Closed-loop robust control of long-term diabetes progression via physical activity management

TL;DR

This paper develops a control-theoretical framework using model predictive control to optimize physical activity recommendations for long-term diabetes management, accounting for patient variability and long-term effects.

Contribution

It introduces a novel feedback control law based on a modified diabetes progression model to quantitatively guide physical activity for diabetes control.

Findings

The control law demonstrates robustness to initial conditions and parameter variations.

Simulation results show effective long-term management of diabetes progression.

The approach supports medical decision-making with quantitative exercise recommendations.

Abstract

Large clinical evidence acknowledges the crucial role played by physical activity in delaying the progression of type-2 diabetes. However, the literature lacks control approaches that leverage exercise for type-2 diabetes control and more in general lacks a quantitative assessment of medical guidelines on the recommended amount of physical activity to be performed, mainly due to the absence of mathematical models that suitably estimate its benefits on diabetes progression. In this work, in order to provide a control-theoretical formulation of the exercise, we design a feedback law in terms of recommended physical activity, following a model predictive control approach, based on a widespread compact diabetes progression model, suitably modified to properly account for the long-term effect of the exercise. Moreover we illustrate how the proposed approach proves to show reliable robustness properties with respect to initial conditions and parameter perturbations, which may be used to reflect inter-patient variability. Results are encouraging in view of the validation of the control law on comprehensive high-dimensional models of diabetes progression, with the aim of translating the prediction of the controller into reasonable recommendations and to quantitatively support medical decision-making.