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

Giorgio Quer1, Jennifer M Radin2, Matteo Gadaleta2

  • 1Scripps Research Translational Institute, La Jolla, CA, USA. gquer@scripps.edu.

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Personal sensor data combined with symptoms can better detect COVID-19 than symptoms alone. Continuous monitoring using wearable devices offers a complementary approach to traditional virus testing for early infection detection.

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Area of Science:

  • Digital Health
  • Infectious Disease Surveillance
  • Wearable Technology

Background:

  • Traditional COVID-19 screening relies on symptom surveys and temperature checks.
  • Subtle physiological changes during infection may be detectable through personal sensor data.

Purpose of the Study:

  • To investigate if passively collected personal sensor data can help identify COVID-19 infections.
  • To assess the efficacy of combining sensor data with symptoms for differentiating COVID-19 positive cases.

Main Methods:

  • A smartphone app collected smartwatch, activity tracker, and self-reported symptom data from 30,529 US participants.
  • A model was developed to differentiate COVID-19 positive from negative cases using symptom and sensor data.

Main Results:

  • A combined model of symptom and sensor data achieved an AUC of 0.80, outperforming a symptom-only model (AUC 0.71).
  • This demonstrates significantly improved discrimination (P < 0.01) between COVID-19 positive and negative symptomatic individuals.

Conclusions:

  • Continuous, passively captured sensor data can complement infrequent virus testing.
  • Personal sensor data holds potential for enhanced, real-time infectious disease monitoring and early detection.