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Digital health technologies (DHTs) are transformative solutions for health care challenges; however, sustaining long-term adherence remains a significant barrier, limiting their effectiveness. This systematic review aims to identify and categorize factors influencing adherence to DHTs and to identify theoretical foundations used to predict it. This review was conducted according to the PICO (population, intervention, comparison, outcome) strategy and followed the Cochrane Handbook and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. The protocol was prospectively registered on PROSPERO (CRD42024628168). Literature searches were performed in December 2024 in PubMed, PsycINFO, Scopus, and IEEE Xplore for studies published between 2019 and 2024 in English, Portuguese, or Spanish. Studies were eligible if they investigated factors influencing adherence to DHTs or theoretical foundations and tools predicting adherence. Nonpeer-reviewed studies, study protocols, and studies that did not explicitly report adherence outcomes were excluded. Risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Tools. Data were synthesized narratively through inductive thematic analysis, with factors influencing adherence extracted and categorized. In total, 61 studies were included, mostly quantitative and conducted in Europe and North America. The populations were mainly patients with medical conditions, and most studies focused on mobile health apps. Study quality was moderate to high. The findings highlight a complex and multifaceted range of factors influencing adherence, which were categorized into four key domains: (1) personal factors (sociodemographic characteristics, health status, user characteristics, and personal beliefs and perceptions), (2) technology and intervention content factors (infrastructure and accessibility, user experience and performance, and content and features of the intervention), (3) social and support system factors (family and informal support and health care professional support), and (4) contextual factors. Among the theoretical foundations identified, the Unified Theory of Acceptance and Use of Technology (UTAUT) emerged as the most frequently applied. The findings highlight the need for integrative, health-specific models that combine behavioral, technological, and clinical aspects. Future research should focus on developing standardized adherence metrics and exploring the interactions between these factors to improve predictive models. However, the evidence base is limited by heterogeneity in study designs and adherence definitions, potential publication, and language bias.

Depression is a prevalent mental health disorder and a leading cause of disability worldwide, creating substantial personal and societal burdens. Digital mental health interventions have emerged as accessible and scalable solutions, with artificial intelligence (AI)-driven chatbots increasingly applied to deliver therapeutic content, monitor symptoms, and provide personalized support. However, limited evidence exists on how chatbot interaction features influence treatment adherence and clinical outcomes in depression. This systematic review aimed to evaluate the clinical effectiveness of AI-driven chatbots for depression and to examine the associations between chatbot characteristics, treatment outcomes, and user adherence. A systematic review and meta-analysis were conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, searching 6 databases (Web of Science, Scopus, PubMed, IEEE Xplore, Embase, and APA PsycINFO) for randomized controlled trials (RCTs) published before May 30, 2025. Eligible studies involved individuals with depression or depressive symptoms receiving AI-driven chatbot, conversational agent, or virtual therapist interventions, with outcomes assessed using the Patient Health Questionnaire-9 (PHQ-9). Data extraction included chatbot type, interaction features, adherence, and standardized mean differences (SMDs) for symptom change. Risk of bias was assessed using the Cochrane Risk of Bias tool version 2 (RoB 2). Random-effects meta-analyses were performed with the Hartung-Knapp-Sidik-Jonkman adjustment. This review was preregistered on the Open Science Framework. A total of 11 RCTs involving 2220 participants (1091 in the intervention and 1129 in the control groups) were included. Using a random-effects model with Hartung-Knapp-Sidik-Jonkman adjustment, AI-driven chatbots showed a small-to-moderate reduction in depressive symptoms compared with control conditions, but the effect was not statistically significant (SMD=-0.46, 95% CI -1.02 to 0.10; P=.01; 95% prediction interval -1.50 to 0.58). Subgroup analyses of adherence did not show significant differences across the reported chatbot-type subgroups. In contrast, exploratory analyses of interaction features revealed more consistent patterns for adherence. Emotional responsiveness, structured feedback strategies, and interaction frequency were associated with higher adherence in high-scoring subgroups, whereas dialogue depth, self-disclosure encouragement, and user agency level showed weaker or inconsistent associations. For clinical outcomes, associations with interaction features were less consistent and more heterogeneous. This systematic review provides an interaction-focused synthesis of AI-driven chatbot interventions for depression, examining how interaction features relate to clinical outcomes and user adherence. Although overall effects were not statistically significant, emotional responsiveness, structured feedback, and interaction frequency were consistently associated with higher adherence. Engagement and outcomes may be influenced by distinct mechanisms. Limitations include the small number of RCTs, heterogeneity, reliance on study-reported descriptions, and potential publication bias. These findings highlight the importance of interaction design in developing scalable digital mental health interventions.

Cancer survivors often experience declining engagement in digital health interventions (DHIs). However, predictors of engagement with provider-guided DHIs remain unclear. Nurse WRITE (Nurse Written Representational Intervention To Ease Symptoms), an effective 8-week nurse-directed symptom management DHI, offers an opportunity to identify factors influencing engagement and enhance intervention efficacy evaluation. This study aims to (1) understand engagement phenomena (dimensions, influencing factors, and challenges), and (2) assess the relationship between engagement and patient symptom control in Nurse WRITE. In this secondary analysis of the Nurse WRITE arm of a 3-arm symptom management trial, we examined data from 68 women with recurrent ovarian cancer to assess socioaffective and cognitive engagement through message board activity, as well as behavioral engagement through website usage data. Regression analyses examined patient characteristics, engagement, and symptom control perceptions. Through content analysis, we explored participant challenges and activities before disengagement. Education was significantly associated with selected cognitive, socioaffective, and behavioral engagement indicators, including cognitive activity count, total word count, completion of symptom care plans, and plan reviews after false discovery rate correction. The most common engagement challenges included worsening health and treatment, busy family life, and website difficulties. Moderate and low engagers also experienced confusion about the intervention timeline and process. Among low engagers, 63.2% (24/38) discontinued communication at specific intervention phases: introduction (8/24, 33.3%), symptom representational assessment (5/24, 20.8%), and goal setting and planning (5/24, 20.8%). Improved symptom control at the end of the intervention was significantly associated with overall engagement, cognitive and socioaffective activity count, question completion percentage, total word count, and completed symptom care plans after false discovery rate correction. Education was associated with selected cognitive, socioaffective, and behavioral engagement indicators in Nurse WRITE. Future provider-guided DHIs should consider strategies to support participants with lower educational attainment, address common engagement barriers, and reengage participants during critical intervention phases. Meaningful engagement across cognitive, socioaffective, and behavioral dimensions may be important for improving outcomes while balancing protocol adherence with flexibility.

Incentivization in clinical trial participation can be challenging, with many studies failing to meet recruitment or retention goals despite traditional compensation strategies. Digital health evolves, and with it, new approaches can emerge to engage participants meaningfully. We propose unique digital images as a novel, symbolic incentive for clinical trials. Digital images combine qualities such as personalization, ownership, and digital visibility, which may drive engagement more effectively than monetary rewards alone. In our illustrative study, participants complete artificial intelligence–personalized digital therapeutics training using CURATE.DTx, generating individualized learning trajectories. These are transformed into digital artworks and minted as nonfungible tokens given as a reward upon trial completion. This concept integrates gamification, personalization, and blockchain technology to support both intrinsic and extrinsic motivation. We explore the implications for decentralized health care, long-term behavior change, and participant recognition in the context of preventive medicine and longevity science. Our aim is to encourage research into the use of digital incentives to transform the trial participant experience and promote sustained engagement in health interventions.

The widespread adoption of the internet has established online health information-seeking behavior (OHISB) as a primary channel for public health knowledge acquisition, potentially influencing patient adherence behaviors and physician-patient dynamics. However, the underlying pathways, particularly the role of physician-patient communication efficacy and the differential impact of various digital platforms, remain underexplored, especially among rural populations. This study examined the association between OHISB and patient adherence among rural residents in China, with a specific focus on the mediating role of physician-patient communication efficacy and the moderating roles of different platform types. A cross-sectional survey was conducted from June 2023 to October 2024 using multistage stratified sampling across 6 Chinese provinces. Participants were rural residents aged 18 to 70 years with recent health care experiences. Data from 7004 valid questionnaires were analyzed. A fixed-effects model assessed the primary association, with robustness checked via least absolute shrinkage and selection operator regression. Mediation analysis using the bootstrap method examined the indirect association through physician-patient communication efficacy, and interaction terms tested the moderating effects of platform type (internet hospitals, professional platforms, WeChat accounts, short video apps, and search engines). OHISB showed a significant positive direct association with patient adherence (β=0.260; P<.001). Physician-patient communication efficacy exhibited a significant negative indirect association with patient adherence (β=-0.026; P<.001), accounting for 9.29% of the total association. Platform type significantly moderated this association: internet hospitals (β=0.099; P=.04), professional platforms (β=0.081; P=.04), and WeChat accounts (β=0.032; P=.03) enhanced the positive association between OHISB and patient adherence, whereas short video platforms (β=-0.034; P=.006) and search engines (β=-0.204; P<.001) weakened it. Online health information seeking among rural residents was directly associated with better patient adherence, but this benefit was partially attenuated by a negative indirect association through reduced physician-patient communication efficacy. The association between OHISB and adherence varied significantly by platform type. This finding suggests the need for digital health equity strategies, interventions to improve communication efficacy and health literacy, and graded management of health information platforms.

Internet-based cognitive behavioral therapies (iCBTs) are typically categorized into 2 types: therapist-assisted and self-guided. Both formats have accumulated substantial evidence supporting their cost-effectiveness and efficacy in treating a range of mental health conditions. However, therapist-assisted iCBTs tend to show lower dropout rates than self-guided versions. The relatively high dropout rates in self-guided programs suggest that some degree of therapist involvement may be necessary to improve engagement and treatment adherence. Yet, the specific reasons for therapist support in iCBT and its functions in improving engagement and treatment adherence remain an underexplored area of research. This study aimed to explore patients' experiences with therapist-assisted iCBT to identify the elements they perceive as important for treatment adherence and to clarify the role of therapist support in the iCBT process. This study draws on 89 semistructured in-depth interviews with iCBT users. Patients took part in 9 different therapist-assisted iCBT programs (depression [n=32], anxiety disorder [n=17], obsessive-compulsive disorder [n=10], bipolar disorder [n=5], social phobia [n=5], bulimia [n=3], alcohol abuse [n=1], panic disorder [n=10], and insomnia [n=6]), all provided nationwide by Helsinki University Hospital in Finland. The interviews were transcribed verbatim and analyzed with the qualitative Gioia method. Three key categories help explain why users consider therapist support essential for adherence in iCBTs: (1) the strengthening of individual autonomy, (2) the therapist's commitment to strengthening the therapeutic alliance, and (3) assistance with emotion regulation. Therapist support was shown to be pivotal, often conveyed through small, text-based gestures that had a meaningful impact. The role of the therapist should not be diminished in the pursuit of digitalization, as human support remains a critical element of effective iCBT.

Mental ill health is a leading cause of disability worldwide, but access to evidence-based support remains limited. Digital mental health interventions offer a timely and low-cost solution. However, improvements in clinical outcomes are reliant on user engagement, which can be low for digital interventions. User characteristics, including demographics and personality traits, could be used to personalize platforms to promote longer-term engagement and improved outcomes. This study aims to investigate how character strengths, a set of positive personality traits, influence engagement patterns with moderated online social therapy, a national digital mental health platform offering individualized, evidence-based digital mental health treatment for young people aged 12-25 years. Data from 6967 young people who enrolled with moderated online social therapy between August 2021 and July 2023 were analyzed. Longitudinal analyses were used to investigate whether scores on 3-character strength dimensions (\"social harmony,\" \"positive determination,\" and \"courage and creativity\") were associated with (1) an accelerated or decelerated rate of dropout from the platform and (2) patterns of engagement over the first 12 weeks following onboarding. Engagement metrics were time spent on the platform, number of sessions on the platform, use of the embedded social network, and messages with the clinical team. On average, young people used the platform for 72.64 (SD 106.64) days. The 3-character strengths were associated with distinct engagement patterns during this time. Individuals scoring higher on \"social harmony\" demonstrated an accelerated dropout rate (coefficient=-0.15, 95% CI -0.26 to -0.04; P=.008). Interestingly, higher scores on this character strength were associated with high rates of initial engagement but a more precipitous decline in platform use over the first 12 weeks, in terms of time spent on the platform (β=-.01; SE 0.00; t2748=-5.05; P<.001) and the number of sessions completed (β=-.00; SE 0.00; t2837=-2.26; P=.02). In contrast, higher scores on \"positive determination\" and \"courage and creativity\" predicted more modest initial platform use but steadier engagement over time, in terms of time spent on the platform (\"positive determination\": β=.01; SE 0.00; t2748=4.05; P<.001 and \"courage and creativity\": β=.01; SE 0.00; t2748=2.66; P=.008). Contrary to our predictions, character strengths did not predict use of the embedded social network or the number of messages sent to the clinical team. Our findings illustrate how character strengths predict distinct engagement trajectories on a digital mental health platform. Specifically, individuals higher on \"social harmony\" showed high initial engagement that quickly declined, while those higher on \"positive determination\" and \"courage and creativity\" demonstrated lower initial engagement but a steadier use of the platform over time. The findings of this study demonstrate an opportunity for digital mental health interventions to be tailored to individual characteristics in a way that would promote greater initial and ongoing engagement.

Research typically shows a higher preference for professionally led face-to-face mental health interventions over digital ones. It remains unclear in which circumstances digital self-help tools are preferred. To address this gap, it is important to examine user characteristics that may help predict when digital interventions are more desirable, ultimately guiding their design to enhance engagement and appeal. This study aims to examine how distress severity and capacity to invest effort relate to intervention preferences, using an ecological assessment of individuals seeking to receive feedback on their mental health. A comprehensive digital mental health screening tool with automated feedback was developed and advertised on social media. The sample comprised 684 adult participants aged 18 to 82 years who opted to complete the screening to receive feedback on their mental health state. Participants completed questionnaires measuring general psychological distress, depression, generalized anxiety, and demographics. The Kessler Psychological Distress Scale-6 was used as the primary measure for distress. Participants were also presented with questions measuring capacity to invest effort and preferences for a professional therapist versus digital self-help tools and for psychotherapy versus a mobile app. The effectiveness of distress, capacity to invest effort, and background characteristics in predicting preferences (a professional vs digital self-help tools; psychotherapy vs a mobile app) was examined using hierarchical linear regressions. The distributions of dichotomized preferences were plotted against distress and capacity to invest effort for transparent visualization. A hierarchical linear regression found that distress, capacity to invest, and currently being in psychotherapy significantly predicted preference for a professional versus digital self-help tools. Distress (β=.25, 95% CI .18-.32; P<.001) and capacity to invest effort (β=.23, 95% CI .16-.30; P<.001) were the strongest predictors, with similar effect size. The model explained 20% of the variance in preference, with the capacity to invest effort uniquely contributing 5%. Most participants experiencing distress with low capacity (158/239, 66.1%) preferred digital self-help tools, whereas most participants experiencing distress with high capacity (147/243, 60.5%) favored a professional. Similar results were obtained when using the Patient Health Questionnaire-4 as an alternative distress measure. Capacity to invest effort remained significant (β=.18, 95% CI .10-.26; P<.001) when predicting a preference for psychotherapy versus a mobile app, while distress was not significant (β=-.03, 95% CI -.10 to .05; P=.51). This study highlights that the preference for digital interventions is driven by a reduced capacity to invest effort in an intervention. Attempts to reduce the mental health treatment gap through digital interventions should focus on optimizing the effort elicited by users to improve desirability and engagement.

Mobile health (mHealth) holds promise for enhancing patient care, yet attrition in its use remains a major barrier. Low retention rates limit its potential impact, while barriers to accessing or adopting mHealth vary across populations and countries. These differences in utilization of mHealth may exacerbate health inequalities, contributing to the digital health divide. We aimed to conduct a systematic review and meta-analysis to investigate the factors associated with inequalities in mHealth utilization across different implementation phases, including access, adoption, adherence, and maintenance. This systematic review and meta-analysis analyzed mHealth research from 2000 to May 30, 2024, using databases, including PubMed, Web of Science, MEDLINE, and ProQuest. Eligible studies included smartphones, mHealth apps, wearables, and inequality indicators across 4 mHealth phases: access, adoption, adherence, and maintenance. Excluded studies were nonpeer-reviewed, opinion-based, or not in English. Extracted data included study characteristics, target populations, health outcomes, and inequality factors like age, gender, socioeconomic status, and digital literacy. Factors were categorized using a digital health equity framework (biological, behavioral, sociocultural, digital, health care system, and physical domains). Meta-analyses were performed using a random-effects model for factors reported in at least three studies, with heterogeneity assessed by the I² statistic. Among 1990 studies, 62 studies met the inclusion criteria, and 30 studies underwent meta-analysis. The phases of mHealth utilization were access (n=23, 37%), adoption (n=47, 76%), adherence (n=9, 15%), and maintenance (n=2, 3%). Meta-analysis showed older age was negatively associated with mHealth adoption (odds ratio [OR] 0.47, 95% CI 0.23-0.93), while higher education and income were positively associated in both access and adoption phases. Employment showed significant associations in the access phase (OR 1.49, 95% CI 1.08-2.05), whereas comorbidities (OR 1.39, 95% CI 1.03-1.86) and private insurance (OR 1.63, 95% CI 1.07-2.48) were significantly associated with adoption of mHealth. Women (OR 1.24, 95% CI 1.06-1.45) and physically active individuals (OR 1.64, 95% CI 1.07-2.50) were more likely to adopt mHealth. The conceptual framework outlined in this study highlights the multifaceted nature of mHealth utilization across all the phases of mHealth engagement. To address these inequalities, tailored and personalized interventions are required at each phase of mHealth utilization. Targeted efforts can enhance digital access for older and low-income adults while promoting engagement through education, insurance support, and healthy behaviors, thereby promoting equitable and effective mHealth use. By recognizing the interconnectedness of these domains, policy makers and health care stakeholders can design interventions that not only address the phase-specific barriers but also bridge broader inequalities in health care access and engagement.

Digital interventions have emerged as a promising approach to support vaping cessation, particularly among youth and young adults. Mobile apps, text messaging programs, telehealth-delivered contingency management, and web-based or social media interventions offer scalable and accessible alternatives to traditional cessation methods. However, there is considerable variation in how these interventions are designed, implemented, and evaluated, with inconsistencies in engagement strategies, theoretical frameworks, and long-term effectiveness. This scoping review aimed to map the current landscape of digital interventions for vaping cessation and identify key strategies, effectiveness outcomes, and implementation challenges. The following questions were addressed: (1) What digital interventions have been developed or evaluated for vaping cessation? (2) What evidence exists regarding the effectiveness of these digital interventions in promoting vaping cessation and user engagement? (3) What key barriers and facilitators influence the adoption, adherence, and success of digital vaping cessation interventions? (4) What gaps remain in the literature, and what areas should future research prioritize to enhance the design and effectiveness of digital vaping cessation tools? This scoping review followed the Joanna Briggs Institute methodology and adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A systematic search was conducted in CINAHL, MEDLINE, and PsycINFO using search terms related to vaping cessation and digital health interventions. Studies examining mobile apps, text messaging programs, social media or web-based interventions, and telehealth coaching explicitly designed for vaping cessation were included. A narrative synthesis was conducted to identify common themes, barriers, and facilitators. Sixteen studies were identified, including SMS text messaging programs, mobile apps, telehealth-delivered contingency management, and web-based or social media interventions. Many interventions reported moderate to high abstinence rates. Programs incorporating personalized messaging, behavioral tracking, and social and interactive features demonstrated greater retention and cessation success. However, minimal application of evidence-based behavior change frameworks, inconsistent reporting of engagement metrics, reliance on self-reported abstinence, and scalability limitations were noted. Digital interventions show promise for vaping cessation, particularly among youth and young adults, but current evidence highlights both opportunities and limitations. Effective interventions leverage personalization and social support to enhance engagement and quit outcomes. However, challenges such as high dropout rates, accessibility barriers, and limited use of rigorous evaluation methods persist. Future research should prioritize hybrid approaches that combine digital support with human interaction, apply equity-focused design principles, and adopt pragmatic, theory-driven evaluation methods to accelerate translation from pilot success to sustainable public health impact.