Physical education and psychosocial vulnerability in adolescent suicide-related risk: a transnational cross-sectional explainable machine-learning analysis of student health survey data
5 affiliations
- 1Tianjin College, University of Science and Technology Beijing , ,
- 2Quality Control Office, Kailuan Mental Health Center , ,
- 3Department of Radiology, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University , ,
- 4Department of Cardiology, Suzhou BenQ Medical Center , ,
- 5Department of Neurology, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University , ,
Abstract
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.
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BibTeX
@article{liu2026physical,
title = {{Physical education and psychosocial vulnerability in adolescent suicide-related risk: a transnational cross-sectional explainable machine-learning analysis of student health survey data}},
author = {Heyi Liu and Jianbin Guo and Xinwei Ma and Cheng Shi and Xiaoming Ma and Jingwei Li},
journal = {Frontiers in psychiatry},
year = {2026},
month = oct,
doi = {10.3389/fpsyt.2026.1898064},
url = {https://europepmc.org/article/PMC/PMC13674449},
note = {CC BY},
}
RIS
TY - JOUR
TI - Physical education and psychosocial vulnerability in adolescent suicide-related risk: a transnational cross-sectional explainable machine-learning analysis of student health survey data
AU - Heyi Liu
AU - Jianbin Guo
AU - Xinwei Ma
AU - Cheng Shi
AU - Xiaoming Ma
AU - Jingwei Li
PY - 2026
DA - 2026/10/02
JO - Frontiers in psychiatry
DO - 10.3389/fpsyt.2026.1898064
UR - https://europepmc.org/article/PMC/PMC13674449
AB - 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.
SN - 1664-0640
ER -