Key result
Wearable-derived HRV and accelerometry machine learning identifies acute emergency stress with ~0.7 AUC.
Why the study?
Chronic stress is the main cause of health problems in high-risk jobs, motivating the search for a non-invasive technique using wearable sensors for objective stress monitoring.
Does continuous monitoring of HRV and accelerometry accurately classify acute stressful emergency incidents in firefighters?
Observational (n=26)
Yes
Does continuous monitoring of HRV and accelerometry accurately classify acute stressful emergency incidents in firefighters?
Effect estimate: AUC 0.7
Continuous psychophysiological monitoring using wearable ECG and accelerometers offers a feasible, objective method for detecting acute occupational stress in real-world settings.
Supports wearable stress detection in firefighters; hypothesis-generating and requires prospective validation before clinical use.
Chronic stress is the main cause of health problems in high-risk jobs. Wearable sensors can become an ecologically valid method of stress level assessment in real-life applications. We sought to determine a non-invasive technique for objective stress monitoring. Data were collected from firefighters during 24-h shifts using sensor belts equipped with a dry-lead electrocardiograph (ECG) and a three-axial accelerometer. Levels of stress experienced during fire incidents were evaluated via a brief self-assessment questionnaire. Types of physical activity were distinguished basing on accelerometer readings, and heart rate variability (HRV) time series were segmented accordingly into corresponding fragments. Those segments were classified as stress/no-stress conditions. Receiver Operating Characteristic (ROC) analysis showed true positive classification as stress condition for 15% of incidents (while maintaining almost zero False Positive Rate), which parallels the amount of truly stressful incidents reported in the questionnaires. These results show a firm correspondence between the perceived stress level and physiological data. Psychophysiological measurements are reliable indicators of stress even in ecological settings and appear promising for chronic stress monitoring in high-risk jobs, such as firefighting.
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Meina et al. (2020) conducted an observational in Occupational stress (n=26). Wearable ECG and accelerometer sensors vs. Non-incident/routine periods was evaluated on Classification of stress conditions (incident vs. non-incident epochs) using HRV and accelerometry features (AUC 0.7). Machine learning classification of combined HRV and accelerometry data successfully identified stress-inducing emergency incidents with an AUC of 0.7, corresponding closely to self-reported psychological stress.
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