Gullapalli Bhanu Teja, Carreiro Stephanie, Chapman Brittany P, Ganesan Deepak, Sjoquist Jan, Rahman Tauhidur
University of Massachusetts Amherst, USA.
Division of Medical Toxicology, Department of Emergency Medicine University of Massachusetts Medical School, USA.
Proc ACM Interact Mob Wearable Ubiquitous Technol. 2021 Sep;5(3). doi: 10.1145/3478107. Epub 2021 Sep 14.
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 coefficient of 0.85.
阿片类药物使用障碍是一种具有重大社会和经济后果的医学病症。虽然无处不在的生理传感技术已被广泛采用并大量用于监测日常活动以及提供有针对性的干预措施以改善人类健康，但利用这些技术在自然环境中检测药物使用情况在很大程度上仍未得到充分探索。我们工作的长期目标是开发一种移动技术系统，该系统能够识别与阿片类药物相关的高风险事件（即处方阿片类药物使用过程中耐受性的发展、阿片类药物使用障碍背景下的复吸事件），并及时部署干预措施以降低过量用药导致发病和死亡的风险。在当前论文中，我们通过提出一个关键问题迈出了第一步：能否使用从腕戴式传感器获取的生理信号来检测阿片类药物的使用情况？招募了36名因急性疼痛病症入院并在临床护理中接受阿片类镇痛药物治疗的个体。在此期间（1 - 14天），受试者佩戴一个非侵入性腕部传感器，该传感器持续测量生理信号（心率、皮肤温度、加速度、皮肤电活动和心跳间期）。我们总共收集了2070小时（约86天）的生理数据，并观察到总共339次阿片类药物给药。我们的结果令人鼓舞，表明使用通道 - 时间注意力时间卷积网络（CTA - TCN）模型，我们能够在一个时间窗口内检测到阿片类药物给药，F1分数为0.80，特异性为0.77，灵敏度为0.80，曲线下面积为0.77。我们还在这个时间窗口内预测给药的确切时刻，归一化平均绝对误差为8.6%，相关系数为0.85。
