Developing a Wearable Sensor-Based Digital Biomarker of Opioid Dependence.

Background: Repeated opioid exposure leads to a variety of physiologic adaptations that develop at different rates and may foreshadow risk of opioid-use disorder (OUD), including dependence and withdrawal. Digital pharmacovigilance strategies that use noninvasive sensors to identify physiologic adaptations to opioid use represent a novel strategy to facilitate safer opioid prescribing. This study aims to identify wearable sensor-derived features associated with opioid dependence by comparing opioid-naïve individuals to chronic opioid users with acute pain and developing a machine-learning model to distinguish between the 2 groups.

Evaluation of a digital tool for detecting stress and craving in SUD recovery: An observational trial of accuracy and engagement.

Background: Digital health interventions offer opportunities to expand access to substance use disorder (SUD) treatment, collect objective real-time data, and deliver just-in-time interventions: however implementation has been limited. RAE (Realize, Analyze, Engage) Health is a digital tool which uses continuous physiologic data to detect high risk behavioral states (stress and craving) during SUD recovery.

Methods: This was an observational study to evaluate the digital stress and craving detection during outpatient SUD treatment.

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).

Standing strong amid a pandemic: How a global online team project stands up to the public health crisis.

The annual instructional virtual team Project X brings together professors and students from across the globe to engage in client projects. The 2020 project was challenged by the global disruption of the COVID-19 pandemic. This paper draws on a quantitative dataset from a post-project survey among 500 participating students and a qualitative narrative inquiry of personal experiences of the faculty members.

Using wearable technology to detect prescription opioid self-administration.

Appropriate monitoring of opioid use in patients with pain conditions is paramount, yet it remains a very challenging task. The current work examined the use of a wearable sensor to detect self-administration of opioids after dental surgery using machine learning. Participants were recruited from an oral and maxillofacial surgery clinic.