Analysis of the exercise intention-behavior gap among college students using explainable machine learning
IntroductionThe 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...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1613553/full |
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| author | Cui Cui Cui Cui Jixin Yin |
| author_facet | Cui Cui Cui Cui Jixin Yin |
| author_sort | Cui Cui |
| collection | DOAJ |
| description | IntroductionThe 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.MethodsThis 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.ResultsAmong 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.DiscussionThese 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. |
| format | Article |
| id | doaj-art-050413ca96334367b25fc21f4c4c78bd |
| institution | DOAJ |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj-art-050413ca96334367b25fc21f4c4c78bd2025-08-20T03:13:43ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-07-011310.3389/fpubh.2025.16135531613553Analysis of the exercise intention-behavior gap among college students using explainable machine learningCui Cui0Cui Cui1Jixin Yin2Department of Sports, Huanghe Jiaotong University, Jiaozuo, Henan, ChinaGangneung Wonju University, Gangneung, Gangwon Province, Republic of KoreaDepartment of Sports, Huanghe Jiaotong University, Jiaozuo, Henan, ChinaIntroductionThe 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.MethodsThis 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.ResultsAmong 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.DiscussionThese 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.https://www.frontiersin.org/articles/10.3389/fpubh.2025.1613553/fullphysical activity promotioncollege studentintention-behavior gapexplainable machine learningfeature engineering |
| spellingShingle | Cui Cui Cui Cui Jixin Yin Analysis of the exercise intention-behavior gap among college students using explainable machine learning Frontiers in Public Health physical activity promotion college student intention-behavior gap explainable machine learning feature engineering |
| title | Analysis of the exercise intention-behavior gap among college students using explainable machine learning |
| title_full | Analysis of the exercise intention-behavior gap among college students using explainable machine learning |
| title_fullStr | Analysis of the exercise intention-behavior gap among college students using explainable machine learning |
| title_full_unstemmed | Analysis of the exercise intention-behavior gap among college students using explainable machine learning |
| title_short | Analysis of the exercise intention-behavior gap among college students using explainable machine learning |
| title_sort | analysis of the exercise intention behavior gap among college students using explainable machine learning |
| topic | physical activity promotion college student intention-behavior gap explainable machine learning feature engineering |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1613553/full |
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