The Carbohydrate Counting in Adolescents With Type 1 Diabetes (CCAT)Study

Bishop, Franziska K.; Maahs, David M.; Spiegel, Gail; Owen, Darcy; Klingensmith, Georgeanna J.; Bortsov, Andrey V.; Thomas, Joan; Mayer‐Davis, Elizabeth J.

doi:10.2337/diaspect.22.1.56

Cited by 73 publications

(88 citation statements)

References 15 publications

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“…4 Another obstacle is lack of competency in carbohydrate counting; several studies have shown that inaccuracy in carbohydrate counting is widespread among children and adults. 3,[5][6][7] Because these dosage calculations can be both complex and time-consuming, individuals often rely on empirical estimates of their insulin needs, which can limit their ability to achieve optimal glycemic control. 8 Many current insulin pump systems feature bolus advisor capability, which automatically calculates insulin boluses to address carbohydrate intake and out-of-range BG levels.…”

mentioning

confidence: 99%

Safe and Efficacious Use of Automated Bolus Advisors in Individuals Treated With Multiple Daily Insulin Injection (MDI) Therapy

Parkin¹,

Barnard

Hinnen

2015

J Diabetes Sci Technol

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A key challenge for many patients with diabetes who are treated with intensive insulin therapy is calculating accurate bolus insulin dosages.1,2 Bolus insulin calculation requires individuals to utilize several factors, including insulin-tocarbohydrate (I:CHO) ratios, insulin sensitivity factors (ISFs), target blood glucose (BG) range, current BG values, anticipated physical activity, and general health status. 3Making these calculations is especially problematic in individuals with deficits in literacy and numeracy, which are common in many societies and, thus, among individuals with diabetes. 4 Another obstacle is lack of competency in carbohydrate counting; several studies have shown that inaccuracy in carbohydrate counting is widespread among children and adults. 3,[5][6][7] Because these dosage calculations can be both complex and time-consuming, individuals often rely on empirical estimates of their insulin needs, which can limit their ability to achieve optimal glycemic control. 8Many current insulin pump systems feature bolus advisor capability, which automatically calculates insulin boluses to address carbohydrate intake and out-of-range BG levels. Use of an automated bolus advisor (BA) can address many of the obstacles associated with intensive insulin therapy by reducing the burden of intensive insulin regimens,increasing treatment satisfaction, 3,9,10 addressing deficits in numeracy and carbohydrate counting, 1,3,9,11 and improving glycemic outcomes. 8,12,13 However, because insulin pump use has not been widely adopted due to perceived complexity of insulin pumps and the extensive training required, 14,15 individuals treated with multiple daily insulin injections (MDIs) do not benefit from automated BA technology in their self-management regimens.Although a growing number of smartphone apps and devices with BA technology are in development, none of the apps are FDA-approved, and only 2 stand-alone devices with integrated BG monitoring capability are commercially AbstractNumerous studies have shown that use of integrated automated bolus advisors (BAs) provides significant benefits to individuals using insulin pump devices, including improved glycemic control and greater treatment satisfaction. Within the past few years, BA devices have been developed specifically for individuals treated with multiple daily insulin injection (MDI) therapy; however, many clinicians who treat these individuals may be unfamiliar with insulin pump therapy and, thus, BA use. Findings from the Automated Bolus Advisor Control and Usability Study (ABACUS) revealed that BA use can be efficacious and clinically meaningful in MDI therapy, and that most patients are willing and able to use this technology appropriately when adequate clinical support is provided. The purpose of this article is to review key learnings from ABACUS and provide practical advice for initiating BA use and monitoring therapy.

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mentioning

confidence: 99%

Safe and Efficacious Use of Automated Bolus Advisors in Individuals Treated With Multiple Daily Insulin Injection (MDI) Therapy

Parkin¹,

Barnard

Hinnen

2015

J Diabetes Sci Technol

View full text Add to dashboard Cite

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“…In a study with 2530 children and children with diabetes, 73 percent were within 10-15 grams of the actual carbohydrate amount. [80] However, a study by Bishop and colleagues [81] found that in their sample of 48 adolescents aged 12 to 18, most youth could not accurately count carbohydrates. However they found that children who did successfully count carbohydrates had significantly lower A1C levels.…”

Section: New Technology Influencing Adherence and Glycemic Controlmentioning

confidence: 99%

Contributing Factors to Poor Adherence and Glycemic Control in Pediatric Type 1 Diabetes: Facilitating a Move Toward Telehealth

McNamara¹,

Reid²,

Freedland³

et al. 2011

Type 1 Diabetes - Complications, Pathogenesis, and Alternative Treatments

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“…Diabetics have to attend courses on CHO counting based on empirical rules; however even well trained patients face difficulties, according to studies. [4][5][6] In Brazeau et al, 4 the average error in CHO counting by 50 T1D adults was 15.4 ± 7.8 grams, while in Bishop et al 5 only 11 of 48 adolescent T1D patients estimated daily CHO with an error less than 10 grams. In a study involving children with T1D and their caregivers, CHO estimations were inaccurate by at least ± 15 grams for 27% of the meals.…”

mentioning

confidence: 94%

Computer Vision-Based Carbohydrate Estimation for Type 1 Patients With Diabetes Using Smartphones

Anthimopoulos

Dehais

Shevchik

et al. 2015

J Diabetes Sci Technol

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Individuals with type 1 diabetes (T1D) use prandial insulin doses to balance the effects of a meal.1 The meal's carbohydrate (CHO) content is a key factor in determining the optimal dose and maintaining normal blood glucose levels. According to clinical studies in insulin-depended children and adolescents, an error of ± 10 grams in CHO counting does not affect postprandial glycemia, 2 yet an error of ± 20 grams substantially impairs the postprandial control. Diabetics have to attend courses on CHO counting based on empirical rules; however even well trained patients face difficulties, according to studies. [4][5][6] In Brazeau et al, 4 the average error in CHO counting by 50 T1D adults was 15.4 ± 7.8 grams, while in Bishop et al 5 only 11 of 48 adolescent T1D patients estimated daily CHO with an error less than 10 grams. In a study involving children with T1D and their caregivers, CHO estimations were inaccurate by at least ± 15 grams for 27% of the meals. 6 The global spread of diabetes-together with the proven inability of diabetics to accurately assess their diet-raised the urgent need for automated tools and services that will support T1D patients with CHO counting. Recently, the ubiquity of smartphones with enhanced capabilities, along with recent advances in computer vision, has permitted the development of image analysis applications for automatic assessment of food intake. 7 The input of such a system consists of a few images or a short video of the upcoming meal, as captured by the user's smartphone. The data are then processed-either locally or on a remote server-to automatically assess the Abstract Background: Individuals with type 1 diabetes (T1D) have to count the carbohydrates (CHOs) of their meal to estimate the prandial insulin dose needed to compensate for the meal's effect on blood glucose levels. CHO counting is very challenging but also crucial, since an error of 20 grams can substantially impair postprandial control. Method: The GoCARB system is a smartphone application designed to support T1D patients with CHO counting of nonpacked foods. In a typical scenario, the user places a reference card next to the dish and acquires 2 images with his/her smartphone. From these images, the plate is detected and the different food items on the plate are automatically segmented and recognized, while their 3D shape is reconstructed. Finally, the food volumes are calculated and the CHO content is estimated by combining the previous results and using the USDA nutritional database. Results: To evaluate the proposed system, a set of 24 multi-food dishes was used. For each dish, 3 pairs of images were taken and for each pair, the system was applied 4 times. The mean absolute percentage error in CHO estimation was 10 ± 12%, which led to a mean absolute error of 6 ± 8 CHO grams for normal-sized dishes. Conclusion:The laboratory experiments demonstrated the feasibility of the GoCARB prototype system since the error was below the initial goal of 20 grams. However, further improvements and evaluation are needed pr...

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The Carbohydrate Counting in Adolescents With Type 1 Diabetes (CCAT)Study

Abstract: Feature Article / Carbohydrate Counting in Adolescents

Cited by 73 publications

References 15 publications

Safe and Efficacious Use of Automated Bolus Advisors in Individuals Treated With Multiple Daily Insulin Injection (MDI) Therapy

Safe and Efficacious Use of Automated Bolus Advisors in Individuals Treated With Multiple Daily Insulin Injection (MDI) Therapy

Contributing Factors to Poor Adherence and Glycemic Control in Pediatric Type 1 Diabetes: Facilitating a Move Toward Telehealth

Computer Vision-Based Carbohydrate Estimation for Type 1 Patients With Diabetes Using Smartphones

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