Table of Contents
Fetching ...

A Model-based Approach for Glucose Control via Physical Activity

Pierluigi Francesco De Paola, Alessandro Borri, Alessia Paglialonga, Pasquale Palumbo, Fabrizio Dabbene

TL;DR

The paper addresses the need for quantitative real-time exercise prescriptions to slow long-term T2D progression. It develops an MPC-based control framework applied to a modified glucose–insulin progression model that incorporates a long-term exercise state $V_l$ and an average exercise input $u_{eq}$ distributed over a period $T$. Results show that open-loop progression drives glucose toward a hyperglycemic steady state ($G ightarrow 600$ mg/dl), while the MPC controller delays and can reverse progression toward normoglycemia ($G ightarrow 100$ mg/dl), with the model translating $u_{eq}$ into concrete exercise durations around 300 minutes/week early on. These findings support the potential for model-driven, actionable exercise guidance and motivate validation on higher-dimensional progression models for broader clinical translation, including integration with meal effects and inter-patient variability.

Abstract

The role played by physical activity in slowing down the progression of type-2 diabetes is well recognized. However, except for general clinical guidelines, quantitative real-time estimates of the recommended amount of physical activity, based on the evolving individual conditions, are {still missing} in the literature. The aim of this work is to provide a control-theoretical formulation of the exercise encoding all the exercise-related features (intensity, duration, period). Specifically, 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 account for the long-term effects of regular exercise. Preliminary simulations show promising results, well aligned with clinical evidence. These findings can be the basis for further validation of the control law on high-dimensional diabetes progression models to ultimately translate the predictions of the controller into meaningful recommendations.

A Model-based Approach for Glucose Control via Physical Activity

TL;DR

The paper addresses the need for quantitative real-time exercise prescriptions to slow long-term T2D progression. It develops an MPC-based control framework applied to a modified glucose–insulin progression model that incorporates a long-term exercise state and an average exercise input distributed over a period . Results show that open-loop progression drives glucose toward a hyperglycemic steady state ( mg/dl), while the MPC controller delays and can reverse progression toward normoglycemia ( mg/dl), with the model translating into concrete exercise durations around 300 minutes/week early on. These findings support the potential for model-driven, actionable exercise guidance and motivate validation on higher-dimensional progression models for broader clinical translation, including integration with meal effects and inter-patient variability.

Abstract

The role played by physical activity in slowing down the progression of type-2 diabetes is well recognized. However, except for general clinical guidelines, quantitative real-time estimates of the recommended amount of physical activity, based on the evolving individual conditions, are {still missing} in the literature. The aim of this work is to provide a control-theoretical formulation of the exercise encoding all the exercise-related features (intensity, duration, period). Specifically, 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 account for the long-term effects of regular exercise. Preliminary simulations show promising results, well aligned with clinical evidence. These findings can be the basis for further validation of the control law on high-dimensional diabetes progression models to ultimately translate the predictions of the controller into meaningful recommendations.

Paper Structure

This paper contains 5 sections, 1 equation, 2 figures, 1 table.

Figures (2)

  • Figure 1: Basal glucose concentration as a function of time in the open loop case (dashed line) and in the controlled case (solid line).
  • Figure 2: Recommended duration of single exercise session as a function of time computed by means of the inverse map in the MPC-controlled case ($\bar{u} = 50\%, T= 2$ days).