Key result
Real-time stress monitoring using physiological features and an XGBoost machine learning algorithm identified stress in ICU nurses with an accuracy of 0.88.
Why the study?
Quantification of stress in ICU nurses has been limited to subjective ratings, leaving a gap in continuous, real-time physiological measurement needed to assess the negative outcomes of stress.
Does a machine learning model based on continuous physiological measurements accurately identify real-time stress in ICU nurses?
Observational (n=23)
Does a machine learning model based on continuous physiological measurements accurately identify real-time stress in ICU nurses?
Machine learning models using continuous physiological measurements, particularly skin temperature, can accurately identify real-time stress in ICU nurses.
Answers are grounded in this paper and cite their sources.
May aid real-time stress management in ICU nurses; leaves open validation and outcome impact in larger studies.
The aim of this study is to explore real-time stress monitoring models (based on physiological features) for intensive care unit (ICU) nurses. The quantification of stress in ICU nurses has been limited to subjective ratings, with a general gap in continuous measurement; real-time stress monitoring based on continuous physiological measurement is needed to assess the negative outcome of stress. Electrodermal activity, eye tracking, accelerometer data, and skin temperatures were recorded continuously through 12-hour shifts for ICU nurses (23 participants). A machine learning algorithm was applied to identify stress over time based on physiological features. eXtreme Gradient Boosting (XGBoost) was performed with an accuracy of 0.88. Skin temperature contributed the most to real-time stress identification for monitoring. Future work should investigate the efficacy of using skin temperature for stress identification in real-time for ICU nurses.
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Zhang et al. (2022) conducted an observational in Stress (n=23). Continuous physiological measurement (electrodermal activity, eye tracking, accelerometer, skin temperature) was evaluated on Stress identification accuracy (Accuracy 0.88). Real-time stress monitoring using physiological features and an XGBoost machine learning algorithm identified stress in ICU nurses with an accuracy of 0.88.
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