A Mobile Health Contraception Decision Support Intervention for Latina Adolescents: Implementation Evaluation for Use in School-Based Health Centers
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
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