Chronic stress is an important factor for a variety of health problems, highlighting the importance of early detection of stress‐related problems. This methodological pilot study investigated whether the physiological response to and recovery from a stress task can differentiate healthy participants and persons with stress‐related complaints.

Healthy participants (n = 20) and participants with stress‐related complaints (n = 12) participated in a laboratory stress test, which included 3 stress tasks. Three physiological signals were recorded: galvanic skin response (GSR), heart rate (HR), and skin temperature (ST). From these signals, 126 features were extracted, including static (eg, mean) and dynamic (eg, recovery time) features. Unsupervised feature selection reduced the set to 26 features. A logistic regression model was developed for 6 feature sets, analysing single‐parameter and multiparameter models as well as models using recovery vs response‐related features. The highest classification performance (accuracy = 78%) was obtained using the response‐related feature set, including all physiological signals and using GSR‐related features. A worse performance was obtained using single‐signal feature sets based on HR (accuracy = 66%) and ST (accuracy = 59%). Response‐related features outperformed recovery‐related features (accuracy = 63%).

Participants with stress‐related complaints may be differentiated from healthy controls by physiological responses to stress tasks. We aimed to bring attention to new exploratory methodologies; further research is needed to validate and replicate the results on larger populations and patients on different areas along the stress continuum.