Abstract
HIGHLIGHTS: What are the main findings? Heart rate patterns associated with stress are more chaotic during the day and at the beginning of the academic semester. There are persistent correlations in the heart rate data and less regular, less predictable heart rate patterns and rhythms during stress events. What are the implications of the main findings? Current stress monitoring tools and models heavily rely on heart rate variability. Trend analysis techniques, such as autocorrelation and detrended fluctuation analysis, show promise for documenting stress-induced cardiac behavior. ABSTRACT: (1) Background: Recent studies indicated the prevalence of stress among students. The increased level of stress is concerning due to its association with cardiovascular diseases. This study examined stress within the academic setting and its effects on heart rate patterns, addressing a gap in analysis methods beyond heart rate variability. (2) Methods: The data were collected from 125 students at a large university in Texas who were highly likely to experience stress disorders. Students were asked to wear a smartwatch for the duration of an academic semester to report their stress events. (3) Results: A total of 1513 stress events were reported. The highest frequency of stress events was reported at the beginning of the week, particularly on Tuesdays, and mostly between 10 am and 6 pm. Results also showed significant increases in the number of significant lags, the number of peaks in autocorrelation plots, and the scaling exponent in DFA plots. This indicates persistent correlations in the heart rate data and less regular, less predictable heart rate patterns and rhythms than during non-stress moments. (4) Conclusions: Findings underscore the importance of using time series analysis to understand the complexities in heart rate rhythm associated with stress, with the potential to inform future stress monitoring capabilities.