This paper is in the following e-collection/theme issue:

Artificial Intelligence in Diabetes Care and Prevention (14) Wearable Devices and Sensors (736) Fitness Trackers and Smart Pedometers/Accelerometers (514) Digital Biomarkers and Digital Phenotyping (155) Machine Learning (1194)

Published on in Vol 6, No 1 (2021): Jan-Mar

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23364, first published .
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

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

Authors of this article:

Benjamin Lam 1 Author Orcid Image ;   Michael Catt 2 Author Orcid Image ;   Sophie Cassidy 3 Author Orcid Image ;   Jaume Bacardit 1 Author Orcid Image ;   Philip Darke 1 Author Orcid Image ;   Sam Butterfield 1 Author Orcid Image ;   Ossama Alshabrawy 4 Author Orcid Image ;   Michael Trenell 5 Author Orcid Image ;   Paolo Missier 1 Author Orcid Image
Article Authors Cited by (10) Tweetations (10) Metrics

Benjamin Lam   1 , BSc ;   Michael Catt   2 , PhD ;   Sophie Cassidy   3 , PhD ;   Jaume Bacardit   1 , PhD ;   Philip Darke   1 , BSc ;   Sam Butterfield   1 , BSc ;   Ossama Alshabrawy   4 , PhD ;   Michael Trenell   5 , PhD ;   Paolo Missier   1 , PhD

1 School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom

2 Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom

3 Faculty of Medicine and Health, University of Sydney, Sydney, Australia

4 Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom

5 Faculty of Medical Sciences, The Medical School, Newcastle University, Newcastle upon Tyne, United Kingdom

Corresponding Author:

  • Benjamin Lam, BSc
  • School of Computing
  • Newcastle University
  • Urban Sciences Building
  • 1 Science Square
  • Newcastle upon Tyne, NE4 5TG
  • United Kingdom
  • Phone: 44 7704111910
  • Email: b.p.lam1@ncl.ac.uk