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

OptimAAPP, a smartphone insulin dose calculator for carbohydrate, fat, and protein: A cross-over, randomised controlled trial in adolescents and adults with type 1 diabetes using multiple daily injection therapy

AIMS

To (1) evaluate the efficacy of OptimAAPP, a smartphone insulin dose calculator for carbohydrate, fat, and protein in managing glycaemia compared with carbohydrate counting in adolescents and adults with type 1 diabetes using flexible multiple daily injection therapy (MDI, ≥4 injections/day) and (2) assess user acceptability of OptimAAPP.

METHODS

In this free-living trial, participants aged 12-50 years were randomised to use carbohydrate counting or OptimAAPP for meal insulin dose calculation for 3 months, then use the alternate method for 3 months. The primary outcome, time-in-range (3.9-10.0 mmol/L) was measured in weeks 3-4 of each arm using continuous glucose monitoring. The acceptability of OptimAAPP was assessed at end intervention using a purpose-designed questionnaire.

RESULTS

An intention-to-treat analysis of 41 participants, mean age 28 ± 12 years and HbA1c 56 ± 10 mmol/mol (7.3 ± 0.9%) found no significant difference in glycaemic outcomes when using OptimAAPP compared with carbohydrate counting including time-in-range (70.5 vs. 67.6%, p = 0.102), above range (24.5% vs. 28.0%, p = 0.068), below range (4.9% vs. 4.4%, p = 0.318), and coefficient of variation (32.2% vs. 33.3%, p = 0.136). There was no severe hypoglycaemia. Participants reported that OptimAAPP was easy to use (79%), and they were confident in giving the recommended doses (82%). Barriers to use were the small food database and the time associated with food entry.

CONCLUSIONS

In adolescents and adults using flexible MDI therapy, OptimAAPP use did not produce glycaemic outcomes that were significantly different from carbohydrate counting. Participant views of OptimAAPP indicate a high level of acceptability. Increasing the size of the food database will likely enhance the user experience.

Bridging the Gap in Carbohydrate Counting With a Mobile App: Needs Assessment Survey

BACKGROUND

Carbohydrate counting (CC) can be burdensome and difficulty with adherence has been reported. Automated CC through mobile apps offers innovative solutions to ease this burden.

OBJECTIVE

This cross-sectional web-based survey aims to identify (1) perceived barriers to CC by Canadians living with type 1 diabetes (T1D) and (2) app features that would help reduce these barriers. The secondary objective aims to compare apps used by participants with the suggested app features.

METHODS

People with T1D aged 14 years and older, living in Canada, were recruited through the BETTER Canadian registry, diabetes organizations, and social media. Participants completed a 39-question web-based survey (closed- and open-ended) to identify barriers in CC, preferred CC app features, and current app use. Respondents rated barriers and app features using a 5-point Likert scale. The features were cross-referenced in each app reported being used by participants. Descriptive statistics summarized barriers and app feature preferences, and statistical analyses identified differences by age, app use, and insulin modality. Mean scores (out of 5) were compared using 2-tailed t tests or nonparametric tests. Open-ended questions were analyzed using inductive thematic analysis.

RESULTS

Participants (N=196; woman: n=145, 74%; mean age 40 [SD 17] years; mean diabetes duration 22 (14) years; relied on CC to determine insulin doses at mealtimes: n=178, 90.8%) reported barriers related to carbohydrate identification, nutrient interaction, and insulin dose calculation, as well as psychosocial factors. Preferred app features included nutrient analysis (165/196, 84.2%), personalization (151/196, 77.1%), insulin bolus calculation (145/196, 74%), and health care professional support (135/196, 68.8%). Among the 16 apps used by participants, most (12/16, 75%) supported nutrient analysis but only one offered bolus calculations or health care professional support, and none offered personalization. Users on injections reported greater barriers to blood glucose monitoring for insulin adjustments compared to exclusive pump users (mean score of 3.87, SD 1.22 vs mean 3.30, SD 1.28; P=.001). They also expressed higher needs for meal logs in an electronic food journal (mean 4.06, SD 1.18 vs mean 3.69, SD 1.17; P=.01), bolus dose suggestions (mean 4.37, SD 0.98 vs mean 3.84, SD 1.26; P=.001), and app personalization (mean 4.47, SD 0.86 vs mean 3.93, SD 1.21; P<.001). No significant differences were observed based on age or app use. The thematic analysis revealed participants' perceptions of suggested barriers and features, as well as new barriers such as calculation errors from unreliable food data and nutrition labels, fear of eating disorders, limited app reliability, and insufficient health care support, with suggestions for technology-based solutions.

CONCLUSIONS

CC mobile apps currently used do not meet the needs of people with T1D. A novel CC app with app features such as photo recognition, reliable nutrient values, and personalized bolus calculations could reduce the CC burden.

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.

The role of real-time continuous glucose monitoring in diabetes management and how it should link to integrated personalized diabetes management

Diabetes is a complex metabolic condition that demands tailored, individualized approaches for effective management. Real-time continuous glucose monitoring (rtCGM) systems have improved in terms of design, usability and accuracy over the years and play a pivotal role in the delivery of integrated personalized diabetes management (iPDM). iPDM is a comprehensive multidisciplinary approach that combines individualized care strategies utilizing technologies and interventions and encourages the active involvement of the person with diabetes in the care provided. The use of stand-alone rtCGM and its integration with other diabetes technologies, such as hybrid automated insulin delivery, have enabled improved glycaemic and quality of life outcomes for people with diabetes. As the uptake of rtCGM and associated technologies is increasing and becoming the standard of care for people with diabetes, it is important that efforts are focused on associated goals such as reducing health inequalities in terms of access, aligning structured education with rtCGM usage, choosing the right technology based on needs and preferences, and minimizing burden while aiming for optimal glucose outcomes. Utilizing rtCGM in other settings than outpatients and in diabetes cohorts beyond type 1 and type 2 diabetes needs further exploration. This review aims to provide an overview of the role of rtCGM and how best to link rtCGM to iPDM, highlighting its role in enhancing personalized treatment strategies.

Adaptive bolus calculators for people with type 1 diabetes: A systematic review

AIM

To conduct a systematic review of studies assessing adaptive insulin bolus calculators for people with type 1 diabetes (T1D).

METHODS

Electronic databases (Medline, Embase and Web of Science) were systematically searched from date of inception to 13 October 2022 for single-arm or randomized controlled studies assessing adaptive bolus calculators only, in children or adults with T1D on multiple daily injections or insulin pumps with glycaemic outcomes reported. The Clinicaltrials.gov registry was searched for recently completed studies evaluating decision support in T1D. The quality of extracted studies was assessed using the Standard Quality Assessment criteria and the Cochrane Risk of Bias assessment tool.

RESULTS

Six studies were identified. Extracted data were synthesized in a descriptive review because of heterogeneity. All the studies were small feasibility studies or were not suitably powered, and all were deemed to be at a high risk of performance and detection bias because they were unblinded. Overall, these studies did not show a significant glycaemic improvement. Two studies showed a reduction in postprandial time below range or an incremental change in blood glucose concentration; however, these were in controlled environments over a short duration.

CONCLUSIONS

There are limited clinical trials evaluating adaptive bolus calculators. Although results from small trials or in-silico data are promising, further studies are required to support personalized and adaptive management of T1D.

Reducing potential errors associated with insulin administration: An integrative review

RATIONALE, AIMS, OBJECTIVES

Around one-third of medication errors resulting in death within 48 h involve insulin therapy. Despite a growing number of interventional strategies that have been published over the past decade, it remains unclear which of these interventions is effective in reducing insulin errors. Therefore, the study aimed to synthesize interventions to reduce the frequency of insulin errors in either home or health care settings.

METHODS

This integrative review was conducted based on Whittemore and Knafl's four steps, which includes problem identification, literature search, data analysis and presentation. Six databases including Cumulative Index of Nursing and Allied Health Literature (CINAHL), Medline, PubMed, Scopus, PsychInfo and Cochrane were searched from January 2010 through July 2021. The level of evidence quality was assessed according to the Johns Hopkins Nursing Evidence-Based Practice grading scale.

RESULTS

Sixteen studies meeting inclusion criteria were reviewed. The results provide strong support for teaching patients how to use automated bolus calculators and educating patients to self-administer insulin to prevent insulin errors in the home setting. Computerized protocols, education and double-checking procedures were also found to be effective strategies for minimizing insulin errors in healthcare settings.

CONCLUSION

While the strategies might be effective in reducing insulin administration errors in the home settings, computerized protocols, continuing education and the manual validation of insulin products appear to be the most effective strategies for reducing such insulin errors in healthcare settings. Understanding these findings may help clinicians and patients to decrease the number of insulin errors administration.

Insulin pumps in children - a systematic review

BACKGROUND

Insulin pump therapy is a real breakthrough in managing diabetes Mellitus, particularly in children. It can deliver a tiny amount of insulin and decreases the need for frequent needle injections. It also helps to maintain adequate and optimal glycemic control to reduce the risk of metabolic derangements in different tissues. Children are suitable candidates for pump therapy as they need a more freestyle and proper metabolic control to ensure adequate growth and development. Therefore, children and their caregivers should have proper education and training and understand the proper use of insulin pumps to achieve successful pump therapy. The pump therapy continuously improves to enhance its performance and increase its simulation of the human pancreas. Nonetheless, there is yet a long way to reach the desired goal.

AIM

To review discusses the history of pump development, its indications, types, proper use, special conditions that may enface the children and their families while using the pump, its general care, and its advantages and disadvantages.

METHODS

We conducted comprehensive literature searches of electronic databases until June 30, 2022, related to pump therapy in children and published in the English language.

RESULTS

We included 118 articles concerned with insulin pumps, 61 were reviews, systemic reviews, and meta-analyses, 47 were primary research studies with strong design, and ten were guidelines.

CONCLUSION

The insulin pump provides fewer needles and can provide very tiny insulin doses, a convenient and more flexible way to modify the needed insulin physiologically, like the human pancreas, and can offer adequate and optimal glycemic control to reduce the risk of metabolic derangements in different tissues.

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.

American Association of Clinical Endocrinology Clinical Practice Guideline: The Use of Advanced Technology in the Management of Persons With Diabetes Mellitus

OBJECTIVE

To provide evidence-based recommendations regarding the use of advanced technology in the management of persons with diabetes mellitus to clinicians, diabetes-care teams, health care professionals, and other stakeholders.

METHODS

The American Association of Clinical Endocrinology (AACE) conducted literature searches for relevant articles published from 2012 to 2021. A task force of medical experts developed evidence-based guideline recommendations based on a review of clinical evidence, expertise, and informal consensus, according to established AACE protocol for guideline development.

MAIN OUTCOME MEASURES

Primary outcomes of interest included hemoglobin A1C, rates and severity of hypoglycemia, time in range, time above range, and time below range.

RESULTS

This guideline includes 37 evidence-based clinical practice recommendations for advanced diabetes technology and contains 357 citations that inform the evidence base.

RECOMMENDATIONS

Evidence-based recommendations were developed regarding the efficacy and safety of devices for the management of persons with diabetes mellitus, metrics used to aide with the assessment of advanced diabetes technology, and standards for the implementation of this technology.

CONCLUSIONS

Advanced diabetes technology can assist persons with diabetes to safely and effectively achieve glycemic targets, improve quality of life, add greater convenience, potentially reduce burden of care, and offer a personalized approach to self-management. Furthermore, diabetes technology can improve the efficiency and effectiveness of clinical decision-making. Successful integration of these technologies into care requires knowledge about the functionality of devices in this rapidly changing field. This information will allow health care professionals to provide necessary education and training to persons accessing these treatments and have the required expertise to interpret data and make appropriate treatment adjustments.

Therapy Adjustments Based on Trend Arrows Using Continuous Glucose Monitoring Systems

Continuous glucose monitoring (CGM) systems use trend arrows to accurately display the anticipated glucose curve for the user. These are used for both "real-time" glucose monitoring and for intermittent scanning glucose monitoring. Trend arrow data are used by people with diabetes to make corrections to their glucose control. It is essential that they are correctly interpreted when adjusting insulin doses and to ensure that appropriate treatment decisions are made. The aim of this article is to provide general treatment guidance for diabetes teams and for people with diabetes using CGM in the context of trend arrows. This is based on previous recommendations for interpreting trend arrows without losing sight of the need for individual therapy adjustment.

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.

Insulin pump initiation and education for children and adolescents - a qualitative study of current practice in New Zealand

Purpose

Worldwide, the use of insulin pumps for the management of type 1 diabetes is increasing. There are no national or international published guidelines and few guidance recommendations detailing the education and training required to commence insulin pump therapy. The aim of this study is to describe current clinical practice regarding initiation of insulin pump therapy in children and adolescents with type 1 diabetes in New Zealand.

Methods

Pediatric diabetes nurse specialists from selected New Zealand hospitals (n = 16) were identified and invited to participate in this qualitative study. For those consenting, structured interviews were conducted. The questions covered basic hospital demographics and various aspects of insulin pump initiation including pump start planning, education, and aspects of follow-up and after-care.

Results

The response rate was 100% (16 out of 16 hospitals). Diabetes clinics interviewed varied in size from 50 to 450 pediatric patients and frequency of insulin pump use from 11% - 46%. Clinical practice differed between clinics. Important differences related to: use of continuous glucose monitoring (12/16); and differing views on immediate vs. delayed use of pump advanced features. Location of pump starts also varied, with both in-patient (2/16) and out-patient (14/16) approaches seen. The motivations and beliefs relating to these various pump start approaches also varied.

Conclusions

Differences seen between hospitals reflected team preference, and possibly a lack of consensus/guidance from the medical literature. Lessons may be learnt and further rationalisation and improvement in education remains possible by combining and adopting strengths from different hospitals.

Technology in Diabetes Treatment: Update and Future

Worldwide the number of people with diabetes mellitus is increasing. There are estimations that diabetes is one of the leading causes of death. The most important goals for the treatment of diabetes are self-management of the disease and an optimal quality of diabetes control. In the therapy new technologies, like real-time continuous interstitial glucose monitoring, continuous subcutaneous insulin infusion (CSII), electronic tools for the monitoring of therapeutic approaches, automated bolus calculators for insulin and electronic tools for education and information of patients, have become widespread and play important roles. All these efforts are related to the interaction between patients, caregivers, scientists or researchers and industry. The presentation of different aspects of new technological approaches in the present article should give more information about different technologies. However, because of the rather quickly appearance of new technologies, the presentation can only be a spotlight. Further studies are mandatory to analyze the effects and long-term benefits of each technology and electronic device.

App-Based Insulin Calculators: Current and Future State

PURPOSE OF REVIEW

To perform a comprehensive literature review and critical assessment of peer-reviewed manuscripts addressing the efficacy, safety, or usability of insulin calculator apps.

RECENT FINDINGS

Managing diabetes with insulin can be complex, and literacy and numeracy skills pose barriers to manual insulin dose calculations. App-based insulin calculators are promising tools to help people with diabetes administer insulin safely and have potential to improve glycemic control. While a large number of apps which assist with insulin dosing are available, there is limited data evaluating their efficacy, safety, and usability. Recently, a need for regulatory oversight has been recognized, but few apps meet federal standards. Thus, choosing an appropriate app is challenging for both patients and providers. An electronic literature review was performed to identify insulin calculator apps with either evidence for efficacy, safety or usability published in peer-reviewed literature or with FDA/CE approval. Twenty apps were identified intended for use by patients with diabetes on insulin. Of these, nine included insulin calculators. Summaries of each app, including pros and cons, are provided. Insulin-calculator apps have the potential to improve self-management of diabetes. While current literature demonstrates improvements in quality of life and glycemic control after use of these programs, larger trials are needed to collect outcome and safety data. Also, further human factor analysis is needed to assure these apps will be adopted appropriately by people with diabetes. App features including efficacy and safety data need to be easily available for consumer review and decision making. Higher standards need to be set for app developers to ensure safety and efficacy.

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.

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.

Diabetes in Childhood and Adolescence

BACKGROUND

The incidence of type 1 diabetes mellitus in childhood and adolescence is steadily rising and now stands at 22.9 new cases per year per 100 000 persons up to age 15.

METHODS

This review is based on pertinent publications retrieved by a selective literature search, with special attention to the current German S3 guideline on diabetes in childhood and adolescence.

RESULTS

Polydipsia, polyuria, and weight loss are the characteristic presenting symptoms of diabetes mellitus. The acutely presenting patient needs immediate stabilization because of the danger of rapid metabolic decompensation (risk of keto - acidosis, 21.1%). Long-term insulin therapy can be delivered either by subcutaneous injection or by an insulin pump. The goals of treatment are the near-normalization of glucose metabolism (HbA1c <7.5%), the avoidance of acute complications (hypoglycemia and ketoacidosis), the reduction of diabetes-specific sequelae (retinopathy, nephropathy, neuropathy, hypertension, and hyperlipidemia), unrestricted participation in age-appropriate everyday activities, and normal physical and psychosocial development. Children and adolescents with diabetes need individualized treatment with frequent adjustments and holistic overall care so that these goals can be effectively met.

CONCLUSION

Every physician must be able to diagnose the initial presentation of diabetes and to initiate the first steps in its management. The patient should be referred as soon as possible to a diabetes team that has experience in the treatment of children and adolescents.

Issues and Ideas in Bolus Advisor Research With Commentary on "A Methodology to Compare Insulin Dosing Algorithms in Real-Life Settings"

The accompanying article by Groat et al in this issue presents a methodology to compare glucose outcomes from insulin bolus dose recommendations observed retrospectively from a novel iDecide bolus calculator with glucose outcomes from the prospective bolus recommendations provided by a current insulin pump. The methodology in this article evaluates a bolus calculator algorithm and also evaluates calculations for two additional lifestyle behaviors, exercise and alcohol intake, that are modifiable risk factors associated with diabetes. This methodology for evaluation of glycemic outcomes from bolus calculators could be expanded further using much larger existing bolus calculator databases. This would complement current verifications done through in-silico testing with the University of Virginia/Padua 300 patient type 1 diabetes simulator. This approach merits serious consideration, yet the actual dose recommendations provided by the iDecide calculator introduce wider lessons on how bolus calculator glucose outcomes might be better optimized.

Evaluation of the timing and coordination of prandial insulin administration in the hospital

AIMS

The objective of this study was to examine the relationship between measures of coordinated insulin delivery and capillary blood glucose (CBG) levels among hospitalized patients and to assess nurse perceptions of insulin administration.

METHODS

Hospitalized patients (n=451) receiving rapid acting insulin analog (RAIA) using carbohydrate counting were retrospectively analyzed. Nurses (n=35) were asked to complete an 18-item anonymous survey assessing perception of RAIA dosing.

RESULTS

The median time from breakfast CBG to RAIA dose was 93 (IQR 57-138) min. There was no association between timeliness measures and mean CBG at lunch or dinner. Hypoglycemia was rare (N=2). More than half (54%) of nurses were confident all of the time in determining the correct dose of RAIA, though none were confident in administering it on time. The majority of nurses perceived an electronic dosing calculator and a patient reminder to notify the nurse at the end of the meal favorably.

CONCLUSIONS

The data demonstrate suboptimal coordination of CBG monitoring and insulin doses using a flexible meal insulin dosing strategy, though there was minimal impact on glycemic control. Nurses reported high confidence in the ability to calculate the correct insulin dose but not in the ability to administer it on time.

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.

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.

Evidence-based Mobile Medical Applications in Diabetes

This article reviews mobile medical applications that are commercially available in the United States or European Union (EU) and are (1) associated with published data of clinical outcomes in the peer-reviewed literature during the past 5 years, (2) cleared by the US Food and Drug Administration (FDA) in the United States, or (3) a recipient of a CE (Conformité Européenne) mark by the EU. Many of these applications have been shown to positively affect outcomes in the short term, but long-term studies are needed. Until more data are available, consumers and professionals can consider guidance based on FDA/CE status.

Qualitative assessment of user experiences of a novel smart phone application designed to support flexible intensive insulin therapy in type 1 diabetes

Background

Modern flexible multiple daily injection (MDI) therapy requires people with diabetes to manage complex mathematical calculations to determine insulin doses on a day to day basis. Automated bolus calculators assist with these calculations, add additional functionality to protect against hypoglycaemia and enhance the record keeping process, however uptake and use depends on the devices meeting the needs of the user. We aimed to obtain user feedback on the usability of a mobile phone bolus calculator application in adults with T1DM to inform future development of mobile phone diabetes support applications.

Methods

Adults with T1DM who had previously received education in flexible MDI therapy were invited to participate. Eligible respondents attended app education and one month later participated in a focus group to provide feedback on the features of the app in relation to usability for patient-based flexible MDI and future app development.

Results

Seven adults participated in the app training and follow up interview. App features that support dose adjustment to reduce hypoglycaemia risk and features that enable greater efficiency in dose calculation, record keeping and report generation were highly valued.

Conclusions

Adults who are self managing flexible MDI found the Rapidcalc mobile phone app to be a useful self-management tool and additional features to further improve usability, such as connectivity with BG meter and food databases, shortcut options to economise data entry and web based storage of data, were identified. Further work is needed to ascertain specific features and benefit for those with lower health literacy.

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.

Promoting health and reducing costs: a role for reform of self-monitoring of blood glucose provision within the National Health Service

AIM

To determine the cost-effectiveness of all options for the self-monitoring of blood glucose funded by the National Health Service, providing guidance for disinvestment and testing the hypothesis that advanced meter features may justify higher prices.

METHODS

Using data from the Health and Social Care Information Centre concerning all 8 340 700 self-monitoring of blood glucose-related prescriptions during 2013/2014, we conducted a cost-minimization analysis, considering both strip and lancet costs, including all clinically equivalent technologies for self-monitoring of blood glucose, as determined by the ability to meet ISO-15197:2013 guidelines for meter accuracy.

RESULTS

A total of 56 glucose monitor, test strip and lancet combinations were identified, of which 38 met the required accuracy standards. Of these, the mean (range) net ingredient costs for test strips and lancets were £0.27 (£0.14-£0.32) and £0.04 (£0.02-£0.05), respectively, resulting in a weighted average of £0.28 (£0.18-£0.37) per test. Systems providing four or more advanced features were priced equal to those providing just one feature. A total of £12 m was invested in providing 42 million self-monitoring of blood glucose tests with systems that fail to meet acceptable accuracy standards, and efficiency savings of £23.2 m per annum are achievable if the National Health Service were to disinvest from technologies providing lesser functionality than available alternatives, but at a much higher price.

CONCLUSION

The study uncovered considerable variation in the price paid by the National Health Service for self-monitoring of blood glucose, which could not be explained by the availability of advanced meter features. A standardized approach to self-monitoring of blood glucose prescribing could achieve significant efficiency savings for the National Health Service, whilst increasing overall utilisation and improving safety for those currently using systems that fail to meet acceptable standards for measurement accuracy.

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.

Can an electronic glycaemic notebook associated with an insulin calculator improve HbA1c in diabetic patients on a multiple insulin injections regimen? A 26-week observational real-life study

BACKGROUND

Automated insulin calculators (AICs) with carbohydrate counting (CHC) have been shown to be effective in improving glycated haemoglobin (HbA1c) levels. By contrast, use of AICs without CHC, with predetermined prandial insulin doses modified according to a correction factor and modulated as a function of glycaemia, has not yet been investigated.

METHODS

This comparative, retrospective, observational and non-randomized study took place over a 6-month period of routine clinical practice. It evaluated the use of Free-style InsuLinx® and Free-style Neo® Abbott Diabetes Care (AIC) in easy mode (no CHC). All patients performed a basal-prandial insulin dosing schedule, and were not educated as to how to determine carbohydrate intake. Changes in HbA1c and capillary blood glucose levels, insulin therapy, frequency of blood glucose tests and body weight were analyzed 6 months prior to inclusion (T-6), at the time of inclusion (T0) and 6 months later (T+6). From T-6 to T0 (period A), patients used a standard blood glucose meter and adjusted their insulin doses themselves, and from T0 to T+6 (period B), each patient was provided with an AIC on easy mode function.

RESULTS

Of the 230 patients, 221 were retained at the end of the study (126 type 1 diabetes mellitus (T1DM) and 95 type 2 diabetes mellitus (T2DM)). At T-6, average (±standard error of mean) HbA1c level was 8.3 ± 0.1%; T1DM: 8.5 ± 0.1% and T2DM: 8.0 ± 0.1%, respectively. At T0, the average HbA1c level was 8.4 ± 0.1% (p = 0.02); T1DM: 8.5 ± 0.1% (ns) and T2DM: 8.2 ± 0.1% (p = 0.004). At T+6, with AIC in easy mode, average HbA1c level decreased significantly to 7.7 ± 0.1% (p < 0.0001); T1DM: 8.0 ± 0.1% (p < 0.0001) and T2DM: 7.5 ± 0.1% (p < 0.0001). At T+6, in all diabetics, blood glucose monitoring frequency increased by 0.4/day (p < 0.0001). Insulin correction amounted to 14% of changes in predetermined prandial insulin doses.

CONCLUSION

Routine clinical use of an AIC without CHC improved self-management of blood glucose and on average, decreased HbA1c levels by 0.52% in T1DM and 0.80% in T2DM after 6 months.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Technology to Reduce Hypoglycemia

Hypoglycemia is a major barrier toward achieving glycemic targets and is associated with significant morbidity (both psychological and physical) and mortality. This article reviews technological strategies, from simple to more advanced technologies, which may help prevent or mitigate exposure to hypoglycemia. More efficient insulin delivery systems, bolus advisor calculators, data downloads providing information on glucose trends, continuous glucose monitoring with alarms warning of hypoglycemia, predictive algorithms, and finally closed loop insulin delivery systems are reviewed. The building blocks to correct use and interpretation of this range of available technology require patient education and appropriate patient selection.

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.

The benefits, limitations, and cost-effectiveness of advanced technologies in the management of patients with diabetes mellitus

Hypoglycemia mitigation is critical for appropriately managing patients with diabetes. Advanced technologies are becoming more prevalent in diabetes management, but their benefits have been primarily judged on the basis of hemoglobin A1c. A critical appraisal of the effectiveness and limitations of advanced technologies in reducing both A1c and hypoglycemia rates has not been previously performed. The cost of hypoglycemia was estimated using literature rates of hypoglycemia events resulting in hospitalizations. A literature search was conducted on the effect on A1c and hypoglycemia of advanced technologies. The cost-effectiveness of continuous subcutaneous insulin infusion (CSII) and real-time continuous glucose monitors (RT-CGM) was reviewed. Severe hypoglycemia in insulin-using patients with diabetes costs $4.9-$12.7 billion. CSII reduces A1c in some but not all studies. CSII improves hypoglycemia in patients with high baseline rates. Bolus calculators improve A1c and improve the fear of hypoglycemia but not hypoglycemia rates. RT-CGM alone and when combined with CSII improve A1c with a neutral effect on hypoglycemia rates. Low-glucose threshold suspend systems reduce hypoglycemia with a neutral effect on A1c, and low-glucose predictive suspend systems reduce hypoglycemia with a small increase in plasma glucose levels. In short-term studies, artificial pancreas systems reduce both hypoglycemia rates and plasma glucose levels. CSII and RT-CGM are cost-effective technologies, but their wide adoption is limited by cost, psychosocial, and educational factors. Most currently available technologies improve A1c with a neutral or improved rate of hypoglycemia. Advanced technologies appear to be cost-effective in diabetes management, especially when including the underlying cost of hypoglycemia.

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.

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.

Prandial insulin dosing using the carbohydrate counting technique in hospitalized patients with type 2 diabetes

OBJECTIVE

To compare a modified fixed meal dosing strategy to flexible meal dosing in hospitalized patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

Patients (N = 126) with refractory hyperglycemia or requiring at least 20 units of insulin per day were randomly assigned to fixed meal dosing (including withholding the dose if less than half of the meal tray was consumed) or flexible meal dosing based upon carbohydrate intake. The inpatient diabetes management team made all treatment adjustments. Outcomes included day 3 mean glucose, 72-h glucose trend analysis, hypoglycemia (<3.9 mmol/L), and inpatient diabetes treatment satisfaction.

RESULTS

The mean glucose on day 3 was 9.5 and 8.8 mmol/L in the fixed and flexible meal groups, respectively (P = 0.26). The frequency of hypoglycemia was 23 and 39% overall in the fixed and flexible meal groups (P = 0.08), with half of events occurring in the morning. There was a wide range of carbohydrate intake (median 51 g/meal, 10-90% range 26-72 g on day 3). The fixed dose group required significantly more prandial insulin overall and more correction insulin over time. There was no difference in composite treatment satisfaction or dosing miscalculations between groups.

CONCLUSIONS

A fixed meal dosing strategy provided similar glucose control as flexible meal dosing, when managed by an inpatient diabetes treatment team. However, a larger sample size would be needed to definitively evaluate a treatment effect of flexible meal dosing in the hospital. Further study is needed to improve the delivery of bolus insulin in hospitalized patients.

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.