Using Fitbit data to monitor the heart rate evolution patterns of college students

Abstract

Objective

To investigate what social, psychological, personality, and behavioral factors affect overtime heart rate changes of college students. Participants: The daily heart rates of over 600 undergraduates at the University of Notre Dame were unobtrusively recorded via Fitbit devices from August 16, 2015, to May 13, 2017. Method: Latent Growth-Curve modeling strategy is utilized to examine how daily mean heart rate and its standard deviation change over time, and what foregoing factors predict observed changes. Results: The mean heart rate increased and its standard deviation stayed the same over the 637 days. Heart rate levels go up with that of social contacts, an indicator of peer influence. Both daily heart rate levels and changes are also affected by multiple external factors. Conclusion: Human heart rate is not only a physiological phenomenon but also a social-psychological one, as it is systematically affected by peer networks, social contexts, and human activities.

Keywords:

• Heart rate
• Latent Growth-Curve Model (LGCM)
• peer influence
• social networks
• sensor data

Conflict of interest disclosure

The authors have no conflicts of interest to report. The authors confirm that the research presented in this article met the ethical guidelines, including adherence to the legal requirements of the United States and received approval from the Institutional Review Board of the University of Notre Dame.

Additional information

Funding

Funding was provided by the National Institutes of Health (grant #1 R01 HL117757-01A1).