PHYSIOLOGICAL, ANTHROPOMETRIC, STRENGTH, AND MUSCLE POWER CHARACTERISTICS CORRELATES WITH RUNNING PERFORMANCE IN YOUNG RUNNERS RODOLFO A. DELLAGRANA,1,2 LUIZ G.A. GUGLIELMO,2 BRUNO V. SANTOS,1 SARA G. HERNANDEZ,1 SE´RGIO G. DA SILVA,1 AND WAGNER DE CAMPOS1 1

Department of Physical Education, Federal University of Parana´, Curitiba, Brazil; and 2Department of Physical Education, Federal University of Santa Catarina, Floriano´polis, Brazil

ABSTRACT Dellagrana, RA, Guglielmo, LGA, Santos, BV, Hernandez, SG, da Silva, SG, and Campos, W. Physiological, anthropometric, strength, and muscle power characteristics correlates with running performance in young runners. J Strength Cond Res 29(6): 1584–1591, 2015—The purpose of this study was to investigate the relationship between physiological, anthropometric, strength, and muscle power variables and a 5-km time trial (5kmT) in young runners. Twenty-three runners volunteered to participate in this study. Height, body mass, body fat, and fat-free mass (FFM) were measured. The subjects underwent laboratory testing to determine maximal oxygen uptake (V_ O2 max), velocity at ventilatory threshold (VVT ), running economy (RE), velocity associated with maximal oxygen uptake (vV_ O2 max), and peak velocity (PV). Peak torque, total work, and power were measured by an isokinetic dynamometer at 608$s21 and 2408$s 21 angular velocities. Right and left knee flexor and extensor torques were evaluated. Finally, the participants performed a 5kmT. Multiple regression and correlation analysis were used to determine the variables that significantly related to 5kmT. Strength and muscle power variables did not correlate with 5kmT. However, most physiological variables were associated with 5kmT. Velocity at ventilatory threshold alone explains 40% of the variance in 5kmT. The addition of the RE at speed 11.2 km$h21 (RE11.2) and FFM to the prediction equation allowed for 71% of the adjusted variance in 5kmT to be predicted. These results show that strength and muscle power variables are not good predictors of 5kmT; however, the physiological variables presented high prediction capacity in the

Address correspondence to Rodolfo A. Dellagrana, [email protected] yahoo.com.br. 29(6)/1584–1591 Journal of Strength and Conditioning Research Ó 2015 National Strength and Conditioning Association

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5kmT. Moreover, the anthropometric measures showed significant influence in performance prediction.

KEY WORDS running test, isokinetic test, endurance performance, anthropometry, young athletes INTRODUCTION

T

raditionally, the variables that have the most significant correlation with the performance of endurance runners are maximum oxygen uptake _ O2max), anaerobic threshold (AT), and running (V economy (RE) (3,22,29,30). Many studies have shown that high-value V_ O2max is a prerequisite for success in middledistance and long-distance runners; however, V_ O2max was not a good predictor of performance in groups of athletes with a small interindividual variation in V_ O2max (1,3,18). Therefore, other factors, such as RE, velocity associated with _ O2max), or peak velocity (PV) can maximal oxygen uptake (vV be better predictors of endurance performance in runners with similar V_ O2max values (10,22). Furthermore, the indices related to blood lactate represent aerobic capacity effectively and are used as predictors of endurance running performance (3,11). However, some studies have shown that performances in middle-distance and long-distance running may be limited not only by physiological factors, but also by muscle power factors (24,25). Thus, strength training for increases in strength and power has been reported to be beneficial in leading to increased rapid force production, contributing to increased running speed in long-distance events (31). Furthermore, specific neural adaptation and increased stiffness of the muscle-tendon system, which allows the body to store and use elastic energy more effectively, may be fundamental in middle-distance and long-distance running (25,27). Moreover, anthropometric variables are also determinants of running performance (32). According to Berg (4), the winners and finishers usually have low height and body mass (BM). These differences in elite athletes are decisive for competitions. Therefore, a strong predictive relationship between physiological variables and endurance performance has

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Journal of Strength and Conditioning Research frequently been reported in young runners (1,8,13). The relationship between physiological, neuromuscular, and anthropometric characteristics and endurance performance is still debatable in young runners. In young runners, only the study by Cole et al. (8) examined the relationship between physiological, strength, and muscle power variables with a 5-km (cross-country) run time, observ_ O2max ing significant relationships between race time and V _ and between vVO2max and RE; however, muscle strength and power were not significantly related to race time. Furthermore, the relationship between the predictors of endurance performance adjusted for anthropometric variables has not been tested in young runners. Therefore, the aims of this study were (a) to analyze the relationship between physiological, anthropometric, strength, and muscle power variables with running performance in young runners, and (b) to analyze the prediction of physiological, anthropometric, strength, and muscle power variables in 5-km distance performance.

METHODS Experimental Approach to the Problem

This study measured 23 young runners for physiological (V_ O2max, AT, RE, vV_ O2max, and PV), strength (peak torque [PT] and total work [TW]), muscle power, and anthropometric (height, BM, body fat percentage [%BF], and fat-free mass [FFM]) variables. Therefore, the research was divided into 4 phases. In the first phase, subjects were grouped according to the Tanner stages of maturation (33), determined by selfassessment as stage 1 (prepubertal), stages 2, 3, and 4 (pubertal), or stage 5 (postpubertal). All of the participants were in stage 5. In addition, the number of hours per week of sports training was self-reported by athletes using a specific questionnaire (26). Anthropometric evaluation was measured, followed by incremental treadmill protocol. In the second phase, a submaximal treadmill test was administered. In the third phase, the runners reported to the physical therapy clinic, where strength and power were evaluated on the isokinetic dynamometer. Finally, in the fourth phase, the athletes performed the performance simulation event over a 5-km distance. Subjects

Twenty-three, moderately trained young runners, all male, were recruited to participate in the study. All subjects had a minimum of 6 months of experience with training and endurance events and, in the period that preceded this performance study, had been training 6 days per week, with a weekly volume that oscillated between 60 and 80 km. Before data collection, the research was approved by the Human Research Ethics Committee of the Federal University of Parana´ (protocol no. 0064.0.091.000-10). Parental permission and the participants’ consent were obtained in writing before participation. Anthropometric Evaluation

Body mass was measured on a scale with 0.1 kg accuracy (Toledo, model 2096, Sa˜o Paulo, Brazil). Height was measured with a stadiometer with 0.1 cm accuracy (Sanny, Sa˜o Paulo,

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Brazil), according to the techniques described by Gordon (16). Body fat percentage was estimated from the equation of 2 skinfolds (tricipital and calf ) proposed by Slaughter (28) for adolescents, with the use of an adipometer with 0.1 mm accuracy (WCS Technology, Curitiba, Brazil). Fat-free mass was calculated from the %BF value. Determination of V_ O2max, Ventilatory Threshold, vV_ O2max, and Peak Velocity

Maximum oxygen uptake was measured using an incremental protocol performed on a motorized treadmill (Imbramed Super ATL, Porto Alegre, Brazil) with the gradient set at 1%. The initial speed was set at 10 km$h21 for 1 minute and was then incremented by 1 km$h21 every 1 minute, until voluntary exhaustion. Throughout the tests, the respiratory and pulmonary gas-exchange variables were measured using a breath-by-breath ParvoMedics gas analyzer (TrueOne Metabolic Measurement System 2400; Salt Lake City, UT, USA). The equipment was calibrated with known gas samples for O2 and CO2, whereas the ventilation flow was calibrated using a syringe with a volume of 3 L (Hans Rudolf, Kansas City, KS, USA). The rate of perceived exertion (RPE) was measured using OMNI category scale, which consists of 11 statements

TABLE 1. Anthropometric, physiological, and performance (5kmT) characteristics of the athletes.* Variables Anthropometric variables Age (y) Height (m) BM (kg) %BF FFM (kg) Physiological variables V_ O2max (L$min21) VVT (km$h21) vV_ O2max (km$h21) PV (km$h21) RE 11.2 km$h21 (L$min21) RE 12.8 km$h21 (L$min21) RE 14.4 km$h21 (L$min21) Performance 5kmT (min)

Mean 6 SD

CV (%)

18.00 1.73 64.31 11.63 56.74

6 6 6 6 6

0.90 0.05 7.99 2.87 6.52

5.00 2.89 12.42 24.68 11.49

4.08 14.22 18.22 18.43 2.57

6 6 6 6 6

0.57 1.08 0.95 0.87 0.27

13.97 7.59 5.21 4.72 10.51

2.83 6 0.35

12.37

3.04 6 0.39

12.83

18.47 6 1.17

6.33

*CV = coefficient of variation; BM = body mass; %BF = body fat percentage; FFM = fat-free mass; V_ O2max = maximal oxygen uptake; VVT = velocity at ventilatory threshold; vV_ O2max = velocity associated with maximal oxygen uptake; PV = peak velocity; RE = running economy; 5kmT = 5-km time trial.

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Performance Characteristics in Young Runners

TABLE 2. Strength and muscle power characteristics of the athletes.* Variables Isokinetic variables Extensors PT 608$s21 (Nm) PT 608$s21 (Nm$kg21) TW 608$s21 (J) TW 608$s21 (J$kg21) PW 2408$s21 (W) PW 2408$s21 (W$kg21) Flexors PT 608$s21 (Nm) PT 608$s21 (Nm$kg21) TW 608$s21 (J) TW 608$s21 (J$kg21) PW 2408$s21 (W) PW 2408$s21 (W$kg21)

Mean 6 SD

CV (%)

193.22 300.62 209.13 325.0 222.98 349.90

6 6 6 6 6 6

33.15 37.65 37.86 45.16 39.84 63.75

17.15 12.52 18.10 13.89 17.87 18.22

118.35 183.83 134.87 209.86 151.08 236.10

6 6 6 6 6 6

24.31 28.61 26.94 34.32 31.55 47.03

20.54 15.60 19.97 16.35 20.88 19.9

a third investigator would have to independently analyze the exercise test data to detect VT (15). However, in this study, the differences between the VT values detected by the 2 investigators did not differ by more than 3% for any participant. Velocity associated with maximal oxygen uptake was defined as the minimal velocity at which V_ O2max occurred (6). The peak velocity (PV) was defined as the last velocity that was maintained for a full minute (23). Running Economy

Running economy was determined using a discontinuous *CV = coefficient of variation; PT = peak torque; TW = total work; PW = Power. protocol. Athletes ran at 3 different speeds (11.2 km$h21; 12.8 km$h21; 14.4 km$h21). Participants exercised at each velocity for 6 minutes with scored from 0 to 10 (from extremely easy to extremely hard) the gradient set at 1%; at the conclusion of each 6-minute (35). Heart rate (HR) was also monitored throughout the tests period, the treadmill speed was reduced to 4.0 km$h21 for (Polar Electro OY, Finland). 2 minutes (8). V_ O2 (mL$kg21$min21) was averaged between The criteria for achieving a V_ O2max required participants the fifth and sixth minutes at each velocity and taken as the to meet 2 of the following: (a) a plateau in V_ O2 (change of reference for an athlete’s RE. Among the analyzed speeds ,150 mL$m21 in the last 2 stages); (b) RER of $1.0; (c) (11.2 km$h21; 12.8 km$h21; 14.4 km$h21), we chose 11.2 peak HR at the end of the test of $95% of age predicted km$h21 (RE11.2) because it is the speed that relates better to maximum (220 2 age); and (d) RPE $9. Therefore, V_ O2max the moderate exercise intensity domain, to reduce the poswas defined as the highest V_ O2 value attained after reaching sibility of V_ O2 slow component. the aforementioned criteria. HRmax was defined as the highest value recorded during the test. The ventilatory threshold (VT) was determined by the excess CO2 method (ExCO2) (36). A visual inspection to determine the VT was carried out independently by 2 experienced investigators. The VT values detected by the 2 investigators were then compared. If the 2 VT values were within 3% (mL$kg21$min21), then those values were averaged and accepted. If the 2 VT values were more than 3% different,

Isokinetic Evaluation

Right and left knee flexor and extensor torques were evaluated using an isokinetic dynamometer (Cybex NORM, Ronkonkoma, NY, USA). After warming up on a treadmill (5 minutes at self-selected pace), participants were positioned seated with their hips and thighs firmly strapped to the seat of the dynamometer, with the hip angle at 858. The dynamometer arm axis was visually aligned with the anatomical axis of the knee joint. The tests were repeated for the contralateral limb and the lever arm length, elevaTABLE 3. Mean 6 SD of the vV_ O2max, PV, VVT, and mean velocity maintained at tion of the dynamometer _ 5 km, and percentages of vVO2max, PV, and VVT in 5 km.* head and seat position for %vV_ O2max %PV %VVT km$h21 each subject was recorded by an experienced researcher. vV_ O2max 18.22 6 0.95 A gravity-correction procedure PV 18.43 6 0.87 100.23 6 1.10 was performed according to the VVT 14.22 6 1.08 78.05 6 4.48 78.22 6 4.44 5kmT 16.30 6 1.06 89.60 6 5.57 89.80 6 5.60 114.98 6 6.98 manufacturer’s instructions. During testing, the range of _ O2max = velocity associated with maximal oxygen uptake; PV = peak velocity; VVT = *vV motion at the knee was about velocity at ventilatory threshold; 5kmT = 5-km time trial. 1108 (08 for knee fully extended). The testing protocol

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Figure 1. Linear correlation between physiological variables and 5kmT. A) VVT vs. 5kmT. B) V_ O2max (L$min21) vs. 5kmT. C) RE, 11.2 km$h (L$min21). D) vV_ O2max vs. 5kmT. E) PV vs. 5kmT. VVT = velocity at ventilatory threshold; 5kmT = 5-km time trial; V_ O2max = maximal oxygen uptake; RE = running economy; vV_ O2max = velocity associated with maximal oxygen uptake; PV = peak velocity.

consisted of open-chain isokinetic movements with concentric quadriceps and hamstring contractions (3 repetitions at 608$s21 and 5 repetitions at 2408$s21). Three submaximal repetitions were performed for familiarization. The variables analyzed were PT, TW, power (PW), and hamstring/quadriceps ratio. Peak torque, TW, and power were normalized for

BM (7). In this study, the dominant lower limb was considered for analysis. Running Performance (5-km Time Trial)

The runners performed a simulated event over a 5-km distance. This event was held at an outdoor 400-m track. VOLUME 29 | NUMBER 6 | JUNE 2015 |

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Performance Characteristics in Young Runners

Figure 2. Linear correlation between anthropometric variables and 5kmT. A) FFM vs. 5kmT. B) Height vs. 5kmT. FFM = fat-free mass; 5kmT = 5-km time trial.

Before the event, the athletes performed a standard warm-up (specific running exercises), followed by static stretching (for quadriceps, hamstrings, and calf ).

RESULTS

The anthropometric, physiological, 5 km performance, strength, and muscle power characteristics are presented in Statistical Analyses Tables 1 and 2. Small variation in the physiological, anthroData normality was verified using the Shapiro-Wilk test. pometric (except %BF), and performance characteristics showed homogeneity among athletes. However, the isokiValues are presented as mean, SD, and coefficient of variance netic variables presented a higher CV. (CV). The correlation between the time of the event over 5 km and the physiological (V_ O2max, VT, RE, vV_ O2max, and The relationship between the velocity at ventilatory _ O2max, and PV with mean velocity of PV), anthropometric (BM, height, %BF, and FFM), strength threshold (VVT), vV the simulated event (5kmT) are presented in Table 3. It was (PT and TW), and muscle power variables was analyzed by observed that the mean velocity of the 5-km event was above Pearson’s product-moment correlation. Multiple regression _ O2max and PV the VVT (114.98 6 6.98%) and below the vV analysis (stepwise method) was performed to identify the (89.60 6 5.57% and 89.80 6 5.60%, respectively). Therefore, independent variables that were able to predict the 5-km these results ensure that the young runners performed their time trial (5kmT). The defaults’ inclusion and exclusion crimaximum effort in the simulated event over a 5-km distance. teria, namely the probability of the F to enter #0.05 and the The correlation between the physiological variables and probability of F to remove $0.10, were used in this analysis. performance in the 5kmT is presented in Figure 1. It can be For all statistics, the significance level was set at p # 0.05. observed that the correlation analysis revealed that FFM and height (Figure 2), but not BM (r = 0.07, p = 0.743) and % TABLE 4. Linear correlation between strength, muscle power variables, and BF (r = 0.03, p = 0.909), were 5kmT.* significant related with 5kmT. In the correlation analysis 5kmT between the strength and musIsokinetic variables Extensors p Flexors p cle power variables and 5 km performance, no significant cor0.09 0.701 0.21 0.348 PT 608$s21 (Nm) 21 21 relation was observed (Table 4). PT 608$s (Nm$kg ) 20.14 0.512 0.09 0.668 Multiple regression analysis TW 608$s21 (J) 20.02 0.936 0.10 0.660 TW 608$s21 (J$kg21) 20.06 0.784 0.03 0.884 was performed using 5kmT as PW 2408$s21 (W) 0.04 0.867 0.11 0.621 the dependent variable with PW 2408$s21 (W$kg21) 20.38 0.068 0.02 0.944 physiological and anthropometric (VVT, RE11.2, vV_ O2max, *5kmT = 5-km time trial; PT = peak torque; TW = total work; PW = power. PV, FFM, and height) as independent variables. Through

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19.3 (r = 0.97), and 42.2 km (r = 0.98). When the intensity of the TABLE 5. Coefficients of multiple regression of the physiological (VVT and RE) _ O2max, the event is below the vV and anthropometric variables (FFM) with 5kmT.* performance seems to depend Homoscedasticity more on aerobic capacity (AT) _ O2max), than aerobic power (V 2 SEE VIF (range) Equation (5kmT) R even when associated with RE (11). Thus, in our study, the run5kmT = 25.64 2 0.71 (VVT) 2 0.71† 0.677 1.2–5.5 3.38 (RE11.2) + 0.21 (FFM) ning speed of 5 km was below _ O2max (89.60 6 5.57%) the vV *VVT = velocity at ventilatory threshold; RE11.2 = running economy at velocity of 11.2 21 21 (Table 3). km$h (L$min ); FFM = body fat-free mass; 5kmT = 5-km time trial; SEE = standard error of estimate; VIF = variance inflation factor. Correlation analyses indicate †p # 0.05. that FFM and height are significant and positive related with 5kmT. These findings showed the importance of control anthropometric variables of young runners. In accordance with that, previous restepwise analysis, the independent variables were VVT, searches showed that approximately 20–30% of the variance RE11.2, and FFM (Table 5). The results showed that the in the performance (800, 1500, and 5000 m) can be explained VVT with the RE11.2 and FFM are able to explain 71% of by 3 anthropometric variables (chest girth, upper leg length, the 5-km performance. and triceps skinfold) in young runners (32); therefore, Tanaka DISCUSSION and Matsuura (32) observed that anthropometric variables are as important as physiological variables. The main aim of this study was to verify the relationship During adolescence, it may be observed that there is a great between and prediction capacity of the physiological, variation in body composition. The decline of %BF and anthropometric, strength, and muscle power variables in increase of FFM among young athletes occur because of the 5-km run time. Our main findings are that the growth spurt, biological maturation, and systematic training physiological variables (VVT, RE11.2, vV_ O2max, and PV) are (20). Therefore, it is clear of the importance of considering the the important determinants of the 5kmT in young runners. variation in growth and maturation process in young athletes Some studies with young endurance runners already found involved in sports, where the body composition variables are a variety of physiological responses related to performance essential to performance. Concerning long-distance running, in middle-distance and long-distance events (1,8). However, in general, elite endurance runners are of small height and these studies did not asses physiological responses related to have low BM (4,19). Several factors may explain the advanperformance adjusted with anthropometric variables. Thus, tage of smaller height and low BM in running. Previous studan interesting finding of this study was that a regression ies have observed that smaller runners possess slim lower model adjusted by physiological variables and FFM showed limbs, which require less muscular effort in leg swing (19), a high prediction value (71%) (Table 5). and also reduce the ground reaction forces (4) during running In this study, almost all the physiological variables (VVT, exercise. Another advantage for elite runners with limited RE11.2, vV_ O2max, and PV) demonstrated a significant corremass is heat accumulation: heavier runners produce and store lation with 5kmT. Similar results have been observed in more heat at a given submaximal running velocity, increasing studies conducted with endurance-trained adults (22,30) at higher environmental temperatures (21). and adolescents (1,8,13). Therefore, performance in endurIn our study, no significant differences were shown in the ance running depends on several factors, including a high correlation between strength and power variables (PT, cardiac output, a high oxygen delivery to working muscles, TW, and PW for extensors and flexors) and 5kmT. This the ability to sustain a high percentage of V_ O2max for long agrees with the study conducted by Cole et al. (8), in which periods of time, and the ability to move efficiently (14). the isokinetic variables (PT) and power assessed by vertical The VVT was the physiological variable that presented the _ O2max, jump testing presented no correlation with cross-country highest correlation with the 5kmT, followed by vV running (5 km) in trained adolescents. However, a study RE11.2, and PV, respectively. This agrees with the results of with well-trained male distance runners (age, 32.0 6 0.26 other studies conducted with adolescent (1,13) and adult years) found an inverse correlation between power (evalu(11,29,30) runners, both demonstrated that AT is a better preated by jump test) and 5kmT (17). Similarly, other studies dictor of long-distance events. Additionally, a landmark study that evaluated middle-distance runners (800 and 1500 m) by Farrel et al. (12) has shown a strong correlation between found a significant correlation between power (evaluated velocity corresponding to onset of plasma lactate accumulation by jump test) and performance (race time) (2,29). The and performance at 3.2 (r = 0.91), 9.7 (r = 0.96), 15 (r = 0.97), VOLUME 29 | NUMBER 6 | JUNE 2015 |

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Performance Characteristics in Young Runners results found in our study may be explained by the test used, in which the subject cannot perform a movement specific to a sport involving several joints because a joint is evaluated and the body does not move in the isokinetic dynamometer (34). Therefore, isokinetic dynamometry is not an effective method of evaluating the mechanical efficiency of endurance running. In the 5-km race, VVT, RE11.2, and FFM explained 71% of the performance variation of the young runners. A previous study conducted with adult middle-distance and longdistance runners indicated that the physiological and anthropometric variables are able to predict the performance. In endurance runners, the V_ O2max, vV_ O2max, lactate threshold, and RE (classic model) plus the peak treadmill velocity (PTV) explained 97.8% of the variation in the 16-km run time (22). In middle-distance runners, the V_ O2max, %BF, and RE explained 89% of the performance variation in the 800-m run time (9). Moreover, other investigations have reported that endurance running performance is dependent on the distance of the event (11,29). Nevertheless, the relationship between physiological characteristics and endurance running performance is clear for trained runners. Although our findings indicate that some physiological variables are good predictors of distance running performance for young runners, other variables such as growth, body composition, and maturity may influence the performance of long-distance running. New studies looking into the factors that predict endurance running performance in young runners may shed light on the matter and help us to understand the main characteristics responsible for success in running performance.

PRACTICAL APPLICATIONS Physiological variables (VVT, vV_ O2max, RE11.2, and PV) were important predictors for 5kmT in young runners, which is in line with previous reports (8). Additionally, we showed that VVT, RE11.2, and FFM are best physiological predictors of performance. Although there is a relative contribution of the anaerobic energy system in 5-km events (5), we showed that strength and muscle power variables were not good predictors of the 5kmT in young runners. Moreover, it is important to highlight that our study showed that the regression analysis with physiological and anthropometric variables is able to explain the 5kmT. These results showed the practical importance of anthropometric evaluation in the selection of young runners and also to monitor performance, whereas growth and maturation status may have influence. Therefore, our results highlight that some physiological variables (VVT, vV_ O2max, RE11.2, and PV) considered critical to long-distance performance events (1,3,13,22,30), should be analyzed with caution in young runners, because changes in body size and composition can affect the relationship between performance and its predictor variables.

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VOLUME 29 | NUMBER 6 | JUNE 2015 |

1591

Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.

Physiological, anthropometric, strength, and muscle power characteristics correlates with running performance in young runners.

The purpose of this study was to investigate the relationship between physiological, anthropometric, strength, and muscle power variables and a 5-km t...
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