Developing a Novel Mobile App to Support HIV Testing and Pre-Exposure Prophylaxis Uptake Among Men Who Have Sex With Men: Formative and Technical Pilot Study
Notice bibliographique
Résumé
BACKGROUND: Young sexual minority men (YSMM) are disproportionately impacted by HIV in the United States. HIV or sexually transmitted infection (STI) testing rates and pre-exposure prophylaxis (PrEP) uptake are low in this priority population. Novel strategies are needed to increase access to HIV and STI prevention services among YSMM. OBJECTIVE: This study aims to describe the development and assess the feasibility and acceptability of LYNX, a mobile app to increase HIV testing and PrEP uptake among YSMM. METHODS: Informed by the Information-Motivation-Behavioral Skills model, the LYNX app was refined through 4 iterative focus groups in 2 US cities among YSMM aged 15 to 24 years. The LYNX app includes SexPro, an innovative tool that provides a personalized sexual health protection score, a sex diary to track sexual partners, HIV and STI testing information and reminders, access to home HIV and STI test kits, and geospatial-based testing and PrEP clinic site information. The refined app was then tested for feasibility and acceptability in a 2-month technical pilot. Baseline and 2-month follow-up assessments and exit interviews were completed. Self-reported app acceptability and use based on paradata were reported. RESULTS: In iterative focus groups among 30 participants (age: mean 20, SD 3 years; Black: 12/30, 40%; Hispanic or Latinx: 13/30, 43%), the app's design was well-received. Participants recommended providing information on how the SexPro score was calculated and how they could improve their score, changes to the language in the sex diary tailored for YSMM, providing a chat feature to facilitate communication between staff and app users, and gamification features to increase overall youth engagement with the app. These recommendations were incorporated into the app. In the technical pilot among 17 participants (age: mean 22.4, SD 1.6 years; Black: 4/17, 24%; Hispanic or Latinx: 8/17, 47%), the mean system usability score was 70 out of 100, falling in the "good" range. Use of the app was high over the 2-month pilot (app opened an average of 8.5, SD 8.0 times with an average duration of 3.8, SD 3.2 min/session), indicating good feasibility. The most commonly used features included the testing feature (n=15, 100%), activity calendar (n=14, 93%), and diary (n=13, 86%). Overall, 11 (79%) participants were likely to continue using LYNX, and 10 (71%) participants were likely to recommend it to a friend. In exit interviews, there was a high level of acceptability of the content, interface, and features of the LYNX app. CONCLUSIONS: Following a user-centered design approach, we tailored the LYNX app to increase HIV and STI testing and PrEP uptake among YSMM in the United States. Our positive findings support further testing of this mobile health tool in an upcoming effectiveness trial in broader youth populations. TRIAL REGISTRATION: ClinicalTrials.gov NCT03177512; https://clinicaltrials.gov/study/NCT03177512. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/10659.
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 | 0,011 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 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) | 0,003 | 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 ».