Circadian Rhythm of Heart Rate Assessed by Wearable Devices Tends to Correlate with the Circadian Rhythm of Salivary Cortisol Concentration in Healthy Young Adults

Abstract

The objective of this study was to determine whether the circadian rhythm of heart rate or step count using wearable devices was related to that of the salivary cortisol levels and to test the possibility that the data from wearable devices could be used as an indicator of circadian rhythm misalignment, which is emerging as a cause of insomnia and mood disorders. Methods: The heart rate and step count were continuously measured in 12 healthy young adults using wearable wrist devices for 5 days, and saliva was sampled every 4 hours, excluding sleeping time, for a total of 48 hours to measure the circadian rhythm of salivary cortisol concentration. Cortisol concentrations were assessed using the enzyme-linked immunosorbent assay. The cosinor analysis for the three measurements, salivary cortisol concentrations, heart rate, and step count, was used to estimate the circadian rhythm. Results: The mean values of the acrophase of the cosine-fitted curve of cortisol, heart rate, and step count were 9.06, 15.84, and 19.09, respectively, while those of the amplitude were 7.70, 12.60, and 10.68, respectively. In addition, the mean values of the mesor of the cosine-fitted curve for cortisol, heart rate, and step count were 17.19, 73.55, and 45.45, respectively, and those of robustness were 0.82, 0.56, and 0.18, respectively. There was a possible positive correlation between the acrophase of the cosine-fitted curve of salivary cortisol and that of heart rate (r=0.55, p=0.064). However, there was no correlation between the acrophase of the cosine-fitted curve of salivary cortisol and that of step count (r=-0.2, p=0.533). Conclusion: The findings suggest that the heart rate measured using the wearable activity tracker was a relatively reliable biomarker of circadian rhythm.

Distinct Circadian Assessments From Wearable Data Reveal Social Distancing Promoted Internal Desynchrony Between Circadian Markers

“…Two recent techniques have been developed which now allow researchers to supplement these typical intensive experiments through mobile assessment of circadian rhythms using wearables (5,6). Wearables typically collect data on wrist movement (actigraphy) and heart rate, each of which can separately be used to estimate various outputs of the circadian clock in the body (5)(6)(7)(8)(9)(10)(11). Using mathematical models and activity measurements collected by the Apple Watch, Huang et al recently showed that DLMO can be predicted to within ∼1 h in non-shift workers (5), which is much more accurate (in terms of mean absolute error between a laboratory DLMO measurement and predicted phase) than cosinor analysis of ambulatory circadian rest activity [the average difference between in laboratory DLMO and the acrophase from cosinor analysis of rest-activity cycle is 4.47 and 4.6 h from Huang et al (5) and Woelders et al (12), respectively].…”

Machine Learning–Based Prediction of Attention-Deficit/Hyperactivity Disorder and Sleep Problems With Wearable Data in Children

“…Second, wearable device data do not guarantee accurate measurement values. Therefore, the wearable data may contain errors before processing . For example, wearable devices tend to be underestimated in a controlled environment and overestimated in a free environment .…”

“…Therefore, the wearable data may contain errors before processing. 51 For example, wearable devices tend to be underestimated in a controlled environment and overestimated in a free environment. 52 Third, the diagnosis of mental disorders should be multifaceted based on expert knowledge and clinical experience.…”

Changes in cortisol awakening responses (CAR) in menopausal women through short-term marine healing retreat program with specific factors affecting each CAR index

“…Throughout the program, the heart rate was automatically monitored every 5 min using a Mi Band 5 (Xiaomi, China) [ 25 ]. The measured heart rate data were analyzed using a cosine wave function, and the best-fitting model was determined using the least squares regression method [ 26 ].…”

“…The measured heart rate data were analyzed using a cosine wave function, and the best-fitting model was determined using the least squares regression method [ 26 ]. Using MATLAB software (MathWorks, USA), we extracted three variables reflecting circadian rhythms: amplitude, mesor, and acrophase [ 25 ].…”