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

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dc.contributor.advisorSabharwal, Ashutoshen_US
dc.contributor.committeeMemberAllen, Geneveraen_US
dc.contributor.committeeMemberPatel, Ankiten_US
dc.contributor.committeeMemberBeier, Margareten_US
dc.contributor.committeeMemberKerr, Daviden_US
dc.creatorPai, Amrutaen_US
dc.date.accessioned2023-08-09T14:37:48Zen_US
dc.date.created2023-05en_US
dc.date.issued2023-04-21en_US
dc.date.submittedMay 2023en_US
dc.date.updated2023-08-09T14:37:48Zen_US
dc.descriptionEMBARGO NOTE: This item is embargoed until 2025-05-01en_US
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.en_US
dc.embargo.lift2025-05-01en_US
dc.embargo.terms2025-05-01en_US
dc.format.mimetypeapplication/pdfen_US
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>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/115067en_US
dc.language.isoengen_US
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.en_US
dc.subjectmealsen_US
dc.subjectdieten_US
dc.subjectdata miningen_US
dc.subjecthabitsen_US
dc.subjectcalorieen_US
dc.subjectcompensationen_US
dc.subjectglucoseen_US
dc.subjectphysical activityen_US
dc.subjectCGMen_US
dc.subjectdiet-trackingen_US
dc.titleA Computational Analysis of Meal Events Using Food Diaries and Continuous Glucose Monitorsen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentElectrical and Computer Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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