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

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

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

Conditions
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Non-Medical Opioid Use

Intervention / Treatment
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* Behavioral: RL-supported IVR intervention


Participation Criteria
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Eligibility Criteria
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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 ED 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

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Health Services Research
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: RL-supported IVR intervention<br>Participants in the intervention group receive brief, non-tailored information related to decreasing opioid analgesic risk via pamphlets given at the ED plus the RL-supported IVR intervention. | Behavioral: RL-supported IVR intervention<br>* Participants in the intervention group receive interactive voice response calls where they are asked to report information about their health and medications using their touch-tone phone. Based on their responses, participants may receive brief or extended motivational messages during the call, or they may be assigned to receive a 20 minute motivational enhancement session with a study therapist over the phone.<br>|
| No Intervention: Enhanced Usual Care<br>Participants in the enhanced usual care group receive brief, non-tailored information related to decreasing opioid analgesic risk via pamphlets given at the ED. | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Level of Non-Medical Opioid Use | 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. | Change over time (1-, 3-, and 6-months post-ED visit) | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Emergency Department (ED) Utilization | ED use will be assessed with adapted items from the Treatment Service Review. A count of visits will be measured for the six months of follow-up. | Change over time (1-, 3-, and 6-months post-ED visit) | 
| Driving after consuming opioids | The investigators have adapted an item from the National Survey of Drinking and Driving Attitudes and Behaviors to apply to opioids. Frequency is assessed with a 5-point Likert scale and will be examined as a categorical variable for each follow-up assessment. | Change over time (1-, 3-, and 6-months post-ED visit) | 
| Overdose Risk Behaviors | The investigators have developed a measure based on items from the published literature on heroin overdose by adding risk factors specific to opioid analgesics. This measure will be a summary score of all items for each follow-up assessment. | Change over time (1-, 3-, and 6-months post-ED visit) | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Opioid Analgesics, Opioid Safety, Emergency Department, Motivational Enhancement, Interactive Voice Response, Reinforcement Learning, mHealth