| Literature DB >> 33506230 |
Allison Shapiro1, Nicole Marinsek1, Ieuan Clay1, Benjamin Bradshaw1, Ernesto Ramirez1, Jae Min1, Andrew Trister2, Yuedong Wang3, Tim Althoff4, Luca Foschini1.
Abstract
The fight against COVID-19 is hindered by similarly presenting viral infections that may confound detection and monitoring. We examined person-generated health data (PGHD), consisting of survey and commercial wearable data from individuals' everyday lives, for 230 people who reported a COVID-19 diagnosis between March 30, 2020, and April 27, 2020 (n = 41 with wearable data). Compared with self-reported diagnosed flu cases from the same time frame (n = 426, 85 with wearable data) or pre-pandemic (n = 6,270, 1,265 with wearable data), COVID-19 patients reported a distinct symptom constellation that lasted longer (median of 12 versus 9 and 7 days, respectively) and peaked later after illness onset. Wearable data showed significant changes in daily steps and prevalence of anomalous resting heart rate measurements, of similar magnitudes for both the flu and COVID-19 cohorts. Our findings highlight the need to include flu comparator arms when evaluating PGHD applications aimed to be highly specific for COVID-19.Entities:
Keywords: COVID-19; Fitbit; SARS-CoV-2; activity tracker; digital health; flu; influenza-like illness; person-generated health data; symptom presentation
Year: 2020 PMID： 33506230 PMCID： PMC7815963 DOI： 10.1016/j.patter.2020.100188
Source DB: PubMed Journal: Patterns (N Y) ISSN： 2666-3899