Introduction: The aim of this project was to map the landscape of who is doing what and where in digital mental health, and to provide recommendations that may assist in targeting communication efforts and funding calls. To address this, the project consisted of three studies:
This report presents the comprehensive results of digital mental health interventions across both academic and commercial landscapes in LMICs. The findings highlight the diverse range of interventions, technologies, and mental health conditions addressed, as well as the geographical distribution and stakeholder involvement in the development and testing of these interventions.
Key Findings:
Types of Interventions and Technologies
The most common types of digital mental health interventions identified were for treatment purposes, followed by diagnosis, prediction, monitoring, and prevention. Mobile and tablet apps were the most frequently used technology, particularly for treatment interventions. For example, the Inuka app in Kenya and Zimbabwe matches people to community health volunteers, supports mental health screening, and provides a medium for delivery of problem-solving therapy to address depression and anxiety. Websites and web-based platforms were also common, such as the Deprexis platform, developed in Germany and adapted for Brazil, which delivers CBT for depression, and Healthy Psychological Station in China, consisting of tailored CBT for depression and anxiety. Emerging technologies such as machine learning, AI, and virtual reality were increasingly being explored, particularly for diagnosis and prediction purposes. For instance, a machine learning model developed in India aimed to predict suicide attempts with 95% accuracy by analysing individual behaviour, whilst virtual reality exposure therapy for obsessive compulsive disorder is being trialled in the Dominican Republic. It is important to underscore the responsible use of these emerging technologies. For example, ensuring ethical standards and cultural relevance in their development and deployment when imported from high-income countries is crucial for their successful integration and acceptance in low- and middle-income countries.
Mental Health Conditions Targeted
Within the focus of this report, which covered anxiety, depression and psychosis (broadly defined), depression and anxiety disorders were the most frequently targeted conditions across all intervention types. We also identified interventions focusing on schizophrenia, bipolar disorder, obsessive-compulsive disorder, post-traumatic stress disorder, and psychosis. For example, the CONEMO app in Brazil and Peru focused on addressing symptoms of depression, the Bipolar Tracking Assistant (BTA) in Iran aimed to predict and monitor bipolar episodes, and the GOGBRAIN app in India was developed to tackle schizophrenia.
Geographical Distribution
The majority of digital mental health interventions were developed and tested in East Asia and the Pacific, particularly in China. Latin America and the Caribbean, South Asia, and the Middle East and North Africa, also had a noteworthy number of interventions. Sub-Saharan Africa had the fewest interventions, highlighting a potential gap in research and implementation in this region. For instance, while numerous interventions were identified in countries such as China, India, and Brazil, only a handful of studies were found in countries such as Kenya, Nigeria, and South Africa. Figures throughout the report are presented with and without interventions developed in China, as although it has developed a large number of digital mental health interventions, it was not the direct focus of this work.
Stakeholder Involvement
The involvement of people with lived experience and other stakeholders (e.g., carers, teachers, coaches) in the development and testing of interventions was inconsistently reported. Only 36 papers (26 without China) reported some form of stakeholder involvement, with varying levels of detail provided. When mentioned, their involvement varied in nature from more meaningful cultural adaptation to less meaningful consultation such as user experience testing and feedback. For example, the SHARP project in India and the US involved patients, family members, and clinicians in co-designing and adapting the mindLAMP app to ensure cultural relevance and improve usability. Experts emphasized the importance of close collaboration with local communities to ensure cultural relevance, highlighting the unique context of each LMIC.
Intervention Development Stages
Most interventions were in the early stages of development, such as pilot or feasibility trials. Fewer interventions were in the effectiveness testing or implementation stages, highlighting the need for more research on the scalability and real-world impact of these interventions. For instance, while numerous pilot and RCT studies were identified for mobile app-based interventions in countries like China, India, and Brazil, relatively few studies assessed their effectiveness in real-world settings or their implementation at scale.
Barriers and Challenges
Key barriers reported by intervention users included stigma, difficulties with internet access, and lack of cultural adaptation. For example, participants in studies from Pakistan and Indonesia cited stigma as a major barrier to accessing mental health services, including digital interventions. Researchers and developers cited challenges in generalizing interventions, ensuring adherence, and maintaining engagement. For instance, studies from China and Brazil reported high dropout rates and low engagement as significant challenges in evaluating the effectiveness of digital interventions. Lack of resources, including human, infrastructure, and economic resources, and health inequalities were identified as broader barriers to access. Experts also raised concerns about increased investments in the field without systematic exploration or holistic cultural adaptation, particularly when apps are brought from high-income countries to LMICs.
Partnerships and Funding
Europe and the United States were identified as main partners in Latin America, with the UK involved to a smaller extent. Experts in LMICs emphasized the need for partnerships and funding from high-income countries to address the treatment gap, provide training, facilitate collaboration, and ensure the sustainability of interventions. They highlighted several areas where support could be beneficial, such as:
Regional Highlights:
East Asia and the Pacific
This region was dominated by interventions from China, with a focus on treatment and the use of mobile apps, websites, and emerging technologies like AI and machine learning. For example, the XiaoE chatbot in China used AI to deliver cognitive-behavioural therapy for depression, while the Mommy Go app provided web-based support for perinatal depression. Coping Camp is another app from China aimed at tackling depression and anxiety in high school students. Other countries in the region, such as Indonesia and Vietnam, had a smaller number of interventions, primarily focusing on depression and anxiety treatment using mobile apps and websites, such as the Guided Act and Feel – Indonesia (GAF-ID) web-based platform for depression.
Latin America and the Caribbean
Interventions in this region primarily originated from academic institutions in countries like Brazil, Mexico, and Colombia. They focused on addressing depression and anxiety using mobile apps and websites. For instance, the Conemo app in Brazil and Peru used a combination of behavioural activation and mobile technology to address depression, while the Cuida tu Ánimo app in Colombia and Chile provided early intervention for anxiety and depression. A chatbot developed in Argentina, ‘Tess’, is testing the use of AI to send reminders, psychoeducation, and emotional support responses to users with depression and anxiety. Partnerships with the US and Europe were reported in several studies.
South Asia
India and Pakistan had the most interventions in this region, focusing on treatment and diagnosis using mobile apps and machine learning. For example, the TreadWill app in India delivered cognitive-behavioural therapy for depression and anxiety, whilst the POD Adventures app used gamification to teach problem solving concepts for depression and anxiety in students. An example from Pakistan is the Thinking Health Programme involving peer-support for patients with depression. Collaborations with the US and UK were a crucial component in some studies, such as the SHARP project, supported by the Wellcome Trust and leading to the development of the MINDLamp platform and its adaptation into Hindi.
Middle East and North Africa
Iran and Egypt had the most interventions in this region, with a mix of treatment, diagnosis, and prediction tools using various technologies. For example, the Happy Mom platform in Iran provided cognitive-behavioural therapy for mothers with depression, while a PTSD Coach Online-Arabic was developed in Egypt to manage PTSD symptoms in trauma-exposed adults . Other countries, such as Lebanon, had a smaller number of interventions, primarily focusing on depression, such as Step-by-step, which is an illustrative narrative program embedded in a digital platform with psychoeducation components.
Sub-Saharan Africa
Few interventions were identified in this region, mostly focusing on depression and anxiety treatment using mobile apps. For instance, the Inuka app in Kenya and Zimbabwe uses a problem-solving therapy delivered by lay health workers to address depression and anxiety, and the Kumasha app in South Africa uses behavioural activities components to address depression. One mental health start up, Blueroom Care, is a text, video, or voice chat-based online therapy app that connects users with licensed therapists and mental health professionals. However, the limited number of interventions highlights the need for more research and implementation efforts in this region.
Strengths and limitations: Findings presented are the result of comprehensive searches and triangulation of information in the three studies that were carried out in this project. The media and literature reviews utilised comprehensive approaches to gather data on digital mental health interventions in LMICs, though both faced limitations. The media review employed social listening tools (Brandwatch and Pulsar) to analyse content from digital platforms, focusing on interventions but excluding telemedicine, teletherapy, and China, and limited to freely available media, potentially missing premium content. Repeated mentions and out-of-scope tools led to an initial high volume of posts, but a final count of fewer unique interventions. The literature review used a systematic database search with pre-defined criteria, though pragmatic adjustments for timely results may have excluded some studies, particularly those with very new or uncommon digital methods that were not explicitly described as such in the title. Single screening and data extraction were conducted due to time constraints, which could introduce errors but were deemed suitable for this review's scope. Both reviews mapped a wide range of digital interventions, though some studies lacked detail on user involvement and barriers, and descriptions of the interventions themselves were sometimes insufficient. The expert consultation was targeted to address specific research questions and complement the review findings. Despite the demographic, professional, and geographical diversity captured, the small number of participants should be taken into account when interpreting findings.
Conclusion: This overview highlights the diverse landscape of digital mental health interventions in LMICs, with a growing focus on the use of mobile apps, websites, and emerging technologies for the treatment and diagnosis of common mental health conditions. While promising interventions have been identified across various regions, significant gaps remain in terms of geographical coverage, stakeholder involvement, and the scalability and sustainability of interventions. Sub-Saharan Africa, in particular, emerged as a region with limited research and information on implementation of interventions, despite the high burden of mental health conditions.
The findings also underscore the importance of cultural relevance, stakeholder engagement, and implementation research in the development and evaluation of digital mental health interventions in LMICs. Collaborative efforts between researchers, clinicians, technology developers, and people with lived experience are crucial to ensure that interventions are acceptable, feasible, and effective in the local contexts.
Targeted funding and communication efforts are needed to address the identified challenges and opportunities in this field. This includes prioritising early-stage implementation research which can uncover critical insights into user engagement, technological infrastructure, and healthcare integration, which are essential for the sustainability and scalability of these interventions. It also includes promoting culturally relevant interventions, supporting the development of interventions for a broader range of mental health conditions, fostering partnerships between LMICs and high-income countries, promoting the integration of digital interventions into existing healthcare systems, encouraging the responsible use of emerging technologies, providing funding for prevention and early intervention strategies, and supporting research on the long-term impact and cost-effectiveness of digital interventions.
By addressing these recommendations, funders and researchers can contribute to the development of a more robust and equitable evidence base for digital mental health interventions in LMICs, ultimately improving access to mental healthcare and promoting the well-being of populations in these settings.