Incentives to Decrease Opioid Use - Pilot

• ClinicalTrials.gov Identifier: NCT04235582
• Recruitment Status: Enrolling by invitation
• First Posted: January 22, 2020
• Last Update Posted: January 29, 2021
• Sponsor: Aurora Health Care
• Collaborators: University of Chicago; University of California, Berkeley
• Information provided by (Responsible Party): Aurora Health Care

Study Description

Brief Summary:

The purpose of this study is to address two key questions in the literature on incentives for substance use. The first question is whether it is more effective to directly incentivize the outcome of interest - drug abstinence - or to incentivize behaviors that are inputs into the production of abstinence. This study will compare two versions of the incentive program: one that incentivizes inputs to achieving abstinence and one that incentivizes the outcome of abstinence. The second question is how to optimize the size of incentives over time to maximize incentive effectiveness. This will be done by randomly varying the size and timing of incentives offered to participants in both the Inputs and Outcomes groups. The incentive amounts will then be varied across participants and time to fit a structural model of abstinence behaviors over time. The model will be used to describe the optimal shape of incentives over time.

Condition or disease	Intervention/treatment	Phase
Substance-Related Disorders; Opioid-Related Disorders; Behavior Therapy	Behavioral: App + Inputs Contingency Management; Behavioral: App + Outcomes Contingency Management	Not Applicable

Detailed Description:

Numerous studies have tested whether providing incentives to encourage abstinence from drugs can further reduce drug abuse in a drug-treatment setting. The results are promising: Incentives to reduce opioid abuse increase the average duration of abstinence by 25 - 60% relative to medication and counseling alone. Similar effects have been demonstrated repeatedly across a wealth of populations, substance-abuse disorders, and payment methodologies.

Despite evidence that incentives are effective and the increasing need for effective approaches to combat the addiction crisis, incentive programs have not been widely implemented. A key barrier is that while the benefits are largely borne by patients and taxpayers, there are large logistical costs that must be borne by clinics: most existing incentive programs involve manual, in-person measurement of behaviors, and prize or voucher purchase and delivery by clinic staff. The significant clinic-level legwork necessary to set up these programs, including setting up behavioral and payment tracking systems, training staff, etc., have prevented the programs from scaling widely. In sum, prior experience has consistently shown that incentives increase duration of treatment and decrease substance abuse, but the logistical complications remain a hurdle to implementation.

This will be the first randomized evaluation of an innovative, scalable incentives program for opioid addiction delivered through a mobile application. The application, which was developed by our implementing partner, DynamiCare Health, provides a "turnkey" solution that health clinics can easily prescribe. The app enables remote monitoring of behavior; for example, drug tests can be administered in patients' homes, as patients submit "selfie-videos" showing them taking saliva drug tests, which are then verified by trained remote staff. Treatment adherence can similarly be checked through GPS tracking for on-site methadone pharmacotherapy. The efficacy of this approach has not been tested rigorously before.

This study will address two key knowledge gaps in the logistics of existing incentive program design for opioid addiction. First, the first technology t for remote monitoring of abstinence behavior for opioid use will be tested. Remote monitoring of abstinence from cigarettes and alcohol has been integral in reducing the costs and extending the potential reach of incentive programs for people with nicotine/tobacco and alcohol use disorders (e.g. to vulnerable or rural populations), and this study promises to do the same for opioid addiction. The second gap is in remote delivery of incentives. After a behavior is verified, the app will deliver incentives to patients as cash available on a linked debit card. The delay between monitoring of the target behavior and the delivery of financial incentives has been shown to be a significant moderator of treatment effect size. This technology allows patients to receive incentives almost immediately following the undertaking of the incentivized behavior: a first in incentives for opioid addiction.

Another novel feature of this design is that can allow assessment of a gap in the literature on incentive delivery: comparing both the isolated effects of incentives and of the monitoring needed to implement an incentive program. In addition to a control group, this study includes both monitoring groups and incentives groups. While existing literature on incentives for addiction has included either a monitoring group or a control group, this is the first to include both, such that a comparison can be made between incentives that are distal (inputs) and proximal (outcome) to the targeted abstinence behavior.

Finally, this study will directly address two key open questions in the literature on incentives for drug-users. The first question is whether it is more effective to directly incentivize the outcome of interest - drug abstinence - or to incentivize behaviors that are inputs into the production of abstinence. Similarly designed studies did not detect different effects on abstinence from incentivizing treatment attendance and incentivizing cocaine abstinence among cocaine users (both were effective): however, not only was this study for a different substance use disorder, but because of differential rates of test submission among these two groups, the results were not conclusive. This study will similarly compare two versions of the incentive program: one that incentivizes inputs to achieving abstinence, and one that incentivizes the outcome of abstinence. To address differential test submission rates, the impacts of the intervention will be measured via urine drug-tests administered identically to patients in both treatments.

The second question is how to optimize the size of incentives over time to maximize incentive effectiveness. This will be assessed by randomly varying the size and timing of incentives offered to participants in both the Inputs and Outcomes groups. The variation in incentive amounts across participants and time to fit a structural model of abstinence behaviors over time. The model will be used to describe the optimal shape of incentives over time.

The results of this intervention will be directly relevant for potential users of this or similar mobile applications for incentive provision among people with opioid-use disorders, including insurers, treatment facilities, and governments.

Study Design

• Study Type: Interventional (Clinical Trial)
• Actual Enrollment: 36 participants
• Allocation: Randomized
• Intervention Model: Parallel Assignment
• Masking: Single (Care Provider)
• Primary Purpose: Treatment
• Official Title: Encouraging Opioid Abstinence Behavior: Incentivizing Inputs and Outcomes - Pilot
• Actual Study Start Date: August 25, 2020
• Estimated Primary Completion Date: October 2021
• Estimated Study Completion Date: October 2021

Arms and Interventions

Arm	Intervention/treatment
Experimental: Outcomes Group; During the intervention period, the "Outcomes" group will receive incentives for abstaining from drug use. Patients in this group will receive the same services and urine drug-test schedule as standard of care and a similar mobile app and debit card as the Inputs group. However, the app will prompt patients in this group to submit saliva drug tests through their mobile phones on a random schedule (averaging three tests per week). Patients will receive immediate financial rewards in exchange for submitting drug-negative samples. Saliva tests typically have a window of detection between 24-48 hr after drug use.	Behavioral: App + Outcomes Contingency Management; The app has programmed contingencies whereby incentives can be earned for outcomes of abstaining from drug use (e.g., drug-negative saliva samples on saliva opioid tests).
Experimental: Inputs Group; Will receive incentives for behaviors that are inputs to abstaining from drug use. Patients in this group will receive the same services and urine drug-test schedule as standard of care. Additionally, patients will be registered for a mobile phone app provided by DynamiCare Health and provided with a linked debit card. The app will prompt patients to complete actions that are inputs to abstinence an average of three times per week. These actions will be tailored to the patient's individual needs, and may include: Drug adherence to prescribed SUD pharmacotherapy; Attendance at individual and group psychotherapy sessions	Behavioral: App + Inputs Contingency Management; The app has programmed contingencies whereby incentives can be earned for behaviors that are inputs to abstaining from drug use (e.g., attending psychotherapy or taking SUD psychopharmacology).
Experimental: Combination Group; Will receive interventions from both Inputs and Outputs groups, as well as standard of care therapy services and urine drug tests an average of three times per week, total. Interventions include incentives for: Drug adherence to prescribed SUD pharmacotherapy; Attendance at individual and group psychotherapy sessions; Random saliva tests	Behavioral: App + Inputs Contingency Management; The app has programmed contingencies whereby incentives can be earned for behaviors that are inputs to abstaining from drug use (e.g., attending psychotherapy or taking SUD psychopharmacology).; Behavioral: App + Outcomes Contingency Management; The app has programmed contingencies whereby incentives can be earned for outcomes of abstaining from drug use (e.g., drug-negative saliva samples on saliva opioid tests).

Outcome Measures

Primary Outcome Measures :

1. Continuous Abstinence from Opioid Use [ Time Frame: Time = 4 weeks ]
   The primary endpoint is the longest period of continuous abstinence from illicit opioids, where abstinence is measured using lab-verified in-person urine-tests, saliva tests, or subject reporting (in the absence of urine testing).
2. Continuous Abstinence from Opioid Use [ Time Frame: Time = 8 weeks ]
   The primary endpoint is the longest period of continuous abstinence from illicit opioids, where abstinence is measured using lab-verified in-person urine-tests, saliva tests, or subject reporting (in the absence of urine testing).
3. Continuous Abstinence from Opioid Use [ Time Frame: Time = 12 weeks ]
   The primary endpoint is the longest period of continuous abstinence from illicit opioids, where abstinence is measured using lab-verified in-person urine-tests, saliva tests, or subject reporting (in the absence of urine testing).

Secondary Outcome Measures :

1. Negative Urinalysis Frequency [ Time Frame: Time = Day 0 through 12 weeks ]
   Percent of standard of care urinalyses results classified as negative
2. Negative Saliva Analysis Frequency [ Time Frame: Time = Day 0 through 12 weeks ]
   Percent of random saliva tests classified as negative
3. Psychotherapy Attendance [ Time Frame: Time = Day 0 through 12 weeks ]
   The percent of psychotherapy visits attended.
4. Psychotherapy Completion [ Time Frame: 12 weeks ]
   Whether or not the patient completed the scheduled psychotherapy program.
5. Medication Adherence [ Time Frame: Time = Day 0 through 12 weeks ]
   % adherence to medication assisted therapies (i.e., % of prescribed medication-assisted treatments taken as prescribed)
6. World Health Organization Quality of Life Brief (WHOQOL-BREF) survey [ Time Frame: Week 4 ]
   Scores on World Health Organization Quality of Life Brief (WHOQOL-BREF) survey on a scale of 1-100 where higher is greater quality of life
7. World Health Organization Quality of Life Brief (WHOQOL-BREF) survey [ Time Frame: Week 8 ]
   Scores on World Health Organization Quality of Life Brief (WHOQOL-BREF) survey on a scale of 1-100 where higher is greater quality of life
8. World Health Organization Quality of Life Brief (WHOQOL-BREF) survey [ Time Frame: Week 12 ]
   Scores on World Health Organization Quality of Life Brief (WHOQOL-BREF) survey on a scale of 1-100 where higher is greater quality of life

Eligibility Criteria

• Ages Eligible for Study: 18 Years and older (Adult, Older Adult)
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: No

Criteria

Inclusion

1. Age at least 18 years old;
2. Meet DSM-5 opioid use disorder criteria as evidenced by an opioid disorder CPT code F11 (opioid related disorders);
3. Have access to a smartphone (iOS or Android) with data plan and willing to download DynamiCare app;
4. Are in day (PHP) or partial day (IOP) AODA treatment in Aurora Health's Behavioral Health Program;
5. Are currently prescribed or will be prescribed within 1-4 days oral buprenorphine for their OUD;

6. Are likely to be helped by contingency management because at least ONE of the following conditions is true:

   1. Were first enrolled in day or partial day opioid treatment no longer than 1 week prior to providing informed consent.
   2. Currently using non-medical opioids.
   3. Regularly missing scheduled AODA appointments.
7. Understands English.

Exclusion Criteria:

1. Have evidence of active (non-substance related) psychosis that might impair participation as determined by the PI.
2. Has significant cognitive impairment that might confound participation as determined by the PI or are so significantly cognitively impaired that they have a legal guardian.

Note that pregnant women are not excluded from participating in the study

Contacts and Locations

Locations

United States, California

Univeresity of California - Berkeley

Berkeley, California, United States, 94720

United States, Illinois

University of Chicago

Chicago, Illinois, United States, 60637

United States, Wisconsin

Aurora Psychiatric Hospital

Wauwatosa, Wisconsin, United States, 53213

Sponsors and Collaborators

Aurora Health Care

University of Chicago

University of California, Berkeley

Investigators

• Principal Investigator: Mindy R Waite, PhD; Aurora Health Care, Inc.

More Information

Publications:

Benishek LA, Dugosh KL, Kirby KC, Matejkowski J, Clements NT, Seymour BL, Festinger DS. Prize-based contingency management for the treatment of substance abusers: a meta-analysis. Addiction. 2014 Sep;109(9):1426-36. doi: 10.1111/add.12589. Epub 2014 May 23.

Kurti AN, Davis DR, Redner R, Jarvis BP, Zvorsky I, Keith DR, Bolivar HA, White TJ, Rippberger P, Markesich C, Atwood G, Higgins ST. A Review of the Literature on Remote Monitoring Technology in Incentive-Based Interventions for Health-Related Behavior Change. Transl Issues Psychol Sci. 2016 Jun;2(2):128-152.

Lussier JP, Heil SH, Mongeon JA, Badger GJ, Higgins ST. A meta-analysis of voucher-based reinforcement therapy for substance use disorders. Addiction. 2006 Feb;101(2):192-203. Review.

Petry NM, Alessi SM, Carroll KM, Hanson T, MacKinnon S, Rounsaville B, Sierra S. Contingency management treatments: Reinforcing abstinence versus adherence with goal-related activities. J Consult Clin Psychol. 2006 Jun;74(3):592-601.

Schottenfeld RS, Chawarski MC, Pakes JR, Pantalon MV, Carroll KM, Kosten TR. Methadone versus buprenorphine with contingency management or performance feedback for cocaine and opioid dependence. Am J Psychiatry. 2005 Feb;162(2):340-9.

• Responsible Party: Aurora Health Care
• ClinicalTrials.gov Identifier: NCT04235582
• Other Study ID Numbers: 19-1095
• First Posted: January 22, 2020
• Last Update Posted: January 29, 2021
• Last Verified: January 2021

Individual Participant Data (IPD) Sharing Statement:

• Plan to Share IPD: No

• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No

Keywords provided by Aurora Health Care:

opioid-use disorder; substance-use disorder; addiction; contingency management; dynamicare

Additional relevant MeSH terms:

Disease; Substance-Related Disorders; Opioid-Related Disorders; Pathologic Processes; Chemically-Induced Disorders; Mental Disorders