Challenges and Mitigation Strategies in the Development and Feasibility Assessment of a Digital Mental Health Intervention for Depression (VMood): Mixed Methods Feasibility Study (Preprint)
Notice bibliographique
Résumé
BACKGROUND Worldwide, the COVID-19 pandemic contributed to further gaps in mental health care, particularly in low- and middle-income countries such as Vietnam, where care is inaccessible for 90% of those who need it. There has subsequently been a considerable increase in the use of digital mental health interventions such as smartphone apps. Presently, the evidence for such interventions is limited, especially in cases in which the interventions have been adapted from evidence-based in-person formats. Implementation science aims to promote the incorporation of scientific findings into practice. A key determinant of implementation success is an intervention’s usability. Hurdles to usability include an intervention being too confusing or time-intensive to use. Facilitators include incorporating a greater number of engagement features and integrating human support. OBJECTIVE The aim of this implementation science feasibility study was to describe the challenges and mitigation strategies used in the development, usability testing, and implementation of a digital depression intervention (VMood smartphone app) developed in Vietnam. VMood was adapted from an evidence-based in-person intervention originally developed in Canada that is grounded in principles of cognitive behavioral therapy with supportive coaching by a lay health or social services worker. The research team is currently testing the effectiveness and cost-effectiveness of VMood in a randomized controlled trial across 8 provinces in Vietnam informed by the results of this feasibility assessment. METHODS This mixed methods feasibility study was organized using an implementation outcome framework focused on acceptability, adoption, appropriateness, and feasibility. This study involved three data collection components: (1) usability testing (interviews and focus groups with app user and provider participants who tested VMood in 1 Vietnamese province), (2) app metrics (from the early phase of the randomized controlled trial in the same province but from different municipalities), and (3) discourse data (notes from various team meetings, communications, and reports on VMood’s development and implementation). Qualitative data were analyzed using thematic content analysis. App use data were analyzed using basic descriptive statistics. RESULTS The findings of the 3 data components showed that there were seven main challenges: (1) challenges with recruitment and uptake of the app, (2) challenges with use and engagement, (3) screening challenges, (4) digital divide, (5) limitations to digital applications for mental health, (6) technological challenges, and (7) funding and policy constraints. Various solutions to help mitigate the challenges were used by the team. CONCLUSIONS The findings contribute important evidence on the challenges to the development and feasibility assessment of a digital depression app adapted from an in-person intervention in Vietnam. The findings have applicability for others looking to develop and implement digital interventions in similar contexts, serving as a unique opportunity to share the lessons learned regarding the development and testing process.
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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 | 0,146 | 0,087 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».