Contributions of training and non-training physical ...

J Phys Fitness Sports Med, 3(2): 261-268 (2014)DOI: 10.7600/jpfsm.3.261

JPFSM: Regular Article

Contributions of training and non-training physical activity tophysical activity level in female athletes

Asumi Yoshida1,2, Kazuko Ishikawa-Takata2*, Motoko Taguchi3, Satoshi Nakae4,

Shigeho Tanaka3,4 and Mitsuru Higuchi3

Received: October 9, 2013 / Accepted: February 5, 2014

Abstract We compared the physical activity level (PAL) and training and non-training physi-cal activity (PA) between rhythmic gymnasts and lacrosse players. In addition, we aimed to clarify the contribution of training and non-training PA to the PAL. Our study subjects were 11 female rhythmic gymnasts and 11 female lacrosse players. PAL was calculated from the total en-ergy expenditure (TEE) as assessed by the doubly labeled water (DLW) method, and the resting metabolic rate (RMR) was measured using indirect calorimetry. Daily PA and sleep durations were assessed using an activity diary. The intensity (metabolic equivalent, MET) of non-training PA was measured using a tri-axial accelerometer. The amount (MET･h) of training was calcu-lated by subtracting the amount of PA and sleep outside of training from the TEE. There were no significant differences in PAL between rhythmic gymnasts (2.59 ± 0.63) and lacrosse players (2.43 ± 0.46). Rhythmic gymnasts had a longer duration and larger amount of training PA and a shorter duration and smaller amount of non-training PA than did lacrosse players. The mean intensities of training and non-training PA were not significantly different between the groups. PAL was positively correlated with the amount of training in both rhythmic gymnasts (γs = 0.818) and lacrosse players (γs = 0.882). There were no significant relationships between PAL and non-training PA in both groups. Our results indicate that the amount of training strongly affects PAL in these athletes.Keywords : physical activity level, doubly labeled water, accelerometer, training

Introduction

The estimated energy requirement for adults is calcu-lated by multiplying the resting metabolic rate (RMR) by physical activity level (PAL). In athletes, the Japan Institute of Sports Sciences (JISS) determined PAL values (JISS PALs) for four sporting categories during both in season and off season1). The JISS PALs during the train-ing season are 2.50, 2.00, 2.00, and 1.75 for “endurance sports,” “explosive sports,” “ball sports,” and “other sports,” respectively. The JISS PALs in the training season were determined based on only seven previous studies using the doubly labeled water (DLW) method in athletes from seven dif-ferent sports2-8). The JISS PALs for “explosive sports” and “ball sports” were based on the fact that nearly 75% of

Japanese athletes showed PAL values of 2.2 or less, and the mean PAL reported in the previous studies was 2.03. The JISS PAL value for “endurance sports” was based on the observation that general endurance athletes had a longer duration and larger amount of training and lighter body weights than did athletes in other sports. The basis for the JISS PAL figure for “other sports” was not clear. PAL varied among studies, and even among the same sports, during the training season. In female swimmers during the training season, PAL values varied from 1.714) to 2.459). In addition, PAL increased to 3.0 with a longer duration of training during training camp10). Likewise, PAL varied among different sports classified in the same category by the JISS1). For instance, whereas the mean PAL value among male baseball players was reported as 2.6611), that of male soccer players was 2.118). Thus, it appears to be difficult to determine the PAL value ac-cording to the sporting category. In addition, Carlsohn *Correspondence: [email protected]

1 Graduate School of Sport Sciences, Waseda University, 2-579-15 Mikajima, Tokorozawa, Saitama 359-1192, Japan2 Department of Nutritional Education, National Institute of Health and Nutrition, 1-23-1 Toyama, Shinjuku-ku, Tokyo

162-8636, Japan3 Faculty of Sport Sciences, Waseda University, 2-579-15 Mikajima, Tokorozawa, Saitama 359-1192, Japan4 Department of Nutritional Science, National Institute of Health and Nutrition, 1-23-1 Toyama, Shinjuku-ku, Tokyo

162-8636, Japan

262 JPFSM : Yoshida A, et al.

et al.12) reported no difference in PAL as measured by an activity record between athletic and nonathletic adoles-cents. Based on these findings, they considered that the increased energy expenditure during training physical ac-tivity (PA) was countered by the decreased energy expen-diture during non-training PA. There is a need to clarify the relationship between PAL and training PA, or non-training PA, separately. In the present study, we first examined PAL and the du-ration, intensity, and amount of training and non-training PA between sporting categories in which the contents of training are expected to vary substantially. We then deter-mined whether training or non-training PA had a stronger association with PAL in athletes.

Materials and Methods

Subjects. Twenty-two female athletes from a physical education college (11 rhythmic gymnasts and 11 lacrosse players) participated in this study. Rhythmic gymnas-tics is thought to be categorized as an “other sport” and lacrosse is thought to be categorized as a “ball sport” among the four categories classified by the JISS1). All subjects participated in national competitions. Written informed consent was obtained from all subjects, and the study protocol was approved by the Ethics Committee of the National Institute of Health and Nutrition and the Eth-ics Committee of Waseda University.

Study design. The experiment was performed during the training season of 2007-2008. Subjects underwent mea-surement of body weight (BW), body height (BH), body composition (percentage of body fat (BF) and fat free mass (FFM)), RMR, and total energy expenditure (TEE). TEE was measured over 8 consecutive days. The subjects underwent measurement of RMR during the follicular phase of their menstrual cycle and as close to the TEE measurement period as possible. The RMR measurement date was determined before or after TEE measurement. BH was measured on the first day of TEE measurement, and BW was measured in the fasting state before giving DLW and on the last day of the study. Body composition was measured within 4 days of the end of TEE measure-ment. Subjects were instructed to maintain their usual eating and activity patterns and to make no conscious attempt to lose or gain weight during the experimental pe-riod.

Anthropometry and body composition. BW with mini-mal clothing was measured to the nearest 0.1 kg using an electronic scale (UC-321; A&D Co., Ltd., Tokyo, Japan). BH was measured to the nearest 0.1 cm using a stadiome-ter (YL-65; Yagami Inc., Nagoya, Japan). The body mass index (BMI) was calculated by dividing BW (kg) by the square of the BH (m2). The BF and the FFM were mea-sured using dual-energy X-ray absorptiometry (Hologic

QDR-4500 DXA Scanner; Hologic Inc., Waltham, MA, USA).

Measurement of RMR. After an overnight fast at our facility, RMR was measured by indirect calorimetry. Sub-jects remained awake in the supine position at a comfort-able room temperature (20–25°C) wearing a facemask for at least 30 minutes. Two samples of expired gas were collected in Douglas bags for 10-minute periods, and the mean of the two values was used for the analysis. The O2 and CO2 concentrations were measured using a gas analyzer (rhythmic gymnasts: AE-300S; Minato Medical Science Inc., Osaka, Japan, and lacrosse players: ARCO-1000; Arco System Inc., Kashiwa, Japan). The volume of expired air was measured using a dry gas volume meter (DC-5; Shinagawa Co., Ltd., Tokyo, Japan) and then con-verted to volume at a standard temperature, pressure, and dry gas. The gas exchange results were converted to RMR (kcal/day) using the Weir equation13). According to a previous study (unpublished data, n = 95) that measured RMR by two different gas analyzers, RMR measured by the AE-300S was significantly lower than that measured by the ARCO-1000 (difference: -31 ± 26 kcal/day, estimation error: -2.6 ± 2.1%). In the present study, when we compared the results between groups us-ing the adjusted RMR with an estimation error of -2.6% for the AE-300S in rhythmic gymnasts, they were not changed. Therefore, we used the nonadjusted RMR data in this analysis. Measurement of TEE. In the present study, TEE was measured using the DLW method as a gold standard for measuring free-living TEE. After collecting a baseline urine sample, a single dose of approximately 1.4 g/kg BW of H2

18O (10.0 atom%; Taiyo Nippon Sanso Inc., Tokyo, Japan) and 0.06 g/kg BW of 2H2O (99.9 atom%; Cam-bridge Isotope Laboratories Inc., Andover, MA, USA) was orally administered to each subject. The subjects were then asked to collect urine samples at eight prede-termined times during the study period, at the same time of day. All samples were stored by freezing at -30°C in airtight parafilm-wrapped containers and then analyzed in our laboratory. Gas samples for isotope ratio mass spectrometry (IRMS) were prepared by equilibration of the urine sample with a gas. The gas used for equilibration of 18O was CO2, and that used for equilibration of 2H was H2. A Pt catalyst was used for equilibration of 2H. The isotopic analyses were conducted using IRMS machines (Finnigan DELTAplus; Thermo Fisher Scientific Inc., Waltham, MA, USA). All samples and the corresponding references were analyzed in duplicate. The intercepts (2H and 18O enrichment at time zero, respectively) and elimination rates (kH and kO) were cal-culated using least-squares linear regression generated on all data points after logarithmic transformation as a func-

263JPFSM : Physical activity levels in female athletes

tion of the elapsed time from dose administration. The 2H and 18O zero-time intercepts were used to determine the 2H and 18O dilution spaces. Total body water (TBW) was calculated from the mean value of the dilution spaces of 2H divided by 1.041 and that of 18O divided by 1.007. The rate of CO2 production (rCO2) was determined as follows: rCO2 = 0.4554 × TBW × (1.007kO – 1.041kH). Calculation of TEE (kcal/day) was performed using a modification of the Weir equation13) based on rCO2 (mol/day) and the respiratory quotient. The food quotient (FQ), which was derived from the food consumption data, was calculated by the formula of Black et al.14) and was used instead of respiratory quotient. This assumes that under conditions of no BW change, the FQ must equal the respi-ratory quotient14). The dietary survey for calculating the FQ was conducted using a self-administered food record during the TEE measurement period. In the present study, average FQ values (rhythmic gymnast FQ = 0.873 and lacrosse player FQ = 0.868) were adopted in each group. PAL was estimated by dividing the TEE by the RMR.

Assessment of components of daily behavior. Compo-nents of daily behavior were assessed using accelerometry data and an activity diary. Subjects were asked to wear a tri-axial accelerometer (Activity Monitor EW4800; Panasonic Electric Works Co., Ltd., Osaka, Japan) during waking hours over the same time period as the TEE mea-surements, except while training or bathing. The acceler-ometers were worn on the hip above the iliac crest. The validity of the activity monitor was previously established in a study showing that the activity monitor-estimated TEE was comparable with the TEE measured using the DLW method, and the two values were significantly correlated (r = 0.835)15). In addition, the energy expenditure assessed by the monitor was significantly correlated with O2 con-sumption while walking or running at seven speeds rang-ing from 40 to 160 m/min and during seven daily activities (performing self-care while standing, changing clothes, cooking, simulating eating supper, washing dishes, doing laundry, and using a vacuum cleaner) (R2 = 0.86)16). In the present study, 60-second epochs were used to collect data. At the end of the measurement periods, the accelerometers were returned to the researchers and the stored data were downloaded to a computer. Data for the day were included in the analysis if sub-jects wore the accelerometer for more than 90% of the assumed wearing time as calculated by subtracting the duration of training and bathing from all waking hours. The reason for adopting the criterion of 90% in this study is as follows. To determine which criterion to adopt, the intraclass correlation coefficient (ICC) was calculated as the agreement between the assumed and actual observed durations of wearing the accelerometer. An ICC value of >0.70 was generally considered to be highly reliable17). We calculated each ICC when the actual duration of wear-ing the accelerometer was more than 60%, 70%, 80%, or

90% of the assumed wearing duration. Consequently, only the criterion of 90% was associated with an ICC of >0.70 in both rhythmic gymnasts (ICC = 0.938) and lacrosse players (ICC = 0.770). An activity diary was used to report the periods in which the subject was not wearing the accelerometer during the day. The subjects recorded the time points at which the accelerometer was attached and removed in an activity diary. Components of daily behavior were clas-sified as training, sleep, or other PA (non-training PA). The duration of each component was determined using the activity diary and accelerometer data. In the present study, we expressed the amount (MET･h) as intensity (metabolic equivalent, MET) × duration (hours). The amount of sleep was calculated as 1.0 MET18) multiplied by the duration of sleep. The amount of non-training PA was calculated as the intensity of non-training PA as mea-sured by accelerometry multiplied by the duration. The intensity of bathing was fixed at 1.5 METs18). The inten-sity for the non-wearing time during non-training PA was estimated as the average MET value for non-training PA. The amount of training was calculated based on the TEE and non-training PA using the following equation: amount of training = TEE (kcal/day) / BW (kg) / 1.05 – amount of sleep – amount of non-training PA – amount of bathing – estimated amount of non-wearing-time PA. Bathing and non-wearing-time PA amounts were only used to estimate the amount of training and were not included in non-train-ing PA. The mean intensity of training PA was calculated as the amount of training PA divided by the duration of training PA. Non-training PA was classified into four subgroups: sedentary (SED, 1.0-1.5 METs), light (LPA, > 1.5 to < 3 METs), moderate (MPA, 3 to < 6 METs), and vigorous (VPA, ≥ 6 METs). The amount of time spent engaging in each intensity level of non-training PA was calculated by multiplying each intensity by the duration assessed using the accelerometer.

Statistical analysis. Data are presented as means with standard deviations (SD) or medians with ranges. Be-cause the amount and duration of non-training PA differed between rhythmic gymnasts and lacrosse players, the amount and duration of the different intensities of non-training PA were expressed as fractions. Statistical sig-nificance was set at p < 0.05 for all analyses. Continuous normally distributed variables were compared using Stu-dent’s t-test, and non-normally distributed variables were compared using the Mann-Whitney U test. Distributions of time and amount of non-training PA at different inten-sities between rhythmic gymnasts and lacrosse players were compared using the χ2-test. Spearman’s rank corre-lation coefficient analysis was performed to examine the relationship between PAL and the duration or amount of training or non-training PA. All statistical analyses were performed using the statistical computing package IBM


SPSS Statistics (version 20; IBM Corporation, Somers, NY, USA).

Results

The characteristics of the subjects are shown in Table 1. Age, BW, BMI, and percent BF were significantly higher in lacrosse players than in rhythmic gymnasts. There were no significant differences in the TEE, RMR, or PAL be-tween the groups. Table 2 shows the duration, amount, and intensity of training, non-training PA, and sleep. Rhythmic gymnasts had a longer training time and shorter durations of non-training PA and sleep than did lacrosse players. The mean intensities of training and non-training PA were not significantly different between rhythmic gymnasts and lacrosse players. The amounts of each daily behavior were significantly different between the groups. Despite differences in the duration and amount of non-training PA between rhythmic gymnasts and lacrosse players, the pro-portions of sedentary, light, moderate, and vigorous non-training PA were not significantly different (Table 3). PAL was positively correlated with the amount of train-ing in both rhythmic gymnasts (γs = 0.818, p = 0.002) and lacrosse players (γs = 0.882, p < 0.001) (Table 4). The analysis of correlations between training and non-training PA showed a significant negative relationship for both amount (γs = -0.685, p = 0.020) and duration (γs = -0.936,

p < 0.001) in rhythmic gymnasts. Lacrosse players showed no relationship between training and non-training PA.

Discussion

To our knowledge, the present study is the first to in-vestigate the relationship of PAL with training and non-training PA between different sporting categories using the DLW method. The important findings of the present study were as follows: 1) Although rhythmic gymnasts had a longer duration and greater amount of training than did lacrosse players, there was no significant difference in the mean PAL between the two groups. In addition, the proportion of the duration and amount of each intensity level component of non-training PA did not differ. 2) Only the amount of training, not the duration, was strongly as-sociated with PAL in rhythmic gymnasts, lacrosse players, and all subjects. The duration and amount of non-training PA did not affect PAL. Our subjects had mean PALs greater than the represen-tative values provided by the JISS1). In the present study, the mean PAL for lacrosse players (2.42) was higher than the JISS PAL for “ball sports” (2.00). Higher PAL than JISS PAL values were also reported in male baseball players (PAL = 2.66)11) and soccer players (PAL = 2.11)8). Both of these sports are included in the category “ball sports.” Similarly, our gymnasts had a higher average PAL (2.59) than the JISS PAL for “other sports” (1.75).

Table 1. Characteristics of subjects.

Data are presented as means ± standard deviations. BH: body height, BW: body weight, BMI: body mass index, BF: body fat, FFM: fat-free mass, RMR: resting metabolic rate, TEE: total en-ergy expenditure, PAL: physical activity level. PAL = TEE / RMR. Continuous normally distrib-uted variables were compared with Student’s t test (*), and non-normally distributed variables were compared with the Mann-Whitney U test (†). Statistical significance was set at p < 0.05.

Rhythmic gymnasts (n=11)

Lacrosse players (n=11) p value

Age (yrs)† 19.6 ± 0.8 21.0 ± 0.4 < 0.001

BH (cm)* 160.7 ± 4.3 158.9 ± 4.7 0.360

BW (kg)* 48.9 ± 4.8 56.0 ± 4.6 0.002

BMI (kg/m2)* 18.9 ± 1.2 22.2 ± 1.7 < 0.001

BF (%)* 18.2 ± 3.6 22.5 ± 2.6 0.004

FFM (kg)* 37.1 ± 2.8 39.0 ± 2.1 0.086

RMR (kcal/day)* 1119 ± 195 1219 ± 167 0.213

TEE (kcal/day)* 2798 ± 247 2901 ± 280 0.372

PAL* 2.59 ± 0.63 2.43 ± 0.46 0.498


Previous studies of “other sports” found that both young gymnasts (PAL = 1.98)5) and female synchronized swim-mers (PAL = 2.18)6) showed a higher PAL than the JISS PAL. In the present study, there was considerable interindi-vidual variability in PAL within sporting categories. PAL values ranged from 1.82 to 3.88 (0.63 SD) for rhythmic gymnasts and from 1.82 to 3.29 (0.46 SD) for lacrosse players. Hill et al.19) reported that the average (± SD) PAL was 2.85 (± 0.9) in female light-weight rowers. In addi-tion, Ebine et al.6) found that the mean (± SD) PAL was 2.18 (± 0.43) in female synchronized swimmers. Thus, even among individuals participating in the same sporting category, the same sport, or the same team, PAL varied greatly. Indeed, our subjects were members of the same university clubs, and substantial interindividual variabil-ity was observed in the intensity, duration, and amount of training. This finding indicates that belonging to the same team does not necessarily mean that athletes undergo the same intensity or duration of training based on the sub-

jects’ positions or levels within the teams. Therefore, it may not be sufficient to estimate PAL based on sporting categories, considering the possible over- or underestima-tion of the energy requirements in each individual. Although rhythmic gymnasts had a larger amount of training than did lacrosse players, there was no differ-ence in PAL between the two sporting categories. This was caused by the fact that rhythmic gymnasts had a smaller amount of non-training PA than that of lacrosse players. However, there were no significant differences in the mean PA intensity during non-training PA or in the proportion of the amount of PA at each intensity level to the amount of total non-training PA between the two sporting categories. Carlsohn et al.12) reported no differ-ences in the TEE or PAL as measured using an activity record validated by the DLW method between adolescent competitive athletes and nonathletic adolescents, despite the fact that the energy cost for exercise training was sig-nificantly higher in athletes. They suggested that the re-duced non-training PA compensated for the higher energy

Table 2. Duration, amount, and intensity of each daily behavior assessed by the doubly labeled water method and accelerometry.

Values are medians (25th-75th percentiles). PA: physical activity, MET: metabolic equivalent. The amount (MET･h) was calculated by multiplying the duration (hours) by the intensity (MET). Variables were compared using the Mann-Whitney U test. Statistical significance was set at p < 0.05. The intensity of sleep was fixed at 1.0 MET.


Lacrosse players(n=11) p value

Duration (min/day)

Training 324 184 < 0.001(224 - 483) (148 - 208)

Non-training PA 682 792 0.010(514 - 770) (738 - 829)

Sleep 400 447 0.023(391 - 411) (426 - 465)

Intensity (METs/day)

Training 4.6 6.3 0.116 (4.4 - 5.5) (4.9 - 8.5)

Non-training PA 1.7 1.7 0.519 (1.5 - 1.7) (1.6 - 1.7)

Amount (METs･h/day)

Training 30.8 19.5 0.016 (18.8 - 38.6) (14.6 - 22.7)

Non-training PA 19.0 21.6 0.007 (14.7 - 20.0) (20.1 - 22.9)

Sleep 6.7 7.5 0.019 (6.5 - 6.8) (7.1 - 7.8)


with PAL in both rhythmic gymnasts and lacrosse play-ers, although the duration and amount of training differed between these sports and among individuals. A previous study using different methods also reported the impor-tance of training volume in athletes. A study of male endurance runners20) showed that the TEE, as assessed by the heart rate, was strongly correlated only with the exer-cise energy expenditure, and not with the energy expendi-

expenditure during training in adolescent athletes because of the athletes’ need to rest and recover following training sessions. However, in our subjects, the smaller amount of non-training PA among rhythmic gymnasts was only attributed to the shorter duration of non-training PA. In addition, we observed no increase in the proportion of sedentary or light PA. The amount of training had the greatest association

Table 3. Fraction of the duration and amount of different intensities of non-training physical activity assessed by accelerometry.

Table 4. Correlations of physical activity level and the duration or amount of training and non-training physical activity.

Values are means ± standard deviations. SED: sedentary activity, LPA: light physical activity, MPA: moderate physical activity, VPA: vigorous physical activity. No significant differences between sporting categories were found by χ2-test analysis (p < 0.05).

PAL: physical activity level, PA: physical activity. Correlations between variables were assessed using Spearman’s rank correlation coefficients (γs). Statistical significance was set at p < 0.05.


Lacrosse players (n=11) p value

Duration (%)

SED 60.4 ± 9.7 56.4 ± 4.4

LPA 32.3 ± 7.4 36.3 ± 4.8

MPA 7.1 ± 2.8 7.0 ± 1.8

VPA 0.2 ± 0.3 0.2 ± 0.3 0.946

Amount (%)

SED 42.0 ± 10.3 38.1 ± 4.2

LPA 41.5 ± 6.4 45.6 ± 5.4

MPA 15.6 ± 5.3 15.3 ± 3.8

VPA 1.0 ± 1.4 1.0 ± 1.4 0.938


Lacrosse players (n=11)

Total (n=22)

γs p γs p γs p

Duration (min/day)

Training 0.182 0.593 0.336 0.312 0.210 0.348

Non-training PA - 0.164 0.631 0.509 0.110 0.075 0.739

Amount (METs･h/day)

Training 0.818 0.002 0.882 < 0.001 0.733 < 0.001

Non-training PA - 0.324 0.331 0.391 0.235 0.074 0.744


ture in non-training PA or sleep. In both sporting categories of the present study, while the duration of training had no association with PAL, the amount of training was significantly positively correlated with PAL. This result suggests that it is more important to assess the amount of training, not the duration of train-ing, to estimate PAL in athletes during the normal training season. Motonaga et al.20) also suggested that the observed exercise energy expenditure, rather than the duration of training or the assumed exercise intensity, should be used for nutritional management of endurance athletes because the amount of training has the greatest effect on the TEE. Some limitations of the present study must be acknowl-edged. We indirectly determined the amount of training by deducting the amount of non-training PA, sleep, bath-ing, and non-wearing time activity assessed by an accel-erometer and an activity diary from the TEE measured by the DLW method. Although the DLW method has been considered the gold standard in the estimation of TEE in free-living individuals, accelerometry has been shown to under- or overestimate the energy expenditure compared to indirect calorimetry in ordinary people21). Thus, the ac-curacy of assessing each daily behavior may differ. In ad-dition, we included in our analysis days on which subjects wore the accelerometer for more than 90% of the assumed wear-time. As a result, the number of days included in the analysis varied widely (2-8 days) among individuals. Likewise, we did not consider between-day differences in the daily distribution of each intensity of non-training PA between training and non-training days. It was not pos-sible in the current analysis to examine whether athletes changed their non-training PA patterns between training days and non-training days because 8 of the 22 female athletes had no non-training days during the study period. In addition, our subjects trained 3 to 7 days a week, and 86% of them (19 of 22 subjects) had more than 5 train-ing days. If athletes train less frequently, their PAL values may be more strongly influenced by non-training PA. Fi-nally, their individual level or role within the team might affect the duration or amount of training even within the same team, but we did not examine these differences in the present study. The present study suggests that the JISS PALs1) may have underestimated the mean PAL for athletes, at least in the “ball sports” and “other sports” categories. In ad-dition, the present study indicates that athletes belonging to the same sporting category can have considerable vari-ability in their PAL. Therefore, it is likely inappropriate to determine a single PAL value for a sporting category. Our findings indicate that the PAL for athletes should be classified based on the duration and intensity of training rather than on sporting categories, or be estimated using equations consistent with those of training. Further re-search is required to clarify the relationship between PAL and each daily behavior, especially in the duration and intensity of training.

Acknowledgments

The authors would like to thank the subjects and their coaching staff for their cooperation during the data collection.

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Conflict of interest

None declared.


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Contributions of training and non-training physical ...

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