Mobile Phone Technology for Preventing HIV and Related Youth Health Problems, Sexual Health, Mental Health, and Substance Use Problems in Southwest Uganda (Youth Health SMS): Protocol for a Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: East and Southern Africa have the highest HIV incidence and prevalence in the world, with adolescents and young adults being at the greatest risk. Despite effective combination prevention tools, including the recently available pre-exposure prophylaxis (PrEP), HIV incidence among adolescents and young adults in Uganda remains high, and PrEP use remains low. Mental health and substance use (behavioral health) play a role in sexual behavior and decision-making, contributing to an increase in the risk for acquiring HIV. Interventions that target multiple HIV risk factors, including sexual and mental health and problematic substance use, are crucial to ending the HIV epidemic. Yet few interventions addressing HIV related health disparities and comorbidities among adolescents and young adults in East and Southern Africa currently exist. OBJECTIVE: This study aims to evaluate the acceptability and feasibility of Kirabo, an SMS text message intervention informed by the information, motivation, and behavior model and to be disseminated through secondary schools. The study will gather preliminary estimates of Kirabo's effectiveness in increasing HIV testing and linking users to mental health counselors. METHODS: We identified Mobile 4 Reproductive Health for adaptation using the assessment, decision, administration, production, topical experts, integration, training, testing (ADAPT-ITT) framework. Mobile 4 Reproductive Health is an evidence-based automated 2-way SMS text messaging and interactive voice response platform that offers sexual and reproductive health information and links users to HIV clinics in East Africa. Through ADAPT-ITT we refined our approach and created Kirabo, an SMS text message-based intervention for linking adolescents and young adults to health services, including HIV testing and mental health counseling. We will conduct a 2-arm randomized controlled trial in Masaka, Uganda. Adolescents (N=200) will be recruited from local schools. Baseline sociodemographic characteristics, HIV test history, and behavioral health symptoms will be assessed. We will evaluate acceptability and feasibility using surveys, interviews, and mobile phone data. The preliminary efficacy of Kirabo in increasing HIV testing and linking users to mental health counselors will be evaluated immediately after the intervention and at the 3-month follow-up. We will also assess the intervention's impact on self-efficacy in testing for HIV, adopting PrEP, and contacting a mental health counselor. RESULTS: Intervention adaptation began in 2019. A pretest was conducted in 2021. The randomized controlled trial, including usability and feasibility assessments and effectiveness measurements, commenced in August 2023. CONCLUSIONS: Kirabo is a tool that assists in the efforts to end the HIV epidemic by targeting the health disparities and comorbidities among adolescents in Uganda. The intervention includes local HIV clinic information, PrEP information, and behavioral health screening, with referrals as needed. Increasing access to prevention strategies and mitigating factors that make adolescents and young adults susceptible to HIV acquisition can contribute to global efforts to end the HIV epidemic. TRIAL REGISTRATION: ClinicalTrials.gov NCT05130151; https://clinicaltrials.gov/study/NCT05130151. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/49352.
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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 | 0.022 | 0.018 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.089 | 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".