MySafeRx™: An Integrated Mobile Platform for Buprenorphine Adherence
Funding Source: NIDA - National Institute on Drug Abuse (R34DA040086)
Funding Period: 04/15/2016 – 03/31/2019
Principal Investigator: Zev Schuman-Olivier, MD
Other project staff: Jacob Borodovsky; Qays Munir; Jackson Steinkamp; Lisa Marsch, PhD; Hai Yi Xie, PhD; Mark McGovern, PhD;
Opioid use disorder represents a serious public health issue. Rates of overdose deaths have been rising among young adults. Additionally, acute Hepatitis C (HCV) infection rates are increasing fast among young adults, especially in rural areas. Buprenorphine/Naloxone (B/N) is a partial opioid mu-receptor agonist dosed daily that prevents opioid withdrawal, blocks opioid euphoria and can prevent opioid overdose. Extended treatment with B/N increases rates of abstinence and retention in outpatient treatment and decreases risk of transmitting HCV. Younger adults have difficulty stabilizing, achieving abstinence, and remaining active in office-based opioid treatment (OBOT) with B/N as compared to older adults. Retaining young adults in B/N treatment is important because dropout is linked with relapse and overdose death. Low levels of adherence to buprenorphine during treatment is associated with dropout from treatment, therefore an intervention that supports medication adherence among young adults during periods of instability during OBOT may offer a critical mechanism for improving treatment outcomes among young adults with opioid use disorder.
MySafeRx™ is a novel integrated mobile platform for medication adherence. This innovative solution has the potential to be a new gold standard for remote adherence monitoring and diversion prevention. The MySafeRx™ platform, which was designed specifically for use with controlled substances, such as B/N, during addiction and mental health treatment, focuses on simultaneously enhancing medication adherence, preventing medication diversion, and providing daily recovery coaching support during periods of clinical instability (e.g. patients with opioid use disorder receiving OBOT with B/N). MySafeRx™ uses a mobile application that provides the capacity for daily remote supervised self-administration of B/N by integrating confidential text messaging, daily videoconferencing with visual observation of medication taking, and secure, electronic medication dispensers (i.e., Medicasafe 3000 device). The MySafeRx™ remote check-in coaching encounter, provides relational recovery coaching and motivational enhancement, during a critical daily window around the time of medication taking. Finally, by providing regular reports to prescribers through a dedicated prescriber web interface, MySafeRx™ increases prescribers' awareness of the level of medication adherence and recovery stability.
This study aims to assess feasibility of a 6-week course of the MySafeRx™ integrated platform provided for a population of vulnerable young adults (<34 years old) with opioid use disorder, enrolling patients during B/N induction and established B/N patients with recent illicit opioid use. The study aims to evaluate both patient and prescriber levels of acceptability and usability, and assess treatment effects on evaluation of patient stability over time. Finally, this study also aims to evaluate initial clinical efficacy of MySafeRx™ compared to standard care based on weekly TLFB self-reports of B/N adherence and on percent negative weekly opioid urine toxicology (during 6 weeks of trial).
Targeting neural self-regulation with technology for overweight teens
Funding Source: Dartmouth SYNERGY Translational Program (Methodology and Technology Innovations for Translational Research Awards (MITRA))
Project Period: 7/1/16-6/30/17
Principal Investigator(s): Catherine Stanger, PhD; Multiple PI: Todd Heatherton, PhD, Psychology and Brain Sciences
Other Project Staff: Co-Investigators: James Sargent, MD, Professor, Pediatrics; William Kelley, PhD, Professor, Psychology and Brain Sciences; Emily Scherer, PhD, Assistant Professor, Biomedical Data Sciences.
This project proposes a novel integration of basic neural and clinical intervention science designed to test the impact of a self-regulation mechanism and intervention model on health outcomes for overweight teens. A growing literature supports the important role of self-regulation, i.e., the process of managing emotional, motivational and cognitive resources to align mental states and behavior with personal goals, in predicting health behavior and health outcomes among overweight teens and adults. In our model, self-regulatory success results from the interaction of two underlying neurobehavioral systems—cognitive control and reward. Strengths and weaknesses in these systems combine to facilitate or hinder the initiation of key health behaviors. A large literature supports the relations between specific neural networks involved in cognitive control and reward, their malleability, especially in adolescence, and their relevance to weight loss, exercise and other health behaviors including medical adherence. This pilot study will test two distinct mobile health intervention approaches that target these self-regulation systems (cognitive control vs. reward).
A multimethod approach to the measurement of self-regulation will be used. Self-regulation measures of cognitive control and reward will include: self-reports (trait self-control, food craving), event-related neural activity during laboratory tasks (delay discounting, go/no go, cue-reactivity), and fMRI resting state network measurement (default, frontoparietal, cingular opercular, and reward subnetworks). We will identify (sets of) behavioral and neural measures that best differentiate cognitive control and reward system phenotypes among overweight teens. This project takes a highly innovative approach, linking functional neuroimaging findings to real-world behavior, using cutting-edge resting state functional connectivity methodology to examine individual differences in malleable brain networks that represent desire strength and self-control capacity, and using advanced computational modeling to study these interacting systems.
We propose to randomly assign 30 overweight teens (BMI%ile >95) to receive one of two interventions: behavioral economic incentives targeting exercise and self-regulation training targeting inhibitory control (Go/No-Go Training; GNGT). BEI will involve delivery of incentives for improving the frequency of daily exercise (verified by fitbit uploads via the teen’s smartphone). GNGT will be delivered via a smartphone app that targets the cognitive capacity to respond to healthy foods while inhibiting responses to unhealthy foods. The effects of these distinct interventions on reward and cognitive control and their relation to health behavior (BMI and exercise outcomes) will be tested. Results from these studies will contribute important knowledge about how self-regulation impacts health behaviors, and how interventions can be targeted to specific self-regulation systems. They will also identify evidence based approaches to improve outcomes for overweight teens.
Public Health Relevance:
This project involves an innovative, interdisciplinary pilot study with clear potential for translation into patient-‐oriented care and for improving population health by targeting obesity, a major public health crisis. In particular, it represents an innovative blending of basic research on self-regulation, and intervention research for a clinical population in critical need.
Applying Machine Learning to Instagram Data to Identify Substance Use
Funding Source: Office of the Provost Seed Funding: Pilot
Funding Period: 2016 – 2017
Principal Investigator: Amar K. Das, MD, PhD
Other Project Staff: Benjamin S. Crosier, PhD; Lisa A. Marsch, PhD; Saeed Hassanpour, PhD
Substance use disorders (SUDs) affect 1 in 10 Americans and cost the country $700 billion annually. Only 10% of those with SUDs receive treatment. A major contributor to this low rate is inadequate screening of individuals who may be at risk because current screening methods struggle to reach a sufficiently large audience. Further, screening is infrequently performed within typical healthcare settings due to resource constraints and limited expertise. Additionally, SUDs are the most stigmatized health problem and are perceived more negatively than criminal history or HIV, resulting in millions not seeking treatment. This project addresses needs related to screening by developing an automated tool that can be tailored to screen a vast number of people with a single user click for behavioral health issues as they emerge as public health concerns, such as the nation’s current opioid epidemic. This tool can be used in research applications or as the foundation of an integrated e-health screening and treatment system
It is possible to avoid the shortcomings of traditional measures by designing fully automated screeners that utilize social media data to identify indicators of risk behavior. Social media data are used to predict everything from purchasing patterns to epidemics. Instagram, the most popular photo sharing application in the world, is uniquely suited to this challenge. This platform has 400 million active users (6% of the global population), 75 million of which use the service daily. Seventy-eight million of these users are in the United States, evenly split across gender (49% female). Most importantly, 90% of Instagram users are under 35. Many young social media users are leaving Facebook, as it is being increasingly adopted by older generations. This is crucial to substance abuse research as young adulthood is a critical developmental phase regarding the initiation of substance abuse. Therefore, Instagram offers the best fitting, largest, and most diverse population to target. Considering that only 10% of those with SUDs engage in treatment via traditional service delivery models, Instagram, when coupled with next generation screeners, provides a novel way to reach out to a segment of the remaining 90%. This is possible via the large-scale delivery of recruitment materials with advertisements inside of Instagram. Social media provides a novel and comprehensive solution of outreach and data collection.
This two phase project first distributes a traditional web-based SUD screener to a large, representative sample on Instagram. Profiles can then be associated with the responses to this screener with advanced data analytic strategies based in machine learning. Specifically, natural language processing (sentiment, content, and valence analysis of text data) and image analysis (classification of visual elements) can be used to create prognostic variables from the unstructured data presented by text and images. These variables can be combined with other information including post frequency and timing to predict substance use risk as captured by a standardized screening tool. This prediction is made with a classification algorithm also based in machine learning that automatically identifies the most predictive features as well as providing a concrete estimate of accuracy. Machine learning has been previously applied in a similar fashion, performing computer-aided screening and diagnosis with imaging (e.g., X-Ray) data (8). The present project adapts this cutting-edge approach to a new source of input data (Instagram) and a novel set of health issues (SUDs) within an ecosystem where delivery to millions is a tangible goal.
Public Health Relevence :
This project develops a scalable, automated, high fidelity, ultra low burden, and easily distributed screening tool that directly addresses the shortcomings of traditional screening procedures. Such a tool makes it possible to detect a vast population of those in need. The wide dissemination made possible by social media marketing platforms encourages unprecedented reach and the screener itself promotes increasingly accurate assessment.
Addiction Recovery Support on Social Media: Systematic Review and Social Media Big Data Analytics
Funding Source: National Institute on Drug Abuse – Center for Technology and Behavioral Health P30 Pilot Core
Project Period: 2016 – 2017
Principal Investigator: Sunny Jung Kim, PhD
Other Project Staff: Lisa A. Marsch, PhD; Alan J. Budney, PhD; Amarendra Das, MD, PhD; Alistair J. O’Malley, PhD; Andrea L. Meier, MS, LADC, LCMHC; David MacKinnon, PhD
Project Summary: Addiction recovery support is a prevalent social media phenomenon exponentially spreading through networks of social media users with substance use problems. Communication features afforded on social media (e.g., share, like, comment) can provide considerable social support for recovery and promote health motivation for people with substance use disorders (SUDs). User-generated social media content and network ties among people with SUDs offer unprecedented opportunities for observing the dynamics in recovery processes at scale in a naturalistic manner, and examining the effects of those communications on psychological and behavioral changes in recovery processes (e.g., relapses and withdrawal symptoms).
Throughout two phases of multidisciplinary studies, the team will examine four primary dimensions: 1) the characteristics of users engaged in recovery support communications, 2) the characteristics of recovery support communications, 3) the social-psychological predictors/mechanisms of recovery support communications, and 4) the recovery outcomes of social media-based recovery support. A systematic review (Phase I) as well as a social media Big Data and survey-based typology (Phase II) with these four core dimensions will provide a novel, scientific foundation for harnessing social media as an observational tool for recovery processes and/or as a platform to offer recovery support for SUDs.
Prior to Phase II, the team will also conduct a series of surveys and experiments to address ethical dilemmas and challenges in Big Data-driven social media research. The proposed research activities and the findings from this study will provide theoretical and empirical evidence in generating evidence-based research protocols, consent forms, and guidelines applicable to social media-based big data research in a sensitive domain such as drug addiction.
Bethany McLeman, BA
Research Project Manager, Clinical Trials Network Northeastern Node, Center for Technology and Behavioral Health
Communications; Substance Use Disorder; Opioid Epidemic; Health Technology
Bethany is engaged in research administration and program management with the Northeast Node of the Clinical Trials Network and Center for Technology and Behavioral Health. Bethany graduated Cum Laude with a BA in English from Keene State College. Before joining the CTN, Bethany managed multiple concurrent learning collaborates and federally-funded studies in addictions research. Bethany’s professional experiences include grant development and management, technology, administration, database design and maintenance, and communication coordination of large networks of researchers. Bethany was born in New Hampshire and is dedicated to helping improve primary care treatment to better impact people with substance use disorders, their families, and their communities. Bethany enjoys spending time outside with her family, both two- and four-legged members, and is always ready to go to the beach.
Meier, A., Lambert-Harris, C., McGovern, M. P., Xie, H., An, M., & McLeman, B. (2014). Co-occurring prescription opioid use problems and posttraumatic stress disorder symptom severity. American Journal of Drug and Alcohol Abuse, 40(4), 304-311. doi: 10.3109/00952990.2014.910519
Meier, A., McGovern, M. P., Lambert-Harris, C., McLeman, B., Franklin, A., Saunders, E. C., Xie, H. (2015). Adherence and competence in two manual-guided therapies for co-occurring substance use and posttraumatic stress disorders: Clinician factors and patient outcomes. American Journal of Drug Alcohol and Abuse, 41(6), 527-534. doi: 0.3109/00952990.2015.1062894.
Saunders, E. C., McLeman, B. M., McGovern, M. P., Xie, H., Lambert-Harris, C., & Meier, A. (2015). The influence of family and social problems on treatment outcomes of persons with co-occurring substance use disorders and PTSD. Journal of Substance Use. doi: 10.3109/14659891.2015.1005184.
McGovern, M. P., Lambert-Harris, C., Xie, H., Meier, A., McLeman, B., & Saunders, E. C. (2015). A randomized controlled trial of treatments for co-occurring substance use disorders and post-traumatic stress disorder. Addiction, 110(7), 1194-204. doi: 10.1111/add.12943.
Saunders, E. C., McGovern, M. P., Xie, H., Lambert-Harris, C., Meier, A., & McLeman, B. M. (2015). The impact of addiction medications on treatment outcomes for persons with co-occurring PTSD and opioid use disorders. American Journal on Addictions. Advance online publication. doi: 10.1111/ajad.12292
Nordstrom, B., Saunders, E. C., McLeman, B., Meier, A., Lambert-Harris, C., Tanzman, B., et al. (2016). Using a regional learning collaborative strategy to implement office-based opioid treatment. Journal of Addiction Medicine. In press.
McGovern, M. P., Lambert-Harris, C., McLeman, B., Meier, A., & Saunders, E. C. (Under Review). The Consolidated Framework for Implementation Research: Operationalizing a model of barriers and facilitators. Journal of Behavioral Health Services and Research.
McGovern, M. P., Lambert-Harris, C., Saunders, E. C., Meier, A., & McLeman, B. (Under Review). Operationalizing a model of barriers and facilitators to implementing evidence-based practices: The Consolidated Framework for Implementation Research (CFIR) Index. Journal of Behavioral Health and Services Research.
McGrath, A. C., Presseau, C., Crozier, M., McLeman, B., McGovern, M. P., & Capone, C. (Under Review). Co-occurring PTSD and substance use disorders: Differences in patients presenting across Veteran and civilian treatment settings. Substance Use and Misuse.
Meier, A., McGovern, M. P., Lambert-Harris, C., McLeman, B., & Saunders, E. C. (Under Review). A pilot trial of two models of clinical supervision for Integrated Cognitive Behavioral Therapy with co-occurring PTSD and substance use disorders. Drug and Alcohol Dependence.
Meier, A., McGovern, M. P., McLeman, B., Lambert-Harris, C., & Saunders, E. C. (Under Review). Using the Evidence Based Practices Attitude Scale to predict clinician implementation of evidence-based therapies. Implementation Science.