Retention in an mHealth App Aiming to Promote the Use of HIV Pre-Exposure Prophylaxis Among Female Sex Workers in Dar es Salaam, Tanzania: Prospective Cohort Study
Bibliographic record
Abstract
Background: Increasing access to smartphones in sub-Saharan Africa offers an opportunity to leverage mobile health (mHealth) technology to improve access to health care in underserved populations. In the domain of HIV prevention, mHealth interventions can potentially contribute to solving the challenges of suboptimal adherence to pre-exposure prophylaxis (PrEP) and low retention in PrEP services among populations most vulnerable to HIV acquisition. However, there is a gap in the knowledge about the use of such interventions in sub-Saharan Africa. Objective: This study aims to evaluate the extent and predictors of retention in an mHealth app (Jichunge) that aims to promote adherence to PrEP and retention in PrEP care among female sex workers in Dar es Salaam, Tanzania. Methods: A prospective cohort of female sex workers residing in Dar es Salaam were recruited, using respondent-driven sampling. All participants were provided with the Jichunge app as they started PrEP. A questionnaire was used to collect data on sociodemographics and other structural factors, while app use data for the 60-day period following the first 150 days of being in the intervention arm were extracted from the app's back end. A multivariable log-binomial model was used to determine predictors of 6-month retention in the Jichunge app. Results: A total of 470 female sex workers were recruited. Nearly three-quarters of participants (206/284, 72.5%) who came to the 6-month follow-up interview no longer had the Jichunge app on their phones. The majority of these participants (193/206, 93.7%) no longer had access to the app because of issues related to their phones. Data extracted from the back end of the app showed that the use of the app declined over time, and only 13.4% (63/470) of the participants were retained (continued to use the app) after 6 months of intervention. At 6 months, women aged ≥35 years were >2 times more likely to use the app than women aged 18 to 24 years (adjusted risk ratio [aRR] 2.2, 95% CI 1.2-4.1; P=.01). Furthermore, retention in the app was higher among participants who demonstrated high PrEP awareness at baseline (aRR 1.8, 95% CI 1.1-3; P=.01) and among those who had experienced financial difficulties due to health care spending (aRR 1.9, 95% CI 1.2-3.2; P=.01). Conclusions: Most female sex workers (206/284, 72.5%) who were enrolled in PrEP care in Tanzania no longer used the Jichunge app after 6 months. Retention in the app at 6 months was predicted by older age, high PrEP awareness, and financial difficulties due to health care spending. Strategies for the long-term retention of participants in mHealth apps, such as systems for reinstallations of apps, should be considered during the design phase.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".