Article | Scandinavian Conference on Health Informatics 2013; Copenhagen; Denmark; August 20; 2013 | Developing and testing a novel study design for improving hypoglycaemia detection and prediction with continuous glucose monitoring data Link�ping University Electronic Press Conference Proceedings
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Title:
Developing and testing a novel study design for improving hypoglycaemia detection and prediction with continuous glucose monitoring data
Author:
Morten Hasselstrøm Jensen: Department of Health Science and Technology, Aalborg University, Denmark/Center for Information Technology Research In the Interest of Society, UC Berkeley, CA, USA Jenna Hua: School of Public Health, UC Berkeley, CA,USA Mette Dencker Johansen: Department of Health Science and Technology, Aalborg University, Denmark Jay Han: Department of Physical Medicine and Rehabilitation, UC Davis School of Medicin, Sacramento, CA, USA Gnangurudasan Prakasam: Center of Excellence in Diabetes & Endocrinology, Sacramento, CA, USA Ole Hejlesen: Department of Health Science and Technology, Aalborg University, Denmark/Department of Health and Nursing Science, University of Agder, Norway/Department of Computer Science, University of Tromsø, Norway Edmund Seto: Center for Information Technology Research In the Interest of Society, UC Berkeley, CA, USA/School of Public Health, UC Berkeley, CA, USA
Download:
Full text (pdf)
Year:
2013
Conference:
Scandinavian Conference on Health Informatics 2013; Copenhagen; Denmark; August 20; 2013
Issue:
091
Article no.:
010
Pages:
45-49
No. of pages:
5
Publication type:
Abstract and Fulltext
Published:
2013-08-21
ISBN:
978-91-7519-518-6
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press; Linköpings universitet


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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 C’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. 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.

Keywords: Hypoglycaemia; detection; prediction; continuous glucose monitoring; study design.

Scandinavian Conference on Health Informatics 2013; Copenhagen; Denmark; August 20; 2013

Author:
Morten Hasselstrøm Jensen, Jenna Hua, Mette Dencker Johansen, Jay Han, Gnangurudasan Prakasam, Ole Hejlesen, Edmund Seto
Title:
Developing and testing a novel study design for improving hypoglycaemia detection and prediction with continuous glucose monitoring data
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Scandinavian Conference on Health Informatics 2013; Copenhagen; Denmark; August 20; 2013

Author:
Morten Hasselstrøm Jensen, Jenna Hua, Mette Dencker Johansen, Jay Han, Gnangurudasan Prakasam, Ole Hejlesen, Edmund Seto
Title:
Developing and testing a novel study design for improving hypoglycaemia detection and prediction with continuous glucose monitoring data
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