SmartCity 360 2016, SmartCity 360 2015: Smart City 360° pp 166-178 | Cite as

On the Use of Consumer-Grade Activity Monitoring Devices to Improve Predictions of Glycemic Variability

  • Chandra KrintzEmail author
  • Rich Wolski
  • Jordan E. Pinsker
  • Stratos Dimopoulos
  • John Brevik
  • Eyal Dassau
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 166)


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.


Activity monitoring Diabetes Prediction Decision support 


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016

Authors and Affiliations

  • Chandra Krintz
    • 1
    Email author
  • Rich Wolski
    • 1
  • Jordan E. Pinsker
    • 3
  • Stratos Dimopoulos
    • 1
  • John Brevik
    • 4
  • Eyal Dassau
    • 2
  1. 1.Computer Science DepartmentUniversity of CaliforniaSanta BarbaraUSA
  2. 2.Chemical Engineering DepartmentUniversity of CaliforniaSanta BarbaraUSA
  3. 3.William Sansum Diabetes CenterSanta BarbaraUSA
  4. 4.Mathematics DepartmentCalifornia State UniversityLong BeachUSA

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