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

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This summary is machine-generated.

Student stress significantly alters heart rate patterns, showing less predictable rhythms during stressful academic periods. This research highlights the need for advanced time series analysis to monitor student well-being.

Keywords:
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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.