A Peer-Led, Narrative-Based, and Mobile-Supported Intervention in Opioid Use Disorder: Multiphase Qualitative and Longitudinal Observational Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".