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.

Advantages and Disadvantages of Realtime Continuous Glucose Monitoring in People with Type 2 Diabetes

2012 ◽  
Vol 08 (01) ◽  
pp. 22 ◽  
Author(s):  
M Susan Walker ◽  
Stephanie J Fonda ◽  
Sara Salkind ◽  
Robert A Vigersky ◽  
◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Glucose Levels ◽  
Advantages And Disadvantages ◽  
Blood Glucose Levels

Previous research has shown that realtime continuous glucose monitoring (RT-CGM) is a useful clinical and lifestyle aid for people with type 1 diabetes. However, its usefulness and efficacy for people with type 2 diabetes is less known and potentially controversial, given the continuing controversy over the efficacy of self-monitoring of blood glucose (SMBG) in this cohort. This article reviews theextantliterature on RT-CGM for people with type 2 diabetes, and enumerates several of the advantages and disadvantages of this technology from the perspective of providers and patients. Even patients with type 2 diabetes who are not using insulin and/or are relatively well controlled on oral medications have been shown to spend a significant amount of time each day in hyperglycemia. Additional tools beyond SMBG are necessary to enable providers and patients to clearly grasp and manage the frequency and amplitude of glucose excursions in people with type 2 diabetes who are not on insulin. While SMBG is useful for measuring blood glucose levels, patients do not regularly check and SMBG does not enable many to adequately manage blood glucose levels or capture marked and sustained hyperglycemic excursions. RT-CGM systems, valuable diabetes management tools for people with type 1 diabetes or insulin-treated type 2 diabetes, have recently been used in type 2 diabetes patients. Theextantstudies, although few, have demonstrated that the use of RT-CGM has empowered people with type 2 diabetes to improve their glycemic control by making and sustaining healthy lifestyle choices.

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 Evaluating the Infuence of Mood and Stress on Glycemic Variability in T1DM Patients using Glucose Monitoring Sensors and Pools

2020 ◽  
Author(s):  
Sergio Contador Pachón ◽  
Marta Botella Serrano ◽  
Aranzazu Aramendi Zurimendi ◽  
Remedios Rodríguez Martínez ◽  
Esther Maqueda Villaizán ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Glycemic Control ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
P Value ◽  
Glucose Levels ◽  
Blood Glucose Levels

Objective: Assess in a sample of patients with type 1 diabetes mellitus whether mood and stress influence blood glucose levels and variability. Material and Methods: Continuous glucose monitoring was performed on 10 patients with type 1 diabetes, where interstitial glucose values were recorded every 15 minutes. A daily survey was conducted through Google Forms, collecting information on mood and stress. The day was divided into 6 slots of 4-hour each, asking the patient to assess each slot in relation to mood (sad, normal or happy) and stress (calm, normal or nervous). Different measures of glycemic control (arithmetic mean and percentage of time below/above the target range) and variability (standard deviation, percentage coefficient of variation, mean amplitude of glycemic excursions and mean of daily differences) were calculated to relate the mood and stress perceived by patients with blood glucose levels and glycemic variability. A hypothesis test was carried out to quantitatively compare the data groups of the different measures using the Student's t-test. Results: Statistically significant differences (p-value < 0.05) were found between different levels of stress. In general, average glucose and variability decrease when the patient is calm. There are statistically significant differences (p-value < 0.05) between different levels of mood. Variability increases when the mood changes from sad to happy. However, the patient's average glucose decreases as the mood improves. Conclusions: Variations in mood and stress significantly influence blood glucose levels, and glycemic variability in the patients analyzed with type 1 diabetes mellitus. Therefore, they are factors to consider for improving glycemic control. The mean of daily differences does not seem to be a good indicator for variability. Keywords: Diabetes mellitus, continuous glucose monitoring, glycemic variability, average glycemia, glycemic control, stress, mood.

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
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