ArticlePloS one2026
Determinants of intention to use generative AI fitness assistants: Integrating the Theory of Planned Behavior, second-order information system quality, and perceived value.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
As generative artificial intelligence (GAI) becomes embedded in sport and health management, GAI fitness assistants can generate and iteratively refine training plans through conversational interactions; however, their adoption remains shaped by users' trade-offs between perceived quality and risk. Drawing on the Information Systems Success Model and the Theory of Planned Behavior (TPB), and incorporating perceived value, this study analyzed cross-sectional, self-reported data from 342 valid questionnaires using structural equation modeling to examine the relationships among second-order information system quality, subjective norms, attitude, perceived behavioral control, and category-level behavioral intention to use GAI fitness assistants. The results indicate that information system quality was significantly associated with more favorable attitude (β = 0.623) and perceived behavioral control (β = 0.485), with system quality contributing the greatest weight (0.805). Bootstrapped indirect-effect tests showed that subjective norm was significantly associated with behavioral intention through attitude, perceived behavioral control, perceived value, and the serial attitude-to-value pathway, whereas its direct effect was not significant. Perceived value is the strongest predictor of intention (β = 0.447), and attitude (β = 0.275) and perceived behavioral control (β = 0.163) are also important antecedents. Overall, the model explains 50.9% of the variance in intention to use. These findings suggest theoretical and practical implications for improving the stability of GAI fitness assistants, the reliability of their recommendations, and the communication of user value.
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