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

Artificial Intelligence in Diabetes Care and Prevention (38) Wearable Devices and Sensors (987) Fitness Trackers and Smart Pedometers/Accelerometers (610) Digital Biomarkers and Digital Phenotyping (226) Machine Learning (1717)

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 (24) Tweetations (8) Metrics

Journals

  1. Jang B, Kim I. Machine-Learning Algorithms Predict Breast Cancer Patient Survival From Uk Biobank Whole-Exome Sequencing Data. Biomarkers in Medicine 2021;15(16):1529 View
  2. Fregoso-Aparicio L, Noguez J, Montesinos L, García-García J. Machine learning and deep learning predictive models for type 2 diabetes: a systematic review. Diabetology & Metabolic Syndrome 2021;13(1) View
  3. Clark S, Lomax N, Morris M, Pontin F, Birkin M. Clustering Accelerometer Activity Patterns from the UK Biobank Cohort. Sensors 2021;21(24):8220 View
  4. Mistry S, Riches N, Gouripeddi R, Facelli J. Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review. Artificial Intelligence in Medicine 2023;135:102461 View
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  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
  16. Cichosz S, Bender C, Hejlesen O. Explainable Machine Learning-Based Approach to Identify People at Risk of Diabetes Using Physical Activity Monitoring. BioMedInformatics 2024;5(1):1 View
  17. Birdi S, Rabet R, Durant S, Patel A, Vosoughi T, Shergill M, Costanian C, Ziegler C, Ali S, Buckeridge D, Ghassemi M, Gibson J, John-Baptiste A, Macklin J, McCradden M, McKenzie K, Mishra S, Naraei P, Owusu-Bempah A, Rosella L, Shaw J, Upshur R, Pinto A. Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review. BMC Public Health 2024;24(1) View
  18. Kiran M, Xie Y, Anjum N, Ball G, Pierscionek B, Russell D. Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis. Frontiers in Digital Health 2025;7 View
  19. Zhang Y, Wang J, Zong H, Singla R, Ullah A, Liu X, Wu R, Ren S, Shen B. The comprehensive clinical benefits of digital phenotyping: from broad adoption to full impact. npj Digital Medicine 2025;8(1) View

Books/Policy Documents

  1. Ibáñez-Redin G, Duarte O, Cagnani G, Oliveira O. Machine Learning for Advanced Functional Materials. View
  2. Alcock L, Sigurdsson H, Ray N, Yarnall A, Rochester L. Gait, Balance, and Mobility Analysis. View

Conference Proceedings

  1. Chee L, Hwong H, Sivakumar S. 2023 International Conference on Digital Applications, Transformation & Economy (ICDATE). Diabetes Detection Using Gait Analysis and Machine Learning View
  2. Ali H, Madanain S, White D, Akhter M, Niazi I. Proceedings of the 2024 Australasian Computer Science Week. From wearable activity trackers to Interstitial Glucose: Data to Insight- A proposed scientific journey View
  3. Sridevi P, K P. 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). A Systematic Review of Supervised Learning and Deep Learning Algorithms for Diabetic Detection View