Acceptability, Engagement, and Effects of a Mobile Digital Intervention to Support Mental Health for Young Adults Transitioning to College: Pilot Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: The transition from high school to college can exacerbate mental health problems in young adults yet barriers prevent seamless mental health care. Existing digital support tools show promise but are not yet designed to optimize engagement or implementation. OBJECTIVE: The goal of the research was to test acceptability and effects of an automated digital Mobile Support Tool for Mental Health (MoST-MH) for young adults transitioning to college. METHODS: Youths aged 18 years and older with a current mental health diagnosis preparing to transition to college (n=52; 85% female [45/52], 91% White [48/52]) were recruited from a primary care (n=31) and a mental health clinic (n=21). Participants were randomized 2:1 to either receive MoST-MH (n=34) or enhanced Usual Care (eUC; n=18). MoST-MH included periodic text message and web-based check-ins of emotional health, stressors, negative impacts, and self-efficacy that informed tailored self-care support messages. Both eUC and MoST-MH participants received links to a library of psychoeducational videos and were asked to complete web-based versions of the Mental Health Self-Efficacy Scale (MHSES), College Counseling Center Assessment of Psychological Symptoms (CCAPS), and Client Service Receipt Inventory for Mental Health (C-SRI) monthly for 3 months and the Post-Study System Usability Scale (PSSUQ) at 3-months. RESULTS: MoST-MH participants were sent a median of 5 (range 3 to 10) text message check-in prompts over the 3-month study period and 100% were completed; participants were sent a median of 2 (range 1 to 8) web-based check-in prompts among which 78% (43/55) were completed. PSSUQ scores indicate high usability (mean score 2.0). Results from the completer analysis demonstrated reductions in mental health symptoms over time and significant between-group effects of MoST-MH compared to eUC on depressive symptom severity (d=0.36, 95% CI 0.08 to 0.64). No significant differences in mental health self-efficacy or mental health health care use were observed. CONCLUSIONS: In this pilot trial, we found preliminary evidence that MoST-MH was engaged with at high rates and found to be highly usable and reduced depression symptoms relative to eUC among youth with mental health disorders transitioning to college. Findings were measured during the COVID-19 pandemic, and the study was not powered to detect differences in outcomes between groups; therefore, further testing is needed. TRIAL REGISTRATION: ClinicalTrials.gov NCT04560075; https://clinicaltrials.gov/ct2/show/NCT04560075.
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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 | 0,005 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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