A Previsit Mobile Health App (Health-E You/Salud iTu) for Male Adolescents to Promote Sexual and Reproductive Health Care Receipt: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Male adolescents have significant sexual and reproductive health (SRH) needs and, despite the existence of national guidelines, their SRH care receipt remains poor. Technology-based interventions have been shown to successfully support adolescents' SRH; however, they have primarily focused on female individuals. To date, no such solutions support comprehensive SRH care delivery for male adolescents in primary care settings. Health-E You/Salud iTu (Health-E You) is a mobile web app that was originally designed for female youth and has proven to be effective in improving contraceptive care delivery and use. Adolescents access the app ahead of a clinic appointment, which contains initial screening questions, interactive knowledge items, and contraception decision support with tailored recommendations based on the users' input. The app then provides clinicians with a summary of patients' recommended SRH care. OBJECTIVE: This study aims to adapt Health-E You for use with diverse groups of male adolescents presenting for care. This protocol describes the development, testing, and evaluation of Health-E You for male youth. METHODS: This study consists of 2 stages. Stage 1 involves formative research, design, and user testing of Health-E You for male adolescents. Specifically, we implemented a youth-centered design process, using multiple rounds of qualitative data collection and engaging youth and clinician advisors to design the Health-E You app from April 2023 through April 2024. From May 2025, for approximately 6 months, we are conducting beta testing of the app with youth and its output with clinicians. Transcripts of interviews and focus groups from stage 1 will be analyzed using deductive and inductive thematic analysis to identify knowledge and skill gaps for the app to address as well as desired app features. Quantitative data will be analyzed using descriptive statistics. Stage 2 will involve a stepped-wedge cluster randomized controlled trial with 2752 participants in 8 primary care settings across the United States to assess the impact of Health-E You on sexually active male adolescents' SRH care receipt and condom use. Data will be analyzed with an intention-to-treat approach using separate mixed models for each study outcome with a random intercept to reflect clustering of patients within clinics, adjusted for clinic size. RESULTS: Results will include qualitative data on male adolescents' and clinicians' perspectives on SRH care receipt and delivery, respectively, and their preferences for app design. User testing will provide qualitative and quantitative data on the feasibility, acceptability, and usability of Health-E You in clinical settings. The efficacy trial will evaluate the extent to which the app improves SRH care receipt and condom use among sexually active male adolescents. CONCLUSIONS: The findings from this study will inform future technology-based interventions for male adolescents and improve male adolescents' SRH care receipt in primary care. TRIAL REGISTRATION: ClinicalTrials.gov NCT06525064; https://www.clinicaltrials.gov/study/NCT06525064. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/77780.
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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 | 0.041 | 0.034 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.113 | 0.017 |
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".