Konferensartikel

Developing and testing a novel study design for improving hypoglycaemia detection and prediction with continuous glucose monitoring data

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

Ladda ner artikel

Ingår i: Scandinavian Conference on Health Informatics 2013; Copenhagen; Denmark; August 20; 2013

Linköping Electronic Conference Proceedings 91:10, s. 45-49

Visa mer +

Publicerad: 2013-08-21

ISBN: 978-91-7519-518-6

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

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 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.

Nyckelord

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

Referenser

[1] American Diabetes Association; "Economic costs of diabetes in the U.S. in 2007;" Diabetes Care; vol. 31; no. 3; pp. 596-615; 2008.

[2] The Diabetes Control and Complication Trial Research Group; "The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus;" N Engl J Med; vol. 329; no. 14; pp. 977-86; 1993.

[3] UK Prospective Diabetes Study Group; "Intensive bloodglucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes;" The Lancet; vol. 352; pp. 837-53; 1998.

[4] American Diabetes Association Workgroup on Hypoglycemia; "Defining and Reporting Hypoglycemia in Diabetes;" Diabetes Care; vol. 28; no. 5; pp. 1245-9; 2005.

[5] K Sanders; J Mills; F Martin; and D Horne; "Emotional attitudes in adult insulin-dependent diabetics;" J Psychosom Res; 1975.

[6] Oxford Regional Prospective Study Group; "Microalbuminuria Prevalence Varies with Age; Sex and Puberty in Children with Type 1 Diabetes Followed fro diagnosis in a longitudinal study;" vol. 22; pp. 495-502; 1999.

[8] B Bode et al.; "Alarms Based on Real-Time Sensor Glucose Values Alert Patients to Hypo- and Hyperglycemia: The Guardian Continuous Monitoring System;" Diabetes Tech Ther; vol. 6; no. 2; pp. 105-13; 2004.

[7] K Rebrin; NF Sheppard Jr.; and GM Steil; "Use of Subcutaneous Interstitial Fluid Glucose to Estimate Blood Glucose: Revisiting Delay and Sensor Offset ;" J Diabetes Sci Tech; vol. 4; no. 5; pp. 1087-98; 2010.

[9] Continuous Glucose Monitoring: Quality of Hypoglycemia Detection; "Diabetes; Obesity and Metabolism;" vol. 15; no. 2; pp. 130-5; 2012.

[10] MH Jensen et al.; "Professional Continuous Glucose Monitoring in Subjects with Type 1 Diabetes: Retrospective Hypoglycemia Detection;" J Diabetes Sci Techn; vol. 7; no. 1; pp. 135-43; 2013.

[11] MH Jensen et al.; "Real-Time Hypoglycemia Detection from Continuous Glucose Monitoring Data of Subjects with Type 1 Diabetes;" Diabetes Technol Ther; 2013.

[12] FK Bishop et al.; "The Carbohydrate Counting in Adolescents With Type 1 Diabetes (CCAT) Study;" Diabetes Spectrum; vol. 22; no. 1; pp. 56-62; 2009.

[13] JM Gleeson-Kreig; "Self-monitoring of Physical Activity - Effects on Self-efficacy and Behavior in People With Type 2 Diabetes;" Diabetes Educ; vol. 32; no. 1; pp. 69- 77; 2006.

[14] KD Kulkarni; "Carbohydrate Counting: A Practical Meal-Planning Option for People With Diabetes;" Clinical Diabetes; vol. 23; no. 3; pp. 120-2; 2005.

[15] A de Nazelle et al.; "Improving estimates of air pollution exposure through ubiquitous sensing technologies;" Environ Pollut; vol. 176; pp. 92-9; 2013.

[16] PE Cryer; SN Davis; and H Shamoon; "Hypoglycemia in diabetes;" Diabetes Care; vol. 26; no. 6; pp. 1902-12; 2003.

[18] M Berger and D Rodbard; "Computer simulation of plasma insulin and glucose dynamics after subcutaneous insulin injection;" Diabetes Care; vol. 12; pp. 725-36;
1989.

[17] OK Hejlesen; S Andreassen; R Hovorka; and DA Cavan; "DIAS - the Diabetes Advisory System: An outline of the system and the evaluation results obtained so far;" Comput Methods Programs Biomed; vol. 54; no. 1-2; pp. 49-58; 1997.

[19] SM Pappade et al.; "Neural Network-Based Real-Time Prediction of Glucose in Patients with Insulin-Dependent Diabetes;" Diabetes Tech Ther; vol. 13; no. 2; pp. 135-41; 2011.

[20] F Cameron; G Niemeyer; K Gundy-Burlet; and B Buckingham; "Statistical Hypoglycemia Prediction;" J Diabetes Sci Tech; vol. 2; no. 4; pp. 612-21; 2008.

[21] CC Palerm and BW Bequette; "Hypoglycemia Detection and Prediction Using Continuous Glucose Monitoring— A Study on Hypoglycemic Clamp Data;" J Diabetes Sci Tech; vol. 1; no. 5; pp. 624-29; 2007.

[22] C Perez-Gandia et al.; "Artificial Neural Network Algorithm for Online Glucose Prediction from Continuous Glucose Monitoring ;" Diabetes Tech Ther; vol. 12; no. 1; pp. 81-8; 2010.

[23] E Dassau et al.; "Real-Time Hypoglycemia Prediction Suite Using Continuous Glucose Monitoring;" Diabetes Care; vol. 33; no. 6; pp. 1249-54; 2010.

[24] WL Clarke; "The Original Clarke Error Grid Analysis (EGA);" Diabetes Tech Ther; vol. 7; no. 5; pp. 776-9; 2005.

Citeringar i Crossref