Evaluation of the Use of Digital Mental Health Platforms and Interventions: Scoping Review

A Scoping Review of Digital-Based Intervention for Reducing Risk of Suicide Among Adults

Suicide is a serious public health problem, especially among adults. Risk factors for suicide include the presence of mental health disorders, history of previous suicide attempts; substance or alcohol use and lack of social support. The impact of suicide risk includes psychological loss, as well as the trauma and emotional stress that can be felt by the families and communities left behind. Digital interventions have emerged as a promising alternative for suicide risk prevention. Previous research has focused on the findings of various designs, which did not provide clear intervention information to inform the implementation of the intervention. This study aims to describe a digital intervention to reduce the risk of suicidal behavior in adults. The design used in this study was a scoping review. The authors conducted a literature search from the Scopus, PubMed, and CINAHL databases. Inclusion criteria in this study were articles discussing digital interventions aimed at preventing suicide risk in adult populations, English language, full-text, RCT or quasi-experiment design, and publication period of the last 10 years (2014-2024). The major keywords used in the article search were suicide prevention, digital intervention, and adults. Data extraction used manual table and data analysis used descriptive qualitative with a content approach. The results showed that there were 9 articles that discussed digital-based interventions to reduce suicide risk in adults. The various types of digital interventions used were smartphone apps, online learning modules, and game-based interventions. These interventions offer significant potential in reducing the risk of suicidal behavior in adults. Digital interventions have an important role in reducing the risk of suicidal behavior in adults by considering aspects of suitability to individual needs and understanding digital literacy. Then, the development of mental health services and public health policies presented needs to be done with collaboration between stakeholders in suicide prevention efforts.

Outcomes of Best-Practice Guided Digital Mental Health Interventions for Youth and Young Adults with Emerging Symptoms: Part II. A Systematic Review of User Experience Outcomes

Although many young people demonstrate resilience and strength, research and clinical evidence highlight an upward trend in mental health concerns among those aged 12 to 25 years. Youth-specific digital mental health interventions (DMHIs) aim to address this trend by providing timely access to mental health support for young people (12-25 years). However, there is a considerable gap in understanding young people user experiences with digital interventions. This review, co-designed with Australia's leading mental health organization Beyond Blue, utilizes a systematic methodology to synthesize evidence on user experience in youth-oriented digital mental health interventions that are fully or partially guided. Five relevant online databases were searched for articles published from 2018 to 2023, yielding 22,482 articles for screening and 22 studies were included in the present analysis. User experience outcomes relating to satisfaction and engagement were assessed for each included intervention, with experience indicators relating to usefulness, usability, value, credibility, and desirability being examined. Elements associated with positive/negative outcomes were extracted. Elements shown to positively influence user experience included peer engagement, modern app-based delivery, asynchronous support, and personalized content. In contrast, users disliked static content, homework/log-keeping, the requirement for multiple devices, and social media integration. Asynchronous interventions showed high satisfaction but faced engagement issues, with combined asynchronous/synchronous interventions reporting better completion rates. DMHIs offer a promising platform for youth mental health support and has the potential to dramatically increase the reach of interventions through the adoption of technological and user experience best practices. While young people respond positively to many aspects of intervention modernization, such as interactive, app-based design, other concepts, such as social media integration, they need to be adopted by the field more cautiously to ensure trust and engagement.Trial Registration CRD42023405812.

Predictors of adherence to a publicly available self-guided digital mental health intervention

Low adherence to self-guided digital mental health interventions (DMHIs) have raised concerns about their real-world effectiveness. Naturalistic data from self-guided DMHIs are often not available, hindering our ability to assess adherence among real-world users. This study aimed to analyze 3 years of user data from the public launch of an empirically supported 12-session self-guided DMHI, to assess overall program adherence rates and explore predictors of adherence. Data from 984 registered users were analyzed. Results showed that only 14.8% of users completed all 12 modules and 68.6% completed less than half of the modules. Users who were younger, had milder depression, had never seen a mental health provider, and who rejected signing-up for weekly program emails completed significantly more modules. Results add to concerns about the generalizability of controlled research on DMHIs due to lower adherence outside of research trials. This study highlights the potential of user data in identifying key factors that may be related to adherence. By examining adherence patterns among different sub-sets of users, we can pinpoint and focus on individuals who may adhere and benefit more from self-guided programs. Findings could also have implications for guiding intervention personalization for individuals who struggle to complete DMHIs.

What are practitioners' views of how digital health interventions may play a role in online child sexual abuse service delivery?

Introduction

Online child sexual abuse (OCSA) affects considerable numbers of children globally and is associated with a variety of mental health problems. Existing practitioner studies suggest that young people are infrequently asked about online abuse and practitioners have a fragmented understanding of the problems experienced or how they might approach them. There are very few evidence-based interventions that guide clinical assessment or practice. Digital Health Interventions (DHIs) have the potential to be an effective option where children and young people's services are challenged, including accessibility and anonymity. The aim of this study was to explore mental health practitioners' views of how DHIs may play a role in supporting young people who have experienced OCSA, and the role they can play in healthcare delivery.

Method

In-depth qualitative interviews and one focus group were conducted with 25 child mental health professionals across two sites (Manchester and Edinburgh). Data was analyzed using reflexive thematic analysis.

Results

Three overarching themes and 9 sub-themes were identified: (1) feeling a little bit lost; (2) seeing potential problems; and (3) knowing what works. Practitioners expressed interest in a DHI to support this client group and saw it as a way of managing waiting lists and complementing existing therapies. They felt that many young people would see this as a preferred medium to in-person therapy, would be empowering, and offers new ways of learning how to stay safe online. However, there were concerns about how much time would be needed by staff to deliver a DHI, anxieties about safety issues in relation to content and data protection, some of which may be unique to this population of young people, and concerns about the absence of a therapeutic relationship with vulnerable children.

Discussion

Our findings indicated that practitioners were uncertain about working with children subjected to OCSA but were receptive to the possibility of using a DHI to support their practice and to reduce waiting lists. Concerns were expressed about the time needed for staff training and support as well as concerns over patient safety and the lack of evidence about the effectiveness of an unsupported DHI.

[Digital technologies to improve mental health]

The burden of mental diseases is enormous and constantly growing worldwide. The resulting increase in demand for psychosocial help is also having a negative impact on waiting times for psychotherapy in Germany. Digital interventions for mental health, such as interventions delivered through or with the help of a website (e.g. "telehealth"), smartphone, or tablet app-based interventions and interventions that use text messages or virtual reality, can help. This article begins with an overview of the functions and range of applications of digital technologies for mental health. The evidence for individual digital forms of interventions is addressed. Overall, it is shown that digital interventions for mental health are likely to be cost-effective compared to no therapy or a non-therapeutic control group. Newer approaches such as "digital phenotyping" are explained in the article. Finally, individual papers from the "Leibniz ScienceCampus Digital Public Health" are presented, and limitations and challenges of technologies for mental health are discussed.

What digital health technology types are used in mental health prevention and intervention? Review of systematic reviews for systematization of technologies

Digital health technology has been widely applied to mental health interventions worldwide. Using digital phenotyping to identify an individual's mental health status has become particularly important. However, many technologies other than digital phenotyping are expected to become more prevalent in the future. The systematization of these technologies is necessary to accurately identify trends in mental health interventions. However, no consensus on the technical classification of digital health technologies for mental health interventions has emerged. Thus, we conducted a review of systematic review articles on the application of digital health technologies in mental health while attempting to systematize the technology using the Delphi method. To identify technologies used in digital phenotyping and other digital technologies, we included 4 systematic review articles that met the inclusion criteria, and an additional 8 review articles, using a snowballing approach, were incorporated into the comprehensive review. Based on the review results, experts from various disciplines participated in the Delphi process and agreed on the following 11 technical categories for mental health interventions: heart rate estimation, exercise or physical activity, sleep estimation, contactless heart rate/pulse wave estimation, voice and emotion analysis, self-care/cognitive behavioral therapy/mindfulness, dietary management, psychological safety, communication robots, avatar/metaverse devices, and brain wave devices. The categories we defined intentionally included technologies that are expected to become widely used in the future. Therefore, we believe these 11 categories are socially implementable and useful for mental health interventions.

Mind-body internet and mobile-based interventions for depression and anxiety in adults with chronic physical conditions: A systematic review of RCTs

This review summarizes the effectiveness of scalable mind-body internet and mobile-based interventions (IMIs) on depression and anxiety symptoms in adults living with chronic physical conditions. Six databases (MEDLINE, PsycINFO, SCOPUS, EMBASE, CINAHL, and CENTRAL) were searched for randomized controlled trials published from database inception to March 2023. Mind-body IMIs included cognitive behavioral therapy, breathwork, meditation, mindfulness, yoga or Tai-chi. To focus on interventions with a greater potential for scale, the intervention delivery needed to be online with no or limited facilitation by study personnel. The primary outcome was mean change scores for anxiety and depression (Hedges' g). In subgroup analyses, random-effects models were used to calculate pooled effect size estimates based on personnel support level, intervention techniques, chronic physical condition, and survey type. Meta-regression was conducted on age and intervention length. Fifty-six studies met inclusion criteria (sample size 7691, mean age of participants 43 years, 58% female): 30% (n = 17) neurological conditions, 12% (n = 7) cardiovascular conditions, 11% cancer (n = 6), 43% other chronic physical conditions (n = 24), and 4% (n = 2) multiple chronic conditions. Mind-body IMIs demonstrated statistically significant pooled reductions in depression (SMD = -0.33 [-0.40, -0.26], p<0.001) and anxiety (SMD = -0.26 [-0.36, -0.17], p<0.001). Heterogeneity was moderate. Scalable mind-body IMIs hold promise as interventions for managing anxiety and depression symptoms in adults with chronic physical conditions without differences seen with age or intervention length. While modest, the effect sizes are comparable to those seen with pharmacological therapy. The field would benefit from detailed reporting of participant demographics including those related to technological proficiency, as well as further evaluation of non-CBT interventions. Registration: The study is registered with PROSPERO ID #CRD42022375606.

The intersection of technology and mental health: enhancing access and care

In recent times, technology has increasingly become a central force in shaping the landscape of mental health care. The integration of various technological advancements, such as teletherapy, virtual care platforms, mental health apps, and wearable devices, holds great promise in improving access to mental health services and enhancing overall care. Technology's impact on mental health care is multi-faceted. Teletherapy and virtual care have brought about a revolution in service delivery, eliminating geographical barriers and offering individuals convenient and flexible access to therapy. Mobile mental health apps empower users to monitor their emotional well-being, practice mindfulness, and access self-help resources on the move. Furthermore, wearable devices equipped with biometric data can provide valuable insights into stress levels and sleep patterns, potentially serving as valuable indicators of mental health status. However, integrating technology into mental health care comes with several challenges and ethical considerations. Bridging the digital divide is a concern, as not everyone has equal access to technology or the necessary digital literacy. Ensuring privacy and data security is crucial to safeguard sensitive client information. The rapid proliferation of mental health apps calls for careful assessment and regulation to promote evidence-based practices and ensure the delivery of quality interventions. Looking ahead, it is vital to consider future implications and adopt relevant recommendations to fully harness technology's potential in mental health care. Continuous research is essential to evaluate the efficacy and safety of digital interventions, fostering collaboration between researchers, mental health professionals, and technology developers. Proper training on ethical technology utilization is necessary for mental health practitioners to maintain therapeutic boundaries while leveraging technological advancements responsibly.

Patterns of engagement in a digital mental health service during COVID-19: a cohort study for children and young people

Introduction

The COVID-19 pandemic increased public use of digital mental health technologies. However, little is known about changes in user engagement with these platforms during the pandemic. This study aims to assess engagement changes with a digital mental healthcare service during COVID-19.

Methods

A cohort study based on routinely collected service usage data from a digital mental health support service in the United Kingdom. Returning users aged 14-25 years that interacted for a maximum of two months were included. The study population was divided into pre-COVID and COVID cohorts. Demographic and usage information between cohorts were compared and usage clusters were identified within each cohort. Differences were tested using Chi-squared, two-sample Kolmogorov-Smirnov tests and logit regressions.

Results

Of the 624,103 users who joined the service between May 1, 2019, and October 1, 2021, 18,889 (32.81%) met the inclusion criteria: 5,048 in the pre-COVID cohort and 13,841 in the COVID cohort. The COVID cohort wrote more journals; maintained the same focus on messaging practitioners, posting discussions, commenting on posts, and having booked chats; and engaged less in writing journals, setting personal goals, posting articles, and having ad-hoc chats. Four usage profiles were identified in both cohorts: one relatively disengaged, one focused on contacting practitioners through chats/messages, and two broadly interested in writing discussions and comments within the digital community. Despite their broad similarities, usage patterns also exhibited differences between cohorts. For example, all four clusters had over 70% of users attempting to have ad-hoc chats with practitioners in the pre-COVID cohort, compared to one out of four clusters in the COVID cohort. Overall, engagement change patterns during the COVID-19 pandemic were not equal across clusters. Sensitivity analysis revealed varying strength of these defined clusters.

Discussion

Our study identified changes in user activity and engagement behavior within a digital mental healthcare service during the COVID-19 pandemic. These findings suggest that usage patterns within digital mental health services may be susceptible to change in response to external events such as a pandemic. Continuous monitoring of engagement patterns is important for informed design and personalized interventions.

Facilitators of and Barriers to Integrating Digital Mental Health Into County Mental Health Services: Qualitative Interview Analyses

BACKGROUND

Digital mental health interventions (DMHIs) represent a promising solution to address the growing unmet mental health needs and increase access to care. Integrating DMHIs into clinical and community settings is challenging and complex. Frameworks that explore a wide range of factors, such as the Exploration, Preparation, Implementation, Sustainment (EPIS) framework, can be useful for examining multilevel factors related to DMHI implementation efforts.

OBJECTIVE

This paper aimed to identify the barriers to, facilitators of, and best practice recommendations for implementing DMHIs across similar organizational settings, according to the EPIS domains of inner context, outer context, innovation factors, and bridging factors.

METHODS

This study stems from a large state-funded project in which 6 county behavioral health departments in California explored the use of DMHIs as part of county mental health services. Our team conducted interviews with clinical staff, peer support specialists, county leaders, project leaders, and clinic leaders using a semistructured interview guide. The development of the semistructured interview guide was informed by expert input regarding relevant inner context, outer context, innovation factors, and bridging factors in the exploration, preparation, and implementation phases of the EPIS framework. We followed a recursive 6-step process to conduct qualitative analyses using inductive and deductive components guided by the EPIS framework.

RESULTS

On the basis of 69 interviews, we identified 3 main themes that aligned with the EPIS framework: readiness of individuals, readiness of innovations, and readiness of organizations and systems. Individual-level readiness referred to the extent to which clients had the necessary technological tools (eg, smartphones) and knowledge (digital literacy) to support the DMHI. Innovation-level readiness pertained to the accessibility, usefulness, safety, and fit of the DMHI. Organization- and system-level readiness concerned the extent to which providers and leadership collectively held positive views about DMHIs as well as the extent to which infrastructure (eg, staffing and payment model) was appropriate.

CONCLUSIONS

The successful implementation of DMHIs requires readiness at the individual, innovation, and organization and system levels. To improve individual-level readiness, we recommend equitable device distribution and digital literacy training. To improve innovation readiness, we recommend making DMHIs easier to use and introduce, clinically useful, and safe and adapting them to fit into the existing client needs and clinical workflow. To improve organization- and system-level readiness, we recommend supporting providers and local behavioral health departments with adequate technology and training and exploring potential system transformations (eg, integrated care model). Conceptualizing DMHIs as services allows the consideration of both the innovation characteristics of DMHIs (eg, efficacy, safety, and clinical usefulness) and the ecosystem around DMHIs, such as individual and organizational characteristics (inner context), purveyors and intermediaries (bridging factor), client characteristics (outer context), as well as the fit between the innovation and implementation settings (innovation factor).