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

In silico evaluation of pramlintide dosing algorithms in artificial pancreas systems

Pramlintide's capability to delay gastric emptying has motivated its use in artificial pancreas systems, accompanying insulin as a control action. Due to the scarcity of pramlintide simulation models in the literature, in silico testing of insulin-plus-pramlintide strategies is not widely used. This work incorporates a recent pramlintide pharmacokinetics/pharmacodynamics model into the T1DM UVA/Padova simulator to adjust and validate four insulin-plus-pramlintide control algorithms. The proposals are based on an existing insulin controller and administer pramlintide either as independent boluses or as a ratio of the insulin infusion. The results of the insulin-pramlintide algorithms are compared against their insulin-alone counterparts, showing an improvement in the time in range between 3.00% and 10.53%, consistent with results reported in clinical trials in the literature. Future work will focus on individualizing the pramlintide model to the patients' characteristics and evaluating the implemented strategies under more challenging scenarios.

Case report: Ketogenic diet alleviated anxiety and depression associated with insulin-dependent diabetes management

Differentiating between an irrational versus a rational fear of hypoglycemia has treatment implications and presents significant challenge for clinicians facing patients with type 1 diabetes, illustrated in this case. A 39-year-old woman with autoimmune-positive insulin-dependent diabetes sought help to alleviate severe diabetes distress, and symptoms of depression and anxiety, associated with unpredictable drastic blood glucose drops. After exhausting conventional methods, she adopted a ketogenic diet (KD). Her glucose values decreased from around 20 mmol/L to 12 mmol/L (360 mg/dL to 216 mg/dL) in the first days. Then, by combining a KD with an insulin pump, her time in optimal glucose range increased from 8 to 51% after 2 months, reducing her HbA1c with 25 mmol/mol (2.2%). This reduced biological and psychological stress, immediately improving her mental health and renewing her hope for the future. The main concerns regarding KD in patients with comorbid type 1 diabetes is the assumed increased risk of ketoacidosis, theoretical depletion of glycogen stores, and a potential adverse effect of saturated fat on cardiovascular risk factors. These concerns are evaluated against existing empirical evidence, suggesting instead that a KD may protect against acidosis, hypoglycemia, and cardiovascular risk. The present case, together with available data, indicate that patients with type 1 diabetes experiencing high levels of biological and psychological stress should be informed of the expected benefits and possible risks associated with a KD, to ensure their right to take informed decisions regarding their diabetes management.

Assessing Carbohydrate Counting Accuracy: Current Limitations and Future Directions

Diabetes mellitus is a prevalent chronic autoimmune disease with a high impact on global health, affecting millions of adults and resulting in significant morbidity and mortality. Achieving optimal blood glucose levels is crucial for diabetes management to prevent acute and long-term complications. Carbohydrate counting (CC) is widely used by patients with type 1 diabetes to adjust prandial insulin bolus doses based on estimated carbohydrate content, contributing to better glycemic control and improved quality of life. However, accurately estimating the carbohydrate content of meals remains challenging for patients, leading to errors in bolus insulin dosing. This review explores the current limitations and challenges in CC accuracy and emphasizes the importance of personalized educational programs to enhance patients' abilities in carbohydrate estimation. Existing tools for assessing patient learning outcomes in CC are discussed, highlighting the need for individualized approaches tailored to each patient's needs. A comprehensive review of the relevant literature was conducted to identify educational programs and assessment tools dedicated to training diabetes patients on carbohydrate counting. The research aims to provide insights into the benefits and limitations of existing tools and identifies future research directions to advance personalized CC training approaches. By adopting a personalized approach to CC education and assessment, healthcare professionals can empower patients to achieve better glycemic control and improve diabetes management. Moreover, this review identifies potential avenues for future research, paving the way for advancements in personalized CC training and assessment approaches and further enhancing diabetes management strategies.

Associations Between Psychosocial Needs, Carbohydrate-Counting Behavior, and App Satisfaction: A Randomized Crossover App Trial on 92 Adults With Diabetes

To examine whether psychosocial needs in diabetes care are associated with carbohydrate counting and if carbohydrate counting is associated with satisfaction with diabetes applications' usability, a randomized crossover trial of 92 adults with type 1 or 2 diabetes requiring insulin therapy tested two top-rated diabetes applications, mySugr and OnTrack Diabetes. Survey responses on demographics, psychosocial needs (perceived competence, autonomy, and connectivity), carbohydrate-counting frequency, and application satisfaction were modeled using mixed-effect linear regressions to test associations. Participants ranged between 19 and 74 years old (mean, 54 years) and predominantly had type 2 diabetes (70%). Among the three tested domains of psychosocial needs, only competence-not autonomy or connectivity-was found to be associated with carbohydrate-counting frequency. No association between carbohydrate-counting behavior and application satisfaction was found. In conclusion, perceived competence in diabetes care is an important factor in carbohydrate counting; clinicians may improve adherence to carbohydrate counting with strategies designed to improve perceived competence. Carbohydrate-counting behavior is complex; its impact on patient satisfaction of diabetes application usability is multifactorial and warrants consideration of patient demographics such as sex as well as application features for automated carbohydrate counting.

In Silico Validation of Personalized Safe Intervals for Carbohydrate Counting Errors

For patients with Type 1 diabetes mellitus (T1DM), accurate carbohydrate counting (CC) is essential for successful blood glucose regulation. Unfortunately, mistakes are common and may lead to an incorrect dosage of prandial insulin. In this work, we aim to demonstrate that each person has their own limits for CC errors, which can be computed using patient-specific data. To validate the proposed method, we tested it using several scenarios to investigate the effect of different CC errors on postprandial blood glucose. Virtual subjects from the T1DM Simulator were used in a clinical trial involving 450 meals over 90 days, all following the same daily meal plan but with different intervals for CC errors near, below, and above the limit computed for each patient. The results show that CC errors within personalized limits led to acceptable postprandial glycemic fluctuations. In contrast, experiments where 50% and 97.5% of the meals present a CC error outside the computed safe interval revealed a pronounced degradation of the time in range. Given these results, we consider the proposed method for obtaining personalized limits for CC errors an excellent starting point for an initial assessment of patients' capabilities in CC and to provide appropriate ongoing education.

Effect on Glycemic Control of an Early Intensive Dietary Structured Education Program for Newly Diagnosed Children with Type 1 Diabetes in Jordan

Methods

This is a retrospective medical chart review study at Jordan University Hospital. The glycemic control of children who were diagnosed with T1D and included in the SEP between June 2017 and December 2019, was compared with those who were exposed to the conventional diabetes education, between January 2014 and December 2016. Various factors were assessed for the possible effects on the SEP outcomes.

Results

The average age at diagnosis for the 112 persons with diabetes (PwD) included in the dietary SEP was 8.30 ± 3.87 years. Glycated hemoglobin was lower in children in the SEP group at 6 months (P value = 0.001) and 12 months (P=0.032) but not at 24 months (P=0.290). SEP had better effect on patients older than 5 years. The possible predictors of glycemic control for the SEP group at 12 months included the mother's educational level and the number of hospital admissions due to DKA and hyperglycemia during the first year after diagnosis.

Conclusion

Our dietary SEP was associated with better glycemic control than conventional diabetes education, at 6 and 12 months after diagnosis. It had a positive effect, mainly in PwD patients who are older than 5 years and had higher maternal educational level.

Simplifying carb counting: A randomized controlled study - Feasibility and efficacy of an individualized, simple, patient-centred carb counting tool

INTRODUCTION

The purpose of this study was to introduce and test a simple, individualized carbohydrate counting tool designed for persons with Type 1 Diabetes Mellitus (T1DM) in order to determine whether the tool improved A1C levels for participants with age, education or language barriers.

METHODS

In a randomized controlled trial, 85 participants were offered six diabetes instructional sessions free of charge over a six-month period. Forty-one received guidance using the regular carbohydrate counting (RCC) method. Forty-four received guidance using an individualized 'Simple Carb Counting' (SCC), involving two customized tables prepared for participants.

RESULTS

The simple, individualized SCC tool for carbohydrate counting was non-inferior to the standard method of RCC. The SCC tool was more effective among participants aged 40 and older, while no differences were found when comparing participants by education level. Irrespective of intervention group, all participants improved their A1C level (9.9% = 13.2 mmol/L vs 8.6% = 11.1 mmol/L, p = .001). A greater improvement in A1C level was seen in newly diagnosed participants (-6.1 vs -0.7, p = .005, -3.4 vs 0.9, p = .032) in both the RCC and SCC groups. All participants expressed improved emotional level per their PAID5 questionnaires (Problem Areas in Diabetes Scale-PAID), (10.6 (±5.7) vs 9.5 (±5.7), p = .023), with women reporting greater improvement than men.

CONCLUSIONS

SCC is a simple, individualized, feasible, low-tech tool for carbohydrate counting, which promotes and enables accurate insulin dosing in people with T1DM. It was found more effective among participants aged 40 and older. Additional studies are needed to corroborate these findings.

Bolus Insulin calculation without meal information. A reinforcement learning approach

In continuous subcutaneous insulin infusion and multiple daily injections, insulin boluses are usually calculated based on patient-specific parameters, such as carbohydrates-to-insulin ratio (CR), insulin sensitivity-based correction factor (CF), and the estimation of the carbohydrates (CHO) to be ingested. This study aimed to calculate insulin boluses without CR, CF, and CHO content, thereby eliminating the errors caused by misestimating CHO and alleviating the management burden on the patient. A Q-learning-based reinforcement learning algorithm (RL) was developed to optimise bolus insulin doses for in-silico type 1 diabetic patients. A realistic virtual cohort of 68 patients with type 1 diabetes that was previously developed by our research group, was considered for the in-silico trials. The results were compared to those of the standard bolus calculator (SBC) with and without CHO misestimation using open-loop basal insulin therapy. The percentage of the overall duration spent in the target range of 70-180 mg/dL was 73.4% and 72.37%, <70 mg/dL was 1.96 and 0.70%, and >180 mg/dL was 23.40 and 24.63%, respectively, for RL and SBC without CHO misestimation. The results revealed that RL outperformed SBC in the presence of CHO misestimation, and despite not knowing the CHO content of meals, the performance of RL was similar to that of SBC in perfect conditions. This algorithm can be incorporated into artificial pancreas and automatic insulin delivery systems in the future.

Evaluation of carbohydrate counting knowledge among individuals with type 1 diabetes mellitus in Saudi Arabia: a cross-sectional study

Introduction

Carbohydrate counting (CC) is an important nutritional strategy to improve glycaemic outcomes among patients with diabetes. Few studies have investigated CC knowledge among individuals with type 1 diabetes mellitus (T1DM) in Saudi Arabia. Therefore, we aimed to evaluate CC knowledge in Saudi adults with T1DM.

Study design and methods

A cross-sectional study was conducted between December 2021 and February 2022, including 224 patients with T1DM from the University Diabetes Center, Riyadh. Adults aged ≥18 years, diagnosed with T1DM for >1 year, and residing in Saudi Arabia were included. CC knowledge was assessed using a previously well-studied tool (AdultCarbQuiz), which was translated into Arabic and tested for validity by a group of dieticians. Descriptive statistics were used for data analysis, and bivariate and regression analyses were conducted.

Results

The AdultCarbQuiz questionnaire-Arabic version had good validity and reliability (Cronbach's α: 0.87). The CC method was used by 54% of the participants. The mean CC knowledge score was 23.01±7.31. A significant negative linear relationship between the participants' CC knowledge scores, and age and glycated haemoglobin (HbA1c) levels, was revealed by simple regression analysis. Furthermore, significant independent variables related to CC knowledge scores were CC use, HbA1c levels, being taught about CC (>5 times), insulin pump usage and DM duration (≤15 years).

Conclusions

Approximately half of the patients used the CC method. The mean CC knowledge scores were better in patients who used the CC method, were more frequently taught about CC, were treated using an insulin pump, and had a shorter DM duration than their counterparts. Therefore, designing and implementing a well-structured nutrition education programme tailored to individuals with diabetes is crucial to provide them with up-to-date dietary information, as well as the necessary knowledge and skills, to improve their outcomes and manage their condition.

Internal model control based module for the elimination of meal and exercise announcements in hybrid artificial pancreas systems

BACKGROUND AND OBJECTIVES

Hybrid artificial pancreas systems outperform current insulin pump therapies in blood glucose regulation in type 1 diabetes. However, subjects still have to inform the system about meals intake and exercise to achieve reasonable control. These patient announcements may result in overburden and compromise controller performance if not provided timely and accurately. Here, a hybrid artificial pancreas is extended with an add-on module that releases subjects from meals and exercise announcements.

METHODS

The add-on module consists of an internal-model controller that generates a "virtual" control action to compensate for disturbances. This "virtual" action is converted into insulin delivery, rescue carbohydrates suggestions, or insulin-on-board limitations, depending on a switching logic based on glucose measurements and predictions. The controller parameters are tuned by optimization and then related to standard parameters from the open-loop therapy. This module is implemented in a hybrid artificial pancreas system proposed by our research group for validation. This hybrid system extended with the add-on module is compared with the hybrid controller with carbohydrate counting errors (hybrid) and the hybrid controller with an alternative unannounced meal compensation module based on a meal detection algorithm (meal detector). The validation used the educational version of the UVa/Padova simulator to simulate the three controllers under two scenarios: one with only meals and another with meals and exercise. The exercise was modeled as a temporal increase of the insulin sensitivity resulting in the glucose drop usually related to an aerobic exercise.

RESULTS

For the scenario with only meals, the three controllers achieved similar time in range (proposed: 85.1 [77.9,88.1]%, hybrid: 84.0 [75.9,86.4]%, meal detector: 81.9 [79.3,83.8]%, median [interquartile range]) with low time in moderate hypoglycemia. Under the scenario with meals and exercise, the proposed module reduces 4.61% the time in hypoglycemia achieved with the other controllers, suggesting an acceptable amount of rescues (27.2 [23.7, 31.0] g).

CONCLUSIONS

The proposed add-on module achieved promising results: it outperformed the meal-detector-based controller, even achieving a postprandial performance as good as the hybrid controller (with carbohydrate counting errors). Also, the rescue suggestion feature of the module mitigated exercise-induced hypoglycemia with admissible rescue amounts.

Impact of Carbohydrate Counting Error on Glycemic Control in Open-Loop Management of Type 1 Diabetes: Quantitative Assessment Through an In Silico Trial

BACKGROUND

In the management of type 1 diabetes (T1D), systematic and random errors in carb-counting can have an adverse effect on glycemic control. In this study, we performed an in silico trial aiming at quantifying the impact of different levels of carb-counting error on glycemic control.

METHODS

The T1D patient decision simulator was used to simulate 7-day glycemic profiles of 100 adults using open-loop therapy. The simulation was repeated for different values of systematic and random carb-counting errors, generated with Gaussian distribution varying the error mean from -10% to +10% and standard deviation (SD) from 0% to 50%. The effect of the error was evaluated by computing the difference of time inside (∆TIR), above (∆TAR) and below (∆TBR) the target glycemic range (70-180mg/dl) compared to the reference case, that is, absence of error. Finally, 3 linear regression models were developed to mathematically describe how error mean and SD variations result in ∆TIR, ∆TAR, and ∆TBR changes.

RESULTS

Random errors globally deteriorate the glycemic control; systematic underestimations lead to, on average, up to 5.2% more TAR than the reference case, while systematic overestimation results in up to 0.8% more TBR. The different time in range metrics were linearly related with error mean and SD (R²>0.95), with slopes of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>β</mml:mi><mml:mrow><mml:mi>M</mml:mi><mml:mi>E</mml:mi><mml:mi>A</mml:mi><mml:mi>N</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>21</mml:mn></mml:mrow></mml:math>, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>β</mml:mi><mml:mrow><mml:mi>S</mml:mi><mml:mi>D</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>07</mml:mn></mml:mrow></mml:math> for ∆TIR, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>β</mml:mi><mml:mrow><mml:mi>M</mml:mi><mml:mi>E</mml:mi><mml:mi>A</mml:mi><mml:mi>N</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>25</mml:mn></mml:mrow></mml:math>, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>β</mml:mi><mml:mrow><mml:mi>S</mml:mi><mml:mi>D</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mo>+</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>06</mml:mn></mml:mrow></mml:math> for ∆TAR, and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>β</mml:mi><mml:mrow><mml:mi>M</mml:mi><mml:mi>E</mml:mi><mml:mi>A</mml:mi><mml:mi>N</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>05</mml:mn></mml:mrow></mml:math>, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>β</mml:mi><mml:mrow><mml:mi>S</mml:mi><mml:mi>D</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mo>+</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>01</mml:mn></mml:mrow></mml:math> for ∆TBR.

CONCLUSIONS

The quantification of carb-counting error impact performed in this work may be useful understanding causes of glycemic variability and the impact of possible therapy adjustments or behavior changes in different glucose metrics.

Digital Solutions to Diagnose and Manage Postbariatric Hypoglycemia

Postbariatric hypoglycemia (PBH) is an increasingly recognized late metabolic complication of bariatric surgery, characterized by low blood glucose levels 1-3 h after a meal, particularly if the meal contains rapid-acting carbohydrates. PBH can often be effectively managed through appropriate nutritional measures, which remain the cornerstone treatment today. However, their implementation in daily life continues to challenge both patients and health care providers. Emerging digital technologies may allow for more informed and improved decision-making through better access to relevant data to manage glucose levels in PBH. Examples include applications for automated food analysis from meal images, digital receipts of purchased food items or integrated platforms allowing the connection of continuously measured glucose with food and other health-related data. The resulting multi-dimensional data can be processed with artificial intelligence systems to develop prediction algorithms and decision support systems with the aim of improving glucose control, safety, and quality of life of PBH patients. Digital innovations, however, face trade-offs between user burden vs. amount and quality of data. Further challenges to their development are regulatory non-compliance regarding data ownership of the platforms acquiring the required data, as well as user privacy concerns and compliance with regulatory requirements. Through navigating these trade-offs, digital solutions could significantly contribute to improving the management of PBH.

Feasibility Study of an Automated Carbohydrate Estimation System Using Thai Food Images in Comparison With Estimation by Dietitians

Carbohydrate counting is essential for well-controlled blood glucose in people with type 1 diabetes, but to perform it precisely is challenging, especially for Thai foods. Consequently, we developed a deep learning-based system for automatic carbohydrate counting using Thai food images taken from smartphones. The newly constructed Thai food image dataset contained 256,178 ingredient objects with measured weight for 175 food categories among 75,232 images. These were used to train object detector and weight estimator algorithms. After training, the system had a Top-1 accuracy of 80.9% and a root mean square error (RMSE) for carbohydrate estimation of <10 g in the test dataset. Another set of 20 images, which contained 48 food items in total, was used to compare the accuracy of carbohydrate estimations between measured weight, system estimation, and eight experienced registered dietitians (RDs). System estimation error was 4%, while estimation errors from nearest, lowest, and highest carbohydrate among RDs were 0.7, 25.5, and 7.6%, respectively. The RMSE for carbohydrate estimations of the system and the lowest RD were 9.4 and 10.2, respectively. The system could perform with an estimation error of <10 g for 13/20 images, which placed it third behind only two of the best performing RDs: RD1 (15/20 images) and RD5 (14/20 images). Hence, the system was satisfactory in terms of accurately estimating carbohydrate content, with results being comparable with those of experienced dietitians.

Prospective Independent Evaluation of the Carbohydrate Counting Accuracy of Two Smartphone Applications

INTRODUCTION

Smartphone applications (apps) have been designed that help patients to accurately count their carbohydrate intake in order to optimize prandial insulin dose matching. Our aim was to evaluate the accuracy of two carbohydrate (carb) counting apps.

METHODS

Medical students, in the role of mock patients, evaluated meals using two smartphone apps: Foodvisor^® (which uses automatic food photo recognition technology) and Glucicheck^® (which requires the manual entry of carbohydrates with the help of a photo gallery). The macronutrient quantifications obtained with these two apps were compared to a reference quantification.

RESULTS

The carbohydrate content of the entire meal was underestimated with Foodvisor^® (Foodvisor^® quantification minus gold standard quantification = - 7.2 ± 17.3 g; p < 0.05) but reasonably accurately estimated with Glucicheck^® (Glucicheck^® quantification minus gold standard quantification = 1.4 ± 13.4 g; ns). The percentage of meals with an absolute error in carbohydrate quantification above 20 g was greater for Foodvisor^® compared to Glucicheck^® (30% vs 14%; p < 0.01).

CONCLUSION

The carb counting accuracy was slightly better when using Glucicheck^® compared to Foodvisor^®. However, both apps provided a lower mean absolute carb counting error than that usually made by T1D patients in everyday life, suggesting that such apps may be a useful adjunct for estimating carbohydrate content.

Reliability and Validity of a Carbohydrate-Counting Knowledge Questionnaire for Young Australians With Type 1 Diabetes

OBJECTIVE

To test the reliability and validity of a carbohydrate-counting knowledge questionnaire in young Australians with type 1 diabetes mellitus (T1DM).

METHODS

Children or young adults (<20 years) with T1DM, or their parents, completed the 72-item Australian PedCarbQuiz (AusPCQ), adapted from the American PedCarbQuiz, and an expert assessment of carbohydrate-counting knowledge. Responses were scored and summed (0-72, higher scores = greater knowledge). Internal reliability was assessed using Cronbach α, and relative validity using Spearman correlations (with HbA1c) and Bland-Altman analysis (with the expert assessment).

RESULTS

Australian PedCarbQuiz reliability (n = 44, mean score = 59.7 ± 5.6) was acceptable (α = 0.83). There was a lack of agreement (mean bias = 10.7, P = 0.008) and significant proportional bias between AusPCQ scores and expert assessments (β = -0.73 [95% confidence interval, -1.82 to -0.79]; P < 0.001).

CONCLUSIONS AND IMPLICATIONS

The AusPCQ was shown to be reliable but not valid in a small sample. Testing in a larger sample is warranted.

Evaluation of Meal Carbohydrate Counting Errors in Patients with Type 1 Diabetes

AIM

Correct estimation of meal carbohydrate content is a prerequisite for successful intensified insulin therapy in patients with diabetes. In this survey, the counting error in adult patients with type 1 diabetes was investigated.

METHODS

Seventy-four patients with type 1 diabetes estimated the carbohydrate content of 24 standardized test meals. The test meals were categorized into 1 of 3 groups with different carbohydrate content: low, medium, and high. Estimation results were compared with the meals' actual carbohydrate content as determined by calculation based on weighing. A subgroup of the participants estimated the test meals for a second (n=35) and a third time (n=22) with a mean period of 11 months between the estimations.

RESULTS

During the first estimation, the carbohydrate content was underestimated by -28% (-50, 0) of the actual carbohydrate content. Particularly meals with high mean carbohydrate content were underestimated by -34% (-56, -13). Median counting error improved significantly when estimations were performed for a second time (p<0.001).

CONCLUSIONS

Participants generally underestimated the carbohydrate content of the test meals, especially in meals with higher carbohydrate content. Repetition of estimation resulted in significant improvements in estimation accuracy and is important for the maintenance of correct carbohydrate estimations. The ability to estimate the carbohydrate content of a meal should be checked and trained regularly in patients with diabetes.

Assessing Mealtime Macronutrient Content: Patient Perceptions Versus Expert Analyses via a Novel Phone App

Background: People with type 1 diabetes estimate meal carbohydrate content to accurately dose insulin, yet, protein and fat content of meals also influences postprandial glycemia. We examined accuracy of macronutrient content estimation via a novel phone app. Participant estimates were compared with expert nutrition analyses performed via the Remote Food Photography Method© (RFPM©). Methods: Data were collected through a novel phone app. Participants were asked to take photos of meals/snacks on the day of and day after scheduled exercise, enter carbohydrate estimates, and categorize meals as low, typical, or high protein and fat. Glycemia was measured via continuous glucose monitoring. Results: Participants (n = 48) were 15-68 years (34 ± 14 years); 40% were female. The phone app plus RFPM© analysis captured 88% ± 29% of participants' estimated total energy expenditure. The majority (70%) of both low-protein and low-fat meals were accurately classified. Only 22% of high-protein meals and 17% of high-fat meals were accurately classified. Forty-nine percent of meals with <30 g of carbohydrates were overestimated by an average of 25.7 ± 17.2 g. The majority (64%) of large carbohydrate meals (≥60 g) were underestimated by an average of 53.6 ± 33.8 g. Glycemic response to large carbohydrate meals was similar between participants who underestimated or overestimated carbohydrate content, suggesting that factors beyond carbohydrate counting may impact postprandial glycemic response. Conclusions: Accurate estimation of total macronutrients in meals could be leveraged to improve insulin decision support tools and closed loop insulin delivery systems; development of tools to improve macronutrient estimation skills should be considered.

Modeling Carbohydrate Counting Error in Type 1 Diabetes Management

Background: The error in estimating meal carbohydrates (CHO) amount is a critical mistake committed by type 1 diabetes (T1D) subjects. The aim of this study is both to investigate which factors, related to meals and subjects, affect the CHO counting error most and to develop a mathematical model of CHO counting error embeddable in T1D patient decision simulators to conduct in silico clinical trials. Methods: A published dataset of 50 T1D adults is used, which includes a patient's CHO count of 692 meals, dietitian's estimates of meal composition (used as reference), and several potential explanatory factors. The CHO counting error is modeled by multiple linear regression, with stepwise variable selection starting from 10 candidate predictors, that is, education level, insulin treatment duration, age, body weight, meal type, CHO, lipid, energy, protein, and fiber content. Inclusion of quadratic and interaction terms is also evaluated. Results: Larger errors correspond to larger meals, and most of the large meals are underestimated. The linear model selects CHO (P < 0.00001), meal type (P < 0.00001), and body weight (P = 0.047), whereas its extended version embeds a quadratic term of CHO (P < 0.00001) and interaction terms of meal type with CHO (P = 0.0001) and fiber amount (P = 0.001). The extended model explains 34.9% of the CHO counting error variance. Comparison with the CHO counting error description previously used in the T1D patient decision simulator shows that the proposed models return more credible realizations. Conclusions: The most important predictors of CHO counting errors are CHO and meal type. The mathematical models proposed improve the description of patients' behavior in the T1D patient decision simulator.

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

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.

The dietary education trial in carbohydrate counting (DIET-CARB Study): study protocol for a randomised, parallel, open-label, intervention study comparing different approaches to dietary self-management in patients with type 1 diabetes

INTRODUCTION

Clinical guidelines recommend that patients with type 1 diabetes (T1D) learn carbohydrate counting or similar methods to improve glycaemic control. Although systematic educating in carbohydrate counting is still not offered as standard-of-care for all patients on multiple daily injections (MDI) insulin therapy in outpatient diabetes clinics in Denmark. This may be due to the lack of evidence as to which educational methods are the most effective for training patients in carbohydrate counting. The objective of this study is to compare the effect of two different educational programmes in carbohydrate counting with the usual dietary care on glycaemic control in patients with T1D.

METHODS AND ANALYSIS

The study is designed as a randomised controlled trial with a parallel-group design. The total study duration is 12 months with data collection at baseline, 6 and 12 months. We plan to include 231 Danish adult patients with T1D. Participants will be randomised to one of three dietician-led interventions: (1) a programme in basic carbohydrate counting, (2) a programme in advanced carbohydrate counting including an automated bolus calculator or (3) usual dietary care. The primary outcome is changes in glycated haemoglobin A1c or mean amplitude of glycaemic excursions from baseline to end of the intervention period (week 24) between and within each of the three study groups. Other outcome measures include changes in other parameters of plasma glucose variability (eg, time in range), body weight and composition, lipid profile, blood pressure, mathematical literacy skills, carbohydrate estimation accuracy, dietary intake, diet-related quality of life, perceived competencies in dietary management of diabetes and perceptions of an autonomy supportive dietician-led climate, physical activity and urinary biomarkers.

ETHICS AND DISSEMINATION

The protocol has been approved by the Ethics Committee of the Capital Region, Copenhagen, Denmark. Study findings will be disseminated widely through peer-reviewed publications and conference presentations.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov Registry (NCT03623113).

Introducing a very low carbohydrate diet for a child with type 1 diabetes

Type 1 diabetes mellitus is a serious autoimmune disease for which no cure is available. The treatment includes insulin therapy, carbohydrate counting, eating healthy foods, exercising regularly, and maintaining a healthy weight. The goal is to keep blood glucose levels close to normal most of the time to delay or prevent complications. Despite the increase in the use of insulin pumps and continuous glucose monitors in recent years, the management of type 1 diabetes remains suboptimal in terms of glycaemic control and normal glycated haemoglobin (HbA1c) level. This article discusses the case of a child with type 1 diabetes who was successfully treated with a very low-carbohydrate diet, resulting in normal levels of HbA1c and normal blood glucose 95% of the time in a range of 70-180 mg/dL (4.0 mmol/L-10 mmol/L). Therefore, further studies are needed to verify how a very low carbohydrate diet impacts child development.

Artificial Pancreas: Clinical Study in Latin America Without Premeal Insulin Boluses

BACKGROUND

Emerging therapies such as closed-loop (CL) glucose control, also known as artificial pancreas (AP) systems, have shown significant improvement in type 1 diabetes mellitus (T1DM) management. However, demanding patient intervention is still required, particularly at meal times. To reduce treatment burden, the automatic regulation of glucose (ARG) algorithm mitigates postprandial glucose excursions without feedforward insulin boluses. This work assesses feasibility of this new strategy in a clinical trial.

METHODS

A 36-hour pilot study was performed on five T1DM subjects to validate the ARG algorithm. Subjects wore a subcutaneous continuous glucose monitor (CGM) and an insulin pump. Insulin delivery was solely commanded by the ARG algorithm, without premeal insulin boluses. This was the first clinical trial in Latin America to validate an AP controller.

RESULTS

For the total 36-hour period, results were as follows: average time of CGM readings in range 70-250 mg/dl: 88.6%, in range 70-180 mg/dl: 74.7%, <70 mg/dl: 5.8%, and <50 mg/dl: 0.8%. Results improved analyzing the final 15-hour period of this trial. In that case, the time spent in range was 70-250 mg/dl: 94.7%, in range 70-180 mg/dl: 82.6%, <70 mg/dl: 4.1%, and <50 mg/dl: 0.2%. During the last night the time spent in range was 70-250 mg/dl: 95%, in range 70-180 mg/dl: 87.7%, <70 mg/dl: 5.0%, and <50 mg/dl: 0.0%. No severe hypoglycemia occurred. No serious adverse events were reported.

CONCLUSIONS

The ARG algorithm was successfully validated in a pilot clinical trial, encouraging further tests with a larger number of patients and in outpatient settings.

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

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.

A Comparative Study on Carbohydrate Estimation: GoCARB vs. Dietitians

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.

Food exchange estimation by children with type 1 diabetes at summer camp

BACKGROUND

As exchange counting poses difficulty for children with type 1 diabetes (T1D) attending diabetes camps, they often guesstimate food amount without performing an exchange calculation. The aim of the study was to compare the accuracy of estimation with exchange counting using the mobile food exchange calculator (MFEC).

METHODS

During a summer camp, 25 children with T1D on pumps estimated the number of carbohydrate (CE) and fat/protein exchanges (FPE) appropriate for main meals. Afterwards, the number of exchanges was counted with MFEC and electronic scales.

RESULTS

There was a difference between CE (p<0.0001) and FPE (p<0.0001) estimations and counting using MFEC. The youth miscalculated the true values of ≥1 CE and ≥1 FPE by 31% and 23%, respectively. They more often underestimated than overestimated CE and FPE (p<0.0001). The estimation error increased with younger age.

CONCLUSIONS

Carbohydrate counting caused significant error in the exchange number. The use of MFEC facilitates correct exchange calculation. Patients should weigh food and calculate exchanges themselves using mobile applications.