Encouraging Abstinence Behavior in a Drug Epidemic

Opioid Use Clinical Trial

Official title:

Encouraging Abstinence Behavior in a Drug Epidemic

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04927143
• Other study ID #: 21.102E
• Status: Recruiting
• Phase: Phase 2
• Start date: September 15, 2021
• Est. completion date: December 2024

Study information

• Verified date: February 2024
• Source: Aurora Health Care
• Contact: Gary Dennison
• Phone: 414-385-1913
• Email: gary.dennison[@]aah.org
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

Combatting the rise of the opioid epidemic is a central challenge of U.S. health care policy. A promising approach for improving welfare and decreasing medical costs of people with substance abuse disorders is offering incentive payments for healthy behaviors. This approach, broadly known as "contingency management" in the medical literature, has repeatedly shown to be effective in treating substance abuse. However, the use of incentives by treatment facilities remains extremely low. Furthermore, it is not well understood how to design optimal incentives to treat opioid abuse. This project will conduct a randomized evaluation of two types of dynamically adjusting incentive schedules for people with opioid use disorders or cocaine use disorders: "escalating" schedules where incentive amounts increase with success to increase incentive power, and "de-escalating" schedules where incentive amounts decrease with success to improve incentive targeting. Both schemes are implemented with a novel "turnkey" mobile application, making them uniquely low-cost, low-hassle, and scalable. Effects will be measured on abstinence outcomes, including longest duration of abstinence and the percentage of negative drug tests. In combination with survey data, variation from the experiment will shed light on the barriers to abstinence more broadly and inform the understanding of optimal incentive design.

Description:

Over the past decade, the annual number of drug-related deaths more than doubled in the United States (Swensen, 2015). In particular, over the 2001-2013 period, overdose deaths involving prescription pain relievers tripled while those involving heroin increased fivefold (NIDA, 2015). Further, the COVID-19 pandemic is thought to have significantly increased drug use, especially opioids, cocaine, and methamphetamines. This upward-sloping trend has steepened in the past few years. Drug overdoses are now the principal cause of death among Americans aged less than 50. A primary cause of this escalating public health crisis is the abuse of opioids (e.g., prescription pain relievers and heroin), which is estimated to concern more than two million Americans (New York Times, 2017).

Many studies in the medical literature have tested whether providing incentives to encourage abstinence from drugs can further reduce drug abuse in a drug-treatment setting. The results are very promising: Incentives to reduce opioid abuse increase the average duration of abstinence by 25 - 60% relative to medication and counseling alone (Petry et al., 2005; Schottenfeld et al., 2005; Petry et al., 2010; Ling et al., 2013). Similar effects have been demonstrated repeatedly across a wealth of populations, substance-abuse disorders, and payment methodologies (Lussier et al., 2006; Davis et al., 2016; Higgins, 2016). A meta-analysis of psychosocial treatments concluded that providing incentives for abstinence behavior was the intervention with the greatest effect size in treating substance use disorders (Dutra et al., 2008). Despite their costs, incentive programs have been estimated to be cost-effective, with the estimated benefits - including benefits to participants and to taxpayers from lower health care costs and higher earnings - estimated to be on the order of 20 times as large as normal program costs (WSIPP, 2017). Although such estimates are somewhat speculative, the case for scaling up incentive programs is strong.

And yet, despite evidence that incentives are effective and the ever-more-dire need for effective approaches to combat the addiction crisis, incentive programs have not been scaled up widely to date. 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: 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 (Benishek et al., 2014).

We propose to conduct the first randomized evaluation of an innovative, scalable incentives program for drug addiction delivered through a mobile application. The application, which was developed by our implementing partner, DynamiCare Health (henceforth "DynamiCare"), 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 drug addiction. First, we will test the first technology that we know of for remote monitoring of abstinence behavior for drug use. 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 our study promises to do the same for illicit drug addiction (see for a review of remote monitoring technologies for incentive delivery). Our 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 (Lussier, Heil, Mongeon, Badger, & Higgins, 2006). Our technology allows patients to receive incentives almost immediately following the undertaking of the incentivized behavior: a first in incentives for drug addiction.

The second question is how to optimize the size of incentives over time to maximize incentive effectiveness. We propose to do this by randomly varying the size and timing of incentives offered to participants across groups. We will then use the variation in incentive amounts across participants and time to fit a structural model of abstinence behaviors over time. We will then use the model 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 substance use disorders, including insurers, treatment facilities, and governments.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 600
• Est. completion date: December 2024
• Est. primary completion date: September 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

1. Age at least 18 years old;

2. Meet DSM-5 OUD, CoUD, or MUD criteria as evidenced by an OUD CPT code F11* (opioid related disorders), a CoUD CPT code F14* (cocaine related disorders), a MUD CPT code F15.1/F15.2 or other clinical notes indicating illicit opioid/cocaine/methamphetamine use for treatment;

3. Have access to a smartphone (iOS or Android) with data plan and willing to download DynamiCare app;

4. Have an email and can access it from their smartphone;

5. Are in residential, day (PHP), partial day (IOP), or outpatient (OP) AODA treatment;

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

1. Were first enrolled in residential, PHP, or IOP substance use treatment no longer than 2 treatment weeks (14 days/encounters of treatment) prior to providing informed consent.

2. Used non-medical opioids, cocaine, and/or methamphetamine within the last 21 days.

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.

Related Conditions & MeSH terms

• Cocaine Use
• Cocaine Use Disorder
• Methamphetamine Abuse
• Methamphetamine-dependence
• Opioid Use
• Opioid-Related Disorders
• Opioid-use Disorder
• Substance Use
• Substance-Related Disorders

Intervention

Device:
• App-Based Contingency Management
Participants will receive financial incentives for submitting randomly generated drug-negative saliva tests across the intervention period.

Behavioral:
• Sham Control
Participants get access to the DynamiCare app but will not be provided with financial incentives.

Locations

Country	Name	City	State
United States	Rogers Behavioral Health	Oconomowoc	Wisconsin
United States	Advocate Aurora Behavioral Health Services	Wauwatosa	Wisconsin

Sponsors (4)

Lead Sponsor	Collaborator
Aurora Health Care	Rogers Behavioral Health, University of California, Santa Cruz, University of Chicago

Country where clinical trial is conducted

United States, 

References & Publications (24)

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. — View Citation

Davis DR, Kurti AN, Skelly JM, Redner R, White TJ, Higgins ST. A review of the literature on contingency management in the treatment of substance use disorders, 2009-2014. Prev Med. 2016 Nov;92:36-46. doi: 10.1016/j.ypmed.2016.08.008. Epub 2016 Aug 8. — View Citation

Dutra L, Stathopoulou G, Basden SL, Leyro TM, Powers MB, Otto MW. A meta-analytic review of psychosocial interventions for substance use disorders. Am J Psychiatry. 2008 Feb;165(2):179-87. doi: 10.1176/appi.ajp.2007.06111851. Epub 2008 Jan 15. — View Citation

Higgins ST, Washio Y, Lopez AA, Heil SH, Solomon LJ, Lynch ME, Hanson JD, Higgins TM, Skelly JM, Redner R, Bernstein IM. Examining two different schedules of financial incentives for smoking cessation among pregnant women. Prev Med. 2014 Nov;68:51-7. doi: 10.1016/j.ypmed.2014.03.024. Epub 2014 Apr 2. — View Citation

Hutchinson ML, Chisolm MS, Tuten M, Leoutsakos JM, Jones HE. The efficacy of escalating and fixed contingency management reinforcement on illicit drug use in opioid-dependent pregnant women. Addict Disord Their Treat. 2012 Sep;11(3):150-153. doi: 10.1097/ADT.0b013e318264cf6d. — View Citation

Kirby KC, Carpenedo CM, Dugosh KL, Rosenwasser BJ, Benishek LA, Janik A, Keashen R, Bresani E, Silverman K. Randomized clinical trial examining duration of voucher-based reinforcement therapy for cocaine abstinence. Drug Alcohol Depend. 2013 Oct 1;132(3):639-45. doi: 10.1016/j.drugalcdep.2013.04.015. Epub 2013 May 13. — View Citation

Lamb RJ, Kirby KC, Morral AR, Galbicka G, Iguchi MY. Shaping smoking cessation in hard-to-treat smokers. J Consult Clin Psychol. 2010 Feb;78(1):62-71. doi: 10.1037/a0018323. — View Citation

Ling W, Hillhouse M, Ang A, Jenkins J, Fahey J. Comparison of behavioral treatment conditions in buprenorphine maintenance. Addiction. 2013 Oct;108(10):1788-98. doi: 10.1111/add.12266. Epub 2013 Jul 12. — View Citation

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. doi: 10.1111/j.1360-0443.2006.01311.x. — View Citation

Packer RR, Howell DN, McPherson S, Roll JM. Investigating reinforcer magnitude and reinforcer delay: a contingency management analog study. Exp Clin Psychopharmacol. 2012 Aug;20(4):287-92. doi: 10.1037/a0027802. Epub 2012 Jun 11. — View Citation

Petry NM, Alessi SM, Barry D, Carroll KM. Standard magnitude prize reinforcers can be as efficacious as larger magnitude reinforcers in cocaine-dependent methadone patients. J Consult Clin Psychol. 2015 Jun;83(3):464-72. doi: 10.1037/a0037888. Epub 2014 Sep 8. — View Citation

Petry NM, Alessi SM, Marx J, Austin M, Tardif M. Vouchers versus prizes: contingency management treatment of substance abusers in community settings. J Consult Clin Psychol. 2005 Dec;73(6):1005-14. doi: 10.1037/0022-006X.73.6.1005. — View Citation

Petry NM, Barry D, Alessi SM, Rounsaville BJ, Carroll KM. A randomized trial adapting contingency management targets based on initial abstinence status of cocaine-dependent patients. J Consult Clin Psychol. 2012 Apr;80(2):276-85. doi: 10.1037/a0026883. Epub 2012 Jan 9. — View Citation

Petry NM, Martin B. Low-cost contingency management for treating cocaine- and opioid-abusing methadone patients. J Consult Clin Psychol. 2002 Apr;70(2):398-405. doi: 10.1037//0022-006x.70.2.398. — View Citation

Petry NM, Weinstock J, Alessi SM, Lewis MW, Dieckhaus K. Group-based randomized trial of contingencies for health and abstinence in HIV patients. J Consult Clin Psychol. 2010 Feb;78(1):89-97. doi: 10.1037/a0016778. — View Citation

Prendergast ML, Podus D, Chang E, Urada D. The effectiveness of drug abuse treatment: a meta-analysis of comparison group studies. Drug Alcohol Depend. 2002 Jun 1;67(1):53-72. doi: 10.1016/s0376-8716(02)00014-5. Erratum In: Drug Alcohol Depend. 2006 Sep 1;84(1):133. — View Citation

Rash CJ, Petry NM. Contingency management treatments are equally efficacious for both sexes in intensive outpatient settings. Exp Clin Psychopharmacol. 2015 Oct;23(5):369-76. doi: 10.1037/pha0000035. Epub 2015 Jul 13. — View Citation

Roll JM, Higgins ST, Badger GJ. An experimental comparison of three different schedules of reinforcement of drug abstinence using cigarette smoking as an exemplar. J Appl Behav Anal. 1996 Winter;29(4):495-504; quiz 504-5. doi: 10.1901/jaba.1996.29-495. — View Citation

Roll JM, Higgins ST. A within-subject comparison of three different schedules of reinforcement of drug abstinence using cigarette smoking as an exemplar. Drug Alcohol Depend. 2000 Feb 1;58(1-2):103-9. doi: 10.1016/s0376-8716(99)00073-3. — View Citation

Roll, John M., Huber, A., Sodano, R., Chudzynski, J.E., Moynier, E., Shoptaw, S. (2006). A Comparison of Five Reinforcement Schedules for Use in Contingency Management-Based Treatment of Methamphetamine Abuse. Psychological Record, 56(1), 67.

Romanowich P, Lamb RJ. Effects of escalating and descending schedules of incentives on cigarette smoking in smokers without plans to quit. J Appl Behav Anal. 2010 Fall;43(3):357-67. doi: 10.1901/jaba.2010.43-357. — View Citation

Romanowich P, Lamb RJ. The effects of fixed versus escalating reinforcement schedules on smoking abstinence. J Appl Behav Anal. 2015 Spring;48(1):25-37. doi: 10.1002/jaba.185. Epub 2015 Jan 30. — View Citation

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. doi: 10.1176/appi.ajp.162.2.340. — View Citation

Swensen, I.D. (2015). Substance-abuse treatment and mortality. Journal of Public Economics 122, 13-30.

* Note: There are 24 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Abstinence from Opioid and/or Cocaine Use	Percent of outcomes saliva tests negative for the relevant drug (opioids and/or cocaine)	12 weeks

External Links

•   New York Times (2017), "Drug Deaths in America Are Rising Faster Than Ever",