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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Smartphone | Experimental | 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. |
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| Monitoring Only | No Intervention | Participants randomized into the Monitoring Only arm would undergo treatment as usual (TAU) but without the smartphone app. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Smartphone | Device | Adjunctive Smartphone app for improving MAT outcomes |
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| 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 |
| 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 |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Andrew James, Ph.D. | University of Arkansas | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Brain Imaging Research Center | Little Rock | Arkansas | 72227 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 41862055 | Background | 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 Jul;186:209946. doi: 10.1016/j.josat.2026.209946. Epub 2026 Mar 19. | |
| 37016394 | Result | 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. |
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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.
For each publication, relevant data and code will be shared at time of publication. Data and code will be available indefinitely.
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.
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Participants will be randomized to one of two arms: a Monitor Only arm (aka treatment-as-usual, MAT only) and a Smartphone arm (aka OptiMAT plus MAT).
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Primary outcome will be evaluated by co-I Dr. Thompson and staff biostatistician, who will remain blind to participant group membership. Since intervention involves daily use of a smartphone, participants will not be blind to group membership. Care providers all will be blind to participants' group membership. PI Dr. James and/or study staff will enroll, provide training in smartphone use, and troubleshoot technical issues, thus will not be blind to group membership.
| 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 |
| 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 |
| 36824884 | Derived | 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. |
| ID | Term |
|---|---|
| D009293 | Opioid-Related Disorders |
| ID | Term |
|---|---|
| D000079524 | Narcotic-Related Disorders |
| D019966 | Substance-Related Disorders |
| D064419 | Chemically-Induced Disorders |
| D001523 | Mental Disorders |
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