๐Ÿง‘๐Ÿผโ€๐Ÿ’ป Research - August 19, 2025

Analysis of the exercise intention-behavior gap among college students using explainable machine learning.

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โšก Quick Summary

This study explored the exercise intention-behavior gap among college students, revealing that perceived barriers significantly influence students’ physical activity behaviors. Utilizing explainable machine learning, the research highlights the importance of addressing these barriers to promote healthier lifestyles among students. ๐Ÿƒโ€โ™‚๏ธ

๐Ÿ” Key Details

  • ๐Ÿ“Š Dataset: Survey data from TikTok-using college students
  • ๐Ÿงฉ Features used: Gender, academic grade, health belief perceptions, planned behavior perceptions
  • โš™๏ธ Technology: Multiple machine learning models, with SHAP for feature importance
  • ๐Ÿ† Key finding: Perceived barriers were the most influential factor in the intention-behavior gap

๐Ÿ”‘ Key Takeaways

  • ๐Ÿ“‰ Intention-behavior gap is a critical issue affecting college students’ fitness.
  • ๐Ÿ’ก Male students in higher academic grades are less likely to exhibit this gap.
  • ๐Ÿšง Perceived barriers significantly hinder students’ physical activity intentions.
  • ๐ŸŒฑ Supportive campus environments can help bridge the intention-behavior gap.
  • ๐Ÿ“ˆ Health promotion strategies should focus on reducing these barriers.
  • ๐Ÿค– Machine learning provides valuable insights into student behavior patterns.
  • ๐Ÿซ Study conducted among college students, highlighting a relevant public health concern.

๐Ÿ“š Background

The physical fitness of college students has become a growing global public health concern. Many students express a desire to engage in physical activity; however, there exists a significant intention-behavior gapโ€”the disconnect between their intentions and actual behaviors. Understanding this gap is essential for developing effective interventions that promote healthier lifestyles among students.

๐Ÿ—’๏ธ Study

This study utilized survey data from TikTok-using college students, examining various factors such as gender, academic grade, and health beliefs. By employing multiple machine learning models, the researchers aimed to predict the presence of the intention-behavior gap and identify the most influential factors contributing to it. The SHapley Additive exPlanations (SHAP) method was applied to interpret the feature importance of the best-performing model.

๐Ÿ“ˆ Results

The analysis revealed that perceived barriers were the most significant factor contributing to the intention-behavior gap. Additionally, male students in higher academic grades, who reported fewer perceived barriers and stronger subjective norms regarding physical activity, were significantly less likely to exhibit this gap. These findings underscore the importance of addressing perceived barriers to enhance physical activity among students.

๐ŸŒ Impact and Implications

The implications of this study are profound. By focusing on reducing perceived barriers and fostering a supportive campus environment for physical activity, universities can effectively transform students’ intentions into consistent, health-promoting behaviors. This research highlights the potential for targeted health promotion strategies to improve the overall fitness and well-being of college students, ultimately contributing to better public health outcomes. ๐ŸŒŸ

๐Ÿ”ฎ Conclusion

This study emphasizes the critical role of understanding the exercise intention-behavior gap among college students. By leveraging machine learning techniques, we can gain valuable insights into the factors influencing student behavior. Moving forward, it is essential for universities to implement strategies that address perceived barriers and promote a culture of physical activity, paving the way for healthier student populations.

๐Ÿ’ฌ Your comments

What are your thoughts on the findings of this study? How can universities better support students in overcoming barriers to physical activity? Let’s engage in a discussion! ๐Ÿ’ฌ Feel free to share your insights in the comments below or connect with us on social media:

Analysis of the exercise intention-behavior gap among college students using explainable machine learning.

Abstract

INTRODUCTION: The physical fitness of college students is a growing global public health concern. A critical challenge in improving student fitness is addressing the intention-behavior gap-the disconnect between students’ intentions to engage in physical activity and their actual behavior.
METHODS: This study utilized survey data from TikTok-using college students, incorporating variables such as gender, academic grade, health belief perceptions, and planned behavior perceptions. Multiple machine learning models were developed to predict the presence of the intention-behavior gap. The performance of these models was evaluated, and SHapley Additive exPlanations (SHAP) was applied to the best-performing model to interpret feature importance.
RESULTS: Among the models tested, SHAP analysis revealed that perceived barriers were the most influential factor contributing to the intention-behavior gap. Furthermore, the results indicated that male students in higher academic grades, with fewer perceived barriers and stronger subjective norms regarding physical activity, were significantly less likely to exhibit this gap.
DISCUSSION: These findings suggest that university health promotion strategies should focus on reducing perceived barriers, cultivating a supportive campus environment for physical activity, and optimizing the allocation of physical education resources. Such measures may effectively support the transformation of students’ physical activity intentions into consistent, health-promoting behaviors.

Author: [‘Cui C’, ‘Yin J’]

Journal: Front Public Health

Citation: Cui C and Yin J. Analysis of the exercise intention-behavior gap among college students using explainable machine learning. Analysis of the exercise intention-behavior gap among college students using explainable machine learning. 2025; 13:1613553. doi: 10.3389/fpubh.2025.1613553

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