Efficacy of Integrating the Management of Pain and Addiction via Collaborative Treatment (IMPACT) in Individuals With Chronic Pain and Opioid Use Disorder: Protocol for a Randomized Clinical Trial of a Digital Cognitive Behavioral Treatment
Bibliographic record
Abstract
BACKGROUND: Chronic pain is common among individuals with opioid use disorder (OUD) who are maintained on medications for OUD (MOUD; eg, buprenorphine or methadone). Chronic pain is associated with worse retention and higher levels of substance use. Treatment of individuals with chronic pain receiving MOUD can be challenging due to their increased clinical complexity. Given the acute and growing nature of the opioid crisis, MOUD is increasingly offered in a wide range of settings, where high-quality, clinician-delivered, empirically validated behavioral treatment for chronic pain may not be available. Therefore, digital treatments that support patient self-management of chronic pain and OUD have the potential for wider implementation to fill this gap. OBJECTIVE: This study aims to evaluate the efficacy of Integrating the Management of Pain and Addiction via Collaborative Treatment (IMPACT), an interactive digital treatment program with asynchronous coach feedback, compared to treatment as usual (TAU) in individuals with chronic pain and OUD receiving MOUD. METHODS: Adult participants (n=160) receiving MOUD and reporting bothersome or high-impact chronic pain will be recruited from outpatient opioid treatment programs in Connecticut (United States) and randomized 1:1 to either IMPACT+TAU or TAU only. Participants randomized to IMPACT+TAU will complete an interactive digital treatment that includes 9 modules promoting training in pain and addiction coping skills and a progressive walking program. The program is augmented with a weekly personalized voice message from a trained coach based on daily participant-reported pain intensity and interference, craving to use opioids, sleep quality, daily steps, pain self-efficacy, MOUD adherence, and engagement with IMPACT collected through digital surveys. Outcomes will be assessed at 3, 6, and 9 months post randomization. The primary outcome is MOUD retention at 3 months post randomization (ie, post treatment). Secondary outcomes include pain interference, physical functioning, MOUD adherence, substance use, craving, pain intensity, sleep disturbance, pain catastrophizing, and pain self-efficacy. Semistructured qualitative interviews with study participants (n=34) randomized to IMPACT (completers and noncompleters) will be conducted to evaluate the usability and quality of the program and its outcomes. RESULTS: The study has received institutional review board approval and began recruitment at 1 site in July 2022. Recruitment at a second site started in January 2023, with a third and final site anticipated to begin recruitment in January 2024. Data collection is expected to continue through June 2025. CONCLUSIONS: Establishing efficacy for a digital treatment for addiction and chronic pain that can be integrated into MOUD clinics will provide options for individuals with OUD, which reduce barriers to behavioral treatment. Participant feedback on the intervention will inform updates or modifications to improve engagement and efficacy. TRIAL REGISTRATION: ClinicalTrials.gov NCT05204576; https://clinicaltrials.gov/ct2/show/NCT05204576. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/54342.
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.045 | 0.007 |
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".