Wearable sensor data and self-reported symptoms for COVID-19 detection

Abstract

Traditional screening for COVID-19 typically includes survey questions about symptoms and travel history, as well as temperature measurements. Here, we explore whether personal sensor data collected over time may help identify subtle changes indicating an infection, such as in patients with COVID-19. We have developed a smartphone app that collects smartwatch and activity tracker data, as well as self-reported symptoms and diagnostic testing results, from individuals in the United States, and have assessed whether symptom and sensor data can differentiate COVID-19 positive versus negative cases in symptomatic individuals. We enrolled 30,529 participants between 25 March and 7 June 2020, of whom 3,811 reported symptoms. Of these symptomatic individuals, 54 reported testing positive and 279 negative for COVID-19. We found that a combination of symptom and sensor data resulted in an area under the curve (AUC) of 0.80 (interquartile range (IQR): 0.73-0.86) for discriminating between symptomatic individuals who were positive or negative for COVID-19, a performance that is significantly better (P < 0.01) than a model 1 that considers symptoms alone (AUC = 0.71; IQR: 0.63-0.79). Such continuous, passively captured data may be complementary to virus testing, which is generally a one-off or infrequent sampling assay. Owing to the current lack of fast and reliable testing, one of the greatest challenges for preventing transmission of SARS-CoV-2 is the ability to quickly identify, trace and isolate cases before they can further spread the infection to susceptible individuals. As regions across the United States start implementing measures to reopen businesses, schools and other activities, many rely on current screening practices for COVID-19, which typically include a combination of symptom and travel-related survey questions and temperature measurements. However, this method is likely to miss pre-symptomatic or asymptomatic cases, which make up ~40-45% of those infected with SARS-CoV-2, and who can still be infectious 1,2. An elevated temperature (>100 °F (>37.8 °C)) is not as common as frequently believed, being present in only 12% of individuals who tested positive for COVID-19 3 and just 31% of patients hospitalized with COVID-19 (at the time of admission) 4. Smartwatches and activity trackers, which are now worn by one in five Americans 5 , can improve our ability to objectively characterize each individual's unique baseline for resting heart rate 6 , sleep 7 and activity and can therefore be used to identify subtle changes in that user's data that may indicate that they are coming down with a viral illness. Previous research from our group has shown that this method, when aggregated at the population level, can significantly improve real-time predictions for influenza-like illness 8. Consequently, we created a prospective app-based research platform, called DETECT (Digital Engagement and Tracking for Early Control and Treatment), where individuals can share their sensor data, self-reported symptoms, diagnoses and ele...

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

“…In the specific context of COVID-19, our findings support the case made by recent work that data from wearable sensors may provide low-sensitivity testing capability with daily frequency. 22 , 23 , 24 , 35 Low-sensitivity/high-frequency testing when combined with a low-delay confirmatory testing strategy has been shown by computation models to significantly reduce prevalence of spreading with minimal burden on pre-emptive quarantine for false positives. 14 , 16 Therefore, wearables could potentially support use cases, such as return to work and college reopening, 18 where most of the cohort can be asked to wear the sensors frequently.…”

Trajectories of Key Physiological Parameters in COVID-19 Patients Using Continuous Remote Monitoring and Health AI

“…When looking at the ambulatory and out-of-hospital environments, the early identi cation of symptomatic and pre-symptomatic infected individuals would be especially valuable in breaking chains of infections, principally due to increased transmission during this period [44][45][46][47] . A small wearable, wireless RPM device that frequently collects and transmits the data automatically and in real-time could help in achieving that.…”

Pain and Stress Detection Using Wearable Sensors and Devices—A Review

“…Wearable sensors are under rapid development and have been applied in many fields through the years, especially in healthcare. Recently, due to the severe pandemic, wearable sensors are also being used for COVID-19 detection [97]. The expanding needs of wearable sensors in healthcare domain is motivated by the increasing healthcare costs; the success application of wearable sensors in such domain is the result of advanced technologies of microelectronics and wireless communication [98].…”