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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».