Digital Health Interventions for Depression and Anxiety in Low- and Middle-Income Countries: Rapid Scoping Review
Bibliographic record
Abstract
BACKGROUND: Low- 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. OBJECTIVE: The 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. METHODS: A 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. RESULTS: A 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. CONCLUSIONS: Despite 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".