ArticleProceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies2021
OpiTrack: A Wearable-based Clinical Opioid Use Tracker with Temporal Convolutional Attention Networks.
Article in Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploring the Applications of Explainability in Wearable Data Analytics: Systematic Literature Review.Journal of medical Internet research · 2024Pooled it
- Predicting Craving-Related Emotions among Opioid Use Disorder Patients: Preliminary Results.... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks · 2025Article
- Opioid misuse detection from cognitive and physiological data with temporal fusion deep learning.Drug and alcohol dependence · 2025Article
- Leveraging Trauma Informed Care for Digital Health Intervention Development in Opioid Use Disorder.Journal of medical toxicology : official journal of the American College of Medical Toxicology · 2025Review
- Monitoring Substance Use with Fitbit Biosignals: A Case Study on Training Deep Learning Models Using Ecological Momentary Assessments and Passive Sensing.AI (Basel, Switzerland) · 2024Article
- Exploring the Potential of a Smart Ring to Predict Postoperative Pain Outcomes in Orthopedic Surgery Patients.Sensors (Basel, Switzerland) · 2024Article
- Users' Acceptability and Perceived Efficacy of mHealth for Opioid Use Disorder: Scoping Review.JMIR mHealth and uHealth · 2024Article
- Smartphone and Wearable Device-Based Digital Phenotyping to Understand Substance use and its Syndemics.Journal of medical toxicology : official journal of the American College of Medical Toxicology · 2024Article
- Pharmacokinetics-Informed Neural Network for Predicting Opioid Administration Moments with Wearable Sensors.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2024Article
- Opioid Overdose Detection in a Murine Model Using a Custom-Designed Photoplethysmography Device.Ingenierie et recherche biomedicale : IRBM = Biomedical engineering and research · 2023Article
- Digital Biomarker Applications Across the Spectrum of Opioid Use Disorder.Cogent mental health · 2023Article
- Explainable AI for clinical and remote health applications: a survey on tabular and time series data.Artificial intelligence review · 2023Article
- Impact of individual and treatment characteristics on wearable sensor-based digital biomarkers of opioid use.NPJ digital medicine · 2022Article
- Perceptions on wearable sensor-based interventions for monitoring of opioid therapy: A qualitative study.Frontiers in digital health · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Opioid use disorder is a medical condition with major social and economic consequences. While ubiquitous physiological sensing technologies have been widely adopted and extensively used to monitor day-to-day activities and deliver targeted interventions to improve human health, the use of these technologies to detect drug use in natural environments has been largely underexplored. The long-term goal of our work is to develop a mobile technology system that can identify high-risk opioid-related events (i.e., development of tolerance in the setting of prescription opioid use, return-to-use events in the setting of opioid use disorder) and deploy just-in-time interventions to mitigate the risk of overdose morbidity and mortality. In the current paper, we take an initial step by asking a crucial question: Can opioid use be detected using physiological signals obtained from a wrist-mounted sensor? Thirty-six individuals who were admitted to the hospital for an acute painful condition and received opioid analgesics as part of their clinical care were enrolled. Subjects wore a noninvasive wrist sensor during this time (1-14 days) that continuously measured physiological signals (heart rate, skin temperature, accelerometry, electrodermal activity, and interbeat interval). We collected a total of 2070 hours (≈ 86 days) of physiological data and observed a total of 339 opioid administrations. Our results are encouraging and show that using a Channel-Temporal Attention TCN (CTA-TCN) model, we can detect an opioid administration in a time-window with an F1-score of 0.80, a specificity of 0.77, sensitivity of 0.80, and an AUC of 0.77. We also predict the exact moment of administration in this time-window with a normalized mean absolute error of 8.6% and
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.