Impact of individual and treatment characteristics on wearable sensor-based digital biomarkers of opioid use

Abstract

Opioid use disorder is one of the most pressing public health problems of our time. Mobile health tools, including wearable sensors, have great potential in this space, but have been underutilized. Of specific interest are digital biomarkers, or end-user generated physiologic or behavioral measurements that correlate with health or pathology. The current manuscript describes a longitudinal, observational study of adult patients receiving opioid analgesics for acute painful conditions. Participants in the study are monitored with a wrist-worn E4 sensor, during which time physiologic parameters (heart rate/variability, electrodermal activity, skin temperature, and accelerometry) are collected continuously. Opioid use events are recorded via electronic medical record and self-report. Three-hundred thirty-nine discreet dose opioid events from 36 participant are analyzed among 2070 h of sensor data. Fifty-one features are extracted from the data and initially compared pre- and post-opioid administration, and subsequently are used to generate machine learning models. Model performance is compared based on individual and treatment characteristics. The best performing machine learning model to detect opioid administration is a Channel-Temporal Attention-Temporal Convolutional Network (CTA-TCN) model using raw data from the wearable sensor. History of intravenous drug use is associated with better model performance, while middle age, and co-administration of non-narcotic analgesia or sedative drugs are associated with worse model performance. These characteristics may be candidate input features for future opioid detection model iterations. Once mature, this technology could provide clinicians with actionable data on opioid use patterns in real-world settings, and predictive analytics for early identification of opioid use disorder risk.

Serum cytokine and inflammatory markers in individuals with heroin use disorder: potential biomarkers for diagnosis and disease severity

“…The underlying mechanisms for these undesirable outcomes remain unclear, highlighting an urgent need for novel mechanistically-based treatments. There is also a pressing need for objective and quantitative biomarkers for HUD [6][7][8][9][10] . Due to their relative non-invasiveness and practicality, measurement of blood-based biomarkers have emerged as powerful approaches in the study of diverse neuropsychiatric disorders 11,12 .…”

Reducing opioid related deaths for individuals who are at high risk of death from overdose: a co-production study with people housed within prison and hostel accommodation during Covid-19

“…It is imperative that innovative interventions are sought, tested, and implemented to address the complex issue of drug-related deaths ( 12 ) and the use of technology has been identified as a means of supporting and replacing traditional interventions in various contexts ( 13 , 14 ). Wearable technology has great potential in the Harm Reduction space to detect overdose, particularly when people use alone, but has been underutilized ( 15 ). A growing body of literature demonstrates the potential of digital interventions, including wearable devices, for preventing opiate overdose ( 2 , 16–19 ).…”