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Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2021,9,9]]},"abstract":"<jats:p>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 (\u2248 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 R2 coefficient of 0.85.<\/jats:p>","DOI":"10.1145\/3478107","type":"journal-article","created":{"date-parts":[[2021,9,14]],"date-time":"2021-09-14T22:48:23Z","timestamp":1631659703000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":26,"title":["OpiTrack"],"prefix":"10.1145","volume":"5","author":[{"given":"Bhanu Teja","family":"Gullapalli","sequence":"first","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephanie","family":"Carreiro","sequence":"additional","affiliation":[{"name":"Division of Medical Toxicology, Department of Emergency Medicine, University of Massachusetts Medical School, Worcester, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brittany P.","family":"Chapman","sequence":"additional","affiliation":[{"name":"Division of Medical Toxicology, Department of Emergency Medicine, University of Massachusetts Medical School, Worcester, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deepak","family":"Ganesan","sequence":"additional","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan","family":"Sjoquist","sequence":"additional","affiliation":[{"name":"University of Massachusetts Medical School, Worcester, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tauhidur","family":"Rahman","sequence":"additional","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,9,14]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2185677.2185741"},{"key":"e_1_2_1_2_1","volume-title":"Towards better understanding of gradient-based attribution methods for deep neural networks. arXiv preprint arXiv:1711.06104","author":"Ancona Marco","year":"2017","unstructured":"Marco Ancona , Enea Ceolini , Cengiz \u00d6ztireli , and Markus Gross . 2017. 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