scholarly journals Continuous Glucose Monitors and Activity Trackers to Inform Insulin Dosing in Type 1 Diabetes: The University of Virginia Contribution

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5386 ◽  
Author(s):  
Chiara Fabris ◽  
Basak Ozaslan ◽  
Marc D. Breton
Keyword(s):  
Physical Activity ◽  
Type 1 Diabetes ◽  
Glycemic Control ◽  
In Silico ◽  
University Of Virginia ◽  
Percent Time ◽  
Activity Trackers ◽  
Continuous Glucose Monitors ◽  
The University

Objective: Suboptimal insulin dosing in type 1 diabetes (T1D) is frequently associated with time-varying insulin requirements driven by various psycho-behavioral and physiological factors influencing insulin sensitivity (IS). Among these, physical activity has been widely recognized as a trigger of altered IS both during and following the exercise effort, but limited indication is available for the management of structured and (even more) unstructured activity in T1D. In this work, we present two methods to inform insulin dosing with biosignals from wearable sensors to improve glycemic control in individuals with T1D. Research Design and Methods: Continuous glucose monitors (CGM) and activity trackers are leveraged by the methods. The first method uses CGM records to estimate IS in real time and adjust the insulin dose according to a person’s insulin needs; the second method uses step count data to inform the bolus calculation with the residual glucose-lowering effects of recently performed (structured or unstructured) physical activity. The methods were tested in silico within the University of Virginia/Padova T1D Simulator. A standard bolus calculator and the proposed “smart” systems were deployed in the control of one meal in presence of increased/decreased IS (Study 1) and following a 1-hour exercise bout (Study 2). Postprandial glycemic control was assessed in terms of time spent in different glycemic ranges and low/high blood glucose indices (LBGI/HBGI), and compared between the dosing strategies. Results: In Study 1, the CGM-informed system allowed to reduce exposure to hypoglycemia in presence of increased IS (percent time < 70 mg/dL: 6.1% versus 9.9%; LBGI: 1.9 versus 3.2) and exposure to hyperglycemia in presence of decreased IS (percent time > 180 mg/dL: 14.6% versus 18.3%; HBGI: 3.0 versus 3.9), tending toward optimal control. In Study 2, the step count-informed system allowed to reduce hypoglycemia (percent time < 70 mg/dL: 3.9% versus 13.4%; LBGI: 1.7 versus 3.2) at the cost of a minor increase in exposure to hyperglycemia (percent time > 180 mg/dL: 11.9% versus 7.5%; HBGI: 2.4 versus 1.5). Conclusions: We presented and validated in silico two methods for the smart dosing of prandial insulin in T1D. If seen within an ensemble, the two algorithms provide alternatives to individuals with T1D for improving insulin dosing accommodating a large variety of treatment options. Future work will be devoted to test the safety and efficacy of the methods in free-living conditions.

scholarly journals Pediatric Medicaid Patients With Type 1 Diabetes Benefit From Continuous Glucose Monitor Technology

2020 ◽  
pp. 193229682090621
Author(s):  
Sonalee J. Ravi ◽  
Alexander Coakley ◽  
Tim Vigers ◽  
Laura Pyle ◽  
Gregory P. Forlenza ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Insulin Pump ◽  
Diabetes Duration ◽  
Continuous Glucose Monitor ◽  
Percent Time ◽  
Time In Range ◽  
Continuous Glucose Monitors ◽  
Hispanic White

Background: We determined the uptake rate of continuous glucose monitors (CGMs) and examined associations of clinical and demographic characteristics with CGM use among patients with type 1 diabetes covered by Colorado Medicaid during the first two years of CGM coverage with no out-of-pocket cost. Method: We retrospectively reviewed data from 892 patients with type 1 diabetes insured by Colorado Medicaid (Colorado Health Program [CHP] and CHP+, Colorado Medicaid expansion). Demographics, insulin pump usage, CGM usage, and hemoglobin A1c (A1c) were extracted from the medical record. Data downloaded into CGM software at clinic appointments were reviewed to determine 30-day use prior to appointments. Subjects with some exposure to CGM were compared to subjects never exposed to CGM, and we examined the effect of CGM use on glycemic control. Results: Twenty percent of subjects had some exposure to CGM with a median of 22 [interquartile range 8, 29] days wear. Sixty one percent of CGM users had >85% sensor wear. Subjects using CGM were more likely to be younger ( P < .001), have shorter diabetes duration ( P < .001), and be non-Hispanic White ( P < .001) than nonusers. After adjusting for age and diabetes duration, combined pump and CGM users had a lower A1c than those using neither technology ( P = .006). Lower A1c was associated with greater CGM use ( P = .002) and increased percent time in range ( P < .001). Conclusion: Pediatric Medicaid patients successfully utilized CGM. Expansion of Medicaid coverage for CGM may help improve glycemic control and lessen disparities in clinical outcomes within this population.

scholarly journals Improving Efficacy of Inhaled Technosphere Insulin (Afrezza) by Postmeal Dosing: In-silico Clinical Trial with the University of Virginia/Padova Type 1 Diabetes Simulator

2016 ◽  
Vol 18 (9) ◽  
pp. 574-585 ◽  
Author(s):  
Roberto Visentin ◽  
Clemens Giegerich ◽  
Robert Jäger ◽  
Raphael Dahmen ◽  
Anders Boss ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Type 1 Diabetes ◽  
In Silico ◽  
University Of Virginia ◽  
The University

scholarly journals Impact of Carbohydrate Counting Error on Glycemic Control in Open-Loop Management of Type 1 Diabetes: Quantitative Assessment Through an in silico Trial

2021 ◽  
pp. 193229682110123
Author(s):  
Chiara Roversi ◽  
Martina Vettoretti ◽  
Simone Del Favero ◽  
Andrea Facchinetti ◽  
Pratik Choudhary ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
In Silico ◽  
Open Loop ◽  
Random Errors ◽  
Linear Regression Models ◽  
Reference Case ◽  
Counting Error ◽  
The Impact

Background: In the management of type 1 diabetes (T1D), systematic and random errors in carb-counting can have an adverse effect on glycemic control. In this study, we performed an in silico trial aiming at quantifying the impact of different levels of carb-counting error on glycemic control. Methods: The T1D patient decision simulator was used to simulate 7-day glycemic profiles of 100 adults using open-loop therapy. The simulation was repeated for different values of systematic and random carb-counting errors, generated with Gaussian distribution varying the error mean from -10% to +10% and standard deviation (SD) from 0% to 50%. The effect of the error was evaluated by computing the difference of time inside (∆TIR), above (∆TAR) and below (∆TBR) the target glycemic range (70-180mg/dl) compared to the reference case, that is, absence of error. Finally, 3 linear regression models were developed to mathematically describe how error mean and SD variations result in ∆TIR, ∆TAR, and ∆TBR changes. Results: Random errors globally deteriorate the glycemic control; systematic underestimations lead to, on average, up to 5.2% more TAR than the reference case, while systematic overestimation results in up to 0.8% more TBR. The different time in range metrics were linearly related with error mean and SD ( R2>0.95), with slopes of [Formula: see text], [Formula: see text] for ∆TIR, [Formula: see text], [Formula: see text] for ∆TAR, and [Formula: see text], [Formula: see text] for ∆TBR. Conclusions: The quantification of carb-counting error impact performed in this work may be useful understanding causes of glycemic variability and the impact of possible therapy adjustments or behavior changes in different glucose metrics.

scholarly journals The University of Virginia/Padova Type 1 Diabetes Simulator Matches the Glucose Traces of a Clinical Trial

2014 ◽  
Vol 16 (7) ◽  
pp. 428-434 ◽  
Author(s):  
Roberto Visentin ◽  
Chiara Dalla Man ◽  
Boris Kovatchev ◽  
Claudio Cobelli
Keyword(s):  
Clinical Trial ◽  
Type 1 Diabetes ◽  
University Of Virginia ◽  
The University

Correlation between Glycemic Control and Physical Activity Level in Adolescents and Children with Type 1 Diabetes

2015 ◽  
Vol 12 (2) ◽  
pp. 232-237 ◽  
Author(s):  
Cristiane Petra Miculis ◽  
Wagner De Campos ◽  
Margaret Cristina da Silva Boguszewski
Keyword(s):  
Physical Activity ◽  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Physical Activity Level ◽  
Activity Level

Fitness and physical activity in youth with type 1 diabetes mellitus in good or poor glycemic control

2014 ◽  
Vol 16 (1) ◽  
pp. 48-57 ◽  
Author(s):  
Thanh Nguyen ◽  
Joyce Obeid ◽  
Rachel G Walker ◽  
Matthew P Krause ◽  
Thomas J Hawke ◽  
...  
Keyword(s):  
Physical Activity ◽  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Type 1 Diabetes Mellitus ◽  
Poor Glycemic Control

Correlation Between Glycemic Control and Physical Activity Level in Adolescents and Children With Type 1 Diabetes

2015 ◽  
Vol 12 (2) ◽  
pp. 232-237 ◽  
Author(s):  
Cristiane Petra Miculis ◽  
Wagner De Campos ◽  
Margaret Cristina da Silva Boguszewski
Keyword(s):  
Physical Activity ◽  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Physical Activity Level ◽  
Sedentary Time ◽  
Insulin Dose ◽  
Fasting Blood Glucose ◽  
Activity Level ◽  
Sleeping Time

Background:The aim of this study was to correlate glycemic control (GC) and variables of physical activity levels (PAL) in children with type 1 diabetes mellitus (T1DM).Methods:Fifty children and adolescents with T1DM were selected. Personal and medical data for the patients were collected. Physical evaluations of body weight and sexual maturation were undertaken. Bouchard’s questionnaire was applied to evaluate PAL as well as for time spent on physical activities.Results:Sixty-four percent of the subjects were sexually mature. Differences were observed between females and males in insulin dose, duration of light physical activity, and sleeping time (P < .05). Ninety percent presented poor GC and 80% had a low PAL. Fasting blood glucose (FBG) was significantly correlated with PAL, with sedentary time, and with sleeping time. Glycated hemoglobin (HbA1c) was significantly correlated with sedentary time and sleeping time. Among the three groups of PAL (insufficient × moderate × active) there were differences in HbA1c (%), FBG (mg/dL), duration of disease (years), and insulin dose (UI/kg/day) (P < 0.001).Conclusion:GC was significantly correlated with PAL. Among the three groups of physical activity level, the most active group was seen to have the best GC.

scholarly journals Effect of Covid-19 quarantine on diabetes Care in Children

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Miriannette Gayoso ◽  
Whei Ying Lim ◽  
Madhuri S. Mulekar ◽  
Anne-Marie D. Kaulfers
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Private Insurance ◽  
T Test ◽  
Pediatric Endocrinology ◽  
Public Insurance ◽  
School Closures ◽  
The University ◽  
At Home

Abstract Background With the onset of the COVID-19 pandemic and state-mandated school closures in the spring of 2020, the management of type 1 diabetes in children underwent significant changes. The aim of our study was to assess the effect of stay-at-home orders on glycemic control in children. Methods We conducted a retrospective review of 238 children with type 1 and type 2 diabetes who were seen in the Pediatric Endocrinology Clinic at the University of South Alabama. Average Hemoglobin A1c (A1c) levels in the year prior to stay-at home orders (May 2019–April 2020) were compared with A1c values during the quarantine period (May 2020–July 2020) using a paired t-test. We also analyzed the change of A1c level with respect to sex, race, type of diabetes, type of insurance, and mode of insulin administration, using a 2-sample t-test. Results The average A1c significantly increased from 9.2% during the previous year to 9.5% during the quarantine period (p = 0.0097). The increase of A1c was significantly higher in public insurance patients (0.49% increase) compared to private insurance patients (0.03% increase), (p = 0.0137). We also observed a significant association between the direction of change and type of insurance. Forty-eight percent of public insurance patients had an A1c increase of > 0.5% while 54% of private insurance patients had no change or decrease in A1c (p = 0.0079). Conclusions The COVID-19 pandemic resulted in worsening glycemic control in children with type 1 diabetes, with those on public insurance affected in greater proportion than those with private insurance.

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