JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI)-generated lifestyle recommendations are increasingly used to support health behavior change across lifestyle domains. However, plausible or personalized 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 Joanna Briggs Institute (JBI) guidance and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR), we searched Ovid MEDLINE, Ovid Embase, APA PsycInfo via ProQuest, Web of Science Core Collection, Scopus, ACM Digital Library, and IEEE Xplore from inception to 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 articles 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; 8,570 unique records were screened, and 21 studies were included. Most were published in 2025 or 2026 (17/21, 81.0%). Large language model-centered systems were the most common format (12/21, 57.1%). Outcomes were concentrated in acceptability-related perceptions, such as satisfaction/enjoyment, perceived quality/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 from system-level acceptance and technical performance 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. Clinical Trial: Open Science Framework (OSF); bu3ga.