背景:职业倦怠的流行是一个日益严重的问题,而在美国,高达60%的医学生,居民,医师,注册护士会出现症状.可穿戴技术可以提供使用生理标记来预测倦怠和其他形式的痛苦的开始的机会。
目的:本研究旨在确定倦怠的生理生物标志物,并确定当前在使用可穿戴技术进行医疗保健专业人员(HCP)的倦怠预测方面存在哪些差距。
方法:于2022年6月7日对多个数据库进行了全面搜索。没有为搜索设置日期限制。数据库是Ovid:MEDLINE(R),Embase,健康之星,APAPsycInfo,Cochrane中央控制试验登记册,Cochrane系统评价数据库,通过ClarivateAnalytics的WebofScience核心合集,Scopus通过Elsevier,EBSCOhost:学术搜索总理,CINAHL与全文,和业务来源总理。观察焦虑的研究,倦怠,压力,包括使用HCP佩戴的可穿戴设备的抑郁症,HCP被定义为医学生,居民,医师,和护士。使用纽卡斯尔渥太华队列研究质量评估表评估偏差。
结果:最初的搜索产生了505篇论文,其中10项(1.95%)研究纳入本综述.大多数(n=9)使用腕部生物传感器,并描述了观察性队列研究(n=8),偏见的风险很低。虽然没有生理指标可靠地与倦怠或焦虑相关,步数和卧床时间与抑郁症状有关,心率和心率变异性与急性应激有关。研究仅限于长期观察(例如,≥12个月)和大样本量,可穿戴数据与系统级信息的集成有限(例如,敏锐度)来预测倦怠。报告标准也不够,特别是在用于生理测量的设备依从性和采样频率方面。
结论:随着可穿戴设备为人类功能的数字健康评估提供了希望,可以将可穿戴设备视为预测倦怠的前沿。未来的数字健康研究探索可穿戴技术在倦怠预测中的实用性,应解决数据标准化和策略的局限性,以提高研究参与的依从性和包容性。
BACKGROUND: The occupational burnout epidemic is a growing issue, and in the United States, up to 60% of medical students, residents, physicians, and registered nurses experience symptoms. Wearable technologies may provide an opportunity to predict the onset of burnout and other forms of distress using physiological markers.
OBJECTIVE: This study aims to identify physiological biomarkers of burnout, and establish what gaps are currently present in the use of wearable technologies for burnout prediction among health care professionals (HCPs).
METHODS: A comprehensive search of several databases was performed on June 7, 2022. No date limits were set for the search. The databases were Ovid: MEDLINE(R), Embase, Healthstar, APA PsycInfo, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Web of Science Core Collection via Clarivate Analytics, Scopus via Elsevier, EBSCOhost: Academic Search Premier, CINAHL with Full Text, and Business Source Premier. Studies observing anxiety, burnout, stress, and depression using a wearable device worn by an HCP were included, with HCP defined as medical students, residents, physicians, and nurses. Bias was assessed using the Newcastle Ottawa Quality Assessment Form for Cohort Studies.
RESULTS: The initial search yielded 505 papers, from which 10 (1.95%) studies were included in this review. The majority (n=9) used wrist-worn biosensors and described observational cohort studies (n=8), with a low risk of bias. While no physiological measures were reliably associated with burnout or anxiety, step count and time in bed were associated with depressive symptoms, and heart rate and heart rate variability were associated with acute stress. Studies were limited with long-term observations (eg, ≥12 months) and large sample sizes, with limited integration of wearable data with system-level information (eg, acuity) to predict burnout. Reporting standards were also insufficient, particularly in device adherence and sampling frequency used for physiological measurements.
CONCLUSIONS: With wearables offering promise for digital health assessments of human functioning, it is possible to see wearables as a frontier for predicting burnout. Future digital health studies exploring the utility of wearable technologies for burnout prediction should address the limitations of data standardization and strategies to improve adherence and inclusivity in study participation.