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

Artificial Intelligence in Diabetes Care and Prevention (32) Wearable Devices and Sensors (936) Fitness Trackers and Smart Pedometers/Accelerometers (588) Digital Biomarkers and Digital Phenotyping (215) Machine Learning (1585)

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 Lam1 Author Orcid Image ;   Michael Catt2 Author Orcid Image ;   Sophie Cassidy3 Author Orcid Image ;   Jaume Bacardit1 Author Orcid Image ;   Philip Darke1 Author Orcid Image ;   Sam Butterfield1 Author Orcid Image ;   Ossama Alshabrawy4 Author Orcid Image ;   Michael Trenell5 Author Orcid Image ;   Paolo Missier1 Author Orcid Image
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