Using Continuous Glucose Monitoring Values for Bolus Size Calculation in Smart Multiple Daily Injection Systems: No Negative Impact on Post-bolus Glycemic Outcomes Found in Real-World Data

BACKGROUND

Recent evidence shows that it may be safe to estimate bolus sizes based on continuous glucose monitoring (CGM) rather than blood glucose (BG) values using glycemic trend-adjusted bolus calculators. Users may already be doing this in the real world, though it is unclear whether this is safe or effective for calculators not employing trend adjustment.

METHODS

We assessed real-world data from a smart multiple daily injections (MDIs) device users with a CGM system, hypothesizing that four-hour post-bolus outcomes using CGM values are not inferior to those using BG values. Our data set included 184 users and spanned 18 months with 79 000 bolus observations. We tested differences using logistic regression predicting CGM or BG value usage based on outcomes and confirmed initial results using a mixed model regression accounting for within-subject correlations.

RESULTS

Comparing four-hour outcomes for bolus events using CGM and BG values revealed no differences using our initial approach (P > .183). This finding was confirmed by our mixed model regression approach in all cases (P > .199), except for times below range outcomes. Higher times below range were predictive of lower odds of CGM-based bolus calculations (OR = 0.987, P < .0001 and OR = 0.987, P = .0276, for time below 70 and 54 mg/dL, respectively).

CONCLUSIONS

We found no differences in four-hour post-bolus glycemic outcomes when using CGM or BG except for time below range, which showed evidence of being lower for CGM. Though preliminary, our results confirm prior findings showing non-inferiority of using CGM values for bolus calculation compared with BG usage in the real world.

Comparison of Insulin Pump Bolus Calculators Reveals Wide Variation in Dose Recommendations

BACKGROUND

The introduction of insulin pumps with bolus calculators (BCs) has improved glycemic outcomes and quality of life for those with type 1 diabetes. Despite the increased reliance on BCs, the formulas used to derive recommended boluses are not standardized. Our objective was to examine whether recommendations from different pump BCs vary significantly for identical clinical scenarios.

METHODS

Three commercially available insulin pump BCs were programmed with identical settings and then presented with combinations of blood glucose (BG) and carbohydrates (CHOs) to generate a 4-unit bolus. At one- and two-hour time points, while there was insulin-on-board (IOB) present, we simulated various BG and CHO scenarios in order to compare BC-recommended doses.

RESULTS

Differences in suggested doses were noted between BCs, as well as within the same brand. The greatest variation was apparent when BG was below target. Doses suggested by one BC varied depending on whether the IOB resulted from a previous dose given for BG or CHO, while the other two BCs adjusted for total IOB regardless of the source.

CONCLUSIONS

In this simulation study, there were large differences in recommended doses between BCs due to the unique way each manufacturer incorporates IOB into their formulas as well as the pharmacokinetics used to derive the IOB amount. Providers should be aware that identical pump settings will result in a different dose recommendation for each pump brand and advise patients accordingly.

Satisfaction of Healthcare Professionals and People With Diabetes With an Insulin Bolus Calculator Mobile Application

People with diabetes (PWD) who need to take mealtime insulin to help control their blood sugar often have difficulty correctly calculating their dose due to consideration of many factors such as current blood glucose, carbohydrate consumption, active insulin duration, insulin-to-carb ratio, and insulin sensitivity. The Insulin Mentor, a bolus calculator tool in the OneTouch Reveal diabetes management app, uses an algorithm to automate many of these calculations and contains a link to a food diary to help estimate carbohydrate intake. In the current study, healthcare professionals and PWD from United States and Germany responded favorably to simulations of this calculator tool and compared it positively with other apps on the market. The Insulin Mentor may simplify the difficult process of correctly calculating mealtime insulin doses for PWD.

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

BACKGROUND

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).

METHOD

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.

RESULTS

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.

CONCLUSION

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.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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).

CONCLUSIONS

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.

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

BACKGROUND

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.

METHOD

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.

RESULTS

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.

CONCLUSION

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.

A Methodology to Compare Insulin Dosing Recommendations in Real-Life Settings

BACKGROUND

We propose a methodology to analyze complex real-life glucose data in insulin pump users.

METHODS

Patients with type 1 diabetes (T1D) on insulin pumps were recruited from an academic endocrinology practice. Glucose data, insulin bolus (IB) amounts, and self-reported alcohol consumption and exercise events were collected for 30 days. Rules were developed to retrospectively compare IB recommendations from the insulin pump bolus calculator (IPBC) against recommendations from a proposed decision aid (PDA) and for assessing the PDA's recommendation for exercise and alcohol.

RESULTS

Data from 15 participants were analyzed. When considering instances where glucose was below target, the PDA recommended a smaller dose in 14%, but a larger dose in 13% and an equivalent IB in 73%. For glucose levels at target, the PDA suggested an equivalent IB in 58% compared to the subject's IPBC, but higher doses in 20% and lower in 22%. In events where postprandial glucose was higher than target, the PDA suggested higher doses in 25%, lower doses in 13%, and equivalent doses in 62%. In 64% of all alcohol events the PDA would have provided appropriate advice. In 75% of exercise events, the PDA appropriately advised an IB, a carbohydrate snack, or neither.

CONCLUSIONS

This study provides a methodology to systematically analyze real-life data generated by insulin pumps and allowed a preliminary analysis of the performance of the PDA for insulin dosing. Further testing of the methodological approach in a broader diabetes population and prospective testing of the PDA are needed.

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.

Self-Management Behaviors in Adults on Insulin Pump Therapy

BACKGROUND

Successful diabetes management requires behavioral changes. Little is known about self-management behaviors (SMB) in adults on insulin pump (IP) therapy.

OBJECTIVE

Analyze and characterize observed common diabetes SMB in adult participants with type 1 diabetes (T1D) using IPs and to correlate behaviors with glycemic outcomes based on participant's individual glucose targets.

MATERIALS AND METHODS

One month of IP data from adults with T1D were downloaded. Computer programs were written to automatically quantify the observed frequency of expected behaviors such as: insulin bolusing, checking blood glucose (BG), and recording carbohydrate intake, and other interactions with the IP.

RESULTS

Nineteen participants were recruited and 4,249 IP interactions were analyzed to ascertain behaviors. Intersubject variability of adherence to minimally expected behaviors was observed: daily documentation of carbohydrates and BG checks in 76.6 (31.7)% and 60.0 (32.5)%, respectively, and bolusing without consulting the IPBC in 13.0 (16.9)% of delivered boluses, while daily insulin bolus delivery was consistent 96.8 (5.7)%. Higher frequency of adherence to daily behaviors correlated with a higher number of glucose readings at target.

CONCLUSION

Results indicate variability in SMB and do not always match recommendations. Case-scenarios based on observed real-life SMB could be incorporated into interviews/surveys to elucidate ways to improve SMB.

The Calculating Boluses on Multiple Daily Injections (CBMDI) study: A randomized controlled trial on the effect on metabolic control of adding a bolus calculator to multiple daily injections in people with type 1 diabetes

BACKGROUND

Although the insulin bolus calculator is increasingly being used by people with type 1 diabetes (T1D) on multiple daily injection (MDI) therapy, few studies have investigated its effects on glycemic control. The aim of this study was to determine whether adding this device to therapeutic intensification could further improve metabolic control.

METHODS

A 4-month randomized controlled clinical trial was performed comparing subjects undergoing therapeutic intensification and either using the bolus calculator (Cb group) or not (active control [Co] group). Metabolic control, fear of hypoglycemia, and treatment acceptance were evaluated.

RESULTS

In all, 70 people completed the study (42 in the Cb group, 28 in the Co group). There was a significant decrease in HbA1c in both the Cb and Co groups (-7 mmol/mol [-0.7 %] vs -4 mmol/mol [-0.4 %], respectively). There were no significant differences in HbA1c at baseline or the end of the study, or in the decrease in HbA1c, glycemia, or changes in blood glucose levels at the end of the study between the two groups. There was a significant increase in the number of participants with good metabolic control (HbA1c <58 mmol/mol [7.5 %]) in the Cb group (from 16.7 % to 40.5 %), but not in the Co group. The incidence of hypoglycemic events was reduced slightly but significantly only in the Cb group. There was no change in the fear of hypoglycemia at the end of the study. The bolus calculator was well accepted.

CONCLUSIONS

In T1D, adding a bolus calculator to intensive MDI resulted in a significant improvement in metabolic control and slightly decreased the number of hypoglycemic episodes. Metabolic control also improved in the Co group.

The First Use of Bolus Calculator With Speech Analyzer

BACKGROUND

The insulin therapy in type 1 diabetes involves a wide array of restrictions in patients and their families. One of those is a difficulty in estimation of the insulin dose programmed for each meal. The purpose of the study is an assessment of functionalities related to the expert system VoiceDiab-a calculator of meal boluses.

METHODS

The sample group composed of 54 patients, aged 3-52, all suffering from type 1 diabetes, treated with the insulin pump, taking part in the clinics RCT (for adults and a pediatrician), with a randomized allocation to a surveyed group and cross-over. The research methodology was based upon questionnaires and open-ended questions.

RESULTS

40% of respondents recognized the application's usefulness as high (18 of 47), giving it 10 points, and easy to use (70%). Disadvantages of this app comprised lack of some products in the application database (n = 23), troubles with the mobile range ( n = 4), and no option of a manual data input for processing purposes (n = 23). Advantages, that have been mentioned the most frequently included facilitation of measurements (n = 7), enhanced life quality of the patient (n = 8), and a guarantee of prompt and thorough calculations (n = 22). Of the surveyed individuals, 50% reached their diet, while 100% gave a top grade to the application, claiming it had contributed to a more efficient metabolic control.

CONCLUSION

The pilot scheme of the expert system VoiceDiab has potential to become an application, facilitating dosing of the meal insulin and improving the comfort and safety of insulin administering. However, it needs to be modified, as mentioned by the users who have tested the system.

A Randomized Controlled Study of an Insulin Dosing Application That Uses Recognition and Meal Bolus Estimations

BACKGROUND

Throughout the insulin pump therapy, decisions of prandial boluses programming are taken by patients individually a few times every day, and, moreover, this complex process requires numerical skills and knowledge in nutrition components estimation. The aim of the study was to determine the impact of the expert system, supporting the patient's decision on meal bolus programming, on the time in range of diurnal glucose excursion in patients treated with continuous subcutaneous insulin infusion (CSII).

METHODS

The crossover, randomized study included 12 adults, aged 19 to 53, with type 1 diabetes mellitus, duration ranging from 7 to 30 years. Patients were educated in complex food counting, including carbohydrate units (CU) and fat-protein units (FPU). Subsequently, they were randomly allocated to the experimental group (A), which used the expert software named VoiceDiab, and the control group (B), using a manual method of meal-bolus estimation.

RESULTS

It was found that 66.7% of patients within the A group statistically reported a relevant increase in the percentage (%) of sensor glucose (SG) in range (TIR 70-180 mg/dl), compared to the B group. TIR (median) reached 53.9% in the experimental group (A) versus 44% within the control group (B), P < .05. The average difference in the number of hypoglycemia episodes was not statistically significant (-0.2%, SD 11.6%, P = .93). The daily insulin requirement in both groups was comparable-the average difference in total daily insulin dose between two groups was 0.26 (SD 7.06 IU, P = .9).

CONCLUSION

The expert system in meal insulin dosing allows improvement in glucose control without increasing the rates of hypoglycemia or the insulin requirement.

An Adaptive Nonlinear Basal-Bolus Calculator for Patients With Type 1 Diabetes

BACKGROUND

Bolus calculators help patients with type 1 diabetes to mitigate the effect of meals on their blood glucose by administering a large amount of insulin at mealtime. Intraindividual changes in patients physiology and nonlinearity in insulin-glucose dynamics pose a challenge to the accuracy of such calculators.

METHOD

We propose a method based on a continuous-discrete unscented Kalman filter to continuously track the postprandial glucose dynamics and the insulin sensitivity. We augment the Medtronic Virtual Patient (MVP) model to simulate noise-corrupted data from a continuous glucose monitor (CGM). The basal rate is determined by calculating the steady state of the model and is adjusted once a day before breakfast. The bolus size is determined by optimizing the postprandial glucose values based on an estimate of the insulin sensitivity and states, as well as the announced meal size. Following meal announcements, the meal compartment and the meal time constant are estimated, otherwise insulin sensitivity is estimated.

RESULTS

We compare the performance of a conventional linear bolus calculator with the proposed bolus calculator. The proposed basal-bolus calculator significantly improves the time spent in glucose target ( P < .01) compared to the conventional bolus calculator.

CONCLUSION

An adaptive nonlinear basal-bolus calculator can efficiently compensate for physiological changes. Further clinical studies will be needed to validate the results.

Case-Based Reasoning for Insulin Bolus Advice

BACKGROUND

Insulin bolus calculators assist people with Type 1 diabetes (T1D) to calculate the amount of insulin required for meals to achieve optimal glucose levels but lack adaptability and personalization. We have proposed enhancing bolus calculators by the means of case-based reasoning (CBR), an established problem-solving methodology, by individualizing and optimizing insulin therapy for various meal situations. CBR learns from experiences of past similar meals, which are described in cases through a set of parameters (eg, time of meal, alcohol, exercise). This work discusses the selection, representation and effect of case parameters used for a CBR-based Advanced Bolus Calculator for Diabetes (ABC4D).

METHODS

We analyzed the usage and effect of selected parameters during a pilot study (n = 10), where participants used ABC4D for 6 weeks. Retrospectively, we evaluated the effect of glucose rate of change before the meal on the glycemic excursion. Feedback from study participants about the choice of parameters was obtained through a nonvalidated questionnaire.

RESULTS

Exercise and alcohol were the most frequently used parameters, which was congruent with the feedback from study participants, who found these parameters most useful. Furthermore, cases including either exercise or alcohol as parameter showed a trend in reduction of insulin at the end of the study. A significant difference ( P < .01) was found in glycemic outcomes for meals where glucose rate of change was rising compared to stable rate of change.

CONCLUSIONS

Results from the 6-week study indicate the potential benefit of including parameters exercise, alcohol and glucose-rate of change for insulin dosing decision support.

Improved A1C Levels in Type 1 Diabetes with Smartphone App Use

OBJECTIVES

Smartphones are a potentially useful tool in diabetes care. We have developed an application (app) linked to a website, Intelligent Diabetes Management (IDM), which serves as both an insulin bolus calculator and an electronic diabetes diary. We have prospectively studied whether patients using this app improved control of their glucose levels.

METHODS

Patients with type 1 diabetes were recruited. There was a 4-week observation period, midway during which we offered to review the participants' records. The app was then downloaded and participants' diabetes regimens entered on the synchronized IDM website. At 2, 4, 8, 12 and 16 weeks of the active phase, their records were reviewed online, and feedback was provided electronically. The primary endpoint was change in levels of glycated hemoglobin (A1C).

RESULTS

Of the 31 patients recruited, 18 completed the study. These 18 made 572±98 entries per person on the IDM system over the course of the study (≈5.1/day). Their ages were 40.0±13.9 years, the durations of their diabetes were 27.3±14.9 years and 44% used insulin pumps. The median A1C level fell from 8.1% (7.5 to 9.0, IQ range) to 7.8% (6.9 to 8.3; p<0.001). During the observation period, glucose records were reviewed for 50% of the participants. In the active phase, review of the glucose diaries took less time on the IDM website than using personal glucose records in the observation period, median 6 minutes (5 to 7.5 IQ range) vs. 10 minutes (7.5 to 10.5 IQ range; p<0.05).

CONCLUSIONS

Our smartphone app enables online review of glucose records, requires less time for clinical staff and is associated with improved glucose control.

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.

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.

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.

A study of patient experience using a blood glucose meter with an in-built insulin dose calculator

Accurate calculation and adjustment of insulin doses is integral to maintaining glycemic control in insulin treated patients. Difficulties with insulin dose calculations may lead to poor adherence to blood glucose monitoring and insulin treatment regimes, resulting in poor metabolic control. The main objective of this study was to evaluate ease of use and user preference of a high specification touch screen blood glucose meter, which has an in-built insulin calculator, compared to patients' usual method of testing blood glucose and deciding insulin doses. Patients with diabetes on a multiple daily injection insulin regime used the Test Meter without the insulin calculator and 1 of 3 comparator meters, each for a 7-day period. They then used the Test Meter with the in-built calculator for 10 days. Patients completed an ease of use questionnaire after each 7-day period, a preference questionnaire after the second 7-day period, and a questionnaire comparing the Test Meter with their usual method after the final 10-day period. Of 164 patients who completed the study, 76% stated a preference for the Test Meter as a diabetes management tool compared to their usual method. A small number of patients preferred familiar methods and/or calculating insulin doses themselves. The log book function of meters was important to most patients. The Test Meter system with in-built insulin calculator supports people to better manage their diabetes and increases their confidence. Patients have different needs and preferences which should be acknowledged and supported in a patient centered health service.

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.

Recommendations for Insulin Dose Calculator Risk Management

Several studies have shown the usefulness of an automated insulin dose bolus advisor (BA) in achieving improved glycemic control for insulin-using diabetes patients. Although regulatory agencies have approved several BAs over the past decades, these devices are not standardized in their approach to dosage calculation and include many features that may introduce risk to patients. Moreover, there is no single standard of care for diabetes worldwide and no guidance documents for BAs, specifically. Given the emerging and more stringent regulations on software used in medical devices, the approval process is becoming more difficult for manufacturers to navigate, with some manufacturers opting to remove BAs from their products altogether. A comprehensive literature search was performed, including publications discussing: diabetes BA use and benefit, infusion pump safety and regulation, regulatory submissions, novel BAs, and recommendations for regulation and risk management of BAs. Also included were country-specific and international guidance documents for medical device, infusion pump, medical software, and mobile medical application risk management and regulation. No definitive worldwide guidance exists regarding risk management requirements for BAs, specifically. However, local and international guidance documents for medical devices, infusion pumps, and medical device software offer guidance that can be applied to this technology. In addition, risk management exercises that are algorithm-specific can help prepare manufacturers for regulatory submissions. This article discusses key issues relevant to BA use and safety, and recommends risk management activities incorporating current research and guidance.

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.

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

Since the early 2000s, there has been an exponentially increasing development of new diabetes-applied technology, such as continuous glucose monitoring, bolus calculators, and "smart" pumps, with the expectation of partially overcoming clinical inertia and low patient compliance. However, its long-term efficacy in glucose control has not been unequivocally proven. In this issue of Journal of Diabetes Science and Technology, Sussman and colleagues evaluated a tool for the calculation of the prandial insulin dose. A total of 205 insulin-treated patients were asked to compute a bolus dose in two simulated conditions either manually or with the bolus calculator built into the FreeStyle InsuLinx meter, revealing the high frequency of wrong calculations when performed manually. Although the clinical impact of this study is limited, it highlights the potential implications of low diabetesrelated numeracy in poor glycemic control. Educational programs aiming to increase patients' empowerment and caregivers' knowledge are needed in order to get full benefit of the technology.

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.

Use of an automated bolus calculator reduces fear of hypoglycemia and improves confidence in dosage accuracy in patients with type 1 diabetes mellitus treated with multiple daily insulin injections

BACKGROUND

Many patients do not intensify their insulin regimens. It is believed that lack of adherence may be largely due to fear of hypoglycemia. We hypothesized that utilization of an automated bolus calculator (bolus advisor) might reduce fear of hypoglycemia and encourage patients to achieve improved glycemic control.

METHOD

We surveyed 1,412 type 1 diabetes mellitus (T1DM) patients treated with multiple daily insulin injection therapy at 270 hospitals in the United Kingdom and Republic of Ireland to assess their attitudes and behaviors regarding insulin therapy after use of a bolus advisor (Accu-Chek® Aviva Expert blood glucose meter and bolus advisor system, Roche Diagnostics). The device automatically calculates bolus dosages based on current blood glucose values, anticipated meal intake, and other parameters.

RESULTS

Five hundred eighty-eight T1DM patients responded to the survey. Respondents were predominantly female, age <1 to 70 years, with diabetes duration of <1 to >15 years. Respondents had 4-12 weeks prior experience using the bolus advisor. 76.7% of respondents indicated current bolus advisor use to calculate insulin boluses for meals/snacks always or quite often. 52.0% of respondents indicated that fear of hypoglycemia was reduced (39.0%) or significantly reduced (13.0%). 78.8% indicated that confidence in the insulin dose calculation improved (50.8%) or significantly improved (28.0%). 89.3% indicated that the bolus advisor made bolus calculation easy or very easy compared with manual calculation.

CONCLUSIONS

Most patients felt that using the bolus advisor was easier than manual bolus calculation, improved their confidence in the accuracy of their bolus dosage, and reduced their fear of hypoglycemia. Randomized trials are needed to confirm these perceptions and determine whether bolus advisor use improves clinical outcomes.

Analysis of use of an automated bolus calculator reduces fear of hypoglycemia and improves confidence in dosage accuracy in type 1 diabetes mellitus patients treated with multiple daily insulin injections

In this issue of Journal of Diabetes Science and Technology, Barnard and colleagues evaluate the use of the ACCU-CHEK® Aviva Expert blood glucose meter/bolus advisor system in patients with type 1 diabetes mellitus. Hypoglycemia is a major limiting factor to intensive glucose control, and fear of hypoglycemia, especially in those who have experienced severe reactions, is a major barrier. The bolus advisor improved overall glucose control and increased adherence by overcoming the patients' fear of hypoglycemia, giving them more confidence to give adequate doses of insulin to control hyperglycemia. In this review, we discuss other human factors that become barriers to intensive control, which can benefit from new technologies, including numeracy literacy, information overload, time required for diabetes self-care, and device incompatibility.

Guidelines for optimal bolus calculator settings in adults

Bolus insulin calculators (BCs) became available in insulin pumps in 2002 and are being integrated into glucose meters and portable device applets for use with multiple daily injections. A retrospective analysis of continuous subcutaneous insulin infusion data from the Actual Pump Practices (APP) study is used in this article to generate formulas for more precise BC settings. A well-designed BC determines accurate bolus doses for carbohydrate intake and for correcting elevated glucose levels. It should also provide the logic necessary to track residual bolus insulin and reduce bolus recommendations to minimize insulin stacking. To provide appropriate bolus doses, a BC requires accurate settings for the carbohydrate factor or insulin:carbohydrate ratio, glucose correction factor, duration of insulin action, and correction target. We provide guidelines to select BC settings from the user's current total daily dose (TDD) of insulin and to determine more appropriate BC settings from an improved TDD based on the mean glucose level.

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.

Bolus calculator with nutrition database software, a new concept of prandial insulin programming for pump users

Bolus calculators are effective tools in controlling blood glucose levels in patients treated with insulin. Diabetics is a new software devised for patients to facilitate and improve self-managing for prandial insulin dosing and for better controlling food intake. This device contains two integral parts: a nutrition database and a bolus calculator. The algorithm is based on a formula in which carbohydrate (CHO) and either fat and/or protein (FP) products are engulfed in insulin. The insulin dose setting is programmed individually for CHO in a normal bolus (N-W) and for FP in a square-wave bolus (S-W). The device calculates the dose of insulin for N-W or S-W, suggests the optimal kind of bolus, and indicates the timing in hours for an S-W bolus. In addition, this calculator, which contains a nutrition database and insulin dosing software, helps determine the correct type of necessary boluses for selected foods.