A Peer-Led, Narrative-Based, and Mobile-Supported Intervention in Opioid Use Disorder: Multiphase Qualitative and Longitudinal Observational Study
Notice bibliographique
Résumé
Background: The ongoing opioid epidemic has been associated with increases in emergency department visits and hospitalizations for drug overdose and injection-related infections. These encounters with the health care system provide an opportunity to offer drug treatment linkage and support for people with opioid use disorder (OUD). There is a need for interventions that enhance linkage to and engagement in treatment with medication for opioid use disorder (MOUD) for people with OUD identified in hospital settings as they transition back to community settings. Objective: The mTools4life (Johns Hopkins University) study aimed to develop and evaluate a peer-led intervention integrating narrative-based health communication into a mobile health (mHealth) app to increase posthospitalization engagement in MOUD and reduce substance use. Methods: The formative phase of the study consisted of semistructured interviews with people with OUD and clinicians who provide care to people with OUD. Interviews sought to identify salient content to include in visual narratives within the mHealth app and information that may increase motivations for behavior change related to MOUD engagement. The intervention was developed in accordance with the information-motivation-behavioral skills model, transportation theory, and the transtheoretical model. The pilot phase of mTools4Life (Johns Hopkins University) aimed to evaluate the acceptability and usability of the intervention. People with OUD were recruited from the Johns Hopkins Hospital Emergency Department and consented to receive the intervention for a 3-month period. Participants completed a study survey at baseline and a 3-month follow-up. Data on demographics, past 30-day substance use, MOUD, and intervention appropriateness and acceptability were obtained at both time points. Additional data on intervention uptake and frequency of use were collected at follow-up. Dependent samples 2-tailed t tests were conducted on continuous data, and Fisher exact tests were conducted on count data. Results: Twenty people with OUD piloted the intervention. The sample was mostly male (13/20, 65%) and non-Hispanic White (13/20, 65%) with a mean age of 41.1 (SD 8.7) years. Most participants (16/20, 80%) completed the 3-month follow-up. Fewer participants reported opioid use at follow-up (9/16, 56.3%) compared to baseline (20/20, 100%; P=.001, and mean days of opioid use out of the past 30 days declined from baseline (19.9, SD 11.7) to follow-up (8.3, SD 11.4; P=.002). MOUD treatment in the prior 3 months was reported by 65% (13/20) of participants at baseline and 81.3% (13/16) at follow-up (P=.46). Most participants used the app (11/16, 68.8%) or engaged with their peer navigator (10/16, 62.5%) during the intervention period. At follow-up, mean acceptability and appropriateness scores (scale 0-5; higher score indicating greater acceptability or appropriateness) were 4.5 (SD 0.5) and 4.3 (SD 0.8), respectively. Conclusions: This study demonstrates the feasibility of the development and deployment of a narrative-based mHealth intervention to support OUD care engagement and preliminary data in support of the intervention's acceptability, appropriateness, and effectiveness.
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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 | 0,010 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».