Search

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.

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 were 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.

Opioid abuse is a rapidly escalating problem in the United States. Effective opioid reversal is achieved with the antidote naloxone, but often does not last as long as the offending opioid, necessitating in-hospital observation. Continuous physiologic monitoring using wearable biosensors represents a potential option to extend monitoring capability outside the clinical setting across the spectrum of opioid abuse including post- naloxone administration. The present study aims to identify the physiologic change that marks the cessation of naloxone's effect. Eleven participants were recruited in the Emergency Department after naloxone administration for an opioid overdose and continuously monitored using a wearable biosensor measuring heart rate, temperature, electrodermal activity and accelerometry. Hilbert transform was used to evaluate a 90- minute post naloxone time point. Physiologic changes were consistent with the onset of opioid drug effect across parameters, but only changes in heart rate and skin temperature research statistical significance.