Coach-Facilitated Web-Based Therapy Compared With Information About Web-Based Resources in Patients Referred to Secondary Mental Health Care for Depression: Randomized Controlled Trial

The Effect of Digital Mental Health Literacy Interventions on Mental Health: Systematic Review and Meta-Analysis

BACKGROUND

Accelerated by technological advancements and the recent global pandemic, there is burgeoning interest in digital mental health literacy (DMHL) interventions that can positively affect mental health. However, existing work remains inconclusive regarding the effectiveness of DMHL interventions.

OBJECTIVE

This systematic review and meta-analysis investigated the components and modes of DMHL interventions, their moderating factors, and their long-term impacts on mental health literacy and mental health.

METHODS

We used a random-effects model to conduct meta-analyses and meta-regressions on moderating effects of DMHL interventions on mental health.

RESULTS

Using 144 interventions with 206 effect sizes, we found a moderate effect of DMHL interventions in enhancing distal mental health outcomes (standardized mean difference=0.42, 95% CI -0.10 to 0.73; P<.001) and a large effect in increasing proximal mental health literacy outcomes (standardized mean difference=0.65, 95% CI 0.59-0.74; P<.001). Uptake of DMHL interventions was comparable with that of control conditions, and uptake of DMHL interventions did not moderate the effects on both proximal mental health literacy outcomes and distal mental health outcomes. DMHL interventions were as effective as face-to-face interventions and did not differ by platform type or dosage. DMHL plus interventions (DMHL psychoeducation coupled with other active treatment) produced large effects in bolstering mental health, were more effective than DMHL only interventions (self-help DMHL psychoeducation), and were comparable with non-DMHL interventions (treatment as usual). DMHL interventions demonstrated positive effects on mental health that were sustained over follow-up assessments and were most effective in enhancing the mental health of emerging and older adults.

CONCLUSIONS

For theory building, our review and meta-analysis found that DMHL interventions are as effective as face-to-face interventions. DMHL interventions confer optimal effects on mental health when DMHL psychoeducation is combined with informal, nonprofessional active treatment components such as skills training and peer support, which demonstrate comparable effectiveness with that of treatment as usual (client-professional interactions and therapies). These effects, which did not differ by platform type or dosage, were sustained over time. Additionally, most DMHL interventions are found in Western cultural contexts, especially in high-income countries (Global North) such as Australia, the United States, and the United Kingdom, and limited research is conducted in low-income countries in Asia and in South American and African countries. Most of the DMHL studies did not report information on the racial or ethnic makeup of the samples. Future work on DMHL interventions that target racial or ethnic minority groups, particularly the design, adoption, and evaluation of the effects of culturally adaptive DMHL interventions on uptake and mental health functioning, is needed. Such evidence can drive the adoption and implementation of DMHL interventions at scale, which represents a key foundation for practice-changing impact in the provision of mental health resources for individuals and the community.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42023363995; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023363995.

Effects of a Self-Guided Transdiagnostic Smartphone App on Patient Empowerment and Mental Health: Randomized Controlled Trial

BACKGROUND

Mental disorders impact both individuals and health systems. Symptoms and syndromes often remain undetected and untreated, resulting in chronification. Besides limited health care resources, within-person barriers such as the lack of trust in professionals, the fear of stigmatization, or the desire to cope with problems without professional help contribute to the treatment gap. Self-guided mental health apps may support treatment seeking by reducing within-person barriers and facilitating mental health literacy. Digital mental health interventions may also improve mental health related self-management skills and contribute to symptom reduction and the improvement of quality of life.

OBJECTIVE

This study aims to investigate the effects of a self-guided transdiagnostic app for mental health on help seeking, reduced stigma, mental health literacy, self-management skills, mental health symptoms, and quality of life using a randomized controlled design.

METHODS

Overall, 1045 participants (recruited via open, blinded, and web-based recruitment) with mild to moderate depression or anxiety-, sleep-, eating-, or somatization-related psychopathology were randomized to receive either access to a self-guided transdiagnostic mental health app (MindDoc) in addition to care as usual or care as usual only. The core features of the app were regular self-monitoring, automated feedback, and psychological courses and exercises. The coprimary outcomes were mental health literacy, mental health-related patient empowerment and self-management skills (MHPSS), attitudes toward help seeking, and actual mental health service use. The secondary outcomes were psychopathological symptom burden and quality of life. Data were collected at baseline and 8 weeks and 6 months after randomization. Treatment effects were investigated using analyses of covariance, including baseline variables as predictors and applying multiple imputation.

RESULTS

We found small but robust between-group effects for MHPSS (Cohen d=0.29), symptoms burden (Cohen d=0.28), and quality of life (Cohen d=0.19) 8 weeks after randomization. The effects on MHPSS were maintained at follow-up. Follow-up assessments also showed robust effects on mental health literacy and preliminary evidence for the improvement of help seeking. Predictors of attrition were lower age and higher personality dysfunction. Among the non-attritors, predictors for deterioration were less outpatient treatment and higher initial symptom severity.

CONCLUSIONS

A self-guided transdiagnostic mental health app can contribute to lasting improvements in patient empowerment. Symptoms of common mental disorders and quality of life improved faster in the intervention group than in the control group. Therefore, such interventions may support individuals with symptoms of 1 or more internalizing disorders, develop health-centered coping skills, prevent chronification, and accelerate symptom improvement. Although the effects for individual users are small and predictors of attrition and deterioration need to be investigated further, the potential public health impact of a self-guided intervention can be large, given its high scalability.

TRIAL REGISTRATION

German Clinical Trials Register DRKS00022531; https://drks.de/search/de/trial/DRKS00022531.

Assessing Patient Adherence to and Engagement With Digital Interventions for Depression in Clinical Trials: Systematic Literature Review

BACKGROUND

New approaches to the treatment of depression are necessary for patients who do not respond to current treatments or lack access to them because of barriers such as cost, stigma, and provider shortage. Digital interventions for depression are promising; however, low patient engagement could limit their effectiveness.

OBJECTIVE

This systematic literature review (SLR) assessed how participant adherence to and engagement with digital interventions for depression have been measured in the published literature, what levels of adherence and engagement have been reported, and whether higher adherence and increased engagement are linked to increased efficacy.

METHODS

We focused on a participant population of adults (aged ≥18 years) with depression or major depressive disorder as the primary diagnosis and included clinical trials, feasibility studies, and pilot studies of digital interventions for treating depression, such as digital therapeutics. We screened 756 unique records from Ovid MEDLINE, Embase, and Cochrane published between January 1, 2000, and April 15, 2022; extracted data from and appraised the 94 studies meeting the inclusion criteria; and performed a primarily descriptive analysis. Otsuka Pharmaceutical Development & Commercialization, Inc (Princeton, New Jersey, United States) funded this study.

RESULTS

This SLR encompassed results from 20,111 participants in studies using 47 unique web-based interventions (an additional 10 web-based interventions were not described by name), 15 mobile app interventions, 5 app-based interventions that are also accessible via the web, and 1 CD-ROM. Adherence was most often measured as the percentage of participants who completed all available modules. Less than half (44.2%) of the participants completed all the modules; however, the average dose received was 60.7% of the available modules. Although engagement with digital interventions was measured differently in different studies, it was most commonly measured as the number of modules completed, the mean of which was 6.4 (means ranged from 1.0 to 19.7) modules. The mean amount of time participants engaged with the interventions was 3.9 (means ranged from 0.7 to 8.4) hours. Most studies of web-based (34/45, 76%) and app-based (8/9, 89%) interventions found that the intervention group had substantially greater improvement for at least 1 outcome than the control group (eg, care as usual, waitlist, or active control). Of the 14 studies that investigated the relationship between engagement and efficacy, 9 (64%) found that increased engagement with digital interventions was significantly associated with improved participant outcomes. The limitations of this SLR include publication bias, which may overstate engagement and efficacy, and low participant diversity, which reduces the generalizability.

CONCLUSIONS

Patient adherence to and engagement with digital interventions for depression have been reported in the literature using various metrics. Arriving at more standardized ways of reporting adherence and engagement would enable more effective comparisons across different digital interventions, studies, and populations.

Guided Internet-Delivered Treatment for Depression: Scoping Review

BACKGROUND

Studies on guided internet-delivered treatment have demonstrated promising results for patients with depressive disorder.

OBJECTIVE

The aim of this study was to provide an overview of this research area and identify potential gaps in the research.

METHODS

In this scoping review, web-based databases were used to identify research papers published between 2010 and 2022 where guided internet-delivered treatment was administered to participants with depressive disorders, a standardized rating scale of depressive symptoms was used as the primary outcome measure, and the treatment was compared with a control condition.

RESULTS

A total of 111 studies were included, and an overview of the studies was provided. Several gaps in the research were identified regarding the design of the studies, treatments delivered, participant representation, and treatment completion.

CONCLUSIONS

This review provides a comprehensive overview of the research area, and several research gaps were identified. The use of other designs and active control conditions is recommended. Future studies should provide access to treatment manuals, and more replications should be conducted. Researchers should aim to include underrepresented populations and provide reports of comorbidities. Definitions of adequate dosage, reports of completion rates, and reasons for treatment dropout are recommended for future studies.

Enhancing COVID Rehabilitation with Technology (ECORT): protocol for an open-label, single-site randomized controlled trial evaluating the effectiveness of electronic case management for individuals with persistent COVID-19 symptoms

BACKGROUND

As of May 2022, Ontario has seen more than 1.3 million cases of COVID-19. While the majority of individuals will recover from infection within 4 weeks, a significant subset experience persistent and often debilitating symptoms, known as "post-COVID syndrome" or "Long COVID." Those with Long COVID experience a wide array of symptoms, with variable severity, including fatigue, cognitive impairment, and shortness of breath. Further, the prevalence and duration of Long COVID is not clear, nor is there evidence on the best course of rehabilitation for individuals to return to their desired level of function. Previous work with chronic conditions has suggested that the addition of electronic case management (ECM) may help to improve outcomes. These platforms provide enhanced connection with care providers, detailed symptom tracking and goal setting, and access to relevant resources. In this study, our primary aim is to determine if the addition of ECM with health coaching improves Long COVID outcomes at 3 months compared to health coaching alone.

METHODS

The trial is an open-label, single-site, randomized controlled trial of ECM with health coaching (ECM+) compared to health coaching alone (HC). Both groups will continue to receive usual care. Participants will be randomized equally to receive health coaching (± ECM) for a period of 8 weeks and a 12-week follow-up. Our primary outcome is the WHO Disability Assessment Scale (WHODAS), 36-item self-report total score. Participants will also complete measures of cognition, fatigue, breathlessness, and mental health. Participants and care providers will be asked to complete a brief qualitative interview at the end of the study to evaluate acceptability and implementation of the intervention.

DISCUSSION

There is currently little evidence about the optimal treatment of Long COVID patients or the use of digital health platforms in this population. The results of this trial could result in rapid, scalable, and personalized care for people with Long COVID which will decrease morbidity after an acute infection. Results from this study will also inform decision making in Long COVID and treatment guidelines at provincial and national levels.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05019963. Registered on 25 August 2021.

Current directions in digital interventions for mood and anxiety disorders

PURPOSE OF REVIEW

The purpose of this review is to provide an overview of the current scope of digital interventions for anxiety and mood disorders, with a focus on smartphone apps, for clinicians and mental healthcare providers.

RECENT FINDINGS

Of the 11 randomized controlled trials analyzed, 7 showed evidence that guided digital interventions (those supported by humans) were effective in improving anxiety and mood symptoms, and 3 showed evidence that unguided (those not supported by humans) interventions were effective. Psychoeducation was the most popular feature of both guided and unguided interventions. Attrition was highest (50%) in the unguided app-based intervention and lowest in the guided interventions. Many studies lacked active control groups and comparison was often made to a nondigital or waitlist control condition.

SUMMARY

Guided digital interventions continue to show promising results and can be used to enhance clinical care with minimal resources although more direct comparisons to existing treatments are necessary to understand their actual efficacy. Unguided self-help apps and chatbots remain promising, but more work is necessary to understand the real-world engagement and efficacy of these interventions.

Characteristics of Mobile Health Platforms for Depression and Anxiety: Content Analysis Through a Systematic Review of the Literature and Systematic Search of Two App Stores

Background

Mobile health (mHealth) platforms show promise in the management of mental health conditions such as anxiety and depression. This has resulted in an abundance of mHealth platforms available for research or commercial use.

Objective

The objective of this review is to characterize the current state of mHealth platforms designed for anxiety or depression that are available for research, commercial use, or both.

Methods

A systematic review was conducted using a two-pronged approach: searching relevant literature with prespecified search terms to identify platforms in published research and simultaneously searching 2 major app stores—Google Play Store and Apple App Store—to identify commercially available platforms. Key characteristics of the mHealth platforms were synthesized, such as platform name, targeted condition, targeted group, purpose, technology type, intervention type, commercial availability, and regulatory information.

Results

The literature and app store searches yielded 169 and 179 mHealth platforms, respectively. Most platforms developed for research purposes were designed for depression (116/169, 68.6%), whereas the app store search reported a higher number of platforms developed for anxiety (Android: 58/179, 32.4%; iOS: 27/179, 15.1%). The most common purpose of platforms in both searches was treatment (literature search: 122/169, 72.2%; app store search: 129/179, 72.1%). With regard to the types of intervention, cognitive behavioral therapy and referral to care or counseling emerged as the most popular options offered by the platforms identified in the literature and app store searches, respectively. Most platforms from both searches did not have a specific target age group. In addition, most platforms found in app stores lacked clinical and real-world evidence, and a small number of platforms found in the published research were available commercially.

Conclusions

A considerable number of mHealth platforms designed for anxiety or depression are available for research, commercial use, or both. The characteristics of these mHealth platforms greatly vary. Future efforts should focus on assessing the quality—utility, safety, and effectiveness—of the existing platforms and providing developers, from both commercial and research sectors, a reporting guideline for their platform description and a regulatory framework to facilitate the development, validation, and deployment of effective mHealth platforms.