grant
Integrating Sleep and Circadian Factors into Machine Learning Models for Personalized Glycemic Response Prediction
Organization COLORADO STATE UNIVERSITYLocation FORT COLLINS, UNITED STATESPosted 5 Feb 2024Deadline 31 Jan 2029
NIHUS FederalResearch GrantFY202521+ years oldAccountingAddressAdultAdult HumanAdult-Onset Diabetes MellitusBehaviorBehavioralBenchmarkingBest Practice AnalysisBiologicalBody CompositionCardiometabolic DiseaseCardiometabolic DisorderCardiovascular DiseasesCell Communication and SignalingCell SignalingCircadian DysregulationCircadian RhythmsClinical TrialsConsumptionContinuous Glucose MonitorD-GlucoseDataData ScienceData SetDevelopmentDextroseEatingEffectivenessFamilyFood IntakeFrequenciesFundingFutureGlucoseHealthHyperglycemiaIndividualInterruptionIntracellular Communication and SignalingKetosis-Resistant Diabetes MellitusLife StyleLifestyleLinkMachine LearningMaturity-Onset Diabetes MellitusMeasurementModelingMonitorNIDDMNational Institutes of HealthNoiseNon-Insulin Dependent DiabetesNon-Insulin-Dependent Diabetes MellitusNoninsulin Dependent DiabetesNoninsulin Dependent Diabetes MellitusNutritionNutrition ResearchNutritional StudyNyctohemeral RhythmParentsParticipantPerformancePhysical activityPhysiologicPhysiologicalPopulationPopulation HeterogeneityPositionPositioning AttributeProductivityPublishingResearchResolutionRisk FactorsRotationSignal TransductionSignal Transduction SystemsSignalingSleepSleep disturbancesSlow-Onset Diabetes MellitusStable Diabetes MellitusStandardizationT2 DMT2DT2DMTechniquesTechnologyTestingTimeTrainingTwenty-Four Hour RhythmType 2 Diabetes MellitusType 2 diabetesType II Diabetes MellitusType II diabetesUnited States National Institutes of HealthValidationaberrant sleepactigraphactigraphyadult onset diabetesadulthoodbenchmarkbiologicbiological signal transductionbiomedical scientistcardiometabolic riskcardiovascular disease riskcardiovascular disordercardiovascular disorder riskcareercircadiancircadian abnormalitycircadian biologycircadian disruptioncircadian disturbancecircadian dysfunctioncircadian impairmentcircadian processcircadian rhythmicitycomputer based predictioncontinuous blood glucose monitorcontinuous blood sugar monitorcontinuous glucose measurementcontinuous monitoringcontinuous sugar monitordaily biorhythmday shiftdevelopmentaldietarydisease riskdisorder riskdisrupted sleepdisturbed sleepdiverse populationsexperiencefeedingglucose metabolismglycemic controlheterogeneous populationhyperglycemicimpaired sleepimprovedindividual heterogeneityindividual variabilityindividual variationindividualized nutritioninterestirregular sleepketosis resistant diabeteslongitudinal data setlongitudinal datasetmachine based learningmachine learning based methodmachine learning based modelmachine learning methodmachine learning methodologiesmachine learning modelmaturity onset diabetesmodel generalizabilitynight shiftnight worknovelparentpersonalized nutritionpopulation diversityprecision nutritionpredict responsivenesspredicting responsepredictive modelingpredictive toolspreventpreventingresolutionsresponseshift workshiftworkskillssleep disruptionsleep dysregulationsleep/wake disruptionsleep/wake disturbancetype 2 DMtype II DMtype two diabetesvalidations
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PROJECT SUMMARY/ABSTRACT
There is increasing consumer interest in using continuous glucose monitors (CGMs) for personalized
nutrition and health optimization. However, while CGMs can identify postprandial glucose spikes that contribute
to type 2 diabetes (T2D) and cardiovascular disease (CVD), the ability to predict abnormal postprandial
glucose…
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