A Computational Analysis of Meal Events Using Food Diaries and Continuous Glucose Monitors

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dc.contributor.advisorSabharwal, Ashutosh
dc.contributor.committeeMemberAllen, Genevera
dc.contributor.committeeMemberPatel, Ankit
dc.contributor.committeeMemberBeier, Margaret
dc.contributor.committeeMemberKerr, David
dc.creatorPai, Amruta
dc.date.accessioned2023-08-09T14:37:48Z
dc.date.created2023-05
dc.date.issued2023-04-21
dc.date.submittedMay 2023
dc.date.updated2023-08-09T14:37:48Z
dc.descriptionEMBARGO NOTE: This item is embargoed until 2025-05-01
dc.description.abstractDiet self-management, through its effect on weight and glycemic control, is one of the cornerstones of Type 2 Diabetes (T2D) prevention and management. A quantitative understanding of bio-behavioral mechanisms of diet is needed to create effective diet self-management tools. Smartphone diet-tracking applications and continuous glucose monitors (CGMs) are emerging devices that enable dense sampling of an individual's diet. Research in diet analysis of app-based food diaries and CGMs have mainly focused on developing aggregate measures of nutrient intake and glucose responses. However, innovative computational analysis is required to infer actionable insights. In this thesis, we develop computational measures for various bio-behavioral aspects of diet by leveraging meal event data collected with food diaries and CGMs. First, we establish recurrent consumption measures across meal events to characterize habitual behavior in an individual's diet. We leverage a large publicly available MyFitnessPal (MFP) food diary dataset to provide novel insights on differences in habitual behavior across individuals and temporal contexts. Next, we develop calorie compensation measures to characterize self-regulatory behavior. A quantitative analysis of calorie compensation measures on the MFP dataset reveals significant meal compensation patterns and their impact on adherence to self-set calorie goals. Finally, we designed an observational study using the MFP app and CGMs to evaluate the impact of meal events on glycemic control in adults with varying hemoglobin a1c levels. We developed elevated meal event count to characterize mealtime glucose responses by exploiting its association with hemoglobin a1c. Elevated meal event count significantly affected glycemic control, suggesting its value as a novel event-driven glycemic target metric. This thesis highlights the value of using CGMs and food diaries to broaden our understanding of diet. The developed measures augment existing intake measures and could be used as a digital bio-behavioral markers to personalize diet self-management strategies.
dc.embargo.lift2025-05-01
dc.embargo.terms2025-05-01
dc.format.mimetypeapplication/pdf
dc.identifier.citationPai, Amruta. "A Computational Analysis of Meal Events Using Food Diaries and Continuous Glucose Monitors." (2023) Diss., Rice University. <a href="https://hdl.handle.net/1911/115067">https://hdl.handle.net/1911/115067</a>.
dc.identifier.urihttps://hdl.handle.net/1911/115067
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectmeals
dc.subjectdiet
dc.subjectdata mining
dc.subjecthabits
dc.subjectcalorie
dc.subjectcompensation
dc.subjectglucose
dc.subjectphysical activity
dc.subjectCGM
dc.subjectdiet-tracking
dc.titleA Computational Analysis of Meal Events Using Food Diaries and Continuous Glucose Monitors
dc.typeThesis
dc.type.materialText
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
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