Adapting mHealth Interventions (PrEPmate and DOT Diary) to Support PrEP Retention in Care and Adherence Among English and Spanish-Speaking Men Who Have Sex With Men and Transgender Women in the United States: Formative Work and Pilot Randomized Trial
Notice bibliographique
Résumé
BACKGROUND: A growing number of mobile health (mHealth) technologies are being developed to support HIV preexposure prophylaxis (PrEP) adherence and persistence; however, most tools have focused on men who have sex with men (MSM), and few are available in Spanish. To maximize the potential impact of these tools in reducing gender and racial/ethnic disparities and promoting health equity, mHealth tools tailored to Spanish-speaking people and transgender women are critically needed. OBJECTIVE: The aim of this study is to adapt and tailor 2 mHealth technologies, PrEPmate and DOT Diary, to support daily PrEP adherence and persistence among Spanish-speaking MSM and English- and Spanish-speaking transgender women and to evaluate the feasibility and acceptability of these tools. METHODS: PrEPmate, an interactive, bidirectional, text messaging intervention that promotes personalized communication between PrEP users and providers, and DOT Diary, a mobile app that promotes self-management of PrEP use and sexual health through an integrated electronic pill-taking and sexual activity diary, were previously developed for English-speaking MSM. We conducted 3 focus groups with 15 English- and Spanish-speaking transgender women and MSM in San Francisco and Miami to culturally tailor these tools for these priority populations. We then conducted a 1-month technical pilot among 21 participants to assess the usability and acceptability of the adapted interventions and optimize the functionality of these tools. RESULTS: Participants in focus groups liked the "human touch" of text messages in PrEPmate and thought it would be helpful for scheduling appointments and asking questions. They liked the daily reminder messages, especially the fun facts, gender affirmations, and transgender history topics. Participants recommended changes to tailor the language and messages for Spanish-speaking and transgender populations. For DOT Diary, participants liked the adherence tracking and protection level feedback and thought the calendar functions were easy to use. Based on participant recommendations, we tailored language within the app for Spanish-speaking MSM and transgender women, simplified the sexual diary, and added motivational badges. In the technical pilot of the refined tools, mean System Usability Scale scores were 81.2/100 for PrEPmate and 76.4/100 for DOT Diary (P=.48), falling in the "good" to "excellent" range, and mean Client Satisfaction Questionnaire scores were 28.6 and 28.3 for PrEPmate and DOT Diary, respectively (maximum possible score=32). Use of both tools was high over the 1-month pilot (average of 10.5 messages received from each participant for PrEPmate; average of 17.6 times accessing the DOT Diary app), indicating good feasibility for both tools. CONCLUSIONS: Using a user-centered design approach, we culturally tailored PrEPmate and DOT Diary to support daily PrEP use among Spanish-speaking MSM and English- and Spanish-speaking transgender women. Our positive findings in a technical pilot support further testing of these mHealth interventions in an upcoming comparative effectiveness trial.
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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 | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| 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 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».