User Acceptability and Adoption of AI-Generated Lifestyle Intervention Recommendations: Scoping Review and Theoretical Integration

Clinical Snapshot

Key Findings

• Effect Size: Not applicable — scoping review with descriptive synthesis only; no pooled effect sizes calculated

• Primary Outcome: Mapping of user acceptability and adoption of AI-generated lifestyle recommendations across 21 empirical studies; acceptability-related perceptions (satisfaction, enjoyment, perceived quality/fit, persuasiveness) were more commonly assessed than adoption-related outcomes (intention, in-study uptake, short-term enactment)

• Nnt Or Sensitivity: Not applicable — no NNT, sensitivity, specificity, or hazard ratio calculable from scoping review data; the review identifies that large language model-centred systems were the most common format (12/21 studies, 57.1%) and that most studies were published in 2025-2026 (17/21, 81%)

• Confidence Interval: Not applicable — no meta-analytic pooling conducted

Clinical Application

The integrative perception-intention-enactment framework provides a conceptual tool for clinicians, health informaticists, and system designers evaluating or commissioning AI-generated lifestyle recommendation systems. Practical application requires that systems elicit and adapt to user context, deliver actionable and credible content, support user autonomy and trust, and account for real-world resource and social constraints. Implementation feasibility in routine clinical settings remains undemonstrated given the preliminary evidence base. AI-generated lifestyle recommendation tools are not yet subject to specific TGA regulatory pathways as therapeutic goods in most configurations, though this is an evolving area as the TGA updates its Software as a Medical Device (SaMD) framework. The RACGP supports digital health integration in primary care but has not issued specific guidance on AI-generated lifestyle recommendations. PBS-listed pharmacotherapies for lifestyle-related conditions (e.g., obesity, type 2 diabetes) may eventually be complemented by AI recommendation tools, but evidence of clinical benefit is insufficient to support this integration at present. Australian primary care clinicians should approach commercially available AI lifestyle tools with caution, noting the absence of long-term efficacy and safety data. The My Health Record ecosystem and national digital health strategy provide infrastructure for future integration but require robust evidence before AI recommendation tools are embedded in care pathways. Adults and caregivers using AI-powered lifestyle recommendation tools for health behaviour change (physical activity, diet, sleep, and related domains); the framework may be relevant to patients with chronic conditions where lifestyle modification is a therapeutic target, though this is not explicitly confirmed in the included studies

Abstract

BACKGROUND: Artificial intelligence (AI)-generated lifestyle recommendations are increasingly used to support health behavior change. However, AI advice does not necessarily mean that users will accept or adopt those recommendations. Although prior reviews have examined AI-enabled lifestyle interventions and health behavior technologies, fewer have focused on whether users accept and adopt AI-generated recommendations. OBJECTIVE: This scoping review aimed to map user acceptability and adoption of AI-generated lifestyle recommendations in user-facing systems used by end users or caregivers. Objectives were to characterize systems and evaluation contexts, clarify how recommendation-level outcomes were conceptualized and measured, synthesize shaping factors, and develop an evidence-informed framework to guide future research, evaluation, and design. METHODS: Following JBI (Joanna Briggs Institute) guidance and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews), we searched Ovid MEDLINE, Ovid Embase, APA PsycInfo via ProQuest, Web of Science Core Collection, Scopus, ACM Digital Library, and IEEE Xplore from database inception to May 5, 2026. The initial search was conducted on November 21, 2025, and an updated search was conducted on May 5, 2026. Eligible studies reported empirical end-user or caregiver data, evaluated AI-generated lifestyle recommendation content delivered without manual review or editing, and reported an acceptability or adoption outcome linked to recommendations. English empirical papers and conference papers were included. Data were charted on study, system, outcome, measurement, factor, and theoretical characteristics. Quality was assessed with the Mixed Methods Appraisal Tool. Findings were synthesized descriptively and through evidence mapping. RESULTS: Searches yielded 12,997 records; 8570 unique records were screened, and 21 studies were included. Most were published in 2025 or 2026 (17/21, 81%). Large language model-centered systems were the most common format (12/21, 57.1%). Outcomes were concentrated in acceptability-related perceptions, such as satisfaction or enjoyment, perceived quality or fit, and persuasiveness, whereas adoption-related outcomes were assessed less often and mainly reflected intention, in-study uptake, or short-term enactment. Factors clustered across system capabilities, content properties, individual states and capacities, and contextual constraints. Findings informed an integrative perception-intention-enactment framework positioning acceptability and adoption as a system-content-user-context process. CONCLUSIONS: This review extends prior AI and digital health reviews by shifting attention toward how users perceive, intend to follow, and enact AI-generated lifestyle recommendations. Acceptability and adoption appear to depend on systems eliciting and adapting to context, content being actionable and credible, users having the capacity to interpret, trust, and engage with recommendations while retaining control, and resources, routines, and social contexts allowing enactment. The framework can guide theory-driven evaluation, outcome selection, and system design by identifying where recommendation processes may succeed or fail, but should be interpreted as preliminary and evidence-informed rather than causal. By integrating implementation, behavioral, and human-AI perspectives, this review provides a foundation for moving AI-generated lifestyle recommendations from technically plausible outputs toward user-centered, context-sensitive, and behaviorally actionable support.

References

1. 1.Ma, M., Sui, T., Zhou, S., Shi, L., & Lau, P. W. C. (2026). User acceptability and adoption of AI-generated lifestyle intervention recommendations: Scoping review and theoretical integration. Journal of Medical Internet Research. https://doi.org/10.2196/93573