The CHALO! 2.0 mHealth-Based Multilevel Intervention to Promote HIV Testing and Linkage-to-Care Among Men Who Have Sex with Men in Mumbai, India: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Current programs to engage marginalized populations such as gay and bisexual individuals and other men who have sex with men (MSM) in HIV prevention interventions do not often reach all MSM who may benefit from them. To reduce the global burden of HIV, far-reaching strategies are needed to engage MSM in HIV prevention and treatment. Globally, including low- and middle-income countries, MSM are now widely using internet-based social and mobile technologies (SMTs; eg, dating apps, social media, and WhatsApp [Meta]), which provides an unprecedented opportunity to engage unreached and underserved groups, such as MSM for HIV prevention and care. OBJECTIVE: This study aimed to assess the effectiveness of a multilevel mobile health (mHealth)-based intervention to improve HIV testing uptake and status neutral linkage-to-care among sexually active MSM reached through internet-based platforms in Mumbai, India. METHODS: In this randomized controlled trial, we will determine whether CHALO! 2.0 (a theory-based multilevel intervention delivered in part through WhatsApp) results in increased HIV testing and linkage-to-care (prevention or treatment). This study is being conducted among 1000 sexually active MSM who are unaware of their HIV status (never tested or tested >6 months ago) and are recruited through SMTs in Mumbai, India. We will conduct a 12-week, 3-arm randomized trial comparing CHALO! 2.0 to 2 control conditions-an attention-matched SMT-based control (also including a digital coupon for free HIV testing) and a digital coupon-only control. The primary outcomes will be HIV testing and status neutral linkage-to-care by 6 months post enrollment. Participants will be followed up for a total of 18 months to evaluate the long-term impact. RESULTS: The study was funded in 2020, with recruitment having started in April 2022 due to delays from the COVID-19 pandemic. Baseline survey data collection began in April 2022, with follow-up surveys starting in July 2022. As of April 2022, we enrolled 1004 participants in the study. The completion of follow-up data collection is expected in January 2025, with results to be published thereafter. CONCLUSIONS: While global health agencies have called for internet-based interventions to engage populations vulnerable to HIV who are not being reached, few proven effective and scalable models exist and none is in India, which has one of the world's largest HIV epidemics. This study will address this gap by testing a multicomponent mHealth intervention to reach and engage MSM at high priority for HIV interventions and link them to HIV testing and prevention or treatment. TRIAL REGISTRATION: ClinicalTrials.gov NCT04814654; https://clinicaltrials.gov/study/NCT04814654. Clinical Trial Registry of India CTRI/2021/03/032280. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/59873.
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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 | 0.029 | 0.025 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.084 | 0.013 |
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".