Development of a Mobile App for Individuals with Co-Occurring Substance Use and Mood Disorders: Integrated Support Now
Notice bibliographique
Résumé
Background: Mood disorders and substance use disorders (SUDs) co-occur at a high rate. Individuals with co-occurring mood and substance use disorders are less likely to complete treatment. Of those who receive treatment, many do not receive adequate care that addresses both disorders. Integrated Group Therapy (IGT) is an evidence-based psychosocial treatment that treats both mood disorders and SUDs, stressing the similarities and relationship between the two disorders. Although IGT is an effective treatment for individuals with mood and substance use disorders, it is not widely available, and for those who do receive IGT, there is no in-the-moment support. Objective: A mobile version of IGT would increase access and provide in-the-moment support for individuals when they need it. The aim of this study is to use exploratory, qualitative user-centered design methodology to interview and observe end users and clinicians who treat individuals with mood disorders and SUDs to inform the design of the mobile app. Methods: Qualitative interviews were conducted with 5 patient participants who were currently receiving treatment for a co-occurring mood and substance use disorder, and 5 clinicians with experience treating patients with mood disorders and/or substance use disorders. All participants completed a short survey to assess demographic information and technology use. Additionally, observations were conducted at 3 IGT inpatient and outpatient groups to triangulate findings across methods. Interviews were audio-recorded and transcribed. Transcripts and field notes were analyzed using thematic analysis using NVivo for Mac (version 11). Results: Patient participants were predominately male (3/5, 60%), age 45-64 (4/5, 80%), unemployed or disabled (3/5, 60%), and white (5/5, 100%). The majority of clinicians were female (4/5, 80%), age 26-44 (5/5, 100%), and white (4/5, 80%). Most patients (4/5, 80%) and clinicians (5/5, 100%) reported feeling comfortable using technology as a treatment tool, and 40% (2/5) of patients indicated that they had experience doing so. Key treatment themes that emerged from the qualitative data included the importance of IGT in helping patients to develop a common language to describe their co-occurring conditions and experiences, visualizing the recovery journey, the importance of independence and freedom to patients throughout treatment, along with varied acceptance and self-perception of one’s recovery journey. With respect to developing a mobile tool, reported patient needs included: in-the-moment support, peer-to-peer support, after-care planning, maintaining structure post-discharge from treatment and opportunities to practice skills. Clinicians corroborated the need for patient peer-to-peer support, help with after-care planning and the opportunity to practice skills. Conclusions: Patients and clinicians were open to the idea of using technology as part of treatment. Several themes emerged to inform the direction of a minimal viable product (MVP) of the app. Next steps include narrowing down to key themes to focus on for the MVP, defining features as relevant to those themes, designing a clickable prototype of the app and conducting iterative feedback sessions with end users.
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Comment cette classification a été obtenuedéplier
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».