A Comparison of Self-Reports and Unobtrusive Multimodal Measures of Stress in a Realistic Simulated Task Environment.

Although a popular measurement method in different fields, self-report measures have multiple shortcomings that stem from their very inherent reliance on the participant's subjective judgment or recall. Given these limitations, in the domain of stress research, unobtrusive measures have been proposed as alternatives. Using a sample of 78 university students who participated in a disaster response simulation in a synthetic task environment, the present study sought to examine whether unobtrusive measures of stress (i.e., physiological, computer interaction, and facial data) can serve as valid substitutes for self-report measures (i.e., state anxiety, task load, and momentary stress). To that end, the study compared the extent to which features from these different modalities accurately (1) classified participants into the stress condition to which they were assigned as well as (2) predicted their performance on a complex synthetic task. The results indicated that the unobtrusive measures individually predicted task performance better than self-report measures, whereas self-report measures predicted the stress condition to which participants were randomly assigned (i.e.,