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Related Experiment Video

Updated: Sep 11, 2025

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity
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Analysis of the exercise intention-behavior gap among college students using explainable machine learning.

Cui Cui1,2, Jixin Yin1

  • 1Department of Sports, Huanghe Jiaotong University, Jiaozuo, Henan, China.

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Summary

College students

Keywords:
college studentexplainable machine learningfeature engineeringintention-behavior gapphysical activity promotion

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Area of Science:

  • Public Health
  • Behavioral Science
  • Sports Science

Background:

  • College student physical fitness is a global public health issue.
  • The intention-behavior gap hinders physical activity engagement among students.

Purpose of the Study:

  • To identify factors influencing the intention-behavior gap in physical activity among college students.
  • To predict the intention-behavior gap using machine learning models.

Main Methods:

  • Survey data from TikTok-using college students.
  • Machine learning models to predict the intention-behavior gap.
  • SHapley Additive exPlanations (SHAP) for feature importance analysis.

Main Results:

  • Perceived barriers were the most significant factor in the intention-behavior gap.
  • Male students with higher academic grades, fewer perceived barriers, and stronger subjective norms were less likely to exhibit the gap.

Conclusions:

  • University health promotion should focus on reducing perceived barriers.
  • Creating supportive campus environments and optimizing physical education resources are crucial.
  • Interventions should aim to translate physical activity intentions into consistent behaviors.