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.

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.

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

These were collected during the 12 weeks the participant had access to the intervention.

This is a pilot and feasibility study with relatively few enrollees.