Background: Most standalone real-time continuous glucose monitoring (RT-CGM) systems provide predictive low and high sensor glucose (SG) threshold alerts. The durations and risk of low and high SG excursions following Guardian™ Connect CGM system predictive threshold alerts were evaluated. Methods: Continuous glucose monitoring system data uploaded between January 2, 2017 and May 22, 2018 by 3133 individuals using multiple daily injections (MDIs) or continuous subcutaneous insulin infusion (CSII) therapy were deidentified and retrospectively analyzed. Glucose excursions were defined as SG values that went beyond a preset low or high SG threshold for ≥15 minutes. For a control group, thresholds were based on the median of the low SG threshold limit (70 mg/dL) and the high SG threshold limit (210 mg/dL) preset by all system users. During periods when alerts were not enabled, timestamps were identified when a predictive alert would have been triggered. The time before low horizon was 17.5 minutes and the time before high horizon was 15 minutes, of all users who enabled alerts. Excursions occurring after a low SG or high SG predictive alert were segmented into prevented, ≤20, 20-60, and >60 minutes. Results: Excursions were prevented after 59% and 39% of low and high SG predictive alerts, respectively. The risk of a low or high excursion occurring was 1.9 ( P < 0.001, 95% CI, 1.88-1.93) and 3.3 ( P < 0.001, 95% CI, 3.20-3.30) times greater, respectively, when alerts were not enabled. Conclusions: The predictive alerts of the RT-CGM system under study can help individuals living with diabetes prevent some real-world low and high SG excursions. This can be especially important for those unable to reach or maintain glycemic control with basic RT-CGM or CSII therapy.
Background: The Guardian Connect continuous glucose monitoring (CGM) system displays current and trending sensor glucose (SG) via smartphone; records insulin, carbohydrate and exercise; and sends predictive high and low SG alerts up to one hour in advance. When used with the Sugar.IQ diabetes assistant application, personalized insights based on behavior patterns (e.g., food log or insulin dose entry) and glycemic outcomes can be tracked with the Glycemic Assist feature. Methods: System data uploaded between June-November of 2018, by 1765 individuals with diabetes were analyzed. Time in target glucose range (TIR, 70-180 mg/dL) and the Glucose Management Indicator (GMI) were compared between those who used (N=530) or did not use Sugar.IQ. Both groups had ≥5 days of SG data and similar demographic and initial glucose profiles. Results: System users had a mean GMI of 7.1%, mean±SD SG of 157.0±49.1mg/dL (8.7±2.7mmol/L), and a mean TIR of 64.5%, over the data upload period. When predictive alerts were enabled, excursions were avoided after 31% of high SG alerts and 62% of low SG alerts. Those who accessed Sugar.IQ experienced a TIR increase of 2.7% (p=0.006) and a mean SG decrease of -3.0% (p<0.001), with reductions in SD of SG (47.9mg/dL to 41.5mg/dL, p<0.001) and coefficient of variation of SG (0.32 to 0.29, p<0.001), versus those who did not. Their GMI was also 6.8% versus 6.9% (p=0.007). Those who tracked food at least once/day increased TIR by 5% (p<0.001). Users considered 88% of insights helpful. Discussion: The Guardian Connect CGM system with Sugar.IQ may advance patient understanding of glucose trends, aid in behavioral change that improves therapy adherence, and lead to better glycemic outcomes. Disclosure S. Arunachalam: Employee; Self; Medtronic. Y. Zhong: None. S. Abraham: Employee; Self; Medtronic MiniMed, Inc. P. Agrawal: Employee; Self; Medtronic MiniMed, Inc. Stock/Shareholder; Self; Medtronic. R. Vigersky: Employee; Self; Medtronic MiniMed, Inc. T.L. Cordero: Employee; Self; Medtronic. F.R. Kaufman: Employee; Self; Medtronic.
Background: Cognitive computing technology providing personalized insights based on correlations between behavior patterns and glycemic outcomes was leveraged to create an interactive mobile assistant for T1D patients. Method: Medtronic MiniMed™ Connect users (N=256) were invited to take part in a 90-day Sugar.IQ app pilot program that began April 11, 2017. The percentage of time in target range (TIR, 70-180 mg/dL), <70mg/dL, >180mg/dL, and excursions (periods >20 minutes and <70mg/dL or >180mg/dL) were collected 30 days before Sugar.IQ onboarding, and compared to those 90 days later (August 2017). Insights delivered to users and user feedback were also analyzed. Results: There were 11,356 sensor-wear days; 10,761 unique Sugar.IQ usage sessions collected; and 4,688 insights delivered (1 every 3 days). Insights included 655 and 699 identifying behaviors associated with more time <70mg/dL and >180mg/dL, respectively. The Sugar.IQ app was used 2.1 times/day. Compared to baseline, TIR was 33 minutes longer per day (P<0.15) and hypoglycemia events reduced by 1.0 per month (P<0.001). A week after receiving insights associated with hypoglycemia, 55% and 54% of users had fewer hypoglycemia and hyperglycemia events, respectively. Hyperglycemia events >2 hours reduced by 1.3 per month (P<0.001). After receiving insights about low glucose associated with boluses delivered at rapid rates of change, users tended to take smaller boluses and consume less carb in the following 7 days. Among insights that included user feedback, 86% of users rated them as “Helpful” vs. “Not helpful.” Conclusion: Timely and personalized insights, such as those provided during the Sugar.IQ pilot, may advance patient understanding of glucose trends, aid in behavioral change that improves therapy adherence, and lead to better outcomes. Disclosure Y. Zhong: None. S. Arunachalam: None. P. Agrawal: None. H. Neemuchwala: None. T.L. Cordero: Employee; Self; Medtronic. F.R. Kaufman: Employee; Self; Medtronic.
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