Feasibility and Acceptability of a mHealth Patient Navigation Intervention to Increase Pre-Exposure Prophylaxis Uptake in Racially and Ethnically Diverse Sexual and Gender Minority Youth in Los Angeles (PrEPresent): Pilot Randomized Controlled Trial
Bibliographic record
Abstract
Background: Pre-exposure prophylaxis (PrEP) is a powerful tool to prevent the transmission of HIV. Interventions promoting PrEP must focus on populations most impacted by systemic barriers to uptake. Historically, young sexual minority men and transgender women have the highest demonstrated rates of new HIV diagnoses, but prevalence within other gender minority populations is now being studied. Few interventions have focused on addressing PrEP uptake with sexual and gender minority (SGM) youth, particularly through mobile health (mHealth) technologies. Built on the successful foundation of the HealthMpowerment Platform, PrEPresent aimed to engage SGM youth across diverse gender, racial, and ethnic identities in the Greater Los Angeles area. Objective: The aim of this study was to evaluate the feasibility and acceptability of a digital peer patient-navigation PrEP uptake app. Methods: PrEPresent incorporated patient activation theory into an mHealth intervention. The study took place over a 6-month period with visits at baseline, 3 months, and 6 months. The intervention period lasted from baseline to 3 months. Control participants received an information-only app. Intervention participants received an enhanced app and access to an interventionist, the PrEPresentative. Intervention participants could meet with the PrEPresentative four times over the 3-month period via phone, Health Insurance Portability and Accountability Act-compliant videoconferencing, or an in-app text messaging. PrEP uptake was measured through survey responses and the UrSure rapid urine test of tenofovir. Results: PrEPresent comprised of 147 sexual and gender-diverse participants-75 participants were randomized into the control arm and 72 into the intervention arm. A total of 48% (71/147) were Latinx and 18% (27/147) were Black or African American. Most (98/147, 67%) were transgender or gender diverse, and the remaining (49/147, 33%) were cisgender men. PrEP was initiated by 25% (14/56) of intervention participants and 19% (11/58) of control participants. In total, 50% (36/72) of intervention participants completed two or more sessions with the interventionist. Intervention participants had an average of 15.93 (SD 15.85) logins compared to 6.31 (SD 9.27) logins for control participants. Average use of the mHealth platform was 9.51 (SD 11.47) minutes for intervention participants and 3.03 (SD 5.70) minutes for control participants. Conclusions: PrEPresent met primary outcome measures of feasibility and acceptability. Despite this, PrEP uptake was low, and use of the platform was low compared to other HealthMpowerment projects. While mHealth offers promising HIV prevention outcomes, fostering active app engagement is crucial in promoting behavior change. Mixed success in initiating PrEP uptake across mHealth interventions involving SGM youth warrants further inquiry into how these platforms can address prevention barriers with this population. Interventions targeting uptake and adherence will need to adapt as the landscape of PrEP delivery evolves with the adoption of on-demand and long-acting injectable modalities.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".