Development of a Mobile Health Application for HIV Prevention Among At-Risk Populations in Urban Settings in East Africa: A Participatory Design Approach
Bibliographic record
Abstract
BACKGROUND: There is limited evidence in Africa on the design and development of mobile health (mHealth) applications to guide best practices and ensure effectiveness. A pragmatic trial for HIV pre-exposure prophylaxis roll-out among key populations in Tanzania is needed. OBJECTIVE: We present the results of the development of a mobile app (Jichunge) intended to promote adherence to pre-exposure prophylaxis (PrEP) among men who have sex with men (MSM) and female sex workers (FSW) in Tanzania. METHODS: A participatory design approach was employed and guided by the information system research framework. MSM and FSW were the target populations. A total of 15 MSM and 15 FSW were engaged in the relevance and design cycles, while the piloting phase included 10 MSM and 20 FSW. RESULTS: The relevance cycle enabled the description of the existing problem, provided the compatible app features for the target population, and identified the need to develop an mHealth app that provides health services in a stigmatizing and discriminating environment. User involvement in the app's design and evaluation provided an opportunity to incorporate social, cultural, and community-specific features that ensured usability. In addition, the participants suggested valuable information to inform the app, text message services, medication registration, and chat platform designs. CONCLUSIONS: The participatory design approach in the development of mHealth apps is useful in identifying and validating population-specific functional features, improve usability, and ensuring future health impacts. Through this participatory process, the Jichunge app took end-user needs, perspectives, and experiences into account, eliciting enthusiasm regarding its potential role in supporting pre-exposure prophylaxis adherence for HIV and related behavioral change promotion. TRIAL REGISTRATION: International Clinical Trials Registry Platform PACTR202003823226570; https://trialsearch.who.int/Trial2.aspx?TrialID=PACTR202003823226570.
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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 | 0.028 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".