Neurocognitive Mechanisms Underlying Smartphone-Assisted Prevention of Relapse in Opioid Use Disorder

The proposed clinical trial would evaluate the use of smartphone applications ("apps", which have well-established efficacy in reducing cigarette and alcohol use) to prevent relapse among patients receiving medication-assisted treatment for opioid use disorder. In addition to standard app-based self-monitoring of drug use and personalized feedback, project innovation is enhanced by the proposed use of location-tracking technology for targeted, personalized intervention when participants enter self-identified areas of high risk for relapse. Furthermore, the proposed sub-study would use longitudinal functional neuroimaging to elucidate the brain-cognition relationships underlying individual differences in treatment outcomes, offering broad significance for understanding and enhancing the efficacy of this and other app-based interventions.

Study Overview

• Status: Enrolling by invitation
• Conditions: Opioid-Related Disorders; Magnetic Resonance Imaging; Mobile Applications; Opiate Substitution Treatment; Craving; Attentional Bias; Ecological Momentary Assessment
• Intervention / Treatment: Device: Smartphone

Detailed Description

The rising public health burden of opioid misuse, coupled with high relapse rates among individuals seeking treatment for opioid use disorder, necessitates novel interventions for improving opioid-related treatment response. Mobile technology such as smartphone-based applications ("apps") represent one such intervention. Although smartphone apps are effective in reducing cigarette and alcohol use, their efficacy for reducing opioid use has not yet been established. The proposed clinical trial would evaluate the app OptiMAT ("Optimizing Medication-Assisted Treatment") to prevent relapse among patients receiving medication-assisted treatment for opioid use disorder. OptiMAT implements two features shown to be effective for reducing substance use: daily self-monitoring of opiate use coupled with personalized feedback. Aim 1 would accrue 255 participants with 1:1 randomization into two arms (OptiMAT vs. Monitoring only) to evaluate differences in monthly opioid use at six months post-enrollment. Aim 2 would enroll a subset of participants (N=120; 60 per arm) into a longitudinal functional neuroimaging (fMRI) study to model the neurocognitive mechanisms underlying individual differences in treatment response. Two putative mechanisms (attentional bias for drug cues and cue-induced craving) promoting abstinence would be studied. Aim 3 would explore the use of location-based geographic ecological momentary assessment (GEMA) for targeted intervention when participants enter self-identified areas of high risk for relapse. Collectively, the proposed aims would (1) evaluate mobile technology applications for reducing opiate use, (2) understand the neurocognitive mechanisms of action to improve upon this and other apps aiming to reduce drug use, and (3) evaluate the role of personalized, contextually-relevant intervention to promote successful treatment outcomes.

• Study Type: Interventional
• Enrollment (Estimated): 336
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • Arkansas
    • Little Rock, Arkansas, United States, 72227
      • Brain Imaging Research Center

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• Sex: male or female
• Age: 18 years and older
• (MRI sub-study): Age: 18-50 years old
• In Phase I treatment of MAT for opioid-use disorder. (Phase I indicates that patient is receiving no more than one week of take-home medications at each weekly clinic visit.)
• Must be willing to use a smartphone if randomized to the smartphone intervention arm
• (MRI sub-study): Native English-speaking

Exclusion Criteria:

• (MRI) Medical history: A history of neurological, cardiovascular, or infectious disease would exclude study participation. A loss of consciousness of 20 or more min or other evidence of brain trauma also would be exclusionary.
• (MRI) Pregnancy: A positive test for pregnancy prior to fMRI would exclude participation, due to unknown effect of high-field MRI on developing fetus.
• (MRI) MRI contraindications: Exclusion criteria for MRI include (1) the presence of non-removable internal (e.g., cardiac pacemakers, aneurysm clips, artificial joints) or external (e.g., piercings, orthodontics) ferromagnetic objects; (2) claustrophobia in a confined MRI environment; (3) medications that interfere with hemodynamic coupling (e.g., beta blockers); (4) hypersensitivity to loud noise; or (5) a body circumference exceeding 60cm due to broad shoulders or morbid obesity

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Treatment
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Triple

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Smartphone; Participants randomized into the Smartphone app arm would use the smartphone app OptiMAT in conjunction with treatment as usual (TAU). Participants would use OptiMAT to complete daily self-assessments of opiate misuse, opiate craving, opiate withdrawal, and mood. The app will personalized feedback for maintaining abstinence goals. The app would also use geographic ecological momentary assessment (GEMA) to intervene via push notification when participants enter areas previously identified as high-risk for opiate use.	Device: Smartphone; Adjunctive Smartphone app for improving MAT outcomes; Other Names: OptiMAT, "Optimizing MAT"
No Intervention: Monitoring Only; Participants randomized into the Monitoring Only arm would undergo treatment as usual (TAU) but without the smartphone app.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Urinalysis - Week 0 (Intake)	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	1 day
Urinalysis - Week 1	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	1 week
Urinalysis - Week 2	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	2 weeks
Urinalysis - Week 3	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	3 weeks
Urinalysis - Week 4	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	4 weeks
Urinalysis - Week 5	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	5 weeks
Urinalysis - Week 6	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	6 weeks
Urinalysis - Week 7	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	7 weeks
Urinalysis - Week 8	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	8 weeks
Urinalysis - Week 9	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	9 weeks
Urinalysis - Week 10	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	10 weeks
Urinalysis - Week 11	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	11 weeks
Urinalysis - Week 12	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	12 weeks
Urinalysis - Week 13	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	13 weeks
Urinalysis - Week 14	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	14 weeks
Urinalysis - Week 15	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	15 weeks
Urinalysis - Week 16	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	16 weeks
Urinalysis - Week 17	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	17 weeks
Urinalysis - Week 18	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	18 weeks
Urinalysis - Week 19	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	19 weeks
Urinalysis - Week 20	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	20 weeks
Urinalysis - Week 21	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	21 weeks
Urinalysis - Week 22	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	22 weeks
Urinalysis - Week 23	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	23 weeks
Urinalysis - Week 24	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	24 weeks
Urinalysis - Week 25	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	25 weeks
Urinalysis - Week 26	Percent of weekly urinalysis tests positive for opioid metabolite other than Suboxone	26 weeks

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
TLFB - Month 0 (Intake)	Days per Month of self-reported opioid misuse from monthly TimeLine FollowBack calendar over past 30 days	1 day
TLFB - Month 1	Days per Month of self-reported opioid misuse from monthly TimeLine FollowBack calendar over past 30 days	1 month
TLFB - Month 2	Days per Month of self-reported opioid misuse from monthly TimeLine FollowBack calendar over past 30 days	2 months
TLFB - Month 3	Days per Month of self-reported opioid misuse from monthly TimeLine FollowBack calendar over past 30 days	3 months
TLFB - Month 4	Days per Month of self-reported opioid misuse from monthly TimeLine FollowBack calendar over past 30 days	4 months
TLFB - Month 5	Days per Month of self-reported opioid misuse from monthly TimeLine FollowBack calendar over past 30 days	5 months
TLFB - Month 6	Days per Month of self-reported opioid misuse from monthly TimeLine FollowBack calendar over past 30 days	6 months
Treatment Continuation - Week 1	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	1 week
Treatment Continuation - Week 2	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	2 weeks
Treatment Continuation - Week 3	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	3 weeks
Treatment Continuation - Week 4	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	4 weeks
Treatment Continuation - Week 5	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	5 weeks
Treatment Continuation - Week 6	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	6 weeks
Treatment Continuation - Week 7	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	7 weeks
Treatment Continuation - Week 8	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	8 weeks
Treatment Continuation - Week 9	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	9 weeks
Treatment Continuation - Week 10	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	10 weeks
Treatment Continuation - Week 11	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	11 weeks
Treatment Continuation - Week 12	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	12 weeks
Treatment Continuation - Week 13	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	13 weeks
Treatment Continuation - Week 14	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	14 weeks
Treatment Continuation - Week 15	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	15 weeks
Treatment Continuation - Week 16	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	16 weeks
Treatment Continuation - Week 17	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	17 weeks
Treatment Continuation - Week 18	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	18 weeks
Treatment Continuation - Week 19	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	19 weeks
Treatment Continuation - Week 20	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	20 weeks
Treatment Continuation - Week 21	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	21 weeks
Treatment Continuation - Week 22	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	22 weeks
Treatment Continuation - Week 23	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	23 weeks
Treatment Continuation - Week 24	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	24 weeks
Treatment Continuation - Week 25	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	25 weeks
Treatment Continuation - Week 26	Binary variable if participant is still in treatment (yes/no). Survival analysis will determine if duration of treatment (i.e. time to treatment discontinuation) differs between study arms	26 weeks

Collaborators and Investigators

• Sponsor: University of Arkansas
• Investigators: Principal Investigator: Andrew James, Ph.D., University of Arkansas

Publications and helpful links

General Publications

• Thompson RG Jr, Bollinger M, Mancino MJ, Hasin D, Han X, Bush KA, Kilts CD, James GA. Smartphone intervention to optimize medication-assisted treatment outcomes for opioid use disorder: study protocol for a randomized controlled trial. Trials. 2023 Apr 4;24(1):255. doi: 10.1186/s13063-023-07213-3.
• Thompson RG Jr, Bollinger M, Mancino M, Hasin D, Han X, Bush KA, Kilts CD, James GA. Smartphone intervention to optimize medication assisted treatment outcomes for opioid use disorder: study protocol for a randomized controlled trial. Res Sq [Preprint]. 2023 Feb 15:rs.3.rs-2511936. doi: 10.21203/rs.3.rs-2511936/v1.
• Bollinger M, Thompson RG Jr, Mancino MJ, Hasin DS, James GA. Geospatial ecological momentary assessment (GEMA) for opioid use disorder: Protocol for a just-in-time adaptive intervention. J Subst Use Addict Treat. 2026 Mar 19;186:209946. doi: 10.1016/j.josat.2026.209946. Online ahead of print.

Study record dates

Study Major Dates

• Study Start (Actual): May 16, 2023
• Primary Completion (Estimated): September 30, 2027
• Study Completion (Estimated): September 30, 2028

Study Registration Dates

• First Submitted: March 21, 2022
• First Submitted That Met QC Criteria: April 12, 2022
• First Posted (Actual): April 20, 2022

Study Record Updates

• Last Update Posted (Actual): May 15, 2026
• Last Update Submitted That Met QC Criteria: May 12, 2026
• Last Verified: April 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Narcotic-Related Disorders; Mental Disorders; Substance-Related Disorders; Chemically-Induced Disorders; Opioid-Related Disorders
• Other Study ID Numbers: 274084

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: Persuant to NIH/NIDA policy for transparency and rigorous experimental design (NOT-MH-14-004, NOT-DA-14-007), all published data will be de-identified and made publicly available through clinical and neuroimaging repositories such as the ENIGMA Addiction Working Group, INDI, or OpenFMRI. To promote open science, data infrastructure will follow the HCP universal BIDS format.
• IPD Sharing Time Frame: For each publication, relevant data and code will be shared at time of publication. Data and code will be available indefinitely.
• IPD Sharing Access Criteria: Data and code will be shared to open science data repositories as described above. Data will be de-identified so that anyone may access it.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; ICF; ANALYTIC_CODE

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes
• product manufactured in and exported from the U.S.: No