Investigating Stress-Related Heart Rate Behavior and Rhythm in College Students Using Trend Analysis Methods

Area of Science:

• Cardiology
• Psychology
• Data Science

Background:

• Student stress is prevalent and linked to cardiovascular issues.
• Existing research often overlooks detailed heart rate pattern analysis beyond variability.
• This study addresses the gap by examining stress impacts on heart rate rhythms.

Purpose of the Study:

• To investigate the effects of academic stress on student heart rate patterns.
• To explore novel analysis methods for heart rate data during stress events.
• To identify specific heart rate rhythm changes associated with student stress.

Main Methods:

• Collected smartwatch data from 125 university students over an academic semester.
• Recorded 1513 student-reported stress events.
• Applied time series analysis techniques, including Detrended Fluctuation Analysis (DFA), to heart rate data.

Main Results:

• Stress events peaked on Tuesdays between 10 am and 6 pm.
• Analysis revealed increased significant lags and peaks in autocorrelation plots during stress.
• DFA plots showed a higher scaling exponent, indicating persistent correlations and less regular heart rate patterns.

Conclusions:

• Time series analysis effectively captures stress-induced complexities in heart rate rhythms.
• Findings suggest potential for developing new stress monitoring tools based on heart rate patterns.
• Understanding these patterns is crucial for student health and well-being.