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

NCT ID: NCT02990377

Last Updated: 2022-01-31

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 459 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2018-11-06
• Study Completion Date: 2021-12-31

Brief 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.

Detailed Description

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.

Conditions

Non-Medical Opioid Use

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: HEALTH_SERVICES_RESEARCH
• Blinding Strategy: NONE

Study Groups

RL-supported IVR intervention

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.

Group Type: EXPERIMENTAL

RL-supported IVR intervention

Intervention Type: BEHAVIORAL

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.

Enhanced Usual Care

Participants in the enhanced usual care group receive brief, non-tailored information related to decreasing opioid analgesic risk via pamphlets given at the ED.

Group Type: NO_INTERVENTION

No interventions assigned to this group

Interventions

RL-supported IVR intervention

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.

Intervention Type: BEHAVIORAL

Eligibility Criteria

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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

National Institute on Drug Abuse (NIDA)

NIH

Sponsor Role: collaborator

University of Michigan

OTHER

Sponsor Role: lead

Responsible Party

Amy S.B. Bohnert

Associate Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Amy S Bohnert, Ph.D.

Role: PRINCIPAL_INVESTIGATOR

University of Michigan

Locations

University of Michigan Medical Center

Ann Arbor, Michigan, United States

Countries

United States

References

Piette JD, Thomas L, Newman S, Marinec N, Krauss J, Chen J, Wu Z, Bohnert ASB. An Automatically Adaptive Digital Health Intervention to Decrease Opioid-Related Risk While Conserving Counselor Time: Quantitative Analysis of Treatment Decisions Based on Artificial Intelligence and Patient-Reported Risk Measures. J Med Internet Res. 2023 Jul 11;25:e44165. doi: 10.2196/44165.

Reference Type: DERIVED

PMID: 37432726 (View on PubMed)

Provided Documents

Document Type: Informed Consent Form

View Document

Other Identifiers

R01DA039159

Identifier Type: NIH

Identifier Source: org_study_id

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