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.

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.

Inclusion Criteria:

• Age at least 18 years old;

• 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

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

• Have an email and can access it from their smartphone;

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

• 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.

• Understands English.

Exclusion Criteria:

• Have evidence of active (non-substance related) psychosis that might impair participation as determined by the PI.
• 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.