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

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 selfreported 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 highstress professionals.

Job Demands and Resources among Healthcare Professionals during Virus Pandemics: A Review and Examination of Fluctuations in Mental Health Strain during COVID‐19

“…From a future research perspective, continued refinement and testing of appropriate measures is a critical future step for this area of research, and may benefit from increased inclusion of less obtrusive, more objective measures. For example, researchers have examined the potential use of psychophysiological responses as a measure of physicians’ cognitive demands while on the job (Johannessen, Szulewski, Radulovic, White, Braund, Howes, Rodenburg, & Davies, 2020), while others are developing methods for objectively capturing physician stress using wearable sensors (Kaczor, Carreiro, Stapp, Chapman, & Indic, 2020). These types of metrics may be very useful for dynamically monitoring healthcare professionals’ physical demands and workload during virus epidemics and pandemics.…”

Pain and Stress Detection Using Wearable Sensors and Devices—A Review

“…As a medical-grade wearable device, it enables researchers to collect multiple physiological data such as BVP for HRV analysis, and EDA that reflects the constantly fluctuating electrical properties of a certain area of skin and peripheral skin temperature. Besides, it also captures motion activity with a 3-axis accelerometer [ 80 , 81 , 82 , 83 ].…”

“… Notes: Empatica E4 wrist band is used in [ 80 , 81 , 82 , 83 ]; AutoSense is used in [ 84 , 85 , 86 ]; SleepSense is used in [ 87 , 88 ]; BN-PPGED is used in [ 89 ]; Cardiosport TP3 is used in [ 90 ]; Q-sensor is used in [ 70 ]; Wahoo chest belt is used in [ 91 ]; BioHarness 3, Shimmer sensor, and MindWave mobile EEG headset are being used as an integrated system for stress monitoring in [ 92 ]; DataLOG is used in [ 93 ]; Device 1 is a EEG wearable sensor developed in Online Predictive Tools for Intervention in Mental Illness (PTIMI) project funded by European Union [ 94 ]; Device 2 is a noninvasive physiological sensor for stress assessment presented in [ 95 ]; Device 3 is used in [ 96 ] which they collect the EMG signals of the left trapezius muscle and then remove the contained ECG signal components. …”

Making Wearable Technology Available for Mental Healthcare through an Online Platform with Stress Detection Algorithms: The Carewear Project

“…Most studies in the literature that try to determine stress levels use a combination of several parameters extracted from one or more physiological signals. Features extracted from EDA are used most often [5,6,8,15,22,23,[41][42][43][44][45][46][47], closely followed by features extracted from the cardiac cycle [5, 6, 15, 16, 21-23, 41, 42, 44, 46, 47]. These last studies mainly use an ECG monitor in different forms to extract the signal [5, 6, 15, 16, 21-23, 41, 42].…”