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.
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.