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
Bibliographic record
Abstract
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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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".