Frontiers in psychiatry · 2026
Physical education and psychosocial vulnerability in adolescent suicide-related risk: a transnational cross-sectional explainable machine-learning analysis of student health survey data.
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Liu Heyi, Guo Jianbin, Ma Xinwei, Shi Cheng, Ma Xiaoming, Li Jingwei
Аннотация
BACKGROUND: Adolescent suicide attempt is a major public health concern linked to psychosocial distress, school environment, health-risk behaviors, and physical activity patterns. This study aimed to develop an explainable XGBoost model for adolescent suicide-attempt status and to determine whether physical inactivity and low physical education (PE) participation could serve as informative school-based indicators.
METHODS: Data were obtained from the Global School-based Student Health Survey conducted in Argentina in 2018, Anguilla in 2016, and Jamaica in 2017. The Argentina dataset was used for model training, while Anguilla and Jamaica were combined as an independent validation cohort. The outcome was self-reported suicide attempt during the past 12 months. Candidate predictors included demographic, anthropometric, dietary, psychosocial, violence- and injury-related, tobacco-related, sexual behavior, physical activity, and PE-related variables. An XGBoost model was developed using nested cross-validation, staged hyperparameter tuning, and Bayesian fine-tuning. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC). SHAP and partial dependence plots were used for interpretation.
RESULTS: A total of 27,710 adolescents were included; 5,165 (18.6%) reported at least one suicide attempt. The final model achieved an independent validation AUC of 0.7928 (95% CI: 0.7790-0.8066). The top 18 SHAP-ranked predictors retained a cumulative 10-fold cross-validated AUC of 0.7865. Loneliness was the most influential predictor, followed by sex, insomnia, early cigarette use, PE days, physical attack, school peer kindness, and physical activity. Physical inactivity and low PE participation showed nonlinear associations with predicted suicide-attempt risk.
CONCLUSIONS: Loneliness and insomnia represented the psychosocial vulnerability profile, while physical inactivity and limited PE participation emerged as potentially informative school-based indicators requiring further longitudinal investigation. These findings support risk-stratified prevention strategies integrating early psychological identification with inclusive, socially supportive physical activity and PE programs.
METHODS: Data were obtained from the Global School-based Student Health Survey conducted in Argentina in 2018, Anguilla in 2016, and Jamaica in 2017. The Argentina dataset was used for model training, while Anguilla and Jamaica were combined as an independent validation cohort. The outcome was self-reported suicide attempt during the past 12 months. Candidate predictors included demographic, anthropometric, dietary, psychosocial, violence- and injury-related, tobacco-related, sexual behavior, physical activity, and PE-related variables. An XGBoost model was developed using nested cross-validation, staged hyperparameter tuning, and Bayesian fine-tuning. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC). SHAP and partial dependence plots were used for interpretation.
RESULTS: A total of 27,710 adolescents were included; 5,165 (18.6%) reported at least one suicide attempt. The final model achieved an independent validation AUC of 0.7928 (95% CI: 0.7790-0.8066). The top 18 SHAP-ranked predictors retained a cumulative 10-fold cross-validated AUC of 0.7865. Loneliness was the most influential predictor, followed by sex, insomnia, early cigarette use, PE days, physical attack, school peer kindness, and physical activity. Physical inactivity and low PE participation showed nonlinear associations with predicted suicide-attempt risk.
CONCLUSIONS: Loneliness and insomnia represented the psychosocial vulnerability profile, while physical inactivity and limited PE participation emerged as potentially informative school-based indicators requiring further longitudinal investigation. These findings support risk-stratified prevention strategies integrating early psychological identification with inclusive, socially supportive physical activity and PE programs.