16: Continuous glucose monitors in CFRD screening: What can they do?

Successful measurements of interstitial glucose are a key component in providing effective care for patients with diabetes. Recently, there has been significant interest in using neural networks to forecast future glucose values from interstitial measurements collected by continuous glucose monitors (CGMs). While prediction accuracy continues to improve, in this work we investigated the effect of physiological sensor location on neural network blood glucose forecasting. We used clinical data from patients with Type 2 Diabetes who wore blinded FreeStyle Libre Pro CGMs (Abbott) on both their right and left arms continuously for 12 weeks. We trained patient-specific prediction algorithms to test the effect of sensor location on neural network forecasting ( N = 13, Female = 6, Male = 7). In 10 of our 13 patients, we found at least one significant ( P < .05) increase in forecasting error in algorithms which were tested with data taken from a different location than data which was used for training. These reported results were independent from other noticeable physiological differences between subjects (eg, height, age, weight, blood pressure) and independent from overall variance in the data. From these results we observe that CGM location can play a consequential role in neural network glucose prediction.

Download Full-text

Diabetes Technology Experiences Among Latinx and Non-Latinx Youth with Type 1 Diabetes

Journal of Diabetes Science and Technology ◽

10.1177/19322968211029260 ◽

2021 ◽

pp. 193229682110292

Author(s):

David Tsai ◽

Jaquelin Flores Garcia ◽

Jennifer L. Fogel ◽

Choo Phei Wee ◽

Mark W. Reid ◽

...

Keyword(s):

Type 1 Diabetes ◽

Young People ◽

Technology Use ◽

Private Insurance ◽

Spanish Speaking ◽

Positive Attitudes ◽

Diabetes Technology ◽

English Speaking ◽

Continuous Glucose Monitors

Background: Diabetes technologies, such as insulin pumps and continuous glucose monitors (CGM), have been associated with improved glycemic control and increased quality of life for young people with type 1 diabetes (T1D); however, few young people use these devices, especially those from minority ethnic groups. Current literature predominantly focuses on white patients with private insurance and does not report experiences of diverse pediatric patients with limited resources. Methods: To explore potential differences between Latinx and non-Latinx patients, English- and Spanish-speaking young people with T1D ( n = 173, ages 11-25 years) were surveyed to assess attitudes about and barriers to diabetes technologies using the Technology Use Attitudes and Barriers to Device Use questionnaires. Results: Both English- and Spanish-speaking participants who identified as Latinx were more likely to have public insurance ( P = .0001). English-speaking Latinx participants reported higher Hemoglobin A1c values ( P = .003), less CGM use ( P = .002), and more negative attitudes about technology (generally, P = .003; and diabetes-specific, P < .001) than either non-Latinx or Spanish-speaking Latinx participants. Barriers were encountered with equivalent frequency across groups. Conclusions: Latinx English-speaking participants had less positive attitudes toward general and diabetes technology than Latinx Spanish-speaking and non-Latinx English-speaking peers, and differences in CGM use were associated with socioeconomic status. Additional work is needed to design and deliver diabetes interventions that are of interest to and supportive of patients from diverse ethnic and language backgrounds.

Download Full-text

Adverse Event Data for Years 2018 to 2020 for Diabetes Devices

Journal of Diabetes Science and Technology ◽

10.1177/19322968211011688 ◽

2021 ◽

pp. 193229682110116

Author(s):

Jan S. Krouwer

Keyword(s):

Adverse Event ◽

Blood Glucose ◽

Adverse Events ◽

Performance Evaluations ◽

Routine Analysis ◽

Event Data ◽

Adverse Event Data ◽

Continuous Glucose Monitors ◽

Device Use ◽

Test Result

Unlike performance evaluations, which are often conducted under ideal conditions, adverse events occur during actual device use for people with diabetes. This report summarizes the number of adverse events for the years 2018 to 2020 for the 3 diabetes devices: blood glucose meters (BG), continuous glucose monitors (CGM), and insulin pumps. A text example of a CGM injury is provided. Possible reasons are suggested for trends. Whereas the rate per test result (events/usage) is exceedingly small, the rate per patient (events/people with diabetes that use insulin) is of concern. Hence, it is important to determine event causes and provide corrective actions. The first step is to put in place routine analysis of adverse event data for diabetes devices.

Download Full-text

Enhanced Accuracy of Continuous Glucose Monitoring during Exercise through Physical Activity Tracking Integration

Sensors ◽

10.3390/s19173757 ◽

2019 ◽

Vol 19 (17) ◽

pp. 3757 ◽

Cited By ~ 1

Author(s):

Alejandro José Laguna Sanz ◽

José Luis Díez ◽

Marga Giménez ◽

Jorge Bondia

Keyword(s):

Physical Activity ◽

Estimation Error ◽

Glucose Monitoring ◽

Linear Regression Models ◽

Activity Tracking ◽

Metabolic Equivalent ◽

Optimal Inputs ◽

Input Model ◽

Continuous Glucose Monitors ◽

Monitoring Devices

Current Continuous Glucose Monitors (CGM) exhibit increased estimation error during periods of aerobic physical activity. The use of readily-available exercise monitoring devices opens new possibilities for accuracy enhancement during these periods. The viability of an array of physical activity signals provided by three different wearable devices was considered. Linear regression models were used in this work to evaluate the correction capabilities of each of the wearable signals and propose a model for CGM correction during exercise. A simple two-input model can reduce CGM error during physical activity (17.46% vs. 13.8%, p < 0.005) to the magnitude of the baseline error level (13.61%). The CGM error is not worsened in periods without physical activity. The signals identified as optimal inputs for the model are “Mets” (Metabolic Equivalent of Tasks) from the Fitbit Charge HR device, which is a normalized measurement of energy expenditure, and the skin temperature reading provided by the Microsoft Band 2 device. A simpler one-input model using only “Mets” is also viable for a more immediate implementation of this correction into market devices.

Download Full-text