagues found that in adults aged 55– 86 years, the minimum VO2 compatible with independent living was 15.4 and 17.7 ml/kg/min for women and men, respectively (Paterson et al., 1999). Below these cut- offs, individuals were likely to require assistance (Paterson et al., 1999). The present study extends the con- cept proposed by Paterson and colleagues, by evaluating cerebrovascular functional capacity at low/moderate exer- cise intensity below these functional thresholds (Jamnick et al., 2020). This study is an ancillary sub- study of the Brain In Motion study (BIM), a quasi- experimental single group pre- /post- intervention study (Tyndall et al., 2013). Here, we aim to determine in a large sample of healthy seden- tary older adults the extent to which a 6- month aerobic ex- ercise intervention is associated with improved objective measures of cardiorespiratory and cerebrovascular func- tions during submaximal exercise at an absolute workload of 65  W (i.e., representing a VO2 of approximately 15– 17 ml/kg/min; Paterson et al., 1999) and a relative inten- sity that corresponds to 40% VO2max. We hypothesize that a 6- month aerobic exercise intervention will lead to a de- crease in CVRi and to an increase in CBF and CVCi during two submaximal workloads (40% VO2max and 65  W). These changes will be above and beyond changes in end- tidal CO2 (PETCO2). We propose that such improvements in cardiovascular and cerebrovascular outcomes after the intervention represent increases in functional capacity that have implications for daily life activities. 2 | MATERIALS AND METHODS Healthy but underactive participants were recruited through fliers, social media, and world of mouth and pro- vided informed written consent prior to enrolment. The University of Calgary Conjoint Health Research Ethics Board provided ethical approval (CHREB: REB 14- 2284). The data that support the findings of this study are avail- able from the corresponding author upon reasonable request. 2.1 | Inclusion/exclusion criteria Participants were required to meet the following criteria to be included in the Brain In Motion study: (Tyndall et al., 2018) (1) age between 50 and 80 years at baseline; (2) re- porting <30 min of moderate exercise 4 days per week or 20 min of vigorous exercise 2 days per week; (3) a body mass index (BMI) of <35 kg/m2; (4) able to walk indepen- dently outside as well as up and down at least 20 stairs; (5) not diagnosed with clinically evident cardiovascular or cerebrovascular disease(s), asthma, type I diabetes melli- tus and/or another condition that would prevent safe ex- ercise; (6) acquire a score ≥24 on the Montreal Cognitive Assessment (MoCA, Rossetti et al., 2011); (7) non- smoker for at least 12 months; (8) no major surgery or trauma in the last 6 months; (9) no diagnosis of neurologic disease; and, (10) clearance obtained from their attending health care professional to participate in the study. Prior to being enrolled, participants were assessed by a study physician, and their medications were noted. Participants were ex- cluded from this ancillary study if they did not complete the 6- month aerobic exercise intervention or had incom- plete gas exchange threshold (GET) and VO2max data pre- and post- intervention (see “Results” section). 2.2 | Exercise intervention Participants took part in a supervised 6- month aero- bic training program that was held three days a week at the Fitness Centre in the Faculty of Kinesiology at the University of Calgary. Each session included a 5- min warm- up, aerobic exercise, a 5- min cool- down, followed by stretching. As participants progressed through the ex- ercise intervention, the duration of aerobic exercise in- creased from 20 to 40 min. As well, the exercise intensity increased from 30%– 45% up to 60%– 70% maximum heart rate reserve (HRR) based on individual VO2max results. Polar® heart rate monitors were worn by each participant throughout the session to ensure compliance to their tar- get heart rate zones. Heart rate data were collected and stored for further analysis using the Polar® Team2 System. Participants were considered compliant if they attended 85% of the total exercise sessions. If a session was missed, participants were strongly encouraged to complete an unsupervised, “make- up” session independently, which 4 of 14 | LAKE et al. was recorded using personal workout logbooks. For fur- ther explanation on the exercise intervention (see Tyndall et al., 2013 and Hall et al., 2019). 2.3 | Testing phases In this report, we focus on data collected immediately prior to the start of the intervention (pre- intervention) and immediately following the completion of the 6- month aerobic exercise intervention (post- intervention). At each phase, participants completed a maximal oxygen uptake (VO2max) test, and a cerebrovascular function test dur- ing submaximal exercise during separate visits within 1– 2  weeks of each other. Several other measurements were collected but they are outside the scope of this report. For further details, please refer to Tyndall et al. (2013). 2.4 | Cardiorespiratory fitness Anthropometrics and exercise data were collected in the Clinical and Translational Exercise Physiology Laboratory, Cumming School of Medicine, University of Calgary by Certified Exercise Physiologist (Canadian Society of Exercise Physiology). Anthropometric data were collected prior to completion of the maximal oxygen uptake test and included measurements of participant's height, weight, and skin folds. Following, maximal oxy- gen uptake (VO2max) was determined using a metabolic cart (Parvo Medics TruOne 2400). Ventilatory volumes and expiratory gases were measured during a ramp exercise test on a programmable motorized treadmill (Quinton TM55). Baseline ventilatory measures were obtained during a three- minute period of quiet stand- ing on the treadmill. Warm- up measures were obtained during a four- minute slow walk at a speed of 1.7 mph and a 0% grade. Following the warm- up period, a com- bination of small increases in velocity and grade that occurred every 30 s were used to elicit a ramp- like test according to previously described methods (McInnis & Balady, 1994). Participants were verbally encouraged throughout the test and exercised to volitional fatigue or until the appearance of symptoms indicating the need to terminate the test according to the American College of Sports Medicine's (ACSM) Indications for Terminating a Symptom- Limited Maximal Exercise Test (Thompson et al., 2010). Recovery measures were obtained for five minutes following the test at a speed of 1.7  mph and grade at 0%. Heart rate was measured with a 12- lead electrocardiogram system (QStress) which monitored heart rhythm at rest (5 min) prior to, during, and post- exercise (3 min). Exercising heart rate, blood pressure (manual brachial measurement), and the participants rating of perceived exertion (RPE) value were measured every two minutes during exercise. Peak heart rate and blood pressure values were recorded at maximal effort. VO2max was determined from the highest 30- s average value during the exercise test. Ventilatory thresholds were determined and verified by two independent inves- tigators according to the V- slope method (Binder et al., 2008). In this report on older sedentary adults, we solely focused on the GET. With incremental exercise inten- sity, GET is associated with an increase in lactate follow- ing which there is a period of isocapnic buffering. At this point VCO2  starts to increase out of proportion to the increase in VO2 indicating the buffering of lactic acid by bicarbonate, but PaCO2 and PETCO2 are relatively stable (i.e., there is no respiratory compensation; Beaver et al., 1986; Poole et al., 2021). 2.5 | Submaximal exercise tests 2.5.1 | Relative workload (40% of VO2max) and Absolute workload (65 W) During the submaximal exercise test, participants were seated on a recumbent cycle ergometer (Lode Corival; Lode BV Medical Technology). Participants first under- went ten minutes of resting air breathing to collect base- line resting end- tidal respiratory values (PETCO2 and PETO2) with a dedicated software program (Chamber, University Laboratory of Physiology, Oxford, UK) while on a mouthpiece connected to a fine capillary attached to a mass spectrometer (AMIS 200; Innovision). Then a second specialized program (BreatheM v2.40, University Laboratory of Physiology, Oxford, UK) was used to accu- rately and continuously record PETCO2 and PETO2 val- ues during the exercise tests with no gas manipulation; participants for the entire duration of the submaximal exercise test simply breathed room air through a mouth- piece with the nose occluded with a nose clip. Heart rate was continuously measured throughout the submaximal exercise test using a 3- lead electrocardiogram system (Micromon 7142 B; Kontron Medical). Beat- by- beat blood pressure was measured contin- uously using a finger pulse photoplethysmography Finometer (Finapres Finometer Pro; Medical Systems) and the finger pressure transducer was positioned at the heart level. A sphygmomanometer (Welch Allyn) was also used to take brachial measurements during rest. Arterial hemoglobin saturation was measured using finger pulse oximetry (3900p; Datex- Ohmeda). Blood flow velocity in the MCAv was non- invasively measured using a 2- MHz transcranial Doppler  | 5 of 14 LAKE et al. ultrasound (TCD) (Toc NeurovisionTM; Multigon Industries Inc.; Leeuwis et al., 2018). MCA location was determined by placing the TCD probe above the zygomatic process near the ear, in the temporal region, and using techniques described by Aaslid et al. (1982). During the first testing session, the TCD probe was man- ually moved and the TCD settings of depth, gain, and amplitude were optimized to find the best signal from the right MCA. Then the probe placement was recorded by tracing the location on the side of the head on a trans- parent sheet together with the TCD settings used. To en- sure accurate placing of the probe and reliability during different sessions the information recorded during the first visit was used post- intervention. In the submaximal exercise test, resting values were collected for 5  min before participants started to cycle. In the first exercise stage (6 min) participants cycled at a work rate relative to 40% of their VO2max values collected during the maximal oxygen uptake testing previously de- scribed. This was followed by a 6- min rest period. In the second exercise stage, all participants, regardless of sex, cycled at an absolute work rate of 65 W for 6 min. Lastly, participants completed 6 min of rest (i.e., recovery phase). 2.6 | Data analyses 2.6.1 | Cerebrovascular measures The TCD signals were collected every 10 ms and averaged values were calculated over each cardiac cycle. Data for the last 30- s interval of each phase of the submaximal ex- ercise test (exercise bouts and rest) were then averaged. MCAv values collected and MAP (obtained by the beat- by- beat data from the Finometer) were subsequently used to calculate CVCi and CVRi: Data were then analyzed using IBM SPSS Statistics, version 25.0 (IBM). Pre- and post- intervention descriptive statistics for the sample are reported in Table 1. Paired sample t- tests were used to compare data pre- /post- intervention. To examine the contribution of changes in PETCO2 to changes in MCAv and CVCi from pre- to post- intervention, we used a series of multiple linear regres- sions with post- scores for MCAv and CVCi (in separate models) as dependent variables, pre- scores as predictors in block one, and changes in PETCO2 (ΔPETCO2 = post- intervention−pre- intervention) as forced confounders. We ran these multiple linear regressions for changes pre- / post- intervention at 40% VO2max and 65  W, separately. The advantage of these analyses is that they allow for the quantification of how much of the variance is explained by changes in PETCO2 by looking at the r2 change of the model that considers the covariate. A value of p < 0.05 was adopted as the minimum level of statistical significance, and all analyses were two- tailed. A Bonferroni correction for multiple comparisons was used with α = 0.05/5 (or 0.01). 3 | RESULTS 3.1 | Subject characteristics Two hundred eighty- six participants were initially en- rolled in the study. Two hundred and thirty- six par- ticipants completed the pre- intervention tests and 206 completed the 6- month exercise intervention. A detailed flowchart of the Brain in Motion study is published else- where (Guadagni et al., 2020; Hall et al., 2019). In this re- port, we examine the complete data for 203 participants due to missing cardiorespiratory data for three partici- pants (66.4 ± 6.4 years, MoCA 27.6 ± 1.4, mean years of completed education 15.9 ± 2.6, 103 females). At pre- intervention, 63 participants reported being on anti- hypertension medications, 6 participants re- ported being on anti- hyperglycemic 11 and 42 partici- pants reports being on lipid- lowering medications. After the intervention, 65 participants reported being on anti- hypertension medication, 6 participants reported being on anti- hyperglycemic and 41 participants reported being on lipid- lowering medications. Please refer Table 1 for pre- / CVRi = [MAP∕MCAv] CVCi = [MCAv∕MAP] TABLE 1 Pre- and post- intervention descriptive statistics for the sample (n = 203) Variables Pre- intervention Post- intervention Mean SD Mean SD Age, years 66.4 6.4 67.0 6.4 BMI, kg/m2 26.9 3.7 26.5 3.6 Height, cm 169.3 9.4 169.3 9.4 Weight, kg 77.6 14.4 76.4 14.2 Waist Girth, cm 96.3 11.3 93.1 11.0 MoCA 27.6 1.4 Education, years 15.9 2.6 Biological sex 103 F 100 M Note: Values are means ± standard deviations (SD); biological sex is expressed as a count. Abbreviations: BMI, body mass index; MoCA, Montreal Cognitive Assessment. 6 of 14 | LAKE et al. post- intervention descriptive statistics for select character- istics of the participants. 3.2 | Cardiorespiratory fitness The GET increased significantly by 4.6% from pre- to post- intervention (17.78  ±  3.39 vs. 18.64  ±  3.36  ml/kg/min, t(202) = −5.99, p < 0.001). VO2max also increased signifi- cantly by 7.1% from pre- to post- intervention (26.12 ± 5.48 vs. 28.11 ± 5.86 ml/kg/min, t(202) = −13.27, p < 0.001). No significant differences were found in heart rate at VO2max from pre- to post- intervention (see Table 2). 3.3 | Responses to submaximal exercise 3.3.1 | Relative workload (40% of VO2max) At pre- intervention participants exercised at an average of 51.0 ± 16.3 W to reach 40% of VO2max. Post- intervention the average workload to reach 40% of VO2max increased to 55.2 ± 16.9 W, an 8.2% increase. CVRi decreased by 2.9% from pre- intervention (CVRi; 2.15  ±  0.59  mmHg/cm/s) to post- intervention (CVRi; 2.09 ± 0.54 mmHg/cm/s), t(199) = 2.47, p = 0.01), MCAv increased by 1.9% from pre- to post- intervention (55.8 ± 12.4 vs. 56.9 ± 12.1 cm/s, t(199) = −2.31, p = 0.022) and CVCi increased by 2.0% from pre- to post- intervention (0.50 ± 0.14 vs. 0.51 ± 0.14 cm/s/mmHg; t(199) = −2.03, p = 0.044). However, the change in CVCi did not remain significant after correction for multiple comparisons (see Tables 3 and 4 and Figure 1). No significant changes were observed in MAP during relative (40% VO2max) submaximal exercise from pre- to post- intervention. HR significantly decreased by 2.6% from pre- intervention (93.2  ±  11.8  bpm) to post- intervention (90.9 ± 10.7 bpm), t(202) = 3.82, p < 0.001 (Figure 2). 3.3.2 | Contribution of changes in PETCO2 to changes in MCAv and CVCi from pre- to post- intervention at relative workload (40% of VO2max) A Multiple Linear Regression on the change in MCAv from pre- to post- intervention while controlling for changes in PETCO2, showed a significant change in MCAv during exercise at a workload of 40% of VO2max from pre- to post- intervention (r = 0.827, r2 change = 0.684, p ≤ 0.001) and a 3.6% contribution of PETCO2 to this change (model 2: r = 0.848, r2 change = 0.036, p ≤ 0.001). Similarly, the change in CVCi from pre- to post- intervention while controlling for changes in PETCO2, showed a significant change in CVCi during exercise at a workload of 40% of VO2max from pre- to post- intervention (r = 0.763, r2 change = 0.582, p ≤ 0.001) and a 5.4% con- tribution of PETCO2 to this change (model 2: r = 0.797, r2 change = 0.054, p ≤ 0.001). 3.3.3 | Absolute workload (65 W) CVRi decreased significantly by 6.3% from pre- intervention to post- intervention (2.37 ± 0.73 vs. 2.23 ± 0.62 mmHg/ cm/s, t(196) = 4.29, p < 0.001). Unlike relative submaxi- mal exercise, during absolute (65 W) submaximal exercise MAP decreased significantly by 3.2% from pre- intervention to post- intervention (121.5 ± 20.1 vs. 117.7 ± 17.9 mmHg, t(198)  =  3.62, p  <  0.001). MCAv increased significantly by 2.1% from pre- to post- intervention (54.3  ±  12.5 vs. 55.4 ± 11.9 cm/s, t(196) = −2.07, p = 0.040). However, the latter change did not remain significant after correction for multiple comparisons. CVCi increased significantly by 6.1% from pre- to post- intervention (0.46  ±  0.14 vs. 0.49 ± 0.14 cm/s/mmHg, t(196) = −3.78, p < 0.001). Please refer to Tables 3 and 4, and Figure 1. HR significantly de- creased by 4.6% from pre- intervention to post- intervention Variables Pre- intervention Post- intervention Mean SD Mean SD Significance GET, ml/kg/min 17.78 3.39 18.64 3.36 <0.001 GET, L/min 1.377 0.359 1.422 0.362 <0.001 HRGET, bpm 120.0 17.0 118.5 15.3 <0.001 VO2max, ml/kg/min 26.12 5.48 28.11 5.86 <0.001 VO2max, L/min 2.029 0.569 2.150 0.598 <0.001 HRmax, bpm 154.9 14.1 156.7 14.6 0.095 RER 1.19 0.09 1.19 0.08 <0.001 Note: Values are means ± standard deviation (SD). Abbreviations: GET, gas exchange threshold; HRGTE, heart rate at GET; HRmax, heart rate at maximal oxygen uptake; RER, respiratory exchange ratio; VO2max, maximal oxygen uptake. TABLE 2 Pre- and post- intervention cardiorespiratory data for all participants  | 7 of 14 LAKE et al. (104.9 ± 16.5 vs. 100.2 ± 15.2 bpm), t(199) = 6.34, p < 0.001 (Figure 2). 3.3.4 | Contribution of changes in PETCO2 to changes in MCAv and CVCi from pre- to post- intervention at absolute workload (65 W) A multiple linear regression on the change in MCAv from pre- to post- intervention while controlling for changes in PETCO2, showed a significant change in MCAv during ex- ercise at a workload of 65 W from pre- to post- intervention (r = 0.801, r2 change = 0.641, p ≤ 0.001) and a 3.4% con- tribution of PETCO2 to this change (model 2: r = 0.822, r2 change = 0.034, p ≤ 0.001). Similarly, the change in CVCi from pre- to post- intervention while controlling for changes in PETCO2, showed a significant change in CVCi during exercise at a workload of 65  W from pre- to post- intervention (r = 0.761, r2 change = 0.580, p ≤ 0.001) and a 2.3% con- tribution of PETCO2 to this change (model 2: r = 0.776, r2 change = 0.023, p ≤ 0.001). 4 | DISCUSSION 4.1 | Major findings This study reports significant improvements in car- diovascular and cerebrovascular indices at the GET and VO2max after a 6- month aerobic exercise intervention in older sedentary adults from the Brain in Motion study. Further, we report evidence of increased functional car- diovascular and cerebrovascular capacity at submaximal exercise workloads. The novelty of this study lies in the investigation of the changes in cerebrovascular indices during submaximal exercise at workloads that mimic the demands of activities of daily function. Previous studies have shown favorable cardiorespiratory adaptations and increased time to fatigue in older individuals after aerobic exercise training (Govindasamy et al., 1992; Poulin et al., 1992). Our study provides additional evidence showing fa- vorable effects of aerobic exercise training to the brain in older adults. We report improvements in cerebrovascular indices during absolute (65 W) submaximal exercise. Specifically, TABLE 3 Pre- and post- intervention cerebrovascular data at rest, relative submaximal exercise (40% of VO2max), and absolute submaximal exercise (65 W) for all participants Variables Rest 40% VO2max Rest 2 65 W Recovery Mean SD Mean SD Mean SD Mean SD Mean SD Pre- intervention HR, bpm 64.7 9.2 93.1† 11.8 69.1 10.4 104.9†,# 16.4 75.4 11.9 MAP, mmHg 97.9 11.8 113.8† 15.2 102.5 12.3 121.4†,# 20.0 101.7 13.1 MCAv, cm/s 51.3 11.2 55.7† 12.4 49.7 10.8 54.2†,# 12.5 48.9 10.6 CVRi, mmHg/cm/s 2.01 0.53 2.15† 0.58 2.17 0.60 2.37†,# 0.73 2.1 0.61 CVCi, cm/s/mmHg 0.53 0.14 0.50† 0.14 0.49 0.13 0.46†,# 0.13 0.48 0.13 PETCO2, mmHg 34.3 3.2 36.8† 3.2 33.4 4.0 35.5†,# 4.4 32.7 3.6 PETO2, mmHg 90.3 4.3 89.6† 3.8 94.6 5.0 92.1†,# 5.8 96.5 4.7 Post- intervention HR, bpm 61.5*** 8.8 90.8†,*** 10.7 65.6*** 9.7 100.3†,#,*** 15.1 71.1*** 11.2 MAP, mmHg 97.7 11.7 113.2† 15.6 102.2 12.6 117.6†,#,*** 18.8 101.4 14.0 MCAv, cm/s 52.7** 11.7 56.8†,* 12.0 50.9* 11.0 55.4†,#,* 11.9 49.8 10.9 CVRi, mmHg/cm/s 1.94** 0.50 2.0†,* 0.54 2.1** 0.52 2.22†,#,*** 0.62 2.14 0.59 CVCi, cm/s/mmHg 0.54 0.14 0.51*,† 0.13 0.50 0.14 0.48†,#,*** 0.14 0.50* 0.14 PETCO2, mmHg 33.8* 3.0 36.3†,** 3.3 33.0 3.5 35.4#,† 3.7 32.2 3.6 PETO2, mmHg 90.1 4.3 89.4† 4.4 94.4 4.7 91.3†,#,** 5.4 96.0 5.2 Note: Values are means ± standard deviation (SD); Rest and Rest 2 were significantly different for each outcome, both pre- and post- intervention (all p < 0.001). Abbreviations: bpm, beats per minute; CVCi, cerebrovascular conductance index; CVRi, cerebrovascular resistance index; HR, heart rate; MAP, mean arterial pressure; MCAv, velocity at the middle cerebral artery; mmHg, millimeters of mercury; PETCO2, end- tidal partial pressure of carbon dioxide; PETO2, end- tidal partial pressure of oxygen. Significant differences found between pre- and post- intervention are represented by asterisk (*p < 0.05, **p < 0.005, ***p < 0.001); † indicates significant differences from Rest 1 to 40% VO2max and Rest 2 to 65 W; # indicates differences from 40% VO2max to 65 W. 8 of 14 | LAKE et al. we observed significant decreases post- intervention of 3.2% in MAP and 6.3% in CVRi, and a 6.1% increase in CVCi. Perhaps not surprisingly, the changes in the cardiovascu- lar and cerebrovascular outcomes during submaximal ex- ercise at the relative workload (40% VO2max) were more modest despite an 8.2% increase in workload (51.0 ± 16.3 to 55.2 ± 16.9 W from pre- to post- intervention). These findings provide evidence supporting the importance of aerobic exercise to confer increases in cardiorespiratory fitness, and in turn, improvements in functional capacity as manifested by improved indices of brain health in older adults. In this study, two different intensities of submaxi- mal exercises were selected to evaluate cerebrovascular functional capacity at exercise intensities that are compa- rable to activities of daily function. First, selecting exer- cise intensities below the GET ensured that exercise was performed in the low/moderate- intensity exercise domain (Jamnick et al., 2020) when there is no respiratory com- pensation and PaCO2 and PETCO2 are stable (thus mini- mizing the possibility of potential confounding factors on our measures of MCAv). Second, 65 W represents a VO2 of approximately 15– 17 ml/kg/min, which has previously been referred to as a critical threshold for independent liv- ing (Paterson et al., 1999). Finally, a relative workload of 40% VO2max provided a window through which to eval- uate the gains in functional capacity with improved car- diorespiratory fitness post- intervention. This conceptual framework is depicted in Figure 3, which illustrates the relationship between heart rate (bpm) and oxygen uptake (VO2, ml/kg/min) during submaximal exercise (65  W) before (pre- intervention) and after (post- intervention) 6 months of aerobic exercise training. Note that the post- intervention heart rate is lower at 65 W and at the GET, despite a 5% higher VO2 at the GET. Moreover, the change in heart rate between submaximal exercise and the GET post- intervention is greater than at pre- intervention indi- cating improved cardiorespiratory fitness (HR ∆1 = 15.1 vs. ∆2 HR = 18.2 bpm). A number of studies have described a biphasic asso- ciation between changes in blood flow to the brain and exercise intensity, characterized by parallel increases in CBF and exercise intensity until ~60% VO2max. After this intensity, and when individuals are closer to the GET, the CBF response tends to plateau or even decrease if the exercise intensity increases to the point of hyper- ventilation induced hypocapnia (Ogoh & Ainslie, 2009; Smith & Ainslie, 2017). In our data, when considering the interplay between some of the main determinants of CBF (i.e., MAP and PETCO2) at different exercise in- tensities, important observations should be made (see Table 3). First, when observing the change in PETCO2 from rest to 40% VO2 and then 65  W, we notice that PETCO2 significantly increases by 7.2% from rest to 40% VO2, but then significantly decreases by 3.6% from 40% VO2 to 65 W. This effect is maintained post- intervention, however somewhat attenuated (3.6% decrease in PETCO2 from 40% VO2 to 65 W pre- intervention vs. % 2.7 decrease post- intervention). This suggests a greater cardiorespiratory fitness post- training when performing exercise at the same intensity (i.e., 65 W). The increase in cardiorespiratory fitness is also confirmed by the blunted increase in HR post- intervention when exercis- ing at higher intensities (+12.5% at pre- intervention vs. +10.3%). Concomitantly, we observe that MCAv follows the same biphasic response with an initial increase at 40% VO2max (+8%), then a decrease (−2.8%) at a greater TABLE 4 Effect sizes and changes (delta pre- /post- intervention) in cerebrovascular data at rest, relative submaximal exercise (40% of VO2max), and absolute submaximal exercise (65 W) for all participants Variables Cohens Dz Delta Rest HR, bpm 0.34 −3.12 MAP, mmHg 0.02 −0.36 MCAV, cm/s 0.11 1.37 CVRi, mmHg/cm/s 0.14 −0.07 CVCi, cm/s/mmHg 0.13 0.02 PETCO2, mmHg 0.13 −0.41 PETO2, mmHg 0.06 −0.25 40% of VO2max HR, bpm 0.20 −2.30 MAP, mmHg 0.03 −0.59 MCAV, cm/s 0.08 1.03 CVRi, mmHg/cm/s 0.10 −0.06 CVCi, cm/s/mmHg 0.07 0.01 PETCO2, mmHg 0.12 −0.46 PETO2, mmHg 0.04 −0.27 65 W HR, bpm 0.29 −4.60 MAP, mmHg 0.19 −3.62 MCAV, cm/s 0.08 1.02 CVRi, mmHg/cm/s 0.19 −0.13 CVCi, cm/s/mmHg 0.14 0.02 PETCO2, mmHg 0.02 −0.12 PETO2, mmHg 0.14 −0.80 Note: Values are means ± standard deviation (SD). Abbreviations: bpm, beats per minute; CVCi, cerebrovascular conductance index; CVRi, cerebrovascular resistance index; HR, heart rate; MAP, mean arterial pressure; MCAV, velocity at the middle cerebral artery; mmHg, millimeters of mercury; PETCO2, end- tidal partial pressure of carbon dioxide; PETO2, end- tidal partial pressure of oxygen.  | 9 of 14 LAKE et al. exercise intensity (65 W), and the same effect persists after training. Conversely, when observing the changes in MAP with increased exercise intensity, MAP signifi- cantly increases by 16.2% from rest to 40% VO2max, and by a further 6.6% at 65 W. However, while this effect is maintained post- intervention it is significantly blunted (6.6% increase in MAP from 40% VO2 to 65  W pre- intervention vs. 3.9% post- intervention). In turn, CVCi linearly decreases (from rest to 40% VO2 to 65 W) in re- sponse to increases in MAP, and this effect is reduced after the intervention, a further indicator of improved cardiovascular function after 6 months of aerobic exer- cise training in older adults. Previous findings of the effects of aging on cardiovas- cular outcomes show a 16% decrease in MCAv within three decades of life (40– 70 years) which is about 0.53% per year (Vriens et al., 1989). Thus, when comparing previously published normative data with our results, the gains that we observe at post- intervention appear to represent an improvement of approximately 5 years in brain health— in other words, the MCAv observed post- intervention represents an average MCAv observed in individuals 5 years younger. The reduction in MAP also showed similar gains. It is known that MAP in older adults is generally higher at both rest and during exercise (Heath et al., 1981). The reduction in MAP ob- served post- exercise at 65 W further confirms the ben- eficial effects of exercise for older adults. Altogether, the exercise- induced gains attenuate, and may perhaps reverse, age- related functional declines in CBF and fos- ter healthy brain function (Williams & Leggett, 1989). This may then translate to a greater capacity to per- form activities of daily life (Shephard, 2009) such as walking independently, shopping, house cleaning, and other activities that involve a VO2 of ~15– 17 ml/kg/min (Paterson et al., 1999). In a recent review, Stillman et al. (2020) describe the effects of exercise on brain health as mediated by multiple mechanisms operating at different system levels. Cellular and molecular effects have been primarily studied in FIGURE 1 Percent (%) changes from pre- to post- intervention in cardiovascular and cerebrovascular health outcomes during (a) relative (40% VO2max) submaximal exercise and (b) absolute (65 W) submaximal exercise FIGURE 2 Heart rate (HR) at relative (40% VO2max) submaximal, absolute (65 W) submaximal, and maximal (VO2max) aerobic exercise pre- and post- intervention (I = intervention) 10 of 14 | LAKE et al. animal models. These studies have shown that exercise increases brain- derived neurotrophic factor that in turn mediates long term potentiation and neuronal prolifera- tion, vascular endothelial growth factor which supports blood vessels, and insulin- like growth factor (IGF)- 1, which influences several neural and angiogenic processes (Maass et al., 2016; Voss et al., 2013). From a brain struc- ture perspective (Davenport et al., 2012; Tyndall et al., 2018), exercise has been shown to promote neurogenesis and increase gray matter volume in particular areas of the brain (e.g., the hippocampus) linked to memory and learning (Erickson et al., 2011), increase cortical thickness and volume in the frontal, parietal and temporal cortex (Batouli & Saba, 2017), and increased white matter micro- structure (Clark et al., 2019). Improvements in functional connectivity have been also associated with better cogni- tive function after exercise (Stillman et al., 2016). In this paper, we examine the contribution of exercise training to the ability of the brain to regulate blood flow to ensure adequate delivery of nutrients and oxygen to the different brain areas. Specifically, we found that exercise training increased blood flow velocity and impacted the ability of the brain vasculature to change (increased conductance, decreased resistance) in response to an external stimulus consisting of bouts of submaximal exercise before and after a 6- month aerobic exercise intervention. Any age- related impairments of cardiovascular function, and consequently CBF regulation, may neg- atively impact the brain's ability to perform cognitive tasks (Barnes & Corkery, 2018; Tarumi & Zhang, 2018). In a recent study from our laboratory (Guadagni et al., 2020), we found improved cerebrovascular function at rest and improved cognition after the 6- month aerobic exercise intervention. We also found novel associations between changes in the ability of the cerebral vascula- ture to react to stimuli (i.e., euoxic hypercapnia) and changes in the cognitive domains of executive functions and verbal fluency confirming the role of exercise to maintain brain health. However, the uniqueness of the current study stems from the findings of improvements, after 6  months of aerobic training, in cardiovascular indices at rest in addition to cerebrovascular function during submaximal exercise at workloads that have been shown to be comparable to activities of daily func- tion (Paterson et al., 1999). Previous studies in similar populations have identified the importance of physical activity in mitigating age- related declines, including sarcopenia and cognitive function (Smith & Ainslie, 2017; Tarumi & Zhang, 2018; Yoo et al., 2018). To our knowledge, this study is the first to provide evidence of improvements in cerebrovascular function during sub- maximal exercise shown to be comparable to workloads FIGURE 3 Relationship between heart rate (bpm) and oxygen uptake (VO2, ml/kg/min) during submaximal exercise (65 W) before (pre- intervention, •) and after (post- intervention, ♦) 6 months of aerobic exercise training. Vertical dash lines represent values before (black) and after (grey) 6 months. The dotted lines follow the VO2 values at 65 W, at gas exchange threshold (GET) and VO2max before (black) and after (grey) 6 months. Delta 1 (∆1) represents changes in submaximal heart rate pre- to post- intervention (HR∆1 = 4.6 bpm). Delta 2 (∆2) represents changes in HR between 65 W and VT1 pre- intervention (HR∆2 = 15.1 bpm). Delta 3 (∆3) represents changes in HR between 65 W and VT1 post- intervention (HR∆3 = 18.2 bpm)  | 11 of 14 LAKE et al. that are required in activities of daily living in a large sample of middle- aged and older adults after an exten- sive period of training. 4.2 | Limitations The present study has some limitations, which warrant a short discussion. First, the lack of a control group in the study precludes the ability to make firm conclusions on the exclusive role of the aerobic exercise intervention in improving cardiovascular and cerebrovascular indices. However, we have previously published data on lack of changes in cerebrovascular outcomes in the 6  months preceding the intervention (Spencer et al., 2015). These previous findings strengthened the role of the interven- tion in the improvements observed in this report. Second, this cohort was composed of healthy, well- educated, mostly Caucasian men and women. As such, our re- sults cannot be generalized to other populations nor to patients with symptom- limited exercise capacity. Third, the two different submaximal workloads were not rand- omized, and this may have impacted results and should be addressed in future studies. Fourth, the use of TCD assumes that the cross- sectional area of the blood ves- sel being insonated (i.e., the MCA) remains unchanged (Poulin et al., 1996). Moreover, we only measured unilat- eral (right) MCA while assessment of other large blood vessels and/or global CBF may have yielded different results (Al- Khazraji et al., 2019). Fifth, the use of finger pulse photoplethysmography to make continuous meas- ures of blood pressure has intrinsic limitations (Maestri et al., 2005), which we have compensated for with the use of a correction factor based on brachial measure- ments. Sixth, participants were asked to complete an ad- ditional unsupervised exercise session once a week and record these and other workouts done on their own in a logbook. These additional sessions were not accounted for in our analyses. Finally, we did not collect any vali- dated direct measure of activities of daily living (ADLs). We however used the Profile of Mood States (POMS) questionnaire to evaluate changes in participants’ mood and vigor from pre- to post- intervention. We found sta- tistically significant decreases (all p  <  0.05, data not shown) post- intervention in the subscales of Confusion (pre- intervention 6.3 ± 2.8, post- intervention 5.9 ± 2.6), Tension (pre- intervention 7.9  ±  3.6, post- intervention 7.3 ± 3.5), and Total Mood Disturbance (pre- intervention 11.5 ± 20.6, post- intervention 7.9 ± 20.2) and a signifi- cant increase (p < 0.001, data not shown) in Vigor (pre- intervention 18.8  ±  5.4, post- intervention 19.8  ±  5.1). These changes may be used as surrogate measures for improvements in daily life. 4.3 | Significance and conclusions Previous reports on the functional ability of older popula- tions have shown progressive declines in aerobic exercise capacity with age, which is associated with reductions in physical functional capacity, and decreases in independ- ence and quality of life (Christou & Seals, 2008; Huggett et al., 2005; Robinson, 1938). Age- related declines in cog- nitive abilities, characterized by chronic and progressive conditions such as dementia, can also reduce the ability to independently perform activities of daily living (Public Health Agency of Canada, 2019). Addressing physical in- activity, an established risk factor of dementia (Livingston et al., 2020), and implementing appropriate exercise inter- ventions, individuals hold promise for reducing the eco- nomic and functional burden of dementia. In conclusion, studies aiming to advance knowledge of the mechanisms underlying the decline in cardiovas- cular and cerebrovascular capacity with aging have im- portant implications for older adults. Our study suggests that aerobic exercise might improve cardiovascular and cerebrovascular indexes at submaximal exercise levels which are comparable to daily life activities. Future stud- ies are needed to confirm these findings and extend them to other populations and to specific activities of daily living. ACKNOWLEDGMENTS We would like to thank all participants of the Brain in Motion study and the Brain in Motion study team. CONFLICT OF INTEREST All authors report no conflict of interest. AUTHOR CONTRIBUTIONS Sonja L. Lake and Veronica Guadagni share co- first authorship and equally contributed to the manuscript. Marc J. Poulin, David B. Hogan, Michael D. Hill, and Todd J. Anderson designed the study; Karen D. Kendall and Michaela Chadder conducted the maximal oxygen uptake (VO2max) tests and analysed the data for deter- mination of GET and RCP; David B. Hogan, Michael D. Hill, Todd J. Anderson, Richard Leigh, and Jean M. Rawling completed medical assessments and provided medical coverage for the VO2max tests; Sonja L. Lake and Veronica Guadagni organized the data and con- ducted the data analyses. All authors interpreted the data. Sonja L. Lake and Veronica Guadagni drafted the manuscript. All authors read, edited and approved the final draft of the manuscript. ORCID Marc J. Poulin  https://orcid.org/0000-0002-1798-6801 12 of 14 | LAKE et al. REFERENCES Aaslid, R., Markwalder, T. M., & Nornes, H. (1982). Noninvasive transcranial Doppler ultrasound recording of flow velocity in basal cerebral arteries. Journal of Neurosurgery, 57, 769– 774. https://doi.org/10.3171/jns.1982.57.6.0769 Ainslie, P. N., Cotter, J. D., George, K. P., Lucas, S., Murrell, C., Shave, R., Thomas, K. N., Williams, M. J., & Atkinson, G. (2008). Elevation in cerebral blood flow velocity with aero- bic fitness throughout healthy human ageing. The Journal of Physiology, 586, 4005– 4010. https://doi.org/10.1113/jphys iol.2008.158279 Ainslie, P. N., & Duffin, J. (2009). Integration of cerebrovascular CO2 reactivity and chemoreflex control of breathing: mechanisms of regulation, measurement, and interpretation. American Journal of Physiology Regulatory, Integrative and Comparative Physiology, 296, R1473– R1495. Al- Khazraji, B. K., Shoemaker, L. N., Gati, J. S., Szekeres, T., & Shoemaker, J. K. (2019). Reactivity of larger intracranial arteries using 7 T MRI in young adults. Journal of Cerebral Blood Flow and Metabolism, 39, 1204– 1214. https://doi.org/10.1177/02716 78X18 762880 Barnes, J. N., & Corkery, A. T. (2018). Exercise improves vascular function, but does this translate to the brain? Brain Plasticity, 4, 65– 79. https://doi.org/10.3233/BPL- 180075 Batouli, S. A. H., & Saba, V. (2017). At least eighty percent of brain grey matter is modifiable by physical activity: A review study. Behavioral Brain Research, 332, 204– 217. Beaver, W. L., Wasserman, K., & Whipp, B. J. (1986). A new method for detecting anaerobic threshold by gas exchange. Journal of Applied Physiology, 60(6), 2020– 2027. https://doi.org/10.1152/ jappl.1986.60.6.2020 Binder, R. K., Wonisch, M., Corra, U., Cohen- Solal, A., Vanhees, L., Saner, H., & Schmid, J. P. (2008). Methodological approach to the first and second lactate threshold in incremental cardiopul- monary exercise testing. European Journal of Cardiovascular Prevention and Rehabilitation, 15, 726– 734. https://doi. org/10.1097/HJR.0b013 e3283 04fed4 Brown, A. D., McMorris, C. A., Longman, R. S., Leigh, R., Hill, M. D., Friedenreich, C. M., & Poulin, M. J. (2010). Effects of cardiore- spiratory fitness and cerebral blood flow on cognitive outcomes in older women. Neurobiology of Aging, 31, 2047– 2057. https:// doi.org/10.1016/j.neuro biola ging.2008.11.002 Chapman, S. B., Aslan, S., Spence, J. S., DeFina, L. F., Keebler, M. W., Didehbani, N., & Lu, H. (2013). Shorter term aerobic exercise improves brain, cognition, and cardiovascular fitness in aging. Frontiers in Aging Neuroscience, 5, 75. https://doi.org/10.3389/ fnagi.2013.00075 Christou, D. D., & Seals, D. R. (2008). Decreased maximal heart rate with aging is related to reduced {beta}- adrenergic responsive- ness but is largely explained by a reduction in intrinsic heart rate. Journal of Applied Physiology, 105, 24– 29. Clark, C. M., Guadagni, V., Mazerolle, E. L., Hill, M., Hogan, D. B., Pike, G. B., & Poulin, M. J. (2019). Effect of aerobic exercise on white matter microstructure in the aging brain. Behavioral Brain Research, 373, 112042. https://doi.org/10.1016/j. bbr.2019.112042 Davenport, M. H., Hogan, D. B., Eskes, G. A., Longman, R. S., & Poulin, M. J. (2012). Cerebrovascular reserve: The link between fitness and cognitive function? Exercise and Sport Sciences Reviews, 40, 153– 158. https://doi.org/10.1097/JES.0b013 e3182 553430 Erickson, K. I., Voss, M. W., Prakash, R. S., Basak, C., Szabo, A., Chaddock, L., Kim, J. S., Heo, S., Alves, H., White, S. M., Wojcicki, T. R., Mailey, E., Vieira, V. J., Martin, S. A., Pence, B. D., Woods, J. A., McAuley, E., & Kramer, A. F. (2011). Exercise training increases size of hippocampus and improves memory. Proceedings of the National Academy of Sciences of the United States of America, 108, 3017– 3022. https://doi.org/10.1073/ pnas.10159 50108 Fontana, L. (2018). Interventions to promote cardiometabolic health and slow cardiovascular ageing. Nature Reviews Cardiology, 15, 566– 577. Franklin, S. S., Wt, G., Wong, N. D., Larson, M. G., Weber, M. A., Kannel, W. B., & Levy, D. (1997). Hemodynamic patterns of age- related changes in blood pressure. The Framingham Heart Study. Circulation, 96, 308– 315. https://doi.org/10.1161/01. CIR.96.1.308 Gajewski, P. D., & Falkenstein, M. (2016). Physical activity and neu- rocognitive functioning in aging - A condensed updated review. European Review of Aging and Physical Activity, 13, 1. https:// doi.org/10.1186/s1155 6- 016- 0161- 3 Govindasamy, D., Paterson, D. H., Poulin, M. J., & Cunningham, D. A. (1992). Cardiorespiratory adaptation with short term train- ing in older men. European Journal of Applied Physiology and Occupational Physiology, 65, 203– 208. https://doi.org/10.1007/ BF007 05082 Guadagni, V., Drogos, L. L., Tyndall, A. V., Davenport, M. H., Anderson, T. J., Eskes, G. A., Longman, R. S., Hill, M. D., Hogan, D. B., & Poulin, M. J. (2020). Aerobic exercise improves cogni- tion and cerebrovascular regulation in older adults. Neurology, https://doi.org/10.1212/WNL.00000 00000 009478 Hall, S. E., Lawal, O. A., Clark, C. M., Tyndall, A. V., Hill, M. D., Sajobi, T. T., & Poulin, M. J. (2019). Novel approach to char- acterize heterogeneity in an aerobic exercise intervention. Medicine and Science in Sports and Exercise, 51, 1506– 1516. https://doi.org/10.1249/MSS.00000 00000 001909 Heath, G. W., Hagberg, J. M., Ehsani, A. A., & Holloszy, J. O. (1981). A physiological comparison of young and older endurance ath- letes. Journal of Applied Physiology: Respiratory, Environmental and Exercise Physiology, 51, 634– 640. https://doi.org/10.1152/ jappl.1981.51.3.634 Hoiland, R. L., Fisher, J. A., & Ainslie, P. N. (2019). Regulation of the cerebral circulation by arterial carbon dioxide. Comprehensive Physiology, 9, 1101– 1154. Huggett, D. L., Connelly, D. M., & Overend, T. J. (2005). Maximal aer- obic capacity testing of older adults: A critical review. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 60, 57– 66. Jamnick, N. A., Pettitt, R. W., Granata, C., Pyne, D. B., & Bishop, D. J. (2020). An examination and critique of current methods to determine exercise intensity. Sports Medicine, 50, 1729– 1756. https://doi.org/10.1007/s4027 9- 020- 01322 - 8 Joyce, W., White, D. W., Raven, P. B., & Wang, T. (2019). Weighing the evidence for using vascular conductance, not resistance, in comparative cardiovascular physiology. The Journal of Experimental Biology, 222. https://doi.org/10.1242/jeb.197426 Kirk- Sanchez, N. J., & McGough, E. L. (2014). Physical exercise and cognitive performance in the elderly: Current perspectives. Clinical Interventions in Aging, 9, 51– 62.  | 13 of 14 LAKE et al. Lautt, W. W. (1989). Resistance or conductance for expression of arterial vascular tone. Microvascular Research, 37, 230– 236. https://doi.org/10.1016/0026- 2862(89)90040 - X Leeuwis, A. E., Smith, L. A., Melbourne, A., Hughes, A. D., Richards, M., Prins, N. D., Sokolska, M., Atkinson, D., Tillin, T., Jäger, H. R., Chaturvedi, N., van der Flier, W. M., & Barkhof, F. (2018). Cerebral blood flow and cognitive functioning in a community- based, multi- ethnic cohort: The SABRE Study. Frontiers in Aging Neuroscience, 10, 279. Livingston, G., Huntley, J., Sommerlad, A., Ames, D., Ballard, C., Banerjee, S., Brayne, C., Burns, A., Cohen- Mansfield, J., Cooper, C., Costafreda, S. G., Dias, A., Fox, N., Gitlin, L. N., Howard, R., Kales, H. C., Kivimäki, M., Larson, E. B., Ogunniyi, A., … Mukadam, N. (2020). Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet, 396, 413– 446. Maass, A., Düzel, S., Brigadski, T., Goerke, M., Becke, A., Sobieray, U., Neumann, K., Lövdén, M., Lindenberger, U., Bäckman, L., Braun- Dullaeus, R., Ahrens, D., Heinze, H. J., Müller, N. G., Lessmann, V., Sendtner, M., & Düzel, E. (2016). Relationships of peripheral IGF- 1, VEGF and BDNF levels to exercise- related changes in memory, hippocampal perfusion and vol- umes in older adults. NeuroImage, 131, 142– 154. https://doi. org/10.1016/j.neuro image.2015.10.084 Maestri, R., Pinna, G., Robbi, E., Capomolla, S., & La Rovere, M. (2005). Noninvasive measurement of blood pressure variability: Accuracy of the Finometer monitor and comparison with the Finapres device. Physiological Measurement, 26, 1125. McInnis, K. J., & Balady, G. J. (1994). Comparison of submaximal exercise responses using the Bruce vs modified Bruce protocols. Medicine and Science in Sports and Exercise, 26, 103– 107. Morey, M. C., Pieper, C. F., & Cornoni- Huntley, J. (1998). Is there a threshold between peak oxygen uptake and self- reported phys- ical functioning in older adults? Medicine and Science in Sports and Exercise, 30, 1223– 1229. https://doi.org/10.1097/00005 768- 19980 8000- 00007 Murrell, C. J., Cotter, J. D., Thomas, K. N., Lucas, S. J., Williams, M. J., & Ainslie, P. N. (2013). Cerebral blood flow and cerebrovas- cular reactivity at rest and during sub- maximal exercise: Effect of age and 12- week exercise training. Age, 35, 905– 920. Ngandu, T., Lehtisalo, J., Solomon, A., Levälahti, E., Ahtiluoto, S., Antikainen, R., Bäckman, L., Hänninen, T., Jula, A., Laatikainen, T., Lindström, J., Mangialasche, F., Paajanen, T., Pajala, S., Peltonen, M., Rauramaa, R., Stigsdotter- Neely, A., Strandberg, T., Tuomilehto, J., … Kivipelto, M. (2015). A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cogni- tive decline in at- risk elderly people (FINGER): A randomised controlled trial. Lancet, 385, 2255– 2263. Ogoh, S., & Ainslie, P. N. (2009). Cerebral blood flow during exercise: Mechanisms of regulation. Journal of Applied Physiology, 107, 1370– 1380. Paterson, D. H., Cunningham, D. A., Koval, J. J., & St Croix, C. M. (1999). Aerobic fitness in a population of independently living men and women aged 55– 86 years. Medicine and Science in Sports and Exercise, 31, 1813– 1820. https://doi. org/10.1097/00005 768- 19991 2000- 00018 Paterson, D. H., & Warburton, D. E. (2010). Physical activity and func- tional limitations in older adults: A systematic review related to Canada's Physical Activity Guidelines. The International Journal of Behavioral Nutrition and Physical Activity, 7, 38. Paulson, O. B., Strandgaard, S., & Edvinsson, L. (1990). Cerebral au- toregulation. Cerebrovascular and Brain Metabolism Reviews, 2, 161– 192. Pentikäinen, H., Savonen, K., Ngandu, T., Solomon, A., Komulainen, P., Paajanen, T., Antikainen, R., Kivipelto, M., Soininen, H., & Rauramaa, R. (2019). Cardiorespiratory fitness and cognition: Longitudinal associations in the FINGER study. Journal of Alzheimer's Disease: JAD, 68, 961– 968. Phillips, A. A., Chan, F. H., Zheng, M. M., Krassioukov, A. V., & Ainslie, P. N. (2016). Neurovascular coupling in humans: Physiology, methodological advances and clinical implications. Journal of Cerebral Blood Flow and Metabolism, 36, 647– 664. Poole, D. C., Rossiter, H. B., Brooks, G. A., & Gladden, L. B. (2021). The anaerobic threshold: 50+ years of controversy. The Journal of Physiology, 599, 737– 767. Poulin, M. J., Liang, P. J., & Robbins, P. A. (1996). Dynamics of the cerebral blood flow response to step changes in end- tidal PCO2 and PO2 in humans. Journal of Applied Physiology, 81(3), 1084– 1095. https://doi.org/10.1152/jappl.1996.81.3.1084 Poulin, M. J., Paterson, D. H., Govindasamy, D., & Cunningham, D. A. (1992). Endurance training of older men: Responses to sub- maximal exercise. Journal of Applied Physiology, 73, 452– 457. Public Health Agency of Canada. (2019). A dementia strategy for Canada: Together we aspire [Online]. www.canada.ca/en/publi c- healt h/servi ces/publi catio ns/disea ses- condi tions/ demen tia- strat egy.html Robinson, S. (1938). Experimental studies of physical fitness in relation to age. Arbeitsphysiologie, 10, 251– 323. https://doi. org/10.1007/BF020 11412 Rossetti, H. C., Lacritz, L. H., Cullum, C. M., & Weiner, M. F. (2011). Normative data for the Montreal Cognitive Assessment (MoCA) in a population- based sample. Neurology, 77, 1272– 1275. https://doi.org/10.1212/WNL.0b013 e3182 30208a Shephard, R. J. (2009). Maximal oxygen intake and independence in old age. British Journal of Sports Medicine, 43, 342– 346. https:// doi.org/10.1136/bjsm.2007.044800 Silverman, A., & Petersen, N. (2021). Physiology, cerebral autoregula- tion. StatPearls [internet]. Statpearls Publishing. Smith, K. J., & Ainslie, P. N. (2017). Regulation of cerebral blood flow and metabolism during exercise. Experimental Physiology, 102, 1356– 1371. https://doi.org/10.1113/EP086249 Spencer, M. D., Tyndall, A. V., Davenport, M. H., Argourd, L., Anderson, T. J., Eskes, G. A., Friedenreich, C. M., Hogan, D. B., Leigh, R., & Meshi, B. (2015). Cerebrovascular responsiveness to hypercapnia is stable over six months in older adults. PLoS One, 10, e0143059. https://doi.org/10.1371/journ al.pone.0143059 Stensvold, D., Viken, H., Steinshamn, S. L., Dalen, H., Støylen, A., Loennechen, J. P., Reitlo, L. S., Zisko, N., Bækkerud, F. H., Tari, A. R., Sandbakk, S. B., Carlsen, T., Ingebrigtsen, J. E., Lydersen, S., Mattsson, E., Anderssen, S. A., Fiatarone Singh, M. A., Coombes, J. S., Skogvoll, E., … Wisløff, U. (2020). Effect of exercise training for five years on all cause mortality in older adults— the Generation 100 study: Randomised controlled trial. BMJ, 371, m3485. Stillman, C. M., Cohen, J., Lehman, M. E., & Erickson, K. I. (2016). Mediators of physical activity on neurocognitive function: A review at multiple levels of analysis. Frontiers in Human Neuroscience, 10, 626. Stillman, C. M., Esteban- Cornejo, I., Brown, B., Bender, C. M., & Erickson, K. I. (2020). Effects of exercise on brain and cognition 14 of 14 | LAKE et al. across age groups and health states. Trends in Neurosciences, 43, 533– 543. https://doi.org/10.1016/j.tins.2020.04.010 Tarumi, T., & Zhang, R. (2018). Cerebral blood flow in normal aging adults: Cardiovascular determinants, clinical implications, and aerobic fitness. Journal of Neurochemistry, 144, 595– 608. Taylor, D. (2014). Physical activity is medicine for older adults. Postgraduate Medical Journal, 90, 26– 32. Thompson, W. R., Gordon, N. F., & Pescatello, L. S.; American College of Sports Medicine. (2010). ACSM’s guidelines for ex- ercise testing and prescription. Wolters Kluwer/Lippincott Williams & Wilkins. Tyndall, A. V., Clark, C. M., Anderson, T. J., Hogan, D. B., Hill, M. D., Stewart Longman, R., & Poulin, M. J. (2018). Protective ef- fects of exercise on cognition and brain health in older adults. Exercise and Sport Sciences Reviews, 46(4), 215– 223. https://doi. org/10.1249/JES.00000 00000 000161 Tyndall, A. V., Davenport, M. H., Wilson, B. J., Burek, G. M., Arsenault- Lapierre, G., Haley, E., Eskes, G. A., Friedenreich, C. M., Hill, M. D., & Hogan, D. B. (2013). The brain- in- motion study: Effect of a 6- month aerobic exercise intervention on cerebrovascular regulation and cognitive function in older adults. BMC Geriatrics, 13, 21. Voss, M. W., Vivar, C., Kramer, A. F., & van Praag, H. (2013). Bridging animal and human models of exercise- induced brain plas- ticity. Trends in Cognitive Sciences, 17, 525– 544. https://doi. org/10.1016/j.tics.2013.08.001 Vriens, E. M., Kraaier, V., Musbach, M., Wieneke, G. H., & van Huffelen, A. C. (1989). Transcranial pulsed Doppler measure- ments of blood velocity in the middle cerebral artery: Reference values at rest and during hyperventilation in healthy volunteers in relation to age and sex. Ultrasound in Medicine & Biology, 15, 1– 8. Williams, L. R., & Leggett, R. W. (1989). Reference values for resting blood flow to organs of man. Clinical Physics and Physiological Measurement, 10, 187– 217. https://doi.org/10.1088/014 3- 0815/10/3/001 Yoo, S. Z., No, M. H., Heo, J. W., Park, D. H., Kang, J. H., Kim, S. H., & Kwak, H. B. (2018). Role of exercise in age- related sarcope- nia. Journal of Exercise Rehabilitation, 14, 551– 558. https://doi. org/10.12965/ jer.18362 68.134 How to cite this article: Lake, S. L., Guadagni, V., Kendall, K. D., Chadder, M., Anderson, T. J., Leigh, R., Rawling, J. M., Hogan, D. B., Hill, M. D., & Poulin, M. J. (2022). Aerobic exercise training in older men and women— Cerebrovascular responses to submaximal exercise: Results from the Brain in Motion study. Physiological Reports, 10, e15158. https://doi.org/10.14814/ phy2.15158

Aerobic exercise training in older men and women-Cerebrovascular responses to submaximal exercise: Results from the Brain in Motion study.

[]

Lake, Sonja L,Guadagni, Veronica,Kendall, Karen D,Chadder, Michaela,Anderson, Todd J,Leigh, Richard,Rawling, Jean M,Hogan, David B,Hill, Michael D,Poulin, Marc J

eng

PMC10117394

Submitted 4 January 2023 Accepted 20 March 2023 Published 17 April 2023 Corresponding author Małgorzata Pałac, [email protected] Academic editor Mike Climstein Additional Information and Declarations can be found on page 11 DOI 10.7717/peerj.15214 Copyright 2023 Pałac et al. Distributed under Creative Commons CC-BY 4.0 OPEN ACCESS Relationship between respiratory muscles ultrasound parameters and running tests performance in adolescent football players. A pilot study Małgorzata Pałac1,2, Damian Sikora1, Tomasz Wolny1,2 and Paweł Linek1,2 1 Musculoskeletal Elastography and Ultrasonography Laboratory, Institute of Physiotherapy and Health Sciences, The Jerzy Kukuczka Academy of Physical Education, Katowice, Śląskie, Poland 2 Musculoskeletal Diagnostic and Physiotherapy - Research Team, The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland ABSTRACT Purpose. Assessing the relationship between ultrasound imaging of respiratory muscles during tidal breathing and running tests (endurance and speed) in adolescent football players. Methods. Ultrasound parameters of the diaphragm and intercostal muscles (shear modulus, thickness, excursion, and velocity), speed (30-m distance), and endurance parameters (multi-stage 20-m shuttle run test) were measured in 22 male adolescent football players. The relation between ultrasound and running tests were analysed by Spearman’s correlation. Results. Diaphragm shear modulus at the end of tidal inspiration was moderately negatively (R = −0.49;p = 0.2) correlated with the speed score at 10 m. The diaphragm and intercostal muscle shear modulus ratio was moderately to strongly negatively correlated with the speed score at 10 m and 30 m (about R = −0.48;p = 0.03). Diaphragm excursion was positively correlated with the speed score at 5 m (R = 0.46;p = 0.04) and 10 m (R = 0.52;p = 0.02). Diaphragm velocity was moderately positively correlated with the speed score at 5 m (R = 0.42;p = 0.06) and 30 m (R = 0.42;p = 0.07). Ultrasound parameters were not significantly related to all endurance parameters (R ≤ 0.36;p ≥ 0.11). Conclusions. Ultrasound parameters of the respiratory muscles are related to speed score in adolescent football players. The current state of knowledge does not allow us to clearly define how important the respiratory muscles’ ultrasound parameters can be in predicting some performance parameters in adolescent athletes. Subjects Kinesiology, Sports Medicine Keywords Athlete, Ultrasonography, Motor skills, Respiration, Diaphragm, Intercostal muscle INTRODUCTION It is well known that respiratory function is related to physical activity and affects exercise performance in athletes. Respiratory muscles (RMs) are an integral part of the respiratory system and physical activity. Their morphology and contractile properties make them useful in endurance types of training (Welch, Kipp & Sheel, 2019). RMs are susceptible to How to cite this article Pałac M, Sikora D, Wolny T, Linek P. 2023. Relationship between respiratory muscles ultrasound parameters and running tests performance in adolescent football players. A pilot study. PeerJ 11:e15214 http://doi.org/10.7717/peerj.15214 fatigue, resulting in reduced performance (Aliverti, 2016; Welch, Kipp & Sheel, 2019) and insufficient oxygen supply to the working muscles (Mcconnell & Lomax, 2006). Studies have shown that RMs training improves RMs’ parameters and decreases muscle fatigue, resulting in a change in respiratory system function (Welch, Kipp & Sheel, 2019). It is also indicated that inspiratory muscle training affects the test results involving time trials or exercise endurance time (Hajghanbari et al., 2013). The main RMs are the diaphragm (DA) and intercostal muscles (IMs). Physiologically, the DA executes about 65% of the respiratory work during inspiration (Moeliono, DM & Nashrulloh, 2022) and affects to a greater extent lung movements (Welch, Kipp & Sheel, 2019). IMs, in turn, contribute to chest expansion (Yoshida et al., 2021), leading to increased inspiratory volume (Yoshida et al., 2019). During inspiration, while the IMs contract, the abdominal muscles gradually relax, and vice versa during expiration. This mechanism has some effects: (a) it prevents rib cage distortion; (b) the DA is unloaded and can act as a flow generator; and (c) the abdominal volume decreases below resting levels (Aliverti, 2016). In football, RM training improves RMs’ strength, which helps to improve exercise tolerance and lower blood lactate levels (Guy, Edwards & Deakin, 2014). Respiration exercises also improve muscle oxygen supply during high-intensity exercise (Archiza et al., 2018). This process can be translated into an improvement in fatigue tolerance and running efficiency of football players (Archiza et al., 2018). Additionally, it was confirmed that in youth football players, the RMs improve aerobic endurance, which is one of the most important parameters of motor preparation in football (Mackała et al., 2020). Spirometry, as a gold standard of assessing respiratory function (Durmic et al., 2015), allows reproducible and standardised assessment of pulmonary function (Lazovic-Popovic et al., 2016). However, spirometry performance is the result of many factors (including airway obstruction, respiratory compliance, and RM strength) that do not allow direct analysis of the RMs (Pałac et al., 2022). In contrast, ultrasound (US) imaging can directly and reliably assess the thickness, excursion, and shear modulus (elasticity) of the RMs (Pałac et al., 2022 ; Zhu et al., 2019). Pałac et al. (2022) also confirmed the reliability of RMs US measurements in adolescent football players. In the literature, some studies have shown the relationship between US parameters of the RMs and spirometry parameters in different populations (Pałac & Linek, 2022). However, a recent systematic review by Pałac & Linek (2022) has shown that the relationship between US parameters and lung function (measured, for example, by spirometry) is inconclusive. Thus, the two methods of measurement should not be used interchangeably, as they measure different aspects (Pałac & Linek, 2022). Taking into account that RMs training affects motor skills and has implications for sports training, it is worth considering these muscles in athletes. Running tests are usually used to assess motor skills such as speed and endurance. According to the literature, speed and endurance depend on the thickness of the lower-extremity muscles, which has been measured using US in young athletes (Stock et al., 2017). Other US parameters have been related to motor skills in elite sports (Sarto et al., 2021). For example, RMs function correlates with postural stability in footballers (León-Morillas et al., 2021), and thus potentially affects motor skills as well. To the best of our knowledge, however, there Pałac et al. (2023), PeerJ, DOI 10.7717/peerj.15214 2/16 have been no studies relating US measurements of RMs with motor skills (endurance and speed) in adolescent football players. We believe that such an analysis is justified, as it may launch the exploration of RMs US measurements that are potentially useful in predicting motor skill performance in athletes. The aim of this preliminary report was to assess the relationship between US of RMs during tidal breathing and selected motor skill (endurance and speed) performance in adolescent football players. Based on the current state of the art, we hypothesised that endurance and speed parameters should be related to the thickness and elasticity of RMs (DA and IMs) in adolescent football players. MATERIALS & METHODS Informed consent The study was approved by the Ethics Committee of the Jerzy Kukuczka Academy of Physical Education in Katowice (Decision No. 9/2020) and conducted in accordance with the guidelines of the Declaration of Helsinki. Before the study, participants and their parents were informed about all procedures performed and have given written consent to participate. All participants provided written informed consent to participate in the study. This research did not receive any external funding. Setting and study design US data were collected in a laboratory setting (Institute of Physiotherapy and Health Sciences, Musculoskeletal Elastography and Ultrasonography Laboratory) by two physiotherapists, whereas endurance and speed measurements were performed by a motor preparation assistant on a football field with an artificial ground surface. Speed and endurance tests were conducted during two consecutive training days. During the first day, speed tests were performed, and on the next day, an endurance test was conducted. All measurements were performed in a preparation phase for the next football season. Due to organisational issues, US was collected one week after the endurance measurements. Participants Adolescent footballers from the professional football academy were considered for the study. We invited all male individuals from a randomly selected team (one age group). The basic criteria of eligibility for the study were (a) all players had to be free of any health or injury issues at the time of testing; (b) no respiratory-related medical history; and (c) no surgical procedure on the pectoral chest, abdominal cavity, pelvic girdle, and/or spine. Information regarding the athletes’ health was obtained by a short interview with the footballers and a coach or physiotherapist working with these athletes in the club. Ultrasound measurements All US measurements were collected by an Aixplorer US scanner (Product Version 12.2.0, Software Version 12.2.0.808; Supersonic Imagine, Aix-en-Provence, France). Linear transducer array (2–10 MHz; SuperLinear 10-2, Vermon, Tours, France) in the SWE mode was used to evaluate the shear modulus and thickness of the ICs and DA on the right side of the body. Each participant laid in the supine position with the right hand Pałac et al. (2023), PeerJ, DOI 10.7717/peerj.15214 3/16 Figure 1 Illustration showing the ultrasound probe placement and orientation (parallel to the ribs). Full-size DOI: 10.7717/peerj.15214/fig-1 placed under the head in order to better visualise the DA. At the beginning, anterior and mid-axillary lines were marked on the participant’s chest, and the US probe was positioned between the lines (Fig. 1). The probe was positioned in the first intercostal space (counting from the bottom) where the lungs did not obscure the DA during tidal breathing. The US measurements were performed in a longitudinal probe position (parallel to the ribs). The participants were asked to relax and breath quietly throughout the procedure. US data were collected twice at the end-tidal inspiration and at the end-tidal expiration, separately. The reliability of RM measurements has been confirmed in previous studies on healthy adolescent football players (Pałac & Linek, 2022). DA excursion was collected in the M-mode on the Aixplorer US scanner coupled with convex transducer array (1–6 MHz, Cristal Curved XC6-1; Vermon, Tours, France). For the excursion measurement, the participant was in the supine position with the upper limbs along the trunk. The probe was placed in the right subcostal area. The participant was asked to take a maximal inspiration and then quietly expire. For the excursion DA measurement, a video collecting the work of breathing before maximal inspiration (tidal expiration) and during maximal inspiration and tidal expiration was recorded. The reliability of DA excursion was confirmed on athletes (Calvo-Lobo et al., 2019). DA excursion amplitude was described as the upright perpendicular distance from the minimum to the maximum point of DA displacement during a given breathing manoeuvre. DA excursion velocity is described as the velocity of DA displacement (during a given breathing pattern). Shear modulus and thickness were calculated from the US images. The Q-Box™ quantitative tool was used to quantify muscle shear modulus. Three separate circles were positioned inside the fascial edge of each muscle, and the shear modulus was automatically calculated. The images were then saved on an external drive in DICOM format and transferred to a computer, where the muscle thickness was measured using RadiAnt DICOM Viewer (Medixant, Poznań, Poland). The DA thickness was measured between the pleural and peritoneal lines. The ICs were measured as the first more superficial Pałac et al. (2023), PeerJ, DOI 10.7717/peerj.15214 4/16 muscle than the DA. The thickness and shear modulus ratio was also measured as the end-inspiratory US value divided by the end-expiratory US value. Running tests Two running tests were used to analyse the participants‘ endurance and speed. All measurements were collected by using photocells of the Witty System (Microgate Bolzano, Italy) with an accuracy of 0.01 s. The Witty System was coupled with Witty Manager (1.14.32 version; Microgate Bolzano, Italy) and connected to a laptop, allowing data collection (Altmann et al., 2019). Both tests were performed on a dry grass football pitch on a sunny day, and the participants wore football kit and boots. Endurance was assessed by a progressive, multi-stage 20-m shuttle run test (MSRT) as a modification of the beep test (Green et al., 2013). The beep test requires athletes to run back and forth (‘‘shuttle’’) between two cones separated by 20 m. The initial speed was 2.22 m/s for 1 min. At the end of the first min, the speed increased to 2.5 m/s and progressively increased by 0.14 m/s each min thereafter. The speed was imposed by audible beeps from pre-recorded audio. Each min stage (level) consisted of multiple ‘‘shuttles’’, and the number depended on the stage speed. Participants were advised to keep running at the pace of the beeps for as long as possible. Once the participant could no longer keep pace with the beeps (i.e., failed to complete two consecutive shuttles in time), the test was terminated (Green et al., 2013). For the purpose of the study, we calculated the parameter ‘‘Total’’ as the total number of completed 20-m repetitions (during the whole test). The following parameters were used for further analysis: Total and calculated VO2max (ml · kg−1· min−1). VO2max was estimated from the maximal speed attained during the test via the previously developed prediction equation −24.4+6.0× maximum aerobic speed (sec) (Léger et al., 1988). The speed test involved running 30 m as fast as possible in a straight line between the photocells. Before the test began, the participants stood adjacent to (i.e., their toes were not touching) the starting line in a standing split-stance position. They were instructed to run as fast as possible and slow down after crossing the finish line. A sound signal marked the beginning of each test. The timer was switched on when the starting line was passed, and measurements were automatically taken at 5 m, 10 m, and 30 m by the photocells positioned at those distances. The timer stopped when the finishing line was passed. Each participant ran the course twice, and the mean scores from both were analysed (Altmann et al., 2019). Statistical analysis Data were analysed using Statistica 13.1 PL (Statsoft, Tulsa, OK, USA) and Excel (Microsoft Corporation, USA) software. Due to the non-normality of the distribution in the Shapiro– Wilk test, we decided to use Spearman’s correlation in the analysis. The correlation value (R) was interpreted as follows: 0 to 0.30 or 0 to −0.30 was considered a weak correlation; 0.31 to 0.50 or −0.31 to −0.50 a moderate correlation; 0.51 to 0.70 or −0.51 to −0.70 a strong correlation; and 0.71 to 1 or −0.71 to −1 a very strong correlation (Hopkins et al., 2009). The significance level was set at p ≤ 0.05. For the a priori analysis, the sample Pałac et al. (2023), PeerJ, DOI 10.7717/peerj.15214 5/16 Figure 2 Flow chart. Full-size DOI: 10.7717/peerj.15214/fig-2 size was determined using G*POWER (Version 3.1.9.7; Universität Kiel, Kiel, Germany) using an alpha of 0.05, a power of 0.80, and an effect size of 0.50 for a two-tailed test. Because Spearman’s rank correlation coefficient is computationally identical to Pearson’s product-moment coefficient, we used the software to calculate the latter. RESULTS Participants Based on the assumptions, the required sample size was determined to be 26. Out of 30 initially invited footballers, 24 met the inclusion criteria. However, during the measurements, two athletes were at a camp with the senior team. Thus, a total of 22 adolescent footballers (two goalkeepers, eight defenders, nine midfielders, three forwards) were included in the final analysis (Fig. 2). Basic data and all parameters measured are shown in Table 1. Speed test vs US DA shear modulus at the end of tidal inspiration was moderately negatively correlated with the speed score at 10 m. The DA shear modulus ratio was moderately negatively correlated with the speed score at 10 m and 30 m. The IC shear modulus ratio was moderately negatively correlated with the speed score at 10 m and strongly negatively correlated with the speed score at 30 m. Additionally, DA excursion was positively correlated with the speed score at 5 m (moderate) and 10 m (strong). DA velocity was moderately positively correlated with the speed score at 5 and 30 m, but statistical significance was borderline (p = 0.06). Detailed R values for each correlation are presented in Table 2. Pałac et al. (2023), PeerJ, DOI 10.7717/peerj.15214 6/16 Table 1 Experimental group characteristics: anthropometric data, ultrasound parameters, endurance test (multi-stage 20-m shuttle run test), and speed test (straight line speed in 5, 10, and 30 m). Characteristic (n = 22) mean ± SD median Anthropometric data Age (yr) 17.1 ± 0.29 17.0 Body mass (kg) 71.4 ± 7.74 70.0 Body height (cm) 180 ± 5.76 180 BMI (kg/m2) 22.1 ± 1.95 22.0 Football practice (yr) 7.77 ± 0.75 8.0 SWE - Shear modulus (kPa) Diaphragm at the end of tidal inspiration 31.2 ± 6.26 31.5 Diaphragm at the end of tidal expiration 29.4 ± 5.60 27.9 Diaphragm ratio 1.07 ± 0.18 1.05 Intercostal muscle at the end of tidal inspiration 27.1 ± 6.23 26.6 Intercostal muscle at the end of tidal expiration 27.0 ± 6.00 25.7 Intercostal muscle ratio 1.01 ± 0.15 0.97 B-mode –Thickness (mm) Diaphragm at the end of tidal inspiration 2.09 ± 0.85 1.82 Diaphragm at the end of tidal expiration 1.71 ± 0.59 1.48 Diaphragm ratio 1.21 ± 0.21 1.20 Intercostal muscle at the end of tidal inspiration 3.98 ± 0.85 4.05 Intercostal muscle at the end of tidal expiration 4.09 ± 0.89 3.97 Intercostal muscle ratio 0.99 ± 0.15 0.95 M-mode Diaphragm excursion (cm) 4.73 ± 1.45 4.59 Diaphragm velocity (cm/s) 2.13 ± 0.89 1.83 Multi stage 20-m shuttle run test Total 127 ± 13.2 122 calculated VO2max (ml · kg−1 ·min−1) 56.2 ± 3.54 55.1 Speed test (s) Distance 5 m 1.03 ± 0.05 1.03 Distance 10 m 1.87 ± 0.52 1.77 Distance 30 m 4.19 ± 0.20 4.14 Notes. SD, standard deviation; BMI, Body Mass Index; SWE, shear wave elastography; ratio, diaphragm at the end of tidal inspi- ration/diaphragm at the end of tidal expiration; Total, total number of completed 20-m repetitions. MSRT vs US US parameters were not significantly related to endurance parameters, although correlations varied from weak to moderate. Detailed R values for each correlation are presented in Table 3. DISCUSSION The preliminary report was designed to assess the relationship between US of RMs during tidal breathing and selected motor skill (endurance and speed) performance in adolescent football players. To the best of our knowledge, there has not yet been a Pałac et al. (2023), PeerJ, DOI 10.7717/peerj.15214 7/16 Table 2 Correlations between ultrasound parameters and speed test results. 5 m 10 m 30 m R p R p R p Shear modulus Diaphragm at the end of tidal inspiration −0.34 0.12 −0.49 0.02* −0.24 0.29 Diaphragm at the end of tidal expiration −0.10 0.66 −0.14 0.55 0.10 0.66 Diaphragm ratio −0.31 0.16 −0.48 0.02* −0.41 0.06 Intercostal muscle at the end of tidal inspiration −0.26 0.26 −0.39 0.08 −0.18 0.44 Intercostal muscle at the end of tidal expiration −0.13 0.58 −0.16 0.49 0.16 0.48 Intercostal muscle ratio −0.28 0.22 −0.47 0.03* −0.54 0.01* Thickness Diaphragm at the end of tidal inspiration −0.07 0.75 −0.06 0.80 0.22 0.34 Diaphragm at the end of tidal expiration −0.27 0.23 −0.12 0.60 0.25 0.25 Diaphragm ratio 0.33 0.13 0.07 0.75 −0.03 0.91 Intercostal muscle at the end of tidal inspiration −0.19 0.42 −0.07 0.78 0.11 0.63 Intercostal muscle at the end of tidal expiration −0.08 0.74 −0.11 0.64 0.05 0.83 Intercostal muscle ratio −0.04 0.86 0.14 0.56 0.07 0.76 M-mode Diaphragm excursion 0.46 0.04* 0.52 0.02* 0.26 0.27 Diaphragm velocity 0.42 0.06 0.34 0.15 0.42 0.07 Notes. SWE, shear wave elastography. *statistically significant p < 0.05. R, correlation coefficient; p, probability value; ratio, diaphragm at the end of tidal inspiration/diaphragm at the end of tidal expiration. study relating the shear modulus, thickness, excursion, and velocity of the DA and ICs with parameters of speed and aerobic endurance based on MSRT in adolescent football players. This preliminary study has shown that US of RMs measurements (shear modulus, thickness, excursion, velocity) corresponded to speed in adolescent athletes. Thus, our initial hypothesis was partially confirmed because footballers with higher values of DA shear modulus at the end of tidal inspiration obtained better results in the 10-m speed test. Similarly, a higher DA and IC shear modulus ratio corresponded to a better speed score at 10 and 30 m, and a higher value of DA excursion and velocity was related to worse scores during the speed test. In turn, our results rejected the hypothesis that RMs are related to endurance in adolescent footballers. Speed Taking all the results together, our study shows that RM shear modulus during tidal breathing may be partially related to the speed score in adolescent footballers. The shear modulus value is related to passive muscle force (Koo & Hug, 2015) and can be used to estimate changes in muscle force (Ate¸s et al., 2015). Chino et al. (2018) showed that DA shear modulus is non-linearly related to inspiratory mouth pressure, increasing rapidly at low inspiratory mouth pressure levels, but less rapidly as mouth pressure reaches higher levels. It can therefore be stated that a higher value of the DA shear modulus indicates greater inspiratory muscle strength. Another study confirmed that DA stiffness increases during Pałac et al. (2023), PeerJ, DOI 10.7717/peerj.15214 8/16 Table 3 Relationship between ultrasound parameters and endurance test (multi-stage 20-m shuttle run) results. Total VO2max R p R p Shear modulus Diaphragm at the end of tidal inspiration −0.16 0.49 0.07 0.76 Diaphragm at the end of tidal expiration −0.17 0.46 −0.05 0.83 Diaphragm ratio 0.03 0.88 0.18 0.42 Intercostal muscle at the end of tidal inspiration 0.03 0.90 0.33 0.15 Intercostal muscle at the end of tidal expiration −0.05 0.84 0.17 0.47 Intercostal muscle ratio 0.18 0.43 0.32 0.16 Thickness Diaphragm at the end of tidal inspiration 0.01 0.98 0.17 0.45 Diaphragm at the end of tidal expiration 0.10 0.66 0.29 0.18 Diaphragm ratio −0.14 0.53 −0.12 0.59 Intercostal muscle at the end of tidal inspiration 0.20 0.40 0.36 0.11 Intercostal muscle at the end of tidal expiration −0.01 0.97 0.10 0.66 Intercostal muscle ratio 0.16 0.48 0.25 0.27 M-mode Diaphragm excursion 0.19 0.41 −0.03 0.91 Diaphragm velocity 0.20 0.41 0.17 0.49 Notes. SWE, shear wave elastography; Total, total number of completed 20-m repetitions; VO2max, calculated VO2max (ml · kg−1 · min−1); R, correlation coefficient; p, probability value; ratio, diaphragm at the end of tidal inspiration/diaphragm at the end of tidal expiration. inspiration (Şendur et al., 2022). Our study shows that a stiffer (higher shear modulus value) DA during tidal inspiration characterised athletes with a better score in the speed test. This may indicate that a stiffer DA improves speed performance. The DA shear modulus value is also related to transdiaphragmatic pressure (Bachasson et al., 2018), which is considered the gold standard for DA examination (Ricoy et al., 2019). Transdiaphragmatic pressure is the main measurement for determining DA strength (Hamnegard et al., 1995) and is clinically relevant because it represents the actual force that drives changes in lung volume and therefore ultimately alveolar ventilation (Bachasson et al., 2018). Sprint running (up to 6 s/up to 40 m) is characterised by anaerobic effort (Sanders et al., 2017). In our study, therefore, it can be assumed that the athletes had an anaerobic effort at the 30-m distance, so they were running at apnoea. It has been suggested that there is increased chest pressure during the initial phase of the speed test, which is linked to the Valsalva test (Turban, 2010). The Valsalva manoeuvre initiates with deep inhalation and DA downward movement (Talasz et al., 2012). Thus, the DA seems to be the main muscle involved in the Valsalva manoeuvre. The increased DA shear modulus during tidal breathing may predispose to a stronger DA contraction during the speed trial, resulting in a better score in the initial phase of running. At a distance of 30 m, the DA and IC shear modulus ratio seems to be more significant. The ratio is calculated by dividing the shear modulus value at the peak of tidal inspiration Pałac et al. (2023), PeerJ, DOI 10.7717/peerj.15214 9/16 by the shear modulus value at the peak of tidal expiration. In our study, the higher the DA and IC shear modulus ratio, the better the speed test score. The ratio score is therefore determined not only by the shear modulus value during inspiration but also during expiration. This means that the best speed scores were achieved by athletes who had a higher RM shear modulus value during tidal inspiration and simultaneously a lower RM shear modulus value during tidal expiration. It may be that a better ability to relax the RMs allows for their greater contraction. When a muscle lengthens, the muscle spindle located inside the muscle is stretched, causing the muscle fibres to contract (Bhattacharyya, 2017). In turn, the comparable correlation values between each of the RMs and speed is probably due to the similar function of the DA and ICs. These muscles both affect chest movement (Ratnovsky, Elad & Halpern, 2008), produce axial rotations of the thorax (Whitelaw et al., 1992), and are important respiratory pump muscles (Han et al., 1993). Consequently, their work must be coordinated (Han et al., 1993). In addition, although the DA is the main RM, when the respiratory workload increases (high breathing efforts), the activity of ICs plays an important role (Ratnovsky, Elad & Halpern, 2008). In view of the previous considerations, it is difficult to explain why footballers characterised by greater DA excursion and velocity during maximal inspiration had worse running scores. It was assumed that the increased stiffness of the DA during tidal breathing allowed greater stiffness of the DA during the Valsalva test because greater stiffness may result in lower DA excursion and velocity. Unfortunately, there are no studies connecting US assessment of RMs to speed in athletes, which greatly limits the interpretability of these preliminary findings. Endurance Some studies have shown that exercises involving the RMs improve endurance by reducing energy demand (Bahenský et al., 2021) and increase aerobic tolerance (Mackała et al., 2020) in youth athletes. It has also been indicated that breathing technique can affect endurance through reduced respiratory work and delayed RM fatigue (Bahenský et al., 2021). This was the reason we hypothesised that endurance should be related to US of RMs in our study. This was not confirmed, as there was no relationship between the endurance and US parameters of RMs. In cited studies (Bahenský et al., 2021; Mackała et al., 2020), RMs strength was measured indirectly by analysing maximal inspiratory and expiratory pressure/forces. In the present study, for the first time, we have evaluated and related RMs with endurance directly by analysing US measurements (shear modulus, thickness, excursion, and velocity). An indirect method of assessing respiratory function is the result of many factors (including airway obstruction, respiratory compliance, and RM strength) that do not allow direct analysis of the RMs (Pałac & Linek, 2022). This may mean that the improvement in endurance in athletes is a more complex phenomenon unrelated to an exclusive change in RM morphology. It is particularly surprising that there was no correlation between DA excursion and aerobic endurance in the present study. DA excursion is related to exercise capacity (Shiraishi et al., 2020) and can predict the improvement in exercise tolerance (Shiraishi et al., 2020) in patients (especially with problems related to the respiratory system). DA Pałac et al. (2023), PeerJ, DOI 10.7717/peerj.15214 10/16 excursion is related to pulmonary parameters like FVC, FEV1, and MIP, whereas DA velocity is related to FVC, MIP, and MEP (Pałac & Linek, 2022). All of these spirometry parameters are related to RM strength (Pałac & Linek, 2022). Thus, it was expected that greater DA excursion would predispose to better endurance in examined football players. Possibly in healthy people (and athletes who achieve higher performance in endurance tests than the non-athlete population), the DA excursion is not as important in order to improve endurance. An alternative explanation of the lack of correlation between DA excursion and aerobic endurance may be the relatively similar endurance (training) level of the footballers studied. However, there is a lack of scientific studies determining the significance of DA excursion in athletes. Hence, the present study results are difficult to interpret definitively. Limitations Due to the small sample size, this study is of a preliminary nature. The study group consisted exclusively of football players from one team and age group, which may explain the high homogeneity of the participants’ motor skills and US parameters. This, in turn, may have influenced the narrow dispersion of the variables and, ultimately, the correlation values. The results should not therefore be generalised to other sports. The participants were included in the analysis regardless of their position; studies have shown that footballers’ profiles can vary according to where they play on the pitch (Oliva-Lozano et al., 2020). US examinations were performed only in the supine position. Another limitation was the collection of US measurements only during tidal breathing (except for excursion—maximal inspiration and tidal expiration). It seems necessary to include US assessment of the RMs during maximal respiratory efforts in future studies. For the purposes of this study, the athletes’ endurance was indirectly determined. The MSRF is used as a test of aerobic capacity (Voss & Sandercock, 2009). The beep test can be used as a health indicator in children and adolescents (Mayorga-Vega et al., 2016), but it is a field test. Thus, the result should not be interpreted as a direct measurement of cardiorespiratory fitness, only as an estimation (Mayorga-Vega et al., 2016). Strength and implications To date, RMs have never been directly investigated in the context of their association with athletes’ performance. Although this is a pilot study, we have shown for the first time that some US parameters of the RMs may be related with motor skills (like speed in our study). From this perspective, we have confirmed that such exploration is justified. US provides an inexpensive and non-invasive tool for assessing RMs on wide populations. The methodology used in this report to assess RMs is easy accessible and reliable. Thus, it seems that the US of RMs in elite athletes is warranted in order to provide deeper insights into the role of RMs in the context of different motor abilities. Previous studies have confirmed the relationship between athletic performance and US parameters of lower-limb muscles (Sarto et al., 2021). It is also worth noting that RMs (mainly DA) function itself is related to pain sensation, stability, and balance. All these aspects are important in high-performance sport. Pałac et al. (2023), PeerJ, DOI 10.7717/peerj.15214 11/16 CONCLUSIONS Shear modulus of the RMs, DA excursion, and velocity are related to speed score in adolescent football players. In the examined population, endurance parameters were not related to any US parameters of RMs. The current state of knowledge does not allow us to conclusively determine how important US parameters of RMs can be in predicting performance parameters (for example endurance and speed) in young athletes. However, the results of the present study point to the need for further research into the role of US measurements of RMs in the development of motor skills. ADDITIONAL INFORMATION AND DECLARATIONS Funding The study was fully funded by the Team of Biomedical Basis of Physiotherapy, The Jerzy Kukuczka Academy of Physical Education in Katowice. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Grant Disclosures The following grant information was disclosed by the authors: The Team of Biomedical Basis of Physiotherapy, The Jerzy Kukuczka Academy of Physical Education in Katowice. Competing Interests The authors declare there are no competing interests. Author Contributions • Małgorzata Pałac conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft. • Damian Sikora performed the experiments, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft. • Tomasz Wolny performed the experiments, authored or reviewed drafts of the article, and approved the final draft. • Paweł Linek conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft. Human Ethics The following information was supplied relating to ethical approvals (i.e., approving body and any reference numbers): The study was approved by the Ethics Committee at the Jerzy Kukuczka Academy of Physical Education in Katowice Data Availability The following information was supplied regarding data availability: The raw data is available in the Supplementary File. Pałac et al. (2023), PeerJ, DOI 10.7717/peerj.15214 12/16 Supplemental Information Supplemental information for this article can be found online at http://dx.doi.org/10.7717/ peerj.15214#supplemental-information. REFERENCES Aliverti A. 2016. The respiratory muscles during exercise. Breathe 12(2):165–168 DOI 10.1183/20734735.008116. Altmann S, Ringhof S, Neumann R, Woll A, Rumpf MC. 2019. Validity and reliability of speed tests used in soccer: a systematic review. PLOS ONE 14(8):e0220982 DOI 10.1371/journal.pone.0220982. Archiza B, Welch JF, Geary CM, Allen GP, Borghi-Silva A, Sheel AW. 2018. Temporal characteristics of exercise-induced diaphragmatic fatigue. Journal of Applied Physiology 124(4):906–914 DOI 10.1152/japplphysiol.00942.2017. Ate¸s F, Hug F, Bouillard K, Jubeau M, Frappart T, Couade M, Bercoff J, Nordez A. 2015. Muscle shear elastic modulus is linearly related to muscle torque over the entire range of isometric contraction intensity. Journal of Electromyography and Kinesiology 25(4):703–708 DOI 10.1016/j.jelekin.2015.02.005. Bachasson D, Dres M, Niérat MC, Gennisson JL, Hogrel JY, Doorduin J, Similowski T. 2018. Diaphragm shear modulus reflects transdiaphragmatic pressure during isovolumetric inspiratory efforts and ventilation against inspiratory loading. Journal of Applied Physiology 126(3):699–707 DOI 10.1152/japplphysiol.01060.2018. Bahenský P, Bunc V, Malátová R, Marko D, GJ Grosicki, Schuster J. 2021. Impact of a breathing intervention on engagement of abdominal, thoracic, and subclavian musculature during exercise, a randomized trial. Journal of Clinical Medicine 10(16):3514 DOI 10.3390/jcm10163514. Bhattacharyya K. 2017. The stretch reflex and the contributions of C David Marsden. Annals of Indian Academy of Neurology 20(1):1–4 DOI 10.4103/0972-2327.199906. Calvo-Lobo C, Almazán-Polo J, Becerro-de Bengoa-Vallejo R, Palomo-López PLosa- IglesiasME, Rodríguez-Sanz D, López-López D. 2019. Ultrasonography comparison of diaphragm thickness and excursion between athletes with and without lum- bopelvic pain. Physical Therapy in Sport 37:128–137 DOI 10.1016/j.ptsp.2019.03.015. Chino K, Ohya T, Katayama K, Suzuki Y. 2018. Diaphragmatic shear modulus at various submaximal inspiratory mouth pressure levels. Respiratory Physiology and Neurobiology 252–253:52–57 DOI 10.1016/j.resp.2018.03.009. Durmic T, Lazovic B, Djelic M, Lazic JS, Zikic D, Zugic V, Dekleva M, Mazic S. 2015. Influências específicas do esporte nos padrões respiratórios em atletas de elite. Jornal Brasileiro de Pneumologia 41(6):516–522 DOI 10.1590/S1806-37562015000000050. Green MS, Esco MR, Martin TD, Pritchett RC, McHugh AN, Williford HN. 2013. Crossvalidation of two 20-m shuttle-run tests for predicting VO2max in female colle- giate soccer players. Journal of Strength and Conditioning Research 27(6):1520–1528 DOI 10.1519/JSC.0b013e318270fcc0. Pałac et al. (2023), PeerJ, DOI 10.7717/peerj.15214 13/16 Guy JH, Edwards AM, Deakin GB. 2014. Inspiratory muscle training improves exercise tolerance in recreational soccer players without concomitant gain in soccer- specific fitness. Journal of Strength and Conditioning Research 28(2):483–491 DOI 10.1519/JSC.0b013e31829d24b0. Hajghanbari B, Yamabayashi C, Buna TR, Coelho JD, Freedman KD, Morton TA, Palmer SA, Toy MA, Walsh C, Sheel AW, Reid WD. 2013. Effects of res- piratory muscle training on performance in athletes: a systematic review with meta-analyses. Journal of Strength and Conditioning Research 27(6):1643–1663 DOI 10.1519/JSC.0b013e318269f73f. Hamnegard CH, Wragg S, Mills G, Kyroussis D, Road J, Daskos G, Bake B, Moxham J, Green M. 1995. The effect of lung volume on transdiaphragmatic pressure. European Respiratory Journal 8(9):1532–1536. Han JN, Gayan-Ramirez G, Dekhuijzen R, Decramer M. 1993. Respiratory function of the rib cage muscles. European Respiratory Journal 6(5):722–728. Hopkins WG, Marshall SW, Batterham AM, Hanin J. 2009. Progressive statistics for studies in sports medicine and exercise science. Medicine and Science in Sports and Exercise 41(1):3–12 DOI 10.1249/MSS.0b013e31818cb278. Koo TK, Hug F. 2015. Factors that influence muscle shear modulus during passive stretch. Journal of Biomechanics 48(12):3539–3542 DOI 10.1016/j.jbiomech.2015.05.038. Lazovic-Popovic B, Zlatkovic-Svenda M, Durmic T, Djelic M, Djordjevic Saranovic S, Zugic V. 2016. Superior lung capacity in swimmers: some questions, more answers! Revista Portuguesa de Pneumologia 22(3):151–156 DOI 10.1016/j.rppnen.2015.11.003. Léger LA, Mercier D, Gadoury C, Lambert J. 1988. The multistage 20 metre shuttle run test for aerobic fitness. Journal of Sports Sciences 6(2):93–101 DOI 10.1080/02640418808729800. León-Morillas F, Lozano-Quijada C, MÁ Lérida-Ortega, León-Garzón MC, Ibáñez Vera AJ, de Oliveira-Sousa SL. 2021. Relationship between respiratory muscle function and postural stability in male soccer players: a case-control study. Healthcare 9(6):644 DOI 10.3390/healthcare9060644. Mackała K, Kurzaj M, Okrzymowska P, Stodółka J, Coh M, Rozek-Piechura K. 2020. The effect of respiratory muscle training on the pulmonary function, lung ventila- tion, and endurance performance of young soccer players. International Journal of Environmental Research and Public Health 17(1):234 DOI 10.3390/ijerph17010234. Mayorga-Vega D, Bocanegra-Parrilla R, Ornelas M, Viciana J. 2016. Criterion-related validity of the distance- and time-based walk/run field tests for estimating cardiores- piratory fitness: a systematic review and meta-analysis. PLOS ONE 11(3):e0151671 DOI 10.1371/journal.pone.0151671. Mcconnell AK, Lomax M. 2006. The influence of inspiratory muscle work history and specific inspiratory muscle training upon human limb muscle fatigue. Journal of Physiology 577(1):445–457 DOI 10.1113/jphysiol.2006.117614. Pałac et al. (2023), PeerJ, DOI 10.7717/peerj.15214 14/16 Moeliono M, DM Sari, Nashrulloh T. 2022. Prediction for the maximum inspi- ratory pressure value from the thoracic expansion measurement in Indone- sian healthy young adults. Canadian Journal of Respiratory Therapy 58:34–38 DOI 10.29390/cjrt-2021-064. Oliva-Lozano JM, Fortes V, Krustrup P, Muyor JM. 2020. Acceleration and sprint profiles of professional male football players in relation to playing position. PLOS ONE 15(8 August):e0236959 DOI 10.1371/journal.pone.0236959. Pałac M, Linek P. 2022. Intra-rater reliability of shear wave elastography for the quantification of respiratory muscles in adolescent athletes. Sensors 22(17):6622 DOI 10.3390/S22176622. Pałac M, Rutka M, Wolny T, Podgórski M, Linek P. 2022. Ultrasonography in assess- ment of respiratory muscles function: a systematic review. Respiration; International Review of Thoracic Diseases 101(9):878–892 DOI 10.1159/000524785. Ratnovsky A, Elad D, Halpern P. 2008. Mechanics of respiratory muscles. Respiratory Physiology and Neurobiology 163(1–3):82–89 DOI 10.1016/j.resp.2008.04.019. Ricoy J, Rodríguez-Núñez N, Álvarez Dobaño JM, Toubes ME, Riveiro V, Valdés L. 2019. Diaphragmatic dysfunction. Pulmonology 25(4):223–235 DOI 10.1016/j.pulmoe.2018.10.008. Sanders GJ, Turner Z, Boos B, Peacock CA, Peveler W, Lipping A. 2017. Aerobic capacity is related to repeated sprint ability with sprint distances less than 40 meters. International Journal of Exercise Science 10(2):197–204. Sarto F, Spörri J, Fitze DP, Quinlan JI, Narici MV, Franchi MV. 2021. Implementing ultrasound imaging for the assessment of muscle and tendon properties in elite sports: practical aspects, methodological considerations and future directions. Sports Medicine 51(6):1151–1170 DOI 10.1007/s40279-021-01436-7. Şendur HN, Şendur AB, Cerit MN, Özhan Oktar S, Yücel C. 2022. Evaluation of diaphragm thickness and stiffness using ultrasound and shear-wave elastography. Ultrasound Quarterly 38(1):89–93 DOI 10.1097/RUQ.0000000000000593. Shiraishi M, Higashimoto Y, Sugiya R, Mizusawa H, Takeda Y, Fujita S, Nishiyama O, Kudo S, Kimura T, Chiba Y, Fukuda K, Tohda Y. 2020. Diaphragmatic excursion correlates with exercise capacity and dynamic hyperinflation in copd patients. ERJ Open Research 6(4):00589-2020 DOI 10.1183/23120541.00589-2020. Stock MS, Mota JA, Hernandez JM, Thompson BJ. 2017. Echo intensity and muscle thickness as predictors Of athleticism and isometric strength in middle-school boys. Muscle and Nerve 55(5):685–692 DOI 10.1002/mus.25395. Talasz H, Kremser C, Kofler M, Kalchschmid E, Lechleitner M, Rudisch A. 2012. Proof of concept: differential effects of Valsalva and straining maneuvers on the pelvic floor. European Journal of Obstetrics and Gynecology and Reproductive Biology 164(2):227–233 DOI 10.1016/j.ejogrb.2012.06.019. Turban JW. 2010. Spontaneous pneumomediastinum from running sprints. Case Reports in Medicine 2010: Article ID 927467 DOI 10.1155/2010/927467. Pałac et al. (2023), PeerJ, DOI 10.7717/peerj.15214 15/16 Voss C, Sandercock G. 2009. Does the twenty meter shuttle-run test elicit max- imal effort in 11- to 16-year-olds?. Pediatric Exercise Science 21(1):55–62 DOI 10.1123/pes.21.1.55. Welch JF, Kipp S, Sheel AW. 2019. Respiratory muscles during exercise: me- chanics, energetics, and fatigue. Current Opinion in Physiology 10:102–109 DOI 10.1016/j.cophys.2019.04.023. Whitelaw WA, Ford GT, Rimmer KP, De Troyer A. 1992. Intercostal muscles are used during rotation of the thorax in humans. Journal of Applied Physiology 72(5):1940–1944 DOI 10.1152/jappl.1992.72.5.1940. Yoshida R, Tomita K, Kawamura K, Nozaki T, Setaka Y, Monma M, Ohse H. 2019. Measurement of intercostal muscle thickness with ultrasound imaging during maximal breathing. Journal of Physical Therapy Science 31(4):340–343 DOI 10.1589/jpts.31.340. Yoshida R, Tomita K, Kawamura K, Setaka Y, Ishii N, Monma M, Mutsuzaki H, Mizukami M, Ohse H, Imura S. 2021. Investigation of inspiratory intercostal muscle activity in patients with spinal cord injury: a pilot study using electromyography, ultrasonography, and respiratory inductance plethysmography. Journal of Physical Therapy Science 33(2):153–157 DOI 10.1589/jpts.33.153. Zhu Z, Li J, Yang D, Du L, Yang M. 2019. Ultrasonography of diaphragm can predict pulmonary function in spinal cord injury patients: a pilot case-control study. Medical Science Monitor 25:5369–5374 DOI 10.12659/MSM.917992. Pałac et al. (2023), PeerJ, DOI 10.7717/peerj.15214 16/16

Relationship between respiratory muscles ultrasound parameters and running tests performance in adolescent football players. A pilot study.

04-17-2023

Pałac, Małgorzata,Sikora, Damian,Wolny, Tomasz,Linek, Paweł

eng

PMC9794057

1 S3 Table. List of candidate factors (n=120). 1. Training factors: Endurance capacity Maximal oxygen consumption Economy of movement (=energy utilization) Strength capacity Power capacity Speed capacity Lactate threshold Lung volume Heart volume Coordination capacity Flexibility capacity Agility capacity Reaction time Recovery speed 2. Metabolism factors: Basal metabolism rate (=calories required to keep the body functioning at rest) Glycolysis capacity (=break down of glucose) Mitochondrial biogenesis (=growth of pre-existing mitochondria) Myoglobin storage capacity (=iron/ oxygen-binding protein) Thermogenesis (=production of heat in the body) Angiogenesis (=formation of new blood vessels) Fat metabolism (break down of fat for energy) Creatine kinase metabolism Lactate dehydrogenase metabolism Lactate buffering system (=regulation of lactate level) 3. Body factors: Weight / BMI Total fat mass Regional fat mass Subcutaneous adipose tissue (=fat under the skin) Visceral adipose tissue (=fat around internal organs) Lean mass (=mass of all organs except body fat including bones, muscles, blood, skin) Bone mineral density Tendon stiffness Number of red blood cells (=erythrocytes) Muscle fibres - hypertrophy capacity (=muscle growth) Muscle fibres - type 1 vs. type 2a/b (=slow vs. fast twitch fibres) Muscle fibres - transformation capacity (type 1 vs. type 2) Muscle fibres - contraction velocity capacity  2 4. Hormone metabolism: Erythropoietin (EPO) level Insulin-like growth factor-1 (IGF-1) level Growth hormone level Cortisol level Epinephrine level Norepinephrine level Testosterone level Dihydrotestosterone level Oestradiol level Dehydroepiandrosterone level Ghrelin level Progesterone level Follicle-stimulating hormone level Gonadocorticoids level Human chorionic gonadotropin level Gonadotropin-releasing hormone level Thyroid hormones level Androstenedione level Anti-Müllerian hormone level 5. Nutrition metabolism: Valine level Leucine level L-carnitine level Carnosine level Creatine level Carbohydrate metabolism Saturated fat metabolism Unsaturated fat metabolism Cholesterol level Omega 3 level Omega 6 level Vitamin deficiencies Vitamin A deficiency Beta carotene deficiency Vitamin B complex vitamins (B1-12) deficiency Vitamin C deficiency Vitamin D deficiency Vitamin E deficiency Vitamin K deficiency Folic acid deficiency Mineral deficiencies Iron deficiency Zinc deficiency Magnesium deficiency Selenium deficiency Gluten intolerance Lactose intolerance Caffeine metabolism Alcohol metabolism Antioxidant level  3 Bicarbonate level Cell hydration status Electrolyte balance/ hydration status Steroid metabolism 6. Immune system: Detoxification process Cytokine responses Healing function of skeletal tissue Healing function of soft tissue Blood pressure regulation 7. Injuries: Risk of left ventricular hypertrophy Risk of metabolic myopathy Risk of stress fractures Risk of upper respiratory tract infections Risk of non-functional overreaching Risk of joint injuries Risk of lumbar disk degeneration Risk of inguinal hernia 8. Psychological factors: Stress resistance Motivation capacity Resilience capacity Concentration capacity Emotion regulation Pain sensitivity Aggression regulation Self-control Self-confidence Risk of eating disorders Risk of addiction Intro vs. extroverted personality Ability to differentiate 9. Environmental factors: Smoking behaviour Alcohol usage Sleep quality Level of fatigue Heat resistance capacity Altitude training sensitivity

Factors associated with high-level endurance performance: An expert consensus derived via the Delphi technique.

12-27-2022

Konopka, Magdalena J,Zeegers, Maurice P,Solberg, Paul A,Delhaije, Louis,Meeusen, Romain,Ruigrok, Geert,Rietjens, Gerard,Sperlich, Billy

eng

PMC9388405

Vol.:(0123456789) Sports Medicine (2022) 52:2283–2295 https://doi.org/10.1007/s40279-022-01680-5 ORIGINAL RESEARCH ARTICLE Decoupling of Internal and External Workload During a Marathon: An Analysis of Durability in 82,303 Recreational Runners Barry Smyth1 · Ed Maunder2 · Samuel Meyler3 · Ben Hunter3 · Daniel Muniz‑Pumares3 Accepted: 27 March 2022 / Published online: 5 May 2022 © The Author(s) 2022 Abstract Aim This study characterised the decoupling of internal-to-external workload in marathon running and investigated whether decoupling magnitude and onset could improve predictions of marathon performance. Methods The decoupling of internal-to-external workload was calculated in 82,303 marathon runners (13,125 female). Inter- nal workload was determined as a percentage of maximum heart rate, and external workload as speed relative to estimated critical speed (CS). Decoupling magnitude (i.e., decoupling in the 35–40 km segment relative to the 5–10 km segment) was classified as low (< 1.1), moderate (≥ 1.1 but < 1.2) or high (≥ 1.2). Decoupling onset was calculated when decoupling exceeded 1.025. Results The overall internal-to-external workload decoupling experienced was 1.16 ± 0.22, first detected 25.2 ± 9.9 km into marathon running. The low decoupling group (34.5% of runners) completed the marathon at a faster relative speed (88 ± 6% CS), had better marathon performance (217.3 ± 33.1 min), and first experienced decoupling later in the marathon (33.4 ± 9.0 km) compared to those in the moderate (32.7% of runners, 86 ± 6% CS, 224.9 ± 31.7 min, and 22.6 ± 7.7 km), and high decoupling groups (32.8% runners, 82 ± 7% CS, 238.5 ± 30.7 min, and 19.1 ± 6.8 km; all p < 0.01). Compared to females, males’ decoupling magnitude was greater (1.17 ± 0.22 vs. 1.12 ± 0.16; p < 0.01) and occurred earlier (25.0 ± 9.8 vs. 26.3 ± 10.6 km; p < 0.01). Marathon performance was associated with the magnitude and onset of decoupling, and when included in marathon performance models utilising CS and the curvature constant, prediction error was reduced from 6.45 to 5.16%. Conclusion Durability characteristics, assessed as internal-to-external workload ratio, show considerable inter-individual variability, and both its magnitude and onset are associated with marathon performance. * Barry Smyth [email protected] Daniel Muniz-Pumares [email protected] 1 Insight Centre for Data Analytics, School of Computer Science, University College Dublin, Dublin, Ireland 2 Sports Performance Research Institute New Zealand, Auckland University Technology, Auckland, New Zealand 3 School of Life and Medical Sciences, University of Hertfordshire, Hatfield AL10 9AB, UK 2284 B. Smyth et al. Key Points The decoupling of internal-to-external workload ratio can be used to quantify the ‘durability’ of endurance athletes during long-duration exercise. We used the decoupling of internal (i.e., heart rate) and external (i.e., grade-adjusted speed) workloads, expressed as a ratio indexed to the 5–10 km segment, to quantify the ‘durability’ of > 80,000 marathon runners. Specifically, we assessed the relationship between the magnitude and onset of this decoupling with marathon performance. There was a large inter-individual variation in the magni- tude and onset of decoupling. However, when classified as low, moderate and high decoupling, athletes experi- encing low decoupling had better marathon performance. Moreover, models of marathon performance were improved when both magnitude and onset decoupling are included. The data presented herein suggest that the decoupling of internal-to-external workload ratio should be taken into consideration during long-duration exercise, as it can contribute to explain marathon performance. 1 Introduction Marathon running has been the subject of considerable inter- est in recent years, and it is generally accepted that multiple factors can affect its performance [1–4]. For example, mod- els explaining marathon performance have typically consid- ered three physiological traits: the maximum oxygen uptake ( ̇VO2 max ), oxygen cost of movement (i.e., running economy), and the fraction of ̇VO2 max that can be maintained for the duration of the marathon [2, 5, 6]. Combined, these physi- ological traits result in a ‘performance metabolic rate’, the highest oxidative metabolic rate that can be sustained for the marathon. Critical speed (CS) is the physiological thresh- old delineating the heavy- and severe-intensity domains, and therefore defines the point at which the maximal metabolic steady-state (MMSS) can be attained, and exercise can be supported mainly from oxidative metabolism [7–10]. It is worth noting that several other terms or approaches have been suggested to correspond with, or permit the approxi- mation of, the MMSS including ventilatory or respiratory thresholds, or thresholds derived from blood lactate concen- tration, such as the maximal lactate steady state [8]. Indeed, criticism of the CS model has been levelled as the concord- ance of estimates with the MMSS can be dependent on the methodology used [11, 12]. Since CS (and its analogous critical power) demarcates the boundary between heavy and severe exercise domains [8, 13, 14], and thus represents a marker of the MMSS, it follows that CS shows a strong asso- ciation with endurance performance—including marathon performance [15, 16]. An interesting finding from studies investigating the abil- ity of CS to predict marathon performance [15, 16] was that faster athletes appear to complete the marathon at higher speeds relative to their CS than slower athletes. Thus, elite marathon runners with an average finishing time of ~ 2 h and 5 min could complete the marathon at ~ 96% of their CS. However, well-trained athletes with an average time of ~ 2 h and 30 min completed the marathon at ~ 93% CS, whereas recreational athletes with an average marathon time of ~ 4 h managed to complete the marathon at ~ 79% CS. A plausible explanation of this apparently linear decrease in marathon speed, relative to CS, with increasing marathon times is that physiological attributes crucial in marathon performance, reflected as the CS, represent the maximum ability of a fully rested athlete, but such physiological attributes deteriorate during prolonged exercise, such as a marathon. Clark et al. [17, 18] recently reported that critical power, the cycling equivalent of CS, decreased by ~ 10–15% following 2 h of heavy exercise. Therefore, if a similar decrease in CS also occurs with prolonged running, it is plausible that mara- thon runners who start the marathon at speeds close to but fractionally below their CS transition into severe intensity exercise (above CS) during a marathon, even if the speed is maintained constant throughout the race. It is plausible that better athletes may be able to preserve physiological traits, and thus maintain speeds closer to CS. Indeed, it has recently been suggested that durability, defined as dete- rioration in physiological characteristics over time during prolonged exercise [19], should be taken into consideration during physiological and performance profiling. The aims of this study, therefore, were to (i) character- ise the decoupling of internal-to-external workload during a marathon in a large cohort of recreational runners; and (ii) investigate whether the magnitude and time of onset of the decoupling could predict marathon performance, and whether taking into consideration the decoupling improved predictions derived from CS alone. Further- more, given recent reports highlighting the differences in fatigability between males and females [20], which may contribute to the observed sex differences in endurance performance [20, 21], we report and compare decoupling traits for male and female athletes separately. We hypoth- esised that marathon runners with faster finishing times would exhibit reduced decoupling of internal-to-external workload ratio compared to runners with slower finishing times. Specifically, we hypothesise that athletes exhib- iting low decoupling and/or late onset in decoupling of 2285 Internal and External Workload Decoupling During Marathon Running internal-to-external workload ratio will be able to per- form closer to their CS. Therefore, we hypothesised that by combining CS with estimations of the magnitude of the decoupling of internal-to-external workload ratio, models of marathon performance would be improved. Finally, we hypothesised that the magnitude of decoupling would be lower in female athletes compared to that observed in their male counterparts. 2 Methods 2.1 Dataset A large dataset of recreational runners was made available to the authors by the running platform Strava® (Strava, Inc., San Francisco, CA, USA) under limited research license. The dataset contained anonymised data and, there- fore, the ethics boards of all institutions (Auckland Univer- sity of Technology, University College Dublin, and Uni- versity of Hertfordshire) deemed the study exempt from ethical approval. Athletes uploaded the data from training sessions, collected through smartphones or other devices (e.g., running pods), into the running platform. The dataset consisted of time, location, distance, and elevation data sampled at 100 m intervals. In addition, heart rate (HR) was available from all training sessions. HR data was pro- cessed in a similar way to running data, and thus aver- aged at 100 m intervals. The characteristics of the dataset used in the current study are provided in Table 1. There were 82,303 runners (~ 16% female) included in this study, for whom training data were available for the ~ 4 months preceding a marathon. For all athletes, the dataset con- tained at least one marathon race. In an attempt to identify genuine marathons, we identified sessions that matched a marathon distance (i.e., 42.2 km), but also contained mul- tiple runners starting at the same time and location. This approach provided a series of candidate marathon races that were manually identified, so that genuine marathon races were differentiated from ‘practice’ marathons. 2.2 Critical Speed and D′ Determination Critical speed and D′, the curvature constant of the speed- duration relationship that represents running capacity above CS, were estimated from raw training data, as previously described [15]. In brief, raw data from all training ses- sions for each athlete were first converted to grade-adjusted speed. This approach accounts for changes in elevation, for instance when running uphill or downhill, and is described in more detail elsewhere [15, 22]. The fastest grade-adjusted speed observed in any training session for each athlete was recorded for a range of distances (400, 800, 1500, 3000, and 5000 m), and then used to construct the distance-time relationship according to a linear model of distance and time [23]. For each athlete, the slope of this line was considered CS, and the intercept of the line the curvature constant, D′ [23]. 2.3 Durability and Decoupling Each marathon was divided into eight 5-km segments plus the final 2 km of the race, and the decoupling of internal- to-external workload ratio was calculated for each segment. The internal workload was determined as a percentage of maximum HR (HRmax). The HRmax for the cohort was given as 178 ± 18 beats per min (bpm) and 187 ± 8 bpm using an age-predicted calculation [24] and the highest HR recorded in any training session, respectively. Therefore, HRmax was defined as the highest HR recorded in any training session for each runner. The external workload was determined as the speed, relative to CS, during the recorded marathon. The first (0–5 km) and last (40–42.2 km) segments of the race were excluded to avoid possible artefacts caused by sudden changes in pace in the first and last few kms of the race, respectively. The decoupling observed in the last 5 km segment of the race (35–40 km) was used to determine the overall magnitude of the decoupling experienced by each athlete, and expressed relative to the 5–10 km segment. Thus, a decoupling of 1.15 indicates that internal-to-external ratio (ratio between %HRmax and %CS) was 15% greater in the 35–40 km segment com- pared to that observed in the 5–10 km segment of the race. To Table 1 Descriptive statistics of the dataset F female runners, M male runners, All all runners F M All Athletes (n) 13,125 69,178 82,303 Age (y) 37 ± 8 40 ± 26 39 ± 24 Finish time (min) 245.2 ± 29.6 223.3 ± 32.5 226.8 ± 33.1 Training sessions (n) 72 ± 33 70 ± 34 70 ± 34 Weeks (n) 18.2 ± 2.6 18.2 ± 2.5 18.2 ± 2.5 Training frequency (sessions·wk−1) 3.9 ± 1.6 3.8 ± 1.7 3.8 ± 1.7 Training volume (km·wk−1) 40.9 ± 15.74 43.0 ± 17.9 42.7 ± 17.6 2286 B. Smyth et al. estimate the onset of decoupling, the race segment from which decoupling remained consistently (i.e., for the remaining of the race) above 1.025 was calculated for each athlete, focusing on the race segments from 10 to 40 km. We converted this race segment into an estimated distance by calculating the mid- point of the segment. Thus, if a decoupling > 1.025 was first detected in the 20–25 km segment of the marathon and sus- tained to the 35–40 km segment, then the onset was assumed to be at 22.5 km. The distance at which decoupling was first observed was converted to time of onset using average running speed. If a decoupling > 1.025 was not detected at all for a run- ner, the onset was assumed to be their either 42.2 km or their finish-time, as appropriate, to represent a runner completing the marathon without decoupling. 2.4 Data Analysis Athletes experiencing a decoupling < 1.1 in the last segment of the race were classified as low decoupling, a decoupling ≥ 1.1 but < 1.2 was considered as moderate, and if the decoupling was ≥ 1.2 it was deemed as high decoupling [19]. In order to investigate whether decoupling experienced by an athlete contributed to explain marathon performance, the correlation between key decoupling characteristics (i.e., magnitude and the onset of decoupling) and absolute (marathon time) and rel- ative (marathon speed relative to CS) marathon performance was determined. To calculate these correlations, athletes were grouped based on their relative performance (in 5% bins, from 70% CS to 90% CS) and absolute performance (in 30 min bins, from 150 to 270 min). Finally, a SciKit learn [Python (Python Software Foundation, Wilmington, DA, USA)] implementa- tion of a gradient boosting regressor [25] was used to predict marathon performance based on CS and D′; this regressor was configured to use n = 5,000 estimators and a learning rate of 0.005 [25]. This approach has already been shown to predict performance with relative success (~ 7% error, Ref. [15]). Therefore, the model was modified to consider CS and D′ as well as durability traits, namely the magnitude and onset of the decoupling. Mean values between sexes and decoupling groups (low vs. moderate, moderate vs. high) were compared with a Welch's t-test (which does not assume equal population variance), and significance was accepted at p < 0.01. Cohen’s d was used as a measure of effect-size, and interpreted as very small (0.01), small (0.20), medium (0.50), large (0.80), very large (1.20) and huge (2.00) [26]. Results are reported as mean ± standard deviation. 3 Results 3.1 Marathon Performance and Critical Speed The overall marathon performance and decoupling char- acteristics of the athletes within the dataset are pre- sented in Table 2. Overall, the marathon was completed at 3.17 ± 0.47 m·s−1, and thus marathon time was ~ 3 h and 47 min ± 33 min. The CS and D′, estimated from raw training data corresponded to 3.72 ± 0.48 m·s−1 and 196 ± 90 m, respectively, and therefore the average mara- thon speed corresponded to 85 ± 7% of CS. Male runners had ~ 10% superior marathon performance and CS com- pared to female runners (both p < 0.01), but females were able to complete the marathon at speeds closer to their CS (87 ± 6 vs. 85 ± 7% CS, respectively; p < 0.01, d = 0.23). 3.2 Internal‑to‑External Workload Decoupling During Marathon Running The average decoupling experienced in the 35–40 km seg- ment was 1.16 ± 0.22. However, there was considerable inter-individual variation. Out of 82,303 runners, 34.5% (28,404 runners) exhibited low decoupling (decoupling < 1.1 in the 35–40 km segment), 32.7% (26,879 runners) moderate decoupling (≥ 1.1 but < 1.2), and 32.8% (27,020 runners) were classified as high decoupling (≥ 1.2). The time-course of decoupling for the low, moderate, and high decoupling groups over the course of a marathon is shown in Fig. 1. The overall magnitude of decoupling was greater for males compared to female runners (1.17 ± 0.22 vs. 1.12 ± 0.16; p < 0.01, d = 0.22). Male runners were relatively evenly distributed in the low, moderate and high decoupling groups (32.3%, 32.6% and 35.1%, respectively), whereas their female counterparts were more frequently classified as low and moderate decoupling compared to high decoupling (46.1%, 33.2% and 20.7%, respectively). The onset of decoupling, when runners first exhibited a continuous decoupling > 1.025 sustained to the end of the marathon, occurred after 25.2 ± 9.9 km. However, there were differences for each decoupling group (Table 2), whereby the onset of the decoupling occurred later in the low decoupling group, compared to the moderate and high decoupling groups. The onset of decoupling occurred first in male runners, irrespective of the magnitude of 2287 Internal and External Workload Decoupling During Marathon Running decoupling experienced (low, moderate or high), as shown in Table 2. When the onset of decoupling was expressed as time, males also experienced earlier decoupling com- pared to female runners (147.3 ± 63.6 vs. 125.1 ± 51.6 min, respectively; p < 0.01, d = 0.41). This held true for all decoupling groups (low, moderate and high decoupling; Table 2, Fig. 2). Table 2 Marathon performance and decoupling characteristics in 83,303 recreational runners ALL represents all athletes in the dataset, whereas F and M represent data from female and male athletes, respectively. The column ‘F v M’ shows whether there was a difference between male and females, where the symbol * depicted a significant difference (p < 0.01) and the corre- sponding effect size The subscripts a, b and c indicate whether a significant difference (p < 0.01) was observed between low vs. moderate decoupling, moderate vs. high decoupling, and low vs. high decoupling, respectively. Decoupling magnitude represents the internal-to-external workload ratio in the 35–40 km segment, and is reported in arbitrary units (AUs) ALL F M M v F Sig d Sig D Sig d Sig d Marathon time (min)  Low decoupling 217.3 ± 33.1 a 0.23 240.5 ± 29.9 a 0.22 211.1 ± 31.1 a 0.31 * 0.95  Moderate decoupling 224.9 ± 31.7 b 0.43 246.9 ± 28.9 b 0.21 220.7 ± 30.4 b 0.53 * 0.87  High decoupling 238.5 ± 30.7 c 0.66 252.9 ± 28.0 c 0.42 236.9 ± 30.6 c 0.84 * 0.53  All athletes 226.8 ± 33.1 245.2 ± 29.6 223.3 ± 32.5 * 0.68 Marathon speed (m·s−1)  Low decoupling 3.31 ± 0.50 a 0.26 2.97 ± 0.38 a 0.22 3.40 ± 0.49 1 0.33 * 0.92  Moderate decoupling 3.19 ± 0.45 b 0.44 2.89 ± 0.36 b 0.21 3.25 ± 0.45 2 0.53 * 0.83  High decoupling 3.00 ± 0.41 c 0.68 2.82 ± 0.34 c 0.42 3.02 ± 0.41 3 0.85 * 0.51  All athletes 3.17 ± 0.47 2.91 ± 0.37 3.22 ± 0.48 * 0.67 Critical speed (m·s−1)  Low decoupling 3.78 ± 0.51 a 0.14 3.39 ± 0.40 1 0.09 3.89 ± 0.48 1 0.23 * 1.10  Moderate decoupling 3.71 ± 0.47 b 0.11 3.35 ± 0.39 3.78 ± 0.45 2 0.19 * 0.98  High decoupling 3.67 ± 0.44 c 0.25 3.36 ± 0.38 3 0.08 3.70 ± 0.43 3 0.42 * 0.80  All athletes 3.72 ± 0.48 3.37 ± 0.39 3.79 ± 0.46 * 0.93 Marathon speed (/CS)  Low decoupling 0.88 ± 0.06 a 0.25 0.88 ± 0.06 a 0.24 0.88 ± 0.07 a 0.25 * 0.04  Moderate decoupling 0.86 ± 0.06 b 0.59 0.86 ± 0.06 b 0.36 0.86 ± 0.06 b 0.61 * 0.07  High decoupling 0.82 ± 0.07 c 0.84 0.84 ± 0.06 c 0.60 0.82 ± 0.07 c 0.85 * 0.34  All athletes 0.85 ± 0.07 0.87 ± 0.06 0.85 ± 0.07 * 0.23 Decoupling magnitude (AU)  Low decoupling 1.01 ± 0.18 a 1.00 1.02 ± 0.12 1 1.33 1.01 ± 0.2 1 0.95  Moderate decoupling 1.15 ± 0.03 b 1.07 1.14 ± 0.03 2 1.67 1.15 ± 0.03 2 1.03 * 0.11  High decoupling 1.33 ± 0.24 c 1.49 1.31 ± 0.16 3 2.18 1.33 ± 0.24 3 1.42 * 0.07  All athletes 1.16 ± 0.22 1.12 ± 0.16 1.17 ± 0.22 * 0.22 Decoupling onset (km)  Low decoupling 33.4 ± 9.0 a 1.32 32.9 ± 9.8 1 1.26 33.6 ± 8.7 a 1.35 * 0.25  Moderate decoupling 22.6 ± 7.3 b 0.49 21.7 ± 7.6 2 0.34 22.8 ± 7.2 b 0.52 * 0.15  High decoupling 19.1 ± 6.8 c 1.79 19.1 ± 7.3 3 1.01 19.2 ± 6.7 c 1.86  All athletes 25.2 ± 9.9 26.3 ± 10.6 25.0 ± 9.8 * 0.13 Decoupling onset (min)  Low decoupling 170.1 ± 53.8 a 1.15 185.1 ± 61.1 1 1.16 166.1 ± 50.9 a 1.14 * 0.36  Moderate decoupling 115.2 ± 40.7 b 0.43 121.0 ± 45.8 2 0.35 114.1 ± 39.6 b 0.43 * 0.17  High decoupling 98.4 ± 37.2 c 1.54 105.3 ± 42.4 3 1.43 97.6 ± 36.5 c 1.56 * 0.21  All athletes 128.6 ± 54.3 147.3 ± 63.6 125.1 ± 51.6 * 0.41 2288 B. Smyth et al. 3.3 Internal‑to‑External Workload Decoupling and Marathon Performance Both relative marathon performance (marathon speed relative to CS) and absolute marathon performance (mar- athon finish time) exhibited a strong association with the magnitude of the decoupling. Athletes exhibiting lower decoupling magnitude completed the marathon at a higher percentage of CS (p < 0.01, R2 = − 0.97) and faster mara- thon time (p < 0.01, R2 = 0.99, Fig. 3). Similarly, a strong association was observed between the onset of decoupling and marathon performance (Fig. 3), whereby athletes who experienced decoupling early during the marathon were able to complete the marathon at a higher fraction of their CS (p < 0.01, R2 = 0.92), and had faster marathon times (p < 0.01, R2 = − 0.99, Fig. 3a, b). 3.4 Prediction of Marathon Performance Marathon performance was predicted with 6.45% error using a model that included CS and D′. Incidentally, marathon predictions based exclusively on CS presented with 6.62% error. However, including either the magnitude of the decou- pling in the 35–40 km segment or the decoupling onset time reduced this error to 5.85% and 5.90%, respectively, which corresponds to relative improvements of 9.3% or 8.5%, respectively (see Fig. 4). When both magnitude and time of onset are included (alongside CS and D′), prediction error falls to 5.16%, which represents an overall improvement of 20.00% compared to the model using CS and D′ only. Overall, the prediction error was lower for female athletes (p < 0.01), irrespective of the model used (Fig. 5). 4 Discussion The primary aim of the present study was to explore the durability characteristics of a large, heterogenous group of recreational runners by calculating the decoupling of inter- nal-to-external workload ratio during marathon running. In addition, we investigated whether the overall magnitude and onset of decoupling experienced by runners contributed to marathon performance, and whether these results were different in male and female runners. The main findings Fig. 1 Time-course of the decoupling of internal-to- external workload for athletes with low, moderate, and high decoupling. Low, moderate and high decoupling was defined as athletes with a decoupling < 1.1, between 1.1 and 1.2, and > 1.2 in the 35–40 km segments. Decoupling is expressed relative to the 5–10 km segment of the marathon Fig. 2 Estimated onset of decoupling during a marathon and decoupling type (low, moderate and high), for male (M) and female (F) runners. The filled circles in the high decoupling indicate a male—female difference (p < 0.01) 2289 Internal and External Workload Decoupling During Marathon Running were that athletes experienced a ~ 1.16 (~ 16%) decou- pling between HR and speed in marathon running, which started after 25.2 ± 9.9 km. However, there was large inter- individual variability, and runners could be classified into low, moderate and high decoupling groups. We found that runners in the low decoupling group completed the race at a higher percentage of their CS, with a faster overall time, and had a later onset of decoupling. Moreover, whilst CS and D′ were able to predict marathon performance, a model that incorporates durability characteristics (i.e., magnitude and onset of the decoupling) reduced the prediction error by 20%. Female runners exhibited a better durability pro- file, as the decoupling exhibited lower magnitude and later onset than that observed in male runners. These findings suggest that durability characteristics, such as its magnitude and onset, should be taking into consideration in marathon running because both parameters were associated with mar- athon performance. Moreover, the results from this study indicate that female runners experience less decoupling than their male counterparts. Fig. 3 The onset (distance and time) and the magnitude of the decou- pling of internal-to-external workload ratio relative to marathon per- formance, where marathon performance is calculated: a relative CS, and b in absolute units (min). Estimated onset of the decoupling of internal-to-external workload relative to marathon performance, where marathon performance is calculated: c relative CS, and d in absolute units (min). Filled markers indicate a significant difference between male and female runners (p < 0.01) and a solid line between two makers indicates a statistically significant difference between consecutive pace bins (p < 0.01) 2290 B. Smyth et al. 4.1 Inter‑Individual Variation in Decoupling Characteristics The large sample of recreational marathon runners ana- lysed in the current study experienced internal-to-external workload decoupling of ~ 1.16, which indicates that the ratio between internal workload (HR) and external workload (grade-adjusted speed, relative to CS) increased by ~ 16% throughout a marathon. However, there was considerable inter-individual variability in the magnitude of decoupling. Fig. 4 Error associated with predictions of marathon performance derived from a CS and D’ only, b CS and D’ plus the magnitude of the decoupling, and c CS and D’ plus the decoupling degree and time to decoupling onset. The error is calculated as the mean absolute dif- ference between the predicted finish-time and the actual finish-time as a fraction of actual finish-time for each finish-time group and the dotted lines show the mean error for male and females for all finish- times. In (a) a filled marker indicates a difference between the corre- sponding male and female means (p < 0.01), and a solid line between two makers indicates a difference between relative pace segments (p < 0.01). The overall R2 value for each finish-time is also shown Fig. 5 Overall performance of different models based exclusively on CS and D′, as well as parameters related to the decoupling of the internal- to-external workload ratio 2291 Internal and External Workload Decoupling During Marathon Running Athletes were classified, based on the magnitude of the decoupling observed in the last 5 km segment of the mara- thon, as low, moderate and high decoupling, as previously suggested [19]. Despite this being an arbitrary classification, we found a remarkably even distribution, and each of the three decoupling groups contained ~ 33% of athletes in the sample. Such inter-individual variability in the magnitude of decoupling supports consideration of durability in physio- logical profiling and performance modelling, as resilience to exercise-induced shifts in intensity domain transitions may contribute to performance capabilities in the latter stages of prolonged events [17–19]. Prolonged exercise, such as marathon running, neces- sitates a physiological steady state, and thus is typically performed at intensities close to, but below, CS [15, 16]. Exercise at intensities that exceed CS (or its cycling analo- gous, critical power) results in an inexorable increase in the concentration of muscle metabolites, such as hydrogen ions and inorganic phosphate, until an intolerable threshold is reached coinciding with the depletion of D′ and the attain- ment of ̇VO2 max , which, ultimately, results in task failure soon afterwards [9, 27, 28]. Alternatively, exercise may be continued after the depletion of D′, but the intensity of exercise must remain below CS [29]. Previous studies have demonstrated that the power profile [30], CS [17, 18] and endurance performance [31] decrease with prolonged, sub- maximal exercise. Combined, the results from these studies and the data presented herein suggest that it is inappropriate to rely exclusively on physiological traits determined in fully rested state athletes to predict endurance performance. It is unlikely that such characteristics, determined at rest, remain constant during prolonged exercise, or that they deteriorate at a constant rate. Instead, athletes appear to exhibit different abilities to preserve their physiological abilities during pro- longed exercise. Thus, monitoring the durability of athletes (e.g., by monitoring the decoupling of internal-to-external workload) should be taken into consideration in physiologi- cal profiling, when prescribing prolonged exercise or aiming to predict endurance (e.g., marathon) performance. 4.2 Decoupling Characteristics and Marathon Performance Previous studies have shown that CS is a strong predictor of marathon performance, with elite marathon runners’ best performances completed at 96% CS [16], and faster rec- reational marathon runners also completing marathons at speeds close to (> 90%), but below, CS [15]. In the present study, athletes in the low decoupling group were able sus- tain a higher fraction of their CS throughout the marathon, which also occurred later in the marathon. The results from the present study demonstrate that marathon runners who exhibited superior durability (i.e., had low decoupling) were also able to run closer to their CS, and also able to complete the marathon faster. The onset of decoupling was estimated to occur when a decoupling of at least 1.025 was first detected. This is, again, an arbitrary threshold representing a 2.5% increase in internal-to-external workload ratio. However, we found that this approach of detecting the onset of decoupling was also associated with marathon performance (Fig. 3). Athletes exhibiting low decoupling were able to complete a further ~ 14 km of the marathon without signs of physi- ological deterioration (Table 2). Moreover, when the onset of decoupling was expressed as time, overall results indicate that decoupling is first observed ~ 128 min into the race (see Table 2). Clark et al. [17] reported that a decrease in critical power was observed following 2 h of cycling at moderate intensities, but not after 80 min. In the present study, how- ever, the onset of decoupling was detected ~ 80 min later in the low decoupling groups compared to the low decou- pling group (~ 105 vs. 185 min, see Table 2). Overall, this study shows that both magnitude of decoupling and onset of decoupling, expressed as distance covered or time elapsed before it was first detected, were associated with marathon performance. Critical speed denotes the highest sustainable oxidative metabolic rate, and thus is strongly associated with endur- ance performance. Indeed, previous studies have shown that marathon performance can be predicted with ~ 7% error using models derived from CS [15]. Similarly, in the pre- sent study marathon performance was predicted with 6.45% error using a model that included CS and D′. The addition of durability traits to this model, namely its magnitude and onset, reduced the prediction error to 5.16%, a 20% improve- ment in accuracy. Therefore, the data presented in the cur- rent study support that models aiming to predict marathon performance, and more generally models of endurance per- formance, should take into consideration the durability of physiological traits. 4.3 Mechanisms Underpinning Decoupling There are several factors that can explain the decoupling of internal-to-external workload decoupling. The mecha- nisms explaining the inter-individual variability in durabil- ity characteristics may be related to skeletal muscle fibre type characteristics given type I fibres are more resilient to exercise-induced loss of mechanical efficiency [32]. There- fore, the muscle metabolic cost of producing a given running speed may be better maintained during marathon running in athletes with a greater proportion of type I fibres, and therefore reduced decoupling between internal and exter- nal work as the race progresses. Similarly, the availability of proteins involved in management of cellular stress, such as the heat shock proteins [33], may promote durability 2292 B. Smyth et al. characteristics by improving the capacity to manage the cel- lular stress generated during prolonged exercise [34]. Dura- bility characteristics may also be related to mitochondrial protein content, as a larger mitochondrial pool may spread the oxidative burden of demanding exercise and therefore reduce mitochondrial damage at the level of the individual mitochondrion during prolonged exercise. These physiologi- cal mechanisms remain speculative and warrant attention from laboratory-based investigations of the determinants of durability characteristics. Further to purely physiological mechanisms, it could be postulated that runners with greater durability are able to preserve a more economical pattern of running through- out the marathon. The greatest sustainable running speed is strongly mediated by running economy (e.g., references [1, 5]). However, the O2 cost of running has been shown to increase concomitantly over increased distances [35]. Elevated levels of markers of muscular fatigue and skeletal muscle damage can interfere with contractile mechanisms through inhibitory effects on α-motoneurons by activating fatigue-sensitive afferent fibres [36]. Consequently, dur- ing periods of prolonged running the force output during the push off phase has been shown to be reduced. Indeed, running induced fatigue has been shown to alter kinemat- ics [37], kinetics [38], as well as stride dynamics [39, 40]. Resultant compensatory alterations in gait pattern to main- tain running speed may result in an upward drift in ̇VO2 , and an increase in internal workload at a given running speed. However, compensatory movement patterns observed along- side and increase in ̇VO2 have been shown to be highly vari- able between runners [41]. Furthermore, it is important to acknowledge the extent of muscular fatigue will be depend- ent on the intensity domain in which exercise is performed. Therefore, further investigations are warranted to elucidate whether diminished running economy is a cause or a conse- quence of durability characteristics. Decoupling was quantified as the internal-to-external ratio [19], and therefore decoupling could represent an increase in internal workload (i.e., HR), decrease in exter- nal workload (i.e., speed), or both. In the current dataset, speed fell following the onset of decoupling by 11.3%, whilst the HR remained constant throughout the marathon, and only increased by 1.6% (or ~ 2 bpm) since decoupling was first detected. These data suggest that during a marathon, a ‘mirror image’ of the slow component was present, whereby workload has to be decreased in order to maintain a constant ̇VO2 [42] or HR [43] during prolonged, submaximal exer- cise. Therefore, factors typically associated with the slow component (e.g., mainly metabolic requirements of fatiguing muscle fibres and additional recruitment of motor units with lower efficiency, see [44] for a review) may also have con- tributed to the observed decoupling of internal-to-external workload ratio. 4.4 Female Runners Exhibit Less Decoupling The results of the present study demonstrate that females displayed a lower magnitude and later onset of decoupling than males (Fig. 2, Table 1). Moreover, there were over twice as many female athletes classified as low decoupling than high decoupling. Previous studies have shown that physiological thresholds that demarcate the exercise inten- sity domains are typically positioned at a higher percentage of ̇VO2 max in females [45]. The data from the current study indicate that, in addition, female runners can also preserve their physiological characteristics better than males, as demonstrated by the low decoupling. Females demonstrate a greater proportional area of type I fibres, greater capillary- to-fibre ratio, greater volumes and densities of mitochon- dria, superior rates of oxidative enzyme activity [46, 47], have greater reliance on fat metabolism than males [48], and may thus be better protected from glycogen depletion. As a result, females may preserve muscular contractile function through better maintenance of glycogen [49], and propensity for greater proportion of fatigue resistance of type I fibres [46, 47]. Combined, whilst males will typically demonstrate a higher CS and better overall marathon performance, these factors may help explain why females were able to complete the marathon at a greater percentage of CS than males and did so whilst experiencing less decoupling. 4.5 Limitations and Future Research Directions For this study, we relied on a large dataset of recreational runners. Using such a large dataset allowed the explora- tion of decoupling characteristics during the marathon, and offers an insight as to whether the internal-to-external work- load experienced during prolonged exercise contributes to explain marathon performance. However, when utilising this approach to use raw training data to calculate CS, it was not possible to verify if participants have performed a maxi- mal effort, for example, checking whether ̇VO2 max has been attained during constant work rate trials [19]. Nonetheless, it is worth noting that this approach has previously been used to estimate CS with a low standard error of estimate (~ 8%) and to successfully predict marathon performance [15]. Data was used for ~ 4 months prior to a marathon event, and so it is likely that some activities included in the data set corresponded to maximal efforts through shorter races (e.g., 5 km) or higher intensity training sessions. Moreover, it has been demonstrated that extraction of data from training results in a high level of agreement with laboratory-based testing when estimating critical power, with low prediction errors (< 5%) [50]. Future research may wish to identify means of verifying maximal efforts to improve CS estimates from training data. It is also worth noting that the CS is an estimation of the upper boundary of the heavy intensity 2293 Internal and External Workload Decoupling During Marathon Running domain, and it was not possible to verify whether this rep- resented the MMSS in the current study. It has been sug- gested that the CS may overestimate the MMSS relative to other methods and is highly dependent on the protocol used [11, 12]. However, the CS has been shown to closely repre- sent the MMSS [14], and is widely regarded as an accurate tool to estimate of the heavy-severe domain transition [8, 13]. Furthermore, other methods used to approximate the heavy-severe boundary, for example, ventilatory thresholds, maximal lactate steady state, etc., were not permissible using the current approach. To quantify internal workload, we used HR data, and it should be acknowledged that HR is likely to exhibit some- what different kinetics to that of ̇VO2 during prolonged exer- cise [43, 51]. Moreover, prolonged exercise can result in fluid loss due to excessive sweating and inadequate fluid replacement, particularly in hot environments. This imposes an additional cardiac strain, which results in a cardiovascu- lar drift (i.e., increased HR, with concomitant reductions in ̇VO2 max [52]). Environmental conditions were not taken into consideration for the current analysis, but it is plausi- ble that the decoupling of internal-to-external workload is increased in hot environments. Moreover, males and females may not be equally affected by exercise-induced dehydra- tion [53]. A question that remains unanswered and warrants further investigation is whether durability traits are sensitive to training adaptations. We would also encourage further research to investigate whether training characteristics, such as training volume, intensity, or the distribution of training load, can influence durability. Nonetheless, the findings from the current study would suggest that training may be able to reduce the decoupling of the internal-to-external workload ratio. 5 Conclusions The internal-to-external ratio during a marathon was ~ 1.16, which represents a 16% increase in internal-to-exter- nal ratio over the course of the marathon, and was first detected ~ 25 km into the marathon. However, there was a large inter-individual variation in both the absolute mag- nitude of the decoupling and its onset. Importantly, both decoupling magnitude and onset were associated with performance, and the inclusion of these durability traits increased the precision of models of marathon performance by ~ 20% compared to those relying exclusively on CS and D′. Females had, overall, a better durability profile, as they exhibited lower decoupling in internal-to-external ratio. The data presented herein, therefore, suggest that appreciation of inter-individual differences in athlete durability may help improve understanding of an individual athlete’s perfor- mance capabilities in marathon running. Declarations Funding Open Access funding provided by the IReL Consortium. The authors received no other funding for this work. Conflicts of interest Authors BS, EM, SM, BH and DM-P declare that they have no conflicting interests. Availability of data The data supporting the findings of the current study are provided by Strava® under a limited research license. The data are thus not publicly available. Requests to access these data should be directed to Strava®. Code availability The code used to analyse the data is available upon reasonable request to Prof. Barry Smyth ([email protected]). Ethics approval The ethics boards of Auckland University of Technol- ogy, University College Dublin, and University of Hertfordshire waived the requirement for ethical approval for the current study. Consent An anonymised dataset from Strava® users was provided to the authors under a limited research license. No new data were gener- ated. Authors’ contributions BS, EM, SM, BH and DM-P designed the study. BS analysed the data and constructed the figures. BS, EM, SM, BH and DM-P interpretated the results, prepared and edited the manu- script, and approved the final version of the manuscript. Open Access This article is licensed under a Creative Commons Attri- bution 4.0 International License, which permits use, sharing, adapta- tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visithttp:// creat iveco mmons. org/ licen ses/ by/4. 0/. References 1. Joyner MJ, Hunter SK, Lucia A, Jones AM. Physiology and fast marathons. J Appl Physiol. 2020;128:1065–8. 2. Jones AM, Kirby BS, Clark IE, Rice HM, Fulkerson E, Wylie LJ, et al. Physiological demands of running at 2-hour marathon race pace. J Appl Physiol. 2021;130:369–79. 3. Hoogkamer W, Kram R, Arellano CJ. How biomechanical improvements in running economy could break the 2-hour marathon barrier. Sport Med. 2017;47:1739–50. 4. Santos-Concejero J, González-Mohíno F, González-Ravé JM, Perrey S, Dewolf AH, Yates BA, et al. Commentaries on viewpoint: physiology and fast marathons. J Appl Physiol. 2020;128:1069–85. 2294 B. Smyth et al. 5. Joyner MJ. Modeling: optimal marathon performance on the basis of physiological factors. J Appl Physiol. 1991;70:683–7. 6. Van Der Zwaard S, Van Der Laarse WJ, Weide G, Bloemers FW, Hofmijster MJ, Levels K, et al. Critical determinants of combined sprint and endurance performance: an integra- tive analysis from muscle fiber to the human body. FASEB J. 2018;32:2110–23. 7. Burnley M, Jones AM. Power–duration relationship: Physiology, fatigue, and the limits of human performance. Eur J Sport Sci. 2018;18:1–12. 8. Jones AM, Burnley M, Black MI, Poole DC, Vanhatalo A. The maximal metabolic steady state: redefining the ‘gold standard.’ Physiol Rep. 2019;7:14098. 9. Nixon RJ, Kranen SH, Vanhatalo A, Jones AM. Steady-state ̇ V O 2 above MLSS: evidence that critical speed better represents maximal metabolic steady state in well-trained runners. Eur J Appl Physiol. 2021;121:3133–44. 10. Poole DC, Burnley M, Vanhatalo A, Rossiter HB, Jones AM. Critical power: an important fatigue threshold in exercise physiol- ogy. Med Sci Sports Exerc. 2016;48:2320. 11. Iannetta D, Ingram CP, Keir DA, Murias JM. Methodologi- cal reconciliation of CP and MLSS and their agreement with the maximal metabolic steady state. Med Sci Sports Exerc. 2022;54:622–32. 12. Gorostiaga EM, Sánchez-Medina L, Garcia-Tabar I. Over 55 years of critical power: Fact or artifact? Scand J Med Sci Sports. 2022;32:116–24. 13. Galán-Rioja MÁ, González-Mohíno F, Poole DC, González-Ravé JM. Relative proximity of critical power and metabolic/ventila- tory thresholds: systematic review and meta-analysis. Sports Med. 2020;50(10):1771–83. 14. Nixon RJ, Kranen SH, Vanhatalo A, Jones AM. Steady-state VO2 above MLSS: evidence that critical speed better represents maximal metabolic steady state in well-trained runners. Eur J Appl Physiol. 2021;121(11):3133–44. 15. Smyth B, Muniz-Pumares D. Calculation of critical speed from raw training data in recreational marathon runners. Med Sci Sports Exerc. 2020;52:2637–45. 16. Jones AM, Vanhatalo A. The ‘Critical Power’ concept: Applications to sports performance with a focus on intermittent high-intensity exercise. Sport Med. 2017;47:65–78. 17. Clark IE, Vanhatalo A, Bailey SJ, Wylie LJ, Kirby BS, Wilkins BW, et al. Effects of two hours of heavy-intensity exercise on the power- duration relationship. Med Sci Sports Exerc. 2018;50:1658–68. 18. Clark IE, Vanhatalo A, Thompson C, Joseph C, Black MI, Blackwell JR, et al. Dynamics of the power-duration relationship during pro- longed endurance exercise and influence of carbohydrate ingestion. J Appl Physiol. 2019;127:726–36. 19. Maunder E, Seiler S, Mildenhall MJ, Kilding AE, et al. The impor- tance of “durability” in the physiological profiling of endurance athletes. Sport Med. 2021;51:1–10. 20. Ansdell P, Thomas K, Hicks KM, Hunter SK, Howatson G, Goodall S. Physiological sex differences affect the integrative response to exercise: acute and chronic implications. Exp Physiol. 2020;105:2007–21. 21. Hunter SK. Sex differences in fatigability of dynamic contractions. Exp Physiol. 2016;101:250–5. 22. Minetti AE, Moia C, Roi GS, Susta D, Ferretti G. Energy cost of walking and running at extreme uphill and downhill slopes. J Appl Physiol. 2002;93:1039–46. 23. Muniz-Pumares D, Karsten B, Triska C, Glaister M. Methodological approaches and related challenges associated with the determination of critical power and W’. J Strength Cond Res. 2019;33:584–96. 24. Inbar O, Oren A, Scheinowitz M, Rotstein A, Dlin R, Casaburi R. Normal cardiopulmonary responses during incremental exercise in 20-to 70-yr-old men. Med Sci Sports Exerc. 1994;26:538–46. 25. Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001;29:1189–232. 26. Sawilowsky SS. New effect size rules of thumb. J Mod Appl Stat Methods. 2009;8:597–9. 27. Black MI, Jones AM, Blackwell JR, Bailey SJ, Wylie LJ, McDonagh STJ, et al. Muscle metabolic and neuromuscular determinants of fatigue during cycling in different exercise intensity domains. J Appl Physiol. 2017;122:446–59. 28. Jones AM, Wilkerson DP, DiMenna F, Fulford J, Poole DC. Mus- cle metabolic responses to exercise above and below the “critical power” assessed using 31P-MRS. Am J Physiol Regul Integr Comp Physiol. 2008;294:585–93. 29. Coats EM, Rossiter HB, Day JR, Miura A, Fukuba Y, Whipp BJ. Intensity-dependent tolerance to exercise after attaining V̇O2max in humans. J Appl Physiol. 2003;95:483–90. 30. Van Erp T, Sanders D, Lamberts RP. Maintaining power output with accumulating levels of work done is a key determinant for success in professional cycling. Med Sci Sports Exerc. 2021;53:1903–10. 31. Noordhof DA. Performance-determining variables in long-dis- tance events : should they be determined from a rested state or after prolonged submaximal exercise? Int J Sport Physiol Perform. 2020;16:647–54. 32. Barclay CJ. Mechanical efficiency and fatigue of fast and slow mus- cles of the mouse. J Physiol. 1996;497:781–94. 33. Henstridge DC, Febbraio MA, Hargreaves M, Henstridge DC. Heat shock proteins and exercise adaptations. Our knowledge thus far and the road still ahead. J Appl Physiol. 2016;120:683–91. 34. Krüger K, Reichel T, Zeilinger C. Role of heat shock proteins 70/90 in exercise physiology and exercise immunology and their diagnostic potential in sports. J Appl Physiol. 2019;126:916–27. 35. Brueckner JC, Atchou G, Capelli C, Duvallet A, Barrault D, Jous- selin E, et al. The energy cost of running increases with the distance covered. Eur J Appl Physiol Occup Physiol. 1991;62:385–9. 36. Millet GY, Lepers R. Alterations of neuromuscular function after prolonged running, cycling and skiing exercises. Sport Med. 2012;34:105–16. 37. Dierks TA, Davis IS, Hamill J. The effects of running in an exerted state on lower extremity kinematics and joint timing. J Biomech. 2010;43:2993–8. 38. Girard O, Millet GP, Slawinski J, Racinais S, Micallef JP. Changes in running mechanics and spring-mass behaviour during a 5-km time trial. Int J Sports Med. 2013;34:832–40. 39. Meardon SA, Hamill J, Derrick TR. Running injury and stride time variability over a prolonged run. Gait Posture. 2011;33:36–40. 40. Mo S, Chow DHK. Stride-to-stride variability and complexity between novice and experienced runners during a prolonged run at anaerobic threshold speed. Gait Posture. 2018;64:7–11. 41. Hunter I, Smith GA. Preferred and optimal stride frequency, stiffness and economy: changes with fatigue during a 1-h high-intensity run. Eur J Appl Physiol. 2007;100:653–61. 42. Grassi B, Rossiter HB, Zoladz JA. Skeletal muscle fatigue and decreased efficiency: two sides of the same coin? Exerc Sport Sci Rev. 2015;43:75–83. 43. Zuccarelli L, Porcelli S, Rasica L, Marzorati M, Grassi B. Compari- son between slow components of HR and VO2 kinetics: functional significance. Med Sci Sports Exerc. 2018;50:1649–57. 44. Jones AM, Grassi B, Christensen PM, Krustrup P, Bangsbo J, Poole DC. Slow component of V̇O2 kinetics: Mechanistic bases and practi- cal applications. Med Sci Sports Exerc. 2011;43:2046–62. 45. Ansdell P, Brownstein CG, Škarabot J, Hicks KM, Howatson G, Thomas K, et al. Sex differences in fatigability and recovery relative to the intensity–duration relationship. J Physiol. 2019;597:5577–95. 46. Haizlip KM, Harrison BC, Leinwand LA. Sex-based differences in skeletal muscle kinetics and fiber-type composition. Physiology. 2015;30:30–9. 2295 Internal and External Workload Decoupling During Marathon Running 47. Simoneau JA, Bouchard C. Human variation in skeletal muscle fiber- type proportion and enzyme activities. Am J Physiol Endocrinol Metab. 1989;257:567–72. 48. Maunder E, Plews DJ, Kilding AE. Contextualising maximal fat oxidation during exercise: determinants and normative values. Front Physiol. 2018;9:599. 49. Ørtenblad N, Westerblad H, Nielsen J. Muscle glycogen stores and fatigue. J Physiol. 2013;591(18):4405–13. 50. Karsten B, Jobson SA, Hopker J, Jimenez A, Beedie C. High agree- ment between laboratory and field estimates of critical power in cycling. Int J Sports Med. 2014;35(04):298–303. 51. Wingo JE, Stone T, Ng J. Cardiovascular drift and maximal oxy- gen uptake during running and cycling in the heat. Med Sci Sports Exerc. 2020;52:1924–32. 52. Wingo JE, Lafrenz AJ, Ganio MS, Edwards GL, Cureton KJ. Car- diovascular drift is related to reduced maximal oxygen uptake during heat stress. Med Sci Sports Exerc. 2005;37:248–55. 53. Wickham KA, Mccarthy DG, Spriet LL, Cheung SS. Sex differences in the physiological responses to exercise-induced dehydration: con- sequences and mechanisms. J Appl Physiol. 2021;131:504–10.

Decoupling of Internal and External Workload During a Marathon: An Analysis of Durability in 82,303 Recreational Runners.

05-05-2022

Smyth, Barry,Maunder, Ed,Meyler, Samuel,Hunter, Ben,Muniz-Pumares, Daniel

eng

PMC9268557

Citation: Motevalli, M.; Wagner, K.-H.; Leitzmann, C.; Tanous, D.; Wirnitzer, G.; Knechtle, B.; Wirnitzer, K. Female Endurance Runners Have a Healthier Diet than Males—Results from the NURMI Study (Step 2). Nutrients 2022, 14, 2590. https:// doi.org/10.3390/nu14132590 Academic Editor: Paul J. Arciero Received: 19 May 2022 Accepted: 21 June 2022 Published: 22 June 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). nutrients Article Female Endurance Runners Have a Healthier Diet than Males—Results from the NURMI Study (Step 2) Mohamad Motevalli 1,2 , Karl-Heinz Wagner 3 , Claus Leitzmann 4, Derrick Tanous 1,2 , Gerold Wirnitzer 5, Beat Knechtle 6,7 and Katharina Wirnitzer 1,2,8,* 1 Department of Sport Science, Leopold-Franzens University of Innsbruck, 6020 Innsbruck, Austria; [email protected] (M.M.); [email protected] (D.T.) 2 Department of Subject Didactics and Educational Research and Development, University College of Teacher Education Tyrol, 6010 Innsbruck, Austria 3 Department of Nutritional Sciences, University of Vienna, 1090 Vienna, Austria; [email protected] 4 Institute of Nutrition, University of Gießen, 35390 Gießen, Germany; [email protected] 5 AdventureV & Change2V, 6135 Stans, Austria; [email protected] 6 Institute of Primary Care, University of Zurich, 8091 Zurich, Switzerland; [email protected] 7 Medbase St. Gallen Am Vadianplatz, 9001 St. Gallen, Switzerland 8 Research Center Medical Humanities, Leopold-Franzens University of Innsbruck, 6020 Innsbruck, Austria * Correspondence: [email protected]; Tel.: +43-(650)-5901794 Abstract: Sex has been recognized to be an important indicator of physiological, psychological, and nutritional characteristics among endurance athletes. However, there are limited data addressing sex-based differences in dietary behaviors of distance runners. The aim of the present study is to explore the sex-specific differences in dietary intake of female and male distance runners competing at >10-km distances. From the initial number of 317 participants, 211 endurance runners (121 fe- males and 90 males) were selected as the final sample after a multi-level data clearance. Participants were classified to race distance (10-km, half-marathon, marathon/ultra-marathon) and type of diet (omnivorous, vegetarian, vegan) subgroups. An online survey was conducted to collect data on sociodemographic information and dietary intake (using a comprehensive food frequency question- naire with 53 food groups categorized in 14 basic and three umbrella food clusters). Compared to male runners, female runners had a significantly greater intake in four food clusters, including “beans and seeds”, “fruit and vegetables”, “dairy alternatives”, and “water”. Males reported higher intakes of seven food clusters, including “meat”, “fish”, “eggs”, “oils”, “grains”, “alcohol”, and “processed foods”. Generally, it can be suggested that female runners have a tendency to consume healthier foods than males. The predominance of females with healthy dietary behavior can be potentially linked to the well-known differences between females and males in health attitudes and lifestyle patterns. Keywords: sex; gender; nutrition; dietary assessment; food frequency; protein; fruit; vegetables; distance running; half-marathon; marathon 1. Introduction The importance of sex-related comparison in sports nutrition topics has been widely discussed over the past decade [1]. It is well-established that the nutritional requirements of athletes are potentially affected by physical and physiological differences between males and females [2,3]. These sex-based differences seem to be more predominant in ultra-endurance athletes who are recommended to pay superior attention to their specific nutritional needs due to the prolonged training/racing activities [4,5]. Sex differences in endurance performance are not limited to the menstrual cycle that causes unfavorable effects on training procedures in female athletes (mainly due to the asso- ciated challenges and anemia rather than hormonal fluctuations) [6,7]. Evidence shows that Nutrients 2022, 14, 2590. https://doi.org/10.3390/nu14132590 https://www.mdpi.com/journal/nutrients Nutrients 2022, 14, 2590 2 of 17 females have a lower oxygen-carrying capacity (due to fewer erythrocytes and hemoglobin levels) than males, which can affect their endurance performance negatively [8]. In addition, females are more susceptible to developing thyroid disorders compared to males [9] result- ing in performance-limiting outcomes, including fatigue [10]. However, males seem to be more prone to cardiovascular abnormalities as it has been shown that cardiac death and coronary heart disease are more prevalent in males than females [11,12], which increases the likelihood of unfavorable health- and performance-related consequences. Considering the fact that male athletes are characterized as being more influenced by risky behaviors such as performance-enhancing substance abuse [13,14], their cardiovascular health is of greater concern. Research indicates that in muscle metabolism pathways during endurance activities, females have a higher capacity to utilize muscle lipids as fuel, and males rely more on muscle and liver glycogen resources [15,16]. To achieve an optimal level of en- durance performance, however, females may need further training adaptations compared to males [17,18] due to the basic sex-specific physical differences (e.g., body mass, muscle mass, and fat mass) [6,19]. Nutritional requirements and patterns may also be affected by sex, whether dependent or independent of the mentioned physical and physiological differences between males and females. It has been shown that female athletes have a greater prevalence of uninten- tional caloric imbalance than males in order to reach and maintain the appropriate body composition required for an optimized level of endurance performance [6,18,19]. Females have also been reported to be generally more health conscious than males, which also can be associated with their attitudes towards food choice, including a greater intake of fruits, vegetables, and whole foods [20]. In contrast, it has been shown that males are more motivated to increase physical activity in their daily routines rather than modifying their nutritional habits [21]. Generally, the various health- and lifestyle-related beliefs between females and males have been predicted to be responsible for up to 50% of sex-specific dietary choices [20]. Dietary assessment is a crucial part of sports nutrition practice, which helps identify nutritional inadequacy (that commonly occurs following restrictive diets) and optimize dietary strategies for improving performance and health. Nutritional concerns, particu- larly energy deficiency, are more critical in both male and female long-distance runners compared to those who run in shorter races [18,22]. Likewise, nutritional requirements are positively associated with increasing intensity, duration, and frequency of running/training sessions [18,23]. Data show that typical daily foods may not fulfill the nutritional needs of endurance runners to support their physiological requirements [22,24]. This concern is more serious for endurance athletes who follow unbalanced and/or inappropriately- planned diets, which has been shown to occur in all diet types (e.g., omnivorous or plant- based diets) [25–27]. It has been reported that even ultra-endurance events can be com- pleted successfully without any health-related consequences by athletes who consume only plant-based foods [28,29]. This finding supports that by following the well-recognized dietary guidelines, appropriately planned plant-based diets can maintain the health of long-distance runners [28,29]. Regardless of the well-established sex differences in physical, physiological, and nutritional characteristics of general populations [30], there is limited evidence comparing dietary intake between male and female endurance athletes, particularly distance runners. Despite the advancement of knowledge in illustrating sex-based differences, the majority of sports nutrition topics have a paucity of female-specific examinations, resulting in the misapplication of many scientific conclusions for female athletes [31]. Available studies regarding the nutrient requirements of endurance athletes [32–34] are not consistent in covering all sex-based differences, or they did not distinguish race distance and diet type of female and male endurance runners [35,36]. Therefore, the present study was conducted to investigate and compare the dietary intake of female and male distance runners across different subgroups of diet type and race distance. It was hypothesized that female runners have a dietary intake more advantageous to health. Nutrients 2022, 14, 2590 3 of 17 2. Materials and Methods 2.1. Study Design and Ethical Approval The present study is a part of the Nutrition and Running High Mileage (NURMI) Study Step 2. The study protocol [37] was approved by the ethics board of St. Gallen, Switzerland (EKSG 14/145; 6 May 2015) with the trial registration number ISRCTN73074080. The methods of the “NURMI Study Step 2” have been previously described in detail [38,39]. 2.2. Participants and Experimental Approach Endurance runners were mainly recruited from Austria, Germany, and Switzerland and were contacted via social media, websites of organizers of marathon events, online running communities, email lists, and runners’ magazines, as well as via additional/other multi-channel recruitments and through personal contacts. Participants were asked to com- plete an online survey within the “NURMI Study Step 2”, which was available in German and English (https://www.nurmi-study.com/en (accessed on 10 May 2022)). Participants were provided with a written description of the procedures and gave their informed consent before completing the questionnaire. The following inclusion criteria were initially required for successful participation in the “NURMI Study Step 2”: (1) written informed consent; (2) at least 18 years of age; (3) questionnaire Step 2 completed; (4) successful participation in a running event of at least half-marathon distance in the past two years. Female and male participants were further categorized according to race distance and kind of diet. Race distance subgroups were half-marathon and (ultra-)marathon (data were pooled since the marathon distance is included in an ultra-marathon); the shortest and longest ultra-marathon distances reported were 50 km and 160 km, respectively. However, a total number of 74 runners who completed the 10-km distance, but had not successfully participated in either a half-marathon or a marathon, also provided accurate and useable answers similar to runners competing over half-marathon or higher. In order to avoid an irreversible loss of these valuable data sets, those who met the inclusion criteria (1) to (3) were kept as additional race distance subgroup. Dietary subgroups were omnivorous (or Western diet, with no restriction on any food items), vegetarian (devoid of all flesh foods, including fish and shellfish, but including eggs and/or dairy products), and vegan diet (devoid of all foods from animal sources, including honey) [40,41] with a minimum of 6-month adherence to the self-reported diet types. 2.3. Data Clearance From the initial number of 317 endurance runners, a total of 106 participants were excluded from the data analysis. Of these, 46 participants did not meet the basic inclusion criteria. In order to control for a minimal status of health linked to a minimum level of fitness and to further enhance the reliability of data sets, the Body Mass Index (BMI) approach following the World Health Organization (WHO) standards [42,43] was applied. On this basis, one participant with a BMI ≥ 30 kg/m2 was excluded from the data analysis since first other health-protective and/or weight loss strategies other than running are necessary to safely reduce body weight. Further, as a result of the specific exclusion criteria for the present study, an additional number of 25 runners were identified and excluded for consuming ≤50% carbohydrates of their total dietary intake (which is lower than the minimum level recommended for maintaining a health-performance association [25,44,45]). Moreover, 34 participants with conflicting statements on water intake (e.g., stated never drinking water) were excluded from the analysis to avoid conflicting data on dietary intake [44]. In addition, a total of 24 runners (11%) had to be shifted to other dietary subgroups: 4 vegan runners: respectively 2 to omnivores and 2 to vegetarian samples; and 20 (9%) vegetarian runners had to be shifted to the omnivores subsample. However, 89% (n = 187) of the recreational runners correctly assessed their kind of diet. As the final sample, 211 runners (121 women and 90 men) with complete data sets were included for statistical analysis. Figure 1 shows the participants’ enrollment and classifications within the present study. Nutrients 2022, 14, 2590 4 of 17 subgroups: 4 vegan runners: respectively 2 to omnivores and 2 to vegetarian samples; and 20 (9%) vegetarian runners had to be shifted to the omnivores subsample. However, 89% (n = 187) of the recreational runners correctly assessed their kind of diet. As the final sam- ple, 211 runners (121 women and 90 men) with complete data sets were included for sta- tistical analysis. Figure 1 shows the participants’ enrollment and classifications within the present study. Figure 1. Participants’ enrollment and classifications by sex. 2.4. Measures and Statistical Modelling Based on the food frequency questionnaire (FFQ) of the “German Health Interview and Examination Survey for Adults (DEGS)” (DEGS-FFQ; with friendly permission of the Robert Koch Institute, Berlin, Germany) [46,47], participants were asked to report their regular food intake based on the consumption frequency (single-choice out of 11 options ranging from “never” to “5 times a day”) and quantity of a broad variety of specific die- tary items (single-choice from various options depending on the food group) particularly in the past four weeks, including meals eaten while out, i.e., in restaurants, canteens, at friends’ houses, etc. Based on the 53 food groups of the DEGS-FFQ and following the Nova classification system of the Food and Agriculture Organization (FAO, Rome, Italy) [48–51], subgroups of foods were categorized with the corresponding questions pooled for a total of 17 food clusters in order to perform quantitative and qualitative data analyses (Table 1). Self-re- ported data, including sociodemographic information, motive(s) for diet type adherence, and pooled food frequency, were linked to sex-based groups. Figure 1. Participants’ enrollment and classifications by sex. 2.4. Measures and Statistical Modelling Based on the food frequency questionnaire (FFQ) of the “German Health Interview and Examination Survey for Adults (DEGS)” (DEGS-FFQ; with friendly permission of the Robert Koch Institute, Berlin, Germany) [46,47], participants were asked to report their regular food intake based on the consumption frequency (single-choice out of 11 options ranging from “never” to “5 times a day”) and quantity of a broad variety of specific dietary items (single-choice from various options depending on the food group) particularly in the past four weeks, including meals eaten while out, i.e., in restaurants, canteens, at friends’ houses, etc. Based on the 53 food groups of the DEGS-FFQ and following the Nova classification system of the Food and Agriculture Organization (FAO, Rome, Italy) [48–51], subgroups of foods were categorized with the corresponding questions pooled for a total of 17 food clusters in order to perform quantitative and qualitative data analyses (Table 1). Self- reported data, including sociodemographic information, motive(s) for diet type adherence, and pooled food frequency, were linked to sex-based groups. Nutrients 2022, 14, 2590 5 of 17 Table 1. Modeling of the Clusters for Food Frequency (Basic Nutrition and Consumption Cluster 1 to 14; Umbrella Cluster for Preparation Cluster 15 to 17). Basic Food Clusters Cluster 1 Grains a—grains b—whole grains cornflakes; white bread; white pasta muesli; wholegrain; mixed bread; wholegrain pasta; wholegrain rice; other grains Cluster 2 Legumes, nuts, and pulses pulses; nuts and seeds; legumes Cluster 3 Fruit and vegetables vegetable juice; fruit; vegetables Cluster 4 Dairy products milk; cheese; yoghurt Cluster 5 Dairy alternatives milk alternatives Cluster 6 Meat a—meat b—processed meat chicken; beef; pork; deer fried nuggets; hamburger; sausage; kebab; pork; processed meat Cluster 7 Meat alternatives tofu; seitan; tempeh; etc. Cluster 8 Fish, shellfish, and seafood Cluster 9 Eggs Cluster 10 Oils and spreads butter; margarine; oils Cluster 11 Sweets and snacks sweets; snacks; salty snacks Cluster 12 Water and unsweetened tea Cluster 13 Beverages Cluster 14 Alcohol Preparation/Umbrella Clusters Cluster 15 Protein a—plant protein b—animal protein legumes and beans; vegetables; grains (couscous, quinoa); dairy alternatives (e.g., soy products); meat alternatives dairy products; eggs; meat and processed meat products; fish, seafood, and shellfish Cluster 16 (Ultra-)processed foods and free/added sugar sugary carbonated drinks; kcal reduced/artificially sweetened drinks; fruit juice; free sugar in tea; free sugar in coffee; cereals; sweet and savory spreads; margarine; pasta; sweets, cakes, and biscuits; salty snacks, butter; processed meat; processed plant products Cluster 17 Free/added sugar Sweet spread; sugary carbonated drinks; fruit juice; free sugar in tea; free sugar in coffee; cereals; sweets, cakes, and biscuits 2.5. Statistical Analysis The statistical software R version 4.1.1 (10 August 2021) Core Team 2018 (R Foundation for Statistical Computing, Vienna, Austria) was used to perform all statistical analyses. Exploratory analysis was done by descriptive statistics: mean values and standard deviation (SD), median and interquartile range (IQR). Chi-square tests (χ2, nominal scale) were conducted to examine the association of sex with nationality, marital status, academic qualification, diet type, race distance, and dietary motives. Kruskal–Wallis tests (ordinal and metric scale) were approximated by using the t or F distributions or using ordinary least squares and standard errors (SE) with R2 to test the association of sex with age, body weight, height, and BMI. Food cluster as the latent variable was derived by 53 manifest parameters (assessing how often and how much consumption of specific dietary items). In order to scale the food consumption displayed by measures, items, and clusters, a heuristic index (as a new composite variable) ranging from 0 to 100 was defined (equivalence in all items; FFQ was calculated by multiplying the reports of both questions, and dividing by Nutrients 2022, 14, 2590 6 of 17 the maximum then). A linear regression model was used to examine significant differences in the intake of specific food clusters by sex and age. The assumptions of the regression analysis have been verified by inspection of graphs of residuals. Differences in respective food clusters between females and males are displayed as effect plots (95% confidence interval). The level of statistical significance was set at p ≤ 0.05. 3. Results From a total of 211 runners (including 121 females and 90 males) with a median age of 38 (IQR 18) years, there were 74 runners of 10-km, 83 half marathoners, and 54 marathoners/ultramarathoners based on race distance, and 95 omnivores, 40 vege- tarians, and 76 vegans based on kind of diet. The majority of endurance runners (96%) were from German-speaking countries (i.e., Germany, Austria, and Switzerland), while 4% of participants were from other countries worldwide. Descriptive analysis showed significant differences between females and males in age (p = 0.023), where males with a median age of 42 (IQR 17) years were older than females with a median age of 37 (IQR 15) years, and BMI (p < 0.001), where males had a higher BMI (22.91 kg/m2, IQR 2.86) compared to females (20.94 kg/m2, IQR 3.05). No significant difference (p > 0.05) was found between male and female runners in academic qualification or marital status. There was a significant sex-based difference in race distance (p < 0.001), where the majority of 10-km runners and half marathoners were female, and most marathon/ultramarathon runners were male. A significant sex-based difference was detected in diet type (p = 0.013), as vegetarian and vegan diets were more common in females and omnivorous were more prevalent in male runners. While endurance runners reported mostly “health & wellbeing” (by 85%) as the main reason/motive to adhere to their self-reported diet types, “social aspects” was the only motive with a significant difference between females and males (41% vs. 65%, respectively; p = 0.010). Table 2 shows the sex-based differences in sociodemographic characteristics of the participants. Table 2. Sociodemographic characteristics of female and male runners. Total n = 211 Females n = 121 Males n = 90 Statistics Age (years) 38 (IQR 18) 37 (IQR 15) 42 (IQR 17) F(1, 209) = 5.26; p = 0.023 Body Weight (kg) 65.0 (IQR 14.1) 59.8 (IQR 10.6) 73.6 (IQR 12.3) F(1, 209) = 189.68; p < 0.001 Height (m) 1.7 (IQR 0.1) 1.7 (IQR 0.1) 1.8 (IQR 0.1) F(1, 209) = 191.83; p < 0.001 BMI (kg/m2) 21.72 (IQR 3.40) 20.94 (IQR 3.05) 22.91 (IQR 2.86) F(1, 209) = 33.21; p < 0.001 Academic Qualification Upper Secondary/Technical A Levels or Equivalent University/Higher Degree No Answer 33% (69) 23% (49) 34% (72) 9% (21) 30% (36) 23% (28) 36% (43) 12% (14) 37% (33) 23% (21) 32% (29) 8% (7) χ2(3) = 2.14; p = 0.709 Marital Status Divorced/Separated Married/Partner Single 5% (11) 68% (143) 27% (57) 7% (8) 61% (74) 32% (39) 3% (3) 77% (69) 20% (18) χ2(2) = 5.75; p = 0.056 Country of Residence Austria Germany Switzerland Other Countries 17% (36) 74% (156) 5% (11) 4% (8) 10% (12) 80% (97) 5% (6) 5% (6) 27% (24) 66% (59) 6% (5) 2% (2) χ2(3) = 11.03; p = 0.012 Race Distance 10-km HM M/UM 35% (74) 39% (83) 26% (54) 45% (55) 38% (46) 17% (20) 21% (19) 41% (37) 38% (34) χ2(2) = 17.95; p < 0.001 Diet Type Omnivorous Vegetarian Vegan 45% (95) 19% (40) 36% (76) 36% (44) 21% (26) 42% (51) 57% (51) 16% (14) 28% (25) χ2(2) = 8.64; p = 0.013 Note. IQR—Interquartile range. BMI—body mass index. km—kilometers. HM—half-marathon. M/UM—marathon/ultra-marathon. Statistical methods: Kruskal–Wallis tests (represented by median and IQR data) and Chi-square tests (represented by prevalence data). Nutrients 2022, 14, 2590 7 of 17 Significant differences between female and male participants were found in the con- sumption of 11 out of 17 food clusters (p < 0.05). Compared to males, female runners reported a greater intake of four food clusters including beans and seeds (p = 0.008), fruit and vegetables (p < 0.001), dairy alternatives (p = 0.012), and water (p = 0.002). In con- trast, males had a higher intake of seven food clusters including grains (p < 0.001), meat (p < 0.001), fish (p = 0.033), eggs (p = 0.041), oils (p = 0.033), alcohol (p < 0.001), and processed foods (p = 0.001). There was no significant difference between female and male runners in the consumption of six food clusters, including dairy (p = 0.159), meat alternatives (p = 0.488), snacks (p = 0.086), beverages (p = 0.350), protein (p = 0.599), and free/added sugar (p = 0.212). Table 3 displays the sex-based differences in intake of 17 food clusters and the subset items. Table 3. Differences between female and male runners in food clusters and items. Females Males Statistics n = 121 n = 90 Part A—Basic clusters FC—1 (Total of grains) 15.43 ± 7.86 21.90 ± 8.16 F(1, 209) = 36.40; p < 0.001 FC—1a (Total of refined grains) 9.99 ± 8.14 15.54 ± 9.57 F(1, 209) = 19.64; p < 0.001 Cornflakes 1.60 ± 3.57 1.44 ± 4.99 F(1, 209) = 2.34; p = 0.127 White bread 6.07 ± 6.35 10.36 ± 9.18 F(1, 209) = 12.03; p = 0.001 White pasta 8.81 ± 8.48 13.84 ± 9.42 F(1, 209) = 16.03; p < 0.001 FC—1b (Total of whole grains) 17.12 ± 8.48 22.95 ± 9.17 F(1, 209) = 22.12; p < 0.001 Muesli 14.89 ± 12.32 18.80 ± 14.00 F(1, 207) = 3.91; p = 0.049 Whole grain bread 14.45 ± 8.54 18.99 ± 9.40 F(1, 209) = 16.23; p < 0.001 Whole grain pasta 9.37 ± 8.11 11.22 ± 9.36 F(1, 209) = 1.65; p = 0.201 Whole grain rice 5.87 ± 6.57 8.96 ± 8.26 F(1, 209) = 7.17; p = 0.008 Other whole grains 6.07 ± 6.35 10.36 ± 9.18 F(1, 209) = 12.03; p = 0.001 FC—2 (Total of beans and seeds) 28.47 ± 13.89 23.70 ± 13.74 F(1, 209) = 7.12; p = 0.008 Nuts & seeds 22.25 ± 13.21 16.11 ± 12.67 F(1, 209) = 13.04; p < 0.001 Legumes 15.98 ± 10.65 15.71 ± 10.74 F(1, 209) = 0.23; p = 0.630 FC—3 (Total of fruit and vegetables) 34.09 ± 13.03 26.84 ± 11.77 F(1, 209) = 19.30; p < 0.001 Vegetable juice 5.48 ± 9.74 5.70 ± 11.58 F(1, 209) = 1.01; p = 0.315 Fruit 19.93 ± 9.30 18.16 ± 8.73 F(1, 209) = 2.92; p = 0.089 Vegetables 34.73 ± 12.56 27.08 ± 10.50 F(1, 209) = 22.01; p < 0.001 FC—4 (Total of dairy) 9.70 ± 12.11 10.77 ± 9.67 F(1, 209) = 2.00; p = 0.159 Milk 7.57 ± 11.31 9.67 ± 11.71 F(1, 209) = 3.00; p = 0.085 Cheese 7.10 ± 8.89 8.12 ± 8.05 F(1, 209) = 1.76; p = 0.187 Yogurt 7.81 ± 11.00 7.17 ± 9.09 F(1, 209) = 0.04; p = 0.833 FC—5: Dairy alternatives 18.08 ± 15.04 13.69 ± 15.51 F(1, 209) = 6.44; p = 0.012 FC—6 (Total of meat) 4.95 ± 9.81 12.46 ± 13.70 F(1, 209) = 19.26; p < 0.001 FC—6a (Total of unprocessed meat) 5.43 ± 10.68 13.04 ± 14.47 F(1, 209) = 17.24; p < 0.001 Chicken 2.42 ± 5.16 4.98 ± 6.35 F(1, 209) = 12.75; p < 0.001 Beef and pork and deer 4.34 ± 8.90 11.25 ± 13.20 F(1, 209) = 18.29; p < 0.001 FC—6b (Total of processed meat) 3.93 ± 8.40 10.52 ± 12.67 F(1, 209) = 19.72; p < 0.001 Fried nuggets 1.32 ± 3.19 2.62 ± 3.64 F(1, 209) = 11.67; p = 0.001 Hamburger 0.43 ± 1.44 1.67 ± 3.10 F(1, 209) = 12.15; p = 0.001 Sausage 0.25 ± 1.20 1.47 ± 3.14 F(1, 209) = 14.23; p < 0.001 Kebab 0.34 ± 1.01 1.57 ± 2.78 F(1, 209) = 15.49; p < 0.001 Other processed meat 4.05 ± 9.51 9.78 ± 13.02 F(1, 209) = 14.40; p < 0.001 FC—7: Meat alternatives 5.99 ± 6.02 6.16 ± 7.44 F(1, 209) = 0.48; p = 0.488 FC—8: Fish 3.80 ± 5.70 5.57 ± 6.90 F(1, 209) = 4.60; p = 0.033 FC—9: Eggs 6.91 ± 8.65 9.16 ± 8.86 F(1, 209) = 4.22; p = 0.041 Nutrients 2022, 14, 2590 8 of 17 Table 3. Cont. Females Males Statistics n = 121 n = 90 FC—10 (Total of oils) 10.24 ± 10.66 15.49 ± 14.99 F(1, 209) = 4.60; p = 0.033 Butter 4.50 ± 8.76 8.00 ± 13.53 F(1, 209) = 0.88; p = 0.348 Margarine 5.92 ± 8.73 7.49 ± 11.36 F(1, 209) = 0.13; p = 0.717 Other oils 4.95 ± 5.36 7.74 ± 7.50 F(1, 209) = 5.71; p = 0.018 FC—11 (Total of snacks) 9.83 ± 6.67 11.81 ± 7.63 F(1, 209) = 2.98; p = 0.086 Sweet snacks 9.77 ± 6.43 10.51 ± 6.78 F(1, 209) = 0.43; p = 0.511 Salty snacks 5.22 ± 6.66 7.66 ± 7.67 F(1, 207) = 6.13; p = 0.014 FC—12 (Total of water) 39.28 ± 22.17 29.92 ± 18.09 F(1, 209) = 9.77; p = 0.002 Water 61.92 ± 28.33 56.16 ± 26.33 F(1, 209) = 2.24; p = 0.136 Unsweetened tea 25.36 ± 17.63 16.52 ± 14.25 F(1, 209) = 17.48; p < 0.001 FC—13: Beverages 14.19 ± 5.22 13.40 ± 4.57 F(1, 209) = 0.88; p = 0.350 FC—14: Alcohol 2.75 ± 3.77 5.06 ± 5.64 F(1, 209) = 13.04; p < 0.001 Part B—Umbrella clusters FC—15 (Total of protein) 39.60 ± 14.30 38.64 ± 13.81 F(1, 209) = 0.28; p = 0.599 FC—15a (Total of plant protein) 35.23 ± 14.88 30.12 ± 13.94 F(1, 209) = 6.40; p = 0.012 FC—15b (Total of animal protein) 12.80 ± 14.72 18.73 ± 14.98 F(1, 209) = 9.04; p = 0.003 FC—16: Processed foods & free/added sugar 23.27 ± 12.62 30.25 ± 15.62 F(1, 209) = 10.81; p = 0.001 FC—17: Free/added sugar 13.62 ± 8.60 16.19 ± 11.21 F(1, 209) = 1.57; p = 0.212 Note. Data are presented as mean ± standard deviation. The values are based on a calculated index ranging from 0 to 100 (points; %), representing an integrated scale from the frequency of food consumption within the past four weeks and the amount of food intake. FC—food clusters. Statistical methods: Kruskal–Wallis tests (F-values). Figure 2 displays the 95% confidence interval to show sex-related differences in food clusters in runners. The food clusters with more than 5% difference between males and females include “grains” (both subclusters: refined and whole grains), “meat” (both subclus- ters: unprocessed and processed meat), “animal protein”, “processed foods & free/added sugar”, “fruit and vegetable”, and “water and unsweetened tea”, where males had a higher consumption compared to the opposite sex in the first four clusters and female in the two latter clusters. Further details regarding the regression results, including p-values, are presented in Table 4. Age was a significant predictor for consumption of the cluster “fruit and vegetables” (p = 0.010), with a marginal (but not significant) association with the two clusters “eggs” (p = 0.058) and “plant protein” (p = 0.056). Table 4. Regression results for age- and sex-based interactions in food clusters. Age Sex * β 95%-CI p β 95%-CI p FC—1a (Total of refined grains) −0.07 [1.08, −1.21] 0.908 5.58 [8.03, 3.13] <0.001 FC—1b (Total of whole grains) −0.48 [0.66, −1.62] 0.407 6.01 [8.46, 3.56] <0.001 FC—2 (Total of beans and seeds) −0.39 [1.41, −2.19] 0.673 −4.63 [−0.77, −8.49] 0.019 FC—3 (Total of fruit and vegetables) −2.11 [−0.51, −3.72] 0.010 −6.45 [−3.01, −9.89] <0.001 FC—4 (Total of dairy) 0.43 [1.88, −1.02] 0.558 0.91 [4.02, −2.20] 0.565 FC—5 (Dairy alternatives) −0.04 [1.95, −2.02] 0.971 −4.38 [−0.12, −8.64] 0.044 FC—6a (Total of unprocessed meat) 1.29 [2.90, −0.32] 0.115 7.13 [10.58, 3.67] <0.001 FC—6b (Total of processed meat) 1.10 [2.45, −0.25] 0.110 6.18 [9.08, 3.28] <0.001 FC—7 (Meat alternatives) 0.32 [1.19, −0.55] 0.470 0.05 [1.91, −1.81] 0.001 Nutrients 2022, 14, 2590 9 of 17 Table 4. Cont. Age Sex * β 95%-CI p β 95%-CI p FC—8 (Fish) 0.49 [1.30, −0.32] 0.239 1.58 [3.32, −0.16] 0.074 FC—9 (Eggs) 1.09 [2.22, −0.04] 0.058 1.84 [4.26, −0.58] 0.136 FC—10 (Total of oils) 0.87 [2.52, −0.78] 0.299 4.92 [8.45, 1.38] 0.007 FC—11 (Total of snacks) −0.11 [0.81, −1.04] 0.808 2.02 [4.00, 0.04] 0.046 FC—12 (Total of water) −1.26 [1.41, −3.93] 0.355 −8.88 [−3.16, −14.61] 0.003 FC—13 (Beverages) 0.30 [0.94, −0.35] 0.366 −0.91 [0.48, −2.29] 0.198 FC—14 (Alcohol) 0.12 [0.72, −0.49] 0.703 2.27 [3.57, 0.97] 0.001 FC—15a (Plant protein) −1.83 [0.05, −3.70] 0.056 −4.43 [−0.41, −8.44] 0.031 FC—15b (Animal protein) 1.48 [3.40, −0.44] 0.130 5.38 [9.50, 1.26] 0.011 FC—16 (Processed foods & free/added sugar) −0.15 [1.67, −1.97] 0.872 7.03 [10.94, 3.13] <0.001 FC—17 (Free/added sugar) −0.14 [1.13, −1.42] 0.826 2.62 [5.36, −0.12] 0.061 Note. * The female sample is considered the reference. β—regression coefficient. CI—confidence interval. p—p-value. FC—food clusters. Statistical methods: Kruskal–Wallis tests (F-values). nts 2022, 14, x FOR PEER REVIEW 9 of 18 Figure 2. Forest plots with 95% confidence interval to display sex-based differences in basic (the left column) and umbrella (the right column) food clusters. Females are considered the reference, and the differences are shown based on the variations of males from females. FC—food clusters. Further details regarding the regression results, including p-values, are presented in Table 4. Age was a significant predictor for consumption of the cluster “fruit and vegeta- bles” (p = 0.010), with a marginal (but not significant) association with the two clusters “eggs” (p = 0.058) and “plant protein” (p = 0.056). Figure 2. Forest plots with 95% confidence interval to display sex-based differences in basic (the left column) and umbrella (the right column) food clusters. Females are considered the reference, and the differences are shown based on the variations of males from females. FC—food clusters. Nutrients 2022, 14, 2590 10 of 17 4. Discussion The present study investigated and compared female and male endurance runners in dietary intake (differentiated by 14 basic clusters and 3 umbrella clusters of food frequency). The most important findings were that (1) females had a significantly higher intake of four food clusters (i.e., “beans and seeds”, “fruit and vegetables”, “dairy alternatives”, and “water”) than males; (2) males had a significantly greater intake of seven food clusters (i.e., “grains”, “meat”, “fish”, “eggs”, “oils”, “alcohol”, and “processed foods”) than females; (3) no significant sex-based difference was observed in the consumption of six food clusters (i.e., “dairy”, “meat alternatives”, “snacks”, “beverages”, “protein”, “free/added sugar”); (4) sex has been found to be a significant predictor for consumption of the majority of food groups; (5) except for “fruit and vegetables” age failed to be a significant predictor of the food groups. As another main outcome, the hypothesis of the present study i.e., “female runners having a more advantageous dietary intake regarding a healthy lifestyle”, was verified. The purpose of the dietary assessment was to identify nutritional inadequacy in order to optimize health-related approaches in general populations and develop individualized dietary strategies for improving the health and performance of athletes [52,53]. Overall, the most common dietary assessment methods include a dietary record, 24-h dietary re- calls, in-depth interviews, and the food frequency questionnaire [52–54]. Evidence has shown that food records, dietary recalls, and detailed interviews are time-consuming and challenging to conduct precisely in athletes [55,56]. On the other hand, food frequency questionnaires have been reported to be a simple, fast, and low-cost method with less bur- den on participants compared to other methods [57]. Hence, food frequency questionnaires can be the most appropriate survey method to assess the dietary intake of athletes [57,58]. Athletes in general–but particularly those who follow restrictive and unbalanced diets–are at higher risk for low energy intake than sedentary people if their diet is not planned appropriately [25,59]. Considering the importance of diet for health status and athletic performance, it is crucial that the first and most important step in any sports nutrition practice is to assess and monitor the dietary intake/status of athletes [56]. In line with the findings from the present study, it has been reported that sex is an important predictor of dietary choices, which mainly originates from different health and lifestyle beliefs between males and females [20]. According to the literature, the influence of sex on dietary intake is not limited only to runners [60,61] but has also been documented in the general population [62,63]. Reports from national dietary investigations on general populations of D-A-CH countries (including Germany, Austria, and Switzerland; home of the majority of participants) also show that sex is a remarkable contributor to dietary intake/patterns [64–67]. Dietary-related sex differences in endurance runners, however, cannot only be attributed to the patterns of supplement intake, as previously reported by the “NURMI Study” [68]. 4.1. Fluid and Alcohol In the present study, data on hydration habits revealed that sex seems to be an influ- encing variable in the consumption of water (with a predominance of females) but not beverages. Comparable results from an investigation of recreational runners showed a significant sex-based difference in the type of fluid intake, where female runners consumed more water, coffee, and tea, and males more sweet beverages or alcoholic drinks [60]. National dietary reports for the German population also indicated a greater consumption of water, coffee, and tea in females than males [67]. Consistently, male runners in the present study reported nearly a two-fold consumption of alcohol compared to females. While data from the Austrian general population show that males had a 3-times greater intake of alcohol than females [65], this ratio was 2:1 in a similar investigation in Switzer- land [66]. According to the dietary recommendations of D-A-CH nutrition organizations, the sex-based differences in the maximum tolerable alcohol intake is also two-fold (i.e., Nutrients 2022, 14, 2590 11 of 17 max. 10 g/day for healthy females and max. 20 g/day for healthy males) [69]. Generally, male athletes are at a higher risk of binge drinking than females [60,70]. 4.2. Carbohydrate Foods The consumption of grains (both refined and whole grains) was higher in males than females in the present study. This finding is inconsistent with the results from the national German report, where females had a higher intake of grains and cereals [67]. Assuming an equal ability of females and males to store and utilize carbohydrates [71], the present finding might be associated with the increased portion of females in 10-km and males in M/UM subgroups. In this regard, it has been reported that sex difference in carbohydrate intake is likely to disappear when the data is adjusted to training volume [61]. Consistent with the present findings, results from a comparable investigation show that female distance athletes tend to consume fewer carbohydrates than males [72]. Grains are not the only source of carbohydrates since other food clusters (e.g., “fruit and vegetables” and “beans and seeds”) also contribute to the carbohydrate supply. Female runners in the present study reported a higher intake of both the clusters “fruit and vegetables” and “beans and seeds” than males. Consistently, it has been documented that females are more eager than males to consume fruits and vegetables [20], and this food cluster showed the highest contrast between the dietary patterns of females and males [73]. The significant predominance of females in the consumption of fruit and vegetables has also been shown by German [64,67], Swiss [66], and Austrian [65] studies on general populations. However, it was unanimously found that the majority of both males and females do not reach the recommendation of five portions of fruits and vegetables per day. Regarding dietary attitudes, while females more frequently than males indicated that vegetables are the major component of a healthy diet, they expressed that the consumption of carbohydrates should be decreased [68]. This finding may be linked to the heightened concerns about body image among females in general populations, and especially female athletes [62,74]. 4.3. Protein and Fat-Based Foods Research has shown that male athletes have a generally higher protein intake than recommended [4,75]. This outcome, however, is not consistent with the nutritional recom- mendations indicating the greater need for baseline protein intake for female endurance athletes due to their higher rate of protein oxidation than males [76,77]. Male runners in the present study reported a generally greater intake of animal protein foods (meat, fish, eggs) than females; however, no sex-based difference was observed in the consumption of dairy products and meat alternatives. The predominance of males in the consumption of meat has been periodically shown in national studies on general populations of Germany [67], Austria [65], and Switzerland [66]. Although animal sources derive approximately 75% of the general protein supply in athletes [78], it has been reported that both male and female marathoners consume a higher portion of plant-based proteins than other athletes and the general population [75,79]. Dietary shifts toward a lower intake of animal sources and more plant foods can result in a lower intake of processed meat (including fast foods) and high-fat foods [80] and consequently improve health and performance [25]. The present findings also indicate a greater consumption of oils and processed foods by male runners. While similar investigations on athletes [3] and general populations [61] support the present findings, the most reasonable justification that has been reported is the lower ability and time of males in preparation of meals, which leads them to consume convenient/fast food and restaurant meals [3]. 4.4. Health Insights in Food Intake In general, the present findings show that female runners have a tendency towards a healthier dietary intake pattern than their male counterparts. It has been reported that female athletes mainly prefer to consume dietary sources containing more micronutrient density to fulfill their health-related concerns [20,30,81], whereas male athletes seem more Nutrients 2022, 14, 2590 12 of 17 interested in consuming macronutrients, especially from protein sources, aiming to main- tain and improve muscle mass and strength. It has also been found that the prevalence of consuming high-fiber meals (as an indicator of a healthy diet) is considerably higher in females than males [20]. The general higher intake of healthier food clusters by female runners appears to be linked to the higher level of females’ health consciousness compared to males [20,82]. Regardless of sex, previous findings from the NURMI project show that runners who follow a vegan diet had a higher level of health consciousness, mainly due to their more beneficial choice of dietary items compared to non-vegan runners [38]. Such sex-based differences in health consciousness and dietary behaviors can also be associated with the well-documented fact that females are generally more interested in diet and health, while males consider physical activity as the main part of a healthy lifestyle [30]. However, it is necessary to consider that regular physical activity, independent of sex, alters the attempts toward a healthier dietary pattern in order to gain further outcomes [83]. As a general fact in sport science, different nutritional requirements of athletes competing in different types of sports should also be considered a potential factor to justify dietary contradictions [36]. Educational level and, more importantly, specific knowledge about nutrition and sport sciences may also be associated with health behaviors, particularly adhering to a healthier diet [84]. In terms of academic qualification, however, there was no significant difference between female and male participants in the present study. The unbalanced distribution of race distance and diet type subgroups across male and female groups may partially contribute to the finding on sex-based dietary differences. Unlike sex, age was not a significant predictor for consumption of the majority of food groups except for one food cluster (i.e., fruit and vegetables). While the null effect of age on general dietary intake can be linked to the fact that male runners were significantly older than female runners, data from dietary studies on general populations indicate that age can be a moderate indicator of dietary patterns [30,73]. It should be considered that most participants in the present study were recreational runners. Evidence indicates that performance level, defined as the term professionalism, can be a key indicator of precise and personally tailored dietary intake and strategies for training and racing independent of age [85,86]. In this regard, the literature reports that the major motives of recreational athletes to take part in sport events are health and/or hobby [87,88], while professional athletes are mainly motivated by performance and competition-related aspects [89]. 4.5. Limitations and Strengths Some limitations in the present investigation should be mentioned. The study was conducted following a cross-sectional design producing self-reported findings; therefore, caution should be taken by interpreting the results. However, several control questions were implemented in different parts of the survey to minimize validity bias and control for contradictory data, and accordingly, participants’ statements were checked for congruency and meaningfulness. The unbalanced distribution of diet type and race distance subgroups among male and female groups (Figure 1) may also be considered as another limitation affecting the sex-based findings and interpretations. Moreover, as a potential selection bias, about half of the endurance runners in the present study stated adhering to a vegan or vegetarian diet, which is markedly higher than the prevalence in general populations. Finally, despite the well-approved validity of a FFQ as a practical method to assess dietary intake and patterns [56,57], especially for athletic populations [57,58], this method seems unsuited to provide details about the macro- and micro-nutrient status of the athletes (on which a considerable number of nutritional recommendations are based on). However, the findings contribute valuable and novel data to current scientific knowl- edge regarding the sex-related patterns of dietary intake among recreational endurance runners categorized across different subgroups of diet types and race distance. Although the present study opens a direction for future interventional studies on athletic populations, future research with larger and more differentiated samples of distance runners will assist Nutrients 2022, 14, 2590 13 of 17 in providing comparable data for a better understanding of the dietary patterns of female and male runners. Finally, the results from the present study will also provide a window into the targeted sex-specific approaches to precisely tailor and personalize the dietary needs and nutritional requirements of male and female distance runners. Endurance runners, their coaches, and sports nutrition specialists can benefit from the results when designing and applying nutritional strategies for long-term adherence to training and competition. 5. Conclusions The sex-based comparison of endurance runners showed that there are remarkable differences between females and males in their dietary intake (assessed by a food frequency questionnaire), supporting the fact that female runners tend to consume healthier foods. While physiological differences between females and males can play a key role in many sex-based nutritional and behavioral variances, it seems that health-oriented attitudes and lifestyle of females can be considered the most reasonable justification for the present findings. However, there is an obvious necessity to design more detailed interventions using further analyses of interacting factors to improve the knowledge of sex differences in dietary choices of endurance athletes and, consequently, to support sports dietitians, nutritionists, and coaches to provide more precise and personalized recommendations. In general, nutrition education, training opportunities, and sports nutrition counseling to expand a runner’s personalized knowledge about health and sports discipline-specific behaviors can be recommended practically to improve the healthy runner lifestyle, including nutritional competencies (e.g., healthy ingredients, nutrients as well as requirements, and foods) in matching the higher exercise-induced demands for active males and females alike. Author Contributions: K.W. conceptualized, designed and developed the study design and the questionnaires together with C.L. and B.K. K.W. performed data analysis together with K.-H.W. and M.M. drafted the manuscript. K.-H.W., C.L. and K.W., helped in drafting the manuscript. K.-H.W., C.L., D.T. and K.W. critically reviewed it. Technical support was provided by G.W. All authors have read and agreed to the published version of the manuscript. Funding: This study has no financial support or funding. Institutional Review Board Statement: The study protocol (available online via https://springerplus. springeropen.com/articles/10.1186/s40064-016-2126-4 (accessed on 10 May 2022)) was approved by the ethics board of St. Gallen, Switzerland on 6 May 2015 (EKSG 14/145). The study is conducted in accordance with the ethical standards of the institutional review board, medical professional codex, and the with the 1964 Helsinki declaration and its later amendments, as of 1996 as well as Data Security Laws and good clinical practice guidelines. Informed Consent Statement: Study participation was voluntary and could be cancelled at any time without provision of reasons and without negative consequences. Informed consent was obtained from all participants included in the study considering the data collected and analyzed exclusively and only in the context of the “NURMI Study”. Data Availability Statement: The datasets generated during and/or analyzed during the current study are not publicly available, but may be made available upon reasonable request. Participants will receive a brief summary of the results of the “NURMI Study”, if desired. Acknowledgments: There were no professional relationships with companies or manufacturers who will benefit from the findings of the present study. Moreover, this research did not receive any specific grant or funding from funding agencies in the public, commercial, or non-profit sectors. Conflicts of Interest: The authors declare no conflict of interest. References 1. Rossi, K.A. Nutritional aspects of the female athlete. Clin. Sports Med. 2017, 36, 627–653. [CrossRef] [PubMed] 2. Tiller, N.B.; Elliott-Sale, K.J.; Knechtle, B.; Wilson, P.B.; Roberts, J.D.; Millet, G.Y. Do sex differences in physiology confer a female advantage in ultra-endurance sport? Sports Med. 2021, 51, 895–915. [CrossRef] [PubMed] Nutrients 2022, 14, 2590 14 of 17 3. Hull, M.V.; Jagim, A.R.; Oliver, J.M.; Greenwood, M.; Busteed, D.R.; Jones, M.T. Gender differences and access to a sports dietitian influence dietary habits of collegiate athletes. J. Int. Soc. Sports Nutr. 2016, 13, 38. [CrossRef] [PubMed] 4. Thomas, D.T.; Erdman, K.A.; Burke, L.M. Position of the Academy of Nutrition and Dietetics, Dietitians of Canada, and the American College of Sports Medicine: Nutrition and Athletic Performance. J. Acad. Nutr. Diet. 2016, 116, 501–528. [CrossRef] 5. Burke, L.M.; Jeukendrup, A.E.; Jones, A.M.; Mooses, M. Contemporary nutrition strategies to optimize performance in distance runners and race walkers. Int. J. Sport Nutr. Exerc. Metab. 2019, 29, 117–129. [CrossRef] 6. Deldicque, L.; Francaux, M. Recommendations for healthy nutrition in female endurance runners: An update. Front. Nutr. 2015, 2, 17. [CrossRef] 7. Burrows, M.; Bird, S. The physiology of the highly trained female endurance runner. Sports Med. 2000, 30, 281–300. [CrossRef] 8. Cheuvront, S.N.; Carter, R.; Deruisseau, K.C.; Moffatt, R.J. Running performance differences between men and women: An update. Sports Med. 2005, 35, 1017–1024. [CrossRef] 9. Fortunato, R.S.; Ferreira, A.C.; Hecht, F.; Dupuy, C.; Carvalho, D.P. Sexual dimorphism and thyroid dysfunction: A matter of oxidative stress? J. Endocrinol. 2014, 221, R31–R40. [CrossRef] 10. Duhig, T.J.; McKeag, D. Thyroid disorders in athletes. Curr. Sports Med. Rep. 2009, 8, 16–19. [CrossRef] 11. Wasfy, M.M.; Hutter, A.M.; Weiner, R.B. Sudden cardiac death in athletes. Methodist Debakey Cardiovasc. J. 2016, 12, 76–80. [CrossRef] 12. Maas, A.H.; Appelman, Y.E. Gender differences in coronary heart disease. Neth. Heart J. 2010, 18, 598–602. [CrossRef] 13. Yusko, D.A.; Buckman, J.F.; White, H.R.; Pandina, R.J. Alcohol, tobacco, illicit drugs, and performance enhancers: A comparison of use by college student athletes and nonathletes. J. Am. Coll. Health 2008, 57, 281–290. [CrossRef] 14. Dandoy, C.; Gereige, R.S. Performance-enhancing drugs. Pediatr. Rev. 2012, 33, 265–272. [CrossRef] 15. Tarnopolsky, M.A.; Rennie, C.D.; Robertshaw, H.A.; Fedak-Tarnopolsky, S.N.; Devries, M.C.; Hamadeh, M.J. Influence of endurance exercise training and sex on intramyocellular lipid and mitochondrial ultrastructure, substrate use, and mitochondrial enzyme activity. Am. J. Physiol. Regul. Integr. Comp. Physiol. 2007, 292, R1271–R1278. [CrossRef] 16. Devries, M.C. Sex-based differences in endurance exercise muscle metabolism: Impact on exercise and nutritional strategies to optimize health and performance in women. Exp. Physiol. 2016, 101, 243–249. [CrossRef] 17. Boldt, P.; Knechtle, B.; Nikolaidis, P.; Lechleitner, C.; Wirnitzer, G.; Leitzmann, C.; Wirnitzer, K. Sex differences in the health status of endurance runners: Results from the NURMI Study (Step 2). J. Strength Cond. Res. 2019, 33, 1929–1940. [CrossRef] 18. Heydenreich, J.; Kayser, B.; Schutz, Y.; Melzer, K. Total energy expenditure, energy intake, and body composition in endurance athletes across the training season: A systematic review. Sports Med. Open 2017, 3, 8. [CrossRef] 19. Bredella, M.A. Sex differences in body composition. Adv. Exp. Med. Biol. 2017, 1043, 9–27. 20. Wardle, J.; Haase, A.M.; Steptoe, A.; Nillapun, M.; Jonwutiwes, K.; Bellisle, F. Gender differences in food choice: The contribution of health beliefs and dieting. Ann. Behav. Med. 2004, 27, 107–116. [CrossRef] 21. Smee, D.; Pumpa, K.; Falchi, M.; Lithander, F. The relationship between diet quality and falls risk, physical function and body composition in older adults. J. Nutr. Health Aging 2015, 19, 1037–1042. [CrossRef] 22. Nikolaidis, P.T.; Veniamakis, E.; Rosemann, T.; Knechtle, B. Nutrition in ultra-endurance: State of the art. Nutrients 2018, 10, 1995. [CrossRef] 23. Kårlund, A.; Gómez-Gallego, C.; Turpeinen, A.M.; Palo-Oja, O.M.; El-Nezami, H.; Kolehmainen, M. Protein supplements and their relation with nutrition, microbiota composition and health: Is more protein always better for sportspeople? Nutrients 2019, 11, 829. [CrossRef] 24. Black, K.; Slater, J.; Brown, R.C.; Cooke, R. Low energy availability, plasma lipids, and hormonal profiles of recreational athletes. J. Strength Cond. Res. 2018, 32, 2816–2824. [CrossRef] 25. Wirnitzer, K.C. Vegan diet in sports and exercise–health benefits and advantages to athletes and physically active people: A narrative review. Int. J. Sports Exerc. Med. 2020, 6, 165. 26. Schröder, S.; Fischer, A.; Vock, C.; Böhme, M.; Schmelzer, C.; Döpner, M.; Hülsmann, O.; Döring, F. Nutrition concepts for elite distance runners based on macronutrient and energy expenditure. J. Athl. Train. 2008, 43, 489–504. [CrossRef] 27. Barber, T.M.; Hanson, P.; Kabisch, S.; Pfeiffer, A.F.H.; Weickert, M.O. The low-carbohydrate diet: Short-term metabolic efficacy versus longer-term limitations. Nutrients 2021, 13, 1187. [CrossRef] 28. Shaw, K.A.; Zello, G.A.; Rodgers, C.D.; Warkentin, T.D.; Baerwald, A.R.; Chilibeck, P.D. Benefits of a plant-based diet and considerations for the athlete. Eur. J. Appl. Physiol. 2022, 122, 1163–1178. [CrossRef] 29. Wirnitzer, K.C.; Kornexl, E. Energy and macronutrient intake of a female vegan cyclist during an 8-day mountain bike stage race. Bayl. Univ. Med. Cent. 2014, 27, 42–45. [CrossRef] 30. Grzymisławska, M.; Puch, E.A.; Zawada, A.; Grzymisławski, M. Do nutritional behaviors depend on biological sex and cultural gender? Adv. Clin. Exp. Med. 2020, 29, 165–172. [CrossRef] 31. Holtzman, B.; Ackerman, K.E. Recommendations and nutritional considerations for female athletes: Health and performance. Sports Med. 2021, 51, 43–57. [CrossRef] [PubMed] 32. Burke, L.M.; Slater, G.; Broad, E.M.; Haukka, J.; Modulon, S.; Hopkins, W.G. Eating patterns and meal frequency of elite Australian athletes. Int. J. Sport Nutr. Exerc. Metab. 2003, 13, 521–538. [CrossRef] [PubMed] 33. Erdman, K.A.; Tunnicliffe, J.; Lun, V.M.; Reimer, R.A. Eating patterns and composition of meals and snacks in elite Canadian athletes. Int J. Sport Nutr. Exerc. Metab. 2013, 23, 210–219. [CrossRef] [PubMed] Nutrients 2022, 14, 2590 15 of 17 34. Nogueira, J.A.D.; Da Costa, T.H.M. Nutrient intake and eating habits of triathletes on a Brazilian diet. Int. J. Sport Nutr. Exerc. Metab. 2004, 14, 684–697. [CrossRef] [PubMed] 35. Vitale, K.; Getzin, A. Nutrition and supplement update for the endurance athlete: Review and recommendations. Nutrients 2019, 11, 1289. [CrossRef] 36. Tiller, N.B.; Roberts, J.D.; Beasley, L.; Chapman, S.; Pinto, J.M.; Smith, L.; Wiffin, M.; Russell, M.; Sparks, S.A.; Duckworth, L.; et al. International Society of Sports Nutrition Position Stand: Nutritional considerations for single-stage ultra-marathon training and racing. J. Int. Soc. Sports Nutr. 2019, 16, 50. [CrossRef] 37. Wirnitzer, K.; Seyfart, T.; Leitzmann, C.; Keller, M.; Wirnitzer, G.; Lechleitner, C.; Rüst, C.A.; Rosemann, T.; Knechtle, B. Prevalence in running events and running performance of endurance runners following a vegetarian or vegan diet compared to non-vegetarian endurance runners: The NURMI study. SpringerPlus 2016, 5, 458. [CrossRef] 38. Wirnitzer, K.; Boldt, P.; Lechleitner, C.; Wirnitzer, G.; Leitzmann, C.; Rosemann, T.; Knechtle, B. Health status of female and male vegetarian and vegan endurance runners compared to omnivores-results from the NURMI study (step 2). Nutrients 2018, 11, 29. [CrossRef] 39. Boldt, P.; Knechtle, B.; Nikolaidis, P.; Lechleitner, C.; Wirnitzer, G.; Leitzmann, C.; Rosemann, T.; Wirnitzer, K. Quality of life of female and male vegetarian and vegan endurance runners compared to omnivores-results from the NURMI study (step 2). J. Int. Soc. Sports Nutr. 2018, 15, 33. [CrossRef] 40. Craig, W.J.; Mangels, A.R.; American Dietetic Association. Position of the American Dietetic Association: Vegetarian diets. J. Am. Diet. Assoc. 2009, 109, 1266–1282. 41. Melina, V.; Craig, W.; Levin, S. Position of the Academy of Nutrition and Dietetics: Vegetarian diets. J. Acad. Nutr. Diet. 2016, 116, 1970–1980. [CrossRef] 42. World Health Organization (WHO). A Healthy Lifestyle—WHO Recommendations. Available online: http://www.euro.who. int/en/health-topics/disease-prevention/nutrition/a-healthy-lifestyle/body-mass-index-bmi (accessed on 10 May 2022). 43. World Health Organization (WHO). Noncommunicable Diseases: Risk Factors. Available online: http://www.who.int/gho/ ncd/risk_factors/bmi_text/en/ (accessed on 10 May 2022). 44. Elmadfa, I.; Leitzmann, C. Ernährung des Menschen (Human Nutrition), 6th ed.; UTB GmbH: Berlin, Germany, 2019; p. 618. 45. König, D. Kohlenhydrate im Sport (Carbohydrates in sport). In Lehrbuch der Sporternährung (Textbook of Sports Nutrition), 1st ed.; Lamprecht, M., Ed.; ÖGSE—Österreichische Gesellschaft für Sporternährung: Graz, Austria, 2007; p. 448. 46. Scheidt-Nave, C.; Kamtsiuris, P.; Goesswald, A.; Hölling, H. German Health Interview and Examination Survey for Adults (DEGS)—Design, objectives and implementations of the first data collection wave. BMC Public Health 2012, 12, 730. [CrossRef] 47. Haftenberger, M.; Heuer, T.; Heidemann, C.; Kube, F. Relative validation of a food frequency questionnaire for national health and nutrition monitoring. Nutr. J. 2010, 9, 36. [CrossRef] 48. Monteiro, C.A.; Cannon, G.; Lawrence, M.; Costa Louzada, M.L.P.; Ereira Machado, P. Ultra-Processed Foods, Diet Quality, and health Using the NOVA Classification System; Food and Agriculture Organization (FAO) of the United Nations (UN): Rome, Italy, 2019. 49. Open Food Facts—Freie Datenbank für Nahrungsmittel. Nova-Gruppen der Lebensmittelverarbeitung Eine Klassifizierung in 4 Gruppen, um den Grad der Verarbeitung von Lebensmitteln Darzustellen. Available online: https://de.openfoodfacts.org/nova (accessed on 23 August 2021). 50. Fachgesellschaft für Ernährungstherapie und Prävention für Professionalität in der Ernährungsberatung (FETeV). 4-Stufen- System für Lebensmittel nach dem Verarbeitungsgrad. Available online: https://fet-ev.eu/lebensmittelbeurteilung/ (accessed on 23 August 2021). 51. Redaktion Ernährungsumschau. Lebensmittelkennzeichnung: 4-Stufen-System für Lebensmittel nach dem Verarbeitungsgrad. ERNÄHRUNGSUMSCHAU 4/2020: M192. Available online: https://www.ernaehrungs-umschau.de/print-news/08-04-2020-4 -stufen-system-fuer-lebensmittel-nach-dem-verarbeitungsgrad/ (accessed on 23 August 2021). 52. Capling, L.; Beck, K.L.; Gifford, J.A.; Slater, G.; Flood, V.M.; O’Connor, H. Validity of dietary assessment in athletes: A systematic review. Nutrients 2017, 9, 1313. [CrossRef] 53. Baranowski, T. Chapter 4, 24-hour recall and diet record methods. In Nutritional Epidemiology, 3rd ed.; Walter, W., Ed.; Oxford University Press: New York, NY, USA, 2013; pp. 49–69. 54. Willett, W. Chapter 5, food frequency methods. In Nutritional Epidemiology, 3rd ed.; Walter, W., Ed.; Oxford University Press: New York, NY, USA, 2013; pp. 70–95. 55. Braakhius, A.K.; Meredith, K.; Cox, G.R.; Hopkins, W.J.; Burke, L.M. Variability in estimation of self reported dietary intake data from elite athletes resulting from processing by different sports dietitians. Int. J. Sport Nutr. Exerc. Metab. 2003, 13, 152–165. [CrossRef] 56. Burke, L.M. Dietary Assessment Methods for the Athlete: Pros and Cons of Different Methods. Sports Sci. Exch. 2015, 28, 1–6. Available online: https://www.gssiweb.org/sports-science-exchange/article/sse-150-dietary-assessment-methods-for- the-athlete-pros-and-cons-of-different-methods (accessed on 18 May 2022). 57. Sunami, A.; Sasaki, K.; Suzuki, Y.; Oguma, N.; Ishihara, J.; Nakai, A.; Yasuda, J.; Yokoyama, Y.; Yoshizaki, T.; Tada, Y.; et al. Validity of a semi-quantitative food frequency questionnaire for collegiate athletes. J. Epidemiol. 2016, 26, 284–291. [CrossRef] 58. Braakhuis, A.J.; Hopkins, W.G.; Lowe, T.E.; Rush, E.C. Development and validation of a food-frequency questionnaire to assess shortterm antioxidant intake in athletes. Int. J. Sport Nutr. Exerc. Metab. 2011, 21, 105–112. [CrossRef] 59. Rogerson, D. Vegan diets: Practical advice for athletes and exercisers. J. Int. Soc. Sports Nutr. 2017, 14, 36. [CrossRef] Nutrients 2022, 14, 2590 16 of 17 60. Škof, B.; Rotovnik Kozjek, N. A comparison of dietary habits between recreational runners and a randomly selected adult population in Slovenia. Zdr. Varst. 2015, 54, 212–221. 61. Heikura, I.A.; Burke, L.M.; Mero, A.A.; Uusitalo, A.L.T.; Stellingwerff, T. Dietary microperiodization in elite female and male runners and race walkers during a block of high intensity precompetition training. Int. J. Sport Nutr. Exerc. Metab. 2017, 27, 297–304. [CrossRef] 62. Davy, S.R.; Benes, B.A.; Driskell, J.A. Sex differences in dieting trends, eating habits, and nutrition beliefs of a group of Midwestern college students. J. Am. Diet Assoc. 2006, 106, 1673–1677. [CrossRef] 63. Sobiecki, J.G.; Appleby, P.N.; Bradbury, K.E.; Key, T.J. High compliance with dietary recommendations in a cohort of meat eaters, fish eaters, vegetarians, and vegans: Results from the European Prospective Investigation into Cancer and Nutrition-Oxford study. Nutr. Res. 2016, 36, 464–477. [CrossRef] 64. Mensink, G.; Truthmann, J.; Rabenberg, M.; Heidemann, C.; Haftenberger, M.; Schienkiewitz, A.; Richter, A. Obst- und Gemüsekonsum in Deutschland. Bundesgesundheitsbl 2013, 56, 779–785. [CrossRef] 65. Österreichischer Ernährungsbericht. Available online: https://broschuerenservice.sozialministerium.at/Home/Download? publicationId=528 (accessed on 11 May 2022). 66. Sechster Schweizerischer Ernährungsbericht. Available online: https://www.blv.admin.ch/dam/blv/de/dokumente/ lebensmittel-und-ernaehrung/publikationen-forschung/6-seb-gesamtbericht-final.pdf.download.pdf/gesamtbericht.pdf (accessed on 11 May 2022). 67. BMEL. Available online: https://www.bmel.de/SharedDocs/Downloads/DE/_Ernaehrung/Forsa-Ern%C3%A4hrungsrport2 018.pdf?__blob=publicationFile&v=353 (accessed on 11 May 2022). 68. Wirnitzer, K.; Motevalli, M.; Tanous, D.R.; Gregori, M.; Wirnitzer, G.; Leitzmann, C.; Rosemann, T.; Knechtle, B. Sex Differences in Supplement Intake in Recreational Endurance Runners—Results from the NURMI Study (Step 2). Nutrients 2021, 13, 2776. [CrossRef] 69. Deutsche Gesellschaft für Ernährung e. V. Available online: https://www.dge.de/wissenschaft/referenzwerte/alkohol/?L=0 (accessed on 11 May 2022). 70. Leasure, J.L.; Neighbors, C.; Henderson, C.E.; Young, C.M. Exercise and alcohol consumption: What we know, what we need to know, and why it is important. Front. Psychiatry 2015, 6, 156. [CrossRef] 71. Wallis, G.A.; Dawson, R.; Achten, J.; Webber, J.; Jeukendrup, A.E. Metabolic response to carbohydrate ingestion during exercise in males and females. Am. J. Physiol. Endocrinol. Metab. 2006, 290, E708–E715. [CrossRef] 72. Burke, L.M.; Cox, G.R.; Culmmings, N.K.; Desbrow, B. Guidelines for daily carbohydrate intake: Do athletes achieve them? Sports Med. 2001, 31, 267–299. [CrossRef] [PubMed] 73. Margetts, B.; Martine, J.A.; Saba, A.; Holm, L.; Kearney, M. Definitions of “healthy” eating: A pan-EU survey of consumer attitudes to food, nutrition and health. Eur. J. Clin. Nutr. 1997, 51, 23–29. 74. Martinsen, M.; Bratland-Sanda, S.; Eriksson, A.K.; Sundgot-Borgen, J. Dieting to win or to be thin? A study of dieting and disordered eating among adolescent elite athletes and non-athlete controls. Br. J. Sports Med. 2010, 44, 70–76. [CrossRef] [PubMed] 75. Gillen, J.B.; Trommelen, J.; Wardenaar, F.C.; Brinkmans, N.Y.J.; Versteegen, J.J.; Jonvik, K.L.; Kapp, C.; de Vries, J.; van den Borne, J.J.G.C.; Gibala, M.J.; et al. Dietary protein intake and distribution patterns of well-trained Dutch athletes. Int. J. Sport Nutr. Exerc. Metab. 2017, 27, 105–114. [CrossRef] 76. Morton, R.W.; Murphy, K.T.; McKellar, S.R.; Schoenfeld, B.J.; Henselmans, M.; Helms, E.; Aragon, A.A.; Devries, M.C.; Banfield, L.; Krieger, J.W.; et al. A systematic review, meta-analysis and meta-regression of the effect of protein supplementation on resistance training-induced gains in muscle mass and strength in healthy adults. Br. J. Sports Med. 2018, 52, 376–384. [CrossRef] 77. Tarnopolsky, M. Protein requirements for endurance athletes. Nutrition 2004, 20, 662–668. [CrossRef] 78. Aguilera, E.; Piñero, P.; Infante Amate, J.; González de Molina, M.; Lassaletta, L.; Sanz Cobeña, A. Emisiones de Gases de Efecto Invernadero En El Sistema Agroalimentario y Huella de Carbono de La Alimentación En España; Real Academia de Ingeniería: Madrid, Spain, 2020. 79. Beis, L.Y.; Willkomm, L.; Ross, R.; Bekele, Z.; Wolde, B.; Fudge, B.; Pitsiladis, Y.P. Food and Macronutrient Intake of Elite Ethiopian Distance Runners. J. Int. Soc. Sports Nutr. 2011, 8, 7. [CrossRef] 80. Health Canada. Canada’s Food Guide. Available online: https://food-guide.canada.ca/en/ (accessed on 13 July 2021). 81. Baranauskas, M.; Jablonskiene, V.; Abaravicius, J.A.; Stukas, R. Actual nutrition and dietary supplementation in Lithuanian elite athletes. Medicina 2020, 56, 247. [CrossRef] 82. Bärebring, L.; Palmqvist, M.; Winkvist, A.; Augustin, H. Gender differences in perceived food healthiness and food avoidance in a Swedish population-based survey: A cross sectional study. Nutr. J. 2020, 19, 140. [CrossRef] 83. Wood, P.D. Physical activity, diet, and health: Independent and interactive effects. Med. Sci. Sports Exerc. 1994, 26, 838–843. [CrossRef] 84. Janiczak, A.; Devlin, B.L.; Forsyth, A.; Trakman, G.L. A systematic review update of athletes’ nutrition knowledge and association with dietary intake. Br. J. Nutr. 2021, 1–14. [CrossRef] 85. Petroczi, A.; Naughton, D.P. The age-gender-status profile of high performing athletes in the UK taking nutritional supplements: Lessons for the future. J. Int. Soc. Sports Nutr. 2008, 5, 2. [CrossRef] 86. Jenner, S.L.; Buckley, G.L.; Belski, R.; Devlin, B.L.; Forsyth, A.K. Dietary intakes of professional and semi-professional team sport athletes do not meet sport nutrition recommendations-a systematic literature review. Nutrients 2019, 11, 1160. [CrossRef] Nutrients 2022, 14, 2590 17 of 17 87. Wirnitzer, K.; Boldt, P.; Wirnitzer, G.; Leitzmann, C.; Tanous, D.; Motevalli, M.; Rosemann, T.; Knechtle, B. Health status of recreational runners over 10-km up to ultra-marathon distance based on data of the NURMI Study Step 2. Sci. Rep. 2022, in press. [CrossRef] 88. Nikolaidis, P.T.; Clemente-Suárez, V.J.; Chlíbková, D.; Knechtle, B. Training, anthropometric, and physiological characteristics in men recreational marathon runners: The role of sport experience. Front. Physiol. 2021, 12, 666201. [CrossRef] 89. Wa´skiewicz, Z.; Nikolaidis, P.T.; Gerasimuk, D.; Borysiuk, Z.; Rosemann, T.; Knechtle, B. What motivates successful marathon runners? The role of sex, age, education, and training experience in polish runners. Front. Psychol. 2019, 10, 1671. [CrossRef]

Female Endurance Runners Have a Healthier Diet than Males-Results from the NURMI Study (Step 2).

06-22-2022

Motevalli, Mohamad,Wagner, Karl-Heinz,Leitzmann, Claus,Tanous, Derrick,Wirnitzer, Gerold,Knechtle, Beat,Wirnitzer, Katharina

eng

PMC7908616

International Journal of Environmental Research and Public Health Article A Longitudinal Exploration of Match Running Performance during a Football Match in the Spanish La Liga: A Four-Season Study † Eduard Pons 1, José Carlos Ponce-Bordón 2,* , Jesús Díaz-García 2, Roberto López del Campo 3, Ricardo Resta 3 , Xavier Peirau 4 and Tomas García-Calvo 2



 Citation: Pons, E.; Ponce-Bordón, J.C.; Díaz-García, J.; López del Campo, R.; Resta, R.; Peirau, X.; García-Calvo, T. A Longitudinal Exploration of Match Running Performance during a Football Match in the Spanish La Liga: A Four-Season Study. Int. J. Environ. Res. Public Health 2021, 18, 1133. https://doi.org/10.3390/ ijerph18031133 Received: 18 November 2020 Accepted: 22 January 2021 Published: 28 January 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Sports Performance Area, FC Barcelona, 08028 Barcelona, Spain; [email protected] 2 Faculty of Sport Sciences, University of Extremadura, 10003 Cáceres, Spain; [email protected] (J.D.-G.); [email protected] (T.G.-C.) 3 LaLiga Sport Research Section, 28043 Madrid, Spain; [email protected] (R.L.d.C.); [email protected] (R.R.) 4 National Institute of Physical Education of Catalunya, 25192 Lleida, Spain; [email protected] * Correspondence: [email protected] † Boulevard of the University s/n. CP: 10003 Caceres, Spain. Abstract: This study aimed to analyze and compare the match running performance during official matches across four seasons (2015/2016–2018/2019) in the top two professional leagues of Spanish football. Match running performance data were collected from all matches in the First Spanish Division (Santander; n = 1520) and Second Spanish Division (Smartbank; n = 1848), using the Mediacoach® System. Total distance and distances of 14–21 km·h−1, 21–24 km·h−1, and more than 24 km·h−1, and the number of sprints between 21 and 24 km·h−1 and more than 24 km·h−1 were analyzed. The results showed higher total distances in the First Spanish Division than in the Second Spanish Division (p < 0.001) in all the variables analyzed. Regarding the evolution of both leagues, physical demands decreased more in the First Spanish Division than in the Second Spanish Division. The results showed a decrease in total distance and an increase in the high-intensity distances and number of sprints performed, although a clearer trend is perceived in the First Spanish Division (p < 0.001; p < 0.01, respectively). Knowledge about the evolution of match running performance allows practitioners to manage the training load according to the competition demands to improve players’ performances and reduce the injury rate. Keywords: longitudinal study; match running performance; professional soccer leagues; sports performance; external load 1. Introduction The external load of soccer matches has been studied in depth over the last two decades, which has improved knowledge on its evolution and trends [1]. Thus, different variables have been analyzed, usually related to the distance covered by the players at different intensities [2], and it should be noted that soccer match running performance has evolved, with significant increases in high-intensity actions [3]. Match physical demands can vary depending on the tactical planning, the opposite team’s playing style or the tactical–technical demands [4]. Research has also shown that these changes could be related to differences between soccer leagues [5]. However, to the best of our knowledge, there are no updated studies on how efforts have evolved in professional leagues’ full seasons. In addition, we found no studies of the analysis and comparison of match running performance from several seasons between two professional soccer leagues to update our knowledge about physical differences at the competitive level and in the evolution of football. Regarding the comparison of match running performance between professional soccer leagues, a previous study analyzed the external load of the top three leagues in English Int. J. Environ. Res. Public Health 2021, 18, 1133. https://doi.org/10.3390/ijerph18031133 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2021, 18, 1133 2 of 10 soccer: the FA Premier League, Championship, and League One [5]. This study concluded that the players in the Premier League, compared to players in the lower leagues such as the Championship and League One, covered less total distance and had fewer high- intensity running distances (p < 0.01). A related study collected physical demand data over four seasons (2006–2010) in two top leagues of English soccer, with similar external load data [6]. Players of the Championship League (2nd) covered more total distance than players of the Premier League (1st). In addition, Championship players covered more high-intensity running distance and performed more sprinting-intensity actions than Premiership players. However, recent research has found the opposite results in this area of study. In this way, authors described and compared the match running performance of the teams of the Spanish First and Second Division leagues during the 2015–2016 season, showing that the Spanish First Division teams covered more total distance than the Spanish Second Division teams [7]. There were differences in the distance covered at high intensity and very high intensity, where teams from the First Division covered more meters at these intensities. In this line, similar results were reported in the analysis of the match running performance of three professional soccer leagues in Norwegian football [8]. They found a higher total distance in the Norwegian first league teams, but differences were nonsignificant. Concerning high-intensity running distances, Norway’s first league teams covered higher sprinting distances than Norway’s second and fourth league teams (p < 0.05). Thus, the most recent studies agree on the presence of higher match physical demands (total and at high intensity) in the top professional soccer leagues. On the other hand, research of the evolution of external load has shown that total distances have been stable over the period from 1967 to 2012 [9]. However, it has also demonstrated that total distance has increased by 2% in the English Premier League over seven consecutive seasons (2006/2007–2012/2013), whereas high-intensity running and sprint distances have increased by 30–50% [3]. Moreover, a longitudinal study of the World Cup final soccer games reported that the soccer game trend evolved towards shorter, higher intensity play periods because players covered a higher sprint distance and they performed sprints more frequently [10]. Although the evolution of match running performance has also been analyzed by ranking tiers, similar trends have been found for all tiers. In this sense, one study reported that, during seven consecutive seasons in the English Premier League, there was an increase in high-intensity running distance (40%) and leading (15%) and explosive (25%) sprints for all tiers, although the average distance covered per sprint decreased [11]. Thus, changes have been observed in the external load of soccer competitions over the last few years. It is difficult to attribute these findings to a single factor. These changes could be explained through the increases in the competition levels of the leagues, the evolution of movement patterns, training specificity based on match physical demand data or a new approach to training [12]. It also could be related to the playing formation or, possibly, the recruitment of players with more explosive characteristics [1,7,11,13]. There are few studies on the evolution of external load over several years. Most of them are outdated and only analyzed the English Premier League. In addition, even if some works compare leagues or analyze the evolution of external load, there are no stud- ies comparing the evolution of leagues of different levels over several years. Therefore, the aim of this study was to analyze and compare the evolution of match running perfor- mance between LaLiga Santander (LL1) and LaLiga Smartbank (LL2) across four seasons (2015/2016–2018/2019). Based on the aforementioned studies [3,7,11], the authors established the following hypotheses. Concerning the match running performance comparison, we expected that the total distances, the distances covered at high intensity, and the number of very high- intensity running efforts would be higher in LL1 than in LL2. On the other hand, we expected that the total distance, the distances covered at high intensity, and the number of very high-intensity running efforts would increase in both professional soccer leagues across the four seasons analyzed. Int. J. Environ. Res. Public Health 2021, 18, 1133 3 of 10 2. Materials and Methods 2.1. Participants The sample included observations of all the matches played over four seasons in LL1 and LL2 (2015/2016, 2016/2017, 2017/2018, and 2018/2019). Two observations were made by match, and one by team. In LL1, 752 team match observations were included in the 2015/2016 season; 744 team match observations were included in the 2016/2017 season; 723 observations were included in the 2017/2018 season and, finally, 731 observations were included in the 2018/2019 season. Similarly, in LL2, 700 team match observations were included in the 2015/2016 season; 744 team match observations were included in the 2016/2017 season; 870 observations were included in the 2017/2018 season and, finally, 731 observations were included in the 2018/2019 season. In addition, 784 observations were excluded due to technical problems in the data collecting system or adverse weather conditions during the match, leading to a total of 5952 team match observations. 2.2. Design and Procedures Match running performance data were collected by a multicamera tracking system called Mediacoach®. This system assesses the distance covered in meters by teams and the number of high-intensity sprints (LaLiga™, Madrid, Spain). It consists of a series of super 4K-High Dynamic Range cameras based on a positioning system (Tracab—ChyronHego VTS) that records and analyzes X and Y positions for each player from several angles, thus providing real-time three-dimensional tracking (tracking data are recorded at 25 Hz). Mediacoach® has been proven to be both reliable and valid and has been used in previous studies [14–16]. Data were provided to the authors by LaLigaTM, and the study received ethical approval from the University of Extremadura, Vice-Rectorate of Research, Transfer and Innovation—Delegation of the Bioethics and Biosafety Commission (Protocol number: 153/2017). 2.3. Study Variables Similarly to previous studies [17–19], the physical demand variables were recorded for each match: (1) total distance covered by soccer teams in meters (TD); (2) distance covered between 14 and 21 km·h−1 (i.e., High-Intensity Running Distance = HIRD); (3) distance covered between 21 and 24 km·h−1 (i.e., Very High-Intensity Running Distance = VHIRD); (4) distance covered at more than 24 km·h−1 (i.e., Sprinting Distance = SpD). These variables were shown and analyzed by matches and separated by halves (first and second half). In addition, the number of sprints performed was registered, as well as (5) the number of very high-intensity running sprints at 21–24 km·h−1 (i.e., SpVHIR), and (6) the number of sprints at more than 24 km·h−1 (i.e., SP). All efforts that implied a minimum movement of one meter, which was maintained for a 1 s minimum, were recorded. Any recording at a speed of over 80% of the value of that category (i.e., >24 km·h−1) was considered as a single register. All these variables show total team values (i.e., all players who participated in matches, starters, nonstarters and substitutes). 2.4. Data Analysis The statistical program SPSS 25.0 was used (Armonk, NY: IBM Corp, 2017) to analyze and treat the data. Firstly, a two-way Analysis of Variance (ANOVA) was used to explore the main differences between the two professional soccer leagues for external load variables (i.e., variables related to distances covered and the number of sprints) across matches and halves. Subsequently, a 2 × 4 Multivariate Analysis of Variance (MANOVA) was used to examine the differences between the two professional soccer leagues across four seasons in different subsets of dependent variables. A split file, where data were separated by seasons, was used to carry out a posthoc comparison between the professional soccer leagues, using Bonferroni posthoc analyses. Thus, MANOVA investigated the evolution of the external load variables, where season and league (LL1 or LL2) were independent variables. Statistical significance was set at p < 0.05, p < 0.01, and p < 0.001. Int. J. Environ. Res. Public Health 2021, 18, 1133 4 of 10 3. Results Table 1 shows the mean match running performance comparison between LL1 and LL2 across the four league seasons. We observed a higher TD in LL1 than in LL2 (p < 0.001). In the analysis of TD by halves, in LL1, TD decreased over the match, as TD was higher in the first half than in the second half, whereas this trend was the opposite in LL2. Similarly, HIRD was higher in LL1 than in LL2 (p < 0.001). Concerning the analysis of the HIRD by halves, this variable was higher in the first half than in the second half in both leagues. VHIRD and SpD were also higher in LL1 than in LL2 (p < 0.001). These two variables were higher in the second halves for these two leagues. Finally, SpVHIR and SP were higher in LL1 (p < 0.001). Table 1. Differences between both professional soccer leagues in match running performance. LL1 LL2 F p M (%) SD M (%) SD TD (m) 109,135 4355 107,895 4110 126 0.00 (***) TD 1st Half (m) 54,826 (50.24%) 2390 53,935 (49.99%) 2386 205 0.00 (***) TD 2nd Half (m) 54,309 (49.76%) 2664 53,960 (50.01%) 2570 26 0.00 (***) HIRD 14–21 km·h−1 (m) 22,436 (20.56%) 2182 21,727 (20.14%) 2005 169 0.00 (***) HIRD 1st Half (m) 11,395 (10.44%) 1222 10,971 (10.17%) 1129 191 0.00 (***) HIRD 2nd Half (m) 11,041 (10.12%) 1186 10,756 (9.97%) 1129 89 0.00 (***) VHIRD 21–24 km·h−1 (m) 3019 (2.77%) 385 2838 (2.63%) 378 331 0.00 (***) VHIRD 1st Half (m) 1504 (1.38%) 230 1409 (1.31%) 223 255 0.00 (***) VHIRD 2nd Half (m) 1515 (1.39%) 234 1429 (1.32%) 231 202 0.00 (***) SpD > 24 km·h−1 (m) 2905 (2.66%) 490 2687 (2.49%) 481 296 0.00 (***) SpD 1st Half (m) 1437 (1.32%) 291 1329 (1.23%) 279 209 0.00 (***) SpD 2nd Half (m) 1467 (1.34%) 304 1357 (1.26%) 299 196 0.00 (***) SpVHIR 21–24 km·h−1 264 (62.12%) 30 249 (62.41%) 30 354 0.00 (***) SP > 24 km·h−1 161 (37.88%) 23 150 (37.59%) 22 287 0.00 (***) Note: *** p < 0.001; TD = Total distance, HIRD = High-intensity running distances, VHIRD = Very high-intensity running distances, SpD = Sprinting distance, SpVHIR = Sprints at very high-intensity running, and SP = Sprints at more than 24 km/h; LL1: LaLiga Santander; LL2: LaLiga Smartbank; % = percentage of the total distance covered. The percentage of SpVHIR and SP takes into account the sum of both variables. Table 2 shows the evolution of TD and HIRD in LL1 and LL2 over these four seasons. We can observe a progressive decrease in TD, especially in LL1. Furthermore, during the second half, TD decreased more in LL2 than in LL1, where it remained more stable. HIRD showed a slight increase in LL1 and a slight decrease in LL2. Concretely, during the first half, HIRD increased slightly in both professional soccer leagues over the four seasons. However, during the second half, HIRD increased in LL1, whereas in LL2, there was a decrease. Int. J. Environ. Res. Public Health 2021, 18, 1133 5 of 10 Table 2. Multivariate Analysis of Variance (MANOVA) to compare TD and HIRD between seasons and professional soccer leagues. LL1 p LL2 p F Sig. Eta Power Variables Season M SD M SD TD (m) 15/16 109,368 4376 d 108,176 3973 bd 1.53 0.20 0.001 0.41 16/17 109,241 4319 d 107,581 4082 ac 17/18 109,321 4189 d 108,205 4238 bd 18/19 108,603 4495 abc 107,530 4062 ac TD 1st Half (m) 15/16 55,009 2387 d 53,974 2244 3.04 0.03 0.002 0.72 16/17 54,900 2381 d 53,775 2230 c 17/18 54,861 2395 d 54,206 2520 bd 18/19 54,526 2374 abc 53,707 2500 c TD 2nd Half (m) 15/16 54,358 2660 54,201 2609 bd 1.67 0.17 0.001 0.44 16/17 54,340 2604 53,806 2618 a 17/18 54,460 2546 d 53,999 2578 18/19 54,077 2827 c 53,822 2434 a HIRD 14–21 km·h−1 (m) 15/16 22,304 2050 c 21,743 1987 bc 1.68 0.17 .001 0.44 16/17 22,267 2112 c 21,383 2022 acd 17/18 22,709 2322 ab 22,044 2023 abd 18/19 22,472 2217 21,688 1910 bc HIRD 1st Half (m) 15/16 11,335 1167 c 10,922 1103 c 1.50 0.21 0.001 0.40 16/17 11,307 1189 c 10,810 1123 c 17/18 11,515 1293 ab 11,174 1154 abd 18/19 11,427 1230 10,939 1087 c HIRD 2nd Half (m) 15/16 10,969 1135 c 10,821 1160 b 2.85 0.04 0.001 0.69 16/17 10,960 1140 c 10,573 1142 acd 17/18 11,194 1259 ab 10,869 1120 b 18/19 11,044 119 10,749 1062 b Note: TD = total distance and HIRD = high-intensity running distances; LL1: LaLiga Santander; LL2: LaLiga Smartbank. Posthoc comparisons: a = significant differences compared with 2015/2016 season; b = significant differences compared with 2016/2017 season; c = significant differences compared with 2017/2018 season; d = significant differences compared with 2018/2019 season. The main difference in the evolution of these professional soccer leagues was the distance covered at very high intensity and sprinting, as shown in Table 3. VHIRD and SpD increased across these four seasons, especially in LL1 (p < 0.001 and p < 0.001, respectively). During the first half, VHIRD increased significantly in both leagues. Likewise, VHIRD also increased during the second half over the four seasons and in LL1 this increase was significant. In addition, VHIRD was higher in LL1 than in LL2 (p < 0.001). For SpD, significant increases were obtained in both leagues (p < 0.001). Concretely, in both halves, SpD increased significantly over the four seasons, but it was higher in LL1 than in LL2 (p < 0.01). Finally, Table 4 shows the evolution of SpVHIR and SP across the four seasons and the comparison between the two professional soccer leagues. For SpVHIR, significant increases were found in both leagues (p < 0.001). Moreover, SpVHIR was higher in LL1 than in LL2 (p < 0.001). On the other hand, SP increased over the four seasons and, in LL2, this increase was significant. SP was also higher in LL1 (p < 0.001) than in LL2. Int. J. Environ. Res. Public Health 2021, 18, 1133 6 of 10 Table 3. MANOVA to compare VHIRD and SpD between seasons and professional soccer leagues. LL1 p LL2 p F Sig. Eta Power Variables Season M SD M SD VHIRD 21–24 km·h−1 (m) 2015/2016 3020 375 2817 47 c 7.19 0.00 0.004 0.98 2016/2017 2988 385 d 2782 47 cd 2017/2018 3013 384 2907 49 abd 2018/2019 3056 396 b 2836 50 bc VHIRD 1st Half (m) 2015/2016 1515 233 1392 358 c 9.65 0.00 0.004 0.99 2016/2017 1485 230 d 1383 362 c 2017/2018 1492 222 1448 392 abd 2018/2019 1523 235 b 1406 385 c VHIRD 2nd Half (m) 2015/2016 1504 225 d 1424 210 c 2.86 0.04 0.005 0.69 2016/2017 1503 225 1399 217 c 2017/2018 1521 244 1459 232 ab 2018/2019 1533 241 a 1429 225 SpD > 24 km·h−1 (m) 2015/2016 2873 468 d 2630 28 c 3.99 0.01 0.003 0.84 2016/2017 2860 502 cd 2636 29 c 2017/2018 2930 486 b 2777 31 abd 2018/2019 2959 500 ab 2689 30 c SpD 1st Half (m) 2015/2016 1432 286 1299 491 c 5.73 0.00 0.002 0.95 2016/2017 1413 303 d 1293 466 cd 2017/2018 1440 284 1382 477 abd 2018/2019 1464 289 b 1335 475 bc SpD 2nd Half (m) 2015/2016 1441 285 cd 1331 281 c 1.56 0.20 0.003 0.41 2016/2017 1447 295 cd 1342 270 c 2017/2018 1489 316 ab 1394 285 ab 2018/2019 1495 317 ab 1354 267 Note. VHIRD = very high-intensity running distances, SpD = sprinting distance; LL1: LaLiga Santander; LL2: LaLiga Smartbank. Posthoc comparisons: a = significant differences compared with 2015/2016 season; b = significant differences compared with 2016/2017 season; c = significant differences compared with 2017/2018 season; d = significant differences compared with 2018/2019 season. Table 4. MANOVA to compare number of sprints at different speed levels between seasons and professional soccer leagues. LL1 p LL2 p F Sig. Eta Power Variables Season M SD M SD No. SpVHIR 21–24 km·h−1 2015/2016 263 29 d 247 224 c 6.85 0.00 0.001 0.98 2016/2017 262 31 d 245 226 c 2017/2018 264 30 255 241 abd 2018/2019 268 32 ab 249 228 c No. SP > 24 km·h−1 2015/2016 160 22 148 291 c 4.93 0.00 0.001 0.91 2016/2017 160 23 148 300 c 2017/2018 161 22 155 303 abd 2018/2019 162 23 150 297 c Note. SpVHIR = sprints at very high-intensity running and SP = sprints at more than 24 km·h−1; LL1: LaLiga Santander; LL2: LaLiga Smartbank. Posthoc comparisons: a = significant differences compared with 2015/2016 season; b = significant differences compared with 2016/2017 season; c = significant differences compared with 2017/2018 season; d = significant differences compared with 2018/2019 season. Int. J. Environ. Res. Public Health 2021, 18, 1133 7 of 10 4. Discussion This study aimed to analyze and compare the evolution of the match running perfor- mance between the top two professional Spanish leagues (LL1 and LL2) across four seasons: 2015/2016–2018/2019. The main findings of the study showed that TD, VHIRD, and SpD were higher in LL1 than in LL2. Concerning the comparison between the first and second halves, we found that high-intensity efforts increased in the second half, especially in LL1. The match running performance evolved during these seasons, showing different changes between the two leagues. Specifically, TD decreased significantly in LL1, whereas VHIRD and SpD increased progressively in both leagues. SpVHIR also increased significantly in both leagues, whereas SP increased significantly only in LL2. Firstly, concerning the match running performance comparison, we expected that all the physical variables analyzed in the present study would be higher in LL1 than in LL2. The results showed that external load was higher in LL1 than in LL2. In particular, the distances covered at high intensity and the number of high-intensity efforts were significantly higher in LL1. These results showed that the league at the higher competitive level had higher physical demands during matches. Our findings agree with previous studies [7,8,20], which compared the top two Spanish and Norwegian professional soccer leagues, finding that the top-tiered leagues were more physically demanding. Several explanations could be used to interpret our results. One reason could be the physical capacity of the players of these teams, such that the LL1 clubs contributed to improving the match running performance of their players [11]. Another reason could be related to the playing formation used by LL1 teams, as certain playing formations imply higher external loads, and LL1 teams may use these more demanding playing formations [13]. Concerning the differences between halves of the matches, it can be observed that the first half of LL1 is more demanding than the second half. Secondly, with respect to the evolution of match running performance during these four seasons, we expected an increase in total distance, the distances covered at high intensity, and the number of very high-intensity running efforts. On the contrary, the changes showed significant decreases in TD in both professional soccer leagues. A possible cause of this may be the playing style used by teams of LaLiga [21] because, in recent years, there has been a gradual increase in teams that prioritized ball possession, confirming that in ball control plays with few transitions players covered less total running distances, although greater distances were covered at high intensity [22]. In addition, the introduction of Video Assistant Referee (VAR) has led to a decrease in effective game time, which has contributed to the decrease in TD [23,24]. In agreement with our hypothesis, where we expected an increase in the distances covered at high intensity and the number of very high-intensity running efforts, the results showed that significant increases in distances covered and efforts performed at high intensity were obtained during the four seasons. In this sense, the significant increases in HIRD and VHIRD are indicators of the evolution and changes occurring in soccer, where players are now trained to perform more high-intensity actions. This has probably been caused by the current training perspective, which increases the presence of high-intensity stimuli according to the competition demands and it decreases the rate of injuries, as achieving optimal player performance while minimizing the risk of injury is the main objective [12,25,26]. These types of efforts are keys to achieving high performances in soccer [27,28] and they are important in decisive situations in professional football. They are the most dominant actions when scoring goals [29]. In this sense, in the 2018/2019 season, VAR was added, which promoted longer recovery times, where high-intensity efforts predominate [24]. Another possible reason could be the tactical evolution of football. Today’s models and playstyles tend to advance defensive pressure lines, resulting in larger spaces and more actions performed at high intensity to take advantage of these spaces. When examining the match running performance separated by halves, TD decreased in LL1 across the second half, contrary to the results shown in LL2, where TD increased. In addition, in LL1, the decrease in TD in the second half was less than in the first halves of Int. J. Environ. Res. Public Health 2021, 18, 1133 8 of 10 the matches. These results could be explained by the high equality between the teams in LL1 and LL2, where the matches are usually decided in the second half. The decrease in TD in LL1 is further supported by the fact that LL1 teams performed a large number of high-intensity efforts compared to LL2 during the first half, which could cause a decrease in TD during the second half [30]. Finally, concerning the comparison of the evolution between the two professional soccer leagues, we found that in LL1 there is a trend toward a progressive increase in VHIRD and SpD, especially in the second half, whereas in LL2, the trend is not clear. On the other hand, VHIRD and SpD increased during the second halves in both professional soccer leagues, contrary to the results reported in previous studies [31]. A possible reason for these results is the higher TD and high-intensity efforts performed by the substitutes during the second halves [32]. Although we stated that the equality between teams was higher in LL2, another possible explanation is the increase in the effect of match status during the second halves. In both leagues, time pressure is higher in the second half. For example, it is not the same to be losing 1–0 at half-time as at 80 min. The effects of time pressure and match status probably increase high-intensity actions [17,33]. 4.1. Limitations and Future Perspectives Taking into account the characteristics of the present study and the novelty of this topic, we considered some limitations with a view to future research. In the 2018/2019 season, VAR was added, which has promoted longer recovery times. In future investigations, we should analyze the differences in the external load before and after the implementation of VAR. In addition, we did not analyze other physical variables such as accelerations and decelerations, which are part of the external load of soccer matches [34]. Thus, these types of physical variables must be analyzed to obtain more information about the match running performance of the competition. Finally, another possible study would be about the different evolution of each team across these seasons (e.g., according to classification or playing style). 4.2. Practical Applications Based on the results obtained, some practical applications can be extracted. Firstly, the paradigm of match running performance has changed across the seasons. Thus, it is also necessary for physical training in soccer to evolve in keeping with current match physical demands to optimize the training process. In this sense, knowledge about the match running performance allows coaches to design soccer training with the correct stimuli to optimize players’ performances. In this regard, this type of stimuli constitutes a methodology for injury prevention and could reduce the injury rate of soccer players. In addition, the evolution of high-intensity efforts is very important in designing specific training tasks that reproduce competition demands. Finally, it was found that the Spanish LL1 is more demanding than LL2, and this information is very important to practitioners who are training in each professional soccer league, since it allows them to discern the different external loads in both the first and second divisions. 5. Conclusions The present research describes and compares the differences in match running per- formances between the top two Spanish professional soccer leagues across four seasons. Firstly, the results showed higher external loads in LL1 than in LL2. Concretely, the dis- tances covered at high intensity are higher in LL1 than in LL2. Secondly, the decrease in total distance and the increase in distance covered and efforts performed at high intensity are the main changes in the external load of soccer in both leagues. Finally, VHIRD and SpD increased during the second halves in both professional soccer leagues. In summary, we must take into account the evolution of the match running performance in training and the teams’ playing styles to ensure that players are trained to perform more high-intensity efforts during the matches. Int. J. Environ. Res. Public Health 2021, 18, 1133 9 of 10 Author Contributions: Conceptualization, T.G.-C.; formal analysis, T.G.-C.; funding acquisition, R.L.d.C. and R.R.; investigation, E.P., J.C.P.-B., J.D.-G., R.L.d.C., R.R., X.P. and T.G.-C.; methodology, J.C.P.-B.; project administration, T.G.-C.; resources, R.L.d.C. and R.R.; visualization, E.P., J.C.P.-B. and J.D.-G.; writing—original draft, J.C.P.-B.; writing—review and editing, E.P., J.D.-G. and T.G.-C. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the European Regional Development Found (ERDF), the Government of Extremadura (Department of Economy and Infrastructure) and LaLiga Research and Analysis Sections. Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of University of Extremadura (Protocol number: 153/2017). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Restrictions apply to the availability of these data. Data was obtained from LaLiga and are available at https://www.laliga.es/en with the permission of LaLiga. Conflicts of Interest: The authors declare no conflict of interest. In addition, the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. References 1. Gabbett, T.J.; Nassis, G.P.; Oetter, E.; Pretorius, J.; Johnston, N.; Medina, D.; Rodas, G.; Myslinski, T.; Howells, D.; Beard, A.; et al. The athlete monitoring cycle: A practical guide to interpreting and applying training monitoring data. Br. J. Sports Med. 2017, 51, 1451–1452. [CrossRef] [PubMed] 2. Rago, V.; Brito, J.; Figueiredo, P.; Costa, J.; Barreira, D.; Krustrup, P.; Rebelo, A. Methods to collect and interpret external training load using microtechnology incorporating GPS in professional football: A systematic review. Res. Sport. Med. 2020, 28, 437–458. [CrossRef] [PubMed] 3. Barnes, C.; Archer, D.T.; Hogg, B.; Bush, M.; Bradley, P.S. The evolution of physical and technical performance parameters in the english premier league. Int. J. Sports Med. 2014, 35, 1095–1100. [CrossRef] [PubMed] 4. Bush, M.D.; Archer, D.T.; Hogg, R.; Bradley, P.S. Factors influencing physical and technical variability in the english premier league. Int. J. Sports Physiol. Perform. 2015, 10, 865–872. [CrossRef] 5. Bradley, P.; Carling, C.; Gomez Diaz, A.; Hood, P.; Barnes, C.; Ade, J.; Boddy, M.; Krustrup, P.; Mohr, M. Match performance and physical capacity of players in the top three competitive standards of English professional soccer. Hum. Mov. Sci. 2013, 32, 808–821. [CrossRef] 6. Di Salvo, V.; Pigozzi, F.; González-Haro, C.; Laughlin, M.S.; De Witt, J.K. Match performance comparison in top English soccer leagues. Int. J. Sports Med. 2013, 34, 526–532. [CrossRef] 7. Gomez-Piqueras, P.; Gonzalez-Villora, S.; Castellano, J.; Teoldo, I. Relation between the physical demands and success in professional soccer players. J. Hum. Sport Exerc. 2019, 14, 1–11. [CrossRef] 8. Sæterbakken, A.; Haug, V.; Fransson, D.; Grendstad, H.N.; Gundersen, H.S.; Moe, V.F.; Ylvisaker, E.; Shaw, M.; Riiser, A.; Andersen, V. Match Running Performance on Three Different Competitive Standards in Norwegian Soccer. Sport. Med. Int. Open 2019, 3, 82–88. [CrossRef] 9. Shalfawi, S.A.; Tjelta, L.I. A critical evaluation of the aerobic capacity demands of elite male soccer players. IJASS Int. J. Appl. Sports Sci. 2016, 28, 200–212. [CrossRef] 10. Wallace, J.L.; Norton, K.I. Evolution of World Cup soccer final games 1966–2010: Game structure, speed and play patterns. J. Sci. Med. Sport 2014, 17, 223–228. [CrossRef] 11. Bradley, P.S.; Archer, D.T.; Hogg, B.; Schuth, G.; Bush, M.; Carling, C.; Barnes, C. Tier-specific evolution of match performance characteristics in the English Premier League: It’s getting tougher at the top. J. Sports Sci. 2016, 34, 980–987. [CrossRef] [PubMed] 12. Ekstrand, J.; Waldén, M.; Hägglund, M. Hamstring injuries have increased by 4% annually in men’s professional football, since 2001: A 13-year longitudinal analysis of the UEFA Elite Club injury study. Br. J. Sports Med. 2016, 50, 731–737. [CrossRef] [PubMed] 13. Bradley, P.S.; Carling, C.; Archer, D.; Roberts, J.; Dodds, A.; di Mascio, M.; Paul, D.; Diaz, A.G.; Peart, D.; Krustrup, P. The effect of playing formation on high-intensity running and technical profiles in English FA premier League soccer matches. J. Sports Sci. 2011, 29, 821–830. [CrossRef] [PubMed] 14. Rivilla-garcía, J.; Calvo, L.C.; Jiménez-Rubio, S.; Paredes-Hernández, V.; Muñoz, A.; Van Den Tillaar, R.; Navandar, A. Characteris- tics of Very High Intensity Runs of Soccer Players in Relation to Their Playing Position and Playing Half in the 2013-14 Spanish la Liga Season. J. Hum. Kinet. 2019, 66, 213–222. [CrossRef] 15. Pons, E.; García-Calvo, T.; Resta, R.; Blanco, H.; López del Campo, R.; Díaz García, J.; Pulido, J.J. A comparison of a GPS device and a multi-camera video technology during official soccer matches: Agreement between systems. PLoS ONE 2019, 14. [CrossRef] Int. J. Environ. Res. Public Health 2021, 18, 1133 10 of 10 16. Felipe, J.L.; Garcia-Unanue, J.; Viejo-Romero, D.; Navandar, A.; Sánchez-Sánchez, J. Validation of a video-based performance analysis system (Mediacoach®) to analyze the physical demands during matches in LaLiga. Sensors 2019, 19, 4113. [CrossRef] 17. Castellano, J.; Blanco-Villaseñor, A.; Álvarez, D. Contextual variables and time-motion analysis in soccer. Int. J. Sports Med. 2011, 32, 415–421. [CrossRef] 18. García-Calvo, T.; Ponce-Bordón, J.C.; Leo, F.M.; López del Campo, R.; Nevado-Garrosa, F.; Pulido, J.J. How does ball possession affect the physical demands in Spanish LaLiga? A multilevel approach. Res. Q. Exerc. Sport. under review. 19. Rey, E.; Costa, P.; Corredoira, F.; Sal, A. Effects of age on physical match performance in professional soccer players. J. Strength Cond. Res. 2019. [CrossRef] 20. Castellano, J.; Casamichana, D. What are the differences between first and second divisions of Spanish football teams? Int. J. Perform. Anal. Sport 2015, 15, 135–146. [CrossRef] 21. Morgans, R.; Adams, D.; Mullen, R.; McLellan, C.; Williams, M.D. Technical and physical performance over an English Championship League season. Int. J. Sport Sci. Coach. 2014, 9, 1033–1042. [CrossRef] 22. Yi, Q.; Gómez, M.A.; Wang, L.; Huang, G.; Zhang, H.; Liu, H. Technical and physical match performance of teams in the 2018 FIFA World Cup: Effects of two different playing styles. J. Sports Sci. 2019, 37, 2569–2577. [CrossRef] [PubMed] 23. Linke, D.; Link, D.; Weber, H.; Lames, M. Decline in match running performance in football is affected by an increase in game interruptions. J. Sport Sci. Med. 2018, 17, 662–667. 24. Errekagorri, I.; Castellano, J.; Echeazarra, I.; Lago-Peñas, C. The effects of the Video Assistant Referee system (VAR) on the playing time, technical-tactical and physical performance in elite soccer. Int. J. Perform. Anal. Sport 2020, 20, 808–817. [CrossRef] 25. Oliva-Lozano, J.M.; Rojas-Valverde, D.; Gómez-Carmona, C.D.; Fortes, V.; Pino-Ortega, J. Worst case scenario match analysis and contextual variables in professional soccer players: A longitudinal study. Biol. Sport 2020, 37, 429–436. [CrossRef] [PubMed] 26. Martin-garcia, A.A.; Castellano, J.; Diaz, A.G.; Cos, F. Positional demands for various-sided games with goalkeepers according to the most demanding passages of match play in football. Biol. Sport 2019, 36, 171–180. [CrossRef] [PubMed] 27. Castellano, J.; Echeazarra, I. Network-based centrality measures and physical demands in football regarding player position: Is there a connection? A preliminary study. J. Sports Sci. 2019, 37, 2631–2638. [CrossRef] 28. Zhou, C.; Zhang, S.; Lorenzo Calvo, A.; Cui, Y. Chinese soccer association super league, 2012–2017: Key performance indicators in balance games. Int. J. Perform. Anal. Sport 2018, 18, 645–656. [CrossRef] 29. Faude, O.; Koch, T.; Meyer, T. Straight sprinting is the most frequent action in goal situations in professional football. J. Sports Sci. 2012, 30, 625–631. [CrossRef] 30. Rampinini, E.; Coutts, A.; Castagna, C.; Sassi, R.; Impellizzeri, F. Variation in Top Level Soccer Match Performance. Int. J. Sports Med. 2007, 28, 1018–1024. [CrossRef] 31. Di Salvo, V.; Gregson, W.; Atkinson, G.; Tordoff, P.; Drust, B. Analysis of high intensity activity in premier league soccer. Int. J. Sports Med. 2009, 30, 205–212. [CrossRef] 32. Lorenzo-Martínez, M.; Padrón-Cabo, A.; Rey, E.; Memmert, D. Analysis of Physical and Technical Performance of Substitute Players in Professional Soccer. Res. Q. Exerc. Sport 2020, 1–8. [CrossRef] 33. Moalla, W.; Fessi, M.S.; Makni, E.; Dellal, A.; Filetti, C.; Salvo, V.D.I.; Chamari, K. Association of physical and technical activities with partial match status in a soccer professional team. J. Strength Cond. Res. 2018, 32, 1708–1714. [CrossRef] [PubMed] 34. Dalen, T.; Jorgen, I.; Gertjan, E.; Havard, H.; Ulrik, W. Player load, acceleration and deceleration during forty-five competitive matches of elite soccer. J. Strength Cond. Res. 2016, 30, 351–359. [CrossRef] [PubMed]

A Longitudinal Exploration of Match Running Performance during a Football Match in the Spanish La Liga: A Four-Season Study.

01-28-2021

Pons, Eduard,Ponce-Bordón, José Carlos,Díaz-García, Jesús,López Del Campo, Roberto,Resta, Ricardo,Peirau, Xavier,García-Calvo, Tomas

eng

PMC4724371

1 3 Int Arch Occup Environ Health (2016) 89:211–220 DOI 10.1007/s00420-015-1064-8 ORIGINAL ARTICLE Measured by the oxygen uptake in the field, the work of refuse collectors is particularly hard work: Are the limit values for physical endurance workload too low? Alexandra M. Preisser1 · Linfei Zhou1 · Marcial Velasco Garrido1 · Volker Harth1 Received: 9 December 2014 / Accepted: 3 June 2015 / Published online: 19 June 2015 © The Author(s) 2015. This article is published with open access at Springerlink.com Conclusion HR as well as the measurement of VO2 can be valuable tools for investigating physiological workload, not only under laboratory conditions but also under normal working conditions in the field. Both in terms of absolute and relative HR and oxygen consumption, employment as a refuse collector should be classified in the upper range of defined heavy work. The limit of heavy work at about 33 % of the individual maximum load at continuous work should be reviewed. Keywords Oxygen uptake · Physically heavy work · Mobile spiroergometry · Relative heart rate · Waste collectors · Endurance work Introduction The organized collecting of waste is essential for a func- tioning community; however, there is no explicit job quali- fication connected with, and the work of garbage collec- tors receives little scientific attention. Collecting waste is described as physically demanding work and as being the cause of various physical disorders with respiratory, gastrointestinal, and musculoskeletal symptoms (Kuijer and Frings-Dresen 2004; Kuijer et al. 2010). This work is regarded as a benchmark for “particularly heavy” work. The definition of “heavy work” is based so far only on the assumption that the endurance limit is 30 % respectively 33 % of the maximum load capacity, taking into account load peaks, manual work, and harmful temperatures (Ilmarinen et al. 1991; Rutenfranz et al. 1976). The deter- mination of an “upper limit” is essential for defining the “reasonableness” of a work—in the sense of the absence of excessive risks to health. There are presently also no indi- cations, showing how the physical performance is with this Abstract Purpose Collecting waste is regarded as a benchmark for “particularly heavy” work. This study aims to determine and compare the workload of refuse workers in the field. We examined heart rate (HR) and oxygen uptake as param- eters of workload during their daily work. Methods Sixty-five refuse collectors from three task- specific groups (residual and organic waste collection, and street sweeping) of the municipal sanitation department in Hamburg, Germany, were included. Performance was determined by cardiopulmonary exercise testing (CPX) under laboratory conditions. Additionally, the oxygen uptake (VO2) and HR under field conditions (1-h morning shift) were recorded with a portable spiroergometry system and a pulse belt. Results There was a substantial correlation of both abso- lute HR and VO2 during CPX [HR/VO2 R 0.89 (SD 0.07)] as well as during field measurement [R 0.78 (0.19)]. Com- pared to reference limits for heavy work, 44 % of the total sample had shift values above 30 % heart rate reserve (HRR); 34 % of the individuals had mean HR during work (HRsh) values that were above the HR corresponding to 30 % of individual maximum oxygen uptake (VO2,max). All individuals had a mean oxygen uptake (VO2,1h) above 30 % of VO2,max. Alexandra M. Preisser and Linfei Zhou are equally contributing first authors. * Alexandra M. Preisser [email protected] 1 Institute for Occupational and Maritime Medicine (ZfAM), University Medical Center Hamburg-Eppendorf, Seewartenstrasse 10, 20459 Hamburg, Germany 212 Int Arch Occup Environ Health (2016) 89:211–220 1 3 heavy work with increasing age. An assessment is required in order to meet the challenges of demographic change in industrialized countries. Only few studies have investi- gated in detail the refuse collectors in different countries with different tasks. Up to now, the heart rate (HR) is used as an indirect indicator of the physiological workload, for example in the Netherlands (Kemper et al. 1990), Japan (Tsujimura et al. 2012), and Brazil (Anjos et al. 2007). The oxygen uptake (VO2), as a direct measure of the metabolic processes, however, was mostly estimated via HR in these groups. So far, the VO2 of refuse collectors was determined only once by means of simulation in the laboratory (Kem- per et al. 1990; Frings-Dresen et al. 1995). The relation between HR and VO2 has not yet been specified under field conditions. This may be due to the fact that the measure- ment of oxygen uptake with a breathing mask for outdoor work in this occupation group is technically particularly challenging. In our view, however, the conclusion of HR on VO2,max requires a review. To our knowledge, there are no recent studies with refuse collectors, who were investi- gated during their daily work with portable spiroergometry to determine the real oxygen uptake. This paper is based on a study about the physiological workload of 65 employees from three task-specific groups [residual waste collection (RWC), organic waste collection (OWC), and street cleaning (SC)] of a municipal sanita- tion department in Germany. Our aim was to categorize the respective workload of these professions under real work- ing conditions as a contribution to the development of a classification of workload in occupational health research. To evaluate the methods in the field of measurement, we also conducted comparisons of the methods of workload measurement. For this purpose, HR and oxygen uptake were determined in field measurements. For comparison, we measured the oxygen uptake by a stationary cycle car- diopulmonary exercise test (CPX). Methods The study group consisted of 65 subjects (62 males and 3 females), aged between 25 and 60, all employees in the municipal sanitation department in Hamburg, Germany. All participants volunteered and were granted compensatory time off by the employer. Before the start of the investiga- tions, there was no selection of participants. The anthropo- metric characteristics of the subjects (Table 1) are repre- sentative in age and sex of the 1544 employees [46.5 (SD 8.6) years; 98 % male) working in refuse collecting in this sanitation department. The examined employees were sub- divided by their occupational tasks into three groups: RWC (n = 35), OWC (12), and SC (18). These jobs are mainly performed by male employees, although there are a few females in street sweeping in Hamburg. There were three women in the last group. The Declaration of Helsinki has been adequately addressed, and written informed consent was obtained from all participants. The study was approved by the Ethics Committee of the Hamburg Medical Associa- tion (register number PV4524). Elements of investigation were specific questioning and physical examination (regarding occupation, symptoms, and disorders according to body functions). Furthermore, spirometry, body plethysmography (MasterScreen™ Body by JAEGER™/CareFusion, Hoechberg, Germany), and CPX were performed with 61 subjects. Four persons were excluded due to cardiorespiratory risk factors. Table 1 Characteristics of study participants SD Standard deviation, BMI body mass index, RWC residual waste collectors, OWC organic waste collectors, SC street cleaners, Allfield subjects submitted to field measurement with portable spiroergometric system Founded significant differences between a male/female; b RWC/SC; c OWC/SC, d OWCfield/allfield, and e SCfield/allfield. The first four differences can be explained by the inclusion of women in the SC group, the latter not N Female Age (years) Height (m) Weight (kg) BMI (kg/m2) Mean SD Mean SD Mean SD Mean SD All 65 3 45.6 8.3 177.7 7.6 89.7 14.7 28.3 3.8 Male 62 45.5 8.4 177.0a 8.3 88.7a 15.3 28.2 3.9 Female 3 3 43.1 11.5 162.0 9.6 65.7 7.4 25.2 4.3 RWC 35 – 47.3b 7.0 179.1 7.4 92.5 15.5 28.8 4.0 OWC 12 – 46.7 8.3 177.8 5.3 94.3 12.8 30.0 4.0 SC 18 3 41.6 9.7 175.0 9.0 81.2b,c 11.1 26.5b,c 2.8 Allfield 13 2 49.7 6.7 174.8 9.3 87.1 15.9 28.3 4.0 RWCfield 5 – 51.1 4.0 178.6 11.5 88.6 12.5 27.6 1.2 OWCfield 3 – 50.4 6.3 175.7 3.8 103.0 14.5 33.4d 4.9 SCfield 5 2 47.8 9.6 170.6 9.0 76.0e 12.3 26.0 2.8 213 Int Arch Occup Environ Health (2016) 89:211–220 1 3 Spirometry represents a measure of forced one-second capacity and vital capacity (FEV1, FVC) performed accord- ing to the criteria of the American Thoracic Society (1995) with the calculation of FEV1/FVC. In addition, body ple- thysmography determines the airways resistance as well as intrathoracic gas volume. CPX was performed according to the recommendations of the German Society of Pneumology (Meyer et al. 2013) with 12-lead ECG monitoring on an electronically braked computer-controlled cycle ergometer (ergoselect 200p/ Ergoline Bitz, Germany) with a continuous increase in the load. This ramp-like protocol enables a precise determina- tion of maximal aerobic and power output and the ventila- tory threshold (VT) (Binder et al. 2008; Meyer et al. 2005). Performance and VO2 and carbon dioxide outputs (VCO2) were measured continuously (Oxycon Pro™ by JAE- GER™/CareFusion, Hoechberg, Germany). CPX was preceded by 2 min of sitting at rest. After a warm-up period of 2 min with an external workload of 25 W, the exercise followed with an increase of 15–25 W/ min (Meyer et al. 2013) depending on the individual fit- ness level. Subjects were verbally encouraged until they could no longer sustain the required crank frequency of 60–70 rpm. Maximum oxygen uptake (VO2,max) was calcu- lated as the average of the highest eight consecutive breaths in the final minute of exercise. The standard equations by Hansen et al. (1984), Reiterer (1975), and Wasserman et al. (2004) for VO2,max and maximal wattage (Pmax) were used for assessment. The VT corresponds to the first VT; it was determined with a combination of VCO2/VO2 slope and increase in minute ventilation (VE) relative to oxygen consumption (VE/VO2), ventilatory equivalent named. This first VT is defined by the increase in VE/VO2 without a concurrent increase in VE/VCO2 (Binder et al. 2008; West- hoff et al. 2013). Forty-one subjects were studied while working with long-term HR measurements (T31 coded transmitter, Polar Electro, Buettelborn, Germany) during a work shift (mean 6.7 h). From this group, 20 subjects (18 males and 2 females) were also connected to mobile CPX (Oxycon Mobile by JAEGER™/CareFusion, Hoechberg, Germany) and to the HR monitoring system for an average of 1.3 h to measure the correlation between HR and oxygen uptake under field conditions (HRfield, VO2,field) (Fig. 1). The field measurement was started before the truck left the depot and thus recorded approximately 30 min of driving plus 1 h of sustained work. The actual HR and oxygen uptake under task-specific work were recorded during the following 1 h of continuous work (HR1h, VO2,1h). Part of the work of the garbage collectors is transporting two-wheeled waste con- tainers (120 l volume) of houses and cellars and the shift of large four-wheeled waste containers (240 l) of storerooms. The path length of an entire work day was estimated with a pedometer and was about 7–10 km. All waste containers were emptied machine-supported into the truck (Fig. 2a, b, photographs with waste worker, spiroergo mask, and gar- bage cans). Occasional waste bags were towed. SC con- sisted of sweeping waste and leaves, sometimes wet leaves, as well as picking up trash. Due to malfunction of the measuring instruments, refusal, and changes in the organi- zation, valid data were obtained for only 13 of 20 subjects. Ahead of the gas exchange measurements in the field via face mask, the mobile CPX unit was volume and gas calibrated. HR and oxygen uptake were both depicted in absolute values and relative to individuals’ maximum val- ues (%HRmax, %VO2,max) and individuals’ values at the VT (%HRVT, %VO2,VT). The difference to maximum val- ues as “reserve values” (%HRR, %VO2,R) was defined as: (HRwork − HRrest)/(HRmax − HRrest) × 100 %, and (VO2,work − VO2,rest)/(VO2,max − VO2,rest) × 100 %, respec- tively. The HR and VO2 at rest (HRrest,VO2,rest) were calcu- lated from the mean values in the first 2 min of the exercise test and the previously measured resting value. Statistics Data are presented as means and standard deviations (SD). To assess the equivalence of linear regression, mean val- ues for Pearson correlation (R), intercept, and slope were Fig. 1 Flowchart of the measurements 214 Int Arch Occup Environ Health (2016) 89:211–220 1 3 determined for each individual. Student’s t test and Wilcoxon test were used to determine whether the mean intercepts and slopes differed from 0 to 1, respectively, and to verify dif- ferences between sample characteristics and differences from reference limits. All calculations were performed using IBM SPSS Statistics 22. For all statistical analyses, the null hypothesis was rejected at a probability of p < 0.05. Results The 65 subjects of the study group showed only low differ- ences in age and in body mass within the total sample for RWC, OWC, and SC, respectively (Table 1). All 61 subjects who could participate in the CPX had normal ECG readings and took no HR-affecting drugs. On the basis of spirometry and body plethysmography, obstructive lung disease (FEV1/ FVC < 70 %) was observed in 21.5 % of subjects. All work- ers diagnosed with pulmonary disorders were active or for- mer smokers (35.4 and 43.1 % of total sample, respectively). The results from HR measurement at work (HRsh) of 41 subjects with an average work shift time of 6.7 h are shown in Table 2. Mean values of the total sample were 100.2 b/ min and 27.9 % HRR, respectively. The HRsh values rela- tive to individuals’ HRmax and to HRVT (%HRmax, %HRVT) determined in the laboratory CPX showed that the OWC had the highest strain compared with the three subgroups (data not shown in detail). HR recorded during one representative work hour (Table 3) showed a slightly higher mean HR1h of 109.2 b/ min and 45.1 % HRR, respectively, for the 13 subjects (for whom also the oxygen uptake was measured) than in the measurement over the whole work shift of the total sam- ple. There were no significant differences between the groups OWC, RWC, and SC for 1 h of measurement. Mean HR values during 1 h as well as during a work shift were close to the HR at VT. Between HR1h and HRsh of these 13 subjects, there was a mean correlation coefficient of R 0.64. The regression of HR1h was slightly but significantly (p < 0.05) higher than HRsh by 10.6 b/min. During the one representative working hour (Table 3), the group mean achieved an oxygen uptake (VO2,1h) of 1103 ml/min. Here too, mean VO2 was close to VO2,VT. The groups did not dif- fer significantly. Fig. 2 a, b Refuse collector with spiroergo mask, equipment, and garbage cans Table 2 Mean heart rate during a work shift (HRsh) of 6.7 h of n = 41, percentage of maximal heart rate (%HRmax), and heart rate at the ventila- tory threshold (%HRVT) from CPX (values relative to heart rate reserve (%HRR)) RWC Residual waste collectors, OWC organic waste collectors, SC street cleaners a Significant difference RWC/OWC b Significant difference RWC/SC N Female Mean HRsh (b/min) HRsh,max (b/min) %HRmax %HRVT %HRR,sh Mean SD Mean SD Mean SD Mean SD Mean SD All 41 3 100.2 11.9 153.7 28.2 63.1 9.2 79.4 12.9 27.9 14.2 RWC 18 – 93.4a,b 11.8 150.6 30.5 58.3a,b 8.6 75.8 16.2 25.3 13.7 OWC 9 – 107.8 7.5 152.8 22.4 68.8 8.9 85.2 8.9 26.1 14.8 SC 14 3 103.9 9.9 158.2 29.8 65.7 7.4 80.1 9.1 32.5 14.2 215 Int Arch Occup Environ Health (2016) 89:211–220 1 3 The results of CPX with measurement of VO2 of all 61 participants and of the 13 subjects with field measured data are depicted in Tables 4 and 5. The three subgroups RWC, OWC, and SC do not differ significantly in this test with respect to Pmax, VO2,max, and HRmax (data not shown). The spiroergometric field measurements’ sample of 13 sub- jects did not differ significantly from the whole group. A relationship between the maximal values from Pmax and VO2,max could be observed with mean correlation coeffi- cient (R) of 0.88; HRmax was weakly correlated with age (R 0.45). Therefore, older participants showed surprisingly a slight increase in HRmax with age (data not shown in detail). The individuals reached values close to age-predicted val- ues with 95.6 % (SD 18.2) VO2,max/VO2,pred, and 90.8 % (SD 7.4) HRmax/HRpred, (Hansen et al. 1984; Reiterer 1975; Wasserman et al. 2004). The linear regression analysis was accomplished to study the relationship between HR and VO2 for CPX and field meas- urement. Data of HR and oxygen uptake during CPX create an individual linear heart/oxygen uptake relationship and a substantial correlation (mean R 0.89, p < 0.001). There was also a linear regression, with a mean correlation coefficient of Table 3 Average of heart rate (HR1h) and oxygen uptake (VO2,1h) during 1 h of work HR1h and VO2,1h relative to maximal heart rate and maximal oxygen uptake during CPX (%HRmax; %VO2,max). HR1h and VO2,1h relative to heart rate and oxygen uptake at the ventilatory threshold (%HRVT; %VO2,VT). And values relative to heart rate reserve (%HRR) RWC Residual waste collection, OWC organic waste collection, SC street cleaning, Allfield subjects submitted to field measurement with portable spiroergometric system N HR1h (b/min) %HRR,1h % HRmax %HRVT VO2,1h (ml) %VO2,max %VO2,VT Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Alla field 13 109.2 12.5 45.1 18.9 71.1 11.5 86.7 17.2 1103 237.3 45.7 9.3 60.0 14.3 RWCfield 5 106.4 15.0 38.3 20.3 66.6 9.8 84.5 22.1 1160 137.0 42.9 5.8 57.8 15.1 OWCfield 3 106.5 9.1 32.7 12.7 67.2 13.5 82.4 14.3 1286 80.2 50.8 7.2 69.1 13.3 SCa field 5 113.7 12.7 59.3 12.6 78.0 10.7 91.6 15.8 935 287.1 45.2 13.0 56.7 14.5 Table 4 Results of CPX tests a The difference to Table 1 can be explained by the exclusion of four people due to cardiac disease or medication N Female Pmax (W) Pmax (W/kg) VO2,max (ml) VO2,max (ml/kg) HRmax (b/min) RERmax Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD All 61a 3 192.1 39.2 2.1 0.5 2623 571 29.2 6.4 158.9 14.3 1.27 0.12 Allfield 13 2 184.5 46.9 2.1 0.5 2458 562 28.4 5.5 155.2 14.1 1.26 0.13 RWCfield 5 – 200.0 17.0 2.3 0.2 2739 398 31.0 2.2 160.0 4.8 1.25 0.15 OWCfield 3 – 180.0 56.3 1.8 0.6 2562 372 25.2 4.6 162.0 27.2 1.22 0.28 SCfield 5 2 171.6 64.8 2.2 0.7 2113 682 27.7 7.8 146.2 6.4 1.28 0.06 Table 5 Heart rate (HRVT), power output (PVT), and oxygen uptake (VO2,VT) at ventilatory threshold (VT) from CPX tests [relative to maximal values from CPX (%Pmax, % VO2,max, %HRmax)] RWC Residual waste collectors, OWC organic waste collectors, SC street cleaners, Allfield subjects submitted to field measurement with portable spiroergometric system N Female P at VT (W) VO2 at VT (ml) HR at VT (b/min) %Pmax %VO2,max %HRmax Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD All 61 3 129.7 43.6 1843 498 125.1 15.3 66.9 16.3 70.3 11.8 79.0 9.3 Allfield 13 2 130.0 49.5 1914 549 128.4 15.8 69.2 12.6 77.1 8.0 82.9 8.4 RWCfield 5 – 143.0 43.1 2109 514 129.6 20.1 70.5 17.0 76.0 9.3 81.0 12.7 OWCfield 3 – 115.0 43.6 1908 400 131.3 19.3 63.1 4.4 74.0 4.5 81.3 2.4 SCfield 5 2 126.0 64.5 1723 684 125.4 11.7 71.6 12.0 80.0 8.6 85.7 5.9 216 Int Arch Occup Environ Health (2016) 89:211–220 1 3 R 0.78 (p < 0.001) between HRfield and VO2,field. The equations obtained here were nearly the same; both regressions for CPX and field measurement are shown in Fig. 3a, b. The correlation between %HRR and %VO2,R during CPX was high (R 0.96). The correlation during field measurement was similar, albeit lower, (R 0.78, both p < 0.001) (Fig. 4a, b). During the field measurement of continuous HRfield and VO2,field, a simultaneous increase and decrease in HR and oxygen uptake could be observed in each individual. In Fig. 5, a typical example is given of one subject. Discussion Relationship of HR to VO2 to determine the validity of measuring methods in the field Heart rate increases linearly as a function of workload intensity and is closely related to oxygen uptake (Arts and Kuipers 1994; Gastinger et al. 2010). Nevertheless, the value of the HR/VO2 relationship can vary between indi- viduals due to metabolic stress or physical training level and therefore should be ascertained individually (Skin- ner et al. 2003). Similarly, interindividual differences are observed when CPX and field measurements during work are compared. To determine the physiological workload of physically demanding work, we investigated the relation between HR and oxygen uptake under field conditions. We could demonstrate that the HR/VO2 relationship was linear not just during the incremental cycle exercise test (CPX) but also in their usual working environment with climatic and other factors. Nevertheless, the range of the correla- tion coefficients shows that HR is more strongly correlated to VO2 during CPX (R 0.89, p < 0.001) than during field measurement (R 0.78, p < 0.001) (Fig. 3a, b). Yet, there is 0 20 40 60 80 100 120 140 160 180 200 220 0 500 1000 1500 2000 2500 3000 HR [b/min] VO2 [ml/min] 0 20 40 60 80 100 120 140 160 180 200 220 0 500 1000 1500 2000 2500 3000 HR [b/min] VO2 [ml/min] (a) (b) Fig. 3 Heart rate (HR) and oxygen uptake (VO2) during CPX with increasing workload by 15–25 W/min (a) and during spiroergo- metric field measurement at work (b), for 13 subjects. a HR (b/ min) = 0.03 × VO2 (ml/min) + 70.95 (drawn trendline); R2 = 0.80; n = 426; p < 0.001. b. HR (b/min) = 0.03 × VO2 (ml/min) + 76.2 (drawn trendline); R2 = 0.65; n = 2191; p < 0.001 -20% 0% 20% 40% 60% 80% 100% 120% 140% -20% 0% 20% 40% 60% 80% 100% %HRR %VO2,R -20% 0% 20% 40% 60% 80% 100% 120% 140% -20% 0% 20% 40% 60% 80% 100% %HRR %VO2,R (a) (b) Fig. 4 Heart rate as a percentage of heart rate reserve (%HRR) in relation to oxygen uptake as a percentage of oxygen uptake reserve (%VO2,R), determined during CPX (a) and field measurement (b), for 13 subjects. a %HRR = 0.925 × %VO2,R (ml/min) − 0.017; R2 = 0.93; n = 325; p < 0.001. b %HRR = 0.783 × %VO2,R (ml/ min) + 0.130; R2 = 0.68; n = 2146; p < 0.001 217 Int Arch Occup Environ Health (2016) 89:211–220 1 3 a significant correlation between HR and VO2 in the field measurement, and furthermore, a congruent increasing and decreasing profile could be demonstrated (Fig. 5). Due to different proportions between HR and VO2, a method based on heart rate reserve (%HRR) and VO2 reserve (%VO2,R) is widely used for the comparison of rel- ative values. Swain and Leutholtz (1997) and recent stud- ies by Lounana et al. (2007) have shown that %HRR data at group level are consistent with %VO2,R. We aimed to find out whether this correlation can also be validated for our group in CPX and especially in spiroergometric field measurement under working conditions. For the incremen- tal exercise testing, we can confirm a substantial correla- tion of %HRR and %VO2,R with R 0.96 (p < 0.01) (Fig. 4a). For spiroergometric field measurement, we found a lower correlation of R 0.78 (p < 0.001). Both regressions show that %HRR does not overestimate %VO2,R as their inter- cepts are close to 0 (Fig. 4b). Possible reasons for lower field correlations between HR and VO2 could be malfunc- tioning in gathering the individual values, different load shapes, and the varyingly high intensity of physical strain of muscle groups with differing efficiency. During work as a refuse collector, especially arm work is performed, while the incremental cycle exercise consists mostly of legwork. A better equivalence between %HRR and %VO2,R for leg- work than for arm work has been described by (Rotstein and Meckel 2000). Additionally, HR can be impaired by further factors, such as temperature, emotion, and physical fitness status (Achten and Jeukendrup 2003). We neverthe- less could demonstrate an equivalence between absolute values of HR and VO2, and equally in relative calculations to HRR and VO2,R in dynamic work, even if it was meas- ured in the field. Fitness and workload capacity evaluated by various thresholds and aspects Because the VT reflects the workload threshold beyond which endurance exercise will not lead to anaerobic metabolism, it can therefore be regarded as the upper limit of intensity during the endurance performance (Binder et al. 2008). The present study showed a high endurance performance for the entire sample during 1 h of work and also during the whole work shift, depending on the HR measurement with a mean of 86.7 % HRVT and 79.4 % HRVT, respectively. The percentage of VO2 during 1 h of work in percentage of VO2,VT was likewise, but lower, with a mean of 60 % VO2,VT. In Fig. 5, which shows a repre- sentative measurement from the field tests, the subject’s HR well exceeded most of the time the individual HRVT. Similarly, VO2,VT was exceeded several times. For individ- ual values relative to the VT (%HRVT, %VO2,VT), our data show that %HRVT may overestimate the real workload; %VO2,VT seems to be more realistic (see Table 5). Fur- thermore, VT not only differs between individuals but also varies depending on the state of training and the type of exercise protocol (Faude et al. 2009). Therefore, the ques- tion arises whether %HRVT is comparable to %VO2,VT. We would recommend to determine VT and likewise the HRVT and VO2,VT, by CPX in the laboratory. This will enable an accurate estimate of %VO2,VT during the field measurement. Fig. 5 Case report: heart rate and oxygen uptake during field measurement (HRfield,VO2,field) of one subject. Individual maxi- mal heart rate, maximal oxygen uptake (HRmax, VO2,max), and the values at ventilatory threshold (HRVT, VO2,VT) are also shown 218 Int Arch Occup Environ Health (2016) 89:211–220 1 3 In our sample, the CPX results are close to the individual predicted and age-dependent values (Table 2). Kroidl et al. (2014) have described the requirements for high, normal, and pathological endurance performance, based on values at VT > 80 %, around 60 %, and <40 % of maximal val- ues, respectively. In comparison, our subjects also reached performance levels in the upper range of normal endurance (Table 2). In the present study, workers show normal ranges of individual fitness. Long work periods with a high level of physical activity did not lead to an increase in maximal oxygen uptake, and only slightly better endurance perfor- mance was observed in them. This seems to be compat- ible with results from previous studies which also investi- gated workers with heavy workload (Ilmarinen et al. 1991; Søgaard et al. 1996). It is commonly suggested that 33–40 % of the indi- vidual’s VO2,max should be the capable workload for 8 h of physical work (Åstrand et al. 2003; Ilmarinen 1992). But %VO2,max depends on the type of exercise performed. According to Kemper et al. (1990), the acceptable limit for refuse collecting work in particular, which mainly consists of arm work combined with legwork, should be at 30 % VO2,max for an 8-h shift. To describe the exercise intensity in our sample, we took HRsh at a given %VO2,max. This method is according to Skinner et al. (2003); they have demonstrated that once VO2,max and the relationship among HR and VO2 are known, the corresponding HR is a good estimate for relative workload. Taking the mean HR values of the 41 subjects in our study who had undergone HRsh measurement, there was a slight exceedance (mean HR 100.2 b/min) of the standards of calculated mean HR value at 30 % VO2,max (96.6 b/min); ns). Here, 66 % of the indi- viduals had mean HRsh values above 30 % VO2max. Frings- Dresen and Kemper 1995, under laboratory conditions, showed that 33–59 % of the subjects, depending on the waste collector activity (bags, different container volumes), exceeded the 30 % of VO2max. Comparing these results with the oxygen uptake of the 13 individuals from the 1-h VO2 measurement, the means even exceeded the reference of 30 % VO2,max significantly (mean VO2,1h 1103 ml/min vs. calculated VO2 at 30 % VO2,max of 737.3 ml/min, p < 0.05). All subjects achieved a mean VO2, which was above the reference limit of 30 % VO2,max, with a total range of 35–69 % VO2,max. These results are consistent with the relation between HR1h and HRsh as the 1-h values were slightly but significantly higher than HRsh. Nevertheless, in both specifications (HR and VO2), very high values have been found, which reflects the high continuous work load of refuse collectors. In general, exercises that are performed with a HRR > 30 % for an 8-h shift are assumed to be at high car- diovascular load (Ilmarinen et al. 1991; Shimaoka et al. 1998). With long-term HR measurement for a work shift of 6.7 h, 39 % of residual waste collectors, 33 % of organic waste collectors, and 39 % of the street cleaners had %HRR,sh values that were higher than 30 % HRR. These findings are consistent with Kuijer et al. (1999), who found 36.4 %HRR for refuse collectors and 22.6 %HRR for street sweepers. Therefore, we can conclude that refuse collectors and street cleaners have high endurance performance and high cardiovascular load during work. Åstrand et al. (2003) specified easy, moderate, and heavy work during an 8-h work shift on the basis of oxy- gen consumption at <600, 600–1000, and >1000 ml/min VO2, respectively, and required a maximum VO2 for work at 40 %VO2,max at <1500, <1500–2500, and >2500 ml/ min, respectively. When compared to Åstrand’s require- ments of workload, the refuse collectors in our study had a mean VO2,1h of 1103 ml/min during work corresponding to 46 % VO2,max (Table 4) and a mean VO2,max of 2623 ml/min during CPX corresponding to oxygen uptake under heavy physical work. This confirms Åstrand’s findings; the work- load of refuse collectors can be classified in the upper field of heavy work. Whether the relatively high physical endur- ance is a health risk for the refuse collectors remains open. In our initial cross-sectional study, we found no evidence to this. Comparison with other occupations Compared to jobs which are commonly referred to as phys- ically heavy, the relative workload found in this study was rather high. The means for HRsh and %HRmax (Table 3) during one work shift are consistent with Wultsch et al. (2012) findings for workers from waste processing (activi- ties were not differentiated). They found mean HRsh 100 b/ min for male and 120 for female, 59 and 65 % HRmax, respectively. Compared to the other investigated profes- sions (workers in metal industry, slaughterhouse work, or healthcare business) referred in this study (Wultsch et al. 2012), our findings on the physical demand of refuse col- lectors were higher. Compared to a study with housekeep- ers which also used a portable spiroergometric system for field measurements (MJ Fröhlich, personal communica- tion), we found similar values at HR1h and VO2,1h to those they determined with 112 b/min and 1.06 l/min, respec- tively. However, compared to portable spiroergometric measurements with lumberjacks (Hagen et al. 1993)—their job is considered to be the hardest form of physical work (with 49 % VO2,max for the younger, 53 % VO2,max for the older, and a HRsh of 138 and 126 b/min, respectively)—our measurement results were rather low. Other studies with refuse collectors have also reported similar HR values to those found in our study. Kemper et al. (1990) have found a mean HRsh of 99.5 b/min in Dutch refuse collectors during one work shift, and—compared to 219 Int Arch Occup Environ Health (2016) 89:211–220 1 3 the threshold value of 30 % VO2,max calculated over HRsh— 30 % of their participants had exceeded that limit. Further- more, they also established a linear relationship between HR and VO2 during work, but they did not describe this correlation further. In a recent study with Brazilian refuse collectors, Anjos et al. (2007) outlined a mean HR for the total working time at 97.6 b/min, 53.4 % HRmax, and 32.8 % HRR; nevertheless, their results were partially lower than those found in the present study. In addition, they identified HR values during the actual working time which can be compared with our values for 1 h of continuous work. A recent Japanese study by Tsujimura et al. (2012) found mean HR values for garbage collectors of 97.5 b/ min, which were similar to the Brazilians but lower than our findings. These studies of refuse collectors, however, determined the workload only by the HR without VO2 field measurements. Conclusion The present study demonstrates that HR and oxygen con- sumption are strongly correlated even during field measure- ments of the heavy dynamic work of the refuse collectors. Therefore, HR measurement is a valuable tool for evaluat- ing the parameters of physiological workload during work. But the correlation between HR and VO2 was stronger under steady conditions in the laboratory, while HR can also be influenced by several external circumstances. In addition, we included only persons without heart disease or medi- cation. In persons with cardiac disease or HR influencing medication, the sole determination of HR cannot replace the measurement of VO2. Therefore, if possible, the determina- tion of VO2 should be aimed in the field measurement. Refuse collectors exceed the upper limits set for physical work stress in the literature (Åstrand et al. 2003; Ilmarinen et al. 1991; Shimaoka et al. 1998). But all investigated employees were in our study within their individual refer- ence limits of physical capacity and aerobic fitness, both in terms of absolute and relative HR as well as in oxygen consumption. The three task-specific groups (RWC, OWC, SC) did not differ in workload. The results of the present study can finally confirm the high workload of refuse col- lectors with the determination of VO2 at work. In addition, the endurance workload of refuse collectors is well above the hitherto recommended limits. The currently applicable limits for an 8-h shift with a maximum of 33–40 % of the individu- al’s VO2 max or HRR > 30 % should be reviewed. Other field measurements with determination of oxygen uptake with other physically hardworking professionals are necessary. Acknowledgments The authors would like to thank L. Herrmann, Stadtreinigung, Hamburg, for his support in recruiting the volunteers, and H.-J. Krankenhagen and A. Frosch for preparing heart rate data in the field measurements. We would like to thank Sabine Bößler and Anne Winkelmann for their excellent support with the technical patient examinations. We are indebted to Cordula Bittner, MD, and Thomas von Münster, MD, who implemented clinical examinations. The study is a part of the investigation: “Ergonomic study of waste collectors in the system garbage collection and street cleaning of the municipal sanitation department in Hamburg” and was funded by a grant from Stadtreinigung, Hamburg. Conflict of interest The authors declare that they have no conflict of interest. Ethical standard All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the Declaration of Helsinki 1964 and its later amendments or comparable ethical standards. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://crea- tivecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. References Achten J, Jeukendrup AE (2003) Heart rate monitoring: applications and limitations. Sports Med 33(7):517–538 American Thoracic Society (1995) Standardization of spirometry. Am J Respir Crit Care Med 152:1107–1136 Anjos LA, Ferreira JA, Damiao JJ (2007) Heart rate and energy expenditure during garbage collection in Rio de Janeiro, Brazil. Cad Saude Publica 23(11):2749–2755 Arts FJ, Kuipers H (1994) The relation between power output, oxy- gen uptake and heart rate in male athletes. Int J Sports Med 15(5):228–231 Åstrand PO, Rodahl K, Dahl HA, Stromme SB (2003) Textbook of work physiology: physiological bases of exercise, 4th edn. Human Kinetics Publishers, Champaign, II Binder RK, Wonisch M, Corra U, Cohen-Solal A, Vanhees L, Saner H, Schmid JP (2008) Methodological approach to the first and second lactate threshold in incremental cardiopulmonary exer- cise testing. Eur J Cardiovasc Prev Rehabil 15(6):726–734 Faude O, Kindermann W, Meyer T (2009) Lactate threshold concepts: how valid are they? Sports Med 39(6):469–490 Frings-Dresen MHW, Kemper HCG, Stassen ARA, Crolla IFAM, Markslag AMT (1995) The daily workload of refuse collectors working with three different collecting methods: a field study. Ergonomics 38:2045–2055 Gastinger S, Sorel A, Nicolas G, Gratas-Delamarche A, Prioux J (2010) A comparison between ventilation and heart rate as indi- cator of oxygen uptake during different intensities of exercise. J Sports Sci Med 9(1):110–118 Hagen KB, Vik T, Myhr NE, Opsahl PA, Harms-Ringdahl K (1993) Physical workload, perceived exertion, and output of cut wood as related to age in motor-manual cutting. Ergonomics 36(5):479–488 Hansen JE, Sue DY, Wasserman K (1984) Predicted values for clinical exercise testing. Am Rev Respir Dis 129(2 Pt 2):S49–S55 220 Int Arch Occup Environ Health (2016) 89:211–220 1 3 Ilmarinen J (1992) Job design for the aged with regard to the decline in their maximal aerobic capacity: part I D guidelines for the practitioner. Int J Ind Ergon 10:53–63 Ilmarinen J, Louhevaara V, Korhonen O, Nygard CH, Hakola T, Suvanto S (1991) Changes in maximal cardiorespiratory capac- ity among aging municipal employees. Scand J Work Environ Health 17(Suppl 1):99–109 Kemper HC, van Aalst R, Leegwater A, Maas S, Knibbe JJ (1990) The physical and physiological workload of refuse collectors. Ergonomics 33(12):1471–1486 Kroidl RF, Schwarz S, Lehnigk B, Fritsch J (2014) Kursbuch Spiroer- gometrie, 3rd edn. Thieme, Stuttgart Kuijer PP, Frings-Dresen MH (2004) World at work: refuse collectors. Occup Environ Med 61(3):282–286 Kuijer PP, Visser B, Kemper HC (1999) Job rotation as a factor in reducing physical workload at a refuse collecting department. Ergonomics 42(9):1167–1178 Kuijer PP, Sluiter JK, Frings-Dresen MH (2010) Health and safety in waste collection: towards evidence-based worker health surveil- lance. Am J Ind Med 53(10):1040–1064 Lounana J, Campion F, Noakes TD, Medelli J (2007) Relation- ship between %HRmax, %HR reserve, %VO2max, and %VO2 reserve in elite cyclists. Med Sci Sports Exerc 39(2):350–357 Meyer T, Lucia A, Earnest CP, Kindermann W (2005) A conceptual framework for performance diagnosis and training prescription from submaximal gas exchange parameters–theory and applica- tion. Int J Sports Med 26(Suppl 1):S38–S48 Meyer FJ, Borst MM, Buschmann HC, Ewert R, Friedmann-Bette B, Ochmann U, Petermann W, Preisser AM, Rohde D, Ruhle KH, Sorichter S, Stahler G, Westhoff M, Worth H (2013) Exercise testing in respiratory medicine. Pneumologie 67(1):16–34 Reiterer W (1975) Methodik eines rektangulären-triangulären Belas- tungstestes. Herz/Kreisl 7(9):457–462 Rotstein A, Meckel Y (2000) Estimation of %VO2 reserve from heart rate during arm exercise and running. Eur J Appl Physiol 83(6):545–550 Rutenfranz J, Hettinger T, Ilmarinen J, Klimmer F (1976) Beurteilung der Eignung zu schwerer körperlicher Arbeit. In: Drasche H, Essing HG, Florian HJ et al (eds) Ökologischer Kurs: Teil Arbe- itsmedizin. Ferdinand Enke, Stuttgart, pp 12–24 Shimaoka M, Hiruta S, Ono Y, Nonaka H, Hjelm EW, Hagberg M (1998) A comparative study of physical work load in Japanese and Swedish nursery school teachers. Eur J Appl Physiol Occup Physiol 77(1–2):10–18 Skinner JS, Gaskill SE, Rankinen T, Leon AS, Rao DC, Wilmore JH, Bouchard C (2003) Heart rate versus %VO2max: age, sex, race, initial fitness, and training response: HERITAGE. Med Sci Sports Exerc 35(11):1908–1913 Søgaard K, Fallentin N, Nielsen J (1996) Work load during floor cleaning: the effect of cleaning methods and work technique. Eur J Appl Physiol Occup Physiol 73(1–2):73–81 Swain DP, Leutholtz BC (1997) Heart rate reserve is equivalent to %VO2 reserve, not to %VO2max. Med Sci Sports Exerc 29(3):410–414 Tsujimura H, Taoda K, Kitahara T (2012) A field study on the physi- ological workload of garbage collectors in the Japanese summer. Ind Health 50(6):556–566 Wasserman K, Hansen JE, Sue DY, Stringer WW, Whipp BJ (2004) Principles of exercise testing and interpretation, 4th edn. Lippin- cott Raven, Baltimore Westhoff M, Ruhle KH, Greiwing A, Schomaker R, Eschenbacher H, Siepmann M, Lehnigk B (2013) Positional paper of the German working group “cardiopulmonary exercise testing” to ventila- tory and metabolic (lactate) thresholds. Dtsch Med Wochenschr 138(6):275–280 Wultsch G, Rinnerhofer S, Tschakert G, Hofmann P (2012) Gov- ernmental regulations for early retirement by means of energy expenditure cut offs. Scand J Work Environ Health 38(4):370–379

Measured by the oxygen uptake in the field, the work of refuse collectors is particularly hard work: Are the limit values for physical endurance workload too low?

06-19-2015

Preisser, Alexandra M,Zhou, Linfei,Velasco Garrido, Marcial,Harth, Volker

eng

PMC9227788

Citation: Machado, J.C.; Góes, A.; Aquino, R.; Bedo, B.L.S.; Viana, R.; Rossato, M.; Scaglia, A.; Ibáñez, S.J. Applying Different Strategies of Task Constraint Manipulation in Small-Sided and Conditioned Games: How Do They Impact Physical and Tactical Demands? Sensors 2022, 22, 4435. https://doi.org/10.3390/ s22124435 Academic Editor: Gregorij Kurillo Received: 17 May 2022 Accepted: 9 June 2022 Published: 11 June 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). sensors Communication Applying Different Strategies of Task Constraint Manipulation in Small-Sided and Conditioned Games: How Do They Impact Physical and Tactical Demands? João Cláudio Machado 1, Alberto Góes 2, Rodrigo Aquino 3 , Bruno L. S. Bedo 4, Ronélia Viana 1, Mateus Rossato 1 , Alcides Scaglia 2,5 and Sérgio J. Ibáñez 6,* 1 Faculty of Physical Education and Physiotherapy, Federal University of Amazonas, Manaus 69067-005, Brazil; [email protected] (J.C.M.); [email protected] (R.V.); [email protected] (M.R.) 2 Faculty of Physical Education, State University of Campinas, Campinas 13083-859, Brazil; [email protected] (A.G.); [email protected] (A.S.) 3 LabSport, Post-Graduate Program in Physical Education, Center of Physical Education and Sports, Federal University of Espírito Santo, Vitória 29075-910, Brazil; [email protected] 4 Biomechanics and Motor Control Laboratory, School of Physical Education and Sports of Ribeirão Preto, University of São Paulo, São Paulo 04024-002, Brazil; [email protected] 5 Laboratory of Sport Pedagogy (LEPE), School of Applied Sciences (FCA), State University of Campinas, Limeira 13484-350, Brazil 6 Optimisation of Training and Sport Performance Research Group, Faculty of Sports Sciences, University of Extremadura, 06006 Badajoz, Spain * Correspondence: [email protected] Abstract: This study aimed to investigate how different strategies of task constraint manipulation impact physical and tactical demands in small-sided and conditioned games (SSCG). Ten recreational U-17 soccer players participated in this study (16.89 ± 0.11 years). We used different strategies of task manipulation to design two 4 vs. 4 SSCG: Structural SSCG and Functional SSCG. In Structural SSCG, pitch format and goal sizes were manipulated, while in Functional SSCG, players were allowed to kick the ball twice and at least 5 passes to shoot at the opponent’s goal. Players participated in four Structural and Functional SSCG, of five minutes duration with a two-minute interval in between. Players’ physical performance and tactical behavior were assessed using the WIMU PROTM inertial device. Structural SSCG stimulated players to cover more distance in sprinting (p = 0.003) and high-speed running (p < 0.001). Regarding tactical behavior, Structural SSCG stimulated players to explore game space better (p < 0.001). Moreover, Functional SSCG stimulated players to be closer to the ball, decreasing the effective playing space (p = 0.008). We conclude that these strategies of task constraint manipulation impact physical and tactical demands of the game. Keywords: soccer; task design; rules; physical demands; tactical behavior 1. Introduction Small-sided and conditioned games (SSCG) are training tasks commonly used by coaches and trainers to provide representative practice scenarios to their players and team [1]. Therefore, several studies have highlighted the importance of SSCG to improve players’ and teams’ performance, where coaches and trainers can manipulate key task con- straints to emphasize specific training contents during the training sessions [2–7]. However, for these games to be considered representative training tasks, the coaches should main- tain the dynamic and functional relationships between crucial sources of information and players’ actions present in the competitive environment [8]. In addition, SSCG need to be carefully adjusted to players’ skill levels and the training content emphasized by coaches and trainers [9–11]. Therefore, the representative training task design needs to respect Sensors 2022, 22, 4435. https://doi.org/10.3390/s22124435 https://www.mdpi.com/journal/sensors Sensors 2022, 22, 4435 2 of 8 the adjustment of task difficulty, complexity, and intensity levels to the player’s intrinsic dynamics [9,11–13]. Previous studies reported acute effects of task manipulation (e.g., number of players, the dimension and shape of the pitch, the quantity and location of goals) on players’ and teams’ performance during SSCG [2–4]. In addition, the impact of rule constraints can be considered a determinant to achieving the physical and tactical stimulus [12,14]. As an example, Machado et al. [12] highlighted two different strategies of task constraint manipulation: (i) modification of structural elements of the game (e.g., number of players, pitch dimension, goal sizes, etc.), and (ii) rule manipulation. The authors [12] observed that these strategies have a different impact on the tactical behavior of teams, because they were composed of players of different ages and levels of tactical skills. The teams composed of younger players and players with low tactical skills were demonstrated to have more difficulty dealing with SSCG with manipulated rules. In this regard, coaches and trainers must carefully design SSCG using the strategy of rule manipulation. Moreover, Machado and Scaglia [15] highlighted that when the coaches and trainers manipulate structural elements of the game, the key sources of information that regulate players’ actions emerge from the game itself, i.e., from the positioning and movement of teammates and opponents, among others. However, when the coaches manipulate the rules, besides this game information, the players need to manage information from outside the game, which originates from the practitioner’s direct intervention (e.g., players can only kick the ball twice, etc.). Therefore, when the game rules are manipulated inappropriately (e.g., without considering the players’ skills level), the task difficulty and complexity may increase [12,15]. Considering that these different strategies of task manipulation might have other impacts on players’ and teams’ performance, it becomes important to understand the effects of using these different strategies on physical performance and the way players and teams structure the game space. Therefore, this study aimed to investigate how additional task constraint manipulation strategies impact physical and tactical demands in SSCG. 2. Materials and Methods 2.1. Participants Ten U-17 recreational soccer players (16.89 ± 0.11 years) participated in this study. The players belong to a sports participation program and train together twice a week. All the procedures in this research were in accordance with the Resolution of the National Health Council (466/2012) and the Declaration of Helsinki (2013). In addition, this study was ap- proved by the Ethics Committee in Research with Human Beings (N. 73222617.0.0000.5404). 2.2. Design We applied two SSCG specifically designed to emphasize the tactical problem of maintaining ball possession, using different strategies of task constraint manipulation: (i) modification of structural elements of the game (i.e., Structural SSCG) and (ii) modifica- tion of the game through functional elements (i.e., Functional SSCG). Both SSCG have been previously used, with an emphasis on maintaining and circulating ball possession [12]. In the Structural SSCG, we manipulated pitch shape (wider) and goal size, and lo- cation. A 4 vs. 4 game configuration was used on a pitch measuring 47.72 m × 29.54 m (width × length), with two small goals (2.5 m × 1 m) located on both wings (Figure 1). Classical soccer rules were applied, except for offside. In the Functional SSCG, the game functional elements were modified by manipulating the rules to emphasize the tactical problem of maintaining ball possession. We used a Gk + 4 vs. 4 + Gk configuration on a pitch measuring 29.54 m × 47.72 m (width × length), with two centralized 7-a-side goals (Figure 1). Sensors 2022, 22, 4435 3 of 8 The following rules were manipulated: (i) the players were allowed to kick the ball once or twice (an extra point was awarded to the opponent’s team every time players kicked the ball more than twice); (ii) teams needed to exchange at least five passes to shoot at the opponent’s goal; (iii) an extra point was awarded to the team every time the players managed to move the ball from one wing to the other, identified from demarcated areas on the field (see Figure 1). Figure 1. Research experimental design: (A) shows the order of the games applied; (B) shows the small-sided and conditioned games used in this study. Goalkeepers were not allowed to participate in offensive actions, in an attempt to maintain a similar individual playing area between the two SSCG conditions (i.e., 176.2 m2). Four SSCG were performed in each of the conditions, with four minutes duration and two minutes interval between games. The order of the games played was randomized, as shown in Figure 1. 2.3. Analysis of Players’ Physical Performance Figure 1. Research experimental design: (A) shows the order of the games applied; (B) shows the small-sided and conditioned games used in this study. The following rules were manipulated: (i) the players were allowed to kick the ball once or twice (an extra point was awarded to the opponent’s team every time players kicked the ball more than twice); (ii) teams needed to exchange at least five passes to shoot at the opponent’s goal; (iii) an extra point was awarded to the team every time the players managed to move the ball from one wing to the other, identified from demarcated areas on the field (see Figure 1). Goalkeepers were not allowed to participate in offensive actions, in an attempt to maintain a similar individual playing area between the two SSCG conditions (i.e., 176.2 m2). Four SSCG were performed in each of the conditions, with four minutes duration and two minutes interval between games. The order of the games played was randomized, as shown in Figure 1. Sensors 2022, 22, 4435 4 of 8 2.3. Analysis of Players’ Physical Performance Players’ physical performance was analyzed through positional data collected using inertial devices (WIMU ProTM, RealTrack System, Almería, Spain), which have been shown to be valid and reliable [16]. This device is composed of an accelerometer, gyroscope, magnetometer, and 10-Hz global position system (GPS—RealTrack System, Almería, Spain). Each participant wore a t-shirt provided by the manufacturer with a pocket to hold the GPS unit between the scapulae. The software SPROTM (RealTrack System, Almería, Spain) was used to extract the following variables: (i) total distance covered (meters); (ii) distance covered (m) sprinting (>18 km/h−1); (iii) distance covered (m) in high-speed running (HSR—13 km/h−1 to 18 km/h−1); (iv) high acceleration (m) (>2 m/s2); (v) high deceleration (m) (<−2 m/s2); (v) the number of actions performed at a sprint. The ranges of speed were based on a previous study [17]. 2.4. Tactical Behaviour Studies have already used these devices to analyze tactical behaviour [18,19]. The actions performed during the games were tracked in real-time at each instant. Following the matches, data were downloaded and exported to a .csv file using the same version of the appropriate software (SPROTM—RealTrack System, Almería, Spain) for further analysis in MATLAB scripts (The MathWorks Inc., Natick, MA, USA). Hence, the geographic coor- dinates were transformed to cartesian coordinates (x,y) and smoothed with a Butterworth digital filter (third-order; cut-off frequency: 0.4 Hz). The following individual and collective tactical variables were analyzed: (i) spatial exploration index (SEI) [20]; (ii) effective playing space for each team (EPS) [20]; (iii) team width and length [20]; (iv) LpW, used to determine the length-per-width ratio per team [21]; (v) stretch index [22]. The SEI indicates players’ exploratory behavior, where higher values highlight those players that were able to explore more game space [20]. EPS considers the polygonal area of players located on the periphery of play of each team [23]. Team length represents the longitudinal distance between the most distant players, while team width represents the lateral dispersion of players [24]. The stretch index considers the average distance of each player to the team centroid, indicating how much more dispersed players are on the pitch [24]. These variables represent the individual and team space organization during the games, including the way in which players occupy game spaces through their positions and movements. 2.5. Statistical Procedures Data normality distribution and homoscedasticity were verified through Shapiro– Wilk’s and Levene’s tests. To compare external load between Structural SSCG and Rules SSCG, we used a pairwise t-test. Moreover, we used both pairwise t-tests and Wilcoxon’s test to compare players’ and teams’ tactical behavior. Effect size was calculated for each pairwise comparison as follows (ES = z.√n): (i) negligible (<0.1), small (0.1–0.29), medium (0.3–0.49), and large (>0.5) [25]. We used SPSS 21.0 (Chicago, IL, USA) for statistical analysis, and the level of significance was 5% (p < 0.05). 3. Results Regarding physical performance (Table 1), we found that players covered more dis- tance at a sprint (p = 0.003) and HSR (p < 0.001), and also performed a greater number of sprints in Structural SSCG (p = 0.004). However, we did not find significant differences between game conditions (Structural and Functional SSCGs) for total distance covered (p = 0.301), high acceleration (p = 0.168), and high deceleration (p = 0.331). Sensors 2022, 22, 4435 5 of 8 Table 1. Players’ physical performance in different small-sided and conditioned game conditions. Physical Performance Structural SSCG Functional SSCG p-Value Effect Size Total distance covered (m) 501.94 (48.14) 493.95 (46.12) 0.301 0.389 (medium) Distance covered (m) at a sprint (>18 km/h−1) 30.52 (17.56) 10.36 (7.69) 0.003 1.554 (large) Distance covered (m) in high-speed running (HSR—13 km/h−1 to 18 km/h−1) 121.82 (42.81) 77.82 (36.78) <0.001 2.602 (large) High accelerations (m) (>2 m/s2) 75.76 (28.67) 69.09 (19.74) 0.168 1.476 (large) High decelerations (m) (<−2 m/s2) 61.31 (25.65) 56.63 (15.83) 0.331 1.141 (large) Number of sprints 2.19 (1.22) 0.87 (0.61) 0.004 1.464 (large) Regarding players’ tactical behavior (Table 2), we observed that Structural SSCG stimulated players to explore more game space (SEI = p < 0.001). Moreover, observing EPS (p = 0.008) and stretch index (p < 0.001) variables, it was possible to note that Functional SSCG stimulated players to be closer to each other. Through team length (p = 0.001), width (p < 0.001), and LpW ratio (p < 0.001) measures, we observed that Structural SSCG stimulated teams to better explore the width of the pitch. Table 2. Players’ and teams’ tactical behaviors in different small-sided and conditioned game conditions. Tactical Behavior Structural SSCG Functional SSCG p-Value Effect Size Spatial exploration index (SEI) 8.55 (1.45) 7.72 (1.43) <0.001 0.584 (large) Effective playing space (EPS) 85.97 (35.94) 67.06 (25.21) 0.002 0.46 (medium) Team width 21.64 (5.09) 13.56 (3.10) <0.001 1.243 (large) Team length 16.48 (4.31) 13.56 (3.18) <0.001 0.184 (small) LpW ratio 0.78 (0.15) 0.93 (0.13) <0.001 0.62 (large) Stretch index 8.50 (2.01) 6.52 (1.25) <0.001 0.761 (large) 4. Discussion This study aimed to investigate how different strategies of task constraint manipu- lation impact physical and tactical demands in SSCG. We observed that Structural SSCG stimulated players to explore game space better and stimulated teams to expand the EPS further. The rules manipulated in Functional SSCG made it difficult for the players to explore the game space, and as a result, they were able to get closer to other players. The stretch index variable also helped us to verify that players tend to get closer relative to each other in SSCG, which was designed using the strategy of rules manipulation. Praça et al. [14] designed SSCG to emphasize progression to the target and found that players presented higher exploratory behaviors. Machado et al. [10,12] found that these manipulated rules contribute to players having more difficulty exchanging passes, keeping possession of the ball, and inhibiting players’ and teams’ exploratory behavior. The greater difficulty for players to respond to the manipulated rules resulted in players moving closer to their teammates, and behaving more statically on the field. Regarding players’ physical performance, we found that Structural SSCG provoked more sprints than Functional SSCG. Moreover, players covered greater distances in sprint- ing and H.S.R. in Structural SSCG. The behavior of the prementioned tactical variables might help to understand the external load presented in these games (Structural and Functional SSCG). As players move further away from each other and as the effective playing space increases, players have more space to move in high-speed running. Other studies highlighted that when playing space increases, the distances covered by the players in different speed zones also increases [26,27]. Nunes et al. [26] observed that U-15 and U-23 players performed more sprints in SSCG with a larger playing area. Moreover, Sensors 2022, 22, 4435 6 of 8 when rules were manipulated to emphasize tactical content of progression to the target, players covered greater distances in different speed zones, especially in sprinting and high-speed running [14]. In Structural SSCG, the pitch was wider, and two small goals were located on both wings. Modifying these structural elements of the game (pitch shape, goal sizes, and location) stimulated teams to expand the playing space in width. This happens as players tend to manage game space to move the ball from one wing to another to create spaces and score a goal. Even with areas restricted on both sides and with the rule that sought to stimulate the ball circulation from one wing to the other, Functional SSCG did not stimulate players to expand the game space in width. This study aimed to raise an important discussion about the design process of SSCG in soccer, highlighting the impact of different task manipulation strategies on players’ physical performance and tactical behavior. Although it presents important information about this design process, this study has limitations that can be highlighted: a small number of players participated and it did not analyze players’ technical performance, considering whether they solved the game problems. Moreover, this study does not consider players’ initial condition, regarding their tactical skills and physical fitness. However, the results of this study are important to highlight that the exaggeration of rule manipulation negatively impacts the way players structure and move through the game space. Therefore, the design process of a representative task must consider both the strategies of task manipulation and the training content that the coach intends to emphasize. 5. Conclusions We conclude that the strategies of task manipulation used impact players’ physical performance and players’ and teams’ tactical behavior differently. Structural SSCG pro- vided a greater adequate playing space, especially in width, encouraging players to explore the pitch more. Moreover, in Structural SSCG, players performed more sprints and covered greater distances in sprinting and high-speed running speed zones. However, Functional SSCG stimulated players to get closer to their teammates. Therefore, the strategy of task modification by functional element manipulation can be used to increase game complexity level, impacting the way players and teams manage the game space. This study provides important information regarding the impact of different strategies of task manipulation, highlighting the need to carefully modify the structural elements and rules of SSCG to adjust these tasks to players’ skills level, and to the training content that coaches intend to emphasize. Author Contributions: Conceptualization, J.C.M. and R.A.; methodology, J.C.M., R.A., B.L.S.B. and A.G.; software, J.C.M. and A.G.; validation, B.L.S.B. and A.G.; formal analysis, J.C.M., R.V. and A.G.; investigation, J.C.M., R.V. and M.R.; resources, J.C.M.; data curation, J.C.M.; writing—original draft preparation, J.C.M.; writing—review and editing, J.C.M., R.A. and B.L.S.B.; visualization, A.S. and S.J.I.; supervision, S.J.I.; project administration, J.C.M.; funding acquisition, J.C.M. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Secretaria Nacional de Futebol e Defesa dos Direitos do Torcedor (SNFDT), Secretaria Nacional de Esportes do Ministério da Cidadania (grant num- ber TED No. 01/2020). This study has been partially subsidized by the Aid for Research Groups (GR21149) from the Regional Government of Extremadura (Department of Economy, Science and Digital Agenda), with a contribution from the European Union from the European Funds for Regional Development. Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki, approved by the Institutional Ethics Committee of UNICAMP (protocol code 2.250.881), and approved on 31 August 2017. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Not applicable. Sensors 2022, 22, 4435 7 of 8 Conflicts of Interest: The authors declare no conflict of interest. References 1. Davids, K.; Araújo, D.; Correia, V.; Vilar, L. How small-sided and conditioned games enhance acquisition of movement and decision-making skills. Exerc. Sport Sci. Rev. 2013, 41, 154–161. [CrossRef] [PubMed] 2. Clemente, F.M.; Sarmento, H. The effects of small-sided soccer games on technical actions and skills: A systematic review. Hum. Mov. 2020, 21, 100–119. [CrossRef] 3. Clemente, F.M.; Afonso, J.; Castillo, D.; Los Arcos, A.; Silva, A.F.; Sarmento, H. The effects of small-sided soccer games on tactical behavior and collective dynamics: A systematic review. Chaos Solitons Fractals 2020, 134, 109710. [CrossRef] 4. Sarmento, H.; Clemente, F.M.; Harper, L.D.; da Costa, I.T.; Owen, A.; Figueiredo, A.J. Small sided games in soccer–a systematic review. Int. J. Perform. Anal. Sport 2018, 18, 693–749. [CrossRef] 5. Clemente, F.M.; Ramirez-Campillo, R.; Sarmento, H.; Praça, G.M.; Afonso, J.; Silva, A.F.; Rosemann, T.; Knechtle, B. Effects of small-sided game interventions on the technical execution and tactical behaviors of young and youth team sports players: A systematic review and meta-analysis. Front. Psychol. 2021, 12, 1501. [CrossRef] 6. Clemente, F.M.; Afonso, J.; Sarmento, H. Small-sided games: An umbrella review of systematic reviews and meta-analyses. PLoS ONE 2021, 16, e0247067. [CrossRef] 7. Gómez-Carmona, C.D.; Gamonales, J.M.; Pino-Ortega, J.; Ibáñez, S.J. Comparative Analysis of Load Profile between Small-Sided Games and Official Matches in Youth. Sports 2018, 6, 173. [CrossRef] 8. Travassos, B.; Duarte, R.; Vilar, L.; Davids, K.; Araújo, D. Practice task design in team sports: Representativeness enhanced by increasing opportunities for action. J. Sports Sci. 2012, 30, 1447–1454. [CrossRef] 9. Machado, J.C.; Scaglia, A.J. Pedagogia não linear no futebol: Uma busca por estratégias pedagógicas que possam nortear o processo de criação de tarefas representativas. In O Futebol nas Ciências Humanas no Brasil; Giglio, S.S., Proni, M.W., Eds.; Editora da UNICAMP: Campinas, Brazil, 2020; pp. 570–585. 10. Machado, J.C.; Ribeiro, J.; Palheta, C.E.; Alcântara, C.; Barreira, D.; Guilherme, J.; Garganta, J.; Scaglia, A.J. Changing rules and configurations during soccer small-sided and conditioned games. How does it impact teams’ tactical behavior? Front. Psychol. 2019, 10, 1554. [CrossRef] 11. Machado, J.C.; Barreira, D.; Teoldo, I.; Travassos, B.; Júnior, J.B.; dos Santos, J.O.L.; Scaglia, A.J. How Does the Adjustment of Training Task Difficulty Level Influence Tactical Behavior in Soccer? Res. Q. Exerc. Sport 2019, 90, 403–416. [CrossRef] 12. Machado, J.C.; Barreira, D.; Teoldo, I.; Serra-Olivares, J.; Góes, A.; Scaglia, A.J. Tactical behaviour of youth soccer players: Differences depending on task constraint modification, age and skill level. J. Hum. Kinet. 2020, 75, 225. [CrossRef] [PubMed] 13. Ibáñez, S.J.; Pérez-Goye, E.; García-Rubio, J.; Courel-Ibáñez, J. Effects of task constraints on training workload in elite women’s soccer. Int. J. Sports Sci. Coach. 2020, 15, 99–107. [CrossRef] 14. Praça, G.M.; Andrade, A.G.P.; Bredt, S.d.G.T.; Moura, F.A.; Moreira, P.E.D. Progression to the target vs. regular rules in Soccer small-sided Games. Sci. Med. Footb. 2022, 6, 66–71. [CrossRef] [PubMed] 15. Machado, J.C.; Scaglia, A.J. Pedagogia do Esporte e o ensino com jogos. In Pequenos Jogos para Treinar em Grande; Prime Books: Lisbon, Portugal, 2022; pp. 161–190. 16. Bastida Castillo, A.; Gómez Carmona, C.D.; de la Cruz Sánchez, E.; Pino Ortega, J. Accuracy, intra-and inter-unit reliability, and comparison between GPS and UWB-based position-tracking systems used for time–motion analyses in soccer. Eur. J. Sport Sci. 2018, 18, 450–457. [CrossRef] 17. Castagna, C.; Impellizzeri, F.; Cecchini, E.; Rampinini, E.; Alvarez, J.C.B. Effects of intermittent-endurance fitness on match performance in young male soccer players. J. Strength Cond. Res. 2009, 23, 1954–1959. [CrossRef] 18. Clemente, F.M.; Castillo, D.; Los Arcos, A. Tactical analysis according to age-level groups during a 4 vs. 4 plus goalkeepers small-sided game. Int. J. Environ. Res. Public Health 2020, 17, 1667. [CrossRef] 19. López, L.M.G.; Olivares, J.S.; Vicedo, J.C.P.; Pérez, J.A.S.; Córdoba, C.P.; del Campo, D.G.D. Formatos Competitivos en Fútbol Sub-12. Un Análisis desde el Punto de Vista Táctico, Condicional y del Compromiso Deportivo del Joven Futbolista. Kronos Rev. Univ. de la Act. Física y el Deporte 2019, 18, 7. 20. Clemente, F.M.; Sequeiros, J.B.; Correia, A.; Silva, F.G.M.; Martins, F.M.L. Computational Metrics for Soccer Analysis: Connecting the Dots; Springer: Berlin/Heidelberg, Germany, 2018. 21. Folgado, H.; Lemmink, K.A.P.M.; Frencken, W.; Sampaio, J. Length, width and centroid distance as measures of teams tactical performance in youth football. Eur. J. Sport Sci. 2014, 14, S487–S492. [CrossRef] 22. Silva, P.; Duarte, R.; Sampaio, J.; Aguiar, P.; Davids, K.; Araújo, D.; Garganta, J. Field Dimension and Skill Level Constrain Team Tactical Behaviours in Small-Sided and Conditioned Games in Football. J. Sports Sci. 2014, 32, 1888–1896. [CrossRef] 23. Low, B.; Coutinho, D.; Gonçalves, B.; Rein, R.; Memmert, D.; Sampaio, J. A systematic review of collective tactical behaviours in football using positional data. Sports Med. 2020, 50, 343–385. [CrossRef] 24. Coito, N.; Davids, K.; Folgado, H.; Bento, T.; Travassos, B. Capturing and Quantifying Tactical Behaviors in Small-Sided and Conditioned Games in Soccer: A Systematic Review. Res. Q. Exerc. Sport 2022, 93, 189–203. [CrossRef] [PubMed] 25. Fritz, C.O.; Morris, P.E.; Richler, J.J. Effect size estimates: Current use, calculations, and interpretation. J. Exp. Psychol. Gen. 2012, 141, 2. [CrossRef] [PubMed] Sensors 2022, 22, 4435 8 of 8 26. Nunes, N.A.; Gonçalves, B.; Davids, K.; Esteves, P.; Travassos, B. How manipulation of playing area dimensions in ball possession games constrains physical effort and technical actions in under-11, under-15 and under-23 soccer players. Res. Sports Med. 2021, 29, 170–184. [CrossRef] [PubMed] 27. Casamichana, D.; Castellano, J. Time–motion, heart rate, perceptual and motor behaviour demands in small-sides soccer games: Effects of pitch size. J. Sports Sci. 2010, 28, 1615–1623. [CrossRef]

Applying Different Strategies of Task Constraint Manipulation in Small-Sided and Conditioned Games: How Do They Impact Physical and Tactical Demands?

06-11-2022

Machado, João Cláudio,Góes, Alberto,Aquino, Rodrigo,Bedo, Bruno L S,Viana, Ronélia,Rossato, Mateus,Scaglia, Alcides,Ibáñez, Sérgio J

eng

PMC7862540

Vol.:(0123456789) 1 3 European Journal of Applied Physiology (2021) 121:425–434 https://doi.org/10.1007/s00421-020-04535-x ORIGINAL ARTICLE The acute physiological and perceptual effects of recovery interval intensity during cycling‑based high‑intensity interval training Christopher R. J. Fennell1 · James G. Hopker1 Received: 4 May 2020 / Accepted: 13 October 2020 / Published online: 23 October 2020 © The Author(s) 2020 Abstract Purpose The current study sought to investigate the role of recovery intensity on the physiological and perceptual responses during cycling-based aerobic high-intensity interval training. Methods Fourteen well-trained cyclists ( ̇VO2peak : 62 ± 9 mL kg−1 min−1) completed seven laboratory visits. At visit 1, the participants’ peak oxygen consumption ( ̇VO2peak ) and lactate thresholds were determined. At visits 2–7, participants com- pleted either a 6 × 4 min or 3 × 8 min high-intensity interval training (HIIT) protocol with one of three recovery intensity prescriptions: passive (PA) recovery, active recovery at 80% of lactate threshold (80A) or active recovery at 110% of lactate threshold (110A). Results The time spent at > 80%, > 90% and > 95% of maximal minute power during the work intervals was significantly increased with PA recovery, when compared to both 80A and 110A, during both HIIT protocols (all P ≤ 0.001). However, recovery intensity had no effect on the time spent at > 90% ̇VO2peak (P = 0.11) or > 95% ̇VO2peak (P = 0.50) during the work intervals of both HIIT protocols. Session RPE was significantly higher following the 110A recovery, when compared to the PA and 80A recovery during both HIIT protocols (P < 0.001). Conclusion Passive recovery facilitates a higher work interval PO and similar internal stress for a lower sRPE when compared to active recovery and therefore may be the efficacious recovery intensity prescription. Keywords Recovery components · Recovery interval intensity · High-intensity interval training · Near-infrared spectroscopy Abbreviations ANOVA Analysis of variance ACT Active AIT Aerobic interval training B[La] Blood lactate concentration HR Heart rate HRmax Maximal minute heart rate HIIT High-intensity interval training HHb Deoxyhaemoglobin LT Lactate threshold NIRS Near-infrared spectroscopy O2Hb Oxyhaemoglobin O2 Oxygen PA Passive PO Power output RPE Rating of perceived exertion sRPE Session RPE TSI% Tissue saturation index VL Vastus lateralis muscle ̇VO2 Pulmonary oxygen uptake ̇VO2peak Peak oxygen consumption ̇VO2max Maximal oxygen consumption MMP Maximal minute power 80A 80% Power output at lactate threshold 110A 110% Power output at lactate threshold Introduction High-intensity interval training (HIIT) is an intermittent mode of endurance training, characterised by short high- intensity work intervals (4 s to ≥ 10 min). Its discontinuous Communicated by Jean-René Lacour. * James G. Hopker [email protected] 1 School of Sport and Exercise Sciences, University of Kent at Medway, Medway Building, Kent, Chatham ME4 4AG, England, UK 426 European Journal of Applied Physiology (2021) 121:425–434 1 3 nature, by design, allows for the accumulation of a greater amount of time exercising in the ‘red zone’ (i.e. above criti- cal power, the lactate steady state or ≥ 90% of maximal oxy- gen consumption [ ̇VO2max ]; Buchheit and Laursen 2013), than could be tolerated during a single bout of continu- ous intensity exercise (MacDougall and Sale 1981). This is important because there is strong evidence that the per- formance of exercise at higher intensities elicits a greater activation of signalling pathways, associated with specific molecular responses which lead to an enhancement of the adaptive phenotype (Coffey and Hawley 2007). The perfor- mance benefits of HIIT alone are particularly powerful in untrained and recreationally active individuals (Milanovic et al. 2016), whilst highly trained athletes can also further enhance endurance performance by undertaking relatively short periods of HIIT (Hawley et al. 1997; Iaia and Bangsbo 2010; Laursen 2010). The multivariate equation of HIIT programming contains five main components: work interval intensity, work interval duration, number of work intervals, recovery interval inten- sity and recovery interval duration (Tschakert and Hofmann 2013). Researchers have sought to optimise HIIT protocols, placing particular focus on the work interval components as this is where the training stimulus is primarily gener- ated (Buchheit and Laursen 2013; Tschakert and Hofmann 2013). Nevertheless, optimal work interval performance (accumulating time at effective training intensities i.e. ≥ 90% ̇VO2max ), can only be achieved if separated by a correctly programmed recovery interval (Schoenmakers et al. 2019). Therefore, understanding the effects of altering the recov- ery interval components on subsequent work interval per- formance is key when looking to programme an effective HIIT session. There has been a sizeable amount of research focusing specifically on understanding the acute effects of recovery interval intensity during cycling-based aerobic interval train- ing (AIT; long work intervals ≥ 1 min; Barbosa et al. 2016; Coso et al. 2010; Dorado et al. 2004; Monedero and Donne 2000; McAinch et al. 2004; Siegler et al. 2006; Stanley and Buchheit 2014). Researchers investigating recovery inten- sity during cycling-based AIT have tended to use time to exhaustion work intervals (Barbosa et al. 2016; Siegler et al. 2006; Dorado et al. 2004) and fixed intensity work intervals (Stanley and Buchheit 2014; Coso et al. 2010). Whilst only two have utilised self-paced fixed duration work interval prescriptions (McAinch et al. 2004; Monedero and Donne 2000), which have been suggested to be an athlete’s typical approach to HIIT training (Seiler et al. 2011). McAinch et al. (2004), required participants to complete 2 × 20-min self- paced maximal effort work intervals (i.e. isoeffort) separated by a 15-min passive (PA) recovery or active (ACT) recovery at 40% of ̇VO2peak . They found no difference in work per- formed during intervals between the ACT and PA protocols. Monedero and Donne (2000) used 2 × 5-km self-paced maxi- mal effort work intervals separated by either a 20-min PA recovery, a massage, ACT recovery at 50% of ̇VO2max , or a combined ACT recovery/massage. The combined recovery condition was found to be the most effective for maintenance of 5-km performance time. Both studies provide informative insights into the effect of recovery intensity on the perfor- mance of high-intensity AIT. However, further research uti- lising different HIIT protocol designs and recovery intensi- ties is required in order to broaden the understanding of the role of recovery interval intensity on the acute responses to self-paced AIT. The current study therefore sought to inves- tigate the role of recovery intensity on the physiological and perceptual responses during cycling-based AIT. Methods Participants Fourteen trained cyclists participated in the study. All par- ticipants had a minimum of 2 years competitive racing experience and were in training for the next competitive season. According to De Pauw et al. (2013), participants were classified as follows: nine were performance level 3 (trained), four were performance level 4 (highly trained) and one was performance level 5 (professional). The study was completed with full ethical approval, according to the Declaration of Helsinki standards. All participants provided signed informed consent prior to testing, Study design Each participant completed seven visits to the laboratory. Visit 1 being incremental exercise tests to identify the lactate threshold (LT), ̇VO2max and to familiarise the participants with the laboratory environment and equipment. In visits 2–7, participants performed six HIIT sessions in a ran- domised order (using simple randomisation; Roberts and Torgerson 1998) using different recovery intensities: PA, ACT at 80% of power output (PO) at the LT (80A) and ACT at 110% of PO at the LT (110A). The 80A and 110A recov- ery intensities were selected to straddle the LT and intended to provide differing levels of recovery. The 4-min and 8-min work durations were selected having previously been used in HIIT research to bring about training adaptation (Stepto et al. 1999; Seiler et al. 2011). Visits were conducted on non-concurrent days and par- ticipants were instructed to refrain from any exercise in the day prior to testing and intense exercise in 2 days prior. Participants were instructed to arrive euhydrated and in a post-prandial state, having eaten at least 4-h prior to test- ing. Participants were told to not consume caffeine within 427 European Journal of Applied Physiology (2021) 121:425–434 1 3 4-h and alcohol within 24-h of testing. Each participant completed all their visits to the laboratory at the same time of day to avoid any circadian variance. An electric fan was placed 2 m in front of the participants to provide cooling during all tests. Participants used their own bike at all visits, affixed to a Cyclus2 ergometer (PO ± 2% maximal error; Rodger et al. 2016) calibrated to the manufacturer’s instructions (Leip- zig, Germany). At all visits respiratory gas exchange data were assessed using breath by breath gas analysis (Meta- lyzer 3B; CORTEX Biophysik GmbH, Leipzig, Germany). Prior to all testing, the analyser was calibrated according to the manufacturer recommendations. Heart rate (HR) was assessed at all visits using Garmin HR monitors (Garmin, Kansas, USA). Preliminary testing Participants were measured for anthropometric values: height and mass. Prior to starting the LT test resting blood lactate (B[La]) samples were taken. The participants then completed a 10-min warm-up at 50 W followed by an incre- mental exercise test during which PO was initially set at 80 W for 4 min, and then increased by 20 W every 4 min. The 4-min increments continued until B[La] > 4 mmol L−1. Participants completed a cool down for 10 min at 50 W, after which they completed seated rest for 10 min, before com- mencing the ̇VO2max test protocol. During the LT test B[La], samples were collected using fingertip capillary blood 30 s before the end of each stage. Blood samples were analysed using a Biosen C-Line (EKF Diagnostic, London, UK). PO and HR were continuously measured throughout the test, and rating of perceived exer- tion (RPE) measurements were asked at the end of each stage using the Borg 6 to 20-point scale (Borg 1982). The first LT was assessed as the point at which B[La] breaks from linearity (Yoshida et al. 1987). The lactate turnpoint (LTP) was assessed as the second break point after which B[La] begins to rise above 4 mmol L−1 (Faude et al. 2009). The ̇VO2max test protocol started with a 10-min warm-up at 100 W, after which the required cycling PO was increased by 20 W every 1 min until the participant reached volitional exhaustion (operationally defined as a cadence of < 60 revo- lutions/min for > 5 s, despite strong verbal encouragement). PO and HR were measured continuously throughout the test, with RPE measurements taken in the last 10 s of each 1-min stage of the test (Borg 1982). The participant’s ̇VO2peak was assessed as the highest pulmonary oxygen consumption ( ̇VO2 ) that was attained during a 1-min period in the test. Maximal minute power (MMP) and maximal minute heart rate (HRmax) were assessed as the highest mean 1-min PO and HR achieved during the test. HIIT sessions Participants completed both the 6 × 4-min and 3 × 8-min HIIT sessions three times (6 HIIT sessions in total), once with each of the three recovery interval intensities: PA, 80A and 110A. The ACT recovery intensities were calculated as 80% and 110% of the participants PO at the LT (Table 1). During the PA recovery intensity, HIIT session participants were instructed to remain seated with their right leg at the bottom of the pedal stroke. All HIIT sessions had an equal work duration of 24 min. Work intervals were prescribed as self-paced on a ‘maximal session effort’ basis, with participants instructed to achieve the highest PO possible during each interval. Participants were only shown time elapsed during the HIIT sessions. Consistent verbal encouragement was given throughout every session. HIIT sessions commenced with a 10-min warm-up at 100 W and finished with a 10-min cool down at 100 W. Recovery interval durations were a standardised 2:1 work:recovery ratio (2 min and 4 min for the 6 × 4-min and 3 × 8-min HIIT sessions, respectively). PO, HR, near-infrared spectroscopy (NIRS) and respira- tory gas data were measured continuously throughout the HIIT sessions. B[La] was measured via a fingertip capillary blood sample and analysed as outlined above. Samples were taken prior to the warm-up and during the last 30 s of each work interval. RPE measurements were taken during the last 15 s of each work interval (Borg 1982). Session RPE (sRPE) Table 1 Participants characteristics and preliminary test results (mean ± SD) PO power output, LT lactate threshold, LTP lactate turnpoint, VL vastus lateralis muscle, ̇VO2peak maximal oxygen consumption, MMP maximal minute power, HRmax maximal minute heart rate Age (years) 33 ± 13 Height (cm) 176.6 ± 5.9 Mass (kg) 70.6 ± 8.1 VL skin fold (mm) 9.5 ± 2.7 ̇VO2peak (L min−1) 4.3 ± 0.6 Relative ̇VO2peak (mL kg min−1) 62 ± 9 MMP (W) 370 ± 56 Relative MMP (W kg−1) 5.2 ± 0.8 HRmax (bpm) 187 ± 11 PO at LT (W) 205 ± 44 PO at LTP (W) 273 ± 48 RPE at LT (6–20) 11 ± 1 RPE at LTP (6–20) 15 ± 1 80A recovery intensity (W) 164 ± 35 110A recovery intensity (W) 225 ± 48 Years training 6.8 ± 6 Years competing 6.3 ± 5.4 Mean weekly training hours 9.1 ± 2.9 428 European Journal of Applied Physiology (2021) 121:425–434 1 3 measurements were taken using a 0 to 10-point scale at the end of the 10-min cool down (Foster et al. 2001). NIRS data were acquisitioned at 10 Hz from the right vastus lateralis muscle (VL; 8 cm from the knee joint on the vertical axis) using a portable continuous-wave NIRS device (Portamon, Artinis Medical Systems, The Nether- lands), which simultaneously uses the Beer-Lambert and spatially resolved spectroscopy method. Changes in tissue oxyhaemoglobin (O2Hb) and deoxyhaemoglobin (HHb) were measured using the differences in absorption charac- teristics at three wavelengths 770, 850 and 905 nm (corre- sponding to the absorption wavelengths of O2Hb and HHb). An ischemic calibration procedure was performed before each session to scale the NIRS O2Hb and HHb signals to the maximal physiological range, as previously described by Ryan et al. (2013). Skinfold thickness at the site of applica- tion of the NIRS optode was determined before each HIIT sessions using Harpenden skinfold callipers (British indica- tors Ltd, Burgess Hill, UK). Data analyses Time above percentages of MMP, HRmax and ̇VO2peak dur- ing the work intervals was calculated by summing all raw PO, HR and ̇VO2 measures over the established cut off. Raw PO, HR and ̇VO2 data were averaged over each work and recovery interval. The Δ O2Hb and Δ tissue saturation index (TSI%) were calculated as the change from the last 30-s average of the work interval to the last 30-s average of the recovery interval. Statistical analyses Data were presented as individual values or mean ± SD (unless specified otherwise). Statistical analyses were con- ducted using IBM SPSS Statistics 26 (IBM, Armonk, New York, USA). Visual inspection of Q–Q plots and Shap- iro–Wilk statistics were used to check whether data were normally distributed. Three separate two-way repeated measures ANOVA, (1) two HIIT protocols (6 × 4 min vs 3 × 8 min) × three recovery intensities (PA, 80A and 110A); (2) three recovery intensities (PA, 80A and 110A) × num- ber of work intervals; (3) three recovery intensities (PA, 80A and 110A) × number of recovery intervals were used to determine between and within condition effects for all dependent variables. Bonferroni post hoc comparisons were used when a main effect or interaction was significant. Par- tial eta squared (ηp 2) was computed as effect size estimates and were defined as small (ηp 2 = 0.01), medium (ηp 2 = 0.06) and large (ηp 2 = 0.14; Lakens 2013). The significance level was set at P < 0.05 in all cases. Results Participants’ characteristics/anthropometrics are presented in Table 1. The PA recovery protocol resulted in a longer time spent at > 80% MMP (P ≤ 0.001; ηp 2 = 0.54), > 90% MMP (P ≤ 0.001; ηp 2 = 0.62) and > 95% MMP (P ≤ 0.001; ηp 2 = 0.49) during the work intervals, when compared to the 80A and 110A recovery protocols of the 6 × 4-min and 3 × 8-min HIIT sessions. Despite the differences in time spent at high percentages of MMP, there was no effect of recovery intensity on the time spent at > 80% ̇VO2peak (P = 0.10; ηp 2 = 0.15), > 90% ̇VO2peak (P = 0.11; ηp 2 = 0.16) and > 95% ̇VO2peak (P = 0.50; ηp 2 = 0.05) during the work intervals of the 6 × 4-min and 3 × 8-min HIIT sessions (Table 2). There was no effect of recovery intensity on the time spent at > 90% HRmax during the work intervals of the 6 × 4-min HIIT session (P = 0.07; ηp 2 = 0.42). The PA recovery proto- col did increase the time spent at > 95% HRmax (P ≤ 0.001; ηp 2 = 0.53) during the work intervals, when compared to the 80A and 110A recovery protocols of the 6 × 4-min HIIT session. The PA recovery protocol increased the time spent at > 90% HRmax (P = 0.012; ηp 2 = 0.52) during the work inter- vals of the 3 × 8-min HIIT session, when compared to the Table 2 Time spent in seconds above percentages of ̇VO2peak , HRmax and MMP during the work intervals ̇VO2peak peak oxygen consumption, HRmax maximal minute heart rate, MMP maximal minute power, Ω significant difference between PA and 110A, β significant difference between PA and 80A, α significant difference between 80 and 110A Prescription Time at % ̇VO2peak Time at %HRmax Time at %MMP 80 90 95 80 90 95 80 90 95 PA 6 × 4 1168 ± 141 806 ± 266 516 ± 263 1265 ± 63 954 ± 145 591 ± 221 Ωβ 940 ± 386 Ωβ 89 ± 76 Ωβ 52 ± 50 Ωβ 80A 6 × 4 1034 ± 358 669 ± 392 444 ± 328 1272 ± 96 734 ± 267 254 ± 251 625 ± 506 19 ± 28 15 ± 25 110A 6 × 4 1161 ± 372 749 ± 417 523 ± 384 1327 ± 99 902 ± 165 333 ± 236 465 ± 470 26 ± 32 15 ± 23 PA 3 × 8 1217 ± 131 841 ± 321 499 ± 301 1313 ± 59 962 ± 218 β 539 ± 268 654 ± 372 Ωβ 48 ± 39 Ωβ 27 ± 29 Ωβ 80A 3 × 8 1116 ± 334 686 ± 320 383 ± 274 1301 ± 84 817 ± 299 363 ± 288 362 ± 362 19 ± 28 14 ± 24 110A 3 × 8 1101 ± 323 640 ± 373 377 ± 332 1337 ± 54 887 ± 215 350 ± 220 209 ± 215 17 ± 25 10 ± 14 429 European Journal of Applied Physiology (2021) 121:425–434 1 3 80A recovery protocol (P = 0.12) but not the 110A recovery protocol (P > 0.05). There was no effect of recovery intensity on the time spent at > 95% HRmax during the work intervals of the 3 × 8-min HIIT session (P = 0.10; ηp 2 = 0.32; Table 2). Recovery intensity had an effect on perceptual responses with participants reporting a higher sRPE during the 110A recovery protocol, when compared to the PA and 80A recov- ery protocols of the 6 × 4-min HIIT session (PA, 8.3 ± 0.7 vs 80A, 8.7 ± 0.6 vs 110A, 9.1 ± 0.5 [95% CL: PA, 7.9–8.6 vs 80A, 8.3–9.0 vs 110A, 8.8–9.4]; P ≤ 0.001; ηp 2 = 0.81) and the 3 × 8-min HIIT session (PA, 8.6 ± 0.7 vs 80A, 8.5 ± 0.6 vs 110A, 9.1 ± 0.5 [95% CL: PA, 8.2–9.0 vs 80A, 8.1–8.8 vs 110A, 8.8–9.4]; P ≤ 0.001; ηp 2 = 0.79). Statistics and effect size estimations from the ANOVA for each work interval variable are shown in Table 3. There were interactions found between recovery intensity and work interval for PO (3 × 8; Fig. 1b), HR (Fig. 1c, d) and ̇VO2 (Fig. 1e, f). No interactions between recovery intensity and work intervals were found for PO (6 × 4; Fig. 1a), B[La] (Fig. 1g, h) and RPE (Fig. 1i, j). There was a main effect of recovery intensity for PO and B[La] (6 × 4), but not for ̇VO2 , HR, B[La] (3 × 8) and RPE. There was a main effect of work interval number found for PO (6 × 4), HR, ̇VO2 , B[La] and RPE, but not for PO (3 × 8). A main effect of session type was only found for PO. Higher work interval PO was pro- duced during the 6 × 4-min HIIT sessions, when compared to the 3 × 8-min HIIT sessions. Recovery intensity had an effect on the physiologi- cal response of the recovery intervals. Both ACT recov- ery protocols produced significantly higher mean recov- ery interval HR (6 × 4-min: PA, 145 ± 8 vs 80A, 157 ± 11 vs 110A, 164 ± 9 bpm; 3 × 8-min: PA, 128 ± 10 vs 80A, 148 ± 11 vs 110A, 161 ± 12 bpm; P ≤ 0.001; ηp 2 = 0.89) and ̇VO2 (6 × 4-min: PA, 1.9 ± 0.3 vs 80A, 3.4 ± 0.9 vs 110A, 3.8 ± 0.8 L min−1; 3 × 8-min: PA, 1.4 ± 0.2 vs 80A, 3.0 ± 0.6 vs 110A, 3.5 ± 0.7 L min−1; P ≤ 0.001; ηp 2 = 0.91) when compared to the PA protocol, during both HIIT sessions. Percentage HHb was significantly higher at the end of the recovery intervals during the 80A and 110A recovery protocols, when compared to the PA recovery protocols dur- ing both HIIT sessions (P ≤ 0.001; ηp 2 = 0.95; Fig. 2a, b). There was a greater change in percentage O2Hb during the PA recovery intervals, when compared to the 80A and 110A recovery intervals during both HIIT sessions (P ≤ 0.001; ηp 2 = 0.95; Fig. 2c, d). There was a greater change in TSI % during the PA recovery intervals, when compared to the 80A and 110A recovery intervals during both HIIT sessions (P ≤ 0.001; ηp 2 = 0.91; Fig. 2e, f). Discussion The main finding of the study was the prescription of ACT recovery intervals significantly impairs work interval per- formance. Specifically, mean work interval PO (Fig. 1a, b) and time spent > 80%, > 90% and 95% of MMP (Table 2) were significantly higher during the PA recovery protocols, when compared to both ACT recovery protocols. Work interval POs were significantly higher during the 6 × 4-min HIIT protocols compared to the 3 × 8-min HIIT protocols; however, the manipulation of recovery intensity resulted in similar physiological and perceptual responses during the work intervals of both HIIT protocol designs (Table 3). Table 3 Statistics and effect- size estimations from analysis of variance for each work interval variable analysed PO power output, HR heart rate, ̇VO2 oxygen consumption, B[La] blood lactate concentration, RPE rating of perceived exertion *Statistical significance. Effect sizes defined as small (ηp 2 = 0.01), medium (ηp 2 = 0.06), and large (ηp 2 = 0.14) Variable Prescription Interaction (inten- sity × interval) Main effect of recovery intensity Main effect of work interval number Main effect of ses- sion type (6 × 4 vs 3 × 8) P ηp 2 P ηp 2 P ηp 2 P ηp 2 PO 6 × 4 0.11 0.11 0.001* 0.44 0.001* 0.26 < 0.001* 0.68 3 × 8 0.04* 0.17 0.021* 0.26 0.69 0.03 HR 6 × 4 < 0.001* 0.43 0.09 0.19 < 0.001* 0.89 0.21 0.14 3 × 8 < 0.001* 0.50 0.10 0.17 < 0.001* 0.83 ̇VO2 6 × 4 < 0.001* 0.32 0.06 0.20 < 0.001* 0.72 0.84 < 0.01 3 × 8 0.006* 0.24 0.52 0.05 < 0.001* 0.74 B[La] 6 × 4 0.08 0.12 < 0.001* 0.49 < 0.001* 0.59 0.26 0.10 3 × 8 0.10 0.15 0.06 0.22 < 0.001* 0.53 RPE 6 × 4 0.06 0.12 0.09 0.17 < 0.001* 0.87 0.24 0.11 3 × 8 0.19 0.11 0.02 0.26 < 0.001* 0.86 430 European Journal of Applied Physiology (2021) 121:425–434 1 3 431 European Journal of Applied Physiology (2021) 121:425–434 1 3 The ACT recovery intervals increased the oxygen (O2) demand at the exercising muscle, as shown by the greater deoxygenation of the VL (Fig. 2a, b). O2Hb and TSI% were therefore unable to recover to the same extent by the end of the recovery interval, in comparison to the PA protocols (Fig. 2c–f). The increased deoxygenation of the VL mus- cle (an important locomotor muscle during cycling perfor- mance) would potentially impair key recovery processes, such as adenosine triphosphate and phosphocreatine resyn- thesis, and muscle lactate clearance which require the avail- ability of O2 (Spencer et al. 2006). Moreover, insufficient O2 availability (i.e. local hypoxia) has been suggested to affect muscular performance and exaggerate the rate of develop- ment of both central and peripheral fatigue (Amann and Calbet 2008). The more complete recovery provided by the PA protocols may explain the participant’s ability to sustain higher work interval POs, compared to the ACT recovery protocols. Buchheit et al. (2009), Kriel et al. (2016) and Ohya et al. (2013) support the findings of the current study by showing the increased deoxygenation of the VL muscle during ACT recovery lead to a reduction in work interval performance. Time spent at high percentages of ̇VO2peak (≥ 90% and 95%) is often used to quantify the effectiveness of a HIIT protocol (Thevenet et al. 2007; Buchheit and Laursen 2013). When exercising close to ̇VO2peak , the O2 delivery and utilisation systems are maximally stressed, which has been suggested to be an effective stimulus for improving ̇VO2max and endurance performance (Buchheit and Laursen 2013; Midgley et al. 2006). In the current study, recovery intensity had no effect on the duration participants spent at > 90% and > 95% of ̇VO2peak during both HIIT sessions (Table 2), despite the PA recovery protocols significantly reducing ̇VO2 at the start of subsequent work intervals. It has been suggested that commencing work intervals from a lower metabolic rate, as observed in the PA protocols, results in a higher ̇VO2 amplitude and reduces the time to reach a ̇VO2 plateau during subsequent work intervals (Schoenmakers and Reed 2018). In addition, the speed of ̇VO2 response has been shown to be increased at higher work rates (Hill et al. 2002). Thus, the higher work inter- val POs and increased time spent > 90% and > 95% of MMP during the PA recovery protocols would have likely provided a more potent driver for ̇VO2 , in comparison to the significantly lower work intensity of the ACT recovery protocols. The combination of the aforementioned factors provides a likely explanation for the similar times spent at high percentages of ̇VO2peak between PA and ACT recovery protocols. Monitoring HR during training is commonplace for coaches and athletes, whilst HR is not directly related to muscular energy turnover or systemic O2 demand (Buchheit et al. 2012; Wu et al. 2005), accumulated time at > 90% HRmax and > 95% HRmax has been used to quan- tify adaptive effects (Seiler et al. 2011). In the present study, PA recovery lowered mean work and recovery interval HR, yet increased the time spent > 90% HRmax by 52–220 s and > 95% HRmax by 176–337 s, when compared to both ACT recovery protocols (Table 2). Aligned to the ̇VO2 data, it can be inferred that PA recovery results in a faster mean response time and a higher amplitude of ̇VO2 and HR during subsequent work intervals, when com- pared to ACT recovery (performed at ≥ 80% PO at LT). It is improbable that the increase in time > 90% HRmax and > 95% HRmax would elicit a greater adaptive stimu- lus. Nevertheless, our findings indicate that maintaining an elevated ̇VO2 and HR during recovery is not necessary for reaching high fractions of ̇VO2peak and HRmax during subsequent work intervals. Low-intensity ACT recovery between work intervals has been shown to be more effective in the removal of B[La] than PA recovery (Bogdanis et al. 1996; Coso et al. 2010; Siegler et al. 2006; Mandroukas et al. 2011). Current data show the ACT recovery protocols resulted in lower B[La] values when compared to the PA recovery proto- cols, although only significant during the 6 × 4-min HIIT session (Fig. 1g, h). This is unlikely the result of the ACT recovery intervals facilitating a greater removal of B[La] when compared to the PA recovery intervals. As B[La] measurements were taken at the end of the work intervals, it is possible that the lower B[La] values were simply due to the lower work interval intensity of the ACT protocols (Fig. 1a, b). In accordance with evidence showing B[La] does not inhibit exercise performance (Hall et al. 2016), the higher B[La] values attained during the PA protocols did not affect subsequent work interval PO. These data support the prescription of PA recovery for increasing the metabolic stress during HIIT sessions, without affecting work interval performance. Whilst research should be used to guide HIIT design, coaches and athletes are advised to be cautious when extrapolating the findings beyond the scope of the HIIT protocols used. There was a clear linear increase in work interval RPE throughout all HIIT sessions, with reported RPE values reaching ≥ 18 (very hard) at the last work interval (Fig. 1i, Fig. 1 a, b Mean PO, c, d mean HR, e, f mean ̇VO2 , g, h B[La], i, j RPE. Data are displayed per work interval as mean ± SD for the 6 × 4-min and 3 × 8-min HIIT sessions with PA recovery intensity (closed triangles), 80A recovery intensity (open circles) and 110A recovery intensity (closed circles). φ significant difference from inter- val 1, T significant difference from previous interval, Ω significant difference between PA and 110A, β significant difference between PA and 80A, α significant difference between 80A and 110A, χ main effect of recovery intensity (all P < 0.01), $ main effect of work inter- val number (all P < 0.01). *P < 0.05; **P < 0.001 ◂ 432 European Journal of Applied Physiology (2021) 121:425–434 1 3 j). The upward drift in physiological stress throughout the HIIT sessions provides an explanation for the increase in RPE, whilst it is also highly likely that biomechanical and psychological processes also effected the participant’s RPE (Marcora et al. 2009; Ulmer 1996). The higher RPE values reported during the PA protocols maybe linked to Fig. 2 a Percentage HHb during the last 30 s of the recovery intervals during the 6 × 4-min HIIT sessions, b percentage HHb during the last 30 s of the recovery intervals during the 3 × 8-min HIIT sessions, c Δ O2Hb during the recovery intervals of the 6 × 4-min HIIT sessions, d Δ O2Hb during the recovery intervals of the 3 × 8-min HIIT ses- sions, e Δ TSI% during the recovery intervals of the 6 × 4-min HIIT sessions, f Δ TSI% during the recovery intervals of the 3 × 8-min HIIT sessions. PA recovery intensity (closed triangles), 80A recov- ery intensity (open circles) and 110A recovery intensity (closed cir- cles). Values are mean ± SD. φ significant difference from interval 1, T significant difference from previous interval, Ω significant dif- ference between PA and 110A, β significant difference between PA and 80A, α significant difference between 80A and 110A. *P < 0.05; **P < 0.001 433 European Journal of Applied Physiology (2021) 121:425–434 1 3 the higher work interval POs (Fig. 1a, b) and percentages of MMP (Table 2) achieved during the PA protocols in comparison to the ACT protocols. Despite within session RPE being higher during the PA protocols, participants reported significantly higher sRPE values at the end of the 110A recovery protocol when compared to the 80A and PA recovery protocols during both HIIT sessions. This finding is of particular interest from an applied perspective when programming HIIT. A HIIT protocol design which reduces an individual’s sRPE without negatively affect- ing the physiological and metabolic load would likely be seen as a favourable session prescription by both athlete and coach. Conclusion ACT recovery at 80% and 110% of the LT significantly impairs performance PO but has a limited effect on the physiological stress of the work intervals during two closely matched HIIT designs, when compared to PA recovery. Based on current evidence, PA recovery between long ‘aerobic’ work intervals facilitates a higher external training load whilst maintaining a similar internal stress for a lower sRPE and therefore may be the efficacious recovery intensity prescription. Acknowledgements Not applicable. Author contributions CF and JH designed the research. CF conducted the experiments, data collection and data analysis. CF and JH wrote the manuscript. All authors read and approved the manuscript. Funding Not applicable. Data availability Data transparency. Code availability Data analysis software application used (SPSS) openly available. Compliance with ethical standards Conflict of interest Not applicable. Ethical approval The study was completed with full ethical approval, according to the Declaration of Helsinki standards. Consent to participate All participants provided signed informed con- sent prior to testing, Consent to publication All participants consented to having research findings published. All authors consented to publication of manuscript. Open Access This article is licensed under a Creative Commons Attri- bution 4.0 International License, which permits use, sharing, adapta- tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/. References Amann M, Calbet JA (2008) Convective oxygen transport and fatigue. J Appl Physiol 104:861–870. https ://doi.org/10.1152/jappl physi ol.01008 .2007 Barbosa LF, Denadai BS, Greco CC (2016) Endurance performance during severe-intensity intermittent cycling: effect of exercise duration and recovery type. Front Physiol 7:602. https ://doi. org/10.3389/fphys .2016.00602 Bogdanis GC, Nevill ME, Lakomy HKA, Graham CM, Louis G (1996) Effects of active recovery on power output during repeated sprint cycling. Eur J Appl Physiol 74:461–469. https ://doi.org/10.1007/ bf023 37727 Borg GA (1982) Psychophysical bases of perceived exertion. Med Sci Sports Exerc 14:377–381 Buchheit M, Laursen PB (2013) High-intensity interval training, solutions to the programming puzzle: part 1: cardiopulmonary emphasis. Sports Med 43:313–338. https ://doi.org/10.1007/s4027 9-013-0029-x Buchheit M, Cormie P, Abbiss CR, Ahmaidi S, Nosaka KK, Laursen PB (2009) Muscle deoxygenation during repeated sprint running: effect of active vs. passive recovery. Int J Sports Med 30:418–425. https ://doi.org/10.1055/s-0028-11059 33 Buchheit M, Abbiss CR, Peiffer JJ, Laursen PB (2012) Performance and physiological responses during a sprint interval training ses- sion: relationships with muscle oxygenation and pulmonary oxy- gen uptake kinetics. Eur J Appl Physiol 112:767–779. https ://doi. org/10.1007/s0042 1-011-2021-1 Coffey VG, Hawley JA (2007) The molecular bases of training adap- tation. Sports Med 37:737–763. https ://doi.org/10.2165/00007 256-20073 7090-00001 Coso JD, Hamouti N, Aguado-Jimenez R, Mora-Rodriguez R (2010) Restoration of blood pH between repeated bouts of high-intensity exercise: effects of various active-recovery protocols. Eur J Appl Physiol 108:523–532. https ://doi.org/10.1007/s0042 1-009-1248-6 De Pauw K, Roelands B, Cheung SS, de Geus B, Rietjens G, Meeusen R (2013) Guideline to classify subject groups in sport-science research. Int J Sports Physiol Perform 8:111–122. https ://doi. org/10.1123/ijspp .8.2.111 Dorado C, Sanchis-Moysi J, Calbet JAL (2004) Effects of recovery mode on performance, O2 uptake, and O2 deficit during high- intensity intermittent exercise. Can J Appl Physiol 29:227–244. https ://doi.org/10.1139/h04-016 Faude O, Kindermann W, Meyer T (2009) Lactate threshold con- cepts: how valid are they? Sports Med 39:469–490. https ://doi. org/10.2165/00007 256-20093 9060-00003 Foster C, Florhaug JA, Franklin J, Gottschall L, Hrovatin LA, Parker S, Doleshal P, Dodge C (2001) A new approach to monitoring exercise training. J Strength Cond Res 15:109–115 Hall MM, Rajasekaran S, Thomsen TW, Peterson AR (2016) Lac- tate: friend or foe. PM R 8:S8–S15. https ://doi.org/10.1016/j. pmrj.2015.10.018 Hawley JA, Myburgh KH, Noakes TD, Dennis SC (1997) Train- ing techniques to improve fatigue resistance and enhance endurance performance. J Sports Sci 15:325–333. https ://doi. org/10.1080/02640 41973 67335 434 European Journal of Applied Physiology (2021) 121:425–434 1 3 Hill DW, Poole DC, Smith JC (2002) The relationship between power and time to achieve V̇O2max. Med Sci Sports Exerc 34:709–714. https ://doi.org/10.1097/00005 768-20020 4000-00023 Iaia FM, Bangsbo J (2010) Speed endurance training is a powerful stimulus for physiological adaptations and performance improve- ments of athletes. Scand J Med Sci Sports 20:11–23. https ://doi. org/10.1111/j.1600-0838.2010.01193 .x Kriel Y, Kerherve HA, Askew CD, Solomon C (2016) The effect of active versus passive recovery periods during high-intensity inter- mittent exercise on local tissue oxygenation in 18–30-year-old sedentary men. PLoS ONE 11:e0163733. https ://doi.org/10.1371/ journ al.pone.01637 33 Lakens D (2013) Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front Psychol 26:863. https ://doi.org/10.3389/fpsyg .2013.00863 Laursen PB (2010) Training for intense exercise performance: high- intensity or high-volume training? Scand J Med Sci Sports 20:1– 10. https ://doi.org/10.1111/j.1600-0838.2010.01184 .x MacDougall D, Sale D (1981) Continuous vs. interval training: a review for the athlete and the coach. Can J Appl Sport Sci 6:96–97 Mandroukas A, Heller J, Metaxas TI, Sendelides T, Riganas C, Vamva- koudis E, Christoulas K, Stefanidis P, Karagiannis V, Kyparos A, Mandroukas K (2011) Cardiorespiratory and metabolic alterations during exercise and passive recovery after three modes of exer- cise. J Strength Cond Res 25:1664–1672. https ://doi.org/10.1519/ JSC.0b013 e3181 da783 1 Marcora SM, Staiano W, Manning W (2009) Mental fatigue impairs physical performance in humans. J Appl Physiol 106:857–864. https ://doi.org/10.1152/jappl physi ol.91324 .2008 McAinch AJ, Febbraio MA, Parkin JM, Zhao S, Tangalakis K, Sto- janovska L, Carey MF (2004) Effect of active versus passive recovery on metabolism and performance during subsequent exercise. Int J Sport Nutr Exerc Metab 14:185–196. https ://doi. org/10.1123/ijsne m.14.2.185 Midgley AW, McNaughton LR, Wilkinson M (2006) Is there an opti- mal training intensity for enhancing the maximal oxygen uptake of distance runners? Empirical research findings, current opin- ions, physiological rationale, and practical recommendations. Sports Med 36:117–132. https ://doi.org/10.2165/00007 256-20063 6020-00003 Milanovic Z, Sporis G, Weston M (2016) Effectiveness of high-inten- sity interval training (HIIT) and continuous endurance training for VO2max improvements: a systematic review and meta-anal- ysis of controlled trials. Sports Med 45:1469–1481. https ://doi. org/10.1007/s4027 9-015-0365-0 Monedero J, Donne B (2000) Effect of recovery interventions on lactate removal and subsequent performance. Int J Sports Med 21:593– 597. https ://doi.org/10.1055/s-2000-8488 Ohya T, Aramaki Y, Kitagawa K (2013) Effect of duration of active or passive recovery on performance and muscle oxygenation during intermittent sprint cycling exercise. Int J Sports Med 34:616–622. https ://doi.org/10.1055/s-0032-13317 17 Roberts C, Torgerson D (1998) Understanding controlled trial: ran- domisation methods in controlled trials. BMJ 317:1301–1310. https ://doi.org/10.1136/bmj.317.7168.1301 Rodger SM, Plews DJ, McQuillan J, Driller MW (2016) Evaluation of the cyclus ergometer and the stages power meter against the SRM crankset for measurement of power output in cycling. J Sci Cycl 5:16–22 Ryan TE, Brizendine JT, McCully KK (2013) A comparison of exer- cise type and intensity on the noninvasive assessment of skeletal muscle mitochondrial function using near-infrared spectroscopy. J Appl Physiol 114:230–237. https ://doi.org/10.1152/jappl physi ol.01043 .2012 Schoenmakers PPJM, Reed KE (2018) The effects of recovery duration on physiological and perceptual responses of trained runners dur- ing four self-paced HIIT sessions. J Sci Med Sport 22:462–466. https ://doi.org/10.1016/j.jsams .2018.09.230 Schoenmakers PPJM, Hettinga FJ, Reed KE (2019) The moderating role of recovery durations in high-intensity interval-training proto- cols. Int J Sports Physiol Perform 31:1–9. https ://doi.org/10.1123/ ijspp .2018-0876 Seiler S, Joranson K, Olesen BV, Hetlelid KJ (2011) Adaptations to aerobic interval training: interactive effects of exercise intensity and total work duration. Scand J Med Sci Sports 23:74–83. https ://doi.org/10.1111/j.1600-0838.2011.01351 .x Siegler JC, Bell-Wilson J, Mermier C, Faria E, Robergs RA (2006) Active and passive recovery and acid-base kinetics following multiple bouts of intense exercise to exhaustion. Int J Sports Nutr Exerc Metab 16:92–107. https ://doi.org/10.1123/ijsne m.16.1.92 Spencer M, Bishop D, Dawson B, Goodman C, Duffield R (2006) Metabolism and performance in repeated cycle sprints: active versus passive recovery. Med Sci Sports Exerc 38:1492–1499. https ://doi.org/10.1249/01.mss.00002 28944 .62776 .a7 Stanley J, Buchheit M (2014) Moderate recovery unnecessary to sus- tain high stroke volume during interval training. A brief report. J Sports Sci Med 13:393–396 Stepto NK, Hawley JA, Dennis SC, Hopkins WG (1999) Effects of different interval-training programs on cycling time-trial performance. Med Sci Sports Exerc 31:736–741. https ://doi. org/10.1097/00005 768-19990 5000-00018 Thevenet D, Tardieu M, Zouhal H, Jacob C, Abderraouf A, Prioux J (2007) Influence of exercise intensity on time spent at high per- centage of maximal oxygen uptake during an intermittent session in young endurance-trained athletes. Eur J Appl Physiol 102:19– 26. https ://doi.org/10.1007/s0042 1-007-0540-6 Tschakert G, Hofmann P (2013) High-intensity intermittent exercise: methodological and physiological aspects. Int J Sports Physiol Perform 8:600–610. https ://doi.org/10.1123/ijspp .8.6.600 Ulmer HV (1996) Concept of an extracellular regulation of muscu- lar metabolic rate during heavy exercise in humans by psycho- physiological feedback. Experientia 52:416–420. https ://doi. org/10.1007/bf019 19309 Wu HC, Hsu WH, Chen T (2005) Complete recovery time after exhaus- tion in high-intensity work. Ergonomics 48:668–679. https ://doi. org/10.1080/00140 13050 00708 71 Yoshida T, Chida M, Ichioka M, Suda Y (1987) Blood lactate param- eters related to aerobic capacity and endurance performance. Eur J Appl Physiol 56:7–11. https ://doi.org/10.1007/bf006 96368 Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The acute physiological and perceptual effects of recovery interval intensity during cycling-based high-intensity interval training.

10-23-2020

Fennell, Christopher R J,Hopker, James G

eng