Modeling the variability of insulin sensitivity for people with Type 1 Diabetes based on clinical data from an artificial pancreas study.

Type 1 Diabetes is an autoimmune disease that eliminates endogenous insulin production. Without the crucial hormone insulin, which is necessary to equilibrate the blood glucose level, the patient must inject insulin subcutaneously. Treatment must be personalized (timing and size of insulin delivery) to achieve glycaemic equilibrium and avoid long-term comorbidities. Patients are educated on Functional Insulin Therapy (FIT) in order to independently adjust insulin delivery several times a day (at least prior to each meal and physical activity). Among personalized parameters, the Correction Factor is used to occasionally correct hyperglycemia via the injection of an insulin dose (bolus) and its value determines the bolus size. Although well-known in common diabetes practice for chronically poorly controlled patients, the phenomenon of "hyperglycemia induces insulin resistance" on a short term basis in patients with rather well controlled diabetes is presented here. Using a new database of evidence, we show that the insulin sensitivity factor, depends on the current level of glycaemia. This opens the door to refining dosing rules for patients and insulin delivery devices in artificial pancreas systems.

An original piecewise model for computing energy expenditure from accelerometer and heart rate signals.

OBJECTIVE: Activity energy expenditure (EE) plays an important role in healthcare, therefore, accurate EE measures are required. Currently available reference EE acquisition methods, such as doubly labeled water and indirect calorimetry, are complex, expensive, uncomfortable, and/or difficult to apply on real time. To overcome these drawbacks, the goal of this paper is to propose a model for computing EE in real time (minute-by-minute) from heart rate and accelerometer signals. APPROACH: The proposed model, which consists of an original branched model, uses heart rate signals for computing EE on moderate to vigorous physical activities and a linear combination of heart rate and counts per minute for computing EE on light to moderate physical activities. Model parameters were estimated from a given data set composed of 53 subjects performing 25 different physical activities (light-, moderate- and vigorous-intensity), and validated using leave-one-subject-out. A different database (semi-controlled in-city circuit), was used in order to validate the versatility of the proposed model. Comparisons are done versus linear and nonlinear models, which are also used for computing EE from accelerometer and/or HR signals. MAIN RESULTS: The proposed piecewise model leads to more accurate EE estimations ([Formula: see text], [Formula: see text] and [Formula: see text] J kg-1 min-1 and [Formula: see text], [Formula: see text], and [Formula: see text] J kg-1 min-1 on each validation database). SIGNIFICANCE: This original approach, which is more conformable and less expensive than the reference methods, allows accurate EE estimations, in real time (minute-by-minute), during a large variety of physical activities. Therefore, this model may be used on applications such as computing the time that a given subject spent on light-intensity physical activities and on moderate to vigorous physical activities (binary classification accuracy of 0.8155).

Personalization of a compartmental physiological model for an artificial pancreas through integration of patient's state estimation.

Artificial Pancreas (AP) are developed for patients with Type 1 diabetes. This medical device system consists in the association of a subcutaneous continuous glucose monitor (CGM) providing a proxy of the patient's glycaemia and a control algorithm offering the real-time modification of the insulin delivery with an automatic command of the subcutaneous insulin pump. The most complex algorithms are based on a compartmental model of the glucoregulatory system of the patient coupled to an approach of MPC (Model-Predictive-Control) for the command. The automatic and unsupervised control of insulin regulation constitutes a major challenge in AP projects. A given model with its parameterization on the shelf will not directly represent the patient's data behavior and the personalization of the model is a prerequisite before using it in a MPC. The present paper focuses on the personalization of a compartmental showing a method where taking into account the estimation of the patient's state in addition to the parameter estimation improves the results in terms of mean quadratic error.