Children change their body size, shape, and gross motor coordination (GMC) as they grow. Further, GMC is expected to link to changes in children's body size, physical activity (PA), and physical fitness (PF). The objective was to model GMC changes in children followed longitudinally and to investigate associations between these changes and PA and PF levels. A total of 245 children (122 girls) were observed at 6 years of age and followed annually until 9 years. A sequence of allometric models was fitted, that is, 1. body mass, stature, and PA; 2. addition of four PF tests; 3. addition of four more PF tests. In Model 1, changes in GMC are nonlinear, and body mass (-0.60 ± 0.07, P < .001) and stature (2.91 ± 0.35, P < .001) parameter estimates were significant suggesting children with a more linear body size/shape showed higher GMC performances. Girls tend to outperform boys across time, and PA was not associated with GMC changes. Model 2 fitted the data better, and the PF tests (handgrip, standing long jump, 50-yard dash, and shuttle run) were significantly linked to GMC change. In Model 3, adding the remaining PF tests did not change the order of any factors importance. The greatest GMC changes were achieved by children whose body size/shape has an ectomorphic dominance across the years. Considering that leaner and physically fitter children tended to be more coordinated, physical education should also focus on PF development in components related to muscular strength, speed, agility, and aerobic capacity, along with nutritional education to reduce fat mass.

Background. The Tanner-Whitehouse radius-ulna-short bone protocol (TW2 RUS SA) is a widely used indicator of maturity status. The scale for converting ratings to an SA has been revised (TW3 RUS SA) which may have implications for studies of youth athletes.Objectives. To compare TW2 and TW3 RUS SAs in an international sample of youth soccer players and to compare distributions of players by maturity status defined by each SA Methods. SA assessments with the TW RUS method were collated for 1831 soccer players 11-17 years from 8 countries. RUS scores were converted to TW2 and TW3 SAs using the appropriate tables. SAs were compared within chronological age (CA) groups. The difference of SA minus CA with each method was used to classify players as late, average or early maturing. Concordance of maturity classifications with TW2 and TW3 SAs was evaluated with Cohen's Kappa coefficients.Results. For the same RUS score, SAs were systematically and substantially reduced with TW3 compared to TW2; mean differences ranged, on average, from -0.97 to -1.16 years. Kappa coefficients indicated fair concordance of maturity classifications based on TW2 and TW3 SAs.Conclusion. TW3 SAs were systematically lower than corresponding TW2 SAs in youth soccer players. The differences between scales have major implications for the classification of players by maturity status, which is central to many talent development programs.

This study investigated the association of six different anthropometric markers with metabolic syndrome to find the most suited to predict children at risk. Sample comprises 1324 Portuguese youth (701 girls, 623 boys), aged 10-17 years. Six anthropometric markers were included: body mass index (BMI), BMI z-score, tri-ponderal index (TPI), waist circumference (WC), WC/height ratio (WC/H), and WC/H adjusted ratio (WC/H ). A standardized metabolic risk score (zMR) was computed by summing of standardized values for fasting glucose, triglycerides, high-density lipoprotein cholesterol, and mean arterial blood pressure. The associations between zMR and anthropometric markers were assessed using univariate and multivariate analyses. Receiver operating characteristic (ROC) curve analysis was used to identify the optimal values that best predict metabolic risk of each anthropometric marker. Among the studied predictors, BMI z-score, followed by BMI and WC, was most highly associated with zMR, while WC/H was the weakest predictor. ROC analyses showed significant AUCs for all markers, yet the discrimination was poor (AUCs from 0.60 to 0.68), with sensitivity ranging from 45.5% to 67.5% and specificity from 72.6% to 81.9%. The optimal cut-off values to predict metabolic risk were 1.62, 23.1 kg/m , 71.0 cm, 18.0 kg/m , 0.47, and 0.50, for BMI z-score, BMI, WC, TPI, WC/H, and WC/H , respectively. BMI z-score, followed by BMI and WC, were the most relevant anthropometric markers to predict metabolic risk in youth, while WC/H was the worst predictor. Results suggest that anthropometric markers should continue to be used as clinical tools to identify youth at risk.

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