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
Bibliographic record
Abstract
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.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".