Using Wearable Activity Trackers to Predict Type 2 Diabetes: Machine Learning–Based Cross-sectional Study of the UK Biobank Accelerometer Cohort

Using Wearable Activity Trackers to Predict Type 2 Diabetes: Machine Learning–Based Cross-sectional Study of the UK Biobank Accelerometer Cohort

BackgroundBetween 2013 and 2015, the UK Biobank collected accelerometer traces from 103,712 volunteers aged between 40 and 69 years using wrist-worn triaxial accelerometers for 1 week. This data set has been used in the past to verify that individuals with chronic diseases ex...

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Bibliographic Details
Main Authors: Lam, Benjamin, Catt, Michael, Cassidy, Sophie, Bacardit, Jaume, Darke, Philip, Butterfield, Sam, Alshabrawy, Ossama, Trenell, Michael, Missier, Paolo
Format: Article
Language:English
Published: JMIR Publications 2021-03-01
Series:JMIR Diabetes
Online Access:https://diabetes.jmir.org/2021/1/e23364

Internet

https://diabetes.jmir.org/2021/1/e23364

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