Meal Detection and Meal Size Estimation for Type 1 Diabetes Treatment: A Variable State Dimension Approach

2015 ◽  
Author(s):  
Jinyu Xie ◽  
Qian Wang
Keyword(s):  
Type 1 Diabetes ◽  
Goodness Of Fit ◽  
Estimation Error ◽  
Artificial Pancreas ◽  
Young Patients ◽  
Estimation Algorithm ◽  
Glucose Variability ◽  
Meal Size ◽  
Size Estimation

To compensate the glucose variability caused by meals is essential in developing Artificial Pancreas for type 1 diabetes. Most existing algorithms rely on meal announcements and determine the insulin doses based on an Insulin-to-Carbohydrate ratio (I:C ratio). However, patients, especially young patients, often forget to provide meal information under natural living conditions. A Variable State Dimension (VSD) based algorithm is developed to detect meals which are unknown to the controller (unannounced meals). The algorithm is evaluated using an FDA-approved UVa/Padova simulator and has demonstrated to achieve 95% success rate in meal detection with less than 17% false alarm rate. In addition, the average meal size estimation error is no more than 13%. We then integrate the VSD-based meal detection and estimation algorithm with our previous published glucose dynamics model consisting of both insulin and carbohydrate inputs. The goodness of fit for 30min-ahead glucose predictions using meal information provided by the VSD-based algorithm has increased by 86% in average compared to the prediction using a model without meal input based on plasma blood glucose (BG) data. Simulation results also show that compared to several meal detection/estimation algorithms in the literature, the VSD-based algorithm has comparable or shorter detection time.

scholarly journals The UVA/Padova Type 1 Diabetes Simulator Goes From Single Meal to Single Day

2018 ◽  
Vol 12 (2) ◽  
pp. 273-281 ◽  
Author(s):  
Roberto Visentin ◽  
Enrique Campos-Náñez ◽  
Michele Schiavon ◽  
Dayu Lv ◽  
Martina Vettoretti ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
In Silico ◽  
Artificial Pancreas ◽  
Real Life ◽  
Glucose Monitoring ◽  
Glucose Variability ◽  
Self Monitoring ◽  
Inhaled Insulin ◽  
Single Meal

Background: A new version of the UVA/Padova Type 1 Diabetes (T1D) Simulator is presented which provides a more realistic testing scenario. The upgrades to the previous simulator, which was accepted by the Food and Drug Administration in 2013, are described. Method: Intraday variability of insulin sensitivity (SI) has been modeled, based on clinical T1D data, accounting for both intra- and intersubject variability of daily SI. Thus, time-varying distributions of both subject’s basal insulin infusion and insulin-to-carbohydrate ratio were calculated and made available to the user. A model of “dawn” phenomenon based on clinical T1D data has been also included. Moreover, the model of subcutaneous insulin delivery has been updated with a recently developed model of commercially available fast-acting insulin analogs. Models of both intradermal and inhaled insulin pharmacokinetics have been included. Finally, new models of error affecting continuous glucose monitoring and self-monitoring of blood glucose devices have been added. Results: One hundred in silico adults, adolescent, and children have been generated according to the above modifications. The new simulator reproduces the intraday glucose variability observed in clinical data, also describing the nocturnal glucose increase, and the simulated insulin profiles reflect real life data. Conclusions: The new modifications introduced in the T1D simulator allow to extend its domain of validity from “single-meal” to “single-day” scenarios, thus enabling a more realistic framework for in silico testing of advanced diabetes technologies including glucose sensors, new insulin molecules and artificial pancreas.

scholarly journals Artificial Pancreas Systems and Physical Activity in Patients with Type 1 Diabetes: Challenges, Adopted Approaches, and Future Perspectives

2019 ◽  
Vol 13 (6) ◽  
pp. 1077-1090 ◽  
Author(s):  
Sémah Tagougui ◽  
Nadine Taleb ◽  
Joséphine Molvau ◽  
Élisabeth Nguyen ◽  
Marie Raffray ◽  
...  
Keyword(s):  
Physical Activity ◽  
Type 1 Diabetes ◽  
Healthcare Professionals ◽  
Therapeutic Option ◽  
Artificial Pancreas ◽  
Glucose Monitoring ◽  
Glucose Variability ◽  
Delivery Methods ◽  
Current State

Physical activity is important for patients living with type 1 diabetes (T1D) but limited by the challenges associated with physical activity induced glucose variability. Optimizing glycemic control without increasing the risk of hypoglycemia is still a hurdle despite many advances in insulin formulations, delivery methods, and continuous glucose monitoring systems. In this respect, the artificial pancreas (AP) system is a promising therapeutic option for a safer practice of physical activity in the context of T1D. It is important that healthcare professionals as well as patients acquire the necessary knowledge about how the AP system works, its limits, and how glucose control is regulated during physical activity. This review aims to examine the current state of knowledge on exercise-related glucose variations especially hypoglycemic risk in T1D and to discuss their effects on the use and development of AP systems. Though effective and highly promising, these systems warrant further research for an optimized use around exercise.

scholarly journals A Closed-Loop Artificial Pancreas Using Model Predictive Control and a Sliding Meal Size Estimator

2009 ◽  
Vol 3 (5) ◽  
pp. 1082-1090 ◽  
Author(s):  
Hyunjin Lee ◽  
Bruce A. Buckingham ◽  
Darrell M. Wilson ◽  
B. Wayne Bequette
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Closed Loop ◽  
Artificial Pancreas ◽  
Estimation Algorithm ◽  
Substantial Improvement ◽  
Performance Comparison ◽  
Diabetic Subject ◽  
Meal Size ◽  
Size Estimation

The objective of this article is to present a comprehensive strategy for a closed-loop artificial pancreas. A meal detection and meal size estimation algorithm is developed for situations in which the subject forgets to provide a meal insulin bolus. A pharmacodynamic model of insulin action is used to provide insulin-on-board constraints to explicitly include the future effect of past and currently delivered insulin boluses. In addition, a supervisory pump shut-off feature is presented to avoid hypoglycemia. All of these components are used in conjunction with a feedback control algorithm using model predictive control (MPC). A model for MPC is developed based on a study of 20 subjects and is tested in a hypothetical clinical trial of 100 adolescent and 100 adult subjects using a Food and Drug Administration-approved diabetic subject simulator. In addition, a performance comparison of previously and newly proposed meal size estimation algorithms using 200 in silico subjects is presented. Using the new meal size estimation algorithm, the integrated artificial pancreas system yielded a daily mean glucose of 138 and 132 mg/dl for adolescents and adults, respectively, which is a substantial improvement over the MPC-only case, which yielded 159 and 145 mg/dl.

733-P: Postprandial Glucose Variability in People with Type 1 Diabetes Is Individual and Impacted by Physiological and Clinical Parameters

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 733-P
Author(s):  
CASSY F.B. DINGENA ◽  
AILSA MARSH ◽  
RAMZI AJJAN ◽  
MATTHEW CAMPBELL
Keyword(s):  
Type 1 Diabetes ◽  
Glucose Variability ◽  
Postprandial Glucose ◽  
Clinical Parameters

Venular oxygen saturation is increased in young patients with type 1 diabetes and mild nonproliferative diabetic retinopathy

2020 ◽  
Vol 98 (8) ◽  
pp. 800-807 ◽  
Author(s):  
Nina C.B.B. Veiby ◽  
Aida Simeunovic ◽  
Martin Heier ◽  
Cathrine Brunborg ◽  
Naila Saddique ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Diabetic Retinopathy ◽  
Oxygen Saturation ◽  
Young Patients ◽  
Nonproliferative Diabetic Retinopathy

Dual‐hormone artificial pancreas for management of type 1 diabetes: Recent progress and future directions

2021 ◽  
Author(s):  
Marco Infante ◽  
David A. Baidal ◽  
Michael R. Rickels ◽  
Andrea Fabbri ◽  
Jay S. Skyler ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Recent Progress ◽  
Artificial Pancreas ◽  
Future Directions

scholarly journals Anxiety, depression, and glycemic control during Covid-19 pandemic in youths with type 1 diabetes

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Maria Cusinato ◽  
Mariangela Martino ◽  
Alex Sartori ◽  
Claudia Gabrielli ◽  
Laura Tassara ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Psychological Impact ◽  
Glucose Monitoring ◽  
Severe Hypoglycemia ◽  
Well Being ◽  
Glucose Variability ◽  
Threshold Score ◽  
The Impact

Abstract Objectives Our study aims to assess the impact of lockdown during the coronavirus disease 2019 pandemic on glycemic control and psychological well-being in youths with type 1 diabetes. Methods We compared glycemic metrics during lockdown with the same period of 2019. The psychological impact was evaluated with the Test of Anxiety and Depression. Results We analyzed metrics of 117 adolescents (87% on Multiple Daily Injections and 100% were flash glucose monitoring/continuous glucose monitoring users). During the lockdown, we observed an increase of the percentage of time in range (TIR) (p<0.001), with a significant reduction of time in moderate (p=0.002), and severe hypoglycemia (p=0.001), as well as the percentage of time in hyperglycemia (p<0.001). Glucose variability did not differ (p=0.863). The glucose management indicator was lower (p=0.001). 7% of youths reached the threshold-score (≥115) for anxiety and 16% for depression. A higher score was associated with lower TIR [p=0.028, p=0.012]. Conclusions Glycemic control improved during the first lockdown period with respect to the previous year. Symptoms of depression and anxiety were associated with worse glycemic control; future researches are necessary to establish if this improvement is transient and if psychological difficulties will increase during the prolonged pandemic situation.

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