Momentary dietary lapse prediction for obesity management: Developing the Eating Behaviour Lapse Inventory Survey Singapore (eBLISS) and a machine learning lapse prediction model.

Journal: Computer methods and programs in biomedicine
Published Date: May 30, 2025

Abstract

BACKGROUND: As the obesity prevalence continues to rise, effective interventions that promote dietary adherence and address the intricate array of factors contributing to dietary lapses are warranted.

Authors

  • H S J Chew
    Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore. Electronic address: nurchs@nus.edu.sg.
  • M Shridhar
    High performance computing-artificial intelligence (HPC-AI), National University of Singapore, Singapore. Electronic address: manish.s@nus.edu.sg.
  • H Kuang
    From the Calgary Stroke Program (H.K., W.Q., M.N., D.C., N.M., M.G., M.D.H., A.M.D., B.K.M.).
  • J Wang
    Joint Laboratory of Modern Agricultural Technology International Cooperation; Key Laboratory of Animal Production, Product Quality, and Security; College of Animal Science and Technology, Jilin Agricultural University, Changchun, China. moa4short@outlook.com.
  • M S Furqan
    Digital and Smart Health Office, National Healthcare Group, Singapore. Electronic address: mohammad.shaheryar@hotmail.com.

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