使用机器学习技术预测儿童从事体育活动和体育活动的主观幸福感:来自多国研究的证据
Josivaldo de Souza-Lima1,2, Gerson Ferrari3, Rodrigo Yáñez-Sepúlveda1
1Facultad de Educación y Ciencias Sociales, Instituto del Deporte y Bienestar, Universidad Andres Bello, Las Condes, Santiago 7550000, Chile.
Children (Basel, Switzerland)
|August 28, 2025
概括
机器学习模型有效地预测了儿童的主观幸福感 (SWB),揭示了与体育参与的密切联系. 这些先进的方法接近理论限制,为体力活动提供了洞察力.
科学领域:
- 儿童心理学
- 计算社会科学
- 运动科学
背景情况:
- 传统的模型在儿童主观福祉 (SWB) 中与非线性关系作斗争.
- 身体活动是SWB的已知相关性,但复杂的模式在全球范围内仍未得到充分探索.
- 这项研究解决了了解不同人群中SWB驱动器的先进分析技术的需求.
研究的目的:
- 评估机器学习 (ML) 用于预测儿童的SWB,重点是体育参与.
- 使用大型国际数据集探索SWB预测的理论限制.
- 确定6至14岁儿童中SWB的关键预测因素及其与体育活动的关系.
主要方法:
- 分析了35个国家的128,184份ISCWeB调查记录.
- 使用GroupKFold交叉验证和留下一个国家的验证,以获得可靠的,可通用的结果.
- 采用XGBoost和LightGBM,并使用SHAP进行解释和启动以进行错误估计.
主要成果:
- 机器学习模型 (XGBoost,LightGBM) 的表现优于OLS,达到0.504的R2.
- 体育参与变量对SWB进行了积极预测 (SHAP值:+0.15-0.25),对模型的准确性做出了重大贡献.
- ML模型捕获了68.1%的可解释差异,接近理论可预测性极限.
结论:
- 与传统方法相比,机器学习显著提高了儿童SWB的预测能力.
- 这些发现强调了体育参与对儿童福祉的重要性,并表明了数据驱动干预的潜力.
- 建议进行进一步的纵向研究以确定因果关系并减轻自我报告数据中的文化偏差.
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