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

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          Abstract

          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.

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          Breaking prolonged sitting reduces postprandial glycemia in healthy, normal-weight adults: a randomized crossover trial.

          Sedentary behavior is a risk factor for cardiometabolic disease. Regularly interrupting sedentary behavior with activity breaks may lower this risk. We compared the effects of prolonged sitting, continuous physical activity combined with prolonged sitting, and regular activity breaks on postprandial metabolism. Seventy adults participated in a randomized crossover study. The prolonged sitting intervention involved sitting for 9 h, the physical activity intervention involved walking for 30 min and then sitting, and the regular-activity-break intervention involved walking for 1 min 40 s every 30 min. Participants consumed a meal-replacement beverage at 60, 240, and 420 min. The plasma incremental area under the curve (iAUC) for insulin differed between interventions (overall P < 0.001). Regular activity breaks lowered values by 866.7 IU · L(-1) · 9 h(-1) (95% CI: 506.0, 1227.5 IU · L(-1) · 9 h(-1); P < 0.001) when compared with prolonged sitting and by 542.0 IU · L(-1) · 9 h(-1) (95% CI: 179.9, 904.2 IU · L(-1) · 9 h(-1); P = 0.003) when compared with physical activity. Plasma glucose iAUC also differed between interventions (overall P < 0.001). Regular activity breaks lowered values by 18.9 mmol · L(-1) · 9 h(-1) (95% CI: 10.0, 28.0 mmol · L(-1) · 9 h(-1); P < 0.001) when compared with prolonged sitting and by 17.4 mmol · L(-1) · 9 h(-1) (95% CI: 8.4, 26.3 mmol · L(-1) · 9 h(-1); P < 0.001) when compared with physical activity. Plasma triglyceride iAUC differed between interventions (overall P = 0.023). Physical activity lowered values by 6.3 mmol · L(-1) · 9 h(-1) (95% CI: 1.8, 10.7 mmol · L(-1) · 9 h(-1); P = 0.006) when compared with regular activity breaks. Regular activity breaks were more effective than continuous physical activity at decreasing postprandial glycemia and insulinemia in healthy, normal-weight adults. This trial was registered with the Australian New Zealand Clinical Trials registry as ACTRN12610000953033.
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            Continuous glucose monitoring: A review of the technology and clinical use.

            Continuous glucose monitoring (CGM) is an increasingly adopted technology for insulin-requiring patients that provides insights into glycemic fluctuations. CGM can assist patients in managing their diabetes with lifestyle and medication adjustments. This article provides an overview of the technical and clinical features of CGM based on a review of articles in PubMed on CGM from 1999 through January 31, 2017. A detailed description is presented of three professional (retrospective), three personal (real-time) continuous glucose monitors, and three sensor integrated pumps (consisting of a sensor and pump that communicate with each other to determine an optimal insulin dose and adjust the delivery of insulin) that are currently available in United States. We have reviewed outpatient CGM outcomes, focusing on hemoglobin A1c (A1C), hypoglycemia, and quality of life. Issues affecting accuracy, detection of glycemic variability, strategies for optimal use, as well as cybersecurity and future directions for sensor design and use are discussed. In conclusion, CGM is an important tool for monitoring diabetes that has been shown to improve outcomes in patients with type 1 diabetes mellitus. Given currently available data and technological developments, we believe that with appropriate patient education, CGM can also be considered for other patient populations.
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              Assessment of risk for severe hypoglycemia among adults with IDDM: validation of the low blood glucose index.

              To evaluate the clinical/research utility of the low blood glucose index (LBGI), a measure of the risk of severe hypoglycemia (SH), based on self-monitoring of blood glucose (SMBG). There were 96 adults with IDDM (mean age 35+/-8 years, duration of diabetes 16+/-10 years, HbA1 8.6+/-1.8%), 43 of whom had a recent history of SH (53 did not), who used memory meters for 135+/-53 SMBG readings over a month, and then for the next 6 months recorded occurrence of SH. The SMBG data were mathematically transformed, and an LBGI was computed for each patient. The two patient groups did not differ with respect to HbA1, insulin units per day, average blood glucose (BG) and BG variability. Patients with history of SH demonstrated a higher LBGI (P 5) risk groups. Over the following 6 months low-, moderate-, and high-risk patients reported 0.4, 2.3, and 5.2 SH episodes, respectively (P = 0.001). The frequency of future SH was predicted by the LBGI and history of SH (R2 = 40%), while HbA1, age, duration of diabetes, and BG variability were not significant predictors. LBGI provides an accurate assessment of risk of SH. In the traditional relationship history of SH-to-future SH, LBGI may be the missing link that reflects present risk. Because it is based on SMBG records automatically stored by many reflectance meters, the LBGI is an effective and clinically useful on-line indicator for SH risk.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                06 December 2019
                December 2019
                : 19
                : 24
                : 5386
                Affiliations
                Center for Diabetes Technology, University of Virginia, 560 Ray C Hunt Dr, Charlottesville, VA 22903, USA; bo3rp@ 123456virginia.edu (B.O.); mb6nt@ 123456virginia.edu (M.D.B.)
                Author notes
                [* ]Correspondence: cf9qe@ 123456virginia.edu
                Author information
                https://orcid.org/0000-0002-8537-1892
                Article
                sensors-19-05386
                10.3390/s19245386
                6961036
                31817678
                808f7bad-2b51-4d56-9d89-336908ab9f9a
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 31 October 2019
                : 21 November 2019
                Categories
                Article

                Biomedical engineering
                continuous glucose monitors,activity trackers,smart insulin dosing,type 1 diabetes

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