A Mobile Health Platform for Clinical Monitoring in Early Psychosis: Implementation in Community-Based Outpatient Early Psychosis Care
Notice bibliographique
Résumé
BACKGROUND: A growing body of literature indicates that smartphone technology is a feasible add-on tool in the treatment of individuals with early psychosis (EP) . However, most studies to date have been conducted independent of outpatient care or in a research clinic setting, often with financial incentives to maintain user adherence to the technology. Feasibility of dissemination and implementation of smartphone technology into community mental health centers (CMHCs) has yet to be tested, and whether young adults with EP will use this technology for long periods of time without incentive is unknown. Furthermore, although EP individuals willingly adopt smartphone technology as part of their treatment, it remains unclear whether providers are amenable to integrating smartphone technology into treatment protocols. OBJECTIVE: This study aimed to establish the feasibility of implementing a smartphone app and affiliated Web-based dashboard in 4 community outpatient EP clinics in Northern California. METHODS: EP individuals in 4 clinics downloaded an app on their smartphone and responded to daily surveys regarding mood and symptoms for up to 5 months. Treatment providers at the affiliated clinics viewed survey responses on a secure Web-based dashboard in sessions with their clients and between appointments. EP clients and treatment providers filled out satisfaction surveys at study end regarding usability of the app. RESULTS: Sixty-one EP clients and 20 treatment providers enrolled in the study for up to 5 months. Forty-one EP clients completed the study, and all treatment providers remained in the study for their duration in the clinic. Survey completion for all 61 EP clients was moderate: 40% and 39% for daily and weekly surveys, respectively. Completion rates were slightly higher in the participants who completed the study: 44% and 41% for daily and weekly surveys, respectively. Twenty-seven of 41 (66%) EP clients who completed the study and 11 of 13 (85%) treatment providers who responded to satisfaction surveys reported they would continue to use the app as part of treatment services. Six (15%; 6/41) clients and 3 providers (23%; 3/13) stated that technological glitches impeded their engagement with the platform. CONCLUSIONS: EP clients and treatment providers in community-based outpatient clinics are responsive to integrating smartphone technology into treatment services. There were logistical and technical challenges associated with enrolling individuals in CMHCs. To be most effective, implementing smartphone technology in CMHC EP care necessitates adequate technical staff and support for utilization of the platform.
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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 | 0,002 | 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 | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| 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
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».