Gaussian Process modelling of blood glucose response to free-living physical activity data in people with type 1 diabetes.
Good blood glucose control is important to people with type 1 diabetes to prevent diabetes-related complications. Too much blood glucose (hyperglycaemia) causes long-term micro-vascular complications, while a severe drop in blood glucose (hypoglycaemia) can cause life-threatening coma. Finding the right balance between quantity and type of food intake, physical activity levels and insulin dosage, is a daily challenge. Increased physical activity levels often cause changes in blood glucose due to increased glucose uptake into tissues such as muscle. To date we have limited knowledge about the minute by minute effects of exercise on blood glucose levels, in part due to the difficulty in measuring glucose and physical activity levels continuously, in a free-living environment. By using a light and user-friendly armband we can record physical activity energy expenditure on a minute-by-minute basis. Simultaneously, by using a continuous glucose monitoring system we can record glucose concentrations. In this paper, Gaussian Processes are used to model the glucose excursions in response to physical activity data, to study its effect on glycaemic control.
Valletta
John Joseph
JJ
School of Engineering Sciences, Computational Engineering and Design Group, University of Southampton, Southampton, United Kingdom. jj.valletta@soton.ac.uk
Chipperfield
Andrew J
AJ
Byrne
Christopher D
CD
D000328:Adult; D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis*; D003922:Diabetes Mellitus, Type 1 / Q000097:blood*; D015444:Exercise / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical
D001786:Blood Glucose