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DOI: 10.1055/s-0043-1770167
Use of Circadian Heart Rate Rhythm for Infection Detection
Introduction: During an infectious process, there is a significant change in heart rate in response to inflammation. Measurement of heart rate over 24 hours for long periods is possible using the wrist photoplethysmography technique. Some equipment (smartwatches or smartbands) measure and record the average heart rate together with actigraphy data. This allows analyzing the circadian rhythm of the heart rate as well as the average heart rate in periods estimated by actigraphy such as sleep and wakefulness. Using a database of ~5300 volunteers who used smartwatches, 32 people with COVID19 were found. Of this total, 26 (81%) had changes in the smartwatch registration, and 22 (63%) had changes before the onset of symptoms. Another study showed that in 78% of cases pre-symptomatic signs were observed on the smartwatch with a median of three days before the onset of symptoms.
Aim: The objective of the present work was to develop an algorithm that identifies the risk of infection using the circadian rhythm of activity and rest, estimated periods of sleep and wakefulness by actigraphy and the circadian rhythm of heart rate.
Methods: The physiological data were obtained from a Xiaomi Miband 3 smartband that was connected to a cell phone application. From the data obtained, the following were calculated daily: the amplitude of the heart rate rhythm calculated by the cosinor method, the average heart rate in the period estimated as sleep, the average heart rate in the period estimated as wakefulness and the average heart rate in the 24 hours.
Results: Of the 98 volunteers who used our system in the period, four of them showed symptoms and tested positive for COVID. In all cases, there was a reduction in the amplitude of the heart rate rhythm, an increase in the average heart rate in the 24 hours, in the period estimated as sleep and in the period estimated as wakefulness. In one of the cases, this change occurred before the appearance of symptoms reported by the user.
Conclusions: The present work shows that there are changes detectable by low-cost and widely used equipment and this can help in the management of new infectious diseases. The limitation of this work is that of all the volunteers we did not find asymptomatic positive cases, which makes a specificity analysis difficult. It is possible that of the 94 volunteers who did not test positive for COVID, they became infected but did not show symptoms and therefore did not take the test.
No conflict of interest has been declared by the author(s).
Publication History
Article published online:
15 June 2023
© 2023. Brazilian Sleep Association. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
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