Contingency Management for Drug Use: Does Age Matter?

Opioid Use Disorder Clinical Trial

Official title:

Encouraging Abstinence Behavior in a Drug Epidemic: Does Age Matter?

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05521854
• Other study ID #: Contingency Management: Age
• Status: Recruiting
• Phase: N/A
• Start date: May 1, 2023
• Est. completion date: December 1, 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

The OVERALL AIM is to assess whether app-based incentives are effective for older adults and to quantify the associations between age and both the efficacy and take-up of app-based incentives. This will allow us to determine if older adults with substance use disorders (SUDs) are willing to engage with app-based incentives and whether they perform similarly to their younger counterparts. Because the study will leverage data from an existing study on app-based incentives, a small add-on study is sufficient to address these three aims. This aim will be achieved while simultaneously gathering data that will shed light on the two aims of the first phase of the study: whether app-based incentives are effective overall, and how to optimize the size of incentives over time to maximize their effectiveness.

Description:

Phase 1 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 (Petry et al., 2006; Schottenfeld et al., 2005). Similar effects have been demonstrated repeatedly across a wealth of populations, substance-abuse disorders, and payment methodologies (see Lussier et al., 2006; Higgins et al., 2011; Davis et al., 2016; and Higgins et al., 2016 for reviews of providing incentives for managing addiction).

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 (Benishek et al., 2014). 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.

The investigators 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 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 phase will address two key knowledge gaps in the logistics of existing incentive program design for drug addiction. First, the study will test technology 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 the study promises to do the same for illicit drug addiction (see for a review of remote monitoring technologies for incentive delivery). 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 (Lussier, Heil, Mongeon, Badger, & Higgins, 2006). The 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. This will be performed by randomly varying the size and timing of incentives offered to participants across groups. The variation in incentive amounts will vary across participants and time to fit a structural model of abstinence behaviors over time. This model will be used to describe the optimal shape of incentives over time.

Phase 2 Combating the epidemic of overdose deaths is a central challenge of U.S. health care policy, and substance use disorder (SUD) is increasingly harming older adults. One approach to treating SUD, incentivizing abstinence from substance use, has repeatedly proven effective but has limited evidence base in older populations. A uniquely scalable modality - of increasing interest to policymakers - for implementing this approach is with a mobile application (app) that delivers incentives for abstinence from opioids and stimulants. However, one concern about using an app for incentives is that it may hinder take-up and viability among older populations. Thus, testing the app specifically among older adults is of critical policy importance.

This phase will focus on the effectiveness of the app for older adults (aged 55 and over), and on understanding whether the acceptability and effectiveness of the app is decreasing with older age. Specifically, the primary research aim is to assess whether app-based incentives are effective for older adults. The secondary research aims are to understand whether older adults have lower take-up and engagement with the app than younger adults, and if app effectiveness is mediated by age, thus shedding light on the necessity of age-specific app design features. Effects on abstinence outcomes will be measured for all participants who engage with the app (N=59), and take-up and engagement outcomes will be measured for all participants who are eligible for the randomized controlled trial even if they do not enroll (N=98). It is expected that 15 percent of participants will be older adults. In order to complete the research aims, data will be jointly analyzed from the Roybal-funded study phase and the previous study phase.

The OVERALL AIM is to assess whether app-based incentives are effective for older adults and to quantify the associations between age and both the efficacy and take-up of app-based incentives. This will allow us to determine if older adults with substance use disorders are willing to engage with app-based incentives and whether they perform similarly to their younger counterparts. Because the study will leverage data from an existing study on app-based incentives, a small add-on study is sufficient to address these three aims. This aim will be achieved while simultaneously gathering data that will shed light on the two aims of the first phase of the study: whether app-based incentives are effective overall, and how to optimize the size of incentives over time to maximize their effectiveness.

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 1, 2024
• Est. primary completion date: September 1, 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 Disorder
• Methamphetamine Abuse
• Opioid Use Disorder
• Opioid-Related Disorders

Intervention

Behavioral:
• DynamiCare app (app-based contingency management)
Participants will receive financial incentives for submitting randomly generated drug-negative saliva tests across the intervention period.
• Sham control
Participants get access to the DynamiCare app but will not be provided with financial incentives.

Locations

Country	Name	City	State
United States	Advocate Health Care	Chicago	Illinois
United States	Aurora Behavioral Health Services	Milwaukee	Wisconsin
United States	Rogers Behavioral Health	Oconomowoc	Wisconsin

Sponsors (3)

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

Country where clinical trial is conducted

United States, 

References & Publications (9)

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

Dickert N, Grady C. What's the price of a research subject? Approaches to payment for research participation. N Engl J Med. 1999 Jul 15;341(3):198-203. doi: 10.1056/NEJM199907153410312. No abstract available. — View Citation

Festinger DS, Marlowe DB, Dugosh KL, Croft JR, Arabia PL. Higher magnitude cash payments improve research follow-up rates without increasing drug use or perceived coercion. Drug Alcohol Depend. 2008 Jul 1;96(1-2):128-35. doi: 10.1016/j.drugalcdep.2008.02.007. Epub 2008 Apr 18. — View Citation

Grant RW, Sugarman J. Ethics in human subjects research: do incentives matter? J Med Philos. 2004 Dec;29(6):717-38. doi: 10.1080/03605310490883046. — View Citation

Halpern SD, Chowdhury M, Bayes B, Cooney E, Hitsman BL, Schnoll RA, Lubitz SF, Reyes C, Patel MS, Greysen SR, Mercede A, Reale C, Barg FK, Volpp KG, Karlawish J, Stephens-Shields AJ. Effectiveness and Ethics of Incentives for Research Participation: 2 Randomized Clinical Trials. JAMA Intern Med. 2021 Nov 1;181(11):1479-1488. doi: 10.1001/jamainternmed.2021.5450. — View Citation

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. doi: 10.1037/tps0000067. — 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

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. doi: 10.1037/0022-006X.74.3.592. — 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

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Drug-negative saliva tests	Percent of three (3) scheduled video-verified saliva test results classified as drug-negative	3 months
Primary	App uptake	Percent of eligible population which uptakes app	Through study completion, an average of 1 year
Secondary	Longest period of continuous abstinence	Longest period of continuous abstinence measured using lab-verified in-person urine-tests or video-verified saliva tests	3 months