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

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

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.

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.

Opioid-Related Disorders, Mobile Applications, Opiate Substitution Treatment, Magnetic Resonance Imaging, Craving, Attentional Bias, Ecological Momentary Assessment

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).

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.

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.

Participants randomized into the Monitoring Only arm would undergo treatment as usual (TAU) but without the smartphone app.

Days per Month of self-reported opioid misuse from monthly TimeLine FollowBack calendar over past 30 days

Days per Month of self-reported opioid misuse from monthly TimeLine FollowBack calendar over past 30 days

Days per Month of self-reported opioid misuse from monthly TimeLine FollowBack calendar over past 30 days

Days per Month of self-reported opioid misuse from monthly TimeLine FollowBack calendar over past 30 days

Days per Month of self-reported opioid misuse from monthly TimeLine FollowBack calendar over past 30 days

Days per Month of self-reported opioid misuse from monthly TimeLine FollowBack calendar over past 30 days

Days per Month of self-reported opioid misuse from monthly TimeLine FollowBack calendar over past 30 days

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.