Research Article
On the Use of Consumer-Grade Activity Monitoring Devices to Improve Predictions of Glycemic Variability
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@INPROCEEDINGS{10.1007/978-3-319-33681-7_14, author={Chandra Krintz and Rich Wolski and Jordan Pinsker and Stratos Dimopoulos and John Brevik and Eyal Dassau}, title={On the Use of Consumer-Grade Activity Monitoring Devices to Improve Predictions of Glycemic Variability}, proceedings={Smart City 360°. First EAI International Summit, Smart City 360°, Bratislava, Slovakia and Toronto, Canada, October 13-16, 2015. Revised Selected Papers}, proceedings_a={SMARTCITY360}, year={2016}, month={6}, keywords={Activity monitoring Diabetes Prediction Decision support}, doi={10.1007/978-3-319-33681-7_14} }- Chandra Krintz
Rich Wolski
Jordan Pinsker
Stratos Dimopoulos
John Brevik
Eyal Dassau
Year: 2016
On the Use of Consumer-Grade Activity Monitoring Devices to Improve Predictions of Glycemic Variability
SMARTCITY360
Springer
DOI: 10.1007/978-3-319-33681-7_14
Abstract
This paper examines the use of partial least squares regression to predict glycemic variability in subjects with Type I Diabetes Mellitus using measurements from continuous glucose monitoring devices and consumer-grade activity monitoring devices. It illustrates a methodology for generating automated predictions from current and historical data and shows that activity monitoring can improve prediction accuracy substantially.
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