Wearable sensors enable personalized predictions of clinical laboratory measurements.

Journal Article (Journal Article)

Vital signs, including heart rate and body temperature, are useful in detecting or monitoring medical conditions, but are typically measured in the clinic and require follow-up laboratory testing for more definitive diagnoses. Here we examined whether vital signs as measured by consumer wearable devices (that is, continuously monitored heart rate, body temperature, electrodermal activity and movement) can predict clinical laboratory test results using machine learning models, including random forest and Lasso models. Our results demonstrate that vital sign data collected from wearables give a more consistent and precise depiction of resting heart rate than do measurements taken in the clinic. Vital sign data collected from wearables can also predict several clinical laboratory measurements with lower prediction error than predictions made using clinically obtained vital sign measurements. The length of time over which vital signs are monitored and the proximity of the monitoring period to the date of prediction play a critical role in the performance of the machine learning models. These results demonstrate the value of commercial wearable devices for continuous and longitudinal assessment of physiological measurements that today can be measured only with clinical laboratory tests.

Full Text

Duke Authors

Cited Authors

  • Dunn, J; Kidzinski, L; Runge, R; Witt, D; Hicks, JL; Schüssler-Fiorenza Rose, SM; Li, X; Bahmani, A; Delp, SL; Hastie, T; Snyder, MP

Published Date

  • June 2021

Published In

Volume / Issue

  • 27 / 6

Start / End Page

  • 1105 - 1112

PubMed ID

  • 34031607

Electronic International Standard Serial Number (EISSN)

  • 1546-170X

International Standard Serial Number (ISSN)

  • 1078-8956

Digital Object Identifier (DOI)

  • 10.1038/s41591-021-01339-0

Language

  • eng