A Narrative-Gamified Mental Health App (Kuamsha) for Adolescents in Uganda: Mixed Methods Feasibility and Acceptability Study
Bibliographic record
Abstract
BACKGROUND: Many adolescents in Uganda are affected by common mental disorders, but only a few affordable treatment options are available. Digital mental health interventions offer promising opportunities to reduce these large treatment gaps, but interventions specifically tailored for Ugandan adolescents are limited. OBJECTIVE: This study aimed to determine the feasibility and acceptability of the Kuamsha program, an intervention delivered through a gamified app with low-intensity telephonic guidance, as a way to promote mental health among adolescents from the general population in Uganda. METHODS: A 3-month pre-post single-arm trial was conducted with adolescents aged between 15 and 19 years living in Wakiso District, Central Uganda. The intervention was coproduced with adolescents from the study site to ensure that it was culturally acceptable. The feasibility and acceptability of the intervention were evaluated using an explanatory sequential mixed methods approach. Feasibility was assessed by collecting data on trial retention rates and treatment adherence rates. Acceptability was assessed through a questionnaire and in-depth interviews with participants following the conclusion of the intervention period. As a secondary objective, we explored the changes in participants' mental health before and after the intervention. RESULTS: A total of 31 adolescents were recruited for the study. Results from the study showed high levels of feasibility and acceptability. Trial retention rates exceeded 90%, and treatment adherence was ≥80%. These results, evaluated against our predefined trial progression criteria, indicate a successful feasibility study, with all criteria exceeding the thresholds necessary to progress to a larger trial. App engagement metrics, such as time spent on the app and modules completed, exceeded existing literature benchmarks, and many adolescents continued to use the app after the intervention. In-depth interviews and questionnaire responses revealed high acceptability levels. Depressive symptoms trended toward reduction (mean difference: 1.41, 95% CI -0.60 to 3.42, Cohen d=0.30), although this was not statistically significant (P=.16). Supporting this trend, we also observed a reduction in the proportion of participants with moderate depressive symptoms from 32% (10/31) to 17% (5/29) after the intervention, but this change was also not significant (P=.10). CONCLUSIONS: This study presents evidence to support the Kuamsha program as a feasible and acceptable digital mental health program for adolescents in Uganda. A fully powered randomized controlled trial is needed to assess its effectiveness in improving adolescents' mental health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".