Measurement of respiratory rate using wearable devices and applications to COVID-19 detection
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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/m 2 , 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 D 0 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.
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SciScore for 10.1101/2021.05.15.21257200: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are several limitations to the present work. The dataset of 10,000 participants consisted of individuals who were randomly selected. We did not attempt to exclude subjects with significant sleep apnea (for whom “average” respiration rate may be hard to define). Age, sex, and BMI were provided by the user, and we are unable to …
SciScore for 10.1101/2021.05.15.21257200: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are several limitations to the present work. The dataset of 10,000 participants consisted of individuals who were randomly selected. We did not attempt to exclude subjects with significant sleep apnea (for whom “average” respiration rate may be hard to define). Age, sex, and BMI were provided by the user, and we are unable to verify these demographic data. We have assumed that participants were healthy during the 2 week period of study, but we do not have evidence of this. This limitation is even more important for the COVID-19 study. Although we have assumed that individuals are healthy several days prior to being diagnosed with COVID-19, we do not have any way to confirm this. The date of COVID-19 diagnosis was provided by the participants themselves, and errors in this date can affect our results. Nevertheless, the results presented in this work establish that the respiratory rate is a valuable health metric which can be reliably computed using wearable devices.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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