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

Study Summary

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.

Primary Outcome

The Current Opioid Misuse Measure (COMM) assesses opioid use that is more than prescribed, for non-pain-related reasons, borrowing medications, obtaining medications from sources other than doctors, etc., on a 5-point Likert scale of "Never" to "Very Often." The primary outcome will be a measure of severity created by summing the items.

Interventions

• BEHAVIORAL RL-supported IVR intervention