High-Risk Factors and Gender Differences of Physical Inactivity among Chinese University Students in 2024

Purpose This study systematically identifies the high-risk factors for physical inactivity among Chinese university students, drawing upon data from the Chinese College Students Physical Activity and Health Longitudinal Survey (CPAHLS-CS). It further explores gender differences in these factors, providing a foundation for developing differentiated intervention strategies. Methods A stratified cluster sampling method was employed to conduct a questionnaire survey among 35,696 university students from general higher education institutions across China in 2024. The questionnaire was utilized to assess the students' PA levels and collect data on demographic, psychological, behavioral, and health indicators. Chi-square tests, Pearson correlation analysis, and binary logistic regression were conducted to analyze the risk factors and their gender differences. Results The detection rate of physical inactivity among university students was 74.33%, with a significantly higher rate observed in female students (54.19%) compared to male students (20.14%). Regression analysis revealed that low self-efficacy was a high-risk influencing factor (male OR = 1.926, 95% CI: 1.743–2.128; female OR = 2.080, 95% CI: 1.869–2.314). Myopia and smartphone addiction were identified as common risk factors across genders. Depressive symptoms were a positive predictor of inactivity for both males and females. Notably, anxiety served as a protective factor against physical inactivity solely for female students, whereas elevated BMI increased the risk of inactivity only among male students. Conclusion The issue of physical inactivity among Chinese university students is severe and exhibits marked gender differences. Female students are more susceptible to physical inactivity, while the risk for male students is more closely associated with weight perception. Low self-efficacy was identified as a high-risk influencing factor, whereas depression, smartphone addiction, and myopia are core risk factors across genders. The effects of anxiety and BMI show gender-specific patterns. Moving forward, precisely targeted interventions are needed to improve the PA levels of university students comprehensively.