The factors affecting on estimation of carbohydrate content of meals in carbohydrate counting

Kawamura, Tomoyuki; Takamura, Chihiro; Hirose, Masakazu; Hashimoto, Tomomi; Higashide, Takashi; Kashihara, Yoneo; Hashimura, Kayako; Shintaku, Haruo

doi:10.1297/cpe.24.153

Cited by 35 publications

(39 citation statements)

References 18 publications

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“…This highlights the need for continuous education and training even among adults. It seems indispensable for both parent and children to continuously practice carbohydrate counting under the guidance of health care professionals and appropriate educational materials to maintain the accuracy of estimation over time [ 25 ].…”

Section: Discussionmentioning

confidence: 99%

The Association between the Parents’ Knowledge of Carbohydrate Counting and the Glycaemic Control of the Children with Type 1 Diabetes

Ranasinghe

Senadeera

Senarathna

et al. 2018

International Journal of Pediatrics

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Background Medical nutritional therapy is an important component of type 1 diabetes (T1D) care in children and carbohydrate counting is one such method. We aimed to evaluate the knowledge of carbohydrate counting among parents of children with T1D from Sri Lanka and study its association with the child's glycaemic control. Methods A descriptive cross-sectional study was conducted among parents of children with T1D. HbA1c measurement was used to assess glycaemic control. Knowledge of parent regarding carbohydrate counting was assessed based on a 24-hour dietary recall. Carbohydrate counting knowledge was defined using ratio of carbohydrate content estimated by parents to actual carbohydrate content calculated by researchers (Total, Breakfast, Lunch, Dinner, and Snacks). Ratios obtained were also divided into three groups, underestimation (<0.9), accurate estimation (0.9-1.1), and overestimation (>1.1). A multivariate regression analysis was performed to determine contribution of carbohydrate counting accuracy to glycaemic control (HbA1c). Results Sample size was 181 and mean age of the parents was 38.8±5.9 years. Mean duration of diabetes in the children was 3.7±2.6 years and mean HbA1c level was 8.3±0.9%. On average, parents estimates of carbohydrate count for the total meal were 0.88±0.27 (88%) (range 0.38-1.47) of the actual carbohydrate count. Only 30.5% (n=55) of parents were grouped in the “accurate” estimation category for the total carbohydrate count. Parents of children with diabetes for ≤3 years estimated total carbohydrate count more accurately than the counterparts (p<0.05). Mean HbA1c value of those who “underestimated” was significantly higher than those with “accurate” estimation. In the multivariate analysis accuracy of carbohydrate estimation was associated with a lower HbA1c (β = −0.36; p=0.03). Conclusions Overall knowledge of carbohydrate counting among parents was inadequate. Better knowledge was associated with improved glycaemic control in children and lower incidence of hypoglycaemic episodes. An inverse association was observed between knowledge and duration of diabetes.

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Section: Discussionmentioning

confidence: 99%

The Association between the Parents’ Knowledge of Carbohydrate Counting and the Glycaemic Control of the Children with Type 1 Diabetes

Ranasinghe

Senadeera

Senarathna

et al. 2018

International Journal of Pediatrics

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“…In general, individuals with T1D, even trained and experienced, are not accurate in CHO counting [ 5 , 6 , 7 , 8 ]. In a cross-sectional study, T1D adults ( n = 50) underestimated 63% of the meals analysed, with a mean error of 20% of total CHO content per meal [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, of 61 patients with T1D, 82% overestimated CHO content by 40%, [ 6 ]. Other studies found that high CHO foods were underestimated not only by children, young adults with T1D and their parents, but also by health professionals [ 7 ], whereas adolescents with T1D tended to overestimate them [ 8 ]. Studies have shown that an error of ±10 g in CHO content estimation maintains postprandial control [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study on Carbohydrate Estimation: GoCARB vs. Dietitians

et al. 2018

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GoCARB is a computer vision-based smartphone system designed for individuals with Type 1 Diabetes to estimate plated meals’ carbohydrate (CHO) content. We aimed to compare the accuracy of GoCARB in estimating CHO with the estimations of six experienced dietitians. GoCARB was used to estimate the CHO content of 54 Central European plated meals, with each of them containing three different weighed food items. Ground truth was calculated using the USDA food composition database. Dietitians were asked to visually estimate the CHO content based on meal photographs. GoCARB and dietitians achieved comparable accuracies. The mean absolute error of the dietitians was 14.9 (SD 10.12) g of CHO versus 14.8 (SD 9.73) g of CHO for the GoCARB (p = 0.93). No differences were found between the estimations of dietitians and GoCARB, regardless the meal size. The larger the size of the meal, the greater were the estimation errors made by both. Moreover, the higher the CHO content of a food category was, the more challenging its accurate estimation. GoCARB had difficulty in estimating rice, pasta, potatoes, and mashed potatoes, while dietitians had problems with pasta, chips, rice, and polenta. GoCARB may offer diabetic patients the option of an easy, accurate, and almost real-time estimation of the CHO content of plated meals, and thus enhance diabetes self-management.

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“…Among others, Deeb et al [15] report that carbohydrate-counting errors are not correlated with meal size, while Vasiloglou et al [16] found that larger meals led to larger estimation errors. On the other hand, Kawamura et al [17] found that meals with small amounts of carbohydrate tended to be overestimated. Finally, Reiterer et al [14] note that random errors, such as faulty carb-counting, as opposed to systematic bias errors, are more detrimental to glycemic control.…”

Section: Introductionmentioning

confidence: 97%

In-Silico Evaluation of Glucose Regulation Using Policy Gradient Reinforcement Learning for Patients with Type 1 Diabetes Mellitus

et al. 2020

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In this paper, we test and evaluate policy gradient reinforcement learning for automated blood glucose control in patients with Type 1 Diabetes Mellitus. Recent research has shown that reinforcement learning is a promising approach to accommodate the need for individualized blood glucose level control algorithms. The motivation for using policy gradient algorithms comes from the fact that adaptively administering insulin is an inherently continuous task. Policy gradient algorithms are known to be superior in continuous high-dimensional control tasks. Previously, most of the approaches for automated blood glucose control using reinforcement learning has used a finite set of actions. We use the Trust-Region Policy Optimization algorithm in this work. It represents the state of the art for deep policy gradient algorithms. The experiments are carried out in-silico using the Hovorka model, and stochastic behavior is modeled through simulated carbohydrate counting errors to illustrate the full potential of the framework. Furthermore, we use a model-free approach where no prior information about the patient is given to the algorithm. Our experiments show that the reinforcement learning agent is able to compete with and sometimes outperform state-of-the-art model predictive control in blood glucose regulation.

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The factors affecting on estimation of carbohydrate content of meals in carbohydrate counting

Cited by 35 publications

References 18 publications

The Association between the Parents’ Knowledge of Carbohydrate Counting and the Glycaemic Control of the Children with Type 1 Diabetes

The Association between the Parents’ Knowledge of Carbohydrate Counting and the Glycaemic Control of the Children with Type 1 Diabetes

A Comparative Study on Carbohydrate Estimation: GoCARB vs. Dietitians

In-Silico Evaluation of Glucose Regulation Using Policy Gradient Reinforcement Learning for Patients with Type 1 Diabetes Mellitus

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