Enablers, Challenges, and Lessons Learned From a Digital Health Intervention (Sehatmandi App) in Afghanistan: Qualitative Study

Background The integration of digital health (eHealth) interventions into primary healthcare systems has gained recognition lately in Low- and Middle-Income Countries (LMICs) to enhance healthcare quality, accessibility, and efficiency. These interventions may offer effective strategies in mitigating the burden of chronic diseases by facilitating access to remote healthcare and optimising its processes. This scoping review aims to identify and assess eHealth interventions implemented in primary care settings in LMICs for further development and adaptation. Methods and analysis We will search two electronic databases, such as Scopus and Embase, to identify peer-reviewed studies reporting on eHealth interventions implemented in primary care settings within LMICs. This review will encompass evidence published in the English language without a time frame restriction. We will remove duplicates from the search, and two reviewers will independently assess all articles for eligibility by first screening the title and abstract, followed by a full-text review. Eligible articles will be extracted, and data will be charted according to types of intervention and settings using a standardised form. Ethics and dissemination There is no ethical review required for this scoping review. We plan to disseminate the findings by presentations at conferences and publishing in open-access journal.

BackgroundLow- and middle-income countries (LMICs), which bear a larger proportion of the global mental illness burden, have been disproportionately impacted by the COVID-19 pandemic due to preexisting mental health care system deficiencies. The pandemic has also led to a considerable increase in care delivered through digital mental health interventions (DMHIs), many of which have been adapted from in-person formats. Thus, there is a need to examine their fidelity to the original format along with issues regarding usability and other challenges to and facilitators of their uptake in LMICs. As most DMHIs have been developed in high-income countries, examining their cultural adaptation to LMIC settings is also critical.ObjectiveThe purpose of this research was to conduct a rapid scoping review of the available evidence on DMHIs for depression and anxiety, two of the most common mental disorders, in LMICs.MethodsA rapid scoping review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) and processes for rapid reviews by Tricco et al. The PubMed and PsycINFO databases were searched for records published between January 2020 (when COVID-19 was declared a public health emergency) and January 2025 using a search strategy developed in consultation with a liaison librarian. The pandemic accelerated the development and application of DMHIs, and this time frame was used to capture the recent literature that may have incorporated new methods of application. The search strategy was developed across three domains: (1) digital health interventions, (2) depression or anxiety, and (3) LMICs. Data were charted from the final records according to (1) intervention type; (2) discussions on fidelity, usability, and cultural adaptation; and (3) challenges to and facilitators of their uptake in LMICs.ResultsA total of 80 records were included in the final analysis, with reasons for exclusion (eg, focused on mental health in general, not being a DMHI, or not focused on LMICs) reported. Six DMHI platforms were identified: (1) mobile app, (2) the web, (3) virtual reality, (4) videoconferencing, (5) telemedicine, and (6) social media. Less than half of the records referenced fidelity (16/80, 20%), usability (29/80, 36%), and cultural adaptation (31/80, 39%). Challenges pertained to the technological system, engagement issues, structural barriers, and concerns regarding privacy and confidentiality. Facilitators included widespread mobile phone use, built-in supervision and training features, and convenience.ConclusionsDespite the opportunities that DMHIs offer for reducing the mental health treatment gap, further work examining and improving their fidelity, usability, and cultural adaptation is required. In addition, various challenges to the uptake of DMHIs in LMICs, including contextual issues, structural barriers, and privacy concerns, must be mitigated to avoid contributing further to the digital divide.

Background“Digital public health” has emerged from an interest in integrating digital technologies into public health. However, significant challenges which limit the scale and extent of this digital integration in various public health domains have been described. We summarized the literature about these challenges and identified strategies to overcome them.MethodsWe adopted Arksey and O’Malley's framework (2005) integrating adaptations by Levac et al. (2010). OVID Medline, Embase, Google Scholar, and 14 government and intergovernmental agency websites were searched using terms related to “digital” and “public health.” We included conceptual and explicit descriptions of digital technologies in public health published in English between 2000 and June 2020. We excluded primary research articles about digital health interventions. Data were extracted using a codebook created using the European Public Health Association's conceptual framework for digital public health.Results and analysisOverall, 163 publications were included from 6953 retrieved articles with the majority (64%, n = 105) published between 2015 and June 2020. Nontechnical challenges to digital integration in public health concerned ethics, policy and governance, health equity, resource gaps, and quality of evidence. Technical challenges included fragmented and unsustainable systems, lack of clear standards, unreliability of available data, infrastructure gaps, and workforce capacity gaps. Identified strategies included securing political commitment, intersectoral collaboration, economic investments, standardized ethical, legal, and regulatory frameworks, adaptive research and evaluation, health workforce capacity building, and transparent communication and public engagement.ConclusionDeveloping and implementing digital public health interventions requires efforts that leverage identified strategies to overcome diverse challenges encountered in integrating digital technologies in public health.

BackgroundAlthough gamification continues to be a popular approach to increase engagement, motivation, and adherence to behavioral interventions, empirical studies have rarely focused on this topic. There is a need to empirically evaluate gamification models to increase the understanding of how to integrate gamification into interventions.ObjectiveThe model of gamification principles for digital health interventions proposes a set of five independent yet interrelated gamification principles. This study aimed to examine the validity and reliability of this model to inform its use in Web- and mobile-based apps.MethodsA total of 17 digital health interventions were selected from a curated website of mobile- and Web-based apps (PsyberGuide), which makes independent and unbiased ratings on various metrics. A total of 133 independent raters trained in gamification evaluation techniques were instructed to evaluate the apps and rate the degree to which gamification principles are present. Multiple ratings (n≥20) were collected for each of the five gamification principles within each app. Existing measures, including the PsyberGuide credibility score, mobile app rating scale (MARS), and the app store rating of each app were collected, and their relationship with the gamification principle scores was investigated.ResultsApps varied widely in the degree of gamification implemented (ie, the mean gamification rating ranged from 0.17≤m≤4.65 out of 5). Inter-rater reliability of gamification scores for each app was acceptable (κ≥0.5). There was no significant correlation between any of the five gamification principles and the PsyberGuide credibility score (P≥.49 in all cases). Three gamification principles (supporting player archetypes, feedback, and visibility) were significantly correlated with the MARS score, whereas three principles (meaningful purpose, meaningful choice, and supporting player archetypes) were significantly correlated with the app store rating. One gamification principle was statistically significant with both the MARS and the app store rating (supporting player archetypes).ConclusionsOverall, the results support the validity and potential utility of the model of gamification principles for digital health interventions. As expected, there was some overlap between several gamification principles and existing app measures (eg, MARS). However, the results indicate that the gamification principles are not redundant with existing measures and highlight the potential utility of a 5-factor gamification model structure in digital behavioral health interventions. These gamification principles may be used to improve user experience and enhance engagement with digital health programs.

BackgroundUser dropout is a widespread concern in the delivery and evaluation of digital (ie, web and mobile apps) health interventions. Researchers have yet to fully realize the potential of the large amount of data generated by these technology-based programs. Of particular interest is the ability to predict who will drop out of an intervention. This may be possible through the analysis of user journey data—self-reported as well as system-generated data—produced by the path (or journey) an individual takes to navigate through a digital health intervention.ObjectiveThe purpose of this study is to provide a step-by-step process for the analysis of user journey data and eventually to predict dropout in the context of digital health interventions. The process is applied to data from an internet-based intervention for insomnia as a way to illustrate its use. The completion of the program is contingent upon completing 7 sequential cores, which include an initial tutorial core. Dropout is defined as not completing the seventh core.MethodsSteps of user journey analysis, including data transformation, feature engineering, and statistical model analysis and evaluation, are presented. Dropouts were predicted based on data from 151 participants from a fully automated web-based program (Sleep Healthy Using the Internet) that delivers cognitive behavioral therapy for insomnia. Logistic regression with L1 and L2 regularization, support vector machines, and boosted decision trees were used and evaluated based on their predictive performance. Relevant features from the data are reported that predict user dropout.ResultsAccuracy of predicting dropout (area under the curve [AUC] values) varied depending on the program core and the machine learning technique. After model evaluation, boosted decision trees achieved AUC values ranging between 0.6 and 0.9. Additional handcrafted features, including time to complete certain steps of the intervention, time to get out of bed, and days since the last interaction with the system, contributed to the prediction performance.ConclusionsThe results support the feasibility and potential of analyzing user journey data to predict dropout. Theory-driven handcrafted features increased the prediction performance. The ability to predict dropout at an individual level could be used to enhance decision making for researchers and clinicians as well as inform dynamic intervention regimens.

HIV/STDs and unintended pregnancy persist among adolescents in the United States; thus, effective sexual health interventions that can be broadly disseminated are necessary. Digital health interventions are highly promising because they allow for customization and widespread reach. The current project involved redeveloping and expanding HEART (Health Education and Relationship Training)-a brief, digital sexual health intervention efficacious at improving safer sex knowledge, self-efficacy, and behavior-onto an open-source platform to allow for greater interactivity and accessibility while reducing long-term program costs. The authors describe the process of adapting, reprogramming, and evaluating the new program, which may serve as a guide for investigators seeking to adapt behavioral interventions onto digital platforms. The final product is an open-source intervention that can be easily adapted for new populations. Among 233 adolescents (Mage = 15.06; 64% girls), HEART was highly acceptable and generally feasible to administer, with no differences in acceptability by gender or sexual identity.

Digital health interventions (DHIs) show promise for delivering effective mental health interventions at scale, however significant issues with sustained engagement have been observed. Current efforts to understand, predict, and prevent poor engagement have yielded little success due to the insufficient consideration of how engagement, and the factors driving it, can fluctuate in intensity across the user journey. One under-explored but highly relevant theoretical framework that can be applied in this context is self-regulated learning (SRL), due to the core emphasis on self-guided psychoeducation in digital mental health. SRL refers to the ability to regulate one’s emotions, cognitions, and behaviours to enhance learning outcomes. To investigate the utility and potential of this framework for advancing our knowledge of engagement with DHIs, we conducted semi-structured interviews with digital health users (n = 14) and explored the role of common SRL variables (i.e., drivers) in shaping engagement across the user journey. A template analysis identified four major themes of drivers: (i) motivational beliefs, feelings, and strategies, (ii) metacognitive feedback and control, (iii) emotional experiences, and (iv) physical and social environment. Participants broadly agreed that higher levels across one or more SRL drivers would enhance engagement behaviours, and that the importance of different drivers for shaping engagement can vary temporally. These preliminary findings highlight the relevance of SRL constructs in describing, understanding, and predicting engagement with DHIs, offering insight into potential opportunities for targeted intervention across the user journey to promote engagement. However, further research to quantitatively assess the relationships between SRL drivers and various metrics of engagement in a DHI trial is needed.

Digital health interventions have emerged as promising tools to promote health behavior change and improve health outcomes. However, a comprehensive synthesis of strategies contributing to these interventions is lacking. This study aims to (1) identify and categorize the strategies used in digital health interventions over the past 25 years; (2) explore the differences and changes in these strategies across time periods, countries, populations, delivery methods, and senders; and (3) serve as a valuable reference for future researchers and practitioners to improve the effectiveness of digital health interventions. This study followed a systematic review approach, complemented by close reading and text coding. A comprehensive search for published English academic papers from PubMed, Web of Science, and Scopus was conducted. The search employed a combination of digital health and intervention-related terms, along with database-specific subject headings and filters. The time span covered 25 years, from January 1, 1999, to March 10, 2024. Sample papers were selected based on study design, intervention details, and strategies. The strategies were identified and categorized based on the principles of Behavior Change Techniques and Behavior Strategies. A total of 885 papers involving 954,847 participants met the eligibility criteria. We identified 173 unique strategies used in digital health interventions, categorized into 19 themes. The 3 most frequently used strategies in the sample papers were "guide" (n=492, 55.6%), "monitor" (n=490, 55.4%), and "communication" (n=392, 44.3%). The number of strategies employed in each paper ranged from 1 to 32. Most interventions targeted clients (n=844, 95.4%) and were carried out in hospitals (n=268, 30.3%). High-income countries demonstrated a substantially higher number and diversity of identified strategies than low- and middle-income countries, and the number of studies targeting the public (n=647, 73.1%) far exceeded those focusing on vulnerable groups (n=238, 26.9%). Digital health interventions and strategies have undergone considerable development over the past 25 years. They have evolved from simple approaches to sophisticated, personalized techniques and are trending toward multifaceted interventions, leveraging advanced technologies for real-time monitoring and feedback. Future studies should focus on rigorous evaluations, long-term effectiveness, and tailored approaches for diverse populations, and more attention should be given to vulnerable groups.

BackgroundOlder adults experience a high risk of adverse events during hospital-to-home transitions. Implementation barriers have prevented widespread clinical uptake of the various digital health technologies that aim to support hospital-to-home transitions.ObjectiveTo guide the development of a digital health intervention to support transitions from hospital to home (the Digital Bridge intervention), the specific objectives of this review were to describe the various roles and functions of health care providers supporting hospital-to-home transitions for older adults, allowing future technologies to be more targeted to support their work; describe the types of digital health interventions used to facilitate the transition from hospital to home for older adults and elucidate how these interventions support the roles and functions of providers; describe the lessons learned from the design and implementation of these interventions; and identify opportunities to improve the fit between technology and provider functions within the Digital Bridge intervention and other transition-focused digital health interventions.MethodsThis 2-phase rapid review involved a selective review of providers’ roles and their functions during hospital-to-home transitions (phase 1) and a structured literature review on digital health interventions used to support older adults’ hospital-to-home transitions (phase 2). During the analysis, the technology functions identified in phase 2 were linked to the provider roles and functions identified in phase 1.ResultsIn phase 1, various provider roles were identified that facilitated hospital-to-home transitions, including navigation-specific roles and the roles of nurses and physicians. The key transition functions performed by providers were related to the 3 categories of continuity of care (ie, informational, management, and relational continuity). Phase 2, included articles (n=142) that reported digital health interventions targeting various medical conditions or groups. Most digital health interventions supported management continuity (eg, follow-up, assessment, and monitoring of patients’ status after hospital discharge), whereas informational and relational continuity were the least supported. The lessons learned from the interventions were categorized into technology- and research-related challenges and opportunities and informed several recommendations to guide the design of transition-focused digital health interventions.ConclusionsThis review highlights the need for Digital Bridge and other digital health interventions to align the design and delivery of digital health interventions with provider functions, design and test interventions with older adults, and examine multilevel outcomes.International Registered Report Identifier (IRRID)RR2-10.1136/bmjopen-2020-045596

Understanding ethnic inequalities in the design and implementation of digital health interventions for cardiometabolic disease: a qualitative study

Digital health interventions have gained prominence in the management of chronic diseases like Type 1 Diabetes Mellitus (T1DM), helping patients adhere to their treatment plans. However, the effectiveness of these interventions in improving adherence to T1DM management varies globally. To systematically review existing digital interventions designed to improve adherence to T1DM management. To identify key features of digital tools that enhance adherence and glycaemic control in T1DM patients. Primary outcomes were improvements in patient adherence, measured by frequency of insulin administration, self-monitoring of blood glucose, and HbA1c levels. Secondary outcomes included patient satisfaction and engagement with digital interventions. A systematic review of randomized controlled trials (RCTs) was conducted using the PICO framework. The review encompassed studies from various global regions. Studies included T1DM patients across different age groups. Databases such as PubMed, Cochrane, and Ovid were searched for relevant studies. A total of 12 randomized controlled trials (RCTs) were included in this review, focusing on improving adherence in T1DM patients. The digital interventions evaluated included mobile health applications, continuous glucose monitoring, telemedicine platforms, and educational video games. Most studies demonstrated significant improvements in patient adherence behaviours, such as insulin administration and self-monitoring of blood glucose, reductions in HbA1c levels were also noted. The improvements in HbA1c as seen in this review for individuals with T1DM are in line with findings from prior studies. The evidence showed that digital health technologies could enhance glycaemic control. These findings are in line with other studies where real-time data monitoring equipment of the emergent CGM systems and mobile apps facilitated accurate and timely modification of diabetes self-management. The reviewed studies also highlighted the effectiveness of digital technology-targeted interventions in enhancing the aspect of self-management and health behaviour among T1DM patients. These results are also aligned with the behavioural change theories which highlight the significance of education, perceived self-ability and continuous feedback in adopting good health behaviour. Digital interventions have a positive impact on adherence to T1DM management and glycaemic control. Their application, particularly in low-resource settings, holds great potential for improving health outcomes. Not applicable. The aim of this review was to look at how mobile applications among other digital tools can assist people living with Type 1 Diabetes (T1DM) through continuous glucose monitoring to manage their health condition in a better way. For this purpose, we reviewed 12 different studies and found that interventions delivered through digital tools enhanced patients' adherence to their treatment by helping them lower the blood sugar levels as measured by HbA1c levels. Main findings reflected how digital tools improved health behaviours and self-management of T1DM. This was facilitated by real-time data monitoring and feedback for the diabetes care. In conclusion, Digital health interventions have the potential to positively impact the T1DM treatment leading to improved health outcomes especially in low resource settings.

Digital health interventions have enormous potential to support patients and the public in achieving their health goals. Nonetheless, many digital health interventions are failing to effectively engage patients and the public. One solution that has been proposed is to directly involve patients and the public in the design process of these digital health interventions. Although there is consensus that involving patients and the public in collaborative design is valuable, design teams have little guidance on how to maximize the value of their collaborative design work. The main objective of this study was to understand how the value of patient and public involvement in digital health design can be maximized, from the perspective of design leaders and patient-public partners. Using a qualitative descriptive methodology, we conducted semi-structured interviews with 19 design leaders and 9 patient-public partners. Interviewees agreed that involving patients and the public was valuable, however, they questioned if current collaborative methods were optimized to ensure maximal value. Interviewees suggested that patient and public collaborative design can add value through four different mechanisms: (1) by allowing the design process to be an empowering intervention itself, (2) by ensuring that the digital health intervention will be effectively engaging for users, (3) by ensuring that the digital health intervention will be seamlessly implemented in practice, and (4) by allowing patient-public collaborations extend beyond the initial product design. Overall, interviewees emphasized that although collaborative design has historically focused on improving the digital health product itself, patients and the public have crucial insights on implementation planning as well as how collaborative design can be used as its own empowering intervention. The results of this paper provide clarity about the ways that patient and public collaborative design can be made more valuable. Digital health design teams can use these results to be more intentional about their collaborative design approaches.

BACKGROUND Racialized immigrant youth face a multitude of challenges that heighten their vulnerability to mental illness. Digital mental health has increasingly been recognized as an effective method to improve accessibility to mental health services for communities with limited access to inclusive care, particularly among youth. However, despite the growing emphasis on cultural inclusivity in interventions targeting racialized and marginalized populations, there remains a gap in empirical evidence regarding how culturally inclusive elements are integrated into digital mental health practices and their impact on the effectiveness of these interventions. OBJECTIVE Focusing on Asian immigrant youth in Canada, this protocol outlines our community-engaged project, which aims to assess whether current digital mental health interventions provide effective and culturally accessible support, and how we can collaboratively develop a framework for creating innovative, inclusive, and effective digital interventions to promote youth mental health. METHODS A two-phase community-engaged approach will be used to involve Asian immigrant youth facing mental health challenges, youth leaders, service providers, and stakeholders from East, Southeast, and South Asian communities. Phase one includes scoping reviews on the technology, access, and cultural inclusivity of digital mental health interventions for Asian and racialized newcomer youth in Canada. In phase two, 15 diverse Asian youth leaders and 15 service providers will participate in a 3.5-hour online session to discuss the need for effective digital mental health interventions, share results from the reviews, co-identify key elements of an inclusive and innovative intervention, and outline next steps for forming a community-campus research partnership focused on mental health promotion for Asian immigrant youth. RESULTS This project is expected to yield a co-developed framework outlining key elements of culturally inclusive and effective digital mental health interventions for Asian immigrant youth in Canada. Anticipated outcomes include enhanced understanding of cultural accessibility gaps in existing digital interventions and strengthened community-campus partnerships to support youth mental health. CONCLUSIONS This research aims to improve access to culturally safe and effective digital mental health interventions for Asian immigrant youth. Benefits include enhanced mental health support for this population, increased awareness of mental health issues among stakeholders, and the development of a framework for culturally inclusive mental health services

BackgroundDigital health technologies have been proposed to support hospital-to-home transition for older adults. The COVID-19 pandemic and the associated physical distancing guidelines have propelled a shift toward digital health technologies. However, the characteristics of older adults who participated in digital health research interventions to support hospital-to-home transitions remain unclear. This information is needed to assess whether current digital health interventions are generalizable to the needs of the broader older adult population.ObjectiveThis rapid review of the existing literature aimed to identify the characteristics of the populations targeted by studies testing the implementation of digital health interventions designed to support hospital-to-home transitions, identify the characteristics of the samples included in studies testing digital health interventions used to support hospital-to-home transitions, and create recommendations for enhancing the diversity of samples within future hospital-to-home digital health interventions.MethodsA rapid review methodology based on scoping review guidelines by Arksey and O’Malley was developed. A search for peer-reviewed literature published between 2010 and 2021 on digital health solutions that support hospital-to-home transitions for older adults was conducted using MEDLINE, Embase, and CINAHL databases. The data were analyzed using descriptive statistics and qualitative content analysis. The Sex- and Gender-Based Analysis Plus lens theoretically guided the study design, analysis, and interpretation.ResultsA total of 34 studies met the inclusion criteria. Our findings indicate that many groups of older adults were excluded from these interventions and remain understudied. Specifically, the oldest old and those living with cognitive impairments were excluded from the studies included in this review. In addition, very few studies have described the characteristics related to gender diversity, education, race, ethnicity, and culture. None of the studies commented on the sexual orientation of the participants.ConclusionsThis is the first review, to our knowledge, that has mapped the literature focusing on the inclusion of older adults in digital hospital-to-home interventions. The findings suggest that the literature on digital health interventions tends to operationalize older adults as a homogenous group, ignoring the heterogeneity in older age definitions. Inconsistency in the literature surrounding the characteristics of the included participants suggests a need for further study to better understand how digital technologies to support hospital-to-home transitions can be inclusive.

Adolescents with type 1 diabetes (T1D) experience higher rates of psychological disorders compared with their healthy peers. As poor psychological health has been associated with suboptimal glycaemic control and more frequent complications, there is an urgent need to develop more 'clinically usable' interventions. Digital mental health interventions offer unique advantages compared with in-person interventions; however, what adolescents with T1D want in terms of content and functionality is poorly understood. Accordingly, the current study conducted focus groups to examine the views of adolescents with T1D regarding digital mental health interventions. Four focus groups were conducted, including 16 adolescents with T1D, ranging from 13 to 17years in age (69% female). Transcripts were analysed using directed content analysis to examine (1) 'what adolescents dislike about existing digital mental health interventions?' and (2) 'what adolescents want in future digital mental health interventions?'. Findings provide a preliminary understanding of what adolescents dislike and also the type of content and functional features, which may be important to include in digital mental health programs for this population, such as a peer support feature (reported by 16 of 16), emotion and diabetes check-in feature (11 of 16) and diabetes-relevant content (12 of 16). Early data suggest that digital mental health interventions should include a significant peer support element, diabetes-relevant content and examples, and check-in on their mental health and diabetes self-management regularly, while avoiding fixed responses or modules and non-age-appropriate content. Based on these findings, a digital intervention is currently being developed.