Unraveling Temporal Dependencies in Stress: Insights from Self-Reported Stress Data

Understanding how stress evolves over time is crucial for improving stress detection research. This study examines temporal dependencies in self-reported stress data. We analyzed three self-reported stress datasets to explore how past and present stress levels correlate, utilizing the autocorrelation function (ACF). Our finding quantitatively showed that temporal dependencies in stress levels vary across participants, and the degree of these dependencies differs across datasets collected in different contexts. We provide some insights on how to consider temporal dependencies in self-reported stress for stress detection models, taking into account individual and contextual variations.