Authors
GuanYu Lu, XuPeng Zhang, Shuo Han, ZhenYu Jia, DanPing Shi
Published in
Frontiers in public health. Volume 14. Pages 1921844. Epub Sep 23, 2026.
Abstract
To develop and validate a dynamic risk prediction model (Landmark Supermodel) for identifying college students at risk of physical fitness failure by senior year, and to determine the optimal intervention window through sequential risk updating.
A total of 4,006 college students (2,745 males; 1,261 females) with four-year longitudinal physical fitness test data were included. Ten physical fitness indicators were assessed annually. Sex-stratified Landmark Supermodels were constructed at three landmark time points using Random Forest and XGBoost classifiers with 57 engineered features encompassing static snapshots, cumulative summaries, change trajectories, variability, and interaction terms. Model performance was evaluated by AUC, Brier score, calibration curves, and decision curve analysis.
The Random Forest Landmark Supermodel achieved AUC of 0.842 (95% CI: 0.831-0.853) for males and 0.773 (95% CI: 0.737-0.818) for females. AUC improved from 0.751 (post-freshman) to 0.858 (post-sophomore) to 0.894 (post-junior) in males. At the junior-year landmark, risk stratification separated male students into high-risk (44.3% failure rate), medium-risk (6.7%), and low-risk (1.3%) groups. Vital capacity, endurance running performance, and BMI trajectories emerged as the most important predictive features.
Dynamic landmark risk prediction can accurately identify students at risk of physical fitness failure, with the sophomore year representing the optimal intervention window. The model provides an evidence-based tool for individualized early warning in physical education.
PMID:
42846055
Bibliographic data and abstract were imported from PubMed on 08 Oct 2026.
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