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
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".