A Mobile Health App to Support Patients Receiving Medication-Assisted Treatment for Opioid Use Disorder: Development and Feasibility Study
Notice bibliographique
Résumé
BACKGROUND: Opioid use disorder (OUD) is a public health crisis with more than 2 million people living with OUD in the United States. Medication-assisted treatment (MAT) is an evidence-based approach for the treatment of OUD that relies on a combination of behavioral therapy and medication. Less than half of those living with OUD are accessing this treatment. Mobile technology can enhance the treatment of chronic diseases in readily accessible and cost-effective ways through self-monitoring and support. OBJECTIVE: The aim of this study is to describe the adaptation of a mobile platform for patients undergoing treatment for OUD and preliminary pilot testing results. METHODS: Our study was conducted with patient and provider participants at the University of Virginia MAT clinic and was approved by the institutional review board. The formative phase included semistructured interviews to understand the needs of patients with OUD, providers' perspectives, and opportunities for MAT support via a mobile app. A second round of formative interviews used mock-ups of app features to collect feedback on feature function and desirability. Formative participants' input from 16 interviews then informed the development of a functional smartphone app. Patient participants (n=25) and provider participants (n=3) were enrolled in a 6-month pilot study of the completed platform. Patient app use and usability interviews, including a system usability score and open-ended questions, were completed 1 month into the pilot study. Open-ended responses were analyzed for prevalent themes. RESULTS: Formative interviews resulted in the development of a mobile app, named HOPE, which includes both evidence-based and participant-suggested features. The features included daily prompts for monitoring mood, stress, treatment adherence, and substance use; patient tracking of goals, reminders, and triggering or encouraging experiences; informational resources; an anonymous community board to share support with other patients; and secure messaging for communication between patients and providers. All patient participants engaged with at least one app feature during their first month of pilot study participation, and the daily self-monitoring prompts were the most used. Patients and providers reported high levels of system usability (mean 86.9, SD 10.2 and mean 83.3, SD 12.8, respectively). Qualitative analysis of open-ended usability questions highlighted the value of self-monitoring, access to support through the app, and perceived improvement in connection to care and communication for both patient and provider participants. CONCLUSIONS: The use of the HOPE program by pilot participants, high usability scoring, and positive perceptions from 1-month interviews indicate successful program development. By engaging with end users and eliciting feedback throughout the development process, we were able to create an app and a web portal that was highly usable and acceptable to study participants. Further work is needed to understand the program's effect on clinical outcomes, patient linkage, and engagement in care.
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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 | 0,008 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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 ».