A Mobile Health Intervention to Support Collaborative Decision-Making in Mental Health Care: Development and Usability
Notice bibliographique
Résumé
BACKGROUND: Shared decision-making between clinicians and service users is crucial in mental health care. One significant barrier to achieving this goal is the lack of user-centered services. Integrating digital tools into mental health services holds promise for addressing some of these challenges. However, the implementation of digital tools, such as mobile apps, remains limited, and attrition rates for mental health apps are typically high. Design thinking can support the development of tools tailored to the needs of service users and clinicians. OBJECTIVE: This study aims to develop and beta test a digital tool designed for individuals with severe mental disorders or substance use disorders to facilitate shared decision-making on treatment goals and strategies within mental health services. METHODS: We used a user-centered design approach to develop iTandem, an app facilitating collaborative treatment between service users and clinicians. Through qualitative interviews and workshops, we engaged 6 service users with severe mental disorders or substance use disorders, 6 clinicians, and 1 relative to identify and design relevant app modules. A beta test of iTandem was conducted to refine the app and plan for a pilot trial in a clinical setting. After 6 weeks of app use, 5 clinicians and 4 service users were interviewed to provide feedback on the concept, implementation, and technical issues. Safety and ethical considerations were thoroughly discussed and addressed. RESULTS: To avoid overload for the service users, we applied a pragmatic take on module content and size. Thus, iTandem includes the following 8 modules, primarily based on the needs of service users and clinicians: Sleep (sleep diary), Medication (intake and side effects), Recovery (measures, including well-being and personal recovery, and exercises, including good things and personal strengths), Mood (mood diary and report of daily feelings), Psychosis (level of positive symptoms and their consequences and level of negative symptoms), Activity (goal setting and progress), Substance use (weekly use, potential triggers or strategies used to abstain), and Feedback on therapy (of individual sessions and overall rating of the past week). For the beta testing, service users and clinicians collaborated in choosing 2-3 modules in iTandem to work with during treatment sessions. The testing showed that the app was well received by service users, and that facilitation for implementation is crucial. CONCLUSIONS: iTandem and similar apps have the potential to enhance treatment outcomes by facilitating shared decision-making and tailoring treatment to the needs of service users. However, successful implementation requires thorough testing, iterative development, and evaluations of both utility and treatment effects. There is a critical need to focus on how technology integrates into clinical settings-from development to implementation-and to conduct further research on early health technology assessments to guide these processes.
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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 | 0,007 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| 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 | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 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 ».