Feasibility and User Experience of an AI-Supported mHealth Intervention for Remote Life Goal Setting Based on Flow Theory: Exploratory Within-Participant Study

TL;DR

This study explores how an AI-powered mobile health tool helps people set life goals remotely, finding it feasible and well-accepted with benefits in motivation and accessibility.

Contribution

The study introduces an AI-supported mHealth intervention for life goal setting based on flow theory, comparing it to human-facilitated approaches in a real-world setting.

Findings

AI-supported sessions received higher scores on suggestion appropriateness compared to occupational therapist-facilitated sessions.

AI emphasized actionability and motivation, while human facilitation focused on reflection and emotional safety.

Participants found AI interactions accessible and conducive to health behavior change.

Abstract

Life goal setting contributes substantially 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 artificial intelligence (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.

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-guided and human-guided interventions for self-management and motivation enhancement.

An exploratory, within-participant, 2-condition comparison with a counterbalanced order was conducted among 28 community-dwelling adults (aged between 20 and 76 years) who were smartphone users. Each participant selected 2 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 OT, 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 data were analyzed using Wilcoxon signed-rank tests with effect size calculations and Benjamini-Hochberg correction for multiple comparisons. Qualitative differences were explored using text mining (term frequency–inverse document frequency analysis) and sentiment evaluation.

Both AI-supported and OT-facilitated R-ACS sessions were feasible and successfully delivered tailored suggestions for all participants. AI-supported sessions received higher scores on all rubric items than OT-facilitated sessions, with a statistically significant difference in suggestion appropriateness (z score=3.13; P=.002; r=0.418; false discovery rate–adjusted P=.008). Term frequency–inverse document frequency 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.

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. OT-facilitated support provides complementary strengths by 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.

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