Many patients with Type 1 diabetes estimate their prandial insulin need inappropriately

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.

Self-care and type 1 diabetes mellitus: systematic review of sex-related differences

INTRODUCTION

Self-care is associated with clinical outcomes of adults with T1DM. Some evidence highlighted significant sex-related differences in self-care behaviors in this population. However, the literature on this topic has not been synthesized before. This study aimed to summarize, critically review, and interpret evidence regarding sex-related differences in adults' self-care behaviors with type 1 diabetes mellitus (T1DM).

EVIDENCE ACQUISITION

A systematic search of articles in the following databases was performed: PubMed, Scopus, Embase, and CINAHL. Articles published from 2009 to 2019 were reviewed. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement and flowchart were used to support the systematic review and study reporting. Two reviewers independently screened and extracted data from the included articles.

EVIDENCE SYNTHESIS

From the 596 identified records, only 15 articles were included because pertinent to this review. Both females and males with T1DM must improve their ability to respond to signs and symptoms when they occur. Females are more likely to perform adequate blood glucose monitoring and adequate diet than males. In contrast, males are more likely to show more adequate levels of physical activity.

CONCLUSIONS

Clinicians should pay greater attention to supporting females with T1DM to enhance their behaviors for maintaining an adequate health status and glycemic control. Likely, greater attention is required to sustain males to monitor changes in their signs and symptoms. Self-care management should be improved in both sexes.

Smart Insulin Pens: Advancing Digital Transformation and a Connected Diabetes Care Ecosystem

With the first commercially available smart insulin pens, the predominant insulin delivery device for millions of people living with diabetes is now coming into the digital age. Smart insulin pens (SIPs) have the potential to reshape a connected diabetes care ecosystem for patients, providers, and health systems. Existing SIPs are enhanced with real-time wireless connectivity, digital dose capture, and integration with personalized dosing decision support. Automatic dose capture can promote effective retrospective review of insulin dose data, particularly when paired with glucose data. Patients, providers, and diabetes care teams will be able to make increasingly data-driven decisions and recommendations, in real time, during scheduled visits, and in a more continuous, asynchronous care model. As SIPs continue to progress along the path of digital transformation, we can expect additional benefits: iteratively improving software, machine learning, and advanced decision support. Both these technological advances, and future care delivery models with asynchronous interactions, will depend on easy, open, and continuous data exchange between the growing number of diabetes devices. SIPs have a key role in modernizing diabetes care for a large population of people living with diabetes.

Exercise and nutrition in type 1 diabetes: Insights from the FinnDiane cohort

Type 1 diabetes is a challenging disease, characterized by dynamic changes in the insulin need during life periods, seasons of the year, but also by everyday situations. In particular, changes in insulin need are evident before, during and after exercise and having meals. In the midst of different life demands, it can be very burdensome to achieve tight glycemic control to prevent late diabetes complications, and at the same time, to avoid hypoglycemia. Consequently, many individuals with type 1 diabetes are faced with diabetes distress, decreasing profoundly their quality of life. Today, the nationwide Finnish Diabetic Nephropathy (FinnDiane) Study, launched in 1997, has gathered data from more than 8,000 well-characterized individuals with type 1 diabetes, recruited from 93 centers all over Finland and has established its position as the world's leading project on studying complications in individuals with type 1 diabetes. Studying risk factors and mechanisms of diabetes complications is inconceivable without trying to understand the effects of exercise and nutrition on glycemic control and the development of diabetes complications. Therefore, in this paper we provide findings regarding food and exercise, accumulated during the 25 years of studying lives of Finnish people with type 1 diabetes.

Carbohydrate restriction for diabetes: rediscovering centuries-old wisdom

Carbohydrate restriction, used since the 1700s to prolong survival in people with diabetes, fell out of favor after the discovery of insulin. Despite costly pharmacological and technological developments in the last few decades, current therapies do not achieve optimal outcomes, and most people with diabetes remain at high risk for micro- and macrovascular complications. Recently, low-carbohydrate diets have regained popularity, with preliminary evidence of benefit for body weight, postprandial hyperglycemia, hyperinsulinemia, and other cardiometabolic risk factors in type 2 diabetes and, with more limited data, in type 1 diabetes. High-quality, long-term trials are needed to assess safety concerns and determine whether this old dietary approach might help people with diabetes attain clinical targets more effectively, and at a lower cost, than conventional treatment.

Carbohydrate Counting App Using Image Recognition for Youth With Type 1 Diabetes: Pilot Randomized Control Trial

BACKGROUND

Carbohydrate counting is an important component of diabetes management, but it is challenging, often performed inaccurately, and can be a barrier to optimal diabetes management. iSpy is a novel mobile app that leverages machine learning to allow food identification through images and that was designed to assist youth with type 1 diabetes in counting carbohydrates.

OBJECTIVE

Our objective was to test the app's usability and potential impact on carbohydrate counting accuracy.

METHODS

Iterative usability testing (3 cycles) was conducted involving a total of 16 individuals aged 8.5-17.0 years with type 1 diabetes. Participants were provided a mobile device and asked to complete tasks using iSpy app features while thinking aloud. Errors were noted, acceptability was assessed, and refinement and retesting were performed across cycles. Subsequently, iSpy was evaluated in a pilot randomized controlled trial with 22 iSpy users and 22 usual care controls aged 10-17 years. Primary outcome was change in carbohydrate counting ability over 3 months. Secondary outcomes included levels of engagement and acceptability. Change in HbA1c level was also assessed.

RESULTS

Use of iSpy was associated with improved carbohydrate counting accuracy (total grams per meal, P=.008), reduced frequency of individual counting errors greater than 10 g (P=.047), and lower HbA1c levels (P=.03). Qualitative interviews and acceptability scale scores were positive. No major technical challenges were identified. Moreover, 43% (9/21) of iSpy participants were still engaged, with usage at least once every 2 weeks, at the end of the study.

CONCLUSIONS

Our results provide evidence of efficacy and high acceptability of a novel carbohydrate counting app, supporting the advancement of digital health apps for diabetes care among youth with type 1 diabetes. Further testing is needed, but iSpy may be a useful adjunct to traditional diabetes management.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04354142; https://clinicaltrials.gov/ct2/show/NCT04354142.

Artificial Intelligence in Decision Support Systems for Type 1 Diabetes

Type 1 diabetes (T1D) is a chronic health condition resulting from pancreatic beta cell dysfunction and insulin depletion. While automated insulin delivery systems are now available, many people choose to manage insulin delivery manually through insulin pumps or through multiple daily injections. Frequent insulin titrations are needed to adequately manage glucose, however, provider adjustments are typically made every several months. Recent automated decision support systems incorporate artificial intelligence algorithms to deliver personalized recommendations regarding insulin doses and daily behaviors. This paper presents a comprehensive review of computational and artificial intelligence-based decision support systems to manage T1D. Articles were obtained from PubMed, IEEE Xplore, and ScienceDirect databases. No time period restrictions were imposed on the search. After removing off-topic articles and duplicates, 562 articles were left to review. Of those articles, we identified 61 articles for comprehensive review based on algorithm evaluation using real-world human data, in silico trials, or clinical studies. We grouped decision support systems into general categories of (1) those which recommend adjustments to insulin and (2) those which predict and help avoid hypoglycemia. We review the artificial intelligence methods used for each type of decision support system, and discuss the performance and potential applications of these systems.

New Insight into Diabetes Management: From Glycemic Index to Dietary Insulin Index

BACKGROUND

Despite efforts to control hyperglycemia, diabetes management is still challenging. This may be due to focusing on reducing hyperglycemia and neglecting the importance of hyperinsulinemia; while insulin resistance and resultant hyperinsulinemia preceded diabetes onset and may contribute to disease pathogenesis.

OBJECTIVE

The present narrative review attempts to provide a new insight into the management of diabetes by exploring different aspects of glycemic index and dietary insulin index.

RESULTS

The current data available on this topic is limited and heterogeneous. Conventional diet therapy for diabetes management is based on reducing postprandial glycemia through carbohydrate counting, choosing foods with low-glycemic index and low-glycemic load. Since these indicators are only reliant on the carbohydrate content of foods and do not consider the effects of protein and fat on the stimulation of insulin secretion, they cannot provide a comprehensive approach to determine the insulin requirements.

CONCLUSION

Selecting foods based on carbohydrate counting, glycemic index or glycemic load are common guides to control glycemia in diabetic patients, but neglect the insulin response, thus leading to failure in diabetes management. Therefore, paying attention to insulinemic response along with glycemic response seems to be more effective in managing diabetes.

Dietary carbohydrate intake and cardio-metabolic risk factors in type 1 diabetes

AIMS

Low-carbohydrate diet (LCD) has gained interest among individuals with diabetes as a means to manage glycaemia. We investigated the adherence to LCD in the Finnish Diabetic Nephropathy Study and whether carbohydrate restriction is associated with cardio-metabolic risk factors.

METHODS

Cross-sectional data were available from 902 individuals with type 1 diabetes (44% men, age 47 ± 13 years). Dietary data were collected twice with a 3-day diet record. Mean of the measurements was used. Carbohydrate intake <130 g/day or <26 E% was used as indication of LCD. Individuals reporting LCD were compared to sex-, diabetes duration- and eGFR-matched controls with higher carbohydrate intakes (>253 g/day or >48 E%). In the whole population, carbohydrate-to-fat ratio was calculated and its association with health variables was investigated.

RESULTS

Higher carbohydrate-to-fat ratio was associated with higher blood glucose variability, higher blood pressure, lower HDL cholesterol concentration, and in men with lower waist-to-hip ratio. LCD adherence (n = 69) was associated with lower BMI (25.6 vs. 27.8 kg/m², p = 0.030), lower variability of blood glucose measurements (0.38 vs. 0.45 mmol/l, p = 0.030), and lower diastolic blood pressure (74 vs. 79 mmHg, p = 0.048). Men reporting LCD had higher total (5.1 vs. 4.0 mmol/l, p = 0.007) and non-HDL cholesterol (3.4 vs. 2.7 mmol/l, p = 0.021). Women with LCD had higher HDL-cholesterol concentration (1.9 vs. 1.5 mmol/l, p = 0.014).

CONCLUSIONS

Reduced blood glucose variability, related to LCD, could have clinical relevance to individuals with type 1 diabetes.

Towards a better understanding of postprandial hyperglycemic episodes in people with diabetes: impact on daily functioning

OBJECTIVE

Acute postprandial hyperglycemia (aPPHG) is often symptomatic and can be associated with behavioral changes such as impaired working memory and attention. However, there is little evidence of the impact of aPPHG on the daily lives of patients. The aim of this study was to explore the frequency and severity of aPPHG episodes and their impact on daily functioning in people with insulin-treated diabetes.

METHODS

Adults (n = 1200) with insulin-treated diabetes mellitus type 1 (T1DM) or 2 (T2DM), most of whom experienced aPPHG, were recruited to complete an online cross-sectional survey in the USA and UK. The survey captured self-reported severity and frequency of aPPHG episodes and included a newly developed questionnaire (aPPHG-Q) assessing the impact of aPPHG episodes on patients' daily lives. Data was analyzed separately according to diabetes type and country. Regression analyses were used to assess the relationship between severity or frequency and scores on the aPPHG-Q.

RESULTS

Between 70% and 86% of USA, and 87% and 88% of UK participants reported experiencing aPPHG episodes. Increasing frequency and severity of aPPHG episodes were associated with worse scores on the aPPHG-Q in patients with both T1DM and T2DM in both countries (p < .014) on all subscale scores (excluding the worry and concerns scores for T1DM in the UK), although the magnitude of the association was smaller for aPPHG frequency.

CONCLUSIONS

Increased severity and frequency of aPPHG episodes in patients with insulin-treated diabetes is associated with greater burden and experience of symptoms, and can negatively impact daily functioning.

Mobile Momentary Assessment and Biobehavioral Feedback for Adolescents with Type 1 Diabetes: Feasibility and Engagement Patterns

BACKGROUND

Integration of momentary contextual and psychosocial factors within self-management feedback may provide more specific, engaging, and personalized targets for problem solving.

METHODS

Forty-four youth ages 13-19 with type 1 diabetes (T1D) were provided a Bluetooth meter and completed the 30-day protocol. Participants were randomized to "app + meter" or "meter-only" groups. App + meter participants completed mealtime and bedtime assessment each day. Assessments focused on psychosocial and contextual information relevant for self-management. Graphical feedback integrated self-monitored blood glucose (SMBG), insulin, and Bluetooth-transmitted blood glucose data with the psychosocial and contextual data. App + meter participants completed an interview to identify data patterns.

RESULTS

The median number of momentary assessments per participant was 80.0 (range 32-120) with 2.60 per day. By 2 weeks participants had an average of 40.77 (SD 12.23) assessments. Dose-response analyses indicated that the number of app assessments submitted were significantly related to higher mean daily SMBG (r = -0.44, P < 0.05) and to lower% missed mealtime SMBG (r = -0.47, P < 0.01). Number of feedback viewing sessions was also significantly related to a lower% missed mealtime SMBG (r = -0.44, P < 0.05). Controlling for baseline variables, mixed-effects analyses did not indicate group × time differences in mean daily SMBG. Engagement analyses resulted in three trajectory groups distinguished by assessment frequencies and rates of decline. Engagement group membership was significantly related to gender, mean daily SMBG, and HbA1c values.

CONCLUSIONS

Momentary assessment combined with device data provided a feasible means to provide novel personalized biobehavioral feedback for adolescents with T1D. A 2-week protocol provided sufficient data for self-management problem identification. In addition to feedback, more intensive intervention may need to be integrated for those patients with the lowest self-management at baseline.

Does sucrose affect the glucose variability in patients with type 1 diabetes? a pilot crossover clinical study

OBJECTIVE

The aim of this study was to compare the effects of a sucrose-free diet with a sucrose-added diet on glucose variability in patients with type 1 diabetes.

METHODS

This was a two-way crossover design study in which patients with type 1 diabetes were monitored by blinded continuous glucose monitoring and were selected to receive a sucrose-free diet (<30 g/d), followed by a sucrose-added diet (>80 g/d) for 2 d each. Intra-day glucose variability was assessed by the mean amplitude of glycemic excursions (MAGE), the M-value, J-index, glycemic risk assessment in diabetes equation (GRADE), and continuous overlapping net glycemic action (CONGA_1-3). Between-day variability was determined by mean of daily difference (MODD). Statistical analyses were performed with a significance level of 5%.

RESULTS

Ten patients with type 1 diabetes were evaluated. The participants were a mean of 26.1 ± 7.1 y of age. The mean duration of disease was 16.5 ± 10.5 y, and patients' mean glycated hemoglobin was 7.4% ± 0.8%. The intra- and inter-day glucose variability indexes did not differ between the diet periods (MAGE: 10.2 ± 5.1 and 10.4 ± 6.8mmol/L, P = 0.98; M-value: 12.9 ± 2 and 15.6 ± 1.3mmol/L, P = 0.29; J-index: 50.9 ± 4.4 and 57.7 ± 3.3mmol/L, P = 0.41; GRADE: 7.2 ± 1 and 4.7 ± 5.3mmol/L, P = 0.07; and MODD: 3.9 ± 1 and 4.3 ± 1.5mmol/L, P = 0.28; for the sucrose-free and sucrose-added diets, respectively). CONGA_1-3 were similar for both diet periods (P > 0.05).

CONCLUSIONS

The use of a moderate amount of sucrose, as part of a balanced diet, did not affect the glucose variability or insulin requirements in patients with type 1 diabetes.

Effectiveness and safety of carbohydrate counting in the management of adult patients with type 1 diabetes mellitus: a systematic review and meta-analysis

OBJECTIVE

This study aimed to evaluate the effectiveness and safety of carbohydrate counting (CHOC) in the treatment of adult patients with type 1 diabetes mellitus (DM1).

MATERIALS AND METHODS

We performed a systematic review of randomized studies that compared CHOC with general dietary advice in adult patients with DM1. The primary outcomes were changes in glycated hemoglobin (HbA1c), quality of life, and episodes of severe hypoglycemia. We searched the following electronic databases: Embase, PubMed, Lilacs, and the Cochrane Central Register of Controlled Trials. The quality of evidence was analyzed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE).

RESULTS

A total of 3,190 articles were identified, and two reviewers independently screened the titles and abstracts. From the 15 potentially eligible studies, five were included, and 10 were excluded because of the lack of randomization or different control/intervention groups. Meta-analysis showed that the final HbA1c was significantly lower in the CHOC group than in the control group (mean difference, random, 95% CI: -0.49 (-0.85, -0.13), p = 0.006). The meta-analysis of severe hypoglycemia and quality of life did not show any significant differences between the groups. According to the GRADE, the quality of evidence for severe hypoglycemia, quality of life, and change in HbA1c was low, very low, and moderate, respectively.

CONCLUSION

The meta-analysis showed evidence favoring the use of CHOC in the management of DM1. However, this benefit was limited to final HbA1c, which was significantly lower in the CHOC than in the control group.

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.

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.

A novel pen-based Bluetooth-enabled insulin delivery system with insulin dose tracking and advice

INTRODUCTION

Diabetes is growing in prevalence internationally. As more individuals require insulin as part of their treatment, technology evolves to optimize delivery, improve adherence, and reduce dosing errors. Insulin pens outperform vial and syringe in simplicity, dosing accuracy, and user preference. Bolus advisors improve dosing confidence and treatment adherence. The InPen System offers a novel approach to treatment via a wireless pen that syncs to a mobile application featuring a bolus advisor, enabling convenient insulin dose tracking and more accurate bolus advice among other features. Areas covered: Existing technology for insulin delivery and bolus advice are reviewed. The mechanics and functionality of the InPen device are delineated. Findings from formative testing and usability studies of the InPen system are reported. Future directions for the InPen system in the treatment of diabetes are discussed. Expert opinion: Diabetes management is complex and largely data-driven. The InPen System offers a promising new opportunity to avail insulin pen-users of features known to improve treatment efficacy, which have otherwise primarily been available to those using pumps. Given that the majority of insulin users do not use insulin pumps, the InPen System is poised to improve glucose control in a significant portion of the diabetes population.

Evaluation of the Effect of Carbohydrate Intake on Postprandial Glucose in Patients With Type 1 Diabetes Treated With Insulin Pumps

BACKGROUND

It has been suggested that dietary freedom in functional insulin therapy may be detrimental to glycemic control in type 1 diabetes. This study evaluates the effect of carbohydrate intake on glycemic control and postprandial blood glucose concentrations.

METHODS

Insulin pump data from 148 adults with type 1 diabetes, trained in functional insulin therapy, using pumps for ≥6 months, with ≥2 weeks of consecutive downloaded data, ≥80% use of a bolus calculator, ≥3 capillary blood glucose tests/day, and a concurrent HbA1C, were analyzed. More detailed periprandial data (pre- and postmeal glucose, carbohydrate intake, insulin bolus) were collected from a subset of 105 downloads (3495 meals).

RESULTS

Mean (± SD) age of contributors was 43 ± 13 years, HbA1C 7.84% ± 0.93 (62.19 mmol/mol); daily carbohydrate intake 166 ± 71 g. HbA1C reduced with increased meals/day (r = -.370, P < .0005) and increased with mean carbohydrate content/meal (r = .198, P = .043). However, total daily carbohydrate intake had a weak but significant negative association with HbA1C (r = -.181, P = .027). There was no association between standard deviation of carbohydrate intake and HbA1C (r = .021, P = .802) or between meal carbohydrate content and postprandial change in blood glucose (r = -.004, P = .939) for meals with early postprandial (1-3 hours; n = 390) readings. There was a weak positive correlation (r = .184, P = .008) between meal carbohydrate content and late (4-7 hours; n = 390) postprandial readings.

DISCUSSION

With appropriate training, patients using insulin pumps can accommodate a flexible diet with variable carbohydrate intake, without detriment to glycemic control. However, large carbohydrate meals may contribute to poorer outcomes, through impact on late postprandial glycemia.

Frequent use of an automated bolus advisor improves glycemic control in pediatric patients treated with insulin pump therapy: results of the Bolus Advisor Benefit Evaluation (BABE) study

BACKGROUND

The relationship between frequency and sustained bolus advisor (BA) use and glycemic improvement has not been well characterized in pediatric populations.

OBJECTIVE

The objective of this study is to assess the impact of frequent and persistent BA use on glycemic control among pediatric type 1 diabetes patients.

METHODS

In this 6-month, single-center, retrospective cohort study, 104 children [61 girls, mean age: 12.7 yr, mean HbA1c 8.0 (1.6)% [64 (17.5) mmol/mol]], treated with the Accu-Chek Aviva Combo insulin pump, were observed. Frequency of BA use, HbA1c, hypoglycemia (<70 mg/dL), therapy changes, mean blood glucose, and glycemic variability (standard deviation) was assessed at baseline and month 6. Sub-analyses of the adolescent patient use (12 months) and longitudinal use (24 months) were also conducted.

RESULTS

Seventy-one patients reported high frequency (HF) device use (≥50%); 33 reported low frequency (LF) use (<50%) during the study. HF users achieved lower mean (SE) HbA1c levels than LF users: 7.5 (0.1)% [59 (1.1) mmol/mol] vs. 8.0 (0.2)% [64 (2.2) mmol/mol], p = 0.0252. No between-group differences in the percentage of hypoglycemia values were seen at 6 months. HF users showed less glycemic variability (84.0 vs. 94.7, p = 0.0045) than LF users. More HF patients reached HbA1c target of <7.5 at 6 months 66.2% (+16.9) vs. 27.3% (-9.1), p = 0.0056. Similar HbA1c results were seen in adolescents and BA users at 24 months.

CONCLUSION

Frequent use of the Accu-Chek Aviva Combo insulin pump BA feature was associated with improved and sustained glycemic control with no increase in hypoglycemia in this pediatric population.

Unknown Safety and Efficacy of Smartphone Bolus Calculator Apps Puts Patients at Risk for Severe Adverse Outcomes

Manual calculation of bolus insulin dosages can be challenging for individuals treated with multiple daily insulin injections (MDI) therapy. Automated bolus calculator capability has recently been made available via enhanced blood glucose meters and smartphone apps. Use of this technology has been shown to improve glycemic control and reduce glycemic variability without changing hypoglycemia; however, the clinical utility of app-based bolus calculators has not been demonstrated. Moreover, recent evidence challenges the safety and efficacy of these smartphone apps. Although the ability to automatically calculate bolus insulin dosages addresses a critical need of MDI-treated individuals, this technology raises concerns about efficacy of treatment and the protection of patient safety. This article discusses key issues and considerations associated with automated bolus calculator use.

Modelling the effect of insulin on the disposal of meal-attributable glucose in type 1 diabetes

The management of postprandial glucose excursions in type 1 diabetes has a major impact on overall glycaemic control. In this work, we propose and evaluate various mechanistic models to characterize the disposal of meal-attributable glucose. Sixteen young volunteers with type 1 diabetes were subject to a variable-target clamp which replicated glucose profiles observed after a high-glycaemic-load ([Formula: see text]) or a low-glycaemic-load ([Formula: see text]) evening meal. [6,6-[Formula: see text]] and [U-[Formula: see text];1,2,3,4,5,6,6-[Formula: see text]] glucose tracers were infused to, respectively, mimic: (a) the expected post-meal suppression of endogenous glucose production and (b) the appearance of glucose due to a standard meal. Six compartmental models (all a priori identifiable) were proposed to investigate the remote effect of circulating plasma insulin on the disposal of those glucose tracers from the non-accessible compartments, representing e.g. interstitium. An iterative population-based parameter fitting was employed. Models were evaluated attending to physiological plausibility, posterior identifiability of their parameter estimates, accuracy-via weighted fitting residuals-and information criteria (i.e. parsimony). The most plausible model, best representing our experimental data, comprised: (1) a remote effect x of insulin active above a threshold [Formula: see text] = 1.74 (0.81-2.50) [Formula: see text] min[Formula: see text] [median (inter-quartile range)], with parameter [Formula: see text] having a satisfactory support: coefficient of variation CV = 42.33 (31.34-65.34) %, and (2) steady-state conditions at the onset of the experiment ([Formula: see text]) for the compartment representing the remote effect, but not for the masses of the tracer that mimicked endogenous glucose production. Consequently, our mechanistic model suggests non-homogeneous changes in the disposal rates for meal-attributable glucose in relation to plasma insulin. The model can be applied to the in silico simulation of meals for the optimization of postprandial insulin infusion regimes in type 1 diabetes.

Expert Study: Utility of an Automated Bolus Advisor System in Patients with Type 1 Diabetes Treated with Multiple Daily Injections of Insulin-A Crossover Study

OBJECTIVE

This study was designed to assess the impact of the use of an automated bolus advisor (ABA) on glycemic control, quality of life, and satisfaction in adult patients with type 1 diabetes mellitus treated with multiple daily injections of insulin.

MATERIALS AND METHODS

A crossover, prospective, randomized, controlled, multicenter study of 36 weeks duration was conducted. Patients were randomized to start in either control phase (CP) using a traditional blood glucose meter to calculate insulin doses (Accu-Chek(®) Aviva Nano; Roche Diagnostics, Indianapolis, IN) or intervention phase (IP) using an ABA meter (Accu-Chek Aviva Expert; Roche Diagnostics) and switched to the other phase after a washout period. Each phase was 12 weeks in duration.

RESULTS

Significant glycated hemoglobin (HbA1c) reduction was observed in both phases (CP, initial HbA1c of 8.05 ± 0.7%, final HbA1c of 7.59 ± 0.7% [P < 0.001]; IP, initial HbA1c of 8.13 ± 1%, final HbA1c of 7.61 ± 0.8% [P < 0.001]). Although the trend was to a higher HbA1c reduction in IP, no statistically significant differences were observed between phases (CP, HbA1c -0.39%; IP, HbA1c -0.52% [P = 0.8]). During IP, the number of daily glucose measurements was greater (4.28 ± 1.2 vs. 4.01 ± 1.1 [P < 0.006]), the rate of postprandial hypoglycemia was lower, and an improvement in quality of life and higher satisfaction were observed.

CONCLUSIONS

In this first crossover study comparing the use of an ABA with the standard usual care, the use of an ABA was effective and well accepted. Furthermore, reduction in hypoglycemic events, improvement in adherence and quality of life, and higher treatment satisfaction were observed.

Clinical Application of the Food Insulin Index for Mealtime Insulin Dosing in Adults with Type 1 Diabetes: A Randomized Controlled Trial

BACKGROUND

The Food Insulin Index (FII) is a novel algorithm for ranking foods based on their insulin demand relative to an isoenergetic reference food. We compared the effect of carbohydrate counting (CC) versus the FII algorithm for estimating insulin dosage on glycemic control in type 1 diabetes.

MATERIALS AND METHODS

In a randomized, controlled trial, adults (n = 26) using insulin pump therapy were assigned to using either traditional CC or the novel Food Insulin Demand (FID) counting for 12 weeks. Subjects participated in group education and individual sessions. At baseline and on completion of the trial, glycated hemoglobin A1c (HbA1c), day-long glycemia (6-day continuous glucose monitoring), fasting lipids, and C-reactive protein were determined.

RESULTS

Changes in HbA1c from baseline to 12 weeks were small and not significant in both groups (mean ± SEM; FII vs. CC, -0.1 ± 0.1% vs. -0.3 ± 0.2%; P = 0.855). The incremental area under the curve following breakfast declined significantly among the FID counters with no change in the CC group (FID vs. CC, -93 ± 41 mmol/L/min [P = 0.043] vs. 4 ± 50 mmol/L/min [P = 0.938]; between groups, P = 0.143). The mean amplitude of the glycemic excursion (MAGE) was significantly reduced among the FID counters (FID vs. CC, -6.1 ± 1.0 vs. -1.3 ± 1.0 mmol/L; P = 0.003), and only the FID counters experienced a trend (-44% vs. +11%; P = 0.057) to reduced hypoglycemia.

CONCLUSIONS

In a 12-week pilot study, MAGE and postprandial glycemia following breakfast were significantly improved with FII counting versus CC, despite no significant differences in HbA1c.

Development of a New Tool to Assess Bolus Calculation and Carbohydrate Estimation

BACKGROUND

Carbohydrate estimation and bolus calculation are two important skills for handling intensive insulin therapy and effectively using bolus calculators. Structured assessment of both skills is lacking. A new tool for the assessment of skills in carbohydrate estimation and bolus calculation was developed and evaluated.

MATERIALS AND METHODS

A new assessment tool (SMART) was developed that included 10 items for bolus calculation and 12 items for carbohydrate estimation. In total, 411 patients on intensive insulin treatment were recruited. Different parameters of glycemic control were used as validity criteria.

RESULTS

The SMART tool achieved good reliability for the assessment of bolus calculation (Cronbach's α = 0.78) and sufficient reliability for the assessment of carbohydrate estimation (Cronbach's α = 0.67). A good bolus calculation skill was significantly associated with lower glycated hemoglobin values (r = -0.27), lower mean blood glucose levels (r = -0.29), and higher fluctuation of blood glucose control (r = -0.43). A good carbohydrate estimation skill was significantly associated with a lower frequency of severe hyperglycemia (r = -0.27) and a higher frequency of euglycemia (r = 0.26).

CONCLUSIONS

SMART is a reliable and valid tool for the assessment of both skills. Bolus calculation as well as carbohydrate estimation was associated with glycemic control. With the help of SMART, important skills for the management of intensive insulin therapy can be assessed separately. Thus, in clinical practice patients in need of assistance from a bolus calculator can be identified.

Development of a Smartphone Application to Capture Carbohydrate, Lipid, and Protein Contents of Daily Food: Need for Integration in Artificial Pancreas for Patients With Type 1 Diabetes?

BACKGROUND

Meal lipids (LIP) and proteins (PRO) may influence the effect of insulin doses based on carbohydrate (CHO) counting in patients with type 1 diabetes (T1D). We developed a smartphone application for CHO, LIP, and PRO counting in daily food and assessed its usability in real-life conditions and potential usefulness.

METHODS

Ten T1D patients used the android application for 1 week to collect their food intakes. Data included meal composition, premeal and 2-hour postmeal blood glucose, corrections for hypo- or hyperglycemia after meals, and time for entering meals in the application. Meal insulin doses were based on patients' CHO counting (application in blinded mode). Linear mixed models were used to assess the statistical differences.

RESULTS

In all, 187 meals were analyzed. Average computed CHO amount was 74.37 ± 31.78 grams; LIP amount: 20.26 ± 14.28 grams and PRO amount: 25.68 ± 16.68 grams. Average CHO, LIP, and PRO contents were significantly different between breakfast and lunch/dinner. The average time for meal entry in the application moved from 3-4 minutes to 2.5 minutes during the week. No significant impact of LIP and PRO was found on available blood glucose values.

CONCLUSION

Our study shows CHO, LIP, and PRO intakes can be easily captured by an application on smartphone for meal entry used by T1D patients. Although LIP and PRO meal contents did not influence glucose levels when insulin doses were based on CHO in this pilot study, this application could be used for further investigation of this topic, including in closed-loop conditions.

Carbohydrate-to-Insulin Ratio in a Mediterranean Population of Type 1 Diabetic Patients on Continuous Subcutaneous Insulin Infusion Therapy

BACKGROUND

The carbohydrate-to-insulin ratio (CIR) is initially calculated from the total daily insulin dose (TDID). However, CIR likely presents variations owing to different population characteristics and intraday variations not being taken into account by most formulas. No information is available concerning the Mediterranean population. We investigated the CIR used by patients with type 1 diabetes (T1DM) using continuous subcutaneous insulin infusion (CSII) therapy in a Mediterranean area, to identify possible intraday variations and establish an adequate formula to calculate CIR.

METHODS

Data from 170 T1DM patients from Barcelona were obtained retrospectively from the Spanish National Registry of CSII Therapy (SNR-CSII). Theoretical CIR was calculated using the formula: 500 divided by TDID. This theoretical CIR was compared to the real CIR.

RESULTS

The real CIR was also compared between main meals. Patients with HbA1c < 7% (n = 44) were considered a reference group for accurate bolus calculation and were analyzed as a subgroup. The real CIR used was 11.5 g/UI for breakfast, 12 g/UI for lunch, and 13.3 g/UI for dinner. CIR obtained by the 500/TDID formula for all meals was 15.5 g/UI. We obtained similar results for the group with HbA1c < 7%. The real CIR differed significantly from the theoretical CIR values and between breakfast and the other main meals (P < .005).

CONCLUSIONS

CIR in our population was significantly lower for breakfast than for other meals. CIR using the 500/TDID formula underestimated prandial insulin requirements. A calculation of 350/TDID for breakfast and 400/TDID for lunch and dinner would be more appropriate for this population.

Safe and Efficacious Use of Automated Bolus Advisors in Individuals Treated With Multiple Daily Insulin Injection (MDI) Therapy: Lessons Learned From the Automated Bolus Advisor Control and Usability Study (ABACUS)

Numerous studies have shown that use of integrated automated bolus advisors (BAs) provides significant benefits to individuals using insulin pump devices, including improved glycemic control and greater treatment satisfaction. Within the past few years, BA devices have been developed specifically for individuals treated with multiple daily insulin injection (MDI) therapy; however, many clinicians who treat these individuals may be unfamiliar with insulin pump therapy and, thus, BA use. Findings from the Automated Bolus Advisor Control and Usability Study (ABACUS) revealed that BA use can be efficacious and clinically meaningful in MDI therapy, and that most patients are willing and able to use this technology appropriately when adequate clinical support is provided. The purpose of this article is to review key learnings from ABACUS and provide practical advice for initiating BA use and monitoring therapy.

Perceptions and experiences of using automated bolus advisors amongst people with type 1 diabetes: a longitudinal qualitative investigation

AIMS

We explored people's reasons for, and experiences of, using bolus advisors to determine insulin doses; and, their likes/dislikes of this technology.

SUBJECTS AND METHODS

42 people with type 1 diabetes who had received instruction in use of bolus advisors during a structured education course were interviewed post-course and 6 months later. Data were analysed thematically.

RESULTS

Participants who considered themselves to have poor mathematical skills highlighted a gratitude for, and heavy reliance on, advisors. Others liked and chose to use advisors because they saved time and effort calculating doses and/or had a data storage facility. Follow-up interviews highlighted that, by virtue of no longer calculating their doses, participants could become deskilled and increasingly dependent on advisors. Some forgot what their mealtime ratios were; others reported a misperception that, because they were pre-programmed during courses, these parameters never needed changing. Use of data storage facilities could hinder effective review of blood glucose data and some participants reported an adverse impact on glycaemic control.

DISCUSSION

While participants liked and perceived benefits to using advisors, there may be unintended consequences to giving people access to this technology. To promote effective use, on-going input and education from trained health professionals may be necessary.

Bolus calculators

Matching meal insulin to carbohydrate intake, blood glucose, and activity level is recommended in type 1 diabetes management. Calculating an appropriate insulin bolus size several times per day is, however, challenging and resource demanding. Accordingly, there is a need for bolus calculators to support patients in insulin treatment decisions. Currently, bolus calculators are available integrated in insulin pumps, as stand-alone devices and in the form of software applications that can be downloaded to, for example, smartphones. Functionality and complexity of bolus calculators vary greatly, and the few handfuls of published bolus calculator studies are heterogeneous with regard to study design, intervention, duration, and outcome measures. Furthermore, many factors unrelated to the specific device affect outcomes from bolus calculator use and therefore bolus calculator study comparisons should be conducted cautiously. Despite these reservations, there seems to be increasing evidence that bolus calculators may improve glycemic control and treatment satisfaction in patients who use the devices actively and as intended.

Estimating insulin demand for protein-containing foods using the food insulin index

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.

Insulin pump therapy in youth with type 1 diabetes: toward closed-loop systems

INTRODUCTION

Insulin pump technology has advanced considerably over the past three decades, leading to more favorable metabolic control and less hypoglycemic events when compared with multiple daily injection therapy. The use of insulin pumps is increasing, particularly in children and adolescents with type 1 diabetes.

AREAS COVERED

This review outlines recent developments in insulin pump therapy from a pediatric perspective. 'Smart' pumps, sensor-augmented pump therapy and threshold-suspend feature of insulin pumps are reviewed in terms of efficacy, safety and psychosocial impact. The current status of closed-loop systems focusing on clinical outcomes is highlighted.

EXPERT OPINION

Closed-loop insulin delivery is gradually progressing from bench to the clinical practice. Longer and larger studies in home settings are needed to expand on short- to medium-term outpatient evaluations. Predictive low glucose management and overnight closed-loop delivery may be the next applications to be implemented in daily routine. Further challenges include improvements of control algorithms, sensor accuracy, duration of insulin action, integration and size of devices and connectivity and usability. Gradual improvements and increasing sophistication of closed-loop components lie on the path toward unsupervised hands-off fully closed-loop system.

Bolus calculator and wirelessly communicated blood glucose measurement effectively reduce hypoglycaemia in type 1 diabetic children - randomized controlled trial

BACKGROUND

The aim of this study was to assess the effect of bolus calculator function and wireless communication between insulin pump and blood glucose metre on metabolic control in children with type 1 diabetes, treated with insulin pumps.

METHODS

In this randomized, controlled, 12-week trial, 156 patients, aged 12.9 ± 2.6 years, with a history of diabetes of 5.1 ± 3.3 years and glycated haemoglobin values of 7.3 ± 1.2% (56.3 ± 13.44 mmol/mol) were included. Children were assigned to one of three arms: group A, subjects using bolus calculator and wireless communication between insulin pump and blood glucose metre; group B, subjects using bolus calculator without communication between the devices and group C, control group. Devices were downloaded at 0, 6 and 12 weeks.

RESULTS

There were statistically fewer episodes of hypoglycaemia in children using bolus calculator compared with the control group: A versus C (3.8 ± 3.1 versus 7.8 ± 5.13 episodes/2 weeks, respectively, p < 0.0001); B versus C (3.6 ± 3.3 versus 7.8 ± 5.1 episodes/2 weeks, respectively, p < 0.0001). Patients in group A used bolus calculator function significantly more frequently than patients in group B (4.9 ± 3.4 versus 2.5 ± 2.9 times/24 h, respectively, p = 0.0006). No significant differences in glycated haemoglobin levels were found between the experimental and the control groups: group A versus C (p = 0.699). The use of bolus calculator did not influence post-prandial glycaemia, body mass index-SD score or insulin/kg/24 h.

CONCLUSIONS

Bolus calculator use reduces hypoglycaemic episodes independently of communication between insulin pump and blood glucose metre. Wireless communication between devices results in more frequent bolus calculator use.

Efficacy of carbohydrate counting in type 1 diabetes: a systematic review and meta-analysis

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 (HbA1c) 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 HbA1c 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 HbA1c with carbohydrate counting versus control (95% CI -0·91 to -0·37; p<0·0001).

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.

Glucose meters with built-in automated bolus calculator: gadget or real value for insulin-treated diabetic patients?

Self-monitoring of blood glucose is now widely recognized as efficacious to enhance and facilitate diabetes management. More than just a means of recording and storing data, some blood glucose meters (BGMs) are now designed with an embedded automated bolus calculator (ABC) with the goal to propose patients recommendations about insulin dosage. The growing literature in this field tends to claim that these new smart BGMs make patient's life easier and decision making safer. The main purpose of this review is to verify whether BGMs with a built-in ABC indeed improve the willingness and the ability of insulin-treated patients to make adequate therapeutic decisions and positively impact the metabolic control and the quality of life of ABC users. It appears that, as long as the education provided by caregivers remains a top priority, BGMs with a built-in ABC (more than just electronic gadgets) can be regarded as bringing real value to insulin-treated patients with diabetes.

Barriers to self-management of diabetes

People with diabetes hold major responsibility for the day-to-day management of their chronic condition. The management that, amongst others, includes blood glucose monitoring, medication taking, diet and physical activity, aims at normalizing blood glucose levels. In many individuals, the level of glycaemia, however, frequently exceeds the recommendations. This observation, together with patients' and practitioners' reports, suggests that active self-management is suboptimal. Various reasons, both individual and environment related, contribute to the suboptimal concordance with treatment regimen. The aim of this review is to discuss some of the barriers to optimal diabetes self-management.

Automated bolus advisor control and usability study (ABACUS): does use of an insulin bolus advisor improve glycaemic control in patients failing multiple daily insulin injection (MDI) therapy? [NCT01460446]

BACKGROUND

People with T1DM and insulin-treated T2DM often do not follow and/or adjust their insulin regimens as needed. Key contributors to treatment non-adherence are fear of hypoglycaemia, difficulty and lack of self-efficacy associated with insulin dose determination. Because manual calculation of insulin boluses is both complex and time consuming, people may rely on empirical estimates, which can result in persistent hypoglycaemia and/or hyperglycaemia. Use of automated bolus advisors (BA) has been shown to help insulin pump users to more accurately meet prandial insulin dosage requirements, improve postprandial glycaemic excursions, and achieve optimal glycaemic control with an increased time within optimal range. Use of a BA containing an early algorithm based on sliding scales for insulin dosing has also been shown to improve HbA1c levels in people treated with multiple daily insulin injections (MDI). We designed a study to determine if use of an automated BA can improve clinical and psychosocial outcomes in people treated with MDI.

METHODS/DESIGN

The Automated Bolus Advisor Control and Usability Study (ABACUS) is a 6-month, prospective, randomised, multi-centre, multi-national trial to determine if automated BA use improves glycaemic control as measured by a change in HbA1c in people using MDI with elevated HbA1c levels (#62;7.5%). A total of 226 T1DM and T2DM participants will be recruited. Anticipated attrition of 20% will yield a sample size of 90 participants, which will provide #62;80% power to detect a mean difference of 0.5%, with SD of 0.9%, using a one-sided 5% t-test, with 5% significance level. Other measures of glycaemic control, self-care behaviours and psychosocial issues will also be assessed.

DISCUSSION

It is critical that healthcare providers utilise available technologies that both facilitate effective glucose management and address concerns about safety and lifestyle. Automated BAs may help people using MDI to manage their diabetes more effectively and minimise the risk of long-term diabetes related complications. Findings from a recent study suggest that BA use positively addresses both safety and lifestyle concerns; however, randomised trials are needed to confirm these perceptions and determine whether bolus advisor use improves clinical outcomes. Our study is designed to make these assessments.

TRIAL REGISTRATION

NCT01460446.

In children using intensive insulin therapy, a 20-g variation in carbohydrate amount significantly impacts on postprandial glycaemia

AIM

To determine if an insulin dose calculated for a meal containing 60 g carbohydrate maintains postprandial glycaemic control for meals containing 40, 50, 70 or 80 g carbohydrate.

METHODS

Thirty-four young people (age range 8.5-17.7 years) using intensive insulin therapy consumed five test breakfasts with equivalent fat, protein and fibre contents but differing carbohydrate quantities (40, 50, 60, 70 and 80 g of carbohydrate). The preprandial insulin dose was the same for each meal, based on the subject's usual insulin:carbohydrate ratio for 60 g carbohydrate. Continuous glucose monitoring was used to monitor postprandial glucose over 180 min.

RESULTS

The 40-g carbohydrate meal resulted in significantly more hypoglycaemia than the other meals (P = 0.003). There was a one in three chance of hypoglycaemia between 120 and 180 min if an insulin dose for 60 g carbohydrate was given for 40 g carbohydrate. The glucose levels of subjects on the 80-g meal were significantly higher than the 60- and 70-g carbohydrate meals at all time points between 150 and 180 min (P < 0.01). Subjects consuming the 80-g meal were more likely to have significant hyperglycaemia (blood glucose levels ≥ 12 mmol/l) compared with the other meals (P < 0.001).

CONCLUSIONS

In patients using intensive insulin therapy, an individually calculated insulin dose for 60 g carbohydrate results in postprandial hypoglycaemia or hyperglycaemia for meals containing 40 and 80 g carbohydrate. To calculate mealtime insulin in order to maintain postprandial control, carbohydrate estimations should be within 10 g of the actual meal carbohydrate.

Performance of a glucose meter with a built-in automated bolus calculator versus manual bolus calculation in insulin-using subjects

BACKGROUND

Patients consider multiple parameters in adjusting prandial insulin doses for optimal glycemic control. Difficulties in calculations can lead to incorrect doses or induce patients to administer fixed doses, rely on empirical estimates, or skip boluses.

METHOD

A multicenter study was conducted with 205 diabetes subjects who were on multiple daily injections of rapid/ short-acting insulin. Using the formula provided, the subjects manually calculated two prandial insulin doses based on one high and one normal glucose test result, respectively. They also determined the two doses using the FreeStyle InsuLinx Blood Glucose Monitoring System, which has a built-in, automated bolus calculator. After dose determinations, the subjects completed opinion surveys.

RESULTS

Of the 409 insulin doses manually calculated by the subjects, 256 (63%) were incorrect. Only 23 (6%) of the same 409 dose determinations were incorrect using the meter, and these errors were due to either confirmed or potential deviations from the study instructions by the subjects when determining dose with meter. In the survey, 83% of the subjects expressed more confidence in the meter-calculated doses than the manually calculated doses. Furthermore, 87% of the subjects preferred to use the meter than manual calculation to determine prandial insulin doses.

CONCLUSIONS

Insulin-using patients made errors in more than half of the manually calculated insulin doses. Use of the automated bolus calculator in the FreeStyle InsuLinx meter minimized errors in dose determination. The patients also expressed confidence and preference for using the meter. This may increase adherence and help optimize the use of mealtime insulin.

The cross-sectional associations between sense of coherence and diabetic microvascular complications, glycaemic control, and patients' conceptions of type 1 diabetes

BACKGROUND

Sense of coherence (SOC) has been associated with various self-care behaviours in the general population. As the management of type 1 diabetes heavily relies on self-management, the SOC concept could also prove important in this population. This paper is a report of a study conducted among patients with type 1 diabetes to assess the associations between SOC and glycaemic control, microvascular complications, and patients' conceptions of their disease.

METHODS

Altogether 1,264 adult patients (45% men, age range 18-82 years) with type 1 diabetes participated in this cross-sectional study. SOC was evaluated using a 13-item SOC questionnaire. Standardized assays were used to determine HbA1c. Nephropathy status was based on albumin excretion rate and retinal laser-treatment was used as an indication of severe retinopathy. Patients' subjective conceptions of diabetes were studied using a questionnaire.

RESULTS

Higher SOC scores, reflecting stronger SOC, were associated with lower HbA1c values. Strong SOC was independently associated with reaching the HbA1c level <7.5%. Adjusting for diabetes duration, age at onset, socioeconomic status and HbA1c, weak SOC was associated with the presence of nephropathy among men, but not women. No associations were observed between SOC and severe retinopathy. Four dimensions describing patients' conceptions of HbA1c, complications, diabetes control and hypoglycaemia were formed from the diabetes questionnaire. Weak SOC was independently associated with worse subjective conceptions in the dimensions of HbA1c and hypoglycaemia. Furthermore among men, an association between weak SOC and the complications factor was observed.

CONCLUSION

Interventions to improve patients' SOC, if available, could improve patients' metabolic control and therefore also reduce the incidence of diabetic complications.

Bolus guide: a novel insulin bolus dosing decision support tool based on selection of carbohydrate ranges

BACKGROUND

Optimal continuous subcutaneous insulin infusion (CSII) therapy emphasizes the relationship between insulin dose and carbohydrate consumption. One widely used tool (bolus calculator) requires the user to enter discrete carbohydrate values; however, many patients might not estimate carbohydrates accurately. This study assessed carbohydrate estimation accuracy in type 1 diabetes CSII users and compared simulated blood glucose (BG) outcomes using the bolus calculator and the "bolus guide," an alternative system based on ranges of carbohydrate load.

METHODS

Patients (n = 60) estimated the carbohydrate load of a representative sample of meals of known carbohydrate value. The estimated error distribution [coefficient of variation (CV)] was the basis for a computer simulation (n = 1.6 million observations) of insulin recommendations for the bolus guide and bolus calculator, translated into outcome blood glucose (OBG) ranges (< or =60, 61-200, >201 mg/dl). Patients (n = 30) completed questionnaires assessing satisfaction with the bolus guide.

RESULTS

The CV of typical meals ranged from 27.9% to 44.5%. The percentage of simulated OBG for the calculator and the bolus guide in the <60 mg/dl range were 20.8% and 17.2%, respectively, and 13.8% and 15.8%, respectively, in the >200 mg/dl range. The mean and median scores of all bolus guide satisfaction items and ease of learning and use were 4.17 and 4.2, respectively (of 5.0).

CONCLUSION

The bolus guide recommendation based on carbohydrate range selection is substantially similar to the calculator based on carbohydrate point estimation and appears to be highly accepted by type 1 diabetes insulin pump users.