Advantages and Disadvantages of Realtime Continuous Glucose Monitoring in People with Type 2 Diabetes

2012 ◽  
Vol 08 (01) ◽  
pp. 22 ◽  
Author(s):  
M Susan Walker ◽  
Stephanie J Fonda ◽  
Sara Salkind ◽  
Robert A Vigersky ◽  
◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Glucose Levels ◽  
Advantages And Disadvantages ◽  
Blood Glucose Levels

Previous research has shown that realtime continuous glucose monitoring (RT-CGM) is a useful clinical and lifestyle aid for people with type 1 diabetes. However, its usefulness and efficacy for people with type 2 diabetes is less known and potentially controversial, given the continuing controversy over the efficacy of self-monitoring of blood glucose (SMBG) in this cohort. This article reviews theextantliterature on RT-CGM for people with type 2 diabetes, and enumerates several of the advantages and disadvantages of this technology from the perspective of providers and patients. Even patients with type 2 diabetes who are not using insulin and/or are relatively well controlled on oral medications have been shown to spend a significant amount of time each day in hyperglycemia. Additional tools beyond SMBG are necessary to enable providers and patients to clearly grasp and manage the frequency and amplitude of glucose excursions in people with type 2 diabetes who are not on insulin. While SMBG is useful for measuring blood glucose levels, patients do not regularly check and SMBG does not enable many to adequately manage blood glucose levels or capture marked and sustained hyperglycemic excursions. RT-CGM systems, valuable diabetes management tools for people with type 1 diabetes or insulin-treated type 2 diabetes, have recently been used in type 2 diabetes patients. Theextantstudies, although few, have demonstrated that the use of RT-CGM has empowered people with type 2 diabetes to improve their glycemic control by making and sustaining healthy lifestyle choices.

MO633GLYCAEMIC MARKERS IN PATIENTS WITH TYPE 2 DIABETES UNDERGOING HAEMODIALYSIS EVALUATED BY LONG-TERM CONTINUOUS GLUCOSE MONITORING*

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Tobias Bomholt ◽  
Marianne Rix ◽  
Thomas Peter Almdal ◽  
Filip K Knop ◽  
Susanne Rosthøj ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Blood Glucose ◽  
Glucose Monitoring ◽  
Control Group ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Sensor Glucose ◽  
The Mean ◽  
Mean Blood Glucose

Abstract Background and Aims The reliability of haemoglobin A1c (HbA1c) as a glycaemic marker in patients receiving haemodialysis (HD) remains unknown. To assess accuracy, we compared HbA1c and fructosamine levels with interstitial glucose levels measured by continuous glucose monitoring (CGM) in patients with type 2 diabetes receiving HD. Method The HD group (maintenance HD and type 2 diabetes) comprised 30 patients who completed the study period of 17 weeks; the control group (type 2 diabetes and an estimated glomerular filtration rate >60 mL/min/1.73 m2) comprised 36 individuals. CGM (Ipro2®, Medtronic) for periods up to seven days was performed five times (with four weeks intervals) during a 16-week period. HbA1c and fructosamine were measured at week 17. The mean sensor glucose from CGM was compared with the measured HbA1c, its estimated mean blood glucose (eMBGA1c) and fructosamine levels. Results In the HD group, the mean sensor glucose from CGM was 1.4 (95% confidence interval [CI]: 1.0–1.8) mmol/L higher than the eMBGA1c, whereas the difference was 0.1 mmol/L (95% CI: -0.1–[0.4]; P<0.001) in the control group. Adjusted for the mean sensor glucose, HbA1c was -7.3 (95% CI: -10.0–[-4.7]) mmol/mol lower in the HD group than in controls (P<0.001), whereas no difference was detected for fructosamine (P=0.64). Conclusion HbA1c evaluated by CGM underestimates mean blood glucose levels in patients receiving maintenance HD; fructosamine appears to be more accurate. CGM-assessed blood glucose could complement or replace HbA1c in patients where HbA1c underestimates blood glucose levels.

scholarly journals Practical Considerations for Initiating and Utilizing Flash Continuous Glucose Monitoring in Clinical Practice

2021 ◽  
Author(s):  
Carol H Wysham ◽  
Davida F Kruger
Keyword(s):  
Type 2 Diabetes ◽  
Type 1 Diabetes ◽  
Treatment Adherence ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Treatment Regimens ◽  
Freestyle Libre ◽  
The Us

Abstract Use of continuous glucose monitoring (CGM) has been shown to improve clinical outcomes in type 1 diabetes (T1D) and type 2 diabetes (T2D), including improved glycemic control, better treatment adherence and an increased understanding of their treatment regimens. Retrospective analysis of CGM data allows clinicians and patients to identify glycemic patterns that support and facilitate informed therapy adjustments. There are currently two types of CGM systems: real-time CGM (rtCGM) and flash CGM. The FreeStyle Libre 2 (FLS2) is the newest flash CGM system commercially available. Because the FLS2 system was only recently cleared for use in the US, many endocrinologists and diabetes specialists may be unfamiliar with strengths, limitations and potential of the FSL2 system. This article focuses on practical approaches and strategies for initiating and using flash CGM in endocrinology and diabetes specialty practices.

scholarly journals Blood Glucose Level Forecasting on Type-1-Diabetes Subjects during Physical Activity: A Comparative Analysis of Different Learning Techniques

2021 ◽  
Vol 8 (6) ◽  
pp. 72
Author(s):  
Benedetta De Paoli ◽  
Federico D’Antoni ◽  
Mario Merone ◽  
Silvia Pieralice ◽  
Vincenzo Piemonte ◽  
...  
Keyword(s):  
Physical Activity ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Mean Squared Error ◽  
Glucose Monitoring ◽  
Online Training ◽  
Machine Learning Algorithms ◽  
Glucose Levels ◽  
Blood Glucose Levels

Background: Type 1 Diabetes Mellitus (T1DM) is a widespread chronic disease in industrialized countries. Preventing blood glucose levels from exceeding the euglycaemic range would reduce the incidence of diabetes-related complications and improve the quality of life of subjects with T1DM. As a consequence, in the last decade, many Machine Learning algorithms aiming to forecast future blood glucose levels have been proposed. Despite the excellent performance they obtained, the prediction of abrupt changes in blood glucose values produced during physical activity (PA) is still one of the main challenges. Methods: A Jump Neural Network was developed in order to overcome the issue of predicting blood glucose values during PA. Three learning configurations were developed and tested: offline training, online training, and online training with reinforcement. All configurations were tested on six subjects suffering from T1DM that held regular PA (three aerobic and three anaerobic) and exploited Continuous Glucose Monitoring (CGM). Results: The forecasting performance was evaluated in terms of the Root-Mean-Squared-Error (RMSE), according to a paradigm of Precision Medicine. Conclusions: The online learning configurations performed better than the offline configuration in total days but not on the only CGM associated with the PA; thus, the results do not justify the increased computational burden because the improvement was not significant.

SELF BLOOD GLUCOSE MONITORING UNDERESTIMATES HYPERGLYCEMIA AND HYPOGLYCEMIA AS COMPARED TO CONTINUOUS GLUCOSE MONITORING IN TYPE 1 AND TYPE 2 DIABETES

2018 ◽  
Vol 24 (1) ◽  
pp. 47-52 ◽  
Author(s):  
Devna Mangrola ◽  
Christine Cox ◽  
Arianne S. Furman ◽  
Sridevi Krishnan ◽  
Sidika E. Karakas
Keyword(s):  
Type 2 Diabetes ◽  
Blood Glucose ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Blood Glucose Monitoring

scholarly journals Evaluating the Infuence of Mood and Stress on Glycemic Variability in T1DM Patients using Glucose Monitoring Sensors and Pools

2020 ◽  
Author(s):  
Sergio Contador Pachón ◽  
Marta Botella Serrano ◽  
Aranzazu Aramendi Zurimendi ◽  
Remedios Rodríguez Martínez ◽  
Esther Maqueda Villaizán ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Glycemic Control ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
P Value ◽  
Glucose Levels ◽  
Blood Glucose Levels

Objective: Assess in a sample of patients with type 1 diabetes mellitus whether mood and stress influence blood glucose levels and variability. Material and Methods: Continuous glucose monitoring was performed on 10 patients with type 1 diabetes, where interstitial glucose values were recorded every 15 minutes. A daily survey was conducted through Google Forms, collecting information on mood and stress. The day was divided into 6 slots of 4-hour each, asking the patient to assess each slot in relation to mood (sad, normal or happy) and stress (calm, normal or nervous). Different measures of glycemic control (arithmetic mean and percentage of time below/above the target range) and variability (standard deviation, percentage coefficient of variation, mean amplitude of glycemic excursions and mean of daily differences) were calculated to relate the mood and stress perceived by patients with blood glucose levels and glycemic variability. A hypothesis test was carried out to quantitatively compare the data groups of the different measures using the Student's t-test. Results: Statistically significant differences (p-value < 0.05) were found between different levels of stress. In general, average glucose and variability decrease when the patient is calm. There are statistically significant differences (p-value < 0.05) between different levels of mood. Variability increases when the mood changes from sad to happy. However, the patient's average glucose decreases as the mood improves. Conclusions: Variations in mood and stress significantly influence blood glucose levels, and glycemic variability in the patients analyzed with type 1 diabetes mellitus. Therefore, they are factors to consider for improving glycemic control. The mean of daily differences does not seem to be a good indicator for variability. Keywords: Diabetes mellitus, continuous glucose monitoring, glycemic variability, average glycemia, glycemic control, stress, mood.

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