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August 18, 2025ChildrenOpen Access

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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Machine learning improves predictions of children's subjective well-being from sports participation, suggesting effective intervention pathways.

Children's subjective well-being predictions were improved using machine learning techniques, particularly through sports participation.
The study analyzed 128,184 records, achieving an R2 of up to 0.504 with models like XGBoost and LightGBM.
Cross-country validation methods ensured robustness, while self-reported sports activity positively impacted well-being predictions.
These findings suggest the need for longitudinal studies to examine causality and reduce cultural biases in data collection.

Souza-Lima et al. (2025) studied this question.

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August 18, 2025ChildrenOpen Access

View Full Paper

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Authors

Machine learning improves predictions of children's subjective well-being from sports participation, suggesting effective intervention pathways.

Children's subjective well-being predictions were improved using machine learning techniques, particularly through sports participation.
The study analyzed 128,184 records, achieving an R2 of up to 0.504 with models like XGBoost and LightGBM.
Cross-country validation methods ensured robustness, while self-reported sports activity positively impacted well-being predictions.
These findings suggest the need for longitudinal studies to examine causality and reduce cultural biases in data collection.

Souza-Lima et al. (2025) studied this question.

View Full Paper

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Bookmark