Background/Objectives: Traditional models like ordinary least squares (OLS) struggle to capture non-linear relationships in children’s subjective well-being (SWB), which is associated with physical activity. This study evaluated machine learning (ML) for predicting SWB, focusing on sports participation, and explored theoretical prediction limits using a global dataset. It addresses a gap in understanding complex patterns across diverse cultural contexts. Methods: We analyzed 128,184 records from the ISCWeB survey (ages 6–14, 35 countries), with self-reported data on sports frequency, emotional states, and family support. To ensure cross-country generalizability, we used GroupKFold CV (grouped by country) and leave-one-country-out (LOCO) validation, yielding mean R² = 0.45 ± 0.05, confirming robustness beyond cultural patterns, SHAP for interpretability, and bootstrapping for error estimation. No pre-registration was required for this secondary analysis. Results: XGBoost and LightGBM outperformed OLS, achieving R² up to 0.504 in restricted datasets (sensitivity excluding affective leakage: R² = 0.35), with sports-related variables (e.g., exercise frequency) associated positively with SWB predictions (SHAP values: +0.15–0.25; incremental ΔR² = 0.06 over demographics/family/school base). Using test–retest reliability from literature (r = 0.74), the estimated irreducible RMSE reached 0.941; XGBoost achieved RMSE = 1.323, approaching the predictability bound with 68.1% of explainable variance captured (after noise adjustment). Partial dependence plots showed linear associations with exercise without satiation and slight age decline. Conclusions: ML improves SWB prediction in children, highlighting associations with sports participation, and approaches predictable variance bounds. These findings suggest potential for data-driven tools to identify patterns, such as through physical literacy pathways, informing physical activity interventions. However, longitudinal studies are needed to explore causality and address cultural biases in self-reports.

Background: Fitness is a powerful marker of health associated with physical activity (PA) in university students. However, insufficient PA is a serious health concern among university students. Active commuting provides an opportunity for increased PA levels. Therefore, the aims of this study were (a) to describe the mode of commuting, PA and fitness in university students; (b) to analyze the associations of mode of commuting with PA and fitness; and c) to analyze the relationship between mode of commuting, PA recommendations and fitness. Methods: This was a cross-sectional study. A total of 1257 university students (52.4% women) participated (22.4 ± 5.6 years old). Results: Public commuting was the main mode to and from university. Active and public commuters were more likely to meet the PA recommendations and reported higher muscular strength than those using private commuting. Active and public commuters who met PA recommendations present the highest fitness in most of its components. Conclusions: Achieving the PA recommendations was more relevant than adopting an active mode of commuting in order to have better fitness. Further research targeting a broader understanding of the mode of commuting, PA levels and fitness in university students is needed.

This study examines the association between socioeconomic factors, gender, physical activity, and health satisfaction among Chilean adolescents. It aims to identify key disparities and their implications for public health policies. Cross-sectional study based on the third wave (2016-2019) of the international Children's Worlds survey. A total of 911 adolescents aged 12-13 years from Santiago, Chile, participated. Physical activity levels, socioeconomic indicators, and health satisfaction were assessed. Statistical analyses included t-tests, chi-square tests, ANOVA, and multiple linear regression models stratified by gender. Boys reported higher levels of electronic gaming (64.6 % vs. 35.4 %, p < 0.001), while girls engaged more in outdoor play at lower frequencies (52.8 % vs. 47.2 %, p = 0.045). Perceived safety was a stronger predictor of health satisfaction for girls (β = 0.252, p < 0.001) than for boys (β = 0.251, p < 0.001). Socioeconomic and environmental factors significantly influence adolescent health satisfaction, with gender-specific differences. Policies should focus on improving access to recreational spaces and addressing economic barriers, particularly for girls.

Physical inactivity remains a major modifiable risk factor for non-communicable diseases and continues to exhibit marked socioeconomic and gender disparities in Latin America. Identifying robust and interpretable predictors of inactivity in nationally representative datasets is essential for informing public health strategies. This study compared a survey-weighted logistic regression model and an explainable machine learning approach (XGBoost) to predict physical inactivity among Chilean adults using data from the 2024 National Physical Activity and Sports Survey (ENAFyD; n = 5248). Models were evaluated on a stratified held-out test set (n = 1050) using weighted and unweighted area under the ROC curve (AUC), Brier scores, and calibration curves. Survey-weighted logistic regression achieved a weighted AUC of 0.801, while XGBoost achieved 0.797, demonstrating comparable discrimination. XGBoost showed marginally lower Brier scores, indicating slightly improved probabilistic calibration. Low socioeconomic status, female sex, lower monthly physical activity expenditure, limited facility access, and lower engagement with digital resources were consistently associated with higher inactivity risk. SHAP-style contribution analysis provided additional insight into feature-level influence within the machine learning framework. Overall, both approaches demonstrated similar predictive capacity, supporting the complementary use of classical regression and explainable machine learning for population-level physical inactivity research.

Background/Objectives: University students exhibit high levels of sedentary behavior and low adherence to physical activity recommendations, and immersive virtual reality (IVR) represents an innovative strategy to increase physical activity participation. The aim of this study was to evaluate the effect of a physical activity intervention using IVR on anthropometric variables, physical fitness, and blood pressure in university students. Methods: A randomized controlled trial was conducted with 60 participants (30 control, 30 intervention) over 12 weeks. The intervention group performed three weekly exercise sessions using IVR, while the control group maintained their usual activity. BMI, waist and hip circumferences, handgrip strength, cardiorespiratory fitness, and blood pressure were assessed. Baseline characteristics between groups were compared using Student’s t-test. The effect of the intervention was analyzed using analysis of covariance adjusted for baseline values. Sensitivity analyses were performed to assess between-group changes, and subgroup analyses were conducted to determine the impact of sex. Results: The intervention produced significant improvements in cardiorespiratory fitness (VO₂ and the 20 m shuttle run test); no significant changes were observed in anthropometric variables, strength, or blood pressure. Conclusions: A 12-week intervention with immersive virtual reality-based physical training improves cardiorespiratory fitness in university students, representing a promising tool for health promotion in this population.

Although regular physical activity is recommended for health, highly contaminated air exposure acts to the detriment of the benefits produced in individuals. The purpose of the present study was to compare the accelerometer-measured physical activity and sedentary behavior during the whole day, in-school time, out-of-school time, and school breaks between highly contaminated air days and non-highly contaminated air days in Chilean adolescents. Nineteen adolescents from Santiago of Chile were assessed by a GT3X accelerometer. The vertical axis and steps per minute for the whole day, and both of these variables together with the percentage of time of moderate-to-vigorous physical activity level in the out-of-school time period were higher in highly contaminated air days than in non-highly contaminated air days (p < 0.05; with effect sizes from r = 0.36 to r = 0.46). Results for sedentary behavior were similar in both conditions for every period of time analyzed. These results allow us to conclude the lack of awareness of the participants in this research regarding the health repercussions in relation to the physical activity performed during air pollution exposure. Some strategies in order to improve the Chilean adolescents’ physical activity and sedentary behavior are suggested and discussed.