scholarly journals Risk-Averse Food Recommendation Using Bayesian Feedforward Neural Networks for Patients with Type 1 Diabetes Doing Physical Activities

2020 ◽  
Vol 10 (22) ◽  
pp. 8037
Author(s):  
Phuong Ngo ◽  
Miguel Tejedor ◽  
Maryam Tayefi ◽  
Taridzo Chomutare ◽  
Fred Godtliebsen
Keyword(s):  
Physical Activity ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Physical Activities ◽  
Personalized Recommendation ◽  
Neural Network Models ◽  
High Blood Glucose ◽  
Glucose Levels ◽  
Blood Glucose Levels

Background. Since physical activity has a high impact on patients with type 1 diabetes and the risk of hypoglycemia (low blood glucose levels) is significantly higher during and after physical activities, an automatic method to provide a personalized recommendation is needed to improve the blood glucose management and harness the benefits of physical activities. This paper aims to reduce the risk of hypoglycemia and hyperglycemia (high blood glucose levels), and empowers type 1 diabetes patients to make decisions regarding food choices connected with physical activities. Methods. Traditional and Bayesian feedforward neural network models are developed to provide accurate predictions of the blood glucose outcome and the risks of hyperglycemia and hypoglycemia with uncertainty information. Using the proposed models, safe actions that minimize the risk of both hypoglycemia and hyperglycemia are provided as food recommendations to the patient. Results. The predicted blood glucose responses to the optimal and safe food recommendations are significantly better and safer than by taking random food. Conclusions. Simulations conducted on the state-of-the-art UVA/Padova simulator combined with Brenton’s physical activity model show that the proposed methodology is safe and effective in managing blood glucose during and after physical activities.

scholarly journals Blood Glucose Level Forecasting on Type-1-Diabetes Subjects during Physical Activity: A Comparative Analysis of Different Learning Techniques

2021 ◽  
Vol 8 (6) ◽  
pp. 72
Author(s):  
Benedetta De Paoli ◽  
Federico D’Antoni ◽  
Mario Merone ◽  
Silvia Pieralice ◽  
Vincenzo Piemonte ◽  
...  
Keyword(s):  
Physical Activity ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Mean Squared Error ◽  
Glucose Monitoring ◽  
Online Training ◽  
Machine Learning Algorithms ◽  
Glucose Levels ◽  
Blood Glucose Levels

Background: Type 1 Diabetes Mellitus (T1DM) is a widespread chronic disease in industrialized countries. Preventing blood glucose levels from exceeding the euglycaemic range would reduce the incidence of diabetes-related complications and improve the quality of life of subjects with T1DM. As a consequence, in the last decade, many Machine Learning algorithms aiming to forecast future blood glucose levels have been proposed. Despite the excellent performance they obtained, the prediction of abrupt changes in blood glucose values produced during physical activity (PA) is still one of the main challenges. Methods: A Jump Neural Network was developed in order to overcome the issue of predicting blood glucose values during PA. Three learning configurations were developed and tested: offline training, online training, and online training with reinforcement. All configurations were tested on six subjects suffering from T1DM that held regular PA (three aerobic and three anaerobic) and exploited Continuous Glucose Monitoring (CGM). Results: The forecasting performance was evaluated in terms of the Root-Mean-Squared-Error (RMSE), according to a paradigm of Precision Medicine. Conclusions: The online learning configurations performed better than the offline configuration in total days but not on the only CGM associated with the PA; thus, the results do not justify the increased computational burden because the improvement was not significant.

776-P: Characteristics of Overnight Blood Glucose Levels Are Related to Sleep Quality in People with Type 1 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 776-P
Author(s):  
RACHEL BRANDT ◽  
MINSUN PARK ◽  
LAURIE T. QUINN ◽  
MINSEUNG CHU ◽  
YOUNGKWAN SONG ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Sleep Quality ◽  
Glucose Levels ◽  
Blood Glucose Levels

A Review of Type 1 Diabetes (T1D)

2020 ◽  
pp. 13-49
Author(s):  
Eileen O'Donnell ◽  
Liam O'Donnell
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Acting Insulin ◽  
Short Period ◽  
Sporting Activities ◽  
To Come

The diagnosis of Type 1 Diabetes (T1D) will come as an unwelcome surprise to most people. Within a short period of time, the person will have to come to understand and manage this chronic illness. The terminology associated with the T1D condition will also be totally new to the person: diabetes mellitus, pancreas, hyperglycaemia (hyper), hypoglycaemia (hypo), bolus (fast acting insulin), basal (slow acting insulin), ketones and blood glucose levels. The purpose of this article is to assist newly diagnosed patients' understanding of T1D, people who are already living with T1D, carers of people with T1D, partners and family members of someone with T1D, work colleagues, and friends who participate in the same sporting activities or go on holiday with a person who has T1D. In addition, this article reviews how people living with T1D can still enjoy exercise and maintain the best quality of life possible; whilst controlling the blood glucose levels in their body for the rest of their lives to prevent the onset of complications associated with diabetes.

Effect of delay on measurement of blood glucose levels in young subjects with type 1 diabetes

2009 ◽  
Vol 86 (2) ◽  
pp. e31-e33 ◽  
Author(s):  
Daniela Elleri ◽  
Carlo L. Acerini ◽  
Janet M. Allen ◽  
Anne-Mette F. Larsen ◽  
Malgorzata E. Wilinska ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Young Subjects

scholarly journals The mathematician's control toolbox for management of type 1 diabetes

2014 ◽  
Vol 4 (5) ◽  
pp. 20140042 ◽  
Author(s):  
Marie Csete ◽  
John Doyle
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Feedback Loop ◽  
Artificial Pancreas ◽  
Glucagon Release ◽  
Control Engineering ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Physiological Feedback

Blood glucose levels are controlled by well-known physiological feedback loops: high glucose levels promote insulin release from the pancreas, which in turn stimulates cellular glucose uptake. Low blood glucose levels promote pancreatic glucagon release, stimulating glycogen breakdown to glucose in the liver. In healthy people, this control system is remarkably good at maintaining blood glucose in a tight range despite many perturbations to the system imposed by diet and fasting, exercise, medications and other stressors. Type 1 diabetes mellitus (T1DM) results from loss of the insulin-producing cells of the pancreas, the beta cells. These cells serve as both sensor (of glucose levels) and actuator (insulin/glucagon release) in a control physiological feedback loop. Although the idea of rebuilding this feedback loop seems intuitively easy, considerable control mathematics involving multiple types of control schema were necessary to develop an artificial pancreas that still does not function as well as evolved control mechanisms. Here, we highlight some tools from control engineering used to mimic normal glucose control in an artificial pancreas, and the constraints, trade-offs and clinical consequences inherent in various types of control schemes. T1DM can be viewed as a loss of normal physiologic controls, as can many other disease states. For this reason, we introduce basic concepts of control engineering applicable to understanding pathophysiology of disease and development of physiologically based control strategies for treatment.

Using behavioral interventions to assist children with type 1 diabetes manage blood glucose levels.

2008 ◽  
Vol 23 (3) ◽  
pp. 389-406 ◽  
Author(s):  
Kim Lasecki ◽  
Daniel Olympia ◽  
Elaine Clark ◽  
William Jenson ◽  
Lora Tuesday Heathfield
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Behavioral Interventions ◽  
Glucose Levels ◽  
Blood Glucose Levels
Sign in / Sign up

Export Citation Format

Share Document