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A Caveat to Using Wearable Sensor Data for COVID-19 Detection: The Role of Behavioral Change after Receipt of Test Results.

Jennifer L ClearyYu FangSrijan SenZhenke Wu
Published in: medRxiv : the preprint server for health sciences (2021)
Recent studies indicate that wearable sensors have the potential to capture subtle within-person changes that signal SARS-CoV-2 infection. However, it remains unclear the extent to which observed discriminative performance is attributable to behavioral change after receiving test results. We conducted a retrospective study in a sample of medical interns who received COVID-19 test results from March to December 2020. Our data confirmed that sensor data were able to differentiate between symptomatic COVID-19 positive and negative individuals with good accuracy (area under the curve (AUC) = 0.75). However, removing post-result data substantially reduced discriminative capacity (0.75 to 0.63; delta= -0.12, p=0.013). Removing data in the symptomatic period prior to receipt of test results did not produce similar reductions in discriminative capacity. These findings suggest a meaningful proportion of the discriminative capacity of wearable sensor data for SARS-CoV-2 infection may be due to behavior change after receiving test results.
Keyphrases
  • electronic health record
  • coronavirus disease
  • sars cov
  • big data
  • respiratory syndrome coronavirus
  • healthcare
  • machine learning
  • data analysis
  • sensitive detection