ArticleFrontiers in public health2026
The impact of intention to adopt generative AI for exercise information on exercise adherence among Chinese college students: the mediating role of autonomous motivation and network analysis.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Longitudinal associations between physical activity and negative emotions among college students: the chain mediating roles of exercise self-efficacy and psychological resilience.Frontiers in psychology · 2026Article
- Dynamic interactions between physical activity, exercise adherence, and adverse psychological states in Chinese older adults: a cross-lagged network analysis.Frontiers in public health · 2026Article
- The relationship between restrained eating and physical activity among female university students: a cross-lagged study.Frontiers in nutrition · 2026Article
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Authors and funding
6 authors.
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Abstract
Background: Insufficient exercise adherence among college students is a common health issue, while generative artificial intelligence (AI) provides a new approach for personalized exercise guidance. However, the extent to which the intention to adopt generative AI for exercise information affects exercise adherence, and the role of autonomous motivation in this process, remains underexplored in empirical research. Methods: This study employed a cross-sectional survey design and administered a questionnaire to 1,878 Chinese undergraduates Results: The intention to adopt generative AI for exercise information was significantly positively correlated with exercise adherence among college students ( Conclusion: This study explores the associations and potential mechanisms by which generative AI may relate to exercise adherence via autonomous motivation, supporting a theoretical pathway of "technology adoption-motivation internalization-behavior persistence." The findings offer a novel perspective on the theoretical associations underlying AI-enabled health behaviors and provide preliminary correlational evidence to inform the future design of generative AI applications for health interventions.
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