scholarly journals Physical Activity, Dietary Patterns, and Glycemic Management in Active Individuals with Type 1 Diabetes: An Online Survey

2021 ◽  
Vol 18 (17) ◽  
pp. 9332
Author(s):  
Sheri R. Colberg ◽  
Jihan Kannane ◽  
Norou Diawara
Keyword(s):  
Physical Activity ◽  
Type 1 Diabetes ◽  
Online Survey ◽  
Diabetes Management ◽  
Physically Active ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
And Performance ◽  
Glycemic Management

Individuals with type 1 diabetes (T1D) are able to balance their blood glucose levels while engaging in a wide variety of physical activities and sports. However, insulin use forces them to contend with many daily training and performance challenges involved with fine-tuning medication dosing, physical activity levels, and dietary patterns to optimize their participation and performance. The aim of this study was to ascertain which variables related to the diabetes management of physically active individuals with T1D have the greatest impact on overall blood glucose levels (reported as A1C) in a real-world setting. A total of 220 individuals with T1D completed an online survey to self-report information about their glycemic management, physical activity patterns, carbohydrate and dietary intake, use of diabetes technologies, and other variables that impact diabetes management and health. In analyzing many variables affecting glycemic management, the primary significant finding was that A1C values in lower, recommended ranges (<7%) were significantly predicted by a very-low carbohydrate intake dietary pattern, whereas the use of continuous glucose monitoring (CGM) devices had the greatest predictive ability when A1C was above recommended (≥7%). Various aspects of physical activity participation (including type, weekly time, frequency, and intensity) were not significantly associated with A1C for participants in this survey. In conclusion, when individuals with T1D are already physically active, dietary changes and more frequent monitoring of glucose may be most capable of further enhancing glycemic management.

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 The Development of an Exercise Advisor App for Type 1 Diabetes: Digitization Facilitates More Individualized Guidance

2020 ◽  
pp. 193229682097981
Author(s):  
Sarah M. McGaugh ◽  
Stephanie Edwards ◽  
Howard Wolpert ◽  
Dessi P. Zaharieva ◽  
Nany Gulati ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Diabetes Management ◽  
Management Strategies ◽  
Target Range ◽  
Feeding Strategies ◽  
Insulin Regimen ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Carbohydrate Feeding

Maintaining blood glucose levels in the target range during exercise can be onerous for people with type 1 diabetes (T1D). Using evidence-based research and consensus guidelines, we developed an exercise advisor app to reduce some of the burden associated with diabetes management during exercise. The app will guide the user on carbohydrate feeding strategies and insulin management strategies before, during, and after exercise and provide targeted and individualized recommendations. As a basis for the recommendations, the decision trees for the app use various factors including the type of insulin regimen, time of activity, previous insulin boluses, and current glucose level. The app is designed to meet the various needs of people with T1D for different activities to promote safe exercise practices.

scholarly journals LSTMs and Deep Residual Networks for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3303
Author(s):  
Jeremy Beauchamp ◽  
Razvan Bunescu ◽  
Cindy Marling ◽  
Zhongen Li ◽  
Chang Liu
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Diabetes Management ◽  
Artificial Pancreas ◽  
Real Patient ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Novel Approach ◽  
The 1960S

To avoid serious diabetic complications, people with type 1 diabetes must keep their blood glucose levels (BGLs) as close to normal as possible. Insulin dosages and carbohydrate consumption are important considerations in managing BGLs. Since the 1960s, models have been developed to forecast blood glucose levels based on the history of BGLs, insulin dosages, carbohydrate intake, and other physiological and lifestyle factors. Such predictions can be used to alert people of impending unsafe BGLs or to control insulin flow in an artificial pancreas. In past work, we have introduced an LSTM-based approach to blood glucose level prediction aimed at “what-if” scenarios, in which people could enter foods they might eat or insulin amounts they might take and then see the effect on future BGLs. In this work, we invert the “what-if” scenario and introduce a similar architecture based on chaining two LSTMs that can be trained to make either insulin or carbohydrate recommendations aimed at reaching a desired BG level in the future. Leveraging a recent state-of-the-art model for time series forecasting, we then derive a novel architecture for the same recommendation task, in which the two LSTM chain is used as a repeating block inside a deep residual architecture. Experimental evaluations using real patient data from the OhioT1DM dataset show that the new integrated architecture compares favorably with the previous LSTM-based approach, substantially outperforming the baselines. The promising results suggest that this novel approach could potentially be of practical use to people with type 1 diabetes for self-management of BGLs.

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.

scholarly journals Cabaran Psikologi bagi Remaja yang Menghidap Diabetes Jenis 1

2018 ◽  
Vol 10 (3-2) ◽  
Author(s):  
Ariffi Suraya Rahmani ◽  
Azlina Mohd. Kosnin ◽  
Zakiah Mohamad Ashari
Keyword(s):  
Type 1 Diabetes ◽  
Children And Adolescents ◽  
Diabetes Management ◽  
Public Access ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Negative Effect ◽  
Life Type

Managing a chronic disease is very challenging and requires effective coping strategies to overcome difficulties for maintaining the health and stability of quality of life. Type 1 diabetes is one of the most common chronic diseases in children and adolescents that requires complex treatment and care. Type 1 diabetes management aspects include insulin intake, checking blood glucose levels, monitoring risk and treatment of disease complications, dietary intake, and physical activity. In addition, the lack of understanding of diabetes among family members and friends also makes them difficult to adapt to the disease and can cause them to experience psychological problems and stress. Hence, this paper aims to identify the psychological challenges faced by children and adolescents with type 1 diabetes. The study is conducted by review the literature on 9 published articles from 2010 to 2017 obtained from the Science Direct, SAGE, PubMed and NIH Public Access. The findings have identified several aspects of psychology often experienced by children and adolescents with type 1 diabetes such as depression, anxiety, stress, and distress. This psychological condition can have a negative effect on life skills, glucose control and ability to deal with diabetes management. It is hoped that proper management in encountering psychological challenges will lead to better results.

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

Who Needs Diabetes Education? A Qualitative Analysis of Emerging Adults With Type 1 Diabetes

2021 ◽  
Vol 47 (6) ◽  
pp. 436-446
Author(s):  
Margot E. Porter ◽  
Michelle L. Litchman ◽  
Ernest G. Grigorian ◽  
Julia E. Blanchette ◽  
Nancy A. Allen
Keyword(s):  
Type 1 Diabetes ◽  
Emerging Adults ◽  
Online Community ◽  
Diabetes Management ◽  
Diabetes Education ◽  
Further Education ◽  
Transitional Period ◽  
Primary Care Clinics ◽  
Glycemic Management

Background The purpose of this study is to explore the diabetes self-management education (DSME) needs of emerging adults with type 1 diabetes mellitus (T1DM) because addressing these needs may facilitate optimal glycemic management during this challenging transitional period. Methods A hybrid qualitative design was utilized. Emerging adults and parents of emerging adults were recruited from endocrinology and primary care clinics and through a Utah-specific T1DM online community. Interviews were conducted to asses needs to achieve target A1C. Data were interpreted thematically. Results Emerging adults with T1DM (N = 33) and parents of emerging adults with T1DM (N = 17) were interviewed. Three main themes emerged: (1) mixed desire for personal DSME; (2) I don’t need the education, others do; and (3) health care provider (HCP) attributes that make a difference. Associated subthemes were reported. Conclusions Emerging adults reported that further education for themselves was not needed, although newly diagnosed individuals would benefit from increased training in diabetes management. Although many emerging adults had a supportive social network, they endorsed the need for greater public education to avoid diabetes misinformation. Emerging adults felt more connected with HCPs that had diabetes-specific training (ie, endocrinologist) or those who personally live with T1DM.

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