ABSTRACT
COVID-19, the disease caused by the SARS-CoV-2 virus, can cause shortness of breath, lung damage, and impaired respiratory function. Containing the virus has proven difficult, in large part due to its high transmissibility during the pre-symptomatic incubation. The study's aim was to determine if changes in respiratory rate could serve as a leading indicator of SARS-CoV-2 infections. A total of 271 individuals (age = 37.3 {+/-} 9.5, 190 male, 81 female) who experienced symptoms consistent with COVID-19 were included - 81 tested positive for SARS-CoV-2 and 190 tested negative; these 271 individuals collectively contributed 2672 samples (days) of data (1856 healthy days, 231 while infected with COVID-19 and 585 while infected with something other than COVID-19). To train a novel algorithm, individuals were segmented as follows; (1) a training dataset of individuals who tested positive for COVID-19 (n=57 people, 537 samples); (2) a validation dataset of individuals who tested positive for COVID-19 (n=24 people, 320 samples) ; (3) a validation dataset of individuals who tested negative for COVID-19 (n=190 people, 1815 samples). All data was extracted from the WHOOP system, which uses data from a wrist-worn strap to produce validated estimates of respiratory rate and other physiological measures. Using the training dataset, a model was developed to estimate the probability of SARS-CoV-2 infection based on changes in respiratory rate during night-time sleep. The model's ability to identify COVID-positive individuals not used in training and robustness against COVID-negative individuals with similar symptoms were examined for a critical six-day period spanning the onset of symptoms. The model identified 20% of COVID-19 positive individuals in the validation dataset in the two days prior to symptom onset, and 80% of COVID-19 positive cases by the third day of symptoms.
Subject(s)
Lung Diseases , Dyspnea , Severe Acute Respiratory Syndrome , COVID-19 , Respiratory InsufficiencyABSTRACT
The COVID-19 pandemic incited global and unprecedented restrictions on the behavior of society. The aims of this study were to quantify changes to sleep/wake behavior and exercise patterns (e.g., exercise frequency, modality, and intensity), and the subsequent impact on physiological markers of health (e.g., total sleep duration, social jet lag, resting heart rate, and heart rate variability) with the introduction of physical distancing mandates and recommendations. A retrospective analysis of 50,000 subscribers to the WHOOP platform (mean age = 36.6 {+/-} 10.5; 11,956 females, 38,044 males) was conducted covering the period from January 1st, 2020 through May 15th, 2020. In order to make robust comparisons, this time period was separated into a 68 day baseline period and a 67 day physical distancing period - with a total of 6.3 million sleeps and 4.9 million exercise sessions analyzed. As compared to baseline, during physical distancing, all subjects analyzed in this study dedicated more time to sleep (+0.21 hours), fell asleep earlier (-0.43 hours), woke up earlier (-0.29 hours), obtained more sleep (+0.19 hours) and reduced social jet lag (-0.23 hours). Subjects also increased exercise frequency by an average of 1.1% and increased exercise intensity by spending an average of 1.8% more time in the three highest heart rate zones. These changes to sleep and exercise behavior may have contributed to the observed lowered resting heart rate (-0.9 beats per minute) and increased heart rate variability (+1.3 milliseconds) during physical distancing. A potential explanation for these results is that decreases in business hours-based commitments during physical distancing may have resulted in increased opportunity to engage in exercise and prioritize sleep. Therefore, as the COVID-19 pandemic eases, maintenance of certain aspects of physical distancing (e.g., flexibility to work from home) may result in a healthier population.