A Prescription Digital Therapeutic to Support Unsupervised Buprenorphine Initiation for Patients With Opioid Use Disorder: Protocol for a Proof-of-Concept Study
Bibliographic record
Abstract
BACKGROUND: Home-based (unsupervised) buprenorphine initiation is considered safe and effective, yet many patients report barriers to successful treatment initiation. Prescription digital therapeutics (PDTs) are software-based disease treatments regulated by the US Food and Drug Administration (FDA). The reSET-O PDT was authorized by the FDA in 2018 and delivers behavioral treatment for individuals receiving buprenorphine for opioid use disorder (OUD). A prototype PDT (PEAR-002b) designed for use with reSET-O was developed to assist in unsupervised buprenorphine initiation. OBJECTIVE: The primary objective of this pilot study is to evaluate the acceptability of PEAR-002b in individuals with OUD who use it to support buprenorphine initiation, their unsupervised buprenorphine initiation success rate, and their medication adherence. METHODS: Ten adults with OUD will be recruited for acceptability and feasibility testing. Outcomes will be assessed using week-1 visit attendance, participant interviews and satisfaction surveys, and urine drug screening (UDS). Three tools will be used in the study: PEAR-002b, reSET-O, and EmbracePlus. PEAR-002b includes a new set of features designed for use with reSET-O. The mechanism of action for the combined PEAR-002b and reSET-O treatment is a program of medication dosing support during week 1 of the initiation phase, cognitive behavioral therapy, and contingency management. During the medication initiation phase, participants are guided through a process to support proper medication use. PEAR-002b advises them when to take their buprenorphine based on provider inputs (eg, starting dose), self-reported substance use, and self-reported withdrawal symptoms. This study also administers the EmbracePlus device, a medical-grade smartwatch, to pilot methods for collecting physiologic data (eg, heart rate and skin conductance) and evaluate the device's potential for use along with PDTs that are designed to improve OUD treatment initiation. Home buprenorphine initiation success will be summarized as the proportion of participants attending the post-buprenorphine initiation visit (week 1) and the proportion of participants who experience buprenorphine initiation-related adverse events (eg, precipitated withdrawal). Acceptability of PEAR-002b will be evaluated based on individual participants' ratings of ease of use, satisfaction, perceived helpfulness, and likelihood of recommending PEAR-002b. Medication adherence will be evaluated by participant self-report data and confirmed by UDS. UDS data will be summarized as the mean of individual participants' proportion of total urine samples testing positive for buprenorphine or norbuprenorphine over the 4-week study. RESULTS: This project was funded in September 2019. As of September 2022, participant enrollment is ongoing. CONCLUSIONS: This is the first study to our knowledge to develop a PDT that assists with unsupervised buprenorphine initiation with the intent to better support patients and prescribers during this early phase of treatment. This pilot study will assess the acceptability and utility of a digital therapeutic to assist individuals with OUD with unsupervised buprenorphine initiation. TRIAL REGISTRATION: ClinicalTrials.gov NCT05412966; https://clinicaltrials.gov/ct2/show/NCT05412966. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/43122.
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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 | 0.021 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.067 | 0.012 |
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".