Kaczor Eric E, Carreiro Stephanie, Stapp Joshua, Chapman Brittany, Indic Premananda
Department of Emergency Medicine, Division of Medical Toxicology, University of Massachusetts, Medical School, Worcester, MA.
Department of Emergency Medicine, Division of Medical Toxicology, University of Massachusetts Medical, School, Worcester, MA.
Proc Annu Hawaii Int Conf Syst Sci. 2020;2020:3729-3738. Epub 2020 Jan 7.
Physician stress, and resultant consequences such as burnout, have become increasingly recognized pervasive problems, particularly within the specialty of Emergency Medicine. Stress is difficult to measure objectively, and research predominantly relies on self-reported measures. The present study aims to characterize digital biomarkers of stress as detected by a wearable sensor among Emergency Medicine physicians. Physiologic data were continuously collected using a wearable sensor during clinical work in the emergency department, and participants were asked to self-identify episodes of stress. Machine learning algorithms were used to classify self-reported episodes of stress. Comparing baseline sensor data to data in the 20-minute period preceding self-reported stress episodes demonstrated the highest prediction accuracy for stress. With further study, detection of stress via wearable sensors could be used to facilitate evidence-based stress research and just-in-time interventions for emergency physicians and other high-stress professionals.
医生的压力以及诸如职业倦怠等由此产生的后果,已日益被视为普遍存在的问题,尤其是在急诊医学专业领域。压力难以客观衡量,研究主要依赖自我报告的测量方法。本研究旨在描述急诊医学医生中可穿戴传感器检测到的压力数字生物标志物。在急诊科临床工作期间,使用可穿戴传感器持续收集生理数据,并要求参与者自行识别压力事件。机器学习算法用于对自我报告的压力事件进行分类。将基线传感器数据与自我报告的压力事件前20分钟的数据进行比较,显示出对压力的最高预测准确性。随着进一步研究,通过可穿戴传感器检测压力可用于促进基于证据的压力研究,并为急诊医生和其他高压力职业提供即时干预。