Digital Health Interventions for Depression and Anxiety in Low- and Middle-Income Countries: Rapid Scoping Review
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,047 | 0,152 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,008 | 0,011 |
| Bibliométrie | 0,022 | 0,017 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,007 | 0,007 |
| Science ouverte | 0,004 | 0,006 |
| Intégrité de la recherche | 0,005 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».