Chronological Age, Somatic Maturation and Anthropometric Measures: Association with Physical Performance of Young Male Judo Athletes

for children and adolescents must consider growth and maturation to ensure suitable training and competition, and anthropometric variables could be used as bio-banding strategies in youth sport. This investigation aimed to analyze the association between chronological age, biologic maturation, and anthropometric characteristics to explain physical performance of young judo athletes. Sixty-seven judokas (11.0–14.7 years) were assessed for anthropometric and physical performance. Predicted adult stature was used as a somatic maturation indicator. A Pearson's bivariate correlation was performed to de ne which anthropometric variables were associated with each physical test.

aimed to analyze the association between chronological age, biologic maturation, and anthropometric characteristics to explain physical performance of young judo athletes. Sixty-seven judokas (11.0–14.7 years) were assessed for anthropometric and physical performance. Predicted adult stature was used as a somatic maturation indicator. A Pearson's bivariate correlation was performed to de ne which anthropometric variables were associated with each physical test. A multiple linear hierarchical regression was conducted to verify the effects of age, maturity, and anthropometry on physical performance. The regression models were built with age, predicted adult stature, and the three most signi cantly correlated anthropometric variables for each physical test. Older judokas performed better in most of the physical tests. However, maturation attenuated the age effect in most variables and signi cantly affected upper body and handgrip strength. Anthropometric variables attenuated age and maturity and those associated with body composition signi cantly affected the performance in most tests, suggesting a potential as bio-banding strategies. Future studies should investigate the role of anthropometric variables on the maturity effect in young judokas. Keywords:adolescent athlete; combat sports; body composition; bio-banding 1. Introduction The complex process of growth and maturation must be considered for children and adolescents in sport to ensure suitable training and competition routines. Chronological age is the traditional strategy to categorize young athletes appropriately for their level of development [1]. While growth is the process of increasing body size in whole or in parts, biological maturation refers to physiological and cognitive development towards adulthood. Although maturational events have an established order in which they hap- pen, the moment when they occur and their duration have immense variability between individuals, even at the same age, which affect the physical, technical, and psychological performance of young athletes. This happens more prominently in boys between 13 and 16 years old [2], which may increase the risk of injury [3] and impair motivation due to the performance discrepancy [4], in uencing whether the young athlete will continue in sports Int. J. Environ. Res. Public Health2021,18, 6410.

in boys between 13 and 16 years old [2], which may increase the risk of injury [3] and impair motivation due to the performance discrepancy [4], in uencing whether the young athlete will continue in sports Int. J. Environ. Res. Public Health2021,18, 6410.

Int. J. Environ. Res. Public Health2021,18, 6410 2 of 11 practice long-term [5]. In sports where strength, power, and speed are paramount, and in those where physical contact is inevitable, mature individuals tend to have a physical advantage over their less mature peers, since young people that mature and develop early tend to be taller and heavier [6]. Several investigations have been carried out to examine the effect of growth and maturation on young athletes' performance and to seek alternative strategies to chrono- logical age for the categorization of young athletes, namely those that are based on the use of body size and/or maturational status [7–9]. These strategies are called bio-banding and do not disregard other aspects that must be considered regarding the allocation of young athletes in competitive categories, such as skill level and psychological pro le [10]. However, most of these investigations focus on team sports, mainly in soccer, some with support from of cial sporting entities, and have already resulted in the rst experiences of unof cial tournaments using bio-banding to distribute young athletes in competitive categories, with positive results [11]. In combat sports, less research attention has been given to the plausible effect of maturation over performance [12,13], and to the applicability of bio-banding to different modalities. Nonetheless, grouping young combat athletes based on physical attributes is common (e.g., boxing, judo, taekwondo, wrestling). In these modalities, athletes are grouped based on chronological age and body mass, and compete in weight classes to promote fair competition and reduce potential injuries [14]. Although studies on the topic are still scarce [13], there is evidence of maturation effect within weight categories in young combat sports [15], raising questions about the suitability of body mass as criteria to guarantee equal conditions among athletes, justifying research on the topic. In addition, various research investigated the use of anthropometric variables for detection and prediction of success in young athletes [16], which can potentially also be used as bio-banding alternatives instead of body mass for categorizing young athletes in various sports, including combat sports. Based on the above-mentioned factors, the aim of the

conditions among athletes, justifying research on the topic. In addition, various research investigated the use of anthropometric variables for detection and prediction of success in young athletes [16], which can potentially also be used as bio-banding alternatives instead of body mass for categorizing young athletes in various sports, including combat sports. Based on the above-mentioned factors, the aim of the present investigation was to analyze the association between chronological age, biological maturation, and anthropometric characteristics to explain the physical performance of young judo athletes. Assuming that anthropometric characteristics may mitigate the effect of chronological age and biological maturation on the performance of young judo athletes, bio-banding strategies in judo and other combat sports could be developed using anthropometric variables, aiming to promote training and competition routines best suited to the development stages of young combat sport athletes. 2. Materials and Methods 2.1. Participants This is a cross-sectional study with a convenience sample, consisting of 67 young male judokas aged 11.0–14.7 years old selected from eight clubs in Portugal. To be included in the study, the participants needed to be between 11.0 and 14.9 years old, have at least one year of judo training, and have no physical or psychological contraindications to participation. Parents or legal guardians provided signed informed consent prior to data collection. Verbal consent was also obtained from participants. Two participants from different clubs dropped out of the study once data collection had commenced. The study was conducted in accordance with the Declaration of Helsinki for Human Studies of the World Medical Association and approved by the Ethics Committee of the Faculty of Sports Sciences and Physical Education of the University of Coimbra [CE/FCDEF-UC/00452019]. 2.2. Anthropometric Measures Common anthropometric procedures [17] were adopted. Stature and sitting height (SH) were measured using a portable stadiometer (Seca Bodymeter 206; Seca Deutschland, Hamburg, Germany) and a segmometer (Rosscraft, T.E. and B. Ross, Perth, Australia), respectively. The inferior members' length (IML) was estimated as stature minus SH. Arm span (AS) was measured using a metallic anthropometric tape by assessing the distance between right and left dactylion points, with both arms abducted 90

were measured using a portable stadiometer (Seca Bodymeter 206; Seca Deutschland, Hamburg, Germany) and a segmometer (Rosscraft, T.E. and B. Ross, Perth, Australia), respectively. The inferior members' length (IML) was estimated as stature minus SH. Arm span (AS) was measured using a metallic anthropometric tape by assessing the distance between right and left dactylion points, with both arms abducted 90 degrees. The hand

Int. J. Environ. Res. Public Health2021,18, 6410 3 of 11 length (HL) was measured as the distance between the stylion and dactylion, while the foot length (FL) was measured as a straight distance between the acropodion and pterion points using an anthropometer. Arm circumference (AC) and calf circumference (CC) were measured with a metallic anthropometric tape. All measures were taken to the nearest 0.1 cm. Body mass (BM) was measured to the nearest 0.1 kg using a portable digital scale (Seca Bella 840; Seca Deutschland, Hamburg, Germany). Skinfold thickness was assessed to the nearest 0.1 mm using a Rosscraft skinfold calipers in the following references: triceps, subscapular, suprailiac, and calf. Estimates of fat mass percentage were obtained from the sex-speci c equation derived from the sum of the triceps and subscapular skinfolds [18]. Thereafter, estimated body fat mass (BFM) and body fat-free mass (BFFM) were calculated. 2.3. Biological Maturation Predicted adult stature (PAS) was used as a maturational indicator [19]. It has been adopted in investigations on the biological maturation effect on physical tness of children in general [20] and in research focusing on youth performance in sport [21], due to the feasibility compared with more valid but invasive indicators [22]. Predicted adult stature has been used instead of peak of height velocity (PHV) in studies on bio-banding, since both have correspondence with pubertal status given by stage of pubic hair [10], but the latter seems to have limited application depending on chronological age and actual age at the peak of height velocity [23]. The PAS protocol requires the participants' decimal age, stature, and body mass, as well as the average parental stature. The stature of the parents was collected through a questionnaire attached to the informed consent form. The PAS variable was also expressed as the percentage of predicted adult stature attained (APAS). It is assumed that among children of the same chronological age, individuals with higher APAS are more advanced in somatic maturation compared with individuals with lower APAS [2]. 2.4. Physical Performance The pacer test was used to evaluate aerobic performance, with the number of com- pleted

PAS variable was also expressed as the percentage of predicted adult stature attained (APAS). It is assumed that among children of the same chronological age, individuals with higher APAS are more advanced in somatic maturation compared with individuals with lower APAS [2]. 2.4. Physical Performance The pacer test was used to evaluate aerobic performance, with the number of com- pleted laps being used as performance indicator. Agility was measured using a 10 5 m shuttle-run test, with the time to complete all laps recorded in seconds [24]. The line-drill test was used to evaluate anaerobic performance, with the time taken to complete the course expressed in seconds [25]. Strength was assessed using the following indicators: abdomi- nal muscle strength (AMS), 60-s sit-up test [26]; upper body muscle strength (UBS), 2-kg medicine ball throw [27]; lower body muscle strength (LBS), standing long jump test [24]; and dominant-hand grip strength (HgS), measured by a dynamometer (Lafayette) [24]. The best of two attempts was recorded in kilograms for HgS. 2.5. Procedures All data were collected by the same trained team, in a single visit for each judo club, where the anthropometric measurements were carried out initially, followed by the physical performance assessments. Participants completed a warm-up, under the guidance of a trainee researcher, before each station was completed in circuit form, in the following order: (1) pacer; (2) 2 kg standing medicine ball throw; (3) stand broad jump test; (4) 10 5 m shuttle-run test; (5) sit-ups; (6) handgrip strength; and (7) line-drill test. 2.6. Statistical Analysis Descriptive statistics (ranges, means, standard deviations, and 95% con dence inter- vals) were used for chronological age (CA), APAS, anthropometric characteristics, and physical test performance. The Kolmogorov–Smirnov test was used to test normality: body fat mass, agility, lower body strength, and handgrip strength were signi cant. A Pearson's bivariate correlation, with 95% bias corrected and accelerated con dence limits based on 1000 bootstrap samples, to correct data normality [28], was performed to assess the level of association between all variables. Multiple linear regression, which does not

agility, lower body strength, and handgrip strength were signi cant. A Pearson's bivariate correlation, with 95% bias corrected and accelerated con dence limits based on 1000 bootstrap samples, to correct data normality [28], was performed to assess the level of association between all variables. Multiple linear regression, which does not

Int. J. Environ. Res. Public Health2021,18, 6410 4 of 11 assume the assumption of data normality [28], was conducted to verify the in uence of the maturity indicators and anthropometric variables on physical performance. CA and APAS were previously selected for the rst and second regression models, respectively, based on their impact on the performance of young athletes described in the literature [2]. For each performance test, the three anthropometric variables with the highest correlation coef cient were selected for the third model. Independent variables were inserted into the regression models hierarchically: Model 1 was constituted by CA; Model 2 by CA and APAS; and Model 3 by CA, APAS, and the three anthropometric variables with the highest correlation coef cients for each physical performance test. Most models met all assumptions for the multiple linear regression [28]: error independence (Durbin-Watson values between 1–3), non-multicollinearity (Tolerance values >0.1; VIF values <10), ho- moscedasticity (standardized residual values between 3 and 3) and non-in uential cases (Cook's distance values <1). The exception was LBS, which failed on the assumption of non-in uential cases. Signi cance ofp< 0.05 was adopted in the analyses. IBM SPSS 26.0 software (SPSS, Inc., Chicago, IL, USA) was used in the study. 3. Results Table standard deviations, and 95% con dence intervals for CA, APAS, anthropometric charac- teristics, and physical test performance. Table 1.Descriptive statistics for the total sample (n= 67). Variables Range Mean sd Minimum Maximum Value 95%CI Chronological age (years) 11.01 14.70 12.54 12.30 to 12.78 0.99 APAS (%) 77.0 94.0 84.4 83.2 to 85.5 4.7 Body mass (kg) 27.6 79.6 47.6 44.7 to 50.5 11.2 Body fat mass (kg) 2.1 34.4 9.6 8.0 to 11.1 6.3 Body fat-free mass (kg) 25.5 65.1 38.0 36.1 to 39.9 7.8 Stature (cm) 134.8 176.5 154.0 151.6 to 156.4 9.9 Sitting height (cm) 71.5 93.2 80.0 78.8 to 81.2 5.1 Arm span (cm) 133.0 180.0 154.1 151.5 to 156.7 10.8 Superior members length (cm) 36.2 70.8 60.2 58.9 to 61.5 5.4 Hand length (cm) 14.1 21.3 16.9 16.5 to 17.2 1.5 Inferior members length (cm) 60.3 85.5 74.0 72.7 to

7.8 Stature (cm) 134.8 176.5 154.0 151.6 to 156.4 9.9 Sitting height (cm) 71.5 93.2 80.0 78.8 to 81.2 5.1 Arm span (cm) 133.0 180.0 154.1 151.5 to 156.7 10.8 Superior members length (cm) 36.2 70.8 60.2 58.9 to 61.5 5.4 Hand length (cm) 14.1 21.3 16.9 16.5 to 17.2 1.5 Inferior members length (cm) 60.3 85.5 74.0 72.7 to 75.4 5.5 Foot length (cm) 20.1 29.0 24.4 24.0 to 24.9 2.0 Arm circumference (cm) 19.0 36.0 25.3 24.5 to 26.1 3.3 Calf circumference (cm) 27.0 40.1 32.6 31.8 to 33.4 3.3 Pacer test (m) 140 1740 757 680 to 835 318 Line-drill test (sec) * 30.09 46.60 36.14 35.36 to 36.92 3.20 Agility 10 5 shuttle run (sec) * 15.88 26.25 19.44 18.93 to 19.96 2.12 60-s sit-ups (count) 15 61 41 39 to 44 10 2-kg ball throw (m) 3.19 8.79 5.22 4.93 to 5.52 1.22 Standing long jump (m) 1.12 5.65 1.69 1.55 to 1.83 0.57 Hand grip strength (kgf) 14.0 40.0 24.8 23.4 to 26.2 5.8 95%CI, con dence interval;sd, standard deviation; APAS, attained predicted mature stature; * Runtime tests—lower value represents better performance. Table tional indicators and anthropometric variables, and the physical performance. Signi cant moderate to high correlations were found for CA and APAS with the performance variables, except for agility, in which only CA was signi cant, and except for abdominal strength. Among the measured anthropometric variables, body fat mass correlated with all physical tests associated with running, and with abdominal strength. Body fat-free mass correlated with the neuromuscular strength performance, except for abdominal strength. Body mass, arm circumference, and calf circumference were not selected for any regression model, as no signi cant correlations were found.

Int. J. Environ. Res. Public Health2021,18, 6410 5 of 11 Table 2. Pearson bivariate correlation coef cients between maturational indicators and anthropometric variables, and physical tests performance variables, with 95% bias corrected and accelerated con dence limits based on 1000 bootstrap samples (n= 67). Independent Variables Dependent Variables (Physical Tests Performance) Pacer Test (m) Line-Drill test (s) + Agility 10 5 Shuttle Run (s) + 60-s Sit-Ups (n) 2-kg Ball Throw (m) Stand Long Jump (m) Handgrip Strength (kg) Maturational indicator Age 0.41 ** (0.21; 0.59) 0.45 *** ( 0.61; 0.24) 0.25 * ( 0.48; 0.02) 0.15 ( 0.07; 0.37) 0.63 *** (0.42; 0.80) 0.47 *** (0.25; 0.67) 0.52 *** (0.29; 0.68) APAS (%) 0.39 ** (0.18; 0.57) 0.41 ** ( 0.58; 0.21) 0.19 ( 0.43; 0.12) 0.06 ( 0.20; 0.30) 0.68 *** (0.47; 0.83) 0.47 *** (0.24; 0.68) 0.65 *** (0.49; 0.77) Anthropometry Body mass (kg) 0.07 ( 0.29; 0.19) 0.02 ( 0.22; 0.21) 0.10 ( 0.14; 0.33) 0.09 ( 0.32; 0.17) 0.66 *** (0.48; 0.79) 0.10 ( 0.14; 0.34) 0.66 *** (0.49; 0.78) Body fat mass (kg) 0.41 ** ( 0.56; 0.19) 0.40 ** (0.16; 0.57) 0.32 ** (0.05; 0.53) 0.27 * ( 0.50; 0.03) 0.22 (0.01; 0.42) 0.31 * ( 0.50; 0.10) 0.26 * (0.01; 0.46) Body fat-free mass (kg) 0.23 (0.04; 0.42) 0.29 * ( 0.46; 0.14) 0.11 ( 0.34; 0.10) 0.08 ( 0.16; 0.32) 0.82 *** (0.71; 0.89) 0.40 ** (0.15; 0.61) 0.78 *** (0.66; 0.86) Stature (cm) 0.25 * (0.06; 0.43) 0.34 ** ( 0.48; 0.17) 0.19 ( 0.40; 0.04) 0.05 ( 0.22; 0.31) 0.71 *** (0.55; 0.82) 0.37 ** (0.16; 0.57) 0.73 *** (0.61; 0.82) Sitting height (cm) 0.20 ( 0.02; 0.40) 0.25 * ( 0.40; 0;07) 0.14 ( 0.31; 0.06) 0.05 ( 0.20; 0.30) 0.69 *** (0.51; 0.82) 0.33 ** (0.13; 0.52) 0.71 *** (0.54; 0.82) Arm span (cm) 0.16 ( 0.02; 0.37) 0.34 ** ( 0.50; 0.18) 0.21 ( 0.43; 0.01) 0.11 ( 0.16; 0.34) 0.73 *** (0.60; 0.82) 0.52 ** (0.13; 0.55) 0.70 *** (0.56; 0.81 Superior members length (cm) 0.13 ( 0.08; 0.38) 0.30 * ( 0.54; 0.08)' 0.26

The study examines how age and maturation affect physical performance in young male judo athletes.