Characterizing COVID-19 and Influenza Illnesses in the Real World Via Person-Generated Health Data

Overview

Journal Patterns (N Y)

Date 2021 Jan 28

PMID 33506230

Citations 44

Authors

Allison Shapiro

Nicole Marinsek

Ieuan Clay

Benjamin Bradshaw

Ernesto Ramirez

Jae Min

Andrew Trister

Yuedong Wang

Tim Althoff

Luca Foschini

Affiliations

Soon will be listed here.

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.

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