Self-Guided Smartphone App (Vimbo) for the Reduction of Symptoms of Depression and Anxiety in South African Adults: Pilot Quantitative Single-Arm Study
Bibliographic record
Abstract
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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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".