User-Centered Design of a Gamified Mental Health App for Adolescents in Sub-Saharan Africa: Multicycle Usability Testing Study
Bibliographic record
Abstract
BACKGROUND: There is an urgent need for scalable psychological treatments to address adolescent depression in low-resource settings. Digital mental health interventions have many potential advantages, but few have been specifically designed for or rigorously evaluated with adolescents in sub-Saharan Africa. OBJECTIVE: This study had 2 main objectives. The first was to describe the user-centered development of a smartphone app that delivers behavioral activation (BA) to treat depression among adolescents in rural South Africa and Uganda. The second was to summarize the findings from multicycle usability testing. METHODS: An iterative user-centered agile design approach was used to co-design the app to ensure that it was engaging, culturally relevant, and usable for the target populations. An array of qualitative methods, including focus group discussions, in-depth individual interviews, participatory workshops, usability testing, and extensive expert consultation, was used to iteratively refine the app throughout each phase of development. RESULTS: A total of 160 adolescents from rural South Africa and Uganda were involved in the development process. The app was built to be consistent with the principles of BA and supported by brief weekly phone calls from peer mentors who would help users overcome barriers to engagement. Drawing on the findings of the formative work, we applied a narrative game format to develop the Kuamsha app. This approach taught the principles of BA using storytelling techniques and game design elements. The stories were developed collaboratively with adolescents from the study sites and included decision points that allowed users to shape the narrative, character personalization, in-app points, and notifications. Each story consists of 6 modules ("episodes") played in sequential order, and each covers different BA skills. Between modules, users were encouraged to work on weekly activities and report on their progress and mood as they completed these activities. The results of the multicycle usability testing showed that the Kuamsha app was acceptable in terms of usability and engagement. CONCLUSIONS: The Kuamsha app uniquely delivered BA for adolescent depression via an interactive narrative game format tailored to the South African and Ugandan contexts. Further studies are currently underway to examine the intervention's feasibility, acceptability, and efficacy in reducing depressive symptoms.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 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".