Patient- and Provider-Reported Experiences of a Mobile Novel Digital Therapeutic in People With Opioid Use Disorder (reSET-O): Feasibility and Acceptability Study
Bibliographic record
Abstract
BACKGROUND: Medications for the treatment of opioid use disorder, such as buprenorphine, are effective and essential for addressing the opioid epidemic. However, high dropout rates from medication remain a challenge. Behavioral treatment with contingency management and cognitive behavioral counseling has shown promise for improving the outcomes of buprenorphine treatment but is complicated to deliver. The delivery of behavioral treatment through technology-based platforms has the potential to make it more feasible for widespread dissemination. OBJECTIVE: reSET-O is a prescription digital therapeutic and a commercial adaptation of the Therapeutic Education System, an internet-based program with a Community Reinforcement Approach to cognitive behavioral therapy. It delivers cognitive behavioral therapy modules and contingency management rewards upon completion of modules and negative urine drug screens. This pilot study aims to assess the feasibility and acceptability of reSET-O in a community-based opioid treatment program with a Hub and Spoke model of care as part of a larger strategy to maintain individuals in treatment. Objective and qualitative results, as well as acceptability and likeability of reSET-O, were obtained from 15 individuals. METHODS: English-speaking individuals aged ≥18 years with a diagnosis of current opioid use disorder were recruited after being on buprenorphine for at least 1 week of treatment. Two 12-week prescriptions for reSET-O were written for the 24-week study. Patient reports of drug use and likeability scales of reSET-O were conducted at weeks 4, 8, 12, and 24 of the study. Qualitative interviews were also conducted. A total of 4 providers were recruited and provided feedback on the acceptability and feasibility of reSET-O. RESULTS: Of the 15 participants who participated in this pilot study, 7 (47%) completed 24 weeks, and 8 (53%) were unable to complete because of dropout after enrollment, attrition in treatment, or incarceration. An average of US $96 in contingency management rewards were earned by participants for the completion of modules for the duration of the pilot study. Participants' subjective feedback revealed that reSET-O was easy to use, enjoyable, and helped provide a safe space to admit recurring substance use. CONCLUSIONS: reSET-O was well accepted based on patient and provider feedback in this pilot study; however, adherence and retention in treatment remain areas for improvement. Randomized control trials are needed to assess whether retention of community-based buprenorphine treatment is enhanced through the use of technology-based behavioral interventions such as reSET-O.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".