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Predicting college students' exercise dependence: a machine learning approach.

Yihang Deng¹, Wei Lan², Mingda Si³

¹Department of Physical Education, Neijiang Normal University, Neijiang, China.

Frontiers in Psychology

|February 16, 2026

Summary

This summary is machine-generated.

Artificial intelligence effectively predicts exercise dependence risk in college students. Key predictors include prolonging exercise, dedicating excessive leisure time, and difficulty reducing exercise frequency.

Keywords:

college student ensemble learning (EN)exercise dependence behavior machine learning risk prediction

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Area of Science:

Sports Psychology
Behavioral Science
Artificial Intelligence in Health

Background:

Exercise dependence is a significant concern among college students.
Understanding its psychological mechanisms and predicting risks is crucial for intervention.

Purpose of the Study:

To explore exercise dependence mechanisms in college students using AI.
To develop and validate a robust AI model for predicting exercise dependence risk.

Main Methods:

Collected data from 2,745 college students via questionnaires on exercise dependence, psychological traits, and demographics.
Employed logistic regression, random forest, XGBoost, and multilayer perceptron algorithms.
Integrated models using ensemble learning (stacking) for enhanced predictive accuracy.

Main Results:

The stacking ensemble model achieved a high Area Under the Curve (AUC) of 0.96 for risk prediction.
Identified key predictors: prolonging exercise, excessive leisure time allocation, difficulty reducing frequency, and exceeding planned exercise duration.
Demonstrated the effectiveness of AI in identifying psychological and behavioral drivers of exercise dependence.

Conclusions:

AI methods, particularly ensemble learning, provide accurate and robust prediction of exercise dependence risk in college students.
Findings highlight critical psychological and behavioral factors contributing to exercise dependence.
AI offers a valuable tool for risk monitoring in sports psychology and student mental health contexts.