scholarly journals Lag Time Remains with Newer Real-Time Continuous Glucose Monitoring Technology During Aerobic Exercise in Adults Living with Type 1 Diabetes

2019 ◽  
Vol 21 (6) ◽  
pp. 313-321 ◽  
Author(s):  
Dessi P. Zaharieva ◽  
Kamuran Turksoy ◽  
Sarah M. McGaugh ◽  
Rubin Pooni ◽  
Todd Vienneau ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Aerobic Exercise ◽  
Real Time ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Lag Time ◽  
Monitoring Technology

The impact of real-time continuous glucose monitoring on metabolic control in children with type 1 diabetes

2020 ◽  
Author(s):  
Ruxandra Calapod Ioana ◽  
Irina Bojoga ◽  
Duta Simona Gabriela ◽  
Ana-Maria Stancu ◽  
Amalia Arhire ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Real Time ◽  
Metabolic Control ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
The Impact

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.

Changes in Accuracy of Continuous Glucose Monitoring Using Dexcom G4 Platinum Over the Course of Moderate Intensity Aerobic Exercise in Type 1 Diabetes

2019 ◽  
Vol 21 (6) ◽  
pp. 364-369 ◽  
Author(s):  
Stéphanie Larose ◽  
Rémi Rabasa-Lhoret ◽  
Amélie Roy-Fleming ◽  
Corinne Suppère ◽  
Sémah Tagougui ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Aerobic Exercise ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Moderate Intensity

Improvement of metabolic control after 3-month use of real-time continuous glucose monitoring in patients with type 1 diabetes: a multicenter study in Greece

HORMONES ◽  
2019 ◽  
Vol 18 (4) ◽  
pp. 443-450
Author(s):  
Ioanna Eleftheriadou ◽  
Triantafyllos Didangelos ◽  
Angelos C. Pappas ◽  
Eleni Anastasiou ◽  
Charalampos Vasilopoulos ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Real Time ◽  
Metabolic Control ◽  
Continuous Glucose Monitoring ◽  
Multicenter Study ◽  
Glucose Monitoring
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