Prediction of Children’s Subjective Well-Being from Physical Activity and Sports Participation Using Machine Learning Techniques: Evidence from a Multinational Study

de Souza-Lima, Josivaldo; Ferrari, Gerson; Yáñez-Sepúlveda, Rodrigo; Giakoni-Ramírez, Frano; Muñoz-Strale, Catalina; Alarcon-Aguilar, Javiera; Parra-Saldias, Maribel; Duclos-Bastias, Daniel; Godoy-Cumillaf, Andrés; Merellano-Navarro, Eugenio; Bruneau-Chávez, José; Valdivia-Moral, Pedro

doi:10.3390/children12081083

Open AccessEditor’s ChoiceArticle

Prediction of Children’s Subjective Well-Being from Physical Activity and Sports Participation Using Machine Learning Techniques: Evidence from a Multinational Study

by

Josivaldo de Souza-Lima

^1,2

,

Gerson Ferrari

³

,

Rodrigo Yáñez-Sepúlveda

¹

,

Frano Giakoni-Ramírez

¹

,

Catalina Muñoz-Strale

¹

,

Javiera Alarcon-Aguilar

¹

,

Maribel Parra-Saldias

⁴,

Daniel Duclos-Bastias

⁵

,

Andrés Godoy-Cumillaf

^6,*

,

Eugenio Merellano-Navarro

⁷

,

José Bruneau-Chávez

⁸

and

Pedro Valdivia-Moral

²

¹

Facultad de Educación y Ciencias Sociales, Instituto del Deporte y Bienestar, Universidad Andres Bello, Las Condes, Santiago 7550000, Chile

²

Facultad de Ciencias de la Educación, Universidad de Granada, 18071 Granada, Spain

³

Escuela de Ciencias de la Actividad Física, el Deporte y la Salud, Universidad de Santiago de Chile (USACH), Santiago 7500618, Chile

⁴

Departamento de Educación Física, Deporte y Recreación, Universidad de Atacama, Copiapó 1530000, Chile

⁵

GEO Research Group, Escuela de Educación Física, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile

⁶

Grupo de Investigación en Educación Física, Salud y Calidad de Vida (EFISAL), Facultad de Educación, Universidad Autónoma de Chile, Temuco 4780000, Chile

⁷

Department of Physical Activity Sciences, Faculty of Education Sciences, Universidad Católica del Maule, Talca 3530000, Chile

⁸

Departamento de Educación Física, Deportes y Recreación, Universidad de la Frontera, Temuco 4811230, Chile

^*

Author to whom correspondence should be addressed.

Children 2025, 12(8), 1083; https://doi.org/10.3390/children12081083

Submission received: 29 July 2025 / Revised: 13 August 2025 / Accepted: 15 August 2025 / Published: 18 August 2025

(This article belongs to the Special Issue Lifestyle and Children's Health Development)

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Abstract

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.

Keywords: subjective well-being; machine learning; physical activity; children; XGBoost; SHAP; sports participation; physical literacy

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MDPI and ACS Style

de Souza-Lima, J.; Ferrari, G.; Yáñez-Sepúlveda, R.; Giakoni-Ramírez, F.; Muñoz-Strale, C.; Alarcon-Aguilar, J.; Parra-Saldias, M.; Duclos-Bastias, D.; Godoy-Cumillaf, A.; Merellano-Navarro, E.; et al. Prediction of Children’s Subjective Well-Being from Physical Activity and Sports Participation Using Machine Learning Techniques: Evidence from a Multinational Study. Children 2025, 12, 1083. https://doi.org/10.3390/children12081083

AMA Style

de Souza-Lima J, Ferrari G, Yáñez-Sepúlveda R, Giakoni-Ramírez F, Muñoz-Strale C, Alarcon-Aguilar J, Parra-Saldias M, Duclos-Bastias D, Godoy-Cumillaf A, Merellano-Navarro E, et al. Prediction of Children’s Subjective Well-Being from Physical Activity and Sports Participation Using Machine Learning Techniques: Evidence from a Multinational Study. Children. 2025; 12(8):1083. https://doi.org/10.3390/children12081083

Chicago/Turabian Style

de Souza-Lima, Josivaldo, Gerson Ferrari, Rodrigo Yáñez-Sepúlveda, Frano Giakoni-Ramírez, Catalina Muñoz-Strale, Javiera Alarcon-Aguilar, Maribel Parra-Saldias, Daniel Duclos-Bastias, Andrés Godoy-Cumillaf, Eugenio Merellano-Navarro, and et al. 2025. "Prediction of Children’s Subjective Well-Being from Physical Activity and Sports Participation Using Machine Learning Techniques: Evidence from a Multinational Study" Children 12, no. 8: 1083. https://doi.org/10.3390/children12081083

APA Style

de Souza-Lima, J., Ferrari, G., Yáñez-Sepúlveda, R., Giakoni-Ramírez, F., Muñoz-Strale, C., Alarcon-Aguilar, J., Parra-Saldias, M., Duclos-Bastias, D., Godoy-Cumillaf, A., Merellano-Navarro, E., Bruneau-Chávez, J., & Valdivia-Moral, P. (2025). Prediction of Children’s Subjective Well-Being from Physical Activity and Sports Participation Using Machine Learning Techniques: Evidence from a Multinational Study. Children, 12(8), 1083. https://doi.org/10.3390/children12081083

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Prediction of Children’s Subjective Well-Being from Physical Activity and Sports Participation Using Machine Learning Techniques: Evidence from a Multinational Study

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