Contingency Management for Drug Use: Does Age Matter?

Overview

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.

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.