Developing and testing a novel study design for improving hypoglycaemia detection and prediction with continuous glucose monitoring data
Year: 2013
Type: article
Abstract: Persons with Type 1 diabetes need continuous exogenous insulin supply throughout their life.Determining the optimal insulin treatment in relation to diet and physical activity is one of the main goals of diabetes management, but is difficult, especially for vulnerable populations, such as adolescents.Erroneous treatment may result in both repeated and severe low blood glucose events.Continuous glucose monitoring (CGM) may help in avoiding these events, but is inaccurate compared to traditional glucose monitoring.Models have been developed to significantly improve CGM's detection of insulin-induced events by using information from the CGM signal itself.Additional temporal data on insulin doses, diet and physical activity may improve hypoglycaemia prediction models.In this research, we present and pilot test a study in which a smartphone was used to obtain these data.Data from one female was obtained over a period of two days.CGM and continuous physical activity accelerometry data were collected with minimum and no dropouts, respectively.The collection of diet, insulin and blood glucose data, also, proceeded without problems.These results indicate that it is possible to collect glucose, diet, insulin and physical activity data of high quality.These data will facilitate further development of models for the detection and prediction of low blood glucose.
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funder: University of California, Davis
OpenAlex ID: W2185213073