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移动幸福感：使用机器学习探索儿童身体素养与幸福感之间的关系 ... ...

已有 377 次阅读2023-1-16 16:12 |个人分类:心理学、心理健康、心理咨询|系统分类:科技教育分享到微信

移动幸福感：使用机器学习探索儿童身体素养与幸福感之间的关系

Moving well-being well: Using machine learning to explore the relationship between physical literacy and well-being in children

——原载《应用心理学：健康与福祉》2023年1月10日在线版——

<Applied Psychology: Health and Well-Being> Version online: 10 January 2023

【摘要】体育素养为终身参与体育活动奠定了基础，从而带来积极的健康成果。体育素养与健康之间的直接途径尚未得到彻底研究。使用机器学习分析了儿童（n=1073，平均年龄10.86±1.20岁）身体素养与幸福感之间的关联。在身体能力领域评估了运动能力（TGMD-3和BOT-2）和与健康相关的健身（PACER和平板支撑）。在情感领域评估了动机（运动问卷中的适应行为调节）和信心（修改后的身体活动自我效能量表）。使用KIDSCREEN-27测量幸福感。使用五种机器学习分类器（决策树、随机森林、XGBoost、AdaBoost、k-最近邻）在整个样本和跨子组（性别、社会经济地位 [SES]、年龄）中调查了通过体育素养预测幸福感的准确性。XGBoost通过身体素养预测幸福感，在整个样本中的准确率为87%。低SES参与者的预测准确性最低。身体素养特征的贡献在各个亚组之间存在显着差异。身体素养可以预测儿童的幸福感，但身体素养特征对幸福感的相对贡献在不同亚组之间存在显着差异。

【关键词】儿童、健康、机器学习、体育素养、预测、福祉

[Abstract] Physical literacy provides a foundation for lifelong engagement in physical activity, resulting in positive health outcomes. Direct pathways between physical literacy and health have not yet been investigated thoroughly. Associations between physical literacy and well-being in children (n = 1073, mean age 10.86 ± 1.20 years) were analysed using machine learning. Motor competence (TGMD-3 and BOT-2) and health-related fitness (PACER and plank) were assessed in the physical competence domain. Motivation (adapted-Behavioural Regulation in Exercise Questionnaire) and confidence (modified-Physical Activity Self-Efficacy Scale) were assessed in the affective domain. Well-being was measured using the KIDSCREEN-27. Accuracy of predicting well-being from physical literacy was investigated using five machine learning classifiers (decision tree, random forest, XGBoost, AdaBoost, k-nearest neighbour) in the full sample and across subgroups (sex, socioeconomic status [SES], age). XGBoost predicted well-being from physical literacy with an accuracy of 87% in the full sample. Predictive accuracy was lowest in low SES participants. Contribution of physical literacy features differed substantially across subgroups. Physical literacy predicts well-being in children but the relative contribution of physical literacy features to well-being differs substantially between subgroups.

[Key words] children, health, machine learning, physical literacy, prediction, well-being

论文原文：Úna Britton, Oluwadurotimi Onibonoje, Sarahjane Belton, Stephen Behan, Cameron Peers, Johann Issartel, Mark Roantree (2023). Moving well-being well: Using machine learning to explore the relationship between physical literacy and well-being in children. Applied Psychology: Health and Well-Being. Version online: 10 January 2023.

https://doi.org/10.1111/aphw.12429

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