Measurement of respiratory rate using wearable devices and applications to COVID-19 detection

Abstract

We show that heart rate enabled wearable devices can be used to measure respiratory rate. Respiration modulates the heart rate creating excess power in the heart rate variability at a frequency equal to the respiratory rate, a phenomenon known as respiratory sinus arrhythmia. We isolate this component from the power spectral density of the heart beat interval time series, and show that the respiratory rate thus estimated is in good agreement with a validation dataset acquired from sleep studies (root mean squared error = 0.648 min−1, mean absolute error = 0.46 min−1, mean absolute percentage error = 3%). We use this respiratory rate algorithm to illuminate two potential applications (a) understanding the distribution of nocturnal respiratory rate as a function of age and sex, and (b) examining changes in longitudinal nocturnal respiratory rate due to a respiratory infection such as COVID-19. 90% of respiratory rate values for healthy adults fall within the range 11.8−19.2 min−1 with a mean value of 15.4 min−1. Respiratory rate is shown to increase with nocturnal heart rate. It also varies with BMI, reaching a minimum at 25 kg/m2, and increasing for lower and higher BMI. The respiratory rate decreases slightly with age and is higher in females compared to males for age <50 years, with no difference between females and males thereafter. The 90% range for the coefficient of variation in a 14 day period for females (males) varies from 2.3–9.2% (2.3−9.5%) for ages 20−24 yr, to 2.5−16.8% (2.7−21.7%) for ages 65−69 yr. We show that respiratory rate is often elevated in subjects diagnosed with COVID-19. In a 7 day window from D−1 to D+5 (where D0 is the date when symptoms first present, for symptomatic individuals, and the test date for asymptomatic cases), we find that 36.4% (23.7%) of symptomatic (asymptomatic) individuals had at least one measurement of respiratory rate 3 min−1 higher than the regular rate.

Occurrence of Relative Bradycardia and Relative Tachycardia in Individuals Diagnosed With COVID-19

“…The effect of illness on the RMSSD and respiratory rate may be estimated as: where ρ and ρ control may be RMSSD or respiratory rate, and the angle brackets denote the median filter. Further details of how Fitbit devices compute the respiratory rate during sleep have been described in Natarajan et al (2021) .…”

“…Commercially available wearable devices have been shown to be useful in early detection of COVID-19 and for monitoring symptoms ( Miller et al, 2020a ; Miller et al, 2020b ; Mishra et al, 2020 ; Natarajan et al, 2020b ; Natarajan et al, 2021 ; Quer et al, 2021 ). Radin et al (2021) studied resting heart rate (henceforth RHR) data from Fitbit devices to investigate long term changes following symptom onset.…”

Autonomic Dysfunction during Acute SARS-CoV-2 Infection: A Systematic Review

“…After removing duplicates, 1585 results remained and were evaluated for title and abstract check. Therefore, 65 reports were considered in the full-text review stage, and 20 of those were included in the systematic review [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48], while 45 reports were excluded: 2 papers written in Russian [49,50], 5 conference papers [51][52][53][54][55], 3 comments [56][57][58], 4 case reports or case series [59][60][61][62], 2 literature reviews [63,64], 1 study protocol [65], 12 studies that did not perform a formal evaluation of autonomic functions [66][67][68][69][70][71][72][73][74][75]…”

“…Details regarding the selection process are available in the PRISMA diagram (Figure 1). [66][67][68][69][70][71][72][73][74][75][76][77]; not acute COVID-19 population-Refs. [78][79][80][81][82][83][84][85][86][87][88][89][90]; not in English or Italian-Refs.…”

Best practices in the real-world data life cycle

“…Many current applications rely on manual integration of datasets provided by device manufacturers [23], rather than open access to application programming interfaces (APIs) that allow linkage to data from EHRs. Discussion of these considerations falls outside the scope of this paper.…”