Key personality and training factors influencing athletes’ mental health - based on machine learning - Guoxiao Sun; Jun Wang; Shuangling Zhang (PLoS ONE, 2025)
Athletes face a higher risk of mental health disorders compared to the general population, and prior theoretical and empirical work suggests that personality traits and training-related factors may play important roles in shaping athletes’ mental health. This study aimed to identify the key personality traits and training-related factors associated with mental health outcomes among athletes. A total of 328 athletes (53% male, 17.84 ± 3.155 years) were assessed using standardized measures of demographics, training history, Cattell’s 16 Personality Factors, and mental health status, with psychological symptoms evaluated via the Symptom Checklist-90 (SCL-90). To determine the most influential factors related to mental disorders, both random forest and logistic regression models were applied. The results showed that 44.6% of athletes presented at least one symptom of a mental disorder. According to the random forest model, the most significant personality and training factors included injury impact, athletic level, training years, boldness, sensitivity, apprehension, and tension. Logistic regression analysis further indicated that high boldness, higher athletic level, and longer training years acted as protective factors, while greater injury impact, apprehension, tension, and sensitivity were associated with an increased risk of mental disorders. These results identify key risk and protective factors for mental health disorders among athletes, highlighting the need for individualized approaches in athlete mental health management, and emphasizing the importance of early identification and targeted interventions.