Application of Machine Learning to Predict Dietary Lapses During Weight Loss.

Journal: Journal of diabetes science and technology
PMID: 29792067

Abstract

BACKGROUND: Individuals who adhere to dietary guidelines provided during weight loss interventions tend to be more successful with weight control. Any deviation from dietary guidelines can be referred to as a "lapse." There is a growing body of research showing that lapses are predictable using a variety of physiological, environmental, and psychological indicators. With recent technological advancements, it may be possible to assess these triggers and predict dietary lapses in real time. The current study sought to use machine learning techniques to predict lapses and evaluate the utility of combining both group- and individual-level data to enhance lapse prediction.

Authors

  • Stephanie P Goldstein
    1 Center for Weight, Eating, and Lifestyle Science and Department of Psychology, Drexel University, Philadelphia, PA, USA.
  • Fengqing Zhang
    2 Department of Psychology, College of Arts and Sciences, Drexel University, Philadelphia, PA, USA.
  • John G Thomas
    3 Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, The Miriam Hospital Weight Control and Diabetes Research Center, Providence, RI, USA.
  • Meghan L Butryn
    1 Center for Weight, Eating, and Lifestyle Science and Department of Psychology, Drexel University, Philadelphia, PA, USA.
  • James D Herbert
    4 President's Office, University of New England, Biddeford, ME, USA.
  • Evan M Forman
    1 Center for Weight, Eating, and Lifestyle Science and Department of Psychology, Drexel University, Philadelphia, PA, USA.

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