Background: Insufficient physical exercise is a growing public health concern in China, where only 30.3% of adults exercise regularly. Exploring the key factors associated with physical exercise participation is essential for promoting healthier lifestyles. Method: This study utilized data from the 2021 China General Social Survey (CGSS) to apply a progressive framework of dimensionality reduction, machine learning prediction, and SHAP-based interpretability analysis. A total of 19 potential factors were considered, with LassoCV used for feature selection and multiple models constructed for comparison. Results: The SVM model showed the best predictive performance. SHAP analysis revealed that watching sports events, household registration, educational attainment, subjective well-being, smoking, age, sleep quality, social activities, and residence suitability for physical exercise are the most important factors influencing participation. Higher education, greater subjective well-being, urban residency, frequent sports viewing, and residence suitability for physical exercise were positively associated with participation, while smoking and poor sleep quality were negatively associated with it. Conclusion: This study highlights the value of combining machine learning with interpretability methods to uncover the key predictors of physical exercise. The findings provide new evidence on the social, psychological, and environmental factors associated with Chinese adults’ exercise behavior, offering insights for targeted health promotion strategies.
Lu et al. (Fri,) studied this question.