Continuous glucose monitoring: data management and evaluation by patients and health care professionals – current situation and developments

2018 ◽  
Vol 42 (6) ◽  
pp. 225-233
Author(s):  
Guido Freckmann ◽  
Jochen Mende
Keyword(s):  
Health Care ◽  
Continuous Glucose Monitoring ◽  
Health Care Professionals ◽  
Diabetes Management ◽  
Glucose Monitoring ◽  
Diabetic Patients ◽  
Diabetes Therapy ◽  
Software Applications ◽  
Metabolic States ◽  
Specific Device

Abstract Continuous glucose monitoring (CGM) technology represents a valuable tool for diabetic patients to control and regulate their blood glucose (BG) levels and to reduce adverse metabolic states, for example, by defining glucose alarm thresholds that alert users if the glucose value crosses to an undesired range. Improvement of CGM technology is ongoing, but there are barriers which confine the usefulness of CGM systems. The utility is mainly defined by the operability of the specific device and also by the provided benefit of available CGM software solutions. In order to take best advantage of diabetes therapy, users should be adequately educated in how to use their CGM system and how to interpret the collected data. Different CGM software applications provide partially different CGM reports and statistics. The standardization of this information also would be conducive to the best possible diabetes management.

scholarly journals An Interactive Web Application for the Statistical Analysis of Continuous Glucose Monitoring Data in Epidemiological Studies

2021 ◽  
pp. 193229682098557
Author(s):  
Alysha M. De Livera ◽  
Jonathan E. Shaw ◽  
Neale Cohen ◽  
Anne Reutens ◽  
Agus Salim
Keyword(s):  
Continuous Glucose Monitoring ◽  
Web Application ◽  
Diabetes Management ◽  
Glucose Monitoring ◽  
Epidemiological Studies ◽  
Interactive Software ◽  
Web Based ◽  
Software Applications ◽  
Visualization Of Data ◽  
User Friendly

Motivation: Continuous glucose monitoring (CGM) systems are an essential part of novel technology in diabetes management and care. CGM studies have become increasingly popular among researchers, healthcare professionals, and people with diabetes due to the large amount of useful information that can be collected using CGM systems. The analysis of the data from these studies for research purposes, however, remains a challenge due to the characteristics and large volume of the data. Results: Currently, there are no publicly available interactive software applications that can perform statistical analyses and visualization of data from CGM studies. With the rapidly increasing popularity of CGM studies, such an application is becoming necessary for anyone who works with these large CGM datasets, in particular for those with little background in programming or statistics. CGMStatsAnalyser is a publicly available, user-friendly, web-based application, which can be used to interactively visualize, summarize, and statistically analyze voluminous and complex CGM datasets together with the subject characteristics with ease.

scholarly journals A Position Statement of the Utilization and Support Status of Continuous Glucose Monitoring in Korea

2021 ◽  
Vol 22 (4) ◽  
pp. 225-237
Author(s):  
Won Jun Kim ◽  
Jae Hyun Kim ◽  
Hye Jin Yoo ◽  
Jang Won Son ◽  
Ah Reum Khang ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Diabetes Management ◽  
Insulin Pump ◽  
Insurance Coverage ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Insulin Injection ◽  
Hypoglycemic Agents ◽  
Diabetic Patients ◽  
Oral Hypoglycemic Agents

The accuracy and convenience of continuous glucose monitoring (CGM), which efficiently evaluates glycemic variability and hypoglycemia, are improving. There are two types of CGM: professional CGM and personal CGM. Personal CGM is subdivided into real-time CGM (rt-CGM) and intermittently scanned CGM (isCGM). CGM is being emphasized in both domestic and foreign diabetes management guidelines. Regardless of age or type of diabetes, CGM is useful for diabetic patients undergoing multiple insulin injection therapy or using an insulin pump. rt-CGM is recommended for all adults with type 1 diabetes (T1D), and can also be used in type 2 diabetes (T2D) treatments using multiple insulin injections. In some cases, short-term or intermittent use of CGM may be helpful for patients with T2D who use insulin therapy other than multiple insulin injections and/or oral hypoglycemic agents. CGM can help to achieve A1C targets in diabetes patients during pregnancy. CGM is a safe and cost-effective alternative to self-monitoring blood glucose in T1D and some T2D patients. CGM used in diabetes management works optimally with proper education, training, and follow up. To achieve the activation of CGM and its associated benefits, it is necessary to secure sufficient repetitive training and time for data analysis, management, and education. Various supports such as compensation, insurance coverage expansion, and reimbursement are required to increase the effectiveness of CGM while considering the scale of benefit recipients, policy priorities, and financial requirements.

Indole as a new tentative marker in exhaled breath for non-invasive blood glucose monitoring of diabetic subjects

2021 ◽  
Author(s):  
Herbert Fink ◽  
Tim Maihöfer ◽  
Jeffrey Bender ◽  
Jochen Schulat
Keyword(s):  
Blood Glucose ◽  
Diabetes Management ◽  
Glucose Monitoring ◽  
Blood Glucose Monitoring ◽  
Glucose Measurement ◽  
Diabetic Patients ◽  
Exhaled Breath ◽  
Invasive Approach ◽  
Test Strips ◽  
Non Invasive

Abstract Blood glucose monitoring (BGM) is the most important part of diabetes management. In classical BGM, glucose measurement by test strips involves invasive finger pricking. We present results of a clinical study that focused on a non-invasive approach based on volatile organic compounds (VOCs) in exhaled breath. Main objective was the discovery of markers for prediction of blood glucose levels (BGL) in diabetic patients. Exhaled breath was measured repeatedly in 60 diabetic patients (30 type 1, 30 type 2) in fasting state and after a standardized meal. Proton Transfer Reaction Time of Flight Mass Spectrometry (PTR-ToF-MS) was used to sample breath every 15 minutes for a total of six hours. BGLs were tested in parallel via BGM test strips. VOC signals were plotted against glucose trends for each subject to identify correlations. Exhaled indole (a bacterial metabolite of tryptophan) showed significant mean correlation to BGL (with negative trend) and significant individual correlation in 36 patients. The type of diabetes did not affect this result. Additional experiments of one healthy male subject by ingestion of lactulose and 13C-labeled glucose (n=3) revealed that exhaled indole does not directly originate from food digestion by intestinal microbiota. As indole has been linked to human glucose metabolism, it might be a tentative marker in breath for non-invasive BGM. Clinical studies with greater diversity are required for confirmation of such results and further investigation of metabolic pathways.

scholarly journals Assessment of Hyperglycemia, Hypoglycemia and Inter-Day Glucose Variability Using Continuous Glucose Monitoring among Diabetic Patients on Chronic Hemodialysis

2021 ◽  
Vol 10 (18) ◽  
pp. 4116
Author(s):  
Maria Divani ◽  
Panagiotis I. Georgianos ◽  
Triantafyllos Didangelos ◽  
Vassilios Liakopoulos ◽  
Kali Makedou ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Glycated Albumin ◽  
Glucose Monitoring ◽  
Area Under The Curve ◽  
Glucose Variability ◽  
Hemodialysis Patients ◽  
Chronic Hemodialysis ◽  
Diabetic Patients ◽  
Lower Efficiency ◽  
Target Glucose

Continuous glucose monitoring (CGM) facilitates the assessment of short-term glucose variability and identification of acute excursions of hyper- and hypo-glycemia. Among 37 diabetic hemodialysis patients who underwent 7-day CGM with the iPRO2 device (Medtronic Diabetes, Northridge, CA, USA), we explored the accuracy of glycated albumin (GA) and hemoglobin A1c (HbA1c) in assessing glycemic control, using CGM-derived metrics as the reference standard. In receiver operating characteristic (ROC) analysis, the area under the curve (AUC) in diagnosing a time in the target glucose range of 70–180 mg/dL (TIR70–180) in <50% of readings was higher for GA (AUC: 0.878; 95% confidence interval (CI): 0.728–0.962) as compared to HbA1c (AUC: 0.682; 95% CI: 0.508–0.825) (p < 0.01). The accuracy of GA (AUC: 0.939; 95% CI: 0.808–0.991) in detecting a time above the target glucose range > 250 mg/dL (TAR>250) in >10% of readings did not differ from that of HbA1c (AUC: 0.854; 95% CI: 0.699–0.948) (p = 0.16). GA (AUC: 0.712; 95% CI: 0.539–0.848) and HbA1c (AUC: 0.740; 95% CI: 0.570–0.870) had a similarly lower efficiency in detecting a time below target glucose range < 70 mg/dL (TBR<70) in >1% of readings (p = 0.71). Although the mean glucose levels were similar, the coefficient of variation of glucose recordings (39.2 ± 17.3% vs. 32.0 ± 7.8%, p < 0.001) and TBR<70 (median (range): 5.6% (0, 25.8) vs. 2.8% (0, 17.9)) were higher during the dialysis-on than during the dialysis-off day. In conclusion, the present study shows that among diabetic hemodialysis patients, GA had higher accuracy than HbA1c in detecting a 7-day CGM-derived TIR70–180 < 50%. However, both biomarkers provided an imprecise reflection of acute excursions of hypoglycemia and inter-day glucose variability.

scholarly journals Impact of Diabetes Continuing Education on Knowledge and Practice of Diabetes Care among Health Care Professionals in Yemen

2021 ◽  
pp. 221-230
Author(s):  
Salmeen D. Babelgaith ◽  
Mansour Almetwazi ◽  
Syed Wajid ◽  
Saeed Alfadly ◽  
Ahmed M Shaman ◽  
...  
Keyword(s):  
Health Care ◽  
Continuing Education ◽  
Diabetes Care ◽  
Health Care Professionals ◽  
Diabetes Management ◽  
Knowledge Score ◽  
General Knowledge ◽  
Significant Difference ◽  
Knowledge And Practice ◽  
The Impact

Background: This study aimed to evaluate the Impact of diabetes continuing education on knowledge and practice of diabetes care among health care professionals in Yemen. Methods: A quasi-experimental study was carried out among health care professionals. The original questionnaire consisted of 22 multiple choice questions. A total of 73 HCPs received continuing education (CE) intervention.  Knowledge attitude and practice (KAP) was assessed using a validated questionnaire.  Results: The result showed that majority of the HCPs has a good general knowledge on diabetes and its managements prior to the CE program. Evaluation of the general knowledge score of the HCPs found some improvement in the knowledge score, however the improvement was not significant (p=0.31). The result of this study found that HCPs has good knowledge on monitoring the sign, symptoms and laboratory parameters. Conclusion: Evaluation of the knowledge score on Goal of Diabetes Management of HCPs found significant (p=0.024) improvement in the knowledge score. The results indicated that the lab values were rated as the most important in the goal for the treatment of diabetes patients.  The study also found no significant difference in practice score after CE program among HCPs.

scholarly journals Functional data analysis and prediction tools for continuous glucose-monitoring studies

2020 ◽  
pp. 1-10
Author(s):  
Emrah Gecili ◽  
Rui Huang ◽  
Jane C. Khoury ◽  
Eileen King ◽  
Mekibib Altaye ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Real Time ◽  
Continuous Glucose Monitoring ◽  
Functional Data ◽  
Diabetes Management ◽  
Glucose Monitoring ◽  
Patient Specific ◽  
Prediction Errors ◽  
Medical Monitoring

Abstract Introduction: To identify phenotypes of type 1 diabetes based on glucose curves from continuous glucose-monitoring (CGM) using functional data (FD) analysis to account for longitudinal glucose patterns. We present a reliable prediction model that can accurately predict glycemic levels based on past data collected from the CGM sensor and real-time risk of hypo-/hyperglycemic for individuals with type 1 diabetes. Methods: A longitudinal cohort study of 443 type 1 diabetes patients with CGM data from a completed trial. The FD analysis approach, sparse functional principal components (FPCs) analysis was used to identify phenotypes of type 1 diabetes glycemic variation. We employed a nonstationary stochastic linear mixed-effects model (LME) that accommodates between-patient and within-patient heterogeneity to predict glycemic levels and real-time risk of hypo-/hyperglycemic by creating specific target functions for these excursions. Results: The majority of the variation (73%) in glucose trajectories was explained by the first two FPCs. Higher order variation in the CGM profiles occurred during weeknights, although variation was higher on weekends. The model has low prediction errors and yields accurate predictions for both glucose levels and real-time risk of glycemic excursions. Conclusions: By identifying these distinct longitudinal patterns as phenotypes, interventions can be targeted to optimize type 1 diabetes management for subgroups at the highest risk for compromised long-term outcomes such as cardiac disease or stroke. Further, the estimated change/variability in an individual’s glucose trajectory can be used to establish clinically meaningful and patient-specific thresholds that, when coupled with probabilistic predictive inference, provide a useful medical-monitoring tool.

scholarly journals Closed-loop management of inpatient hyperglycaemia

2019 ◽  
Vol 80 (11) ◽  
pp. 665-669
Author(s):  
CK Boughton ◽  
R Hovorka
Keyword(s):  
Health Care ◽  
Insulin Therapy ◽  
Health Care Professionals ◽  
Glucose Control ◽  
Closed Loop ◽  
Diabetes Management ◽  
Inpatient Setting ◽  
Glucose Levels ◽  
Closed Loop Systems ◽  
Inpatient Diabetes

The prevalence of diabetes in the inpatient setting is increasing, and suboptimal glucose control in hospital is associated with increased morbidity and mortality. Attaining the recommended glucose levels is challenging with standard insulin therapy. Hypoglycaemia and hyperglycaemia are common and diabetes management in hospital can be a considerable workload burden for health-care professionals. Fully automated insulin delivery (closed-loop) has been shown to be safe, and achieves superior glucose control than standard insulin therapy in the hospital, including in those patients receiving haemodialysis and enteral or parenteral nutrition where glucose control can be particularly challenging. Evidence that the improved glucose control achieved using closed-loop systems can translate into improved clinical outcomes for patients is key to support widespread adoption of this technology. The closed-loop approach has the potential to provide a paradigm shift in the management of inpatient diabetes, particularly in the most challenging inpatient populations, and may reduce staff work burden and the health-care costs associated with inpatient diabetes.

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