Reducing Non-Medical Opioid Use: An Automatically Adaptive mHealth Intervention

Age Range:18 - Any
Start Date:November 6, 2018
End Date:June 2021
Contact:Amy S Bohnert, Ph.D.

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In recent years in the U.S., problems associated with opioid prescriptions, including
non-medical use and overdose, increased to historically unprecedented levels and represent a
public health crisis. Emergency departments (EDs) play an important role in opioid
prescribing, particularly to individuals at high risk for adverse opioid-related outcomes.
The purpose of this study is to determine whether a new mobile health (mhealth) intervention
can assist people in the safe use of opioid analgesic (OA) medications after leaving the
emergency department (ED).

The specific aims of this project are to: (1) adapt and enhance an existing motivational
intervention to decrease non-medical opioid use after an ED visit by optimizing intervention
intensity and duration through reinforcement learning (RL); (2) examine the impact of an
RL-supported intervention on non-medical opioid use level during the six months post-ED
visit; and (3) examine the impact of the RL intervention on the opioid-related behaviors and
adverse outcomes of driving after opioid use, overdose risk behaviors, and subsequent
opioid-related ED visits. The secondary aims of this project are to: (SA1) examine whether
baseline level of non-medical opioid use moderates the effects of the intervention; and (SA2)
understand barriers and facilitators of implementation of the intervention based on
qualitative interviews with ED patients.

The proposed study will test the efficacy of an interactive voice response (IVR) and
reinforcement learning (RL) supported motivational intervention delivered after an emergency
department (ED) visit to participants with recent non-medical OA use who receive an OA in the
ED or who are prescribed an OA at ED discharge, compared to enhanced usual care (EUC). In the
intervention condition, IVR calls will ask participants to report information about their
health and medications using their touch-tone phone, and based on their responses they may
receive brief or extended motivational messages during the IVR call, or they may be assigned
to receive a 20 minute motivational enhancement session with a study therapist over the
phone. Because the most helpful intensity of intervention is unknown and likely to vary
between patients, the project will use an artificial intelligence strategy called
reinforcement learning (RL). The RL system will continuously "learn" from the success of
prior actions in similar situations with similar patients in order to select the action most
likely to reduce non-medical opioid use for each participant during each call.

The proposed study will screen ~ 5,600 ED patients to enroll 600 ED participants in the
randomized controlled trial (RCT). Participants will be randomized to the intervention
condition (n=300) or to EUC (n=300). All participants will be re-assessed at 1, 3 and 6
months post-ED visit for level of non-medical OA use and related outcomes. The RCT will be
complemented by qualitative interviews to inform later implementation.

Inclusion Criteria:

- Presenting at the study site emergency department (ED) for a pain related complaint

- Past 3-month non-medical opioid analgesic (OA) use

- Receiving an OA in the ED, or being given an OA prescription to fill after leaving the

Exclusion Criteria:

- Unable to perform informed consent

- Presenting for pain related to acute cancer therapy

- DSM-V moderate or severe opiate (heroin or OA) use disorders (4+ symptoms), or
experiencing tolerance and withdrawal symptoms

- Unable to read/understand English

- Lives 50+ miles from the study site

- Acute risk for self-harm at the time of recruitment

- Currently pregnant
We found this trial at
Ann Arbor, Michigan 48109
Phone: 734-845-3638
Ann Arbor, MI
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