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

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 2020-03-30 and 2020-04-27 (N=41 with wearable data). Compared to self-reported diagnosed flu cases from the same timeframe (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 which lasted longer (median of 12 vs. 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 (RHR) 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.