CGMTSA

CGMTSA analyzes continuous glucose monitoring (CGM) time series data as an R package to provide imputation, outlier detection, calculation of CGM metrics and time series parameters, and visualization for studies of glucose dynamics and diabetes management.

Key Features:

• Missing Data Imputation and Outlier Identification: Time series functions tailored to impute missing CGM values and identify outliers in glucose measurements.
• Calculation of Metrics and Parameters: Computation of recommended CGM metrics alongside key time series parameters for assessing glucose dynamics.
• Interactive and 3D Graphical Visualizations: Generation of interactive and three-dimensional graphs to inspect temporal trends and support time series model optimization.
• Support for CGM Devices and Preprocessing: Compatibility with data from popular CGM devices and support for common data processing steps.

Scientific Applications:

• Diabetes research and management: Time series analyses to characterize glycemic patterns for research and clinical monitoring.
• Clinical decision support and personalized treatment: Derivation of metrics and temporal features to inform clinical decision-making and individualized therapy planning.
• Studies of glucose regulation dynamics: Investigation of acute and long-term temporal dynamics of glucose regulation using CGM time series methods.

Methodology:

Implements time series methodologies for missing data imputation, outlier identification, calculation of CGM metrics and time series parameters, and generation of interactive/3D visualizations while supporting common data processing steps and data from popular CGM devices.