A bolus calculator is an effective means of controlling postprandial glycemia in patients on insulin pump therapy

The effect of bolus advisors on glycaemic parameters in adults with diabetes on intensive insulin therapy: A systematic review with meta-analysis

AIM

To conduct a systematic review with meta-analysis to provide a comprehensive synthesis of randomized controlled trials (RCTs) and prospective cohort studies investigating the effects of currently available bolus advisors on glycaemic parameters in adults with diabetes.

MATERIALS AND METHODS

An electronic search of PubMed, Embase, CINAHL, Cochrane Library and ClinicalTrials.gov was conducted in December 2022. The risk of bias was assessed using the revised Cochrane Risk of Bias tool. (Standardized) mean difference (MD) was selected to determine the difference in continuous outcomes between the groups. A random-effects model meta-analysis and meta-regression were performed. This systematic review was registered on PROSPERO (CRD42022374588).

RESULTS

A total of 18 RCTs involving 1645 adults (50% females) with a median glycated haemoglobin (HbA1c) concentration of 8.45% (7.95%-9.30%) were included. The majority of participants had type 1 diabetes (N = 1510, 92%) and were on multiple daily injections (N = 1173, 71%). Twelve of the 18 trials had low risk of bias. The meta-analysis of 10 studies with available data on HbA1c showed that the use of a bolus advisor modestly reduced HbA1c compared to standard treatment (MD -011%, 95% confidence interval -0.22 to -0.01; I2 = 0%). This effect was accompanied by small improvements in low blood glucose index and treatment satisfaction, but not with reductions in hypoglycaemic events or changes in other secondary outcomes.

CONCLUSION

Use of a bolus advisor is associated with slightly better glucose control and treatment satisfaction in people with diabetes on intensive insulin treatment. Future studies should investigate whether personalizing bolus advisors using artificial intelligence technology can enhance these effects.

Methods for Insulin Bolus Adjustment Based on the Continuous Glucose Monitoring Trend Arrows in Type 1 Diabetes: Performance and Safety Assessment in an In Silico Clinical Trial

BACKGROUND

Providing real-time magnitude and direction of glucose rate-of-change (ROC) via trend arrows represents one of the major strengths of continuous glucose monitoring (CGM) sensors in managing type 1 diabetes (T1D). Several literature methods were proposed to adjust the standard formula (SF) used for insulin bolus calculation by accounting for glucose ROC, but each of them provides different suggestions, making it difficult to understand which should be applied in practice. This work aims at performing an extensive in-silico assessment of their performance and safety.

METHODS

The methods of Buckingham (BU), Scheiner (SC), Pettus/Edelman (PE), Klonoff/Kerr (KL), Aleppo/Laffel (AL), Ziegler (ZI), and Bruttomesso (BR) were evaluated using the UVa/Padova T1D simulator, in single-meal scenarios, where ROC and glucose at mealtime varied between [-2,+2] mg/dL/min and [80,200] mg/dL, respectively. Efficacy of postprandial glucose control was quantitatively assessed by time in, above and below range (TIR, TAR, and TBR, respectively).

RESULTS

For negative ROCs, all methods proved to increase TIR and decrease TAR and TBR vs SF, with KL, PE, and BR being the most effective. For positive ROCs, a general worsening of the performances is present, only BR improved the glycemic control when mealtime glucose was close to hypoglycemia, while SC resulted the safest in the other conditions.

CONCLUSIONS

Insulin bolus adjustment methods are effective for negative ROCs, but they generally appear to overdose for positive ROCs, calling for safer strategies in such a scenario. These results can be useful in outlining guidelines to identify which adjustment to apply based on the mealtime condition.

Bolus Insulin calculation without meal information. A reinforcement learning approach

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

Comparison of three insulin bolus calculators to increase time in range of glycemia in a group of poorly controlled adults Type 1 diabetes in a Brazilian public health service

BACKGROUND

A main factor contributing to insufficient glycemic control, during basal/bolus insulin therapy, is poor self-management bolus. Insulin bolus administration frequency is strongly associated with glycated hemoglobin (A1c) in Type 1 Diabetes (T1D). In the present study, we analyzed the performance of two-bolus calculator's software that could be accessible to T1D patients from a Public Health Service to improve glycemic time in range (TIR) and A1c.

METHODS

This prospective, controlled, randomized, parallel intervention clinical trial was carried out with 111 T1D participants on basal/bolus therapy [multiple daily insulin injections (MDI) or subcutaneous infusion pump (CSII)] with basal A1c ≥ 8.5% for 24 weeks. Patients were divided into 3 groups: 2 interventions: COMBO^® (bolus calculator) and GLIC (mobile application) and 1 control (CSII group). Anthropometrics and metabolic variables were assessed on basal, 3 and 6 months of follow-up.

RESULTS

TIR was increased in 9.42% in COMBO group (29 ± 12% to 38.9 ± 12.7%; p < 0.001) in 8.39% in the GLIC® group (28 ± 15% to 36.6 ± 15.1%; p < 0.001) while remained stable in CSII group (40 ± 11% to 39.3 ± 10.3%). A1c decrease in 1.08% (p < 0.001), 0.64% (p < 0.001) and 0.38% (p = 0.01) at 6 months in relation to basal in the COMBO, GLIC and CSII respectively. Daily basal insulin dose was reduced by 8.8% (p = 0.01) in the COMBO group.

CONCLUSION

The COMBO and a mobile applicative (GLIC) bolus calculator had a similar and a good performance to optimize the intensive insulin treatment of T1D in the public health system with increase in the TIR and reduction in A1C without increase hypoglycemia prevalence.

Automated Bolus Calculators and Connected Insulin Pens: A Smart Combination for Multiple Daily Injection Insulin Therapy

Although automated bolus calculators (ABCs) have become a mainstay in insulin pump therapy, they have not achieved similar levels of adoption by persons with diabetes (PWD) using multiple daily injections of insulin (MDI). Only a small number of blood glucose meters (BGMs) have incorporated ABC functionality and the proliferation of unregulated ABC smartphone apps raised safety concerns and eventually led to Food and Drug Administration (FDA)-mandated regulatory oversight for these types of apps. With the recent introduction of smartphone-connected insulin pens, manufacturer-supported companion ABC apps may offer an ideal solution for PWD and health care professionals that reduces errors of mental math when calculating bolus insulin dosing, increases the quality of diabetes data reporting, and improves glycemic outcomes.

Clinical Evaluation of a Novel CGM-Informed Bolus Calculator with Automatic Glucose Trend Adjustment

Background: Expert opinion guidelines and limited data from clinical trials recommend adjustment to bolus insulin doses based on continuous glucose monitor (CGM) trend data, yet minimal evidence exists to support this approach. We performed a clinical evaluation of a novel CGM-informed bolus calculator (CIBC) with automatic insulin bolus dose adjustment based on CGM trend used with sensor-augmented pump therapy. Materials and Methods: In this multicenter, outpatient study, participants 6-70 years of age with type 1 diabetes (T1D) used the Omnipod^® 5 System in Manual Mode, first for 7 days without a connected CGM (standard bolus calculator, SBC, phase 1) and then for 7 days with a connected CGM using the CIBC (CIBC phase 2). The integrated bolus calculator used stored pump settings plus user-estimated meal size and/or either a manually entered capillary glucose value (SBC phase) or an imported current CGM value and trend (CIBC phase) to recommend a bolus amount. The CIBC automatically increased or decreased the suggested bolus amount based on the CGM trend. Results: Twenty-five participants, (mean ± standard deviation) 27 ± 15 years of age, with T1D duration 12 ± 9 years and A1C 7.0% ± 0.9% completed the study. There were significantly fewer sensor readings <70 mg/dL 4 h postbolus with the CIBC compared to the SBC (2.1% ± 2.0% vs. 2.8 ± 2.7, P = 0.03), while percent of sensor readings >180 and 70-180 mg/dL remained the same. There was no difference in insulin use or number of boluses given between the two phases. Conclusion: The CIBC was safe when used with the Omnipod 5 System in Manual Mode, with fewer hypoglycemic readings in the postbolus period compared to the SBC. This trial was registered at ClinicalTrials.gov (NCT04320069).

An Ensemble Learning Algorithm Based on Dynamic Voting for Targeting the Optimal Insulin Dosage in Type 1 Diabetes Management

People with type 1 diabetes (T1D) need exogenous insulin administrations several times a day. The amount of injected insulin is key for maintaining the concentration of blood glucose (BG) within a physiological safe range. According to well-established clinical guidelines, insulin dosing at mealtime is calculated through an empirical formula which, however, does not take advantage of the knowledge of BG trend provided in real-time by continuous glucose monitoring (CGM) sensors. To overcome suboptimal insulin dosage, we recently used machine learning techniques to build two new models, one linear and one nonlinear, which incorporate BG trend information.In this work, we propose an ensemble learning method for mealtime insulin bolus estimation based on dynamic voting, which combines the two models by taking advantage of where each alternative performs better. Being the resulting model black-box, a tool that enables its interpretability was applied to evaluate the contribution of each feature. The proposed model was trained using a synthetic dataset having information on 100 virtual subjects with different mealtime conditions, and its performance was evaluated within a simulated environment.The benefit given by the ensemble method compared to the single models was confirmed by the high time within the target glycemic range, and the trade-off reached in terms of time spent below and above this range. Moreover, the model interpretation pointed out the key role played by the information on BG dynamics in the estimation of insulin dosage.

Emerging technologies for the management of type 2 diabetes mellitus

Diabetes mellitus is a global health problem affecting 422 million people worldwide, of which 34.2 million live in the United States alone. Complications due to diabetes can lead to considerable morbidity and mortality related to both microvascular and macrovascular disease. While glycosylated hemoglobin testing is the standard test utilized to evaluate glycemic control, emerging targets like "time in range" and "glycemic variability" often provide more accurate assessments of glycemic fluctuations and have implications for diabetes complications and quality of life. Patients with diabetes face considerable burdens of self-care including frequent glucose monitoring, multiple insulin injections, dietary management, and the need to track daily activities, all of which lead to reduced adherence and psychological burnout. From the provider perspective, limited patient data and access to self-management tools lead to treatment inertia and a reduced ability to help patients achieve and maintain their glycemic goals. In the past few decades, there have been considerable advances in treatment-based technology and technological applications designed to help reduce patient burden and provide tools for better self-management. These advances make real-time clinical data available for clinicians to make necessary changes in treatment regimens. In this review, we discuss the latest emerging technologies available for the management of people with type 2 diabetes mellitus.

LSTMs and Deep Residual Networks for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management

To avoid serious diabetic complications, people with type 1 diabetes must keep their blood glucose levels (BGLs) as close to normal as possible. Insulin dosages and carbohydrate consumption are important considerations in managing BGLs. Since the 1960s, models have been developed to forecast blood glucose levels based on the history of BGLs, insulin dosages, carbohydrate intake, and other physiological and lifestyle factors. Such predictions can be used to alert people of impending unsafe BGLs or to control insulin flow in an artificial pancreas. In past work, we have introduced an LSTM-based approach to blood glucose level prediction aimed at “what-if” scenarios, in which people could enter foods they might eat or insulin amounts they might take and then see the effect on future BGLs. In this work, we invert the “what-if” scenario and introduce a similar architecture based on chaining two LSTMs that can be trained to make either insulin or carbohydrate recommendations aimed at reaching a desired BG level in the future. Leveraging a recent state-of-the-art model for time series forecasting, we then derive a novel architecture for the same recommendation task, in which the two LSTM chain is used as a repeating block inside a deep residual architecture. Experimental evaluations using real patient data from the OhioT1DM dataset show that the new integrated architecture compares favorably with the previous LSTM-based approach, substantially outperforming the baselines. The promising results suggest that this novel approach could potentially be of practical use to people with type 1 diabetes for self-management of BGLs.

Intelligent Data-Driven Model for Diabetes Diurnal Patterns Analysis

In type 1 diabetes, diurnal activity routines are influential factors in insulin dose calculations. Bolus advisors have been developed to more accurately suggest doses of meal-related insulin based on carbohydrate intake, according to pre-set insulin to carbohydrate levels and insulin sensitivity factors. These parameters can be varied according to the time of day and their optimal setting relies on identifying the daily time periods of routines accurately. The main issues with reporting and adjustments of daily activity routines are the reliance on self-reporting which is prone to inaccuracy and within bolus calculators, the keeping of default settings for daily time periods, such as within insulin pumps, glucose meters, and mobile applications. Moreover, daily routines are subject to change over periods of time which could go unnoticed. Hence, forgetting to change the daily time periods in the bolus calculator could contribute to sub-optimal self-management. In this paper, these issues are addressed by proposing a data-driven model for identification of diabetes diurnal patterns based on self-monitoring data. The model uses time-series clustering to achieve a meaningful separation of the patterns which is then used to identify the daily time periods and to advise of any time changes required. Further improvements in bolus advisor settings are proposed to include week/weekend or even modifiable daily time settings. The proposed model provides a quick, granular, more accurate, and personalized daily time setting profile while providing a more contextual perspective to glycemic pattern identification to both patients and clinicians.

No Effect of an Automated Bolus Calculator in Pediatric Patients with Type 1 Diabetes on Multiple Daily Injections: The Expert Kids Study

Background: This multicenter crossover study investigated the potential beneficial effect of an automated bolus calculator (ABC) in children and adolescents with type 1 diabetes (T1D) treated with multiple daily injections (MDI). Methods: Participants were randomized to either begin or end with a 5 months intervention versus their regular treatment regimen (control), separated by a 2 months washout period. During the intervention participants were carefully instructed to use the ABC (Accu-Check Aviva Expert) versus manual insulin calculations during the control period. Participants between 8 and 18 years of age with T1D were recruited from clinics in Denmark, Belgium, and Spain. Inclusion criteria included T1D for >1 year, a minimum of 3 months MDI treatment before inclusion, and HbA1c of 7.5%-11% (57-97 mmol/mol). Improvement in HbA1c was the main outcome, and improved quality of life (QoL) and glucose variability (time spent in target glucose) were secondary outcomes. Results: A total of 65 patients with a mean age of 13.25 years and a mean HbA1c of 8.25% (66.7 mmol/mol) were included. Midway evaluation after 2 months of intervention showed no significant difference from the standard care (0.297, 95% confidence interval [CI]: -0.645 to 0.054; P = 0.10). The difference remained insignificant after the 5 months of intervention (-0.143 [95% CI: -0.558 to 0.272; P = 0.51]). Using the ABC did not change the time spent in target glucose range, nor did it change the QoL. Conclusions: Our study did not demonstrate beneficial additive effects of an ABC in children and adolescents with T1D treated with MDI neither in HbA1c, nor in any other endpoint investigated.

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.

Evaluation of Four Blood Glucose Monitoring Systems for Self-Testing with Built-in Insulin Dose Advisor Based on ISO 15197:2013: System Accuracy and Hematocrit Influence

BACKGROUND

Self-monitoring of blood glucose (SMBG) is important in diabetes therapy; however, not all SMBG systems are sufficiently accurate. In addition, some SMBG systems are influenced by the user's hematocrit value.

METHODS

System accuracy and hematocrit influence was evaluated for four SMBG systems with built-in insulin dose advisors (Accu-Chek^® Aviva Expert [1], FreeStyle InsuLinx [2], FreeStyle Precision Neo [3], MyStar DoseCoach^® [4]) based on International Organization for Standardization (ISO) 15197:2013 section 6.3 (system accuracy) and 6.4.3 (packed cell volume [hematocrit]) with three test strip lots for each system. Two different established comparison methods were used to investigate a possible impact of the comparison method on analytical performance data.

RESULTS

Two systems (2, 4) fulfilled ISO 15197:2013 accuracy criteria when the manufacturer's comparison measurement method was applied and showed with all three tested lots 97% to 99.5% of results within ±15 mg/dL and ±15% of the comparison measurement results at blood glucose (BG) concentrations <100 and ≥100 mg/dL, respectively, and 100% of results within consensus error grid zones A and B. Regarding hematocrit influences, two systems (3, 4) showed with all three tested lots ≤10 mg/dL and ≤10% difference between the test sample and the respective control sample for BG concentrations <100 and ≥100 mg/dL, respectively, when using the manufacturer's comparison measurement method.

CONCLUSIONS

When using the manufacturer's comparison measurement method, two out of four SMBG systems fulfilled the minimum system accuracy requirements of ISO 15197:2013. In addition, varying hematocrit levels can affect measurement results with some SMBG systems with built-in insulin dose advisors.

Personalized Adaptive CBR Bolus Recommender System for Type 1 Diabetes

Type 1 diabetes mellitus (T1DM) is a chronic disease. Those who have it must administer themselves with insulin to control their blood glucose level. It is difficult to estimate the correct insulin dosage due to the complex glucose metabolism, which can lead to less than optimal blood glucose levels. This paper presents PepperRec, a case-based reasoning (CBR) bolus insulin recommender system capable of dealing with an unrestricted number of situations in which T1DM persons can find themselves. PepperRec considers several factors that affect glucose metabolism, such as data about the physical activity of the user, and can also cope with missing values for these factors. Based on CBR methodology, PepperRec uses new methods to adapt past recommendations to the current state of the user, and retains updated historical patient information to deal with slow and gradual changes in the patient over time (concept drift). The proposed approach is tested using the UVA/PADOVA simulator with 33 virtual subjects and compared with other methods in the literature, and with the default insulin therapy of the simulator. The achieved results demonstrate that PepperRec increases the amount of time the users are in their target glycaemic range, reduces the time spent below it, while maintaining, or even reducing, the time spent above it.

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.

Insulin Sensitivity Index-Based Optimization of Insulin to Carbohydrate Ratio: In Silico Study Shows Efficacious Protection Against Hypoglycemic Events Caused by Suboptimal Therapy

BACKGROUND AND AIM

The insulin to carbohydrate ratio (CR) is a parameter used by patients with type 1 diabetes (T1D) to calculate the premeal insulin bolus. Usually, it is estimated by the physician based on patient diary, but modern diabetes technologies, such as subcutaneous glucose sensing (continuous glucose monitoring, CGM) and insulin delivery (continuous subcutaneous insulin infusion, CSII) systems, can provide important information for its optimization. In this study, a method for CR optimization based on CGM and CSII data is presented and its efficacy and robustness tested in several in silico scenarios, with the primary aim of increasing protection against hypoglycemia.

METHODS

The method is based on a validated index of insulin sensitivity calculated from sensor and pump data (S_I^SP), area under CGM and CSII curves. The efficacy and robustness of the method are tested in silico using the University of Virginia/Padova T1D simulator, in several suboptimal therapy scenarios: with nominal CR variation, over/underestimation of meal size or suboptimal basal insulin infusion. Simulated CGM and CSII data were used to calculate the optimal CR. The same scenarios were then repeated using the estimated CR and glycemic control was compared.

RESULTS

The optimized CR was efficacious in protecting against hypoglycemic events caused by suboptimal therapy. The method was also robust to possible error in carbohydrate count and suboptimal basal insulin infusion.

CONCLUSIONS

A novel method for CR optimization in T1D, implementable in daily life using CGM and CSII data, is proposed. The method can be used both in open- and closed-loop insulin therapy.

Wearable Continuous Glucose Monitoring Sensors: A Revolution in Diabetes Treatment

Worldwide, the number of people affected by diabetes is rapidly increasing due to aging populations and sedentary lifestyles, with the prospect of exceeding 500 million cases in 2030, resulting in one of the most challenging socio-health emergencies of the third millennium. Daily management of diabetes by patients relies on the capability of correctly measuring glucose concentration levels in the blood by using suitable sensors. In recent years, glucose monitoring has been revolutionized by the development of Continuous Glucose Monitoring (CGM) sensors, wearable non/minimally-invasive devices that measure glucose concentration by exploiting different physical principles, e.g., glucose-oxidase, fluorescence, or skin dielectric properties, and provide real-time measurements every 1–5 min. CGM opened new challenges in different disciplines, e.g., medicine, physics, electronics, chemistry, ergonomics, data/signal processing, and software development to mention but a few. This paper first makes an overview of wearable CGM sensor technologies, covering both commercial devices and research prototypes. Then, the role of CGM in the actual evolution of decision support systems for diabetes therapy is discussed. Finally, the paper presents new possible horizons for wearable CGM sensor applications and perspectives in terms of big data analytics for personalized and proactive medicine.

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.

Insulin Bolus Calculator in a Pediatric Hospital. Safety and User Perceptions

BACKGROUND

Insulin dosing in hospitalized pediatric patients is challenging and requires dosing to be matched with the specific clinical and nutritional circumstances. We implemented a customized subcutaneous insulin bolus dose calculator tool integrated with the electronic health record to improve patient care. Here we describe this tool, its utilization and safety, and assess user satisfaction and perceptions of the tool.

METHODS

Blood glucose results for all patients who received insulin with and without the calculator tool were compared to assess safety. To assess user perceptions and satisfaction, a survey was sent to all identified users who interacted with the tool during the period from May 2015 to the end of November 2015. Survey responses were summarized, mean user satisfaction calculated, and correlation of Likert scale items with overall satisfaction assessed.

RESULTS

Hypoglycemia rates (2.2% and 2.9%, p = 0.17) and severe hypoglycemia rates (0.04% and 0.1%, p = 0.21) were similar for the groups that received insulin with and without the calculator tool. Overall satisfaction for all survey respondents was high (4.05, SD = 0.83). Physicians indicated a slightly higher satisfaction than nurses (4.33 versus 3.94, p = 0.04). User agreement with improvement of quality of care showed the highest correlation with overall satisfaction (r = 0.80, 95% CI 0.7 - 0.87).

CONCLUSION

Implementation of an insulin calculator tool streamlined ordering and administration of insulin in a pediatric academic institution while maintaining patient safety. Users indicated high overall satisfaction with the tool.

Characterization of Exercise and Alcohol Self-Management Behaviors of Type 1 Diabetes Patients on Insulin Pump Therapy

BACKGROUND

There is a lack of systematic ways to analyze how diabetes patients use their insulin pumps to self-manage blood glucose to compensate for alcohol ingestion and exercise. The objective was to analyze "real-life" insulin dosing decisions occurring in conjunction with alcohol intake and exercise among patients using insulin pumps.

METHODS

We recruited adult type 1 diabetes (T1D) patients on insulin pump therapy. Participants were asked to maintain their daily routines, including those related to exercising and consuming alcohol, and keep a 30-day journal on exercise performed and alcohol consumed. Thirty days of insulin pump data were downloaded. Participants' actual insulin dosing behaviors were compared against their self-reported behaviors in the setting of exercise and alcohol.

RESULTS

Nineteen T1D patients were recruited and over 4000 interactions with the insulin pump were analyzed. The analysis exposed variability in how subjects perceived the effects of exercise/alcohol on their blood glucose, inconsistencies between self-reported and observed behaviors, and higher rates of blood glucose control behaviors for exercise versus alcohol.

CONCLUSION

Compensation techniques and perceptions on how exercise and alcohol affect their blood glucose levels vary between patients. Improved individualized educational techniques that take into consideration a patient's unique life style are needed to help patients effectively apply alcohol and exercise compensation techniques.

Boluses in Insulin Therapy

Giving a bolus is one major part in multiple dose insulin therapy (MDI) along with basal insulin substitution. To adjust the bolus optimally, different factors like carbohydrate content and composition of the meal, correction factors, and timing have to be considered. Advances in technologies like bolus advisors can assist the patients but still there a several open questions and technical challenges regarding boluses. This commentary provides an opportunity to address several of the above-mentioned factors influencing the result of bolusing. It shall draw attention to those factors and address the current opportunities, for example, continuous subcutaneous insulin infusion (CSII), as well as the need for further studies which can help to improve diabetes insulin therapy by means of the correct use of boluses.

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.

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.

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.

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.

Managing diabetes in the digital age

The prevalence of diabetes is rising globally. Poor glucose control results in higher rates of diabetes-related complications and an increase in health care expenditure. Diabetes self-management education (DSME) training has shown to improve glucose control, and thus may reduce long-term complications. Implementation of diabetes self-management education programs may not be feasible for all the institutions or in developing countries due to lack of resources and higher costs associated with DSME training. With the increasing use of smartphones and Internet, there is an opportunity to use digital tools for training people with diabetes to self-manage their disease. A number of mobile applications, Internet portal, and websites are available to help patients to improve their diabetes care. However, the studies are limited to show its effectiveness and cost-benefits in diabetes self-management. In addition, there are many challenges ahead for the digital health industry. In this review, we assess the use of newer technologies and digital health in diabetes self-management with a focus on future directions and potential challenges.

Advanced Insulin Bolus Advisor Based on Run-To-Run Control and Case-Based Reasoning

This paper presents an advanced insulin bolus advisor for people with diabetes on multiple daily injections or insulin pump therapy. The proposed system, which runs on a smartphone, keeps the simplicity of a standard bolus calculator while enhancing its performance by providing more adaptability and flexibility. This is achieved by means of applying a retrospective optimization of the insulin bolus therapy using a novel combination of run-to-run (R2R) that uses intermittent continuous glucose monitoring data, and case-based reasoning (CBR). The validity of the proposed approach has been proven by in-silico studies using the FDA-accepted UVa-Padova type 1 diabetes simulator. Tests under more realistic in-silico scenarios are achieved by updating the simulator to emulate intrasubject insulin sensitivity variations and uncertainty in the capillarity measurements and carbohydrate intake. The CBR(R2R) algorithm performed well in simulations by significantly reducing the mean blood glucose, increasing the time in euglycemia and completely eliminating hypoglycaemia. Finally, compared to an R2R stand-alone version of the algorithm, the CBR(R2R) algorithm performed better in both adults and adolescent populations, proving the benefit of the utilization of CBR. In particular, the mean blood glucose improved from 166 ± 39 to 150 ± 16 in the adult populations (p = 0.03) and from 167 ± 25 to 162 ± 23 in the adolescent population (p = 0.06). In addition, CBR(R2R) was able to completely eliminate hypoglycaemia, while the R2R alone was not able to do it in the adolescent population.

An Advanced Bolus Calculator for Type 1 Diabetes: System Architecture and Usability Results

This paper presents the architecture and initial usability results of an advanced insulin bolus calculator for diabetes (ABC4D), which provides personalized insulin recommendations for people with diabetes by differentiating between various diabetes scenarios and automatically adjusting its parameters over time. The proposed platform comprises two main components: a smartphone-based patient platform allowing manual input of glucose and variables affecting blood glucose levels (e.g., meal carbohydrate content and exercise) and providing real-time insulin bolus recommendations; and a clinical revision platform to supervise the automatic adaptations of the bolus calculator parameters. The system implements a previously in silico validated bolus calculator algorithm based on case-based reasoning, which uses information from similar past events (i.e., cases) to suggest improved personalized insulin bolus recommendations and automatically learns from new events. Usability of ABC4D was assessed by analyzing the system usage at the end of a six-week pilot study (n = 10). Further feedback on the use of ABC4D has been obtained from each participant at the end of the study from a usability questionnaire. On average, each participant requested 115 ± 21 insulin recommendations, of which 103 ± 28 (90%) were accepted. The clinical revision software proposed a total of 754 case revisions, where 723 (96%) adaptations were approved by a clinical expert and updated in the patient platform.

Smartphone apps for calculating insulin dose: a systematic assessment

BACKGROUND

Medical apps are widely available, increasingly used by patients and clinicians, and are being actively promoted for use in routine care. However, there is little systematic evidence exploring possible risks associated with apps intended for patient use. Because self-medication errors are a recognized source of avoidable harm, apps that affect medication use, such as dose calculators, deserve particular scrutiny. We explored the accuracy and clinical suitability of apps for calculating medication doses, focusing on insulin calculators for patients with diabetes as a representative use for a prevalent long-term condition.

METHODS

We performed a systematic assessment of all English-language rapid/short-acting insulin dose calculators available for iOS and Android.

RESULTS

Searches identified 46 calculators that performed simple mathematical operations using planned carbohydrate intake and measured blood glucose. While 59% (n = 27/46) of apps included a clinical disclaimer, only 30% (n = 14/46) documented the calculation formula. 91% (n = 42/46) lacked numeric input validation, 59% (n = 27/46) allowed calculation when one or more values were missing, 48% (n = 22/46) used ambiguous terminology, 9% (n = 4/46) did not use adequate numeric precision and 4% (n = 2/46) did not store parameters faithfully. 67% (n = 31/46) of apps carried a risk of inappropriate output dose recommendation that either violated basic clinical assumptions (48%, n = 22/46) or did not match a stated formula (14%, n = 3/21) or correctly update in response to changing user inputs (37%, n = 17/46). Only one app, for iOS, was issue-free according to our criteria. No significant differences were observed in issue prevalence by payment model or platform.

CONCLUSIONS

The majority of insulin dose calculator apps provide no protection against, and may actively contribute to, incorrect or inappropriate dose recommendations that put current users at risk of both catastrophic overdose and more subtle harms resulting from suboptimal glucose control. Healthcare professionals should exercise substantial caution in recommending unregulated dose calculators to patients and address app safety as part of self-management education. The prevalence of errors attributable to incorrect interpretation of medical principles underlines the importance of clinical input during app design. Systemic issues affecting the safety and suitability of higher-risk apps may require coordinated surveillance and action at national and international levels involving regulators, health agencies and app stores.

Method for automatic adjustment of an insulin bolus calculator: in silico robustness evaluation under intra-day variability

BACKGROUND AND OBJECTIVE

Insulin bolus calculators are simple decision support software tools incorporated in most commercially available insulin pumps and some capillary blood glucose meters. Although their clinical benefit has been demonstrated, their utilisation has not been widespread and their performance remains suboptimal, mainly because of their lack of flexibility and adaptability. One of the difficulties that people with diabetes, clinicians and carers face when using bolus calculators is having to set parameters and adjust them on a regular basis according to changes in insulin requirements. In this work, we propose a novel method that aims to automatically adjust the parameters of a bolus calculator. Periodic usage of a continuous glucose monitoring device is required for this purpose.

METHODS

To test the proposed method, an in silico evaluation under real-life conditions was carried out using the FDA-accepted Type 1 diabetes mellitus (T1DM) UVa/Padova simulator. Since the T1DM simulator does not incorporate intra-subject variability and uncertainty, a set of modifications were introduced to emulate them. Ten adult and ten adolescent virtual subjects were assessed over a 3-month scenario with realistic meal variability. The glycaemic metrics: mean blood glucose; percentage time in target; percentage time in hypoglycaemia; risk index, low blood glucose index; and blood glucose standard deviation, were employed for evaluation purposes. A t-test statistical analysis was carried out to evaluate the benefit of the presented algorithm against a bolus calculator without automatic adjustment.

RESULTS

The proposed method statistically improved (p<0.05) all glycemic metrics evaluating hypoglycaemia on both virtual cohorts: percentage time in hypoglycaemia (i.e. BG<70 mg/dl) (adults: 2.7±4.0 vs. 0.4±0.7, p=0.03; adolescents: 7.1±7.4 vs. 1.3±2.4, p=0.02) and low blood glucose index (LBGI) (adults: 1.1±1.3 vs. 0.3±0.2, p=0.002; adolescents: 2.0±2.19 vs. 0.7±1.4, p=0.05). A statistically significant improvement was also observed on the blood glucose standard deviation (BG SD mg/dL) (adults: 33.5±13.7 vs. 29.2±8.3, p=0.01; adolescents: 63.7±22.7 vs. 44.9±23.9, p=0.01). Apart from a small increase in mean blood glucose on the adult cohort (129.9±11.9 vs. 133.9±11.6, p=0.03), the rest of the evaluated metrics, despite showing an improvement trend, did not experience a statistically significant change.

CONCLUSIONS

A novel method for automatically adjusting the parameters of a bolus calculator has the potential to improve glycemic control in T1DM diabetes management.

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.

Use of an integrated strip-free blood glucose monitoring system increases frequency of self-monitoring and improves glycemic control: Results from the ExAct study

Aims

We investigated the impact of using an integrated, strip-free system compared to the use of single-strip systems on testing frequency and glycemic control in individuals with insulin-treated diabetes.

Methods

This multinational, comparative, cluster-randomized, observational study included 311 patients with type 1 and insulin-treated type 2 diabetes who were performing SMBG at suboptimal frequencies. Sites were cluster-randomized to “integrated strip-free” system (EXP group) or any “single-strip” system (CNL group). Testing frequency and HbA1c were measured at baseline, 12 weeks and 24 weeks.

Results

At week 24, the EXP group showed an increase in SMBG frequency from baseline of 4.17 tests/week (95% CI 2.76, 5.58) compared with an increase of 0.53 tests/week (95% CI −0.73, 1.79) among CNL patients, resulting in a between-group difference of 3.63 tests/week (p < 0.0002). Mixed-effects models for repeated measurements (MMRM) controlling for baseline frequency of testing, country and clinical site confirmed a higher SMBG testing frequency in the EXP group compared to the CNL group, with a between-group difference of 2.70 tests/week (p < 0.01). Univariate analysis showed greater HbA1c reductions in the EXP group than CNL group: −0.44% (95% CI −0.59, −0.29) vs. −0.13% (95% CI −0.27, 0.01), respectively, p < 0.0002. MMRM analyses confirmed these HbA1c reductions. A greater percentage of EXP than CNL patients achieved HbA1c reductions of ≥0.5%: 45.1% vs. 29.1%, respectively, p < 0.01.

Conclusions

The use of an integrated, strip-free SMBG system improved testing adherence and was associated with improvements in glycemic control.

Cross-sectional survey and retrospective analysis of a large cohort of adults with type 1 diabetes with long-term continuous subcutaneous insulin infusion treatment

Background. Continuous subcutaneous insulin infusion (CSII) is an established modality for intensive insulin treatment of type 1 diabetes (T1D), but long-term data concerning satisfaction, CSII function use, safety, and efficacy in real-life conditions are scarce. Methods. We analyzed a cohort of adult patients with T1D treated with CSII for more than 1 year in a single diabetes center. We performed a cross-sectional survey in 2010 (tolerance/satisfaction and behavior forms) and a retrospective analysis of medical records (including HbA1c level, hospitalization, and catheter infections). The primary objective was to assess long-term tolerance/satisfaction, and secondary objectives were safety and efficacy. Results. There were 295 patients analyzed. After a median duration of CSII use of 5 years, overall satisfaction was high for about 90% of patients. Mean CSII-related discomfort scores were low for work, recreation, and sleep and moderate for sport and sexual activity (2.5 ± 1.9, 2.6 ± 1.8, 2.6 ± 2.1, 3.4 ± 2.3, and 4.0 ± 2.9 of 10, respectively). Despite a high level of diabetes education, only one third of patients were using advanced CSII functions. During long-term follow-up, the safety of CSII treatment was good; the hospitalization rate was 0.18 patients/year, and catheter infections were scarce. The HbA1c level dropped about -0.5% independently from CSII duration (P < .05). Conclusions. In this adult cohort, satisfaction and tolerance, together with safety, of CSII were maintained at long-term follow up. The sole basic functions of CSII were currently used by patients. A 0.5% decrease in the HbA1c level was maintained during the study period.

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.

Use of an insulin bolus advisor facilitates earlier and more frequent changes in insulin therapy parameters in suboptimally controlled patients with diabetes treated with multiple daily insulin injection therapy: results of the ABACUS trial

BACKGROUND

We assessed the impact of using an automated bolus advisor integrated into a blood glucose meter on the timing and frequency of adjusting insulin therapy parameter settings and whether the availability of this technology would increase blood glucose test strip utilization in diabetes patients treated with multiple daily insulin injection (MDI) therapy.

SUBJECTS AND METHODS

The Automated Bolus Advisor Control and Usability Study (ABACUS) trial, a 26-week, prospective, randomized, controlled, multinational study that enrolled 218 type 1 and type 2 diabetes patients, demonstrated that use of an automated insulin bolus advisor helps improve glycemic control in suboptimally controlled, MDI-treated patients. Patient data were assessed to determine when and how often changes in insulin parameter settings occurred during the study. Patient meters were downloaded to determine blood glucose monitoring frequency.

RESULTS

One hundred ninety-three patients completed the study: 93 control arm (CNL) and 100 intervention (experimental) arm (EXP). Significantly more EXP (47.5%) than CNL (30.7%) patients received one or more changes in their insulin sensitivity factor (ISF) settings during the study (P=0.0191). Changes in ISF settings occurred earlier and more frequently in EXP than CNL patients throughout the study. A similar trend was seen in changes in insulin-to-carbohydrate ratios. There were no differences in daily self-monitoring of blood glucose frequency [mean (SD)] between CNL and EXP patients: 4.7 (1.5) versus 4.6 (1.3) (P=0.4085).

CONCLUSIONS

Use of an automated bolus advisor was associated with earlier, more frequent changes in key insulin parameters, which may have contributed to subsequent improvements in glycemic control but without increased glucose test strip utilization.

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.

6(th) Annual Symposium on Self-Monitoring of Blood Glucose (SMBG) applications and beyond, April 25-27, 2013, Riga, Latvia

International experts in the fields of diabetes, diabetes technology, endocrinology, and pediatrics gathered for the 6(th) Annual Symposium on Self-Monitoring of Blood Glucose (SMBG) Applications and beyond. The aim of this meeting was to continue setting up a global network of experts in this field and provide an international platform for exchange of ideas to improve life for people with diabetes. The 2013 meeting comprised a comprehensive scientific program, parallel interactive workshops, and two keynote lectures. All these discussions were intended to help identify gaps and areas where further scientific work and clinical studies are warranted.

Use of an insulin bolus advisor improves glycemic control in multiple daily insulin injection (MDI) therapy patients with suboptimal glycemic control: first results from the ABACUS trial

OBJECTIVE

Use of automated bolus advisors is associated with improved glycemic control in patients treated with insulin pump therapy. We conducted a study to assess the impact of using an insulin bolus advisor embedded in a blood glucose (BG) meter on glycemic control and treatment satisfaction in patients treated with multiple daily insulin injection (MDI) therapy. The study goal was to achieve >0.5% A1C reduction in most patients.

RESEARCH DESIGN AND METHODS

This was a 26-week, prospective, randomized, controlled, multinational study that enrolled 218 MDI-treated patients with poorly controlled diabetes (202 with type 1 diabetes, 16 with type 2 diabetes) who were 18 years of age or older. Participants had mean baseline A1C of 8.9% (SD, 1.2 [74 mmol/mol]), mean age of 42.4 years (SD, 14.0), mean BMI of 26.5 kg/m(2) (SD, 4.2), and mean diabetes duration of 17.7 years (SD, 11.1). Control group (CNL) patients used a standard BG meter and manual bolus calculation; intervention group (EXP) patients used the Accu-Chek Aviva Expert meter with an integrated bolus advisor to calculate insulin dosages. Glucose data were downloaded and used for therapy parameter adjustments in both groups.

RESULTS

A total of 193 patients (CNL, n = 93; EXP, n = 100) completed the study. Significantly more EXP than CNL patients achieved >0.5% A1C reduction (56.0% vs. 34.4%; P < 0.01). Improvement in treatment satisfaction (Diabetes Treatment Satisfaction Questionnaire scale) was significantly greater in EXP patients (11.4 [SD, 6.0] vs. 9.0 [SD, 6.3]; P < 0.01). Percentage of BG values <50 mg/dL was <2% in both groups during the study.

CONCLUSIONS

Use of an automated bolus advisor resulted in improved glycemic control and treatment satisfaction without increasing severe hypoglycemia.