Paper Info
Title
From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations
Abstract
ABSTRACTSelf-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.
Year
DOI
Venue
2021
10.1145/3411764.3445555
Conference on Human Factors in Computing Systems
Keywords
DocType
Volume
Diabetes self-management,Goal setting,Machine learning,Personal Informatics
Conference
2021
Citations 
PageRank 
References 
1
0.37
0
Authors
13
Name
Order
Citations
PageRank
Elliot G. Mitchell145.88
Elizabeth M Heitkemper2155.12
Marissa Burgermaster343.46
Matthew Levine4218.67
Yishen Miao511.04
Maria L. Hwang610.37
Pooja M. Desai7223.61
Andrea Cassells8172.86
Jonathan N. Tobin9174.55
Tabak Esteban G1011.72
David J Albers1116116.04
Arlene M. Smaldone12314.43
Lena Mamykina13112498.05