Integrating Patient Choice and Collaborative Care Managers to Implement eHealth Tools in Depression: Self-Report Pilot Study
Notice bibliographique
Résumé
Background: Improving mental health treatment within the collaborative care model (CoCM) may be achieved by using e-mental health (e-MH) tools and addressing the challenges to their integration. Objective: This study aims to understand how patients select, engage, and use three self-help e-MH tools for depression, and to explore satisfaction with e-MH tools, with a particular emphasis on care manager interactions. Methods: This was a single-center, nonrandomized, preferred assignment study of two cognitive behavioral therapy-based tools (Moodkit and moodgym) and an educational website (the Depression Center Toolkit). The tools were recommended for use in 15-minute sessions 3 times a week, for 6 weeks, coupled with low-intensity care manager coaching. Utilization of e-MH was also captured during an additional 4 weeks without coaching. Self-report outcome measures were gathered at baseline, weekly for 6 weeks, at week 10, and through activities suggested by the tool. Results: The 32 participants enrolled were predominantly female (n=27, 84%), non-Hispanic Caucasian (n=29, 91%), with a mean age of 41.8 (SD 16.1; range 20 to 78) years. Most participants (n=26, 81%) presented with moderate to moderately severe depression (Patient Health Questionnaire-9=11-19) and a marked level of impairment in different areas of functioning. About 81% (n=26) of the participants initially selected a cognitive behavioral therapy-based tool, and 19% (n=6) selected the educational website. In total, 4 of 32 (12%) participants switched tools within the first week, 6 of 32 (22%) participants dropped out, and one was removed. The remaining 25 active individuals used tools on average 3.0 (SD 2.4) times per week, most time (67%), for 11 to 20 minutes or more at a time. Of the 19 participants reached and surveyed at week 6, 52% (16/31) remained actively engaged with their tools, including 2 users who had switched tools and 8 between 45 and 78 years old. At week 10, about 75% (12/16) of this subgroup were using their tools with no coaching; this represented 49% of the cohort. Satisfaction increased with progressive use of the tool. The care manager's low-intensity coaching lasted on average 7.9 (SD 3.9) minutes and promoted better understanding and greater use of the tools. Other facilitators to adherence consisted of organization, convenience, ease, accessibility, and privacy policies of the tools, while barriers included time constraints, depressive symptoms, and uncertainty about the efficacy of the tool. Conclusions: Uptake of e-MH tools for depression is feasible and associated with significant user satisfaction in CoCM. Low-intensity care manager coaching is consistent with the CoCM and is associated with uptake and ongoing use of e-MH tools. To our knowledge, this is the first study to leverage the care manager's proactive outreach to and routine follow-ups with patients toward engagement in self-help digital tools.
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 | 0,010 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».