Personalized hybrid models for exercise, meal, and insulin interventions in type 1 diabetic children and adolescents

Abstract

Inter- and intrapatient variability in blood glucose (BG) metabolism imposes the need for personalized models in diabetes care. Validation of personalized models using the clinical data of type 1 diabetic (T1D) subjects and inclusion of lifestyle factors like exercise as an input in these personalized models are rarely seen in the literature. In this paper, we have developed personalized BG prediction models with a specialized structure comprising three different classes of models: a mechanistic model for meal absorption dynamics, an empirical model for insulin absorption kinetics and a transfer function model for prediction of personalized BG dynamics. Hence, the proposed model structure is termed a hybrid model (HM). Exercise intensity in these personalized HMs is quantified using a measure called rate of perceived exertion (RPE). The clinical data of 34 T1D subjects are used for model development and two different scenarios of cross validation - same day validation (SDV) and different day validation (DDV). The BG data collected during one of the clinical visits have been used for model development (85-90% of data of this visit) and SDV (the remaining 10-15% data of the same visit); while the data collected during the other day visit have been used for DDV. This is to test the ability of developed HMs in predicting the BG dynamics for a prolonged time period, thereby ensuring their potential to capture intrapatient variability. The fitness and cross validation results of personalized HMs not only show accurate prediction of BG dynamics but also reveal the potential of HMs in capturing both inter- and intrapatient variability. © 2013 American Chemical Society.