On the optimal design of clinical tests for the identification of type 1 diabetes models in the presence of continuous glucose monitoring systems

Model-Based Design of Experiments (MBDoE) techniques can be a valuable tool to improve the information content of clinical tests when the purpose is to identify the set of parameters of physiological models in type 1 diabetes mellitus care. Recent advances on sensor technology allowed for the development of continuous glucose monitoring systems (CGMSs), where measurements can be collected with a frequency which is much higher than possible so far. In this paper a dynamic approach to model-based design of experiments is adopted to specifically tailor the design procedure to the features of a CGMS. A simulated case study is used to assess the impact of CGMSs in the experiment design and successive parameter estimation for the identification of a complex physiological model of glucose homeostasis. Results are compared to an MBDoE applied to a conventional discrete measurement system.