Wearable Sensor Data and Self-reported Symptoms for COVID-19 Detection

by | Oct 29, 2020 | Research Publications

Author(s):

  • Giorgio Quer
  • Jennifer M. Radin
  • Matteo Gadaleta
  • Katie Baca-Motes
  • Lauren Ariniello
  • Edward Ramos
  • Vik Kheterpal
  • Eric J. Topol
  • Steven R. Steinhubl 

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 model1 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.

Documentation:

https://doi.org/10.1038/s41591-020-1123-x

References:
  1. Menni, C. et al. Real-time tracking of self-reported symptoms toÿ predict potential COVID-19.Nat. Med 26, 1037–1040 (2020).CAS  Article  Google Scholar 
  2. Oran, D. P. & Topol, E. J. Prevalence of asymptomatic SARS-CoV-2 infection. Ann. Intern. Med. https://doi.org/10.7326/M20-3012 (2020).
  3. New COVID-19 Test Data (Color Genomics, 2020); https://www.color.com/new-covid-19-test-data-majority-of-people-who-test-positive-for-covid-19-have-mild-symptoms-or-are-asymptomatic
  4. Richardson, S. et al. Presenting characteristics, comorbidities and outcomes among 5,700 patients hospitalized with COVID-19 in the New York City area.JAMA 323, 2052–2059 (2020).CAS  Article  Google Scholar 
  5. Vogels, E. A. About One-in-Five Americans Use a Smart Watch or Fitness Tracker (Pew Research Center, 2020); https://www.pewresearch.org/fact-tank/2020/01/09/about-one-in-five-americans-use-a-smart-watch-or-fitness-tracker/
  6. Quer, G., Gouda, P., Galarnyk, M., Topol, E. J. & Steinhubl, S. R. Inter- and intraindividual variability in daily resting heart rate and its associations with age, sex, sleep, BMI and time of year: retrospective, longitudinal cohort study of 92,457 adults. PLoS ONE 15, e0227709 (2020).CAS  Article  Google Scholar 
  7. Jaiswal, S. J. et al. Association of sleep duration and variability with body mass index: Sleep measurements in a large US population of wearable sensor users. JAMA Intern. Med. https://doi.org/10.1001/jamainternmed.2020.2834 (2020).
  8. Radin, J. M., Wineinger, N. E., Topol, E. J. & Steinhubl, S. R. Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study. Lancet Digit. Health 2, e85–e93 (2020).Article  Google Scholar 
  9. Zhu, G. et al. Learning from large-scale wearable device data for predicting epidemics trend of COVID-19. Discrete Dynamics Nat. Soc. 2020, 6152041 (2020). Google Scholar 
  10. Mishra, T. et al. Early detection of COVID-19 using a smartwatch. Preprint at medRxiv https://doi.org/10.1101/2020.07.06.20147512 (2020).
  11. Natarajan, A., Su, H.-W. & Heneghan, C. Assessment of physiological signs associated with COVID-19 measured using wearable devices. Preprint at https://doi.org/10.1101/2020.08.14.20175265 (2020).
  12. Evidation Health and BARDA Partner on Early Warning System for COVID-19 (Evidation, 2020); https://evidation.com/news/evidationhealthandbardapartner/
  13. Tempredict Study (Oura Health, 2020); https://ouraring.com/ucsf-tempredict-study
  14. Covidentify (Duke University, 2020); https://covidentify.covid19.duke.edu/
  15. Corona Datenspende (Robert Koch Institut, 2020); https://corona-datenspende.de/science/en
  16. Shen, B. et al. Proteomic and metabolomic characterization of COVID-19 patient sera. Cell 182, 59–72 (2020).CAS  Article  Google Scholar 
  17. Sharma, R., Agarwal, M., Gupta, M., Somendra, S. & Saxena, S. K. in Coronavirus Disease 2019 (COVID-19) (ed. Saxena, S.) 55–70 (Springer, 2020).
  18. Tabata, S. et al. Clinical characteristics of COVID-19 in 104 people with SARS-CoV-2 infection on the Diamond Princess cruise ship: a retrospective analysis. Lancet Infect. Dis. 20, 1043–1050 (2020).CAS  Article  Google Scholar 
  19. Ferretti, L. et al. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science 368, eabb6936 (2020).CAS  Article  Google Scholar 
  20. Chau, N. V. V. et al. The natural history and transmission potential of asymptomatic SARS-CoV-2 infection. Clin. Infect. Dis. https://doi.org/10.1093/cid/ciaa711 (2020).
  21. Jing, Q. L. et al. Household secondary attack rate of COVID-19 and associated determinants in Guangzhou, China: a retrospective cohort study. Lancet Infect. Dis. 20, 1141–1150 (2020).CAS  Article  Google Scholar 
  22. Meng, H. et al. CT imaging and clinical course of asymptomatic cases with COVID-19 pneumonia at admission in Wuhan, China. J. Infect. 81, e33–e39 (2020).CAS  Article  Google Scholar 
  23. Inui, S. et al. Chest CT findings in cases from the cruise ship ‘Diamond Princess’ with coronavirus disease 2019 (COVID-19). Radiol. Cardiothorac. Imaging 2, e200110 (2020).Article  Google Scholar 
  24. Long, Q. X. et al. Clinical and immunological assessment of asymptomatic SARS-CoV-2 infections. Nat. Med 26, 1200–1204 (2020).CAS  Article  Google Scholar 
  25. Milechin, L. et al. Detecting pathogen exposure during the non-symptomatic incubation period using physiological data. Preprint at bioRxiv https://doi.org/10.1101/218818 (2017).
  26. Sleep and Sleep Disorders (Center for Disease Control and Prevention, 2020); https://www.cdc.gov/sleep/data_statistics.html
  27. Steinhubl, S. R., Wolff-Hughes, D. L., Nilsen, W., Iturriaga, E. & Califf, R. M. Digital clinical trials: creating a vision for the future. NPJ Digit. Med. 2, 126 (2019).Article  Google Scholar 
  28. Steinhubl, S. R., McGovern, P., Dylan, J. & Topol, E. J. The digitised clinical trial. Lancet 390, 2135 (2017).Article  Google Scholar 
  29. Pratap, A. et al. Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants. NPJ Digit. Med. 3, 21 (2020).Article  Google Scholar 
  30. Coravos, A. et al. Modernizing and designing evaluation frameworks for connected sensor technologies in medicine. NPJ Digit. Med. 3, 37 (2020).Article  Google Scholar 
  31. Bradford, L. R., Aboy, M. & Liddell, K. COVID-19 contact tracing Apps: a stress test for privacy, the GDPR and data protection regimes. J. Law Biosci. https://doi.org/10.1093/jlb/lsaa034 (2020).
  32. Rivera, S. C. et al. The impact of patient-reported outcome (PRO) data from clinical trials: a systematic review and critical analysis. Health Qual. Life Outcomes 17, 156 (2019).Article  Google Scholar 
  33. Basch, E. et al. Overall survival results of a trial assessing patient-reported outcomes for symptom monitoring during routine cancer treatment. JAMA 318, 197–198 (2017).Article  Google Scholar 
  34. Bell, S. K. et al. Frequency and types of patient-reported errors in electronic health record ambulatory care notes. JAMA Netw. Open 3, e205867 (2020).Article  Google Scholar 
  35. Heneghan, C., Venkatraman, S. & Russell, A. Investigation of an estimate of daily resting heart rate using a consumer wearable device. Preprint at medRxiv https://doi.org/10.1101/19008771 (2019).