Impact of individual and treatment characteristics on wearable sensor-based digital biomarkers of opioid use.
Impact of individual and treatment characteristics on wearable sensor-based digital biomarkers of opioid use.
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DOI：
10.1038/s41746-022-00664-z
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发表时间：
2022-08-22
影响因子：
15.2
通讯作者：
中科院分区：
文献类型：
--
作者：
阿片类药物使用障碍是我们这个时代最紧迫的公共卫生问题之一。包括可穿戴传感器在内的移动的健康工具在这一领域具有巨大潜力，但尚未得到充分利用。特别感兴趣的是数字生物标志物，或与健康或病理相关的最终用户生成的生理或行为测量。当前手稿描述了一项在接受阿片类镇痛药治疗急性疼痛疾病的成人患者中进行的纵向观察性研究。研究中的参与者使用腕戴式E4传感器进行监测，在此期间连续收集生理参数（心率/变异性、皮肤电活动、皮肤温度和加速度计）。阿片类药物使用事件通过电子病历和自我报告记录。在2070 h的传感器数据中，分析了36名参与者的339例离散剂量阿片类药物事件。从数据中提取了51个特征，并最初比较阿片类药物给药前后的特征，随后用于生成机器学习模型。基于个体和治疗特征比较模型性能。检测阿片类药物给药的最佳机器学习模型是使用来自可穿戴传感器的原始数据的时间注意力时间卷积网络（CTA-TCN）模型。静脉内药物使用史与更好的模型性能相关，而中年和非麻醉镇痛或镇静药物的联合给药与更差的模型性能相关。这些特征可以是用于未来阿片样物质检测模型迭代的候选输入特征。一旦成熟，这项技术可以为临床医生提供关于现实世界中阿片类药物使用模式的可操作数据，以及用于早期识别阿片类药物使用障碍风险的预测分析。
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.
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DOI：
10.3390/s20226478
发表时间：
2020-11-12
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Moltó IN;Albiach JP;Amer-Cuenca JJ;Segura-Ortí E;Gabriel W;Martínez-Gramage J
通讯作者：
Martínez-Gramage J
影响因子：
3.9
作者：
Karim, Fazle;Majumdar, Somshubra;Chen, Shun
通讯作者：
Chen, Shun
DOI：
10.15585/mmwr.mm7015a1
发表时间：
2021-04-16
期刊：
MMWR. Morbidity and mortality weekly report
影响因子：
--
作者：
Luo F;Li M;Florence C
通讯作者：
Florence C
影响因子：
4.7
作者：
Montag C;Elhai JD;Dagum P
通讯作者：
Dagum P
影响因子：
3.4
作者：
Afshari, Reza;Maxwell, Simon R. J.;Bateman, D. Nicholas
通讯作者：
Bateman, D. Nicholas