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Wearable sensors enable personalized predictions of clinical laboratory measurements.

Publication ,  Journal Article
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 in: Nature medicine
June 2021

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.

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Published In

Nature medicine

DOI

EISSN

1546-170X

ISSN

1078-8956

Publication Date

June 2021

Volume

27

Issue

6

Start / End Page

1105 / 1112

Related Subject Headings

  • Wearable Electronic Devices
  • Vital Signs
  • Movement
  • Monitoring, Physiologic
  • Immunology
  • Humans
  • Heart Rate
  • Galvanic Skin Response
  • Body Temperature
  • Biosensing Techniques
 

Citation

APA
Chicago
ICMJE
MLA
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Dunn, J., Kidzinski, L., Runge, R., Witt, D., Hicks, J. L., Schüssler-Fiorenza Rose, S. M., … Snyder, M. P. (2021). Wearable sensors enable personalized predictions of clinical laboratory measurements. Nature Medicine, 27(6), 1105–1112. https://doi.org/10.1038/s41591-021-01339-0
Dunn, Jessilyn, Lukasz Kidzinski, Ryan Runge, Daniel Witt, Jennifer L. Hicks, Sophia Miryam Schüssler-Fiorenza Rose, Xiao Li, et al. “Wearable sensors enable personalized predictions of clinical laboratory measurements.Nature Medicine 27, no. 6 (June 2021): 1105–12. https://doi.org/10.1038/s41591-021-01339-0.
Dunn J, Kidzinski L, Runge R, Witt D, Hicks JL, Schüssler-Fiorenza Rose SM, et al. Wearable sensors enable personalized predictions of clinical laboratory measurements. Nature medicine. 2021 Jun;27(6):1105–12.
Dunn, Jessilyn, et al. “Wearable sensors enable personalized predictions of clinical laboratory measurements.Nature Medicine, vol. 27, no. 6, June 2021, pp. 1105–12. Epmc, doi:10.1038/s41591-021-01339-0.
Dunn J, Kidzinski L, Runge R, Witt D, Hicks JL, Schüssler-Fiorenza Rose SM, Li X, Bahmani A, Delp SL, Hastie T, Snyder MP. Wearable sensors enable personalized predictions of clinical laboratory measurements. Nature medicine. 2021 Jun;27(6):1105–1112.

Published In

Nature medicine

DOI

EISSN

1546-170X

ISSN

1078-8956

Publication Date

June 2021

Volume

27

Issue

6

Start / End Page

1105 / 1112

Related Subject Headings

  • Wearable Electronic Devices
  • Vital Signs
  • Movement
  • Monitoring, Physiologic
  • Immunology
  • Humans
  • Heart Rate
  • Galvanic Skin Response
  • Body Temperature
  • Biosensing Techniques