Self-Guided Smartphone App (Vimbo) for the Reduction of Symptoms of Depression and Anxiety in South African Adults: Pilot Quantitative Single-Arm Study
Notice bibliographique
Résumé
Background Barriers to mental health assessment and intervention have been well documented within South Africa, in both urban and rural settings. Internationally, evidence has emerged for the effectiveness of technology and, specifically, app-based mental health tools and interventions to help overcome some of these barriers. However, research on digital interventions specific to the South African context and mental health is limited. Objective This pilot study investigated the feasibility of using an app (Vimbo) to treat symptoms of anxiety and depression in South African adults recruited from a community sample. The Vimbo app is a self-guided, cognitive behavioral therapy–based digital intervention for common mental health difficulties developed for the South African context. Methods This pilot study used a naturalistic, single-arm design testing the Vimbo app over 12 weeks, from October 2020 to February 2021. Participants were recruited through the South African Depression and Anxiety Group and social media advertisements online. A 2-week retention period was used to allow for a minimum of 2 datasets. App usage and engagement metrics were extracted directly from the back end of the app. Based on the model, researchers expected many users to discontinue usage when their symptom levels entered a healthy range. Pre-post review of symptom levels was used to reflect on clinical recovery status at discontinuation after the retention period. Results A total of 218 applicants met study eligibility criteria and were invited to download the Vimbo app. Of these, 52% (114/218) of the participants registered with the app, who indicated multiple variances of depression and anxiety symptoms ranging in severity from mild to severe. Two participants users withdrew from the study. Moreover, 69% (77/112) of users were retained, including 8 who had technical issues with their treatment. When comparing broad uptake across all interested participants, chi-square analysis indicated significantly reduced uptake in participants identifying as “unemployed but seeking employment” (χ24=10.47; N=251; P=.03). When considering app usage for the entire cohort (n=69, excluding participants with technical issues), there was a mean of 72.87 (SD 71.425) total module pages read, a mean of 30% (SD 29.473%) of prescribed content completed, and a mean of 19.93 (SD 27.517) times engaging with tools and skills. Conclusions Our findings support the case for continued exploration of app-based interventions for treating depression and anxiety in South Africa. Developing strategies to increase access and improve intervention uptake may prove essential to helping mobile health interventions make as significant an impact as possible. Future research should include a randomized controlled trial with a larger sample to further assess the efficacy of app-based interventions in treating mental health difficulties in South Africa.
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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 | 0,004 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».