Identifying Opioid Withdrawal Using Wearable Biosensors

Wearable biosensors can be used to monitor opioid use, a problem of dire societal consequence given the current opioid epidemic in the US. Such surveillance can prompt interventions that promote behavioral change. Prior work has focused on the use of wearable biosensor data to detect opioid use. In this work, we present a method that uses machine learning to identify opioid withdrawal using data collected with a wearable biosensor. Our method involves developing a set of machine-learning classifiers, and then evaluating those classifiers using unseen test data. An analysis of the best performing model (based on the Random Forest algorithm) produced a receiver operating characteristic (ROC) area under the curve (AUC) of 0.9997 using completely unseen test data. Further, the model is able to detect withdrawal with just one minute of biosensor data. These results show the viability of using machine learning for opioid withdrawal detection. To our knowledge, the proposed method for identifying opioid withdrawal in OUD patients is the first of its kind

Character Strengths are Superpowers: Using Positive Psychology to Help Children Realize Their Potential

Big Brothers Big Sisters (BBBS) is a national non-profit organization that facilitates one-on-one mentoring between at-risk youth and adults in order to build developmental assets and promote the well-being of youth. The organization utilizes mentoring to facilitate strong, caring relationships that help marginalized youth realize their potential. The Great Lakes Bay Chapter of BBBS (Midland, Michigan) seeks to expand positive psychology resources and curriculum to: (a) bring together staff, mentors, mentees, and parents with a common language; (b) build and support strong relationships; and (c) enhance well-being in youth. This project provides a character strengths curriculum to support these goals. The project includes a train-the-trainer model and implementation recommendations to pilot the curriculum at the Great Lakes Bay chapter. If impactful, BBBS can adapt and scale the curriculum for other programs. To meet the diverse needs of the mentees, a research-informed Positive Psychology Playbook is included. The playbook is an expertise kit covering eight topics relevant to the development of at-risk youth. These topics, which include gratitude, grit, growth mindset, optimism, resilience, self- efficacy, character strengths and positive relationships, further equip the Great Lakes Bay Chapter with positive psychology knowledge, tips, and activities to support the development and well-being of marginalized youth

Perceptions on the Opioid Epidemic: A Qualitative Study

Background: Opioid use disorder (OUD) and resultant opioid overdoses have amplified over the last 20 years, despite efforts to identify best practices for treatment. Little research has focused on the disconnect between individuals with OUD and their healthcare providers. Hypothesis: We hypothesize that discrepancies exist between individuals with OUD and their healthcare providers with respect to perceptions of and experiences with medical care, the opioid antidote naloxone, and current treatment paradigms. Highlighting these discrepancies will inform future healthcare models. Methods: Using electronic surveys and semi-structured interviews, we will collect qualitative data from both individuals with OUD and emergency providers to assess knowledge, attitudes, and perceptions towards OUD, and to identify perceived barriers and facilitators to OUD treatment. A sampling methodology geared toward hidden populations, respondent driven sampling (RDS), will be used to recruit individuals with OUD. The initial participants will be enrolled from the emergency department (seeds) and will be asked to recruit three individuals in their social network from the community (waves). Results: Recruitment to date has focused on individuals with OUD: we have enrolled six seeds. Recruitment of additional waves by the seeds has been a challenge; there has only been one response out of a total of fifteen possible referrals. Community Engagement: We seek to enhance our emergency department-based seed recruitment strategy by expanding into the Worcester community. We are specifically looking to partner with community based-harm reduction agencies and other groups that engage individuals with active or past OUD

Objective Measurement of Physician Stress in the Emergency Department Using a Wearable Sensor

Physician stress, and resultant consequences such as burnout, have become increasingly recognized pervasive problems, particularly within the specialty of Emergency Medicine. Stress is difficult to measure objectively, and research predominantly relies on self-reported measures. The present study aims to characterize digital biomarkers of stress as detected by a wearable sensor among Emergency Medicine physicians. Physiologic data was continuously collected using a wearable sensor during clinical work in the emergency department, and participants were asked to self-identify episodes of stress. Machine learning algorithms were used to classify self-reported episodes of stress. Comparing baseline sensor data to data in the 20-minute period preceding self-reported stress episodes demonstrated the highest prediction accuracy for stress. With further study, detection of stress via wearable sensors could be used to facilitate evidence-based stress research and just-in-time interventions for emergency physicians and other high-stress professionals

Towards Device Agnostic Detection of Stress and Craving in Patients with Substance Use Disorder

Novel technologies have great potential to improve the treatment of individuals with substance use disorder (SUD) and to reduce the current high rate of relapse (i.e. return to drug use). Wearable sensor-based systems that continuously measure physiology can provide information about behavior and opportunities for real-time interventions. We have previously developed an mHealth system which includes a wearable sensor, a mobile phone app, and a cloud-based server with embedded machine learning algorithms which detect stress and craving. The system functions as a just-in-time intervention tool to help patients de-escalate and as a tool for clinicians to tailor treatment based on stress and craving patterns observed. However, in our pilot work we found that to deploy the system to diverse socioeconomic populations and to increase usability, the system must be able to work efficiently with cost-effective and popular commercial wearable devices. To make the system device agnostic, methods to transform the data from a commercially available wearable for use in algorithms developed from research grade wearable sensor are proposed. The accuracy of these transformations in detecting stress and craving in individuals with SUD is further explored

Realize, Analyze, Engage (RAE): A Digital Tool to Support Recovery from Substance Use Disorder

Background: Substance use disorders are a highly prevalent group of chronic diseases with devastating individual and public health consequences. Current treatment strategies suffer from high rates of relapse, or return to drug use, and novel solutions are desperately needed. Realize Analyze Engage (RAE) is a digital, mHealth intervention that focusses on real time, objective detection of high-risk events (stress and drug craving) to deploy just-in-time supportive interventions. The present study aims to (1) evaluate the accuracy and usability of the RAE system and (2) evaluate the impact of RAE on patient centered outcomes. Methods: The first phase of the study will be an observational trial of N = 50 participants in outpatient treatment for SUD using the RAE system for 30 days. Accuracy of craving and stress detection algorithms will be evaluated, and usability of RAE will be explored via semi-structured interviews with participants and focus groups with SUD treatment clinicians. The second phase of the study will be a randomized controlled trial of RAE vs usual care to evaluate rates of return to use, retention in treatment, and quality of life. Anticipated findings and future directions: The RAE platform is a potentially powerful tool to de-escalate stress and craving outside of the clinical milieu, and to connect with a support system needed most. RAE also aims to provide clinicians with actionable insight to understand patients\u27 level of risk, and contextual clues for their triggers in order to provide more personalized recovery support