Personal and Social‐Built Environmental Factors of Glucose Variability Among Multiethnic Groups of Adults With Type 2 Diabetes: Research Protocol Using Ecological Momentary Assessment, Continuous Glucose Monitoring, and Actigraphy

Author:

Nam Soohyun1ORCID,Jeon Sangchoon1,Ash Garrett I.2,Weinzimer Stuart2,Dunton Genevieve F.3,Parekh Niyati4,Grey Margaret1,Chen Kai5,Lee Minjung1,Sajdlowska Anna1,Whittemore Robin1

Affiliation:

1. School of Nursing Yale University Orange Connecticut USA

2. School of Medicine Yale University New Haven Connecticut USA

3. Departments of Preventive Medicine and Psychology University of Southern California Los Angeles California USA

4. College of Global Public Health, and Population Health Langone School of Medicine New York New York USA

5. School of Public Health Yale University New Haven Connecticut USA

Abstract

ABSTRACTGlucose variability (GV)—the degree of fluctuation in glucose levels over a certain period of time—is emerging as an important parameter of dynamic glycemic control. Repeated glycemic oscillations have been reported to be the link to diabetes complications. This prospective observational study aims to: (1) identify multilevel risk factors (personal and social‐built environmental factors) associated with high GV; (2) identify “within‐person predictors” of high GV leveraging the intra‐person data to inform future personalized diabetes interventions; and (3) examine which lifestyle factors either mediate or moderate the relationship between emotional well‐being and GV among diverse adults with type 2 diabetes (T2D). We will recruit 200 adults with T2D from the community. All participants will complete baseline surveys assessing demographics, lifestyle, social‐built environmental, and clinical factors. Real‐time dynamic glucose levels will be measured using continuous glucose monitoring (CGM). Sleep, physical activity, diet/eating, and emotional well‐being will be measured with an actigraphy device and a real‐time self‐report tool (ecological momentary assessment [EMA]) across 14 days. Two 24‐h dietary recall data will be collected by online video calls. Generalized linear models, multilevel models, and structural equation models will be developed to achieve the study aims. The findings from the study will identify high‐risk groups of high GV who would benefit from CGM to improve diabetes outcomes and inform the future development of personalized just‐in‐time interventions targeting lifestyle behaviors with an increased understanding of GV and by supporting healthcare providers' clinical decisions.

Publisher

Wiley

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