Engagement-Supportive Mechanisms in Stress-Focused Digital Mental Health Interventions With Adaptive, Conversational, and Context-Aware Systems: Narrative Review.

Background: Stress is a widespread public health concern associated with cardiovascular disease, depression, anxiety, and reduced quality of life. Digital mental health interventions (DMHIs), delivered through smartphones, web platforms, and wearables, offer scalable support for stress management but often struggle to sustain user engagement. Evidence describing how adaptive, conversational, and context-aware mechanisms function together within stress-focused DMHIs remains fragmented. Objective: This review aims to synthesize evidence on engagement-supportive mechanisms in stress-focused DMHIs, with attention to personalization; biometric feedback; just-in-time adaptive interventions (JITAIs); gamification; AI, including large language models (LLMs); and methodological approaches to studying engagement. It also examines methodological, motivational, and experiential dimensions of engagement. Methods: This narrative review was informed by a structured search of PubMed, IEEE Xplore, and Scopus for peer-reviewed studies published between January 2018 and April 2025, followed by an evidence update through August 2026. Eligible studies described stress-focused DMHIs or related affective support interventions reporting at least 1 engagement-supportive mechanism. Randomized controlled trials, qualitative investigations, simulation or modeling studies, and implementation studies were included. Data were extracted using a structured form capturing study characteristics, engagement mechanisms, outcomes, and alignment with the review domains. Descriptive mapping summarized study types and mechanism frequencies, while a narrative synthesis integrated behavioral, experiential, methodological, and theory-informed findings. Results: Thirty-two studies met the inclusion criteria. Randomized or microrandomized controlled trials were the most common design (n=17), followed by qualitative investigations (n=6), design or implementation studies (n=3), mixed methods studies (n=3), simulation or modeling studies (n=2), and a quasi-experimental study (n=1). Dynamic adaptation was identified in 14 studies, conversational support in 10, static tailoring in 9, social or co-design mechanisms in 9, context-aware prompting in 7, gamified reinforcement in 5, biometric-responsive adaptation in 4, and narrative immersion in 2. These mechanisms frequently overlapped within layered adaptive architectures rather than functioning as isolated engagement features. AI-enabled systems included conversational agents, LLM-supported systems, recommender systems, reinforcement learning, and computational modeling approaches. Outcomes included behavioral indicators such as adherence, retention, and prompt receptivity, alongside experiential indicators such as perceived relevance, trust, emotional support, and immersion. Evidence strength varied substantially; many AI-enabled systems contributed feasibility, acceptability, or optimization-oriented evidence rather than definitive comparative effectiveness evidence. Conclusions: Stress-focused DMHIs are increasingly adopting adaptive, conversational, and context-aware engagement-supportive mechanisms, but the evidence base remains heterogeneous and methodologically uneven. Dynamic adaptation, conversational support, and static tailoring were most frequently represented, whereas biometric-responsive adaptation, narrative immersion, and deeply integrated gamified systems were less common. AI-enabled systems may support contextual responsiveness and personalized interaction but require transparent adaptive logic, privacy safeguards, and human-centered evaluation. Future work should prioritize longitudinal, mixed methods research capable of distinguishing behavioral engagement, experiential engagement, feasibility, acceptability, and sustained stress-related outcomes.