Bolus Calculator Research Articles

Automatic Tuning of CIR and ISF with the Adaptive Bolus Calculator (ABC) Approach Is Safe and Reliable—Results from a Hybrid In Silico Evaluation

Introduction: T1DM patients usually determine their bolus insulin requirements using a patient and daytime specific carbohydrate to insulin ratio (CIR) and insulin sensitivity factor (ISF). These factors are tuned by the patients themselves with assistance of their MDs. Recently, the Adaptive Bolus Calculator (ABC) method has been proposed for the automatic tuning of CIRs and ISFs based on data that is routinely collected by T1DM patients. Methods: Data from a recent clinical trial with 12 patients are used for evaluating the ABC method. CIR and ISF values are identified using the ABC method based on the first 6 days of the data, whereas the 7th day is used for the evaluation. The effect of the estimated CIRs and ISFs on the glycemic outcomes is simulated using a deviation based method. The simulation outcomes are compared to the outcomes obtained with the clinically used settings (in the figure below abbreviated as CUS) that the patients employed on the previous 6 days for calculating their bolus insulin needs. Results: The CIRs and ISFs estimated by the ABC approach lead in average to a 6.8% reduction for time in hypoglycemia (BG<70 mg/dl) as compared to the clinically used CIR/ISF settings (avg. time in hypoglycemia: from 9.8% to 3.0%, p<0.01). Time in hyperglycemia (BG>180 mg/dl) on the other hand increases only slightly (avg. time in hyperglycemia: 10.9% vs. 9.3%, p=0.08). Disclosure F. Reiterer: None. L. del Re: None.

Long-Term Use of Closed-Loop Control—Patient-Reported Experience

Objective: Assess patient experience via semi-structured interviews after long-term outpatient use of closed loop control (CLC). Methods: Eleven patients with T1D diagnosed for at least one year participated (10 females), with a mean (SD) age of 47.3 years (12.9), diabetes duration of 26.6 years (17.7), and pre-study HbA1c 7.6% (0.5) or 60 mmol/mol. The CLC system was inControl, composed of a CGM, insulin pump, and control algorithms housed on a smartphone. Participants used inControl for 8-weeks after which they were interviewed. Interviews followed a semi-structured format based on theories of technology acceptance. Numerical ratings were obtained for difficulty, complexity, trust, and long-term use probability on a 5-point Likert scale. Results: Ratings were difficulty - 2.0 (0.8), complexity - 1.9 (0.8), trust - 3.8 (1.0), and 4) probability of long-term use - 3.3 (1.6). Difficult aspects of use were managing the three components and alarms for out of range BGs. The major complexities were finding ways to stop alarms and understanding insulin bolus calculation. Nine patients reported that the device was very useful and had improved BG control, including decreasing low BGs and reducing HbA1c. All reported “over-riding“ the device, most often to give more insulin for high BG correction, and also detaching the system at times to avoid alarms. Trust in the device was high, with 8 patients rating ≥ 4 but there was belief that not enough insulin was given for high BGs. Probability of long-term use varied, with the alarms and burden of multiple components as factors. Conclusions: Patient-reported experiences varied across individuals. The majority did not find the system difficult or complex, found it useful for better BG control, and trusted it. However, there is need for technology improvement to reduce equipment burden and alarms. Findings indicate that users might benefit from patient education before device use to help them understand how the system calculates insulin doses. Disclosure L. Gonder-Frederick: Other Relationship; Self; HFS-Global LLC. S.A. Brown: Research Support; Self; Roche Diagnostics Corporation, Dexcom, Inc., Tandem Diabetes Care, Inc., Medtronic, Ascensia Diabetes Care, TypeZero Technologies, Inc.. J. Shepard: None. L.L. Kollar: None. B. Kovatchev: Consultant; Self; Sanofi-Aventis. Speaker's Bureau; Self; Dexcom, Inc.. Stock/Shareholder; Self; TypeZero Technologies, Inc.. Research Support; Self; Dexcom, Inc., Roche Diagnostics Corporation, Tandem Diabetes Care, Inc., Ascensia Diabetes Care.

The Hybrid Closed-Loop System: Evolution and Practical Applications.

Achievement of well-controlled blood glucose is essential for preventing complications in patients with type 1 diabetes. Since the inception of continuous subcutaneous insulin infusion, the aim has been to develop an artificial pancreas, with the ability to use an automated algorithm to deliver one or more hormones in response to blood glucose with the intent to keep blood sugar as close to a prespecified target as possible. Development and rapid improvement of continuous glucose sensor technology has recently allowed swift progress toward a fully closed-loop insulin delivery system. In 2017, Medtronic began marketing the 670G insulin pump with Guardian 3 sensor. When in auto mode, this is a hybrid closed-loop insulin delivery system that automatically adjusts basal insulin delivery every 5 min based on sensor glucose to maintain blood glucose levels as close to a specific target as possible. Patients receive prandial insulin by entering carbohydrate amount into the bolus calculator. Early studies show improvement in HbA1c in both adults and adolescents with this technology. Initial safety trials showed no occurrence of diabetic ketoacidosis or hypoglycemia. The utility of this device is limited by blood glucose targets of 120 and 150 mg/dL that are unacceptably high for some patients. Notwithstanding recent advances, we are far from a system that is able to replicate islet function in the form of a fully automated, multihormonal blood glucose control device.

In Silico Assessment of Literature Insulin Bolus Calculation Methods Accounting for Glucose Rate of Change.

The standard formula (SF) used in bolus calculators (BCs) determines meal insulin bolus using "static" measurement of blood glucose concentration (BG) obtained by self-monitoring of blood glucose (SMBG) fingerprick device. Some methods have been proposed to improve efficacy of SF using "dynamic" information provided by continuous glucose monitoring (CGM), and, in particular, glucose rate of change (ROC). This article compares, in silico and in an ideal framework limiting the exposition to possibly confounding factors (such as CGM noise), the performance of three popular techniques devised for such a scope, that is, the methods of Buckingham et al (BU), Scheiner (SC), and Pettus and Edelman (PE). Using the UVa/Padova Type 1 diabetes simulator we generated data of 100 virtual subjects in noise-free, single-meal scenarios having different preprandial BG and ROC values. Meal insulin bolus was computed using SF, BU, SC, and PE. Performance was assessed with the blood glucose risk index (BGRI) on the 9 hours after meal. On average, BU, SC, and PE improve BGRI compared to SF. When BG is rapidly decreasing, PE obtains the best performance. In the other ROC scenarios, none of the considered methods prevails in all the preprandial BG conditions tested. Our study showed that, at least in the considered ideal framework, none of the methods to correct SF according to ROC is globally better than the others. Critical analysis of the results also suggests that further investigations are needed to develop more effective formulas to account for ROC information in BCs.

Evaluating optimal utilisation of technology in type 1 diabetes mellitus from a clinical and health economic perspective: protocol for a systematic review

BackgroundTechnology has been implemented since the 1970s with the hope of improving glycaemic control and reducing the burden of complications for those living with type 1 diabetes. A clinical and cost-effectiveness comparison of all available technologies including continuous subcutaneous insulin infusion (CSII), continuous glucose monitors (CGMs), sensor-augmented pump therapy (including either low-glucose suspend or predictive low-glucose suspend), hybrid closed-loop systems, closed-loop (single-hormone or dual-hormone) systems, flash glucose monitoring (FGM), insulin bolus calculators, and ‘smart-device’ applications is currently lacking. This systematic review, network meta-analysis, and narrative synthesis aims to summarise available evidence regarding the clinical and cost-effectiveness of available technologies in the management of patients with type 1 diabetes.MethodsRelevant studies will be searched using a comprehensive strategy through MEDLINE, MEDLINE in-process and other non-indexed citations, EMBASE, PubMed, all evidenced-based medicine reviews, EconLit, Cost-effectiveness Analysis Registry, Research Papers in Economics, Web of Science, PsycInfo, CINAHL, and PROSPERO for randomised controlled trials and economic evaluations. The search strategy will assess if there are combinations of currently available technologies that are superior to each other or to insulin injections and capillary blood glucose testing with regard to glycaemic control, morbidity/mortality, quality of life, and cost-effectiveness. Two reviewers will screen all articles for eligibility and then independently evaluate risk of bias, complete quality assessment, and extract data for included studies. Network meta-analyses will be performed where there is sufficient homogenous clinical data. Narrative synthesis will be performed for heterogeneous clinical data that cannot be pooled for network meta-analysis with critical appraisal of economic evaluations.DiscussionThis systematic review protocol utilises rigorous methodology and pre-determined eligibility criteria to provide a uniquely comprehensive search for a broad spectrum of clinical and economic outcomes in comparing multiple currently available technologies for managing type 1 diabetes. Evidence on which technologies may be most appropriate for particular patient groups will be examined as well as the economic justification for funding of different technologies.Systematic review registrationPROSPERO (CRD42017077221)

Effect of Automated Bolus Calculation on Glucose Variability and Quality of Life in Patients With Type 1 Diabetes on CSII Treatment

PurposeAutomated bolus calculation may benefit patients with poorly controlled type 1 diabetes who are relatively new to continuous subcutaneous insulin infusion (CSII). This study investigated the effect of automated bolus calculation on glucose variability, glucose control, and diabetes-related quality of life in patients with reasonably well-controlled type 1 diabetes, accustomed to treatment with CSII for several years. MethodsThis open-label, single-center study included 32 patients (mean age, 45.9 [15.1] years; 34% male; disease duration, 27.3 [12.9] years; glycosylated hemoglobin [HbA1c] level, 64.6 [12.5] mmol/mol [8.1% (1.1%)]; CSII treatment, 9.0 [7.8] years) who were randomly assigned to receive 4 months’ treatment with a bolus calculator (n = 14) or continuation of standard care without a bolus calculator (n = 18). All participants received dietary counseling on carbohydrate counting. Primary outcome was glucose variability, as assessed by the SD of 7-point glucose profiles. Secondary outcomes included HbA1c, rate of (severe) hypoglycemia, and diabetes-related quality of life. FindingsAfter 4 months of follow-up, glucose variability had improved in the bolus calculator group compared with the control group (change, –0.8 [0.9] vs 0.1 [0.9] mmol/L; P = 0.030). Mean glucose levels did not change in either group (0.4 [1.1] vs 0.3 [0.9] mmol/L; P = 0.95). There were also no differences in change in hypoglycemia rate (–0.6 [1.6] vs –0.4 [1.6] event per patient per week; P = 0.67), HbA1c value (–0.5 [6.6] vs –4.9 [10.6] mmol/mol; P = 0.21), or diabetes-related quality of life between the bolus calculator group and the control group. ImplicationsUse of a bolus calculator modestly improved glucose variability in this relatively small group of patients with longstanding type 1 diabetes on CSII but did not affect other parameters of glycemic control or diabetes-related quality of life.

A Neural-Network-Based Approach to Personalize Insulin Bolus Calculation Using Continuous Glucose Monitoring.

In type 1 diabetes (T1D) therapy, the calculation of the meal insulin bolus is performed according to a standard formula (SF) exploiting carbohydrate intake, carbohydrate-to-insulin ratio, correction factor, insulin on board, and target glucose. Recently, some approaches were proposed to account for preprandial glucose rate of change (ROC) in the SF, including those by Scheiner and by Pettus and Edelman. Here, the aim is to develop a new approach, based on neural networks (NN), to optimize and personalize the bolus calculation using continuous glucose monitoring information and some easily accessible patient parameters. The UVa/Padova T1D Simulator was used to simulate data of 100 virtual adults in a single-meal noise-free scenario with different conditions in terms of meal amount and preprandial blood glucose and ROC values. An NN was trained to learn the optimal insulin dose using the SF parameters, ROC, body weight, insulin pump basal infusion rate and insulin sensitivity as features. The performance of the NN for meal bolus calculation was assessed by blood glucose risk index (BGRI) and compared to the methods by Scheiner and by Pettus and Edelman. The NN approach brings to a small but statistically significant ( P < .001) reduction of BGRI value, equal to 0.37, 0.23, and 0.20 versus SF, Scheiner, and Pettus and Edelman, respectively. This preliminary study showed the potentiality of using NNs for the personalization and optimization of the meal insulin bolus calculation. Future work will deal with more realistic scenarios including technological and physiological/behavioral sources of variability.

Efficacy of automatic bolus calculator with automatic speech recognition in patients with type 1 diabetes: A randomized cross-over trial.

Patients using an insulin pump as part of their diabetes treatment need to calculate insulin bolus doses to compensate for a meal. Some patients do not modify their meal boluses according to changes in the amount and composition of food products in a meal. The lack of correct meal boluses leads to unstable, and therefore harmful, blood glucose levels. The aim of the present study was to test a system supporting bolus determination based on a voice description of a meal. The bolus calculator developed (VoiceDiab) consists of a smartphone application and three remote servers for automatic speech recognition, text analysis, and insulin dosage calculation. Forty-four people with type 1 diabetes (T1D) treated with continuous subcutaneous insulin infusion finished the randomized cross-over study. Patients were randomly allocated to the group in which the VoiceDiab system supported bolus calculation or to an unsupported group, in which patients or their caregivers calculated boluses. After a 14-day washout period, patients from the supported group were switched to the unsupported group, whereas those in the unsupported group were switched to the supported group. There was a significant difference between the supported and unsupported groups in the percentage of patients with 2-h postprandial glycemia within the 70-180 mg/dL range (58.6% vs 46.6%, respectively; P = 0.031). The VoiceDiab system improves postprandial glucose control without increasing the time in hyperglycemia or hypoglycemia. Therefore, it may be useful in the treatment of patients with diabetes on intensive insulin therapy.

Adaptive basal insulin recommender system based on Kalman filter for type 1 diabetes

Type 1 diabetes mellitus is a chronic disease that requires those affected to self-administer insulin to control their blood glucose level. However, the estimation of the correct insulin dosage is not easy due to the complexity of glucose metabolism, which usually leads to blood glucose levels far from the optimal. This paper presents an adaptive and personalised basal insulin recommender system based on Kalman filter theory that can be used with or without continuous glucose monitoring systems. The proposed approach is tested with the UVa/PADOVA simulator with eleven virtual adult subjects. It has been tested in combination with two different bolus calculators, and the performance achieved has been compared with that obtained with the default basal doses of the simulator, which can be assumed as optimal. The achieved results demonstrate that the proposed system rapidly converges to the optimal basal dose, and it can be used with adaptive bolus calculators without the risk of instability.

Impact of erroneous meal insulin bolus with dual-hormone artificial pancreas using a simplified bolus strategy - A randomized controlled trial

Postprandial glucose control remains challenging for patients with type 1 diabetes (T1D). A simplified meal bolus approach with a dual-hormone (insulin and glucagon) closed-loop system (DH-CLS) has been tested; yet, the impact of categorization errors with this strategy is unknown. The objective was to compare, in a randomized controlled inpatient trial, DH-CLS with the simplified meal bolus approach for two different meals properly categorized or overestimated. We tested, in patients with T1D, the simplified strategy with two standardized breakfasts (n = 10 per meal) adequately categorized or overestimated: (1) 75 g and (2) 45 g of carbohydrate. No difference was observed for percentage of time <4.0 mmol/L over a 4-hour post-meal period (primary outcome; median [IQR]: 0[0–0] vs. 0[0–0] for both comparisons, p = 0.47 and 0.31 for the 75 g and 45 g meals, respectively). Despite higher meal insulin boluses with overestimation for both meals (9.2 [8.2–9.6] vs. 8.1 [7.3–9.1] U and 8.4 [7.2–10.4] vs. 4.8 [3.7–5.6] U; p < 0.05), mean glycemia, percentage of time in target range and glucagon infusion did not differ. Additional scenarios were tested in silico with comparable results. These results suggest that the DH-CLS with a simplified meal bolus calculation is probably able to avoid hypoglycemia in the event of meal size misclassification.

Carbohydrate Counting in Children and Adolescents with Type 1 Diabetes.

Carbohydrate counting (CC) is a meal-planning tool for patients with type 1 diabetes (T1D) treated with a basal bolus insulin regimen by means of multiple daily injections or continuous subcutaneous insulin infusion. It is based on an awareness of foods that contain carbohydrates and their effect on blood glucose. The bolus insulin dose needed is obtained from the total amount of carbohydrates consumed at each meal and the insulin-to-carbohydrate ratio. Evidence suggests that CC may have positive effects on metabolic control and on reducing glycosylated haemoglobin concentration (HbA1c). Moreover, CC might reduce the frequency of hypoglycaemia. In addition, with CC the flexibility of meals and snacks allows children and teenagers to manage their T1D more effectively within their own lifestyles. CC and the bolus calculator can have possible beneficial effects in improving post-meal glucose, with a higher percentage of values within the target. Moreover, CC might be integrated with the counting of fat and protein to more accurately calculate the insulin bolus. In conclusion, in children and adolescents with T1D, CC may have a positive effect on metabolic control, might reduce hypoglycaemia events, improves quality of life, and seems to do so without influencing body mass index; however, more high-quality clinical trials are needed to confirm this positive impact.

An automated meal detector and bolus calculator in combination with closed-loop blood glucose control

Abstract The aim of this study is to develop an algorithm for detection of unannounced meals and an insulin bolus calculator (BC) to work in combination with the meal detector. The input of the meal detector are the continuous glucose monitoring (CGM) data and the insulin infusion rate. During daytime, the automated meal detector and the BC control the blood glucose concentration. During nighttime, a model predictive control (MPC) algorithm regulates the basal insulin rate. The meal detector detects the occurrence of a meal, estimates the amount of carbohydrate (CHO) in the meal, and estimates the meal onset time. The BC computes a bolus dose to cover the detected meal. We test the meal detector and the BC on nine virtual type 1 diabetes (T1D) patients. The meal detection algorithm, applied on the virtual patients, has a median detection delay of 40 min, detection sensitivity of 80% and a median meal onset estimation bias of 15 min. The algorithm does not have false positive.

Identification of Mixed-Meal Effects on Insulin Needs and Glycemic Control

Abstract Patients with type 1 diabetes mellitus (T1DM) usually determine their bolus insulin requirements using methods from advanced carbohydrate counting (ACC). In standard ACC only the carbohydrate content of the meal is used, whereas newer research suggests that also fat and protein should be considered. Especially meals with a high fat content are reported to require additional insulin. However, it remains disputed how to account for mixed meal composition appropriately when calculating bolus insulin needs. The current paper shows how the previously published Adaptive Bolus Calculator (ABC) method can be used to identify the effect of the mixed meal composition on the bolus insulin requirements. Additionally, a pragmatic strategy is proposed how the mixed meal composition can be taken into account when determining the bolus insulin needs with the ABC method. It is shown in Deviation Analysis simulations that taking into account the mixed meal composition for calculating the bolus insulin needs leads to a small, but statistically significant improvement in glycemic control.

Impact of Carbohydrate Counting Errors on Glycemic Control in Type 1 Diabetes

Abstract Patients with type 1 diabetes mellitus (T1DM) typically determine their bolus insulin needs based on methods from Advanced Carbohydrate Counting (ACC). In ACC the amount of required bolus insulin is assumed to be directly proportional to the carbohydrate content of the ingested meal. It is well known that many T1DM patients have difficulties to accurately estimate meal carbohydrates. Though these estimates are used for calculating the meal bolus amount, there is a scarcity of data on how estimation errors affect glycemic control. The current paper analyzes the effect of carb counting errors on glycemic outcomes during basal-bolus-therapy in a simulation study using so-called Deviation Analyses. Furthermore, the effect of inaccurate estimates on the settings of the insulin therapy is studied by means of the previously published Adaptive Bolus Calculator (ABC) algorithm. It is found that whereas random carb counting inaccuracies indeed do lead to an inferior glycemic control, systematic biases in the estimates are expected to hardly affect the results since these are usually implicitly accounted for in the therapy settings.

Does maximising the use of bolus calculator glucose meters improves glycaemic control in children and adolescents with type 1 diabetes?

Does maximising the use of bolus calculator glucose meters improves glycaemic control in children and adolescents with type 1 diabetes?

Bolus Advisors: Sources of Error, Targets for Improvement.

Bolus advisors that are designed to improve the accuracy of individual bolus doses relative to a meal's carb content and the current glucose have not substantially changed since they were introduced 15 years ago despite an obvious need for enhancement and innovation. Although some glycemic benefits have been demonstrated, bolus advisors largely ignore the large amounts of clinical data they gather that could have a significant impact on glucose outcomes. Concerns have also been raised regarding the aggressive nature of largely unpublished or poorly explained bolus advisor algorithms. Hypoglycemia and hyperglycemia remain significant risks due to inaccurate bolus advisor settings and the absence of tracking or an inappropriate handling of bolus on board. This review covers common sources for bolus advisor error such as the selection of physiologically inappropriate bolus advisor settings, the use of short duration of insulin action times, poor algorithm logic that tends to cover all carb intake fully, and an excessive reliance on simplistic dosing algorithms. As well as discussing these areas, we provide 21 ways to improve current bolus calculators.

Bolus Calculator Reduces Hypoglycemia in the Short Term and Fear of Hypoglycemia in the Long Term in Subjects with Type 1 Diabetes (CBMDI Study).

Background: In a previous study we demonstrated improvement in metabolic control and reduction in hypoglycemia in people with type 1 diabetes on multiple daily injections, after having used a bolus calculator for 4 months.Objective: To demonstrate whether (1) extending its use (2) or introducing it in the control group, previously subjected to treatment intensification, could further improve metabolic control and related psychological issues.Methods: After the previous clinical trial, in which the subjects were randomized either to treatment with the calculator or to control group for 4 months, both groups used the calculator during an additional 4-month period.Results: In the previous control group, after using the device, HbA1c did not improve (7.86% ± 0.87% vs. 8.01% ± 0.93%, P 0.215), although a significant decrease in postprandial hypoglycemia was observed (2.3 ± 2 vs. 1.1 ± 1.2/2 weeks, P 0.002). In the group in which the treatment was extended from 4 to 8 months, HbA1c did not improve either (7.61 ± 0.58 vs. 7.73 ± 0.65, P 0.209); however this group had a greater perceived treatment satisfaction (12.03 ± 4.26 vs. 13.71 ± 3.75, P 0.007) and a significant decrease in fear of hypoglycemia (28.24 ± 8.18 basal vs. 25.66 ± 8.02 at 8 months, P 0.026).Conclusions: The extension in the use of the calculator or its introduction in a previously intensified control group did not improve metabolic control, although it did confirm a decrease in hypoglycemic episodes in the short term, while the extension of its use to 8 months was associated with a reduction in fear of hypoglycemia and greater treatment satisfaction.

Relevance of Bolus Calculators in Current Hybrid Closed Loop Systems.

Relevance of Bolus Calculators in Current Hybrid Closed Loop Systems.

Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator – in silico evaluation under intra-day variability

Background and ObjectiveCurrent prototypes of closed-loop systems for glucose control in type 1 diabetes mellitus, also referred to as artificial pancreas systems, require a pre-meal insulin bolus to compensate for delays in subcutaneous insulin absorption in order to avoid initial post-prandial hyperglycemia. Computing such a meal bolus is a challenging task due to the high intra-subject variability of insulin requirements. Most closed-loop systems compute this pre-meal insulin dose by a standard bolus calculation, as is commonly found in insulin pumps. However, the performance of these calculators is limited due to a lack of adaptiveness in front of dynamic changes in insulin requirements. Despite some initial attempts to include adaptation within these calculators, challenges remain. MethodsIn this paper we present a new technique to automatically adapt the meal-priming bolus within an artificial pancreas. The technique consists of using a novel adaptive bolus calculator based on Case-Based Reasoning and Run-To-Run control, within a closed-loop controller. Coordination between the adaptive bolus calculator and the controller was required to achieve the desired performance. For testing purposes, the clinically validated Imperial College Artificial Pancreas controller was employed. The proposed system was evaluated against itself but without bolus adaptation. The UVa-Padova T1DM v3.2 system was used to carry out a three-month in silico study on 11 adult and 11 adolescent virtual subjects taking into account inter-and intra-subject variability of insulin requirements and uncertainty on carbohydrate intake. ResultsOverall, the closed-loop controller enhanced by an adaptive bolus calculator improves glycemic control when compared to its non-adaptive counterpart. In particular, the following statistically significant improvements were found (non-adaptive vs. adaptive). Adults: mean glucose 142.2 ± 9.4vs. 131.8 ± 4.2mg/dl; percentage time in target [70, 180]mg/dl, 82.0 ± 7.0vs. 89.5 ± 4.2; percentage time above target 17.7 ± 7.0vs. 10.2 ± 4.1. Adolescents: mean glucose 158.2 ± 21.4vs. 140.5 ± 13.0mg/dl; percentage time in target, 65.9 ± 12.9vs. 77.5 ± 12.2; percentage time above target, 31.7 ± 13.1vs. 19.8 ± 10.2. Note that no increase in percentage time in hypoglycemia was observed. ConclusionUsing an adaptive meal bolus calculator within a closed-loop control system has the potential to improve glycemic control in type 1 diabetes when compared to its non-adaptive counterpart.

Exploring factors influencing HbA1c and psychosocial outcomes in people with type 1 diabetes after training in advanced carbohydrate counting.

The purpose of this secondary analysis of the StenoABC Study was to identify determinants of the changes in HbA1c observed after training of people with type 1 diabetes in advanced carbohydrate counting (ACC) and automated bolus calculator (ABC) use, and further to investigate psychosocial effects of these insulin dosing approaches. Validated diabetes-specific questionnaires were used to assess diabetes treatment satisfaction, problem areas in diabetes, fear of hypoglycemia and diabetes dependent quality of life before and one year after the training. In addition, numeracy was tested (using a non-validated test developed specifically for this study) and behavioral measures (number of daily blood glucose measurements and self-reported use of ACC) were obtained. Associations between change in HbA1c and these measures plus sex, age, diabetes duration and BMI were tested. Numeracy was the only baseline predictor of yearly change in HbA1c identified. Higher levels of numeracy were associated with greater reductions in HbA1c (P=0.031). No associations between change in HbA1c and the behavioral measures investigated were found, nor were any clinically relevant associations between changes in HbA1c and questionnaire scores. Treatment satisfaction increased in all users of ACC (P<0.001). People who also used an ABC reported significantly lower levels of fear of hypoglycemia than people who practiced ACC without such device (P=0.005). Improvements in HbA1c after training in ACC were inversely related to numeracy. Use of an ABC did not compensate for poor numeracy skills. However, device use reduced fear of hypoglycemia compared with ACC without ABC use.