Evaluating the Effectiveness of a Mobile HIV Prevention App to Increase HIV and Sexually Transmitted Infection Testing and Pre-Exposure Prophylaxis Initiation Among Rural Men Who Have Sex With Men in the Southern United States: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Gay and bisexual men who have sex with men (MSM) in the rural United States are less likely to test for HIV and sexually transmitted infections (STIs) and use pre-exposure prophylaxis (PrEP) compared to urban MSM. Given the challenges in building brick-and-mortar facilities serving the sexual health needs of MSM in rural communities, there is a need to evaluate digital engagement strategies. Combine is a smartphone app designed to provide HIV prevention information and resources to MSM that may be particularly well suited to support rural MSM. HealthMindr, an app from which Combine is adapted, has previously been shown to increase HIV testing and PrEP uptake among urban MSM. Because rural MSM face additional barriers to accessing HIV prevention services, a motivational interview (MI) focused on HIV prevention strategies might enhance the effectiveness of Combine to increase uptake of HIV testing, STI testing, and PrEP. OBJECTIVE: This study aims to determine the effectiveness of the Combine app to increase HIV testing, STI testing, and PrEP initiation among rural cisgender MSM. We will also assess the effectiveness of 2 adjunctive interventions: the availability of free HIV and STI self-test kits and the offering of an MI. METHODS: In this type 2 hybrid effectiveness-implementation randomized controlled trial, we will assess the effectiveness of Combine to increase HIV testing, STI testing, and PrEP initiation among rural MSM across the Southern United States. A total of 464 men will be recruited and randomized to 1 of the 4 arms. Participants in all 4 arms will have access to most app features (eg, health resources, quizzes, health care provider locators, and ordering free condoms and lubricants). Using a 2×2 factorial design, half (232/464, 50%) of the participants will be randomized to receive access to free at-home HIV and STI self-test kits and half (232/464, 50%) will be randomized to receive an MI. Participants will complete surveys every 6 months to allow for assessment of self-reported outcomes: app use, HIV testing, STI testing, and PrEP initiation over the 24-month follow-up period. Self-reported PrEP uptake will be verified by dried blood spot testing or uploading a photograph of a PrEP prescription. RESULTS: Participant recruitment began in March 2024. As of July 2025, 395 participants have been enrolled and randomized. Recruitment is expected to be completed by December 2025. CONCLUSIONS: This trial will determine whether the Combine app increases HIV testing, STI testing, and PrEP uptake among rural MSM in the Southern United States. It will also provide critical information about the most effective strategies for implementing digital health interventions for rural MSM. TRIAL REGISTRATION: ClinicalTrials.gov NCT06205368; https://clinicaltrials.gov/study/NCT06205368. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/69540.
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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 | 0.037 | 0.037 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.009 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.074 | 0.011 |
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".