TY - JOUR
T1 - Associations between Sleep Quality and Heart Rate Variability; Implications for a Biological Model of Stress Detection Using Wearable Technology
AU - Chalmers, Taryn
AU - Hickey, Blake A.
AU - Newton, Philip
AU - Lin, Chin Teng
AU - Sibbritt, David
AU - McLachlan, Craig S.
AU - Clifton-Bligh, Roderick
AU - Morley, John W.
AU - Lal, Sara
N1 - Funding Information:
Acknowledgments: We thank the NSW Defence Innovation Network and NSW State Government for financial support of the StressWatch project through grant DINPP2019 S1-06. We also acknowledge the use of FitBit products to obtain heart rate data during this study. The authors also acknowledge the Neuroscience Research Unit, School of Life Sciences, The University of Technology Sydney, where this research was conducted, and the participants for their time and interest.
Funding Information:
Funding: This research was funded by the NSW Defence Innovation Network and NSW State Government, grant number DINPP2019 S1-06, StressWatch Project.
Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/5/1
Y1 - 2022/5/1
N2 - Introduction: The autonomic nervous system plays a vital role in the modulation of many vital bodily functions, one of which is sleep and wakefulness. Many studies have investigated the link between autonomic dysfunction and sleep cycles; however, few studies have investigated the links between short-term sleep health, as determined by the Pittsburgh Quality of Sleep Index (PSQI), such as subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction, and autonomic functioning in healthy individuals. Aim: In this cross-sectional study, the aim was to investigate the links between short-term sleep quality and duration, and heart rate variability in 60 healthy individuals, in order to provide useful information about the effects of stress and sleep on heart rate variability (HRV) indices, which in turn could be integrated into biological models for wearable devices. Methods: Sleep parameters were collected from participants on commencement of the study, and HRV was derived using an electrocardiogram (ECG) during a resting and stress task (Trier Stress Test). Result: Low-frequency to high-frequency (LF:HF) ratio was significantly higher during the stress task than during the baseline resting phase, and very-low-frequency and high-frequency HRV were inversely related to impaired sleep during stress tasks. Conclusion: Given the ubiquitous nature of wearable technologies for monitoring health states, in particular HRV, it is important to consider the impacts of sleep states when using these technologies to interpret data. Very-low-frequency HRV during the stress task was found to be inversely related to three negative sleep indices: sleep quality, daytime dysfunction, and global sleep score.
AB - Introduction: The autonomic nervous system plays a vital role in the modulation of many vital bodily functions, one of which is sleep and wakefulness. Many studies have investigated the link between autonomic dysfunction and sleep cycles; however, few studies have investigated the links between short-term sleep health, as determined by the Pittsburgh Quality of Sleep Index (PSQI), such as subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction, and autonomic functioning in healthy individuals. Aim: In this cross-sectional study, the aim was to investigate the links between short-term sleep quality and duration, and heart rate variability in 60 healthy individuals, in order to provide useful information about the effects of stress and sleep on heart rate variability (HRV) indices, which in turn could be integrated into biological models for wearable devices. Methods: Sleep parameters were collected from participants on commencement of the study, and HRV was derived using an electrocardiogram (ECG) during a resting and stress task (Trier Stress Test). Result: Low-frequency to high-frequency (LF:HF) ratio was significantly higher during the stress task than during the baseline resting phase, and very-low-frequency and high-frequency HRV were inversely related to impaired sleep during stress tasks. Conclusion: Given the ubiquitous nature of wearable technologies for monitoring health states, in particular HRV, it is important to consider the impacts of sleep states when using these technologies to interpret data. Very-low-frequency HRV during the stress task was found to be inversely related to three negative sleep indices: sleep quality, daytime dysfunction, and global sleep score.
KW - rate variability
KW - sleep
KW - stress
KW - wearable technology
UR - http://www.scopus.com/inward/record.url?scp=85129679674&partnerID=8YFLogxK
UR - https://doi.org/10.25905/21638123.v1
U2 - 10.3390/ijerph19095770
DO - 10.3390/ijerph19095770
M3 - Article
AN - SCOPUS:85129679674
SN - 1661-7827
VL - 19
JO - International Journal of Environmental Research and Public Health
JF - International Journal of Environmental Research and Public Health
IS - 9
M1 - 5770
ER -