Journal of Diabetes Science and Technology

Background: Individuals with type 1 diabetes (T1D) have to count the carbohydrates (CHOs) of their meal to estimate the prandial insulin dose needed to compensate for the meal’s effect on blood glucose levels. CHO counting is very challenging but also crucial, since an error of 20 grams can substantially impair postprandial control. Method: The GoCARB system is a smartphone application designed to support T1D patients with CHO counting of nonpacked foods. In a typical scenario, the user places a reference card next to the dish and acquires 2 images with his/her smartphone. From these images, the plate is detected and the different food items on the plate are automatically segmented and recognized, while their 3D shape is reconstructed. Finally, the food volumes are calculated and the CHO content is estimated by combining the previous results and using the USDA nutritional database. Results: To evaluate the proposed system, a set of 24 multi-food dishes was used. For each dish, 3 pairs of images were taken and for each pair, the system was applied 4 times. The mean absolute percentage error in CHO estimation was 10 ± 12%, which led to a mean absolute error of 6 ± 8 CHO grams for normal-sized dishes. Conclusion: The laboratory experiments demonstrated the feasibility of the GoCARB prototype system since the error was below the initial goal of 20 grams. However, further improvements and evaluation are needed prior launching a system able to meet the inter- and intracultural eating habits.

Background: Imprecise carbohydrate counting as a measure to guide the treatment of diabetes may be a source of errors resulting in problems in glycemic control. Exact measurements can be tedious, leading most patients to estimate their carbohydrate intake. In the presented pilot study a smartphone application (BE_AR), that guides the estimation of the amounts of carbohydrates, was used by a group of diabetic patients. Methods: Eight adult patients with diabetes mellitus type 1 were recruited for the study. At the beginning of the study patients were introduced to BE_AR in sessions lasting 45 minutes per patient. Patients redraw the real food in 3D on the smartphone screen. Based on a selected food type and the 3D form created using BE_AR an estimation of carbohydrate content is calculated. Patients were supplied with the application on their personal smartphone or a loaner device and were instructed to use the application in real-world context during the study period. For evaluation purpose a test measuring carbohydrate estimation quality was designed and performed at the beginning and the end of the study. Results: In 44% of the estimations performed at the end of the study the error reduced by at least 6 grams of carbohydrate. This improvement occurred albeit several problems with the usage of BE_AR were reported. Conclusions: Despite user interaction problems in this group of patients the provided intervention resulted in a reduction in the absolute error of carbohydrate estimation. Intervention with smartphone applications to assist carbohydrate counting apparently results in more accurate estimations.

We present snap-n-eat, a mobile food recognition system. The system can recognize food and estimate the calorific and nutrition content of foods automatically without any user intervention. To identify food items, the user simply snaps a photo of the food plate. The system detects the salient region, crops its image, and subtracts the background accordingly. Hierarchical segmentation is performed to segment the image into regions. We then extract features at different locations and scales and classify these regions into different kinds of foods using a linear support vector machine classifier. In addition, the system determines the portion size which is then used to estimate the calorific and nutrition content of the food present on the plate. Previous approaches have mostly worked with either images captured in a lab setting, or they require additional user input (eg, user crop bounding boxes). Our system achieves automatic food detection and recognition in real-life settings containing cluttered backgrounds. When multiple food items appear in an image, our system can identify them and estimate their portion size simultaneously. We implemented this system as both an Android smartphone application and as a web service. In our experiments, we have achieved above 85% accuracy when detecting 15 different kinds of foods.

Background: Diabetes self-management education is an essential element of diabetes care. Systems based on information and communication technology (ICT) for supporting lifestyle modification and self-management of diabetes are promising tools for helping patients better cope with diabetes. An earlier study had determined that diet improved and HbA1c declined for the patients who had used DialBetics during a 3-month randomized clinical trial. The objective of the current study was to test a more patient-friendly version of DialBetics, whose development was based on the original participants’ feedback about the previous version of DialBetics. Method: DialBetics comprises 4 modules: data transmission, evaluation, exercise input, and food recording and dietary evaluation. Food recording uses a multimedia food record, FoodLog. A 1-week pilot study was designed to determine if usability and compliance improved over the previous version, especially with the new meal-input function. Results: In the earlier 3-month, diet-evaluation study, HbA1c had declined a significant 0.4% among those who used DialBetics compared with the control group. In the current 1-week study, input of meal photos was higher than with the previous version (84.8 ± 13.2% vs 77.1% ± 35.1% in the first 2 weeks of the 3-month trial). Interviews after the 1-week study showed that 4 of the 5 participants thought the meal-input function improved; the fifth found input easier, but did not consider the result an improvement. Conclusions: DialBetics with FoodLog was shown to be an effective and convenient tool, its new meal-photo input function helping provide patients with real-time support for diet modification.

The goal of modern diabetes treatment is to a large extent focused on self-management to achieve and maintain a healthy, low HbA1c. Despite all new technical diabetes tools and support, including advanced blood glucose meters and insulin delivery systems, diabetes patients still struggle to achieve international treatment goals, that is, HbA1c < 7.5 in children and adolescents. In this study we developed and tested a mobile-phone-based tool to capture and visualize adolescents’ food intake. Our aim was to affect understanding of carbohydrate counting and also to facilitate doctor–adolescent communication with regard to daily treatment. Furthermore, we wanted to evaluate the effect of the designed tool with regard to empowerment, self-efficacy, and self-treatment. The study concludes that implementing a visualization tool is an important contribution for young people to understand the basics of diabetes and to empower young people to define their treatment challenges. By capturing a picture of their own food, the person’s own feeling of being in charge can be affected and better self-treatment achieved.

Background: In an artificial pancreas (AP), the meals are either manually announced or detected and their size estimated from the blood glucose level. Both methods have limitations, which result in suboptimal postprandial glucose control. The GoCARB system is designed to provide the carbohydrate content of meals and is presented within the AP framework. Method: The combined use of GoCARB with a control algorithm is assessed in a series of 12 computer simulations. The simulations are defined according to the type of the control (open or closed loop), the use or not-use of GoCARB and the diabetics’ skills in carbohydrate estimation. Results: For bad estimators without GoCARB, the percentage of the time spent in target range (70-180 mg/dl) during the postprandial period is 22.5% and 66.2% for open and closed loop, respectively. When the GoCARB is used, the corresponding percentages are 99.7% and 99.8%. In case of open loop, the time spent in severe hypoglycemic events (<50 mg/dl) is 33.6% without the GoCARB and is reduced to 0.0% when the GoCARB is used. In case of closed loop, the corresponding percentage is 1.4% without the GoCARB and is reduced to 0.0% with the GoCARB. Conclusion: The use of GoCARB improves the control of postprandial response and glucose profiles especially in the case of open loop. However, the most efficient regulation is achieved by the combined use of the control algorithm and the GoCARB.

Background: Wearable computing has long been described as the solution to many health challenges. However, the use of this technology as a diabetes patient self-management tool has not been fully explored. A promising platform for this use is the smartwatch—a wrist-worn device that not only tells time but also provides internet connection and ability to communicate information to and from a mobile phone. Method: Over 9 months, the design of a diabetes diary application for a smartwatch was completed using agile development methods. The system, including a two-way communication between the applications on the smartwatch and mobile phone, was tested with 6 people with type 1 diabetes. A small number of participants was deliberately chosen due to ensure an efficient use of resources on a novel system. Results: The designed smartwatch system displays the time, day, date, and remaining battery time. It also allows for the entry of carbohydrates, insulin, and blood glucose (BG), with the option to view previously recorded data. Users were able to record specific physical activities, program reminders, and automatically record and transfer data, including step counts, to the mobile phone version of the diabetes diary. The smartwatch system can also be used as a stand-alone tool. Users reported usefulness, responded positively toward its functionalities, and also provided specific suggestions for further development. Suggestions were implemented after the feasibility study. Conclusions: The presented system and study demonstrate that smartwatches have opened up new possibilities within the diabetes self-management field by providing easier ways of monitoring BG, insulin injections, physical activity and dietary information directly from the wrist.

Objective: The purpose of this study was to investigate whether or not adding a fixed preprandial dose of inhaled insulin to a fully automated closed loop artificial pancreas would improve the postprandial glucose control without adding an increased risk of hypoglycemia. Research Design and Methods: Nine subjects with T1DM were recruited for the study. The patients were on closed-loop control for 24 hours starting around 4:30 pm. Mixed meals (~50 g CHO) were given at 6:30 pm and 7:00 am the following day. For the treatment group each meal was preceded by the inhalation of one 10 U dose of Technosphere Insulin (TI). Subcutaneous insulin delivery was controlled by a zone model predictive control algorithm (zone-MPC). At 11:00 am, the patient exercised for 30 ± 5 minutes at 50% of predicted heart rate reserve. Results: The use of TI resulted in increasing the median percentage time in range (70-180 mg/dl, BG) during the 5-hour postprandial period by 21.6% (81.6% and 60% in the with/without TI cases, respectively, P = .06) and reducing the median postprandial glucose peak by 33 mg/dl (172 mg/dl and 205 mg/dl in the with and without TI cases, respectively, P = .004). The median percentage time in range 80-140 mg/dl during the entire study period was 67.5% as compared to percentage time in range without the use of TI of 55.2% (P = .03). Conclusions: Adding preprandial TI (See video supplement) to an automated closed-loop AP system resulted in superior postprandial control as demonstrated by lower postprandial glucose exposure without addition hypoglycemia.

Background: Oxidative stress is a detrimental feature of diabetes implicated in the progression of the disease and its complications. The relationship between insulin therapy and oxidative stress is complex. This study tested the hypothesis that improved glucose control, rather than insulin dose, is central to reduced oxidative stress in patients with type 2 diabetes following continuous subcutaneous insulin infusion (CSII). Methods: In this 16-week, multicenter study, 54 CSII-naïve patients with type 2 diabetes (age 57 ± 10 years, HbA1c 69 ± 15 mmol/mol [8.5 ± 1.4%], diabetes duration 13 ± 6 years) treated with either oral antidiabetic agents (OAD) alone (n = 17), basal insulin ± OAD (n = 17), or multiple daily injections (MDI) ± OAD (n = 20) were the evaluable group. Diabetes medications except metformin were discontinued, and 16 weeks of CSII was initiated. Insulin dose was titrated to achieve optimal glycemic control. A plasma marker of oxidative stress relevant to cardiovascular disease (oxidized low density lipoprotein [ox-LDL]) was assessed at baseline and week 16. Results: CSII improved glycemic control (HbA1c −13 ± 2 mmol/mol [−1.2 ± 0.2%]; fasting glucose −36.6 ± 8.4 mg/dL; mean glucose excursion −23.2 ± 6.5 mg/dL, mean ± SE; all P < .001) and reduced ox-LDL (–10.5%; P < .05). The antioxidant effect was cohort-independent (P > .05), but was significantly more pronounced in patients on statins (P = .019). The effect of CSII was more closely correlated to improvements in glucose excursion (P = .013) than to insulin dose (P > .05) or reduction in HbA1c (P > .05). Conclusions: CSII induces depression of plasma ox-LDL associated with change in glucose control, rather than with change in insulin dose. The effect is augmented in patients receiving statins.

Background: The PaQ® insulin delivery system is a simple-to-use patch-on device that provides preset basal rates and bolus insulin on demand. In addition to feasibility of use, safety, and efficacy (reported elsewhere), this study analyzed the impact of PaQ on patient-reported outcomes, including barriers to insulin treatment, diabetes-related distress, and attitudes toward insulin therapy in patients with type 2 diabetes on a stable multiple daily injection (MDI) regimen. Methods: This single-center, open-label, single-arm study comprised three 2-week periods: baseline (MDI), transition from MDI to PaQ, and PaQ treatment. Validated questionnaires were administered during the baseline and PaQ treatment periods: Barriers to Insulin Treatment questionnaire (BIT), Insulin Treatment Appraisal Scale (ITAS), and Problem Areas in Diabetes scale (PAID). Results: Eighteen patients (age 59 ± 5 years, diabetes duration 15 ± 7 years, 21% female, HbA1c 7.7 ± 0.7%) completed the questionnaires. There was a strong, significant effect of PaQ use in mean BIT total scores (difference [D] = −5.4 ± 0.7.7, P = .01, effect size [d] = 0.70). Patients perceived less stigmatization by insulin injection (D = −2.2 ± 6.2, P = .18, d = 0.35), increased positive outcome (D = 1.9 ± 6.6, P = .17, d = 0.29), and less fear of injections (1.3 ± 4.8, P = .55, d = 0.28). Mean change in ITAS scores after PaQ device use showed a nonsignificant improvement of 1.71 ± 5.63 but moderate effect size (d = 0.30, P = .14). No increase in PAID scores was seen. Conclusions: The results and moderate to large effects sizes suggest that PaQ device use has beneficial and clinically relevant effects to overcoming barriers to and negative appraisal of insulin treatment, without increasing other diabetes-related distress.

Background: The carbohydrate-to-insulin ratio (CIR) is initially calculated from the total daily insulin dose (TDID). However, CIR likely presents variations owing to different population characteristics and intraday variations not being taken into account by most formulas. No information is available concerning the Mediterranean population. We investigated the CIR used by patients with type 1 diabetes (T1DM) using continuous subcutaneous insulin infusion (CSII) therapy in a Mediterranean area, to identify possible intraday variations and establish an adequate formula to calculate CIR. Methods: Data from 170 T1DM patients from Barcelona were obtained retrospectively from the Spanish National Registry of CSII Therapy (SNR-CSII). Theoretical CIR was calculated using the formula: 500 divided by TDID. This theoretical CIR was compared to the real CIR. Results: The real CIR was also compared between main meals. Patients with HbA1c < 7% (n = 44) were considered a reference group for accurate bolus calculation and were analyzed as a subgroup. The real CIR used was 11.5 g/UI for breakfast, 12 g/UI for lunch, and 13.3 g/UI for dinner. CIR obtained by the 500/TDID formula for all meals was 15.5 g/UI. We obtained similar results for the group with HbA1c < 7%. The real CIR differed significantly from the theoretical CIR values and between breakfast and the other main meals (P < .005). Conclusions: CIR in our population was significantly lower for breakfast than for other meals. CIR using the 500/TDID formula underestimated prandial insulin requirements. A calculation of 350/TDID for breakfast and 400/TDID for lunch and dinner would be more appropriate for this population.

Background: Current treatment guidelines for type 2 diabetes (T2D) recommend individualized intensification of therapy for glycated hemoglobin (A1C) ≥ 7% in most patients. The purpose of this investigation was to explore the ability of an electronic medical record (EMR) to identify glycemic intensification strategies among T2D patients receiving pharmacologic therapy. Methods: Patient records between 2005 and 2011 with documentation of A1C and active prescriptions for any diabetes medications were queried to identify potential candidates for intensification based on A1C ≥ 7% while on 1-2 oral diabetes medications (ODM). Patients with follow-up A1C values within 1 year of index A1C were grouped according to intensification with insulin, GLP-1 receptor agonists (GLP-1RA), a new class of ODM, or no intensification. Changes in A1C and continuation of intensification therapy were determined. Results: A total of 4921 patients meeting inclusion criteria were intensified with insulin (n = 416), GLP-1RA (n = 68), ODM (n = 1408), or no additional therapy (n = 3029). Patients receiving insulin had higher baseline (9.3 ± 2.0 vs 8.3 ± 1.2 vs 8.3 ± 1.3 vs 7.6 ± 1.0%, P < .0001) and follow-up A1C (8.1 ± 1.6 vs 7.5 ± 1.2 vs 7.6 ± 1.3 vs 7.2 ± 1.1%, P < .0001) despite experiencing larger absolute A1C reductions (−1.2 ± 2.1 vs −0.8 ± 1.4 vs −0.7 ± 1.4 vs −0.3 ± 1.1%, P < .0001). Patients receiving GLP-1RA were more obese at baseline (BMI: 33.6 ± 7.1 vs 37.7 ± 6.1 vs 33.7 ± 6.8 vs 32.9 ± 7.1 kg/m², P < .0001) and follow-up (BMI: 33.9 ± 7.3 vs 36.6 ± 6.1 vs 33.8 ± 7.0 vs 32.4 ± 7.0 kg/m², P < .0001) despite experiencing more absolute weight reduction. Insulin was the most and GLP-1RA the least likely therapy to be continued. Conclusions: An EMR allows identification of prescribing practices and compliance with T2D treatment guidelines. Patients receiving intensification of glycemic medications had baseline A1C >8% suggesting that treatment recommendations are not being followed.

Background: This study explores the relationship between education for inpatient diabetes providers and the utilization of insulin order sets, inpatient glucometrics, and length of stay in a large health care system. Methods: The study included patients with and without the diagnosis of diabetes. An education campaign included provider-directed diabetes education administered via online learning modules and in-person presentations by trained individuals. Relationships among provider-attended diabetes education, order set usage, and inpatient glucometrics (hypo- and hyperglycemia) were analyzed, as well as length of stay. Results: Insulin use knowledge scores for all providers averaged 52%, and improved significantly to 93% (P < .001) by the end of the education intervention period. Likewise utilization of electronic basal-bolus order sets increased from a baseline of 20% for patients receiving insulin to 86% within 6 weeks (P < .01) of introduction of order sets. During the study, the incidence of hypoglycemia and hyperglycemia declined from 1.47% to 1.27% and from 23.21% to 17.80%, respectively. However, these improvements were not sustained beyond the completion of the education campaign. Conclusions: Education of diabetes health care providers was provided in a large, multihospital system through the use of online learning modules. Adoption of standardized insulin order sets was associated with an improvement in glucometrics. This educational and quality initiative resulted in overall improvements in insulin knowledge, adherence to recommended order sets, inpatient glucometrics, and patient length of stay. These improvements were not sustained, reinforcing the need for repeated educational interventions for those involved in providing inpatient diabetes care.

While being physically active bestows many health benefits on individuals with type 1 diabetes, their overall blood glucose control is not enhanced without an effective balance of insulin dosing and food intake to maintain euglycemia before, during, and after exercise of all types. At present, a number of technological advances are already available to insulin users who desire to be physically active with optimal blood glucose control, although a number of limitations to those devices remain. In addition to continued improvements to existing technologies and introduction of new ones, finding ways to integrate all of the available data to optimize blood glucose control and performance during and following exercise will likely involve development of “smart” calculators, enhanced closed-loop systems that are able to use additional inputs and learn, and social aspects that allow devices to meet the needs of the users.

Background: Although physical exercise (PE) is recommended for individuals with type 1 diabetes (DM1), participation in exercise is challenging because it increases the risk of severe hypoglycemia and the available therapeutic options to prevent it frequently result in hyperglycemia. There is no clear recommendation about the best timing for exercise. The aim of this study was to compare the risk of hypoglycemia after morning or afternoon exercise sessions up to 36 hours postworkout. Methods: This randomized crossover study enrolled subjects with DM1, older than 18 years of age, on sensor-augmented insulin pump (SAP) therapy. Participants underwent 2 moderate-intensity exercise sessions; 1 in the morning and 1 in the afternoon, separated by a 7 to 14 day wash-out period. Continuous glucose monitoring (CGM) data were collected 24 hours before, during and 36 hours after each session. Results: Thirty-five subjects (mean age 30.31 ± 12.66 years) participated in the study. The rate of hypoglycemia was significantly lower following morning versus afternoon exercise sessions (5.6 vs 10.7 events per patient, incidence rate ratio, 0.52; 95% CI, 0.43-0.63; P < .0001). Most hypoglycemic events occurred 15-24 hours after the session. On days following morning exercise sessions, there were 20% more CGM readings in near-euglycemic range (70-200 mg/dL) than on days prior to morning exercise (P = .003). Conclusions: Morning exercise confers a lower risk of late-onset hypoglycemia than afternoon exercise and improves metabolic control on the subsequent day.

Background: The functional performance of the JuniorSTAR^® (Sanofi, Paris, France) half-unit insulin pen was evaluated through a series of specific objective tests to assess the dose accuracy, pen weight, injection force, and dialing torque. Method: Pens (n = 60) were tested under standard atmospheric conditions with 3 different types of insulins manufactured by Sanofi (insulin glargine, insulin glulisine, and biphasic insulin isophane). The dose accuracy was tested according to the ISO 11608-1:2012 standards. Injection doses of 0.010, 0.155, and 0.300 ml were evaluated. For mean weight evaluation, the pens without the cartridge were weighed on precision balances. The injection force was measured using a texture analyzer and the dialing torque was measured using a torque meter. Results: JuniorSTAR met the ISO 11608-1:2012 criteria for dose accuracy as all the delivered doses were within the predefined limits for all types of insulin tested. The mean weight of the JuniorSTAR pen was 33.4 g (SD = 0.075). The mean injection force was 6.0 N (SD = 0.8), 4.3 N (SD = 0.4), and 5.1 N (SD = 0.6) for insulin glargine, insulin glulisine, and biphasic insulin isophane, respectively. The mean dialing torque was 5.09 Ncm (SD = 0.29) and 5.88 Ncm (SD = 0.53) for setting and correcting a dose, respectively. Conclusions: Together with results from a previously reported usability survey, these results show that the JuniorSTAR reusable, half-unit pen is a lightweight and accurate device for insulin delivery with a dialing torque and injection force suitable for young people with type 1 diabetes.

Background: The study presents a comparison of the glucose-lowering effects, glycemic variability, and insulin doses during treatment with insulin degludec or insulin glargine. Methods: In this open-label, single-center, 2-way crossover study, 13 Japanese diabetic outpatients in the insulin-dependent state on basal-bolus therapy were assigned to receive either insulin glargine followed by insulin degludec, or insulin degludec followed by insulin glargine. Basal insulin doses were fixed in principle, and patients self-adjusted their bolus insulin doses. Seventy-two-hour continuous glucose monitoring was performed 2 weeks after switching the basal insulin. Results: Mean blood glucose (mg/dL) was not significantly different between insulin degludec and insulin glargine over 48 hours (141.8 ± 35.2 vs 151.8 ± 43.3), at nighttime (125.6 ± 40.0 vs 124.7 ± 50.4), or at daytime (149.3 ± 37.1 vs 163.3 ± 44.5). The standard deviation (mg/dL) was also similar (for 48 hours: 48.9 ± 19.4 vs 50.3 ± 17.3; nighttime: 18.7 ± 14.3 vs 13.7 ± 6.7; daytime: 49.3 ± 20.0 vs 44.3 ± 17.7). Other indices of glycemic control, glycemic variability, and hypoglycemia were similar for both insulin analogs. Total daily insulin dose (TDD) and total daily bolus insulin dose (TDBD) were significantly lower with insulin degludec than with insulin glargine (TDD: 0.42 ± 0.20 vs 0.46 ± 0.22 U/kg/day, P = .028; TDBD: 0.27 ± 0.13 vs 0.30 ± 0.14 U/kg/day, P = .036). Conclusions: Insulin degludec and insulin glargine provided effective and stable glycemic control. Insulin degludec required lower TDD and TDBD in this population of patients.

Background: Delays in the time-action profiles of premeal boluses of rapid-acting insulin analogs contribute to early postmeal hyperglycemia in patients with diabetes. We tested whether applying local heat to skin around the injection site to increase the rate of insulin absorption reduces postprandial hyperglycemia in patients with type 2 diabetes. Methods: Fourteen patients with type 2 diabetes (4 females; age 61.6 ± 8.4 years, HbA1c 8.42 ± 1.13%; BMI 29.10 ± 5.61 kg/m²) on intensified insulin therapy underwent 5-hour meal tolerance tests (MTTs) with a standardized liquid meal after an overnight fast on 2 study days. Subjects injected 0.2 U/kg of insulin aspart or lispro subcutaneously into the abdominal skin on both days with and without the use of the InsuPad device. Results: Following the premeal bolus injection of rapid-acting insulin analog, infusion site warming led to a rise in plasma insulin levels to peak concentrations that were significantly earlier than without skin warming (mean ± SD 52 ± 26.7 vs 80 ± 51.3 minutes, P < .005) as well as increase in plasma insulin levels during the first hour after injection (mean ± SD 63.5 ± 32.7 IU vs 48.0 ± 25.0 uU.min/ml, P = .019). As a result, the area under the curve of the postprandial glucose excursion during the first 2 hours (the primary study outcome) and the entire 5 hours after the meal were significantly reduced (P = .007 and P = .03, respectively) with skin warming around the injection site. Discussion and Conclusions: Use of the InsuPad to increase the rate of insulin absorption provides an effective means to achieve better control of postmeal glucose excursions in type 2 diabetic patients receiving premeal injections of rapid-acting insulin analogs.

Aims: The aim was to compare early discontinuation and related treatment costs in type 2 diabetes in primary care after initiation of insulin glargine or human basal insulin (NPH). Methods: Overall, 2765 glargine and 1554 NPH patients from 1072 general practices were analyzed (Disease Analyser). Early discontinuation was defined as switching to a different basal insulin or another insulin treatment regimen within 90 days after first basal insulin prescription (index date, ID). Treatment costs were assessed 365 days prior and post ID in both groups. Propensity score matching and linear regression was used to adjust cost differences (post vs prior ID: discontinued vs continued patients) for age, sex, diabetes duration, antidiabetic comedication, diabetologist care, disease management program participation, costs before ID, and Charlson Comorbidity Index. Results: Within 3 months after ID, 13% of glargine patients switched to other insulin treatment regimens (NPH: 18%; P < .05). After propensity score matching, adjusted cost differences in 146 discontinued versus 1342 continued glargine patients were calculated (NPH: 146 vs 1342). Diabetes-related prescription costs were lower among persistent glargine patients compared to persistent NPH patients (EUR–49 [19]; P = .0109). Mean cost difference for diabetes-related prescriptions was lower among those who persisted on glargine compared to those who switched to other treatment regimens (EUR–74 [42], P = .0780). Conclusions: Treatment persistence within 3 months after basal insulin initiation was significantly higher under insulin glargine compared to NPH. Diabetes-related prescription costs were significantly lower among patients who adhered to insulin glargine compared to persistent NPH patients.

Systems for continuous glucose monitoring (CGM) have been available for a number of years, and numerous clinical studies have been performed with them. Interestingly, in many of these studies patients with an increased risk of hypoglycemic events were excluded. In addition, in most studies subjects were using a pump for insulin delivery. Therefore our knowledge about the benefit of CGM in patients employing multiple daily injections (MDI) of insulin is limited, especially when it comes to a reduction in the risk of low glucose events in high-risk individuals. We are planning to run a 26-week randomized controlled study in Germany (HypoDE, Hypoglycemia in Deutschland) that is focused on evaluating if such a reduction can be observed in patients on MDI with an increased risk of low glucose events. In all, 160 patients will participate in the study, randomized into the intervention group and control group. Ideally one would study if the frequency of severe hypoglycemic events is different between both groups. However, this would require such a large sample size and study duration, so for pragmatic reasons we will use low glucose levels <55 mg/dl (measured by CGM) for at least 20 minutes as a risk marker for severe hypoglycemic events. The results from the HypoDE study shall help determine the advantage of using CGM in subjects with type 1 diabetes with an increased risk of low glucose events treated with MDI.

Background: The GluCath® intra-arterial continuous glucose monitoring (IA-CGM) system uses a novel quenched chemical fluorescence sensing mechanism to optically measure blood glucose when deployed in the radial artery. The aim of this study was to compare the accuracy of the IA-CGM and the FreeStyle Navigator® subcutaneous continuous glucose monitoring (SC-CGM) system with standard care. Methods: After admission to the intensive care unit (ICU), the IA-CGM was inserted via a 20 gauge radial arterial study catheter and the SC-CGM was placed at the abdominal wall of postoperative cardiac surgery patients with an expected ICU length of stay > 24 hours. Each device was calibrated according to manufacturer instructions. Glucose values of both CGM systems were blinded for the clinical staff. Reference blood glucose samples were collected from the study catheter every 1-2 hours for at least 24 hours and analyzed on a Radiometer ABL blood gas analyzer. Results: The IA-CGM and SC-CGM sensors were successfully inserted in 8 subjects. Accuracy assessment was performed with 183 paired points: 85.8% of the IA-CGM measurements and 84.2% of the SC-CGM measurements met ISO 15197:2003 glucometer criteria (within 20%) across a 79-248 mg/dl (4.4-13.8 mmol/L) glucose range. Overall ± SD mean absolute relative difference was 12.3 ± 11.3% for IA-CGM and 11.1 ± 8.3% for SC-CGM (difference −1.2%, 95% CI −3.3 to 0.8; P = .24). Conclusions: The IA-CGM system directly measured arterial blood glucose and did not interfere with clinical care. However, accuracy was similar to that of the less invasive SC-CGM device.

Glucose monitoring either by self-monitoring of blood glucose (SMBG) or continuous glucose monitoring (CGM) plays an important role in diabetes management and in reducing risk for diabetes-related complications. However, despite evidence supporting the role of glucose monitoring in better patient health outcomes, studies also reveal relatively poor adherence rates to SMBG and CGM use and numerous patient-reported barriers. Fortunately, some promising intervention strategies have been identified that promote at least short-term improvements in patients’ adherence to SMBG. These include education, problem solving, contingency management, goal setting, cognitive behavioral therapy, and motivational interviewing. Specific to CGM, interventions to promote greater use among patients are currently under way, yet one pilot study provides data suggesting better maintenance of CGM use in patients showing greater readiness for behavior change. The purpose of this review is to summarize the literature specific to glucose monitoring in patients with diabetes focusing specifically on current adherence rates, barriers to monitoring, and promising intervention strategies that may be ready to deploy now in the clinic setting to promote greater patient adherence to glucose monitoring. Yet, to continue to help patients with diabetes adhere to glucose monitoring, future research is needed to identify the treatment strategies and the intervention schedules that most likely lead to long-term maintenance of optimal glycemic monitoring levels.

Adoption of electronic health records (EHRs) has increased dramatically since the 2009 implementation of the Health Information Technology for Economic and Clinical Health (HITECH) Act. The latest data from the Centers for Disease Control and Prevention (CDC) indicate that the majority of U.S. hospitals and nearly half of U.S. health care professionals have implemented an EHR with advanced functionality.¹ The goals of the HITECH act were not only to incentivize the adoption of EHRs, but also to increase the quality, safety, and efficiency of health care by promoting the concept of “meaningful use.”^2,3 The stepwise implementation of “meaningful use” is now entering the latter stages with a focus on improving patient outcomes.⁴

Objective: The objective of this study was to elucidate whether the degree of autonomic modulation is associated with the degree of microvascular complications in patients with type 1 diabetes. Methods: A total of 290 type 1 individuals with diabetes were randomly recruited during normal visits to outpatient clinics at 4 Danish hospitals. The degree of autonomic modulations was quantified by measuring heart rate variability (HRV) during passive spectral analysis and active tests (valsalva ratio [VT], response to standing [RT], and deep breathing [E:I]). To describe possible associations between severity of microvascular complications and measures of autonomic modulation, multivariate analysis was performed. Results: After adjusting for diabetes duration, sex, age, pulse pressure, heart rate, and smoking, autonomic dysfunction remained significantly correlated with severity of retinopathy, nephropathy, and peripheral neuropathy in individuals with type 1 diabetes patients. Conclusions: Autonomic dysfunction is present in early stages of retinopathy, nephropathy, and peripheral neuropathy in patients with type 1 diabetes.

Hemoglobin A1c (HbA1c) measurement has come to be a cornerstone in modern diabetes therapy. However, the methodological aspects of this type of measurement have been given little attention lately due to its position as an established method of choice. Nevertheless, quite a number of issues face practical application, such as clinically relevant differences between different measurement methods—both lab-based and point-of-care (POCT) systems will show better or worse diabetes management results after switching methods; and there are a number of possible reasons that need to be known and observed in practice. The aim of this review is to draw attention to these problems from a German point of view and provide suggestions for appropriate measures to improve the situation.

HbA1c, a routinely used integrated measure of glycemic control, is traditionally thought to be equivalent to mean blood glucose in hematologically normal individuals. Therefore, particularly as the methodology of measuring HbA1c has been standardized, clinical decisions dependent on mean blood glucose are often predominantly decided based on the interpretation of measured HbA1c. In this commentary, however, now that a more routine method of measuring red cell life span has been developed, we present evidence that the relationship between HbA1c and mean blood glucose is influenced by variation in red blood cell survival even in the hematologically normal. This variation has consequences for the appropriate interpretation of HbA1c in diverse clinical conditions such as the diagnosis of diabetes and management of diabetes in chronic kidney disease.

Patients with diabetes have to take numerous factors/data into their therapeutic decisions in daily life. Connecting the devices they are using by feeding the data generated into a database/app is supposed to help patients to optimize their glycemic control. As this is not established in practice, the different roadblocks have to be discussed to open the road. That large telecommunication companies are now entering this market might be a big help in pushing this forward. Smartphones offer an ideal platform for connectivity solutions.

The reimbursement model for medical devices and supplies used in the management of diabetes in Canada, along with the process for assessing new health technologies, can be complicated. Various provincial programs, including Ontario’s Assistive Devices Program and the Ontario Monitoring for Health Program, reimburse the costs associated with certain devices and supplies for diabetes management. In addition, provincial advisory committees, such as the Ontario Health Technology Advisory Committee, review and make recommendations on the adoption of new health technologies in each province. This article provides an overview of the reimbursement programs available for diabetes devices and supplies and reviews the process for assessing new health technologies using the province of Ontario as an example.