A Theory-Based mHealth Intervention (Getting Off) for Methamphetamine-Using Men Who Have Sex With Men: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Methamphetamine (meth) use among men who have sex with men (MSM) is associated with increased HIV prevalence and transmission and substandard advancement along the HIV prevention and care continuum. Given the growth of mobile health (mHealth) technologies, it is no longer necessary to limit meth treatment options to physical, brick-and-mortar sites, and administration using generic, nontailored content. OBJECTIVE: In a 2-arm randomized controlled trial (RCT; N=300), we aim to evaluate the use of an mHealth intervention (Getting Off) to assess the impact and noninferiority of a cross-platform app (developed from a manualized meth treatment intervention) to help MSM reduce meth use and HIV sexual risk behaviors and improve their advancement along the HIV prevention and care continuum (HIV testing, pre-exposure prophylaxis uptake and persistence, and antiretroviral therapy uptake and adherence). METHODS: Participants will be randomized into 2 arms: arm A, with immediate access to the app (immediate delivery: n=150), or arm B, with delayed access to the app after a 30-day period (delayed delivery: n=150). Participants in both arms will use the same Getting Off app and will have 30 days to complete the 24 sessions. Participants will be assessed at the 1-, 2- (delayed delivery arm only), 3-, 6-, and 9-month timepoints to determine observed treatment effects and will be compared with a historical matched sample of participants (n=~600) who received the brick-and-mortar group-based Getting Off intervention. RESULTS: Recruitment began in January 2019 for phase 1, the formative phase. In January and February 2019, 4 focus groups (N=36) were formed to provide input on the adaptation of the group-based manual intervention to a mobile app. Data collection for phase 2, the RCT, is expected to be completed in January 2023. The final results are anticipated in April 2023. CONCLUSIONS: By creating a culturally responsive mobile app, Getting Off aims to reduce meth use and improve sexual health outcomes among meth-using MSM. The Getting Off app could have significant public health impact by greatly expanding access to effective, affordable, private, culturally competent, and highly scalable meth treatment for MSM. TRIAL REGISTRATION: Clinicaltrials.gov NCT03884946; https://clinicaltrials.gov/ct2/show/NCT03884946. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/22572.
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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 | 0.036 | 0.033 |
| Meta-epidemiology (narrow) | 0.008 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.008 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.097 | 0.016 |
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".