Mobile Health Intervention Tools Promoting HIV Pre-Exposure Prophylaxis Among Adolescent Girls and Young Women in Sub-Saharan Africa: Scoping Review
Notice bibliographique
Résumé
BACKGROUND: In 2022, 3100 adolescent girls and young women in sub-Saharan Africa experienced new HIV infections each week. HIV pre-exposure prophylaxis (PrEP) is effective at preventing HIV but has limited uptake and persistence. Mobile health (mHealth) interventions can improve medication adherence; however, their utility to improve PrEP adherence among adolescent girls and young women is not well established. OBJECTIVE: This scoping review synthesizes evidence supporting mHealth for PrEP among adolescent girls and young women in sub-Saharan Africa and identifies strategies for further evaluation. METHODS: We searched PubMed and Google Scholar databases, expert referrals, and reference lists using the following eligibility criteria: (1) original research study or protocol; (2) English language; (3) publication between January 1, 2012, and August 31, 2023; (4) inclusion of adolescent girls and young women; (5) conducted in sub-Saharan Africa; and (6) use of mHealth tools to promote PrEP uptake, adherence, or persistence. Titles and abstracts were screened by 2 independent researchers. Full-text manuscripts were reviewed against all eligibility criteria to determine the final included studies. The characteristics and results of the included studies were abstracted and synthesized by mHealth tool type. RESULTS: The search identified 482 unique citations. Title and abstract review removed 380 citations primarily for not including adolescent girls and young women or being conducted outside sub-Saharan Africa. The remaining 102 articles underwent full-text review, yielding 31 eligible publications reporting on 21 unique studies. The most common mHealth tool was SMS text message (n=11), followed by app (n=9), telehealth (n=3), website (n=4), and video (n=1). Few publications evaluated effectiveness, and the results were mixed. One study found that SMS text message reminders improved PrEP adherence, and another concluded that SMS text message reminders did not show a significant impact. Two studies found that differentiated service delivery, which included mHealth components, improved PrEP uptake or persistence; however, the findings could not be attributed solely to the mHealth components. Lastly, 1 website was shown to improve PrEP persistence. Several earlier-stage studies focused on values and preferences toward mHealth without reporting the impact on PrEP. CONCLUSIONS: We found few rigorously evaluated mHealth interventions for supporting PrEP among adolescent girls and young women, preventing the ability to draw conclusions on its effectiveness. Studies documented high usability and acceptability but limited assessment of the impact on health outcomes. Secondary uses of mHealth were found for data collection and components of the standard of care. There is substantial room for growth in the innovative use of mHealth to support PrEP among adolescent girls and young women. Consideration of the strengths and limitations of mHealth tools in the local setting, review of past lessons learned, and intentional measurement of mHealth exposure and use could help advance this growing field.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,010 | 0,041 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,005 |
| Bibliométrie | 0,012 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 ».