Addressing Youth Mental Health Through Schools and Primary Care Clinics Using the Connected for Wellness Mobile App: Protocol for a Stepped-Wedge Trial
Notice bibliographique
Résumé
BACKGROUND: Youth in the United States are experiencing rising rates of anxiety, depression, and other mental health challenges, yet many remain underserved due to systemic barriers such as poverty, limited access to care, and shortages in the mental health workforce. Schools and primary care clinics are trusted, community-based settings that offer strategic opportunities for early identification, prevention, and intervention. As part of a public health approach, integrating digital mental health tools into these settings can support broad access, enhance mental health literacy, and promote help-seeking behaviors among all students. When co-designed with youth and embedded within existing care systems, these tools offer a scalable, proactive solution to support well-being across diverse school-aged populations. OBJECTIVE: This protocol describes the design and implementation of a stepped-wedge clinical trial to evaluate the Connected for Wellness mobile app, a youth- and caregiver-facing digital mental health intervention. Developed using participatory informatics and human-centered design principles, the app provides culturally relevant content in English and Spanish. Features include self-guided wellness activities, well-being screeners, psychoeducational videos, and localized service directories. Machine learning algorithms personalize content recommendations based on user inputs and behavioral patterns. METHODS: This trial uses a stepped-wedge cluster randomized design across 20 community-based sites (10 high schools and 10 primary care clinics) in 2 counties in California. These counties were selected for their high proportions of underserved youth populations. All youth aged 13 to 22 years and their caregivers will be invited to access the app. Sites are randomized into 2 implementation waves. The app is introduced site-wide as a universal public health intervention supported by on-site navigators and peer ambassadors. The primary outcomes are derived from a cascade-of-care framework, including identification of mental health need, referral to services, initiation of services, and engagement (defined as ≥3 treatment visits). Data will be collected via anonymous in-app analytics, monthly World Health Organization-Five Well-Being Index assessments, and deidentified electronic health and administrative records. Generalized linear mixed models will be used to evaluate differences in cascade outcomes between pre- and postimplementation phases while accounting for clustering and site-level variability. RESULTS: As of May 2025, the mobile app has been finalized, institutional review board approvals have been secured, and all study sites have been recruited. Participant recruitment is projected to begin in August 2025. Data collection and initial analyses will begin in early 2026, with preliminary findings expected by October 2026. CONCLUSIONS: This study tests a novel digital health intervention integrated into trusted care systems. If effective, the Connected for Wellness mobile app may serve as a scalable strategy to reduce disparities in mental health care access and engagement for youth across the United States. TRIAL REGISTRATION: ClinicalTrials.gov NCT06122688; https://clinicaltrials.gov/study/NCT06122688. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/73721.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,037 | 0,039 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,004 |
| Méta-épidémiologie (sens large) | 0,007 | 0,004 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,004 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,008 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,093 | 0,017 |
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
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».