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.
- Award ID(s):
- 2124282
- NSF-PAR ID:
- 10389032
- Date Published:
- Journal Name:
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Volume:
- 5
- Issue:
- 3
- ISSN:
- 2474-9567
- Page Range / eLocation ID:
- 1 to 29
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Purpose To evaluate how technology access affected substance use disorder (SUD) treatment prior to COVID‐19 for people who use drugs in rural areas.
Methods The Rural Opioid Initiative (January 2018‐March 2020) was a cross‐sectional study of people with prior 30‐day injection drug or nonprescribed opioid use from rural areas of 10 states. Using multivariable mixed‐effect regression models, we examined associations between participant technology access and SUD treatment.
Findings Of 3,026 participants, 71% used heroin and 76% used methamphetamine. Thirty‐five percent had no cell phone and 10% had no prior 30‐day internet use. Having both a cell phone and the internet was associated with increased days of medication for opioid use disorder (MOUD) use (aIRR 1.29 [95% CI 1.11‐1.52]) and a higher likelihood of SUD counseling in the prior 30 days (aOR 1.28 [95% CI 1.05‐1.57]). Lack of cell phone was associated with decreased days of MOUD (aIRR 0.77 [95% CI 0.66‐0.91]) and a lower likelihood of prior 30‐day SUD counseling (aOR 0.77 [95% CI 0.62‐0.94]).
Conclusions Expanding US rural SUD treatment engagement via telemedicine may require increased cell phone and mobile network access.
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Abstract Objective The factors that influence seizure timing are poorly understood, and seizure unpredictability remains a major cause of disability. Work in chronobiology has shown that cyclical physiological phenomena are ubiquitous, with daily and multiday cycles evident in immune, endocrine, metabolic, neurological, and cardiovascular function. Additionally, work with chronic brain recordings has identified that seizure risk is linked to daily and multiday cycles in brain activity. Here, we provide the first characterization of the relationships between the cyclical modulation of a diverse set of physiological signals, brain activity, and seizure timing.
Methods In this cohort study, 14 subjects underwent chronic ambulatory monitoring with a multimodal wrist‐worn sensor (recording heart rate, accelerometry, electrodermal activity, and temperature) and an implanted responsive neurostimulation system (recording interictal epileptiform abnormalities and electrographic seizures). Wavelet and filter–Hilbert spectral analyses characterized circadian and multiday cycles in brain and wearable recordings. Circular statistics assessed electrographic seizure timing and cycles in physiology.
Results Ten subjects met inclusion criteria. The mean recording duration was 232 days. Seven subjects had reliable electroencephalographic seizure detections (mean = 76 seizures). Multiday cycles were present in all wearable device signals across all subjects. Seizure timing was phase locked to multiday cycles in five (temperature), four (heart rate, phasic electrodermal activity), and three (accelerometry, heart rate variability, tonic electrodermal activity) subjects. Notably, after regression of behavioral covariates from heart rate, six of seven subjects had seizure phase locking to the residual heart rate signal.
Significance Seizure timing is associated with daily and multiday cycles in multiple physiological processes. Chronic multimodal wearable device recordings can situate rare paroxysmal events, like seizures, within a broader chronobiology context of the individual. Wearable devices may advance the understanding of factors that influence seizure risk and enable personalized time‐varying approaches to epilepsy care.
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null (Ed.)Background Increased work through electronic health record (EHR) messaging is frequently cited as a factor of physician burnout. However, studies to date have relied on anecdotal or self-reported measures, which limit the ability to match EHR use patterns with continuous stress patterns throughout the day. Objective The aim of this study is to collect EHR use and physiologic stress data through unobtrusive means that provide objective and continuous measures, cluster distinct patterns of EHR inbox work, identify physicians’ daily physiologic stress patterns, and evaluate the association between EHR inbox work patterns and physician physiologic stress. Methods Physicians were recruited from 5 medical centers. Participants (N=47) were given wrist-worn devices (Garmin Vivosmart 3) with heart rate sensors to wear for 7 days. The devices measured physiological stress throughout the day based on heart rate variability (HRV). Perceived stress was also measured with self-reports through experience sampling and a one-time survey. From the EHR system logs, the time attributed to different activities was quantified. By using a clustering algorithm, distinct inbox work patterns were identified and their associated stress measures were compared. The effects of EHR use on physician stress were examined using a generalized linear mixed effects model. Results Physicians spent an average of 1.08 hours doing EHR inbox work out of an average total EHR time of 3.5 hours. Patient messages accounted for most of the inbox work time (mean 37%, SD 11%). A total of 3 patterns of inbox work emerged: inbox work mostly outside work hours, inbox work mostly during work hours, and inbox work extending after hours that were mostly contiguous to work hours. Across these 3 groups, physiologic stress patterns showed 3 periods in which stress increased: in the first hour of work, early in the afternoon, and in the evening. Physicians in group 1 had the longest average stress duration during work hours (80 out of 243 min of valid HRV data; P=.02), as measured by physiological sensors. Inbox work duration, the rate of EHR window switching (moving from one screen to another), the proportion of inbox work done outside of work hours, inbox work batching, and the day of the week were each independently associated with daily stress duration (marginal R2=15%). Individual-level random effects were significant and explained most of the variation in stress (conditional R2=98%). Conclusions This study is among the first to demonstrate associations between electronic inbox work and physiological stress. We identified 3 potentially modifiable factors associated with stress: EHR window switching, inbox work duration, and inbox work outside work hours. Organizations seeking to reduce physician stress may consider system-based changes to reduce EHR window switching or inbox work duration or the incorporation of inbox management time into work hours.more » « less
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Abstract Background and Aims Despite the high prevalence of substance use among people in the US criminal justice system, little is known about the incidence of overdose mortality by use patterns, drug convictions and supervision setting. We examined the associations between these characteristics and overdose mortality.
Design Retrospective cohort study.
Setting and participants Individuals sentenced to prison, jail, probation or jail plus probation for a felony conviction in Michigan, USA from 2003 to 2006.
Measurements Using the National Death Index, we assessed overdose mortality to December 2012. We calculated overdose mortality rates by pre‐sentence opioid use, drug convictions and supervision setting. Multivariable analyses were conducted using competing risks regression with time‐varying covariates.
Findings Among 140 266 individuals followed over a mean of 7.84 years [standard deviation (SD) = 1.52], 14.9% of the 1131 deaths were due to overdose (102.8 per 100 000 person‐years). Over the follow‐up, more than half of overdose deaths occurred in the community (57.7%), nearly a third (28.8%) on probation and 12.8% on parole. The adjusted risk of overdose death was lower on probation [hazard ratio (HR) = 0.71, 95% confidence interval (CI) = 0.60, 0.85] than in the community without probation or parole (HR = 1.00) but not significantly different on parole (HR = 1.13, 95% CI = 0.87, 1.47). Pre‐sentence daily opioid use (HR = 3.54, 95% CI = 3.24, 3.87) was associated with an increased risk. Drug possession (HR = 1.11, 95% CI = 0.93, 1.31) and delivery convictions (HR = 0.92, 95% CI = 0.77, 1.09) were not significantly associated with overdose mortality.
Conclusions Based on the absolute or relative risk, parole, probation and community settings are appropriate settings for enhanced overdose prevention interventions. Ensuring that individuals with pre‐sentence opioid use have access to harm reduction and drug treatment services may help to prevent overdose among people involved with the criminal justice system.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3478107
