Abstract
Respiration rate, heart rate, and heart rate variability (HRV) are some health metrics that are easily measured by consumer devices, which can potentially provide early signs of illness. Furthermore, mobile applications that accompany wearable devices can be used to collect relevant self-reported symptoms and demographic data. This makes consumer devices a valuable tool in the fight against the COVID-19 pandemic. Data on 2745 subjects diagnosed with COVID-19 (active infection, PCR test) were collected from May 21 to September 11, 2020, consisting of PCR positive tests conducted between February 16 and September 9. Considering male (female) participants, 11.9% (11.2%) of the participants were asymptomatic, 48.3% (47.8%) recovered at home by themselves, 29.7% (33.7%) recovered at home with the help of someone else, 9.3% (6.6%) required hospitalization without ventilation, and 0.5% (0.4%) required ventilation. There were a total of 21 symptoms reported, and the prevalence of symptoms varies by sex. Fever was present in 59.4% of male subjects and in 52% of female subjects. Based on self-reported symptoms alone, we obtained an AUC of 0.82 ± 0.017 for the prediction of the need for hospitalization. Based on physiological signs, we obtained an AUC of 0.77 ± 0.018 for the prediction of illness on a specific day. Respiration rate and heart rate are typically elevated by illness, while HRV is decreased. Measuring these metrics, taken in conjunction with molecular-based diagnostics, may lead to better early detection and monitoring of COVID-19.
Highlights
The year 2020 has seen the emergence of a global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus
Zhu et al.[12] studied heart rate, activity, and sleep data collected from Huami wearable devices to potentially identify outbreaks of COVID-19, and concluded that at a population level an anomaly detection algorithm provided correlation with the measured infection rate
Mishra et al.[16] analyzed heart rate, steps, and sleep data collected from Fitbit devices to identify the onset of COVID-19
Summary
The year 2020 has seen the emergence of a global pandemic caused by the severe acute respiratory syndrome coronavirus 2 The disease caused by this virus typically presents as a lower respiratory infection, though many atypical presentations have been reported. This has caused a major health challenge globally due to the apparent high transmissibility of this virus in a previously unexposed population. Zhu et al.[12] studied heart rate, activity, and sleep data collected from Huami wearable devices to potentially identify outbreaks of COVID-19, and concluded that at a population level an anomaly detection algorithm provided correlation with the measured infection rate. Mishra et al.[16] analyzed heart rate, steps, and sleep data collected from Fitbit devices to identify the onset of COVID-19. Given the reporting of symptoms by study participants, we provide an estimate of predicted disease severity based solely on symptoms
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