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Kirsten Norgaard - One of the best experts on this subject based on the ideXlab platform.
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study protocol for optimising glycaemic control in type 1 diabetes treated with multiple daily insulin injections intermittently scanned continuous glucose monitoring Carbohydrate Counting with automated bolus calculation or both a randomised control
BMJ Open, 2020Co-Authors: Anna Lilja Secher, Dorte Vistisen, Ulrik Pedersenbjergaard, Ole Lander Svendsen, Birthe Gaderasmussen, Thomas P Almdal, Liv Dorflinger, Kirsten NorgaardAbstract:Introduction There are beneficial effects of advanced Carbohydrate Counting with an automatic bolus calculator (ABC) and intermittently scanned continuous glucose monitoring (isCGM) in persons with type 1 diabetes. We aim to compare the effects of isCGM, training in Carbohydrate Counting with ABC and the combination of the two concepts with standard care. Methods and analysis A multi-centre randomised controlled trial with inclusion criteria: ≥18 years, type 1 diabetes ≥1 year, injection therapy, HbA1c >53 mmol/mol, whereas daily use of Carbohydrate Counting and/or CGM/isCGM wear are exclusion criteria. Inclusion was initiated in October 2018 and is ongoing. Eligible persons are randomised into four groups: standard care, ABC, isCGM or ABC+isCGM. Devices used are FreeStyle Libre Flash and smart phone diabetes application mySugr. Participants attend group courses according to treatment allocation with different educational contents. Participants are followed for 26 weeks with clinical visits and telephone consultations. At baseline and at study end, participants wear blinded CGM, have blood samples performed and fill in questionnaires on person-related outcomes, and at baseline also on personality traits and hypoglycaemia awareness. The primary outcome is the difference in time spent in normoglycaemia (4–10 mmol/L) at study end versus baseline between the isCGM group and the standard care group. Secondary outcomes will also be analysed. Results are expected in 2020. Ethics and dissemination Regional Scientific Ethics Committee approval (H-17040573). Results will be sought disseminated at conferences and in high impact journals. Trial registration number ClinicalTrial.gov registry (NCT03682237).
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765 p effects of an advanced Carbohydrate Counting course on diet composition in insulin treated type 2 diabetes
Diabetes, 2019Co-Authors: Merete Christensen, Thorhallur I Halldorsson, Anders Gotfredsen, E Hommel, Peter Gaede, Sjurdur F Olsen, Kirsten NorgaardAbstract:Background and Aim: Training in advanced Carbohydrate Counting improves glycemic control in type 1 diabetes and type 2 diabetes. We aimed to investigate, whether a course in advanced Carbohydrate Counting reduced intake of Carbohydrates and glycemic index of Carbohydrates in persons with basal-bolus insulin treated type 2 diabetes. Material and Methods: We conducted a 24-week open-label, randomized controlled study in 79 participants with basal-bolus insulin treated type 2 diabetes. Participants were randomized 1:1 into two groups. ABC group received 6-hour training in advanced Carbohydrate Counting and use of an automated bolus calculator. MC group also received 6-hour training in advanced Carbohydrate Counting but was trained in manual calculation of insulin bolus. Diet composition and intake of macronutrients were assessed at baseline, week 12 and week 24 using a validated electronic food frequencies questionnaire. Results: Baseline characteristics were similar between groups (mean age 62.5 ± 9.6 years, mean HbA1C 72 ± 11 mmol/mol, mean diabetes duration 18.7 ± 7.6 years, mean BMI 33 ± 6 kg/m 2 ). Mean energy intake was significantly reduced in the ABC group from baseline to week 24 (P=0.03), primarily due to significantly reduced intake of Carbohydrates in form of cereals (P=0.005). No significant changes in energy intake were seen in the MC group (P=0.62). Further, glycemic index of ingested Carbohydrates was reduced significantly in the ABC group (P=0.03), but not in the MC group (P=0.09). As previously reported HbA1c had decreased significantly by 9.0 mmol/mol in both groups at week 24. There were no significant changes in BMI throughout the study. Conclusion: Advanced Carbohydrate Counting with the use of an automated bolus calculator may help to reduce energy intake and glycemic index of ingested Carbohydrates in basal-bolus insulin treated type 2 diabetes. Disclosure M.B. Christensen: None. T.I. Halldorsson: None. A. Gotfredsen: None. E. Hommel: None. P. Gaede: None. S.F. Olsen: None. K. Norgaard: Advisory Panel; Self; Abbott, Medtronic, Novo Nordisk A/S. Speaker9s Bureau; Self; Bayer US, Medtronic, Roche Diabetes Care, Rubin Medical, Sanofi, Zealand Pharma A/S. Stock/Shareholder; Self; Novo Nordisk A/S. Funding Roche A/S
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exploring factors influencing hba1c and psychosocial outcomes in people with type 1 diabetes after training in advanced Carbohydrate Counting
Diabetes Research and Clinical Practice, 2017Co-Authors: Signe Schmidt, E Hommel, Dorte Vistisen, Thomas Almdal, Kirsten NorgaardAbstract:Abstract Aims 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. Methods 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. Results 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 Conclusions 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.
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effects of advanced Carbohydrate Counting guided by an automated bolus calculator in type 1 diabetes mellitus stenoabc a 12 month randomized clinical trial
Diabetic Medicine, 2017Co-Authors: E Hommel, Signe Schmidt, Dorte Vistisen, K Neergaard, M Gribhild, Thomas Almdal, Kirsten NorgaardAbstract:Aims To test whether concomitant use of an automated bolus calculator for people with Type 1 diabetes carrying out advanced Carbohydrate Counting would induce further improvements in metabolic control. Methods We conducted a 12-month, randomized, parallel-group, open-label, single-centre, investigator-initiated clinical study. We enrolled advanced Carbohydrate Counting-naive adults with Type 1 diabetes and HbA1c levels 64–100 mmol/mol (8.0–11.3%), who were receiving multiple daily insulin injection therapy. In a 1:1-ratio, participants were randomized to receive training in either advanced Carbohydrate Counting using mental calculations (MC group) or advanced Carbohydrate Counting using an automated bolus calculator (ABC group) during a 3.5-h group training course. For 12 months after training, participants attended a specialized diabetes centre quarterly. The primary outcome was change in HbA1c from baseline to 12 months. Results Between August 2012 and September 2013, 168 participants (96 men and 72 women) were recruited and randomly assigned to the MC group (n = 84) and the ABC group (n = 84). Drop-out rates were 23.8 and 21.4%, respectively (P = 0.712); 130 participants completed the study. The baseline HbA1c was 75 ± 9 mmol/mol (9.0 ± 0.8%) in the MC group and 74 ± 8 mmol/mol (8.9 ± 0.7%) in the ABC group. At 12 months, change in HbA1c was significant within both groups: MC group: -2 mmol/mol (95% CI -4 to -1) or -0.2% (95% CI -0.4 to -0.1; P = 0.017) and ABC group: -5 mmol/mol (95% CI -6 to -3) or -0.5% (95% CI -0.6 to -0.3; P < 0.0001), but HbA1c reductions were significantly greater in the ABC group (P = 0.033). No episodes of severe hypoglycaemia were reported. Conclusions People with Type 1 diabetes initiating advanced Carbohydrate Counting obtained significantly greater HbA1c reductions when guided by an automated bolus calculator (NCT02084498).
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use of advanced Carbohydrate Counting and an automated bolus calculator in clinical practice the boluscal training concept
International Diabetes Nursing, 2015Co-Authors: Merete Meldgaard, Kirsten Norgaard, Camilla Dammfrydenberg, Ulla Vesth, Signe SchmidtAbstract:AbstractBackground: BolusCal® is a newly developed training concept for patients with Type 1 diabetes (T1D) on a basal-bolus regime. The training, which is provided by a diabetes nurse and a dietician, consists of a 4-hour group session followed by a 1-hour follow-up and includes training in advanced Carbohydrate Counting and the use of an automated bolus calculator.Aims: The aim of this article is to describe the BolusCal training concept and to report changes in HbA1c and body mass index (BMI) as well as resources spent 12 months after implementing the BolusCal training concept in routine clinical practice.Methods: During 14 months in 2012–2013, 86 patients with T1D participated in a BolusCal training course. We retrospectively collected patient data from electronic medical records.Results: From training course participation to 12 months, HbA1c decreased from 66 to 57 mmol/mol (8.2–7.4%) (p < 0.001). BMI did not change. Within the first 6 months the number of follow-up consultations ranged from 0 to 9 (...
Hugh D Tildesley - One of the best experts on this subject based on the ideXlab platform.
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Carbohydrate Counting vs sliding scale for insulin dosage estimation
Canadian Journal of Diabetes, 2007Co-Authors: Emma O Billington, Tyler Fraser, Amira Tawashy, Hugh D TildesleyAbstract:ABSTRACT OBJECTIVE To determine if there is a difference in outcome in patients with type 1 diabetes who use different methods to estimate insulin dosage: sliding scale or Carbohydrate Counting. METHODS This study assessed patients who initiated continuous subcutaneous insulin infusion (CSII) and used either sliding scale (n=81) or Carbohydrate Counting (n=44) methodology to determine bolus insulin dosages. Groups were compared in terms of glycosylated hemoglobin (A1C), weight, frequency of hypoglycemia and insulin requirement at CSII initiation and follow-up. Within-person changes in a subgroup that started out using the sliding scale method and then crossed over to Carbohydrate Counting (n=21) were also evaluated. RESULTS At baseline, only A1C differed significantly (p CONCLUSION With only the sliding scale method showing a significant decrease in A1C, Carbohydrate Counting does not appear to be superior for insulin dosage estimation in patients using CSII.
Ulrich Keller - One of the best experts on this subject based on the ideXlab platform.
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Carbohydrate Counting of food
Swiss Medical Weekly, 2011Co-Authors: Karin Hegar, Stefanie Heiber, Michael Brandle, Emanuel Christ, Ulrich KellerAbstract:QUESTIONS UNDER STUDY: Carbohydrate Counting is a principal strategy in nutritional management of type 1 diabetes. The Nutri-Learn buffet (NLB) is a new computer-based tool for patient instruction in Carbohydrate Counting. It is based on food dummies made of plastic equipped with a microchip containing relevant food content data. The tool enables the dietician to assess the patient`s food Counting abilities and the patient to learn in a hands-on interactive manner to estimate food contents such as Carbohydrate content. METHODS: Multicentre randomised controlled trial in 134 patients with type 1 diabetes comparing the use of the Nutri-Learn buffet in determining and improving ability to estimate the Carbohydrate content of food with the use of conventional counselling tools (i.e. pictures and tables). RESULTS: The NLB group showed significantly better Carbohydrate estimation values than the control group. In particular, there was a significant improvement in estimation of starches, fruits and sweets. The NLB was preferred by patients and dieticians in that rating of Carbohydrate was closer to reality than the use of conventional tools, and since the tool has a play element, is interactive and adjustable, and can be used with only minimal knowledge of a specific language. CONCLUSIONS: Adjustment of preprandial insulin doses to the amounts of dietary Carbohydrates ingested during the subsequent meal resulted in improved metabolic control in previous studies. The present study demonstrated that the new tool (Nutri-Learn buffet) improved teaching and learning of Carbohydrate Counting. In addition, it allowed an objective assessment of the Carbohydrate Counting skills of patients by the dietician. The findings therefore suggest that the tool is helpful in nutritional counselling of patients with diabetes mellitus.
K J Bell - One of the best experts on this subject based on the ideXlab platform.
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A randomized comparison of three prandial insulin dosing algorithms for children and adolescents with Type 1 diabetes.
Diabetic Medicine, 2018Co-Authors: P. E. Lopez, K J Bell, Bruce R. King, Elizabeth A. Davis, M. Evans, Timothy W Jones, Patrick Mcelduff, Carmel E SmartAbstract:To compare systematically the impact of two novel insulin-dosing algorithms (the Pankowska Equation and the Food Insulin Index) with Carbohydrate Counting on postprandial glucose excursions following a high fat and a high protein meal. A randomized, crossover trial at two Paediatric Diabetes centres was conducted. On each day, participants consumed a high protein or high fat meal with similar Carbohydrate amounts. Insulin was delivered according to Carbohydrate Counting, the Pankowska Equation or the Food Insulin Index. Subjects fasted for 5 h following the test meal and physical activity was standardized. Postprandial glycaemia was measured for 300 min using continuous glucose monitoring. 33 children participated in the study. When compared to Carbohydrate Counting, the Pankowska Equation resulted in lower glycaemic excursion for 90-240 min after the high protein meal (p < 0.05) and lower peak glycaemic excursion (p < 0.05). The risk of hypoglycaemia was significantly lower for Carbohydrate Counting and the Food Insulin Index compared to the Pankowska Equation (OR 0.76 Carbohydrate Counting vs. the Pankowska Equation and 0.81 the Food Insulin Index vs. the Pankowska Equation). There was no significant difference in glycaemic excursions when Carbohydrate Counting was compared to the Food Insulin Index. The Pankowska Equation resulted in reduced postprandial hyperglycaemia at the expense of an increase in hypoglycaemia. There were no significant differences when Carbohydrate Counting was compared to the Food Insulin Index. Further research is required to optimize prandial insulin dosing. © 2018 Diabetes UK.
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A randomized comparison of three prandial insulin dosing algorithms for children and adolescents with Type 1 diabetes.
Diabetic Medicine, 2018Co-Authors: P. E. Lopez, K J Bell, Bruce R. King, Elizabeth A. Davis, M. Evans, Timothy W Jones, Patrick Mcelduff, Carmel E SmartAbstract:AIM: To compare systematically the impact of two novel insulin-dosing algorithms (the Pankowska Equation and the Food Insulin Index) with Carbohydrate Counting on postprandial glucose excursions following a high fat and a high protein meal. METHODS: A randomized, crossover trial at two Paediatric Diabetes centres was conducted. On each day, participants consumed a high protein or high fat meal with similar Carbohydrate amounts. Insulin was delivered according to Carbohydrate Counting, the Pankowska Equation or the Food Insulin Index. Subjects fasted for 5 h following the test meal and physical activity was standardized. Postprandial glycaemia was measured for 300 min using continuous glucose monitoring. RESULTS: 33 children participated in the study. When compared to Carbohydrate Counting, the Pankowska Equation resulted in lower glycaemic excursion for 90-240 min after the high protein meal (p < 0.05) and lower peak glycaemic excursion (p < 0.05). The risk of hypoglycaemia was significantly lower for Carbohydrate Counting and the Food Insulin Index compared to the Pankowska Equation (OR 0.76 Carbohydrate Counting vs. the Pankowska Equation and 0.81 the Food Insulin Index vs. the Pankowska Equation). There was no significant difference in glycaemic excursions when Carbohydrate Counting was compared to the Food Insulin Index. CONCLUSION: The Pankowska Equation resulted in reduced postprandial hyperglycaemia at the expense of an increase in hypoglycaemia. There were no significant differences when Carbohydrate Counting was compared to the Food Insulin Index. Further research is required to optimize prandial insulin dosing.
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Estimating insulin demand for protein-containing foods using the food insulin index
European Journal of Clinical Nutrition, 2014Co-Authors: K J Bell, D Munns, G Howard, Stephen Colagiuri, Peter Petocz, R. Gray, J. C. Brand-millerAbstract:Background/objective: The Food Insulin Index (FII) is a novel algorithm for ranking foods on the basis of insulin responses in healthy subjects relative to an isoenergetic reference food. Our aim was to compare postprandial glycemic responses in adults with type 1 diabetes who used both Carbohydrate Counting and the FII algorithm to estimate the insulin dosage for a variety of protein-containing foods. Subjects/methods: A total of 11 adults on insulin pump therapy consumed six individual foods (steak, battered fish, poached eggs, low-fat yoghurt, baked beans and peanuts) on two occasions in random order, with the insulin dose determined once by the FII algorithm and once with Carbohydrate Counting. Postprandial glycemia was measured in capillary blood glucose samples at 15–30 min intervals over 3 h. Researchers and participants were blinded to treatment. Results: Compared with Carbohydrate Counting, the FII algorithm significantly reduced the mean blood glucose level (5.7±0.2 vs 6.5±0.2 mmol/l, P =0.003) and the mean change in blood glucose level (−0.7±0.2 vs 0.1±0.2 mmol/l, P =0.001). Peak blood glucose was reached earlier using the FII algorithm than using Carbohydrate Counting (34±5 vs 56±7 min, P =0.007). The risk of hypoglycemia was similar in both treatments (48% vs 33% for FII vs Carbohydrate Counting, respectively, P =0.155). Conclusions: In adults with type 1 diabetes, compared with Carbohydrate Counting, the novel FII algorithm improved postprandial hyperglycemia after consumption of protein-containing foods.
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Estimating insulin demand for protein-containing foods using the food insulin index.
European Journal of Clinical Nutrition, 2014Co-Authors: K J Bell, D Munns, G Howard, Stephen Colagiuri, Peter Petocz, R. Gray, J. C. Brand-millerAbstract:The Food Insulin Index (FII) is a novel algorithm for ranking foods on the basis of insulin responses in healthy subjects relative to an isoenergetic reference food. Our aim was to compare postprandial glycemic responses in adults with type 1 diabetes who used both Carbohydrate Counting and the FII algorithm to estimate the insulin dosage for a variety of protein-containing foods. A total of 11 adults on insulin pump therapy consumed six individual foods (steak, battered fish, poached eggs, low-fat yoghurt, baked beans and peanuts) on two occasions in random order, with the insulin dose determined once by the FII algorithm and once with Carbohydrate Counting. Postprandial glycemia was measured in capillary blood glucose samples at 15–30 min intervals over 3 h. Researchers and participants were blinded to treatment. Compared with Carbohydrate Counting, the FII algorithm significantly reduced the mean blood glucose level (5.7±0.2 vs 6.5±0.2 mmol/l, P=0.003) and the mean change in blood glucose level (−0.7±0.2 vs 0.1±0.2 mmol/l, P=0.001). Peak blood glucose was reached earlier using the FII algorithm than using Carbohydrate Counting (34±5 vs 56±7 min, P=0.007). The risk of hypoglycemia was similar in both treatments (48% vs 33% for FII vs Carbohydrate Counting, respectively, P=0.155). In adults with type 1 diabetes, compared with Carbohydrate Counting, the novel FII algorithm improved postprandial hyperglycemia after consumption of protein-containing foods.
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efficacy of Carbohydrate Counting in type 1 diabetes a systematic review and meta analysis
The Lancet Diabetes & Endocrinology, 2014Co-Authors: K J Bell, Stephen Colagiuri, Peter Petocz, Alan W Barclay, Jennie BrandmillerAbstract:Summary Background Although Carbohydrate Counting is the recommended dietary strategy for achieving glycaemic control in people with type 1 diabetes, the advice is based on narrative review and grading of the available evidence. We aimed to assess by systematic review and meta-analysis the efficacy of Carbohydrate Counting on glycaemic control in adults and children with type 1 diabetes. Methods We screened and assessed randomised controlled trials of interventions longer than 3 months that compared Carbohydrate Counting with general or alternate dietary advice in adults and children with type 1 diabetes. Change in glycated haemoglobin (HbA 1c ) concentration was the primary outcome. The results of clinically and statistically homogenous studies were pooled and meta-analysed using the random-effects model to provide estimates of the efficacy of Carbohydrate Counting. Findings We identified seven eligible trials, of 311 potentially relevant studies, comprising 599 adults and 104 children with type 1 diabetes. Study quality score averaged 7·6 out of 13. Overall there was no significant improvement in HbA 1c concentration with Carbohydrate Counting versus the control or usual care (−0·35% [−3·9 mmol/mol], 95% CI −0·75 to 0·06; p=0·096). We identified significant heterogeneity between studies, which was potentially related to differences in study design. In the five studies in adults with a parallel design, there was a 0·64% point (7·0 mmol/mol) reduction in HbA 1c with Carbohydrate Counting versus control (95% CI −0·91 to −0·37; p Interpretation There is some evidence to support the recommendation of Carbohydrate Counting over alternate advice or usual care in adults with type 1 diabetes. Additional studies are needed to support promotion of Carbohydrate Counting over other methods of matching insulin dose to food intake. Funding None.
Jens M Bruun - One of the best experts on this subject based on the ideXlab platform.
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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
BMJ Open, 2019Co-Authors: Bettina Ewers, Tina Vilsboll, Henrik Ullits Andersen, Jens M BruunAbstract: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).