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本文引用的文献

1
Assessment of Physician Well-being, Part One: Burnout and Other Negative States.医师健康评估,第一部分:倦怠和其他负面状态。
West J Emerg Med. 2019 Mar;20(2):278-290. doi: 10.5811/westjem.2019.1.39665. Epub 2019 Feb 28.
2
Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study.使用可穿戴传感器和手机识别自我报告的压力和心理健康状况的客观生理标志物及可改变行为:观察性研究
J Med Internet Res. 2018 Jun 8;20(6):e210. doi: 10.2196/jmir.9410.
3
Human emotion classification based on multiple physiological signals by wearable system.基于可穿戴系统的多生理信号的人类情感分类
Technol Health Care. 2018;26(S1):459-469. doi: 10.3233/THC-174747.
4
Coverage of Emotion Recognition for Common Wearable Biosensors.常见可穿戴生物传感器的情感识别覆盖。
Biosensors (Basel). 2018 Mar 24;8(2):30. doi: 10.3390/bios8020030.
5
Wearable Biosensors to Evaluate Recurrent Opioid Toxicity After Naloxone Administration: A Hilbert Transform Approach.用于评估纳洛酮给药后复发性阿片类药物毒性的可穿戴生物传感器:一种希尔伯特变换方法。
Proc Annu Hawaii Int Conf Syst Sci. 2018 Jan;2018:3247-3252. Epub 2018 Jan 3.
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Physician Burnout and Well-Being: A Systematic Review and Framework for Action.医生职业倦怠与幸福感:一项系统综述及行动框架
Dis Colon Rectum. 2017 Jun;60(6):567-576. doi: 10.1097/DCR.0000000000000844.
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Relationships of work-related psychosocial risks, stress, individual factors and burnout - Questionnaire survey among emergency physicians and nurses.工作相关心理社会风险、压力、个体因素与职业倦怠的关系——急诊医生和护士问卷调查
Med Pr. 2017 Mar 24;68(2):167-178. doi: 10.13075/mp.5893.00516. Epub 2017 Mar 13.
8
Controlled Interventions to Reduce Burnout in Physicians: A Systematic Review and Meta-analysis.控制干预措施以减少医生的倦怠感:系统评价和荟萃分析。
JAMA Intern Med. 2017 Feb 1;177(2):195-205. doi: 10.1001/jamainternmed.2016.7674.
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Interventions to prevent and reduce physician burnout: a systematic review and meta-analysis.干预措施预防和减少医生倦怠:系统评价和荟萃分析。
Lancet. 2016 Nov 5;388(10057):2272-2281. doi: 10.1016/S0140-6736(16)31279-X. Epub 2016 Sep 28.
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Physician Burnout: Its Origin, Symptoms, and Five Main Causes.医生职业倦怠:其根源、症状及五大主要成因
Fam Pract Manag. 2015 Sep-Oct;22(5):42-7.

使用可穿戴传感器客观测量急诊科医生的压力。

Objective Measurement of Physician Stress in the Emergency Department Using a Wearable Sensor.

作者信息

Kaczor Eric E, Carreiro Stephanie, Stapp Joshua, Chapman Brittany, Indic Premananda

机构信息

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.

出版信息

Proc Annu Hawaii Int Conf Syst Sci. 2020;2020:3729-3738. Epub 2020 Jan 7.

PMID:32015695
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6996921/
Abstract

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.

摘要

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。