Observational Study of Glycemic Impact of Anticipatory and Early-Race Athletic Competition Stress in Type 1 Diabetes.

Athletic competitions and the associated psychological stress are a challenge for people with type 1 diabetes (T1D). This study aims to understand the influence of anticipatory and early race competition stress on blood glucose concentrations and to identify personality, demographic, or behavioral traits indicative in the scope of the impact. Ten recreational athletes with T1D competed in an athletic competition and an exercise-intensity matched non-competition "training" session for comparison.

Meal and Physical Activity Detection from Free-living Data for Discovering Disturbance Patterns to Glucose Levels in People with Diabetes.

Objective: Interpretation of time series data collected in free-living has gained importance in chronic disease management. Some data are collected objectively from sensors and some are estimated and entered by the individual. In type 1 diabetes (T1D), blood glucose concentration (BGC) data measured by continuous glucose monitoring (CGM) systems and insulin doses administered can be used to detect the occurrences of meals and physical activities and generate the personal daily living patterns for use in automated insulin delivery (AID).

Detection of Meals and Physical Activity Events From Free-Living Data of People With Diabetes.

Background: Predicting carbohydrate intake and physical activity in people with diabetes is crucial for improving blood glucose concentration regulation. Patterns of individual behavior can be detected from historical free-living data to predict meal and exercise times. Data collected in free-living may have missing values and forgotten manual entries.

Physical Activity and Psychological Stress Detection and Assessment of Their Effects on Glucose Concentration Predictions in Diabetes Management.

Objective: Continuous glucose monitoring (CGM) enables prediction of the future glucose concentration (GC) trajectory for making informed diabetes management decisions. The glucose concentration values are affected by various physiological and metabolic variations, such as physical activity (PA) and acute psychological stress (APS), in addition to meals and insulin. In this work, we extend our adaptive glucose modeling framework to incorporate the effects of PA and APS on the GC predictions.

Discrimination of simultaneous psychological and physical stressors using wristband biosignals.

Background And Objective: In this work, we address the problem of detecting and discriminating acute psychological stress (APS) in the presence of concurrent physical activity (PA) using wristband biosignals. We focused on signals available from wearable devices that can be worn in daily life because the ultimate objective of this work is to provide APS and PA information in real-time management of chronic conditions such as diabetes by automated personalized insulin delivery. Monitoring APS noninvasively throughout free-living conditions remains challenging because the responses to APS and PA of many physiological variables measured by wearable devices are similar.

Determining Physical Activity Characteristics from Wristband Data for Use in Automated Insulin Delivery Systems.

Algorithms that can determine the type of physical activity (PA) and quantify the intensity can allow precision medicine approaches, such as automated insulin delivery systems that modulate insulin administration in response to PA. In this work, data from a multi-sensor wristband is used to design classifiers to distinguish among five different physical states (PS) (resting, activities of daily living, running, biking, and resistance training), and to develop models to estimate the energy expenditure (EE) of the PA for diabetes therapy. The data collected are filtered, features are extracted from the reconciled signals, and the extracted features are used by machine learning algorithms, including deep-learning techniques, to obtain accurate PS classification and EE estimation.

Simulation Software for Assessment of Nonlinear and Adaptive Multivariable Control Algorithms: Glucose - Insulin Dynamics in Type 1 Diabetes.

A simulator for testing automatic control algorithms for nonlinear systems with time-varying parameters, variable time delays, and uncertainties is developed. It is based on simulation of virtual patients with Type 1 diabetes (T1D). Nonlinear models are developed to describe glucose concentration (GC) variations based on user-defined scenarios for meal consumption, insulin administration, and physical activity.

Online Glucose Prediction Using Computationally Efficient Sparse Kernel Filtering Algorithms in Type-1 Diabetes.

Streaming data from continuous glucose monitoring (CGM) systems enable the recursive identification of models to improve estimation accuracy for effective predictive glycemic control in patients with type-1 diabetes. A drawback of conventional recursive identification techniques is the increase in computational requirements, which is a concern for online and real-time applications such as the artificial pancreas systems implemented on handheld devices and smartphones where computational resources and memory are limited. To improve predictions in such computationally constrained hardware settings, efficient adaptive kernel filtering algorithms are developed in this paper to characterize the nonlinear glycemic variability by employing a sparsification criterion based on the information theory to reduce the computation time and complexity of the kernel filters without adversely deteriorating the predictive performance.

Controlling the AP Controller: Controller Performance Assessment and Modification.

Background: Despite recent advances in closed-loop control of blood glucose concentration (BGC) in people with type 1 diabetes (T1D), online performance assessment and modification of artificial pancreas (AP) control systems remain a challenge as the metabolic characteristics of users change over time.

Methods: A controller performance assessment and modification system (CPAMS) analyzes the glucose concentration variations and controller behavior, and modifies the parameters of the control system used in the multivariable AP system. Various indices are defined to quantitatively evaluate the controller performance in real time.

Multi-Model Sensor Fault Detection and Data Reconciliation: A Case Study with Glucose Concentration Sensors for Diabetes.

Erroneous information from sensors affect process monitoring and control. An algorithm with multiple model identification methods will improve the sensitivity and accuracy of sensor fault detection and data reconciliation (SFD&DR). A novel SFD&DR algorithm with four types of models including outlier robust Kalman filter, locally weighted partial least squares, predictor-based subspace identification, and approximate linear dependency-based kernel recursive least squares is proposed.

Multi-level Supervision and Modification of Artificial Pancreas Control System.

Artificial pancreas (AP) systems provide automated regulation of blood glucose concentration (BGC) for people with type 1 diabetes (T1D). An AP includes three components: a continuous glucose monitoring (CGM) sensor, a controller calculating insulin infusion rate based on the CGM signal, and a pump delivering the insulin amount calculated by the controller to the patient. The performance of the AP system depends on successful operation of these three components.

Multivariable Artificial Pancreas for Various Exercise Types and Intensities.

Background: Exercise challenges people with type 1 diabetes in controlling their glucose concentration (GC). A multivariable adaptive artificial pancreas (MAAP) may lessen the burden.

Methods: The MAAP operates without any user input and computes insulin based on continuous glucose monitor and physical activity signals.

Incorporating Unannounced Meals and Exercise in Adaptive Learning of Personalized Models for Multivariable Artificial Pancreas Systems.

Background: Despite the recent advancements in the modeling of glycemic dynamics for type 1 diabetes mellitus, automatically considering unannounced meals and exercise without manual user inputs remains challenging.

Method: An adaptive model identification technique that incorporates exercise information and estimates of the effects of unannounced meals obtained automatically without user input is proposed in this work. The effects of the unknown consumed carbohydrates are estimated using an individualized unscented Kalman filtering algorithm employing an augmented glucose-insulin dynamic model, and exercise information is acquired from noninvasive physiological measurements.

Adaptive and Personalized Plasma Insulin Concentration Estimation for Artificial Pancreas Systems.

Background: The artificial pancreas (AP) system, a technology that automatically administers exogenous insulin in people with type 1 diabetes mellitus (T1DM) to regulate their blood glucose concentrations, necessitates the estimation of the amount of active insulin already present in the body to avoid overdosing.

Method: An adaptive and personalized plasma insulin concentration (PIC) estimator is designed in this work to accurately quantify the insulin present in the bloodstream. The proposed PIC estimation approach incorporates Hovorka's glucose-insulin model with the unscented Kalman filtering algorithm.

Automatic Detection and Estimation of Unannounced Meals for Multivariable Artificial Pancreas System.

Background: Automatically attenuating the postprandial rise in the blood glucose concentration without manual meal announcement is a significant challenge for artificial pancreas (AP) systems. In this study, a meal module is proposed to detect the consumption of a meal and to estimate the amount of carbohydrate (CHO) intake.

Methods: The meals are detected based on qualitative variables describing variation of continuous glucose monitoring (CGM) readings.

Model-Fusion-Based Online Glucose Concentration Predictions in People with Type 1 Diabetes.

Accurate predictions of glucose concentrations are necessary to develop an artificial pancreas (AP) system for people with type 1 diabetes (T1D). In this work, a novel glucose forecasting paradigm based on a model fusion strategy is developed to accurately characterize the variability and transient dynamics of glycemic measurements. To this end, four different adaptive filters and a fusion mechanism are proposed for use in the online prediction of future glucose trajectories.

Meal Detection and Carbohydrate Estimation Using Continuous Glucose Sensor Data.

A meal detection and meal-size estimation algorithm is developed for use in artificial pancreas (AP) control systems for people with type 1 diabetes. The algorithm detects the consumption of a meal and estimates its carbohydrate (CHO) amount to determine the appropriate dose of insulin bolus for a meal. It can be used in AP systems without manual meal announcements, or as a safety feature for people who may forget entering meal information manually.

Hypoglycemia Detection and Carbohydrate Suggestion in an Artificial Pancreas.

Fear of hypoglycemia is a major concern for many patients with type 1 diabetes and affects patient decisions for use of an artificial pancreas system. We propose an alternative way for prevention of hypoglycemia by issuing predictive hypoglycemia alarms and encouraging patients to consume carbohydrates in a timely manner. The algorithm has been tested on 6 subjects (3 males and 3 females, age 24.