Silvia Gabrielli

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.

Background: Digital microinterventions are increasingly used to support stress self-management in everyday life. While previous research has examined their feasibility, adherence, and preliminary effectiveness, much less is known about how these interventions are experienced over extended periods of real-world use. Objective: This study used collaborative autoethnography (CAE) to examine the everyday experience of living with MINT (Micro-Interventions for Navigating Tension), a Telegram-based chatbot for stress management. Our goal was not to evaluate clinical outcomes but to understand how scheduled microinterventions fit into everyday life, when they were ignored, and when people relied on other ways of coping instead, and to identify implications for the design of future digital mental health tools. Methods: We conducted a 4-week CAE involving 5 digital mental health researchers, who used the MINT chatbot as both participants and researchers. The experience was documented through multiple qualitative and quantitative sources, including reflective diaries, ecological momentary assessments (EMAs), workshop discussions, and wearable data, and analyzed collaboratively using reflexive thematic analysis (RTA). Results: Six interconnected themes described how MINT was experienced in everyday life: (T1) the importance of timing and emotional receptivity, (T2) the need for reciprocity and relational continuity, (T3) the tendency to favor existing coping strategies over suggested exercises, (T4) the meaning of nonengagement during periods of high stress, (T5) the collapse of self-initiated use after reminders were removed, and (T6) how everyday experiences with consumer wearables and general-purpose large language models reshaped expectations of digital mental health systems. Conclusions: Our findings show that CAE complements conventional evaluations of digital mental health interventions by revealing aspects of everyday use that are difficult to capture through feasibility studies, large-scale trials, or looking at usage data. Beyond the specific case of MINT, they suggest that future stress-management systems should better account for users’ receptivity, provide greater continuity across interactions, build on existing coping strategies, and help users move from externally prompted engagement to autonomous self-management.

Digital mental health interventions increasingly use narrative, game design, personalisation and behavioural data to support engagement, but it remains unclear how these features can be integrated without weakening fidelity to the original evidence-based intervention approach. This study examined interdisciplinary perspectives on a narrative based serious game and meaningful engagement framework for preventative digital mental health grounded in Self Help Plus (SH+) and Acceptance and Commitment Therapy (ACT) informed principles. A two-round electronic Delphi (eDelphi) study was conducted involving participants from Human-Computer Interaction (HCI), mental health experts, and representatives of the target user groups. Fifteen participants completed Round 1 and following attrition, twelve completed Round 2. Consensus required at least 70% agreement or disagreement and an interquartile range of no more than 1. Round 1 assessed agreement with core framework assumptions and established consensus on 6/12 items. Round 2 re-rated the six unresolved items with controlled feedback, introduced five clarification items and elicited refinement priorities. Round 1 showed broad support for narrative, game design, behavioural observation, transferability and flexible pacing, but identified tensions around translation of SH+/ACT principles, oversimplification, narrative complexity, behavioural data sufficiency, context specificity and autonomy. Round 2 produced consensus that SH+/ACT principles can be translated into interactive mechanics when the core SH+/ACT content and processes remain stable and adaptation occurs mainly through delivery style, examples, pacing, narrative framing and interaction format. Participants also supported adjustable narrative depth, multidimensional engagement assessment, principle level transferability and guided autonomy. The framework received partial conceptual support, but findings indicate that further development should proceed through a modular minimum viable product (MVP) and empirical testing rather than immediate deployment.

UNSTRUCTURED This submission is a corrigendum to correct an omission in the published article.

BACKGROUND Stress is a ubiquitous public health concern associated with increased risk of cardiovascular disease, depression, anxiety, and reduced quality of life. Digital mental health interventions (DMHIs), delivered via smartphones, web platforms and wearables, offer scalable support for stress management but often struggle to sustain user engagement which is a prerequisite for their effectiveness. Emerging adaptive mechanisms, such as personalisation and gamification, aim to enhance contextual relevance, emotional resonance and user adherence but the evidence describing how these mechanisms function together within stress focused DMHIs, remains fragmented. OBJECTIVE This review aims to synthesise evidence on engagement strategies in stress-focused DMHIs, identifying effective adherence techniques, the influence of personalisation, biometric feedback, Just-In-Time Adaptive Interventions (JITAIs), gamification and Artificial Intelligence (including large language models, LLMs), and methodological approaches to studying engagement. METHODS This narrative review was informed by a systematic search of PubMed, IEEE Xplore, and Scopus (January 2018–April 2025) for peer‑reviewed studies on stress‑focused DHIs reporting at least one engagement feature. Eligible studies included randomised controlled trials, qualitative investigations, simulation or modelling work, and implementation research. Data were extracted using a structured analysis capturing study characteristics, engagement strategies, outcomes, and alignment with the research questions. Descriptive statistics summarised study types and feature frequencies, while a narrative synthesis integrated descriptive mapping with interpretive, theory‑informed analysis organised by research question. RESULTS 22 studies met the inclusion criteria. Randomised controlled trials (10) were most common, followed by qualitative investigations (5) and design or simulation studies (7). 14 studies incorporated personalisation and JITAI features, 2 included biometric feedback, and 3 used gamification elements. 11 studies integrated AI, 7 of which employed generative LLM chatbots. Interventions combining tailored content with contextually timed delivery achieved higher adherence than static programmes. Personalisation often co‑occurred with AI, while biometric and JITAI features appeared mainly in technologically advanced systems. Adaptive components enhanced perceived relevance and emotional engagement but raised concerns about transparency, privacy, and burden. Wearable‑triggered prompts and reinforcement learning demonstrated potential, yet remained sensitive to privacy risks. Gamification boosted short‑term motivation when embedded in meaningful narratives, but showed mixed results when poorly implemented. Qualitative analyses further identified barriers related to power dynamics, cultural relevance, and data transparency. CONCLUSIONS Adaptive personalisation and timely delivery were the most effective engagement strategies, while biometric feedback and gamification remain underused. AI and reinforcement learning enable contextual support, but require safeguards for transparency and fairness. Diverse methods across RCTs, qualitative and implementation research provided complementary insights. Stress management DHIs are evolving toward intelligent, user-centred systems that adapt to emotional and contextual states. Future work should emphasise interpretable AI, culturally grounded co-design and longitudinal, mixed-methods evaluation to sustain engagement and equitable stress reduction.

Background: Chronic Obstructive Pulmonary Disease (COPD) is a major global health burden in which acute exacerbations accelerate progression and increase hospitalizations. Emerging technologies, such as wearable biosensors, artificial intelligence (AI), and digital health tools, enable more proactive disease management. Objectives: This umbrella review synthesized evidence from systematic reviews and meta-analyses on (1) AI-driven prediction of COPD exacerbations using low-cost wearable biosignals, and (2) the effectiveness of digital health interventions on disease management, quality of life, and medication adherence. Methods: A systematic search of PubMed, Scopus, and Web of Science (2015–2025) identified eligible reviews. Methodological quality was assessed using AMSTAR-2, and study overlap was quantified with the Corrected Covered Area (CCA). A narrative synthesis was conducted across two research questions. Protocol registered in PROSPERO (CRD420251164450). Results: Twenty-seven reviews met the inclusion criteria. AI models demonstrated promising internal predictive accuracy but lacked external validation and clinical integration. Digital health interventions, such as mHealth applications and telerehabilitation, showed small to moderate improvements in quality of life and physical function. Reported effects varied considerably (OR = 0.20–2.37; I² = 0–94%), indicating substantial heterogeneity across studies. Evidence for improvements in medication adherence and exacerbation reduction was inconsistent, and most included reviews were rated “Low” or “Critically Low” in methodological quality, limiting the generalizability of findings. Conclusions: AI and digital tools show strong promise for proactive COPD management, particularly through wearable-derived biosignals, outperforming traditional static assessments. However, their clinical readiness remains limited due to small-scale studies, interpretability challenges, inconsistent outcome measures, and a lack of external validation. To support real-world translation and regulatory adoption, future research must prioritize large-scale, rigorous, and equitable studies with standardized methodologies and robust generalizability testing.