Associations of Maternal Prepregnancy Body Mass Index and Gestational Weight Gain With Physical Fitness in Childhood

Predicting VO2max in Children and Adolescents Aged between 6 and 17 Using Physiological Characteristics and Participation in Sport Activities: A Cross-Sectional Study Comparing Different Regression Models Stratified by Gender

Background: The aim of this study is to use different regression models to capture the association between cardiorespiratory fitness VO2max (measured in mL/kg/min) and somatometric characteristics and sports activities and making better predictions. Methods: multiple linear regression (MLR), quantile regression (QR), ridge regression (RR), support vector regression (SVR) with three different kernels, artificial neural networks (ANNs), and boosted regression trees (RTs) were compared to explain and predict VO2max and to choose the best performance model. The sample consisted of 4908 children (2314 males and 2594 females) aged between 6 and 17. Cardiorespiratory fitness was assessed by the 20 m maximal multistage shuttle run test and maximal oxygen uptake (VO2max) was calculated. Welch t-tests, Mann−Whitney-U tests, X2 tests, and ANOVA tests were performed. The performance measures were root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). All analyses were stratified by gender. Results: A comparison of the statistical indices for both the predicted and actual data indicated that in boys, the MLR model outperformed all other models in all indices, followed by the linear SVR model. In girls, the MLR model performed better than the other models in R2 but was outperformed by SVR-RBF in terms of RMSE and MAE. The overweight and obesity categories in both sexes (p < 0.001) and maternal prepregnancy obesity in girls had a significant negative effect on VO2max. Age, weekly football training, track and field, basketball, and swimming had different positive effects based on gender. Conclusion: The MLR model showed remarkable performance against all other models and was competitive with the SVR models. In addition, this study’s data showed that changes in cardiorespiratory fitness were dependent, to a different extent based on gender, on BMI category, weight, height, age, and participation in some organized sports activities. Predictors that are not considered modifiable, such as gender, can be used to guide targeted interventions and policies.

Early Life Factors Associated with Lean Body Mass in Spanish Children: CALINA Study

Early life is critical for the programming of body composition. The literature links perinatal factors with fat mass development and its future effects (e.g., obesity); however, little evidence exists between early life factors and lean body mass (LBM). This study follows up on a cohort of 416 Spanish children at ages six to eight, previously evaluated at birth in the CALINA study. Here, we studied the association between early life factors, LBM, and limb strength. Parental origin/nutritional status, maternal smoking during pregnancy, gestational diabetes/weight gain/age, birth weight (BW), early feeding, and rapid weight gain (RWG) were collected from primary care records. Bioimpedance analysis, dual-energy X-ray absorptiometry, peripheral quantitative computed tomography, and a handgrip/standing long jump test were used to assess fat-free mass index (FFMI), total lean soft tissue mass index (TLSTMI), muscle cross-sectional area index (MCSAI), and limb strength, respectively. In girls, maternal smoking, gestational age, and BW were positively associated with FFM/LSTM. In boys, the parents’ BMI, BW, and RWG were positively associated with FFM/LSTM. BW was associated with handgrip strength in both. Maternal BMI in girls and RWG in boys were negatively associated with the standing long jump. Early life programming plays a key role in determining LBM in children.