Assessing quality of glycemic control: Hypo- and hyperglycemia, and glycemic variability using mobile self-monitoring of blood glucose system.
Sophie Huey-Ming GuoPublished in: Health informatics journal (2019)
Although mobile applications bring potential benefits of metabolic control for patients with diabetes, their effect on glycemic fluctuation has been less widely explored. The goal of this study was to utilize data from the Mobile Self-Monitoring of Blood Glucose System to obtain a picture of the metabolic progression. Twenty-seven adults with type 2 diabetes mellitus were recruited to receive a mobile diabetes self-care system for a six-week period. The approach to the interpretation of glycemic control patterns, utilizes the following methods: 1) Graphical displays of the percentage of hyper-and-hypoglycemia episodes; 2) Pattern recognition of glycemic variability based on a simple equation involving both the standard deviation and the mean. Analytical results reveal that short-term usage of the developed system stabilizes the week-by-week glycemic fluctuations. Four categories were established to distinguish different patterns of patients' glycemic variation. If patterns of glycemic control can be recognized or interpreted by newly designed mobile applications, then the collection and analysis of metabolic variation will greatly help both health care providers and patients in effective diabetes management.
Keyphrases
- glycemic control
- blood glucose
- type diabetes
- weight loss
- end stage renal disease
- insulin resistance
- healthcare
- newly diagnosed
- ejection fraction
- chronic kidney disease
- peritoneal dialysis
- cardiovascular disease
- blood pressure
- clinical trial
- adipose tissue
- metabolic syndrome
- machine learning
- skeletal muscle
- deep learning
- electronic health record
- study protocol
- patient reported
- single cell