Measuring COVID-19 and Influenza in the Real World via Person-Generated Health Data
Authors: Marinsek Nikki , Shapiro Allison , Clay Ieuan , Bradshaw Ben , Ramirez Ernesto , Min Jae , Trister Andrew , Wang Yuedong , Althoff Tim , Foschini Luca 
Published: May 2020

BackgroundSince the beginning of the COVID-19 pandemic, data from smartphones and connected sensors have been used to better understand presentation and management outside the clinic walls. However, reports on the validity of such data are still sparse, especially when it comes to symptom progression and relevance of wearable sensors.ObjectiveTo understand the relevance of Person-Generated Health Data (PGHD) as a means for early detection, monitoring, and management of COVID-19 in everyday life. This type of data include quantifying prevalence and progression of symptoms from self-reports as well as changes in activity and physiological parameters continuously measured from wearable sensors, and contextualizing findings for COVID-19 patients with those from cohorts of flu patients.Design, Setting, and ParticipantsRetrospective digital cohort study of individuals with a self-reported positive SARS-CoV-2 or influenza test followed over the period 2019-12-02 to 2020-04-27. Three cohorts were derived: Patients who self-reported being diagnosed with flu prior to the SARS-CoV-2 pandemic (N=6270, of which 1226 also contributed sensor PGHD); Patients who reported being diagnosed with flu during the SARS-CoV-2 pandemic (N=426, of which 85 also shared sensor PGHD); and patients who reported being diagnosed with COVID-19 (N=230, of which sensor PGHD was available for 41). The cohorts were derived from a large-scale digital participatory surveillance study designed to track Influenza-like Illness (ILI) incidence and burden over time.ExposuresSelf-reported demographic data, comorbidities, and symptoms experienced during a diagnosed ILI episode, including SARS-CoV-2. Physiological and behavioral parameters measured daily from commercial wearable sensors, including Resting Heart Rate (RHR), total step count, and nightly sleep hours.Main Outcomes and MeasuresWe investigated the percentage of individuals experiencing symptoms of a given type (e.g. shortness of breath) across demographic groups and over time. We examined illness duration, and care seeking behavior, and how RHR, step count, and nightly sleep hours deviated from expected behavior on healthy days over the course of the infection episode.ResultsSelf-reported symptoms of COVID-19 present differently from flu. COVID-19 cases tended to last longer than flu (median of 12 vs. 9 days), are uniquely characterized by chest pain/pressure, shortness of breath, and anosmia. The fraction of elevated RHR measurements collected daily from commercial wearable devices rise significantly in the 2 days surrounding ILI symptoms onset, but does not appear to do so in a way specific to COVID-19. Steps lost due to COVID-19 persists for longer than for flu.Conclusion and RelevancePGHD can be a valid source of longitudinal real world data to detect and monitor COVID-19-related symptoms and behaviors at population scale. PGHD may provide continuous, near real-time feedback to intervention effectiveness that otherwise requires waiting for symptoms to develop into contacts with the healthcare system. It has also the potential to increase pre-test probability of other downstream diagnostics. To effectively leverage PGHD for participatory surveillance it is crucial to invest in the creation of trusted, long-term communication channels with individuals through which data can be efficiently collected, consented, and contextualized, while protecting the privacy of individuals and ultimately facilitating the transition in and out of care.