Digital transformation of mental health services

Healthcare providers perspectives on digital, self-guided mental health programs for LGBTQIA+ individuals: A cross-sectional online survey

Digital, self-guided mental health programs are a promising avenue for mental health support for LGBTQIA+ (lesbian, gay, bisexual, trans, Queer, intersex, asexual plus additional sexuality, gender, and romantic identities) people - however, healthcare providers (HCPs) perspectives on programs are largely unknown. The aim of this study was to explore these perspectives. A cross-sectional online survey was distributed across Australia, with a final sample of 540 HCPs from a range of disciplines. Most respondents (419, 81.2 %), reported that digital, self-guided mental health programs would be useful, but 74.5 % (n = 380) also reported that they had concerns. Thematic analysis of open-text responses showed that HCPs believe programs may help overcome access barriers and could be useful as part of a wider care journey. Others were concerned about patient safety, and whether programs could be appropriately tailored to LGBTQIA+ experiences. Content analysis of open-text responses showed affirming language and imagery, content on LGBTQIA+ people's unique challenges, wider health information, and connections to community were important to include in programs. HCPs advocated for programs that offered broad and sub-population specific information. These findings show that HCPs are enthusiastic about digital, self-guided mental health programs, but care should be taken to address key concerns to facilitate future implementation.

A scoping review of the literature on the application and usefulness of the Problem Management Plus (PM+) intervention around the world

BACKGROUND

Given the high rates of common mental disorders and limited resources, task-shifting psychosocial interventions are needed to provide adequate care. One such intervention developed by the World Health Organization is Problem Management Plus (PM+).

AIMS

This review maps the evidence regarding the extent of application and usefulness of the PM+ intervention, i.e. adaptability, feasibility, effectiveness and scalability, since it was introduced in 2016.

METHOD

We conducted a scoping review of seven literature databases and grey literature from January 2015 to February 2024, to identify peer-reviewed and grey literature on PM+ around the world.

RESULTS

Out of 6739 potential records, 42 met the inclusion criteria. About 60% of the included studies were from low- and middle-income countries. Findings from pilot/feasibility trials demonstrated that PM+ is feasible, acceptable and safe. Results from definitive randomised controlled trials at short-term follow-up also suggested that PM+ is effective, with overall moderate-to-large effect sizes, in improving symptoms of common mental health problems. Although PM+ was more effective in reducing symptoms of common mental disorders, it was found to be costlier compared to usual care in the only study that evaluated its cost-effectiveness.

CONCLUSIONS

Our findings indicate that PM+, in its individual and group formats, can be adapted and effectively delivered by trained helpers to target a wide range of common mental health concerns. More effectiveness and implementation evidence is required to understand the long-term impact of PM+, its cost-effectiveness and scalability, and moderators of treatment outcomes such as gender and delivery formats.

Nudges and Prompts Increase Engagement in Self-Guided Digital Health Treatment for Depression and Anxiety: Results From a 3-Arm Randomized Controlled Trial

BACKGROUND

Accessible and effective approaches to mental health treatment are important because of common barriers such as cost, stigma, and provider shortage. The effectiveness of self-guided treatment is well established, and its use has intensified because of the COVID-19 pandemic. Engagement remains important as dose-response relationships have been observed. Platforms such as Facebook (Meta Platform, Inc), LinkedIn (Microsoft Corp), and X Corp (formerly known as Twitter, Inc) use principles of behavioral economics to increase engagement. We hypothesized that similar concepts would increase engagement in self-guided digital health.

OBJECTIVE

This 3-arm randomized controlled trial aimed to test whether members of 2 digital self-health courses for anxiety and depression would engage with behavioral nudges and prompts. Our primary hypothesis was that members would click on 2 features: tips and a to-do checklist. Our secondary hypothesis was that members would prefer to engage with directive tips in arm 2 versus social proof and present bias tips in arm 3. Our tertiary hypothesis was that rotating tips and a to-do checklist would increase completion rates. The results of this study will form a baseline for future artificial intelligence-directed research.

METHODS

Overall, 13,224 new members registered between November 2021 and May 2022 for Evolution Health's self-guided treatment courses for anxiety and depression. The control arm featured a member home page without nudges or prompts. Arm 2 featured a home page with a tip-of-the-day section. Arm 3 featured a home page with a tip-of-the-day section and a to-do checklist. The research protocol for this study was published in JMIR Research Protocols on August 15, 2022.

RESULTS

Arm 3 had significantly younger members (F2,4564=40.97; P<.001) and significantly more female members (χ24=92.2; P<.001) than the other 2 arms. Control arm members (1788/13,224, 13.52%) completed an average of 1.5 course components. Arm 2 members (865/13,224, 6.54%) clicked on 5% of tips and completed an average of 1.8 course components. Arm 3 members (1914/13,224, 14.47%) clicked on 5% of tips, completed 2.7 of 8 to-do checklist items, and completed an average of 2.11 course components. Completion rates in arm 2 were greater than those in arm 1 (z score=3.37; P<.001), and completion rates in arm 3 were greater than those in arm 1 (z score=12.23; P<.001). Engagement in all 8 components in arm 3 was higher than that in arm 2 (z score=1.31; P<.001).

CONCLUSIONS

Members engaged with behavioral nudges and prompts. The results of this study may be important because efficacy is related to increased engagement. Due to its novel approach, the outcomes of this study should be interpreted with caution and used as a guideline for future research in this nascent field.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/37231.

Impact of AI-Powered Solutions in Rehabilitation Process: Recent Improvements and Future Trends

Rehabilitation is an important and necessary part of local and global healthcare services along with treatment and palliative care, prevention of disease, and promotion of good health. The rehabilitation process helps older and young adults even children to become as independent as possible in activities of daily life and enables participation in useful living activities, recreation, work, and education. The technology of Artificial Intelligence (AI) has evolved significantly in recent years. Many activities related to rehabilitation have been getting benefits from using AI techniques. The objective of this review study is to explore the advantages of AI for rehabilitation and how AI is impacting the rehabilitation process. This study aims at the most critical aspects of the rehabilitation process that could potentially take advantage of AI techniques including personalized rehabilitation apps, rehabilitation through assistance, rehabilitation for neurological disorders, rehabilitation for developmental disorders, virtual reality rehabilitation, rehabilitation of neurodegenerative diseases and Telerehabilitation of Cardiovascular. We presented a survey on the newest empirical studies available in the literature including the AI-based technology helpful in the Rehabilitation process. The novelty feature included but was not limited to an overview of the technological solutions useful in rehabilitation. Seven different categories were identified. Illustrative examples of practical applications were detailed. Implications of the findings for both research and practice were critically discussed. Most of the AI applications in these rehabilitation types are in their infancy and continue to grow while exploring new opportunities. Therefore, we investigate the role of AI technology in rehabilitation processes. In addition, we do statistical analysis of the selected studies to highlight the significance of this review work. In the end, we also present a discussion on some challenges, and future research directions.

Additive effects of adjunctive app-based interventions for mental disorders - A systematic review and meta-analysis of randomised controlled trials

Background

It is uncertain whether app-based interventions add value to existing mental health care.

Objective

To examine the incremental effects of app-based interventions when used as adjunct to mental health interventions.

Methods

We searched PubMed, PsycINFO, Scopus, Web of Science, and Cochrane Library databases on September 15th, 2023, for randomised controlled trials (RCTs) on mental health interventions with an adjunct app-based intervention compared to the same intervention-only arm for adults with mental disorders or respective clinically relevant symptomatology. We conducted meta-analyses on symptoms of different mental disorders at postintervention. PROSPERO, CRD42018098545.

Results

We identified 46 RCTs (4869 participants). Thirty-two adjunctive app-based interventions passively or actively monitored symptoms and behaviour, and in 13 interventions, the monitored data were sent to a therapist. We found additive effects on symptoms of depression (g = 0.17; 95 % CI 0.02 to 0.33; k = 7 comparisons), anxiety (g = 0.80; 95 % CI 0.06 to 1.54; k = 3), mania (g = 0.2; 95 % CI 0.02 to 0.38; k = 4), smoking cessation (g = 0.43; 95 % CI 0.29 to 0.58; k = 10), and alcohol use (g = 0.23; 95 % CI 0.08 to 0.39; k = 7). No significant effects were found on symptoms of depression within a bipolar disorder (g = -0.07; 95 % CI -0.37 to 0.23, k = 4) and eating disorders (g = -0.02; 95 % CI -0.44 to 0.4, k = 3). Studies on depression, mania, smoking, and alcohol use had a low heterogeneity between the trials. For other mental disorders, only single studies were identified. Only ten studies had a low risk of bias, and 25 studies reported insufficient statistical power.

Discussion

App-based interventions may be used to enhance mental health interventions to further reduce symptoms of depression, anxiety, mania, smoking, and alcohol use. However, the effects were small, except for anxiety, and limited due to study quality. Further high-quality research with larger sample sizes is warranted to better understand how app-based interventions can be most effectively combined with established interventions to improve outcomes.

Multitask Learning for Mental Health: Depression, Anxiety, Stress (DAS) Using Wearables

This study investigates the prediction of mental well-being factors-depression, stress, and anxiety-using the NetHealth dataset from college students. The research addresses four key questions, exploring the impact of digital biomarkers on these factors, their alignment with conventional psychology literature, the time-based performance of applied methods, and potential enhancements through multitask learning. The findings reveal modality rankings aligned with psychology literature, validated against paper-based studies. Improved predictions are noted with temporal considerations, and further enhanced by multitasking. Mental health multitask prediction results show aligned baseline and multitask performances, with notable enhancements using temporal aspects, particularly with the random forest (RF) classifier. Multitask learning improves outcomes for depression and stress but not anxiety using RF and XGBoost.