JMIR Publications

ABSTRACT

Background:

Life goal setting contributes significantly to well-being and quality of life, particularly among middle-aged and older adults. However, delivering remote goal-setting support remains challenging due to limited professional resources and accessibility barriers. Recent advancements in mobile health (mHealth) technologies, telemedicine, and generative AI present new opportunities for scalable, personalized health behavior interventions. Nevertheless, few studies have compared AI-driven life goal interventions with conventional human-facilitated approaches in real-world settings.

Objective:

This study aimed to evaluate the feasibility and user experience of an AI-supported mHealth intervention for remote life goal setting based on Flow Theory. We compared the AI-supported approach to occupational therapist (OT)-facilitated support and explored the differential characteristics of AI- and human-guided interventions for self-management and motivation enhancement.

Methods:

An exploratory crossover study was conducted with 28 community-dwelling adults (aged 20s to 70s, smartphone users). Each participant selected two personal life goals and completed remote Adjusting the Challenge-Skill Balance (R-ACS) sessions, a structured telemedicine process based on Flow Theory. One goal was supported by an occupational therapist, while the other was facilitated by a generative AI chatbot integrated into an mHealth platform. Following each session, participants completed a 4-item rubric-based questionnaire (5-point Likert scale) assessing the quantity and quality of questions, appropriateness of suggestions, and perceived contribution to goal attainment. Free-text feedback was also collected. Quantitative outcomes were analyzed using Mann-Whitney U tests with effect size estimation and Benjamini-Hochberg correction. Qualitative differences were explored through text mining (TF-IDF analysis) and sentiment evaluation.

Results:

Both AI-supported and OT-facilitated R-ACS sessions were feasible and successfully delivered tailored suggestions for all participants. AI-supported sessions received higher mean scores across all questionnaire items compared to OT-facilitated sessions, with a statistically significant difference in suggestion appropriateness (U=584.5, p=0.00068; FDR-adjusted p=0.00272; r=0.422). TF-IDF analysis of free-text comments revealed that AI-supported sessions emphasized actionability, motivation, and immediacy, while OT-facilitated sessions highlighted reflection, self-understanding, and emotional safety. Participants expressed high acceptance of both intervention types, with AI-supported interactions perceived as particularly accessible and conducive to health behavior change.

Conclusions:

AI-supported mHealth interventions for remote life goal setting based on Flow Theory are feasible, well accepted, and offer potential advantages in immediacy, motivation enhancement, and action-oriented support. Human-facilitated support by occupational therapists provides complementary strengths in fostering reflection and psychological safety. A hybrid R-ACS model that integrates both AI and human expertise may optimize personalized, scalable self-management support for life goal setting. Future randomized controlled trials are warranted to further investigate the long-term impact of AI-driven mHealth interventions on health behavior, well-being, and quality of life. Clinical Trial: Not applicable of this study.