id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-355447-0xuzolt3	Miller, D. J.	Leveraging wearable technology to predict the risk of COVID-19 infection.	2020-06-19		.txt	text/plain	3739	211	49	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). 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 aim of this study was to assess the ability of a novel algorithm to classify changes in respiratory rate as indicative of COVID-19 infection immediately prior to and during the first days of symptoms and to evaluate the model's robustness to instances of similar clinical presentations with differing etiology. The aim of this study was to assess the ability of a novel algorithm to classify changes in respiratory rate, as indicative of COVID-19 infection immediately prior to and during the first days of symptoms.	./cache/cord-355447-0xuzolt3.txt	./txt/cord-355447-0xuzolt3.txt