Data have shown that continuous glucose monitoring (CGM) in patients with type 2 diabetes (T2D) can not only help achieve HbA1c improvements but also identify unforeseen hypoglycemia risk and address glucose variability. CGM in clinical practice can be deployed both in the professional, clinical setting (retrospective review) and being used in the personal, at-home setting (real time). RT-CGM, flash CGM, or blinded CGM (no data displayed to the patient) can be worn for 3, 7, or 14 days and then downloaded in the clinic for interpretation. The current recommendation for accurate or reproducible pattern recognition is to analyze 14 days of CGM data. The data can be evaluated by the clinician, compared to the last CGM profile done and reviewed with the patient to help drive treatment changes and/or improve patient¿s self-management skills. Meanwhile, numerous wearable devices -now linked to the CGM devices- have been introduced allowing to monitor different physiological aspects of diabetic people such as exercise & activity, heart rate & electrocardiogram, eating behaviours, thus completing the characterization of a diabetic person life and generating a really huge amount of data. Such data could potentially help in better predicting the magnitude, tendency, frequency and duration of fluctuations of glucose levels. Along with the aforementioned benefits, the use of multiple wearable devices introduces the necessity of their daily maintenance which could potentially lead to a decline of the perceived quality of life. It is therefore important to understand the benefits of creating big data sets, in relation to the greater computational efforts required to process the data, the introduced overhead to the users for collecting the data, and of course, the repercussion of the misuse of the data. In this project we would like to focus on how far we can get in predicting interstitial glucose levels (IGL) to regulate metabolic control without using large volumes of data.
Only about half of patients with type 2 diabetes are meeting glycemic goals and there has been negligible change in the percentage of individuals achieving their target goals over the last decade. Despite the approval of 40 new treatment options for type 2 diabetes since 2005 these therapies and approaches have not had a meaningful impact on the degree of glycemic control in a large subset of the population with diabetes. Although these treatment options have shown notable efficacy in Randomized Controlled Trials (RCTs) their impact on glycemic control in real-world clinical practice has been minimal.
Not only poor medication adherence but also poor diabetes everyday personalized education are demonstrably key contributors to the disconnect between RCT and real- world results . In this setting during the past recent years CGM has been recongnized as an important tool for patients and clinicians to visualize the important role that diet, exercise, stress management, and the appropriate selection of diabetes medications can have in managing type 2 diabetes (T2D).
Furthermore, numerous wearable devices -now linked to the CGM devices- have been introduced allowing to monitor different physiological aspects of daily lifestyle thus completing the characterization of a diabetic person life and generating a really huge amount of data. Such data could potentially help in better predicting the magnitude, tendency, frequency and duration of fluctuations of glucose levels. Along with these benefits, the use of multiple wearable devices introduces the necessity of their daily maintenance which could potentially lead to a decline of the perceived quality of life of a person with diabetes.
The aim of our project is to understand how many data we should collect in order to mantain accuracy in the prediction of interstitial glucose levels. These data will help clinicians and patients to understand if we really need to collect multiple data from a single subject and reduce the disadvantages associated to data collection and monitoring.