A Mobile Health Contraception Decision Support Intervention for Latina Adolescents: Implementation Evaluation for Use in School-Based Health Centers
Bibliographic record
Abstract
BACKGROUND: Health care providers are a trusted and accurate source of sexual health information for most adolescents, and clinical guidelines recommend that all youth receive comprehensive, confidential sexual health information and services. However, these guidelines are followed inconsistently. Providers often lack the time, comfort, and skills to provide patient-centered comprehensive contraceptive counseling and services. There are significant disparities in the provision of sexual health services for Latino adolescents, which contribute to disproportionately higher rates of teenage pregnancy. To address this, we developed Health-E You or Salud iTu in Spanish, an evidence-informed mobile health (mHealth) app, to provide interactive, individually tailored sexual health information and contraception decision support for English and Spanish speakers. It is designed to be used in conjunction with a clinical encounter to increase access to patient-centered contraceptive information and services for adolescents at risk of pregnancy. Based on user input, the app provides tailored contraceptive recommendations and asks the youth to indicate what methods they are most interested in. This information is shared with the provider before the in-person visit. The app is designed to prepare youth for the visit and acts as a clinician extender to support the delivery of health education and enhance the quality of patient-centered sexual health care. Despite the promise of this app, there is limited research on the integration of such interventions into clinical practice. OBJECTIVE: This study described efforts used to support the successful adoption and implementation of the Health-E You app in clinical settings and described facilitators and barriers encountered to inform future efforts aimed at integrating mHealth interventions into clinical settings. METHODS: This study was part of a larger, cluster randomized control trial to evaluate the effectiveness of Health-E You on its ability to reduce health disparities in contraceptive knowledge, access to contraceptive services, and unintended pregnancies among sexually active Latina adolescents at 18 school-based health centers (SBHCs) across Los Angeles County, California. App development and implementation were informed by the theory of diffusion of innovation, the Patient-Centered Outcomes Research Institute's principles of engagement, and iterative pilot testing with adolescents and clinicians. Implementation facilitators and barriers were identified through monthly conference calls, site visits, and quarterly in-person collaborative meetings. RESULTS: Implementation approaches enhanced the development, adoption, and integration of Health-E You into SBHCs. Implementation challenges were also identified to improve the integration of mHealth interventions into clinical settings. CONCLUSIONS: This study provides important insights that can inform and improve the implementation efforts for future mHealth interventions. In particular, an implementation approach founded in a strong theoretical framework and active engagement with patient and community partners can enhance the development, adoption, and integration of mHealth technologies into clinical practice. TRIAL REGISTRATION: ClinicalTrials.gov NCT02847858; https://clinicaltrials.gov/ct2/show/NCT02847858 (Archived by WebCite at http://www.webcitation.org/761yVIRTp).
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".