From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations.

Journal: Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference
Published Date: May 7, 2021

Abstract

Self-tracking can help personalize self-management interventions for chronic conditions like type 2 diabetes (T2D), but reflecting on personal data requires motivation and literacy. Machine learning (ML) methods can identify patterns, but a key challenge is making actionable suggestions based on personal health data. We introduce GlucoGoalie, which combines ML with an expert system to translate ML output into personalized nutrition goal suggestions for individuals with T2D. In a controlled experiment, participants with T2D found that goal suggestions were understandable and actionable. A 4-week in-the-wild deployment study showed that receiving goal suggestions augmented participants' self-discovery, choosing goals highlighted the multifaceted nature of personal preferences, and the experience of following goals demonstrated the importance of feedback and context. However, we identified tensions between abstract goals and concrete eating experiences and found static text too ambiguous for complex concepts. We discuss implications for ML-based interventions and the need for systems that offer more interactivity, feedback, and negotiation.

Authors

  • Elliot G Mitchell
    Department of Biomedical Informatics, Columbia University.
  • Elizabeth M Heitkemper
    School of Nursing, The University of Texas at Austin.
  • Marissa Burgermaster
    Department of Population Health, Dell Medical School, and Department of Nutritional Sciences, The University of Texas at Austin.
  • Matthew E Levine
    Department of Computing and Mathematical Sciences, California Institute of Technology.
  • Yishen Miao
    Department of Molecular, Cellular, and Developmental Biology, University of California Santa Barbara.
  • Maria L Hwang
    Science and Math, Fashion Institute of Technology.
  • Pooja M Desai
    Department of Biomedical Informatics, Columbia University.
  • Andrea Cassells
    Clinical Directors Network (CDN).
  • Jonathan N Tobin
    Clinical Directors Network (CDN) and The Rockefeller University.
  • Esteban G Tabak
    Courant Institute of Mathematical Sciences.
  • David J Albers
    University of Colorado, Anschutz Medical Campus, Section of Informatics and Data Science, Departments of Pediatrics, Biomedical Engineering, and Biostatistics and Informatics, and Department of Biomedical Informatics, Columbia University.
  • Arlene M Smaldone
    School of Nursing, Columbia University.
  • Lena Mamykina
    Department of Biomedical Informatics, Columbia University.

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