From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations
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.
Additional Information
© 2021 Association for Computing Machinery. This research was funded by the National Institute of Diabetes and Digestive and Kidney Diseases award number R56DK113189 and the National Library of Medicine award number T15LM007079. Thank you to the fellow students of Columbia's Department of Biomedical Informatics, who earned the undying gratitude of the corresponding author by arts-and-crafting food images for the virtual bufet.Attached Files
Accepted Version - nihms-1792256.pdf
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Additional details
- PMCID
- PMC9067367
- Eprint ID
- 114659
- DOI
- 10.1145/3411764.3445555
- Resolver ID
- CaltechAUTHORS:20220510-702319000
- R56DK113189
- NIH
- T15LM007079
- NIH Predoctoral Fellowship
- Created
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2022-05-10Created from EPrint's datestamp field
- Updated
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2022-05-10Created from EPrint's last_modified field