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<p>Urban–rural disparities in physical fitness during early childhood remain insufficiently characterized. This study aimed to identify physical fitness phenotypes among preschool children in urban and rural settings and evaluate their predictability using anthropometric and demographic features. A total of 1049 children aged 3–6 years were assessed using standardized fitness tests. K-means clustering identified latent fitness profiles separately in urban and rural groups. Logistic regression, random forest, and XGBoost models were trained to predict cluster membership using anthropometric and demographic variables. Two distinct fitness phenotypes were consistently identified, with Cluster&#xa0;1 demonstrating superior strength, power, and coordination (<InlineEquation ID="IEq1"><EquationSource Format="TEX">\(p &lt; 0.001\)</EquationSource></InlineEquation>). Supervised models achieved moderate predictive performance (accuracy <InlineEquation ID="IEq2"><EquationSource Format="TEX">\(\approx\)</EquationSource></InlineEquation> 0.79–0.80; AUC <InlineEquation ID="IEq3"><EquationSource Format="TEX">\(\approx\)</EquationSource></InlineEquation> 0.85–0.86). Model interpretation indicated that age and height were the most influential predictors, while body composition and demographic variables contributed less. Preschool children exhibit distinct, region-dependent fitness phenotypes. While these phenotypes are primarily defined by motor performance, their prediction based on anthropometric data alone is moderate, highlighting the importance of functional assessments for early fitness evaluation.</p>

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Regional differences in physical fitness profiles of preschool children in Chongqing China revealed by machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月24日

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  • Jing Gong,
  • Xianglong Yu,
  • Duanhao Wang

摘要

Urban–rural disparities in physical fitness during early childhood remain insufficiently characterized. This study aimed to identify physical fitness phenotypes among preschool children in urban and rural settings and evaluate their predictability using anthropometric and demographic features. A total of 1049 children aged 3–6 years were assessed using standardized fitness tests. K-means clustering identified latent fitness profiles separately in urban and rural groups. Logistic regression, random forest, and XGBoost models were trained to predict cluster membership using anthropometric and demographic variables. Two distinct fitness phenotypes were consistently identified, with Cluster 1 demonstrating superior strength, power, and coordination (\(p < 0.001\)). Supervised models achieved moderate predictive performance (accuracy \(\approx\) 0.79–0.80; AUC \(\approx\) 0.85–0.86). Model interpretation indicated that age and height were the most influential predictors, while body composition and demographic variables contributed less. Preschool children exhibit distinct, region-dependent fitness phenotypes. While these phenotypes are primarily defined by motor performance, their prediction based on anthropometric data alone is moderate, highlighting the importance of functional assessments for early fitness evaluation.