Lead Investigator: Xiaohua Douglas Zhang, University of Kentucky
Title of Proposal Research: Markers derived from continuous glucose monitoring data to guide exercise for T1DM
Vivli Data Request: 8834
Funding Source: None
Potential Conflicts of Interest: None
Summary of the Proposed Research:
This project aims to advance diabetes care by leveraging continuous glucose monitoring (CGM) data and machine learning methodologies, with a focus on type 1 diabetes (T1D). The project builds upon the Type 1 Diabetes and Exercise Initiative (T1DEXI) study to better understand how various exercise types and timings affect glucose response in individuals with T1D.
Aim 1: Perform data mining on the T1DEXI dataset and explore the effects of different exercise sessions on T1D.
Aim 2: Develop a predictive machine learning model for optimal exercise outcomes
Aim 3: Explore the missing value issues in the CGM data from the T1DEXI study and develop methods and strategies to predict missing values in CGM readings.
Requested Studies:
Type 1 Diabetes EXercise Initiative: The Effect of Exercise on Glycemic Control in Type 1 Diabetes Study
Data Contributor: Jaeb Center for Health Research Foundation, Inc.
Study ID: T1-DEXI
Sponsor ID: T1-DEXI
Type 1 Diabetes EXercise Initiative Pediatric Study (T1DexiP): The Effect of Exercise on Glycemic Control in Youth with Type 1 Diabetes
Data Contributor: Jaeb Center for Health Research Foundation, Inc.
Study ID: T1-DEXIP
Sponsor ID: T1-DEXIP