Feasibility and usability of a ChatGPT-based app to support physical activity: A pilot study

Abstract

Background: FysBot is a ChatGPT-based mobile app developed to promote physical activity among adults living with obesity. This pilot study aimed to evaluate the feasibility and usability of FysBot. Methods: A 6-week single-arm pilot study was conducted in which patients from an obesity rehabilitation clinic in Norway used FysBot. This pilot study employed an explanatory sequential mixed-methods design combining questionnaires and post-intervention interviews. Participants completed questionnaires at baseline and weeks 2, 4, and 6, assessing leisure-time physical activity (Godin Leisure-Time Exercise Questionnaire (GODIN)), motivation (Behavioral Regulation in Exercise Questionnaire-2 and relative autonomy index (RAI)), Self-Efficacy for Exercise (SEE), and System Usability Scale (SUS). Semi-structured interviews were conducted to explore user experiences further. Quantitative data were analyzed descriptively, with multiple imputations for missing data, while qualitative data were analyzed thematically. Results: Fifty-three participants were eligible, 36 completed baseline, and 17 completed the final follow-up. App engagement declined steadily, with most participants ceasing use after week 2. The mean SUS score was 51.3, indicating below-average usability. The median of self-reported leisure-time physical activity (GODIN: 34–40) and overall motivation (RAI: 8.3–9.8) showed small, non-significant increases, while identified regulation increased significantly (2.8–3.3; p = 0.04) and SEE decreased (58–49). Qualitative findings supported these results, showing that participants valued the chatbot's motivational potential but experienced technical problems and limited personalization. Conclusions: This study offers insight into the potential of a ChatGPT-based physical activity app for adults living with obesity and highlights key areas for refinement. Future iterations should incorporate user-requested features through iterative co-design, with enhanced personalization and guidance to improve relevance and engagement.