Development of an mHealth App to Support the Prevention of Sexually Transmitted Infections Among Black Men Who Have Sex With Men Engaged in Pre-exposure Prophylaxis Care in New Orleans, Louisiana: Qualitative User-Centered Design Study
Notice bibliographique
Résumé
BACKGROUND: Sexual health disparities exist for Black men who have sex with men (BMSM) in New Orleans, Louisiana. Rates of sexually transmitted infections (STIs) are high for both BMSM and those taking HIV pre-exposure prophylaxis (PrEP). OBJECTIVE: In this study, we introduced an existing PrEP adherence app to new potential users-BMSM engaged in PrEP care in New Orleans-to guide app adaptation with STI prevention features and tailoring for the local context. METHODS: Using a user-centered design, we conducted 4 focus group discussions (FGDs), with interim app adaptations from December 2020 to March 2021. During the FGDs, a video of the app, app website, and mock-ups were shown to participants. We asked about facilitators of and barriers to STI prevention in general, current app use, impressions of the existing app, new app features to potentially facilitate STI prevention, and how the app should be tailored for BMSM. We used applied qualitative thematic analysis to identify themes and needs of the population. RESULTS: Overall, 4 FGDs were conducted with 24 BMSM taking PrEP. We grouped themes into 4 categories: STI prevention, current app use and preferences, preexisting features and impressions of the prep'd app, and new features and modifications for BMSM. Participants noted concern about STIs and shared that anxiety about some STIs was higher than that for others; some participants shared that since the emergence of PrEP, little thought is given to STIs. However, participants desired STI prevention strategies and suggested prevention methods to implement through the app, including access to resources, educational content, and sex diaries to follow their sexual activity. When discussing app preferences, they emphasized the need for an app to offer relevant features and be easy to use and expressed that some notifications were important to keep users engaged but that they should be limited to avoid notification fatigue. Participants thought that the current app was useful and generally liked the existing features, including the ability to communicate with providers, staff, and each other through the community forum. They had suggestions for modifications for STI prevention, such as the ability to comment on sexual encounters, and for tailoring to the local context, such as depictions of iconic sights from the area. Mental health emerged as an important need to be addressed through the app during discussion of almost all features. Participants also stressed the importance of ensuring privacy and reducing stigma through the app. CONCLUSIONS: A PrEP adherence app was iteratively adapted with feedback from BMSM, resulting in a new app modified for the New Orleans context and with STI prevention features. Participants gave the app a new name, PCheck, to be more discreet. Next steps will assess PCheck use and STI prevention outcomes.
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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 | 0,007 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».