scholarly journals Gaussian process modelling of blood glucose response to free-living physical activity data in people with type 1 diabetes

Author(s):  
J.J. Valletta ◽  
A.J. Chipperfield ◽  
C.D. Byrne
Author(s):  
Jason R. Jaggers ◽  
Timothy McKay ◽  
Kristi M. King ◽  
Bradly J. Thrasher ◽  
Kupper A. Wintergerst

Current technology commonly utilized in diabetes care includes continuous glucose monitors (CGMs) and insulin pumps. One often overlooked critical component to the human glucose response is daily physical activity habits. Consumer-based activity monitors may be a valid way for clinics to collect physical activity data, but whether or not children with type 1 diabetes (T1D) would wear them or use the associated mobile application is unknown. Therefore, the purpose of this study was to test the feasibility of implementing a consumer-based accelerometer directly into ongoing care for adolescents managing T1D. Methods: Adolescents with T1D were invited to participate in this study and instructed to wear a mobile physical activity monitor while also completing a diet log for a minimum of 3 days. Clinical compliance was defined as the number of participants who were compliant with all measures while also having adequate glucose recordings using either a CGM, insulin pump, or on the diet log. Feasibility was defined as >50% of the total sample reaching clinical compliance. Results: A total of 57 children and teenagers between the ages of 7 and 19 agreed to participate in this study and were included in the final analysis. Chi-square results indicated significant compliance for activity tracking (p < 0.001), diet logs (p = 0.04), and overall clinical compliance (p = 0.04). Conclusion: More than half the children in this study were compliant for both activity monitoring and diet logs. This indicates that it is feasible for children with T1D to wear a consumer-based activity monitor while also recording their diet for a minimum of three days.


2017 ◽  
Vol 11 (1) ◽  
Author(s):  
Othmar Moser ◽  
Gerhard Tschakert ◽  
Alexander Mueller ◽  
Werner Groeschl ◽  
Thomas R. Pieber ◽  
...  

2011 ◽  
Vol 20 (23-24) ◽  
pp. 3423-3429 ◽  
Author(s):  
Lynn Kilbride ◽  
Jacqui Charlton ◽  
Gillian Aitken ◽  
Gordon W Hill ◽  
Richard CR Davison ◽  
...  

2020 ◽  
Vol 10 (22) ◽  
pp. 8037
Author(s):  
Phuong Ngo ◽  
Miguel Tejedor ◽  
Maryam Tayefi ◽  
Taridzo Chomutare ◽  
Fred Godtliebsen

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.


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