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

Artificial Intelligence in Diabetes Care and Prevention (29) Wearable Devices and Sensors (903) Fitness Trackers and Smart Pedometers/Accelerometers (571) Digital Biomarkers and Digital Phenotyping (198) Machine Learning (1501)

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
Article Authors Cited by (18) Tweetations (10) Metrics

Journals

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  6. Zou X, Liu Y, Ji L. Review: Machine learning in precision pharmacotherapy of type 2 diabetes—A promising future or a glimpse of hope?. DIGITAL HEALTH 2023;9 View
  7. Chellappan D, Rajaguru H. Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance. Diagnostics 2023;13(16):2654 View
  8. Site A, Nurmi J, Lohan E. Machine-Learning-Based Diabetes Prediction Using Multisensor Data. IEEE Sensors Journal 2023;23(22):28370 View
  9. Bibbo D, De Marchis C, Schmid M, Ranaldi S. Machine learning to detect, stage and classify diseases and their symptoms based on inertial sensor data: a mapping review. Physiological Measurement 2023;44(12):12TR01 View
  10. Muse E, Topol E. Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management. Cell Metabolism 2024;36(4):670 View
  11. Alsadi B, Musleh S, Al-Absi H, Refaee M, Qureshi R, El Hajj N, Alam T. An ensemble-based machine learning model for predicting type 2 diabetes and its effect on bone health. BMC Medical Informatics and Decision Making 2024;24(1) View
  12. Rotbei S, Tseng W, Merino-Barbancho B, Haleem M, Montesinos L, Pecchia L, Fico G, Botta A. Evaluating impact of movement on diabetes via artificial intelligence and smart devices systematic literature review. Expert Systems with Applications 2024;257:125058 View
  13. Ali H, Niazi I, White D, Akhter M, Madanian S. Comparison of Machine Learning Models for Predicting Interstitial Glucose Using Smart Watch and Food Log. Electronics 2024;13(16):3192 View
  14. dos Santos M, Heckler W, Bavaresco R, Barbosa J. Machine learning applied to digital phenotyping: A systematic literature review and taxonomy. Computers in Human Behavior 2024;161:108422 View
  15. Serrano D, Luciano F, Anaya B, Ongoren B, Kara A, Molina G, Ramirez B, Sánchez-Guirales S, Simon J, Tomietto G, Rapti C, Ruiz H, Rawat S, Kumar D, Lalatsa A. Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine. Pharmaceutics 2024;16(10):1328 View

Books/Policy Documents

  1. Ibáñez-Redin G, Duarte O, Cagnani G, Oliveira O. Machine Learning for Advanced Functional Materials. View