Self-monitored Blood Glucose Readings Research Articles

Self-Monitoring Blood Glucose Method and Outcome: A Review

Self-monitoring of blood glucose (SMBG) is a valuable technique for diabetes mellitus treatment. Patients with diabetes frequently monitor their blood glucose levels in order to identify hypoglycemia and modify their insulin dosage as necessary. In many large-scale outcome studies, self-monitoring of blood glucose (SMBG) in the management of diabetes plays a vital role, contributing significantly to the outcomes. It is recommended that the patient keep track of their SMBG readings in a log book. For interpreting the SMBG findings, information regarding food intake, medication, and activity may be useful. An explanation of the practical components of the process is required to assess a patient's grasp of SMBG knowledge. For SMBG lancing treatments to be effective, the patient must have a thorough understanding of the stages involved. With many studies suggesting the benefits of SMBG other studies say that SMBG has little clinical effectiveness in improving glycemic control in patients with T2DM who are taking oral medications or eating a low-carbohydrate diet alone, and is thus unlikely to be cost-effective. However, if patients have the ability to modify their treatment dosage then it can be much more effective. In this review we will be looking at the SMBG techniques, outcomes and the relationship with glucose management.

Flash Glucose Monitoring to Assess Glycemic Control and Variability in Hemodialysis Patients: The GIOTTO Study.

Background: Flash glucose monitoring (FGM) is a technology with considerable differences compared to continuous glucose monitoring (CGM), but it has been scarcely studied in hemodialysis patients. Thus, we aimed assessing the performance of FGM in such patients by comparison to self-monitoring of blood glucose (SMBG). We will also focus on estimation of glycemic control and variability, and their relationships with parameters of glucose homeostasis.Methods: Thirty-one patients (20 with type 2 diabetes, T2DM, 11 diabetes-free, NODM) collected readings by FGM and SMBG for about 12 days on average. Readings by FGM and SMBG were compared by linear regression, Clarke error grid, and Bland-Altman analyses. Several indices of glycemic control and variability were computed. Ten patients also underwent oral glucose tolerance test (OGTT) for assessment of insulin sensitivity/resistance and insulin secretion/beta-cell function.Results: Flash glucose monitoring and SMBG readings showed very good agreement in both T2DM and NODM (on average, 97 and 99% of readings during hemodialysis in A+B Clarke regions, respectively). Some glycemic control and variability indices were similar by FGM and SMBG (p = 0.06–0.9), whereas others were different (p = 0.0001–0.03). The majority of control and variability indices were higher in T2DM than in NODM, according to both FGM and SMBG (p = 0.0005–0.03). OGTT-based insulin secretion was inversely related to some variability indices according to FGM (R < −0.72, p < 0.02).Conclusions: Based on our dataset, FGM appeared acceptable for glucose monitoring in hemodialysis patients, though partial disagreement with SMBG in glycemic control/variability assessment needs further investigations.

Effects of physician's diabetes self-management education using Japan Association of Diabetes Education and Care Diabetes Education Card System Program and a self-monitoring of blood glucose readings analyzer in individuals with type 2 diabetes: An exploratory, open-labeled, prospective randomized clinical trial.

Aims/IntroductionThis 6‐month, single‐center, prospective, open‐labeled, randomized trial was designed to investigate whether physicians’ diabetes self‐management education using an education tool developed by the Japan Association of Diabetes Education and Care and a self‐monitoring of blood glucose (SMBG) analyzer improves glycemic control in individuals with type 2 diabetes receiving insulin and SMBG.Materials and MethodsParticipants were randomized into intervention (I) and control (C) groups. Both groups received physicians’ diabetes self‐management education at each hospital visit, whereas the Japan Association of Diabetes Education and Care education tool and the SMBG readings analyzer was used in group I, but not group C. All participants filled out a diabetes treatment‐related quality of life form and an original questionnaire on SMBG use with five questions (Q1–Q5) before and after the study period.ResultsA total of 76 individuals were recruited and randomized. Glycated hemoglobin (HbA1c) was significantly improved during the study period in group I, whereas no significant change was observed in group C. The change in HbA1c was greater in group I, although it did not reach statistical significance. The diabetes treatment‐related quality of life total score was not changed in either group. Interestingly, the score of Q1 (“How important is SMBG to you?”) in the SMBG questionnaire was unchanged in group I, whereas it was significantly decreased in group C. HbA1c change was independently associated with changes in insulin dose and SMBG Q1 score.ConclusionGreater HbA1c‐lowering by physicians’ diabetes self‐management education using the Japan Association of Diabetes Education and Care education tool and SMBG analyzer in individuals with type 2 diabetes receiving insulin and SMBG was suggested, but not confirmed.

Systematic Review of Self-Monitoring of Blood Glucose in Patients With Type 2 Diabetes.

The benefit of self-monitoring of blood glucose in reducing HbA1c in non-insulin-treated participants remains unclear. HbA1c may be improved in this population with said self-monitoring. The aim of this study was to conduct meta-analyses of glycemic control in non-insulin-treated participants with Type 2 diabetes: self-monitoring of blood glucose versus usual care, structured versus unstructured self-monitoring of blood glucose, and use of self-monitoring of blood glucose readings by clinicians to adjust (or modify) therapy versus usual care. MEDLINE, Embase, and Cochrane Central were electronically searched to identify articles published from January 1, 2000, to June 30, 2020. Trials investigating changes in HbA1c were selected. Screening was performed independently by two investigators. Two investigators extracted HbA1c at baseline and follow-up for each trial. Nineteen trials involving 4,965 participants were included. Overall, self-monitoring of blood glucose reduced HbA1c. Preplanned subgroup analysis showed that using self-monitoring of blood glucose readings to adjust therapy contributed significantly to the reduction. No significant improvement in HbA1c was shown in self-monitoring of blood glucose without therapy adjustment. The same difference was observed in structured versus unstructured self-monitoring of blood glucose. HbA1c is improved with clinician therapy modification based on structured self-monitoring of blood glucose readings. Implications are for clinicians to prescribe structured self-monitoring of blood glucose to modify therapy based on the readings and not prescribe unstructured self-monitoring of blood glucose. Participants with suboptimal glycemic control may benefit most. A self-monitoring of blood glucose regimen that improves clinical and cost-effectiveness is presented. Future studies can investigate this regimen specifically.

A Deep Neural Network Application for Improved Prediction of [Formula: see text] in Type 1 Diabetes.

[Formula: see text] is a primary marker of long-term average blood glucose, which is an essential measure of successful control in type 1 diabetes. Previous studies have shown that [Formula: see text] estimates can be obtained from 5-12 weeks of daily blood glucose measurements. However, these methods suffer from accuracy limitations when applied to incomplete data with missing periods of measurements. The aim of this article is to overcome these limitations improving the accuracy and robustness of [Formula: see text] prediction from time series of blood glucose. A novel data-driven [Formula: see text] prediction model based on deep learning and convolutional neural networks is presented. The model focuses on the extraction of behavioral patterns from sequences of self-monitored blood glucose readings on various temporal scales. Assuming that subjects who share behavioral patterns have also similar capabilities for diabetes control and resulting [Formula: see text], it becomes possible to infer the [Formula: see text] of subjects with incomplete data from multiple observations of similar behaviors. Trained and validated on a dataset, containing 1543 real world observation epochs from 759 subjects, the model has achieved the mean absolute error of 4.80 [Formula: see text]mmol/mol, median absolute error of 3.81 [Formula: see text]mmol/mol and [Formula: see text] of 0.71 ± 0.09 on average during the 10 fold cross validation. Automatic behavioral characterization via extraction of sequential features by the proposed convolutional neural network structure has significantly improved the accuracy of [Formula: see text] prediction compared to the existing methods.

927-P: Use of Mobile-Enabled Reminders Feature Is Associated with Improved Behavioral and Glycemic Outcomes in the Real World

In the real-world, lifestyle and environmental barriers can adversely impact self-management of diabetes. Emerging health technologies such as mobile diabetes management apps can potentially help people with diabetes navigate such barriers with features like reminders for self-monitoring blood glucose (SMBG) checks and medications. While such features are designed to help people with diabetes improve the structure and adherence of SMBG and medication regimens, real-world evidence of their effectiveness have been inconclusive. In the current study, we assessed whether the use of reminders features corresponded with improved behavioral and glycemic outcomes in the real world. We compared the distributions of SMBG check rates, and low (&amp;lt;70 mg/dL), in-range (between 70-180 mg/dL), and high SMBG readings (&amp;gt;180 mg/dL) between users of a mobile diabetes management app who had set up reminders and app users who did not (n=500 in each sample, balanced by age, gender, and app use duration). Two-sample Kolmogorov-Smirnov tests showed that app users with reminders performed more frequent SMBG checks (D=.20, P&amp;lt;.001), had more in-range readings (D=.096, P=.020), and had fewer high readings (D=.086, P=.049) than users without reminders. No difference was observed in the distributions of low readings, though a trend may be present (D=.084, P=.059). These findings indicate that mobile-enabled reminders can contribute to improved SMBG behavior and glycemic outcomes in the real world. As mobile health/digital therapeutic tools (and their ever-growing features/capabilities) continue to evolve, patients and clinicians have reason for optimism that such tools can improve diabetes management and reduce burden. Disclosure R. Abad Juan: Employee; Self; Glooko, Inc. T. Sheng: Employee; Self; Glooko, Inc. S. Babikian: None. M. Greenfield: Stock/Shareholder; Self; Glooko, Inc.

Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring.

While A1C is well established as an important risk marker for diabetes complications, with the increasing use of continuous glucose monitoring (CGM) to help facilitate safe and effective diabetes management, it is important to understand how CGM metrics, such as mean glucose, and A1C correlate. Estimated A1C (eA1C) is a measure converting the mean glucose from CGM or self-monitored blood glucose readings, using a formula derived from glucose readings from a population of individuals, into an estimate of a simultaneously measured laboratory A1C. Many patients and clinicians find the eA1C to be a helpful educational tool, but others are often confused or even frustrated if the eA1C and laboratory-measured A1C do not agree. In the U.S., the Food and Drug Administration determined that the nomenclature of eA1C needed to change. This led the authors to work toward a multipart solution to facilitate the retention of such a metric, which includes renaming the eA1C the glucose management indicator (GMI) and generating a new formula for converting CGM-derived mean glucose to GMI based on recent clinical trials using the most accurate CGM systems available. The final aspect of ensuring a smooth transition from the old eA1C to the new GMI is providing new CGM analyses and explanations to further understand how to interpret GMI and use it most effectively in clinical practice. This Perspective will address why a new name for eA1C was needed, why GMI was selected as the new name, how GMI is calculated, and how to understand and explain GMI if one chooses to use GMI as a tool in diabetes education or management.

Efficacy and Safety of Dapagliflozin in Patients with Inadequately Controlled Type 1 Diabetes—DEPICT-2 Study

The DEPICT-2 trial (NCT02460978) evaluated the efficacy and safety of dapagliflozin (DAPA) as add-on to adjustable insulin (INS) in patients (pts) with inadequately controlled T1D (A1c 7.5-10.5%) over 24 weeks. This phase 3 study randomized pts 1:1:1 to DAPA 5 mg (n=271), 10 mg (n=270) or placebo (PBO; n=272) plus INS. INS dose could be adjusted by the investigator according to self-monitored blood glucose readings, local guidance and individual circumstances. At Week 24, DAPA 5 and 10 mg significantly decreased A1c (0.37% and 0.42% reductions in the DAPA 5 mg and 10 mg groups respectively), total daily insulin dose (TDD), and body weight (Table). As measured by masked continuous glucose monitoring (CGM), mean interstitial glucose, mean amplitude of glucose excursion (MAGE) and mean percent of readings within target glycemic range (>70-≤180 mg/dL) vs. PBO were improved. There was an increase in pts who reduced their A1c by ≥0.5% without severe hypoglycemia (odds ratios [95% CI]: 2.71 [1.81, 4.06] and 3.07 [2.05, 4.60] for DAPA 5 and 10 mg respectively). Hypoglycemic events, including severe hypoglycemia were balanced between treatment groups. There were more adjudicated definite diabetic ketoacidosis (DKA) events on DAPA. In conclusion, DAPA vs. PBO as add-on to INS in pts with T1D was well tolerated, improved glycemic control and decreased variability without increasing hypoglycemia but with more DKA events. Disclosure C. Mathieu: Research Support; Self; Novo Nordisk A/S. Advisory Panel; Self; Novo Nordisk A/S. Speaker9s Bureau; Self; Novo Nordisk A/S. Research Support; Self; Sanofi. Speaker9s Bureau; Self; Sanofi. Advisory Panel; Self; Sanofi. Research Support; Self; Merck Sharp & Dohme Corp.. Speaker9s Bureau; Self; Merck Sharp & Dohme Corp.. Advisory Panel; Self; Merck Sharp & Dohme Corp.. Research Support; Self; Eli Lilly and Company. Speaker9s Bureau; Self; Eli Lilly and Company. Advisory Panel; Self; Eli Lilly and Company. Research Support; Self; Novartis AG. Speaker9s Bureau; Self; Novartis AG. Advisory Panel; Self; Novartis AG, Bristol-Myers Squibb Company. Speaker9s Bureau; Self; AstraZeneca. Advisory Panel; Self; AstraZeneca, Pfizer Inc., Janssen Pharmaceuticals, Inc., Boehringer Ingelheim GmbH. Speaker9s Bureau; Self; Boehringer Ingelheim GmbH. Advisory Panel; Self; Hanmi Pharmaceutical. Research Support; Self; Roche Diagnostics Corporation. Advisory Panel; Self; Roche Diagnostics Corporation. Research Support; Self; Medtronic. Advisory Panel; Self; Medtronic, MannKind Corporation. Research Support; Self; Intrexon. Advisory Panel; Self; Intrexon, Dianax, UCB, Inc.. Research Support; Self; Abbott. P. Dandona: Advisory Panel; Self; AstraZeneca. Consultant; Self; AstraZeneca. Research Support; Self; AstraZeneca. P. Gillard: None. P.A. Senior: Consultant; Self; Abbott, AstraZeneca. Research Support; Self; AstraZeneca, Prometic Life Sciences Inc., Novo Nordisk Inc., Sanofi. Consultant; Self; Eli Lilly and Company. Speaker9s Bureau; Self; Eli Lilly and Company, Novo Nordisk Inc.. Consultant; Self; Novo Nordisk Inc., Janssen Pharmaceuticals, Inc.. Speaker9s Bureau; Self; Janssen Pharmaceuticals, Inc.. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc.. Speaker9s Bureau; Self; AstraZeneca, Merck & Co., Inc., Abbott. Research Support; Self; Viacyte, Inc.. C. Hasslacher: None. E. Araki: Speaker9s Bureau; Self; Astellas Pharma US, Inc., MSD K.K., Kowa Pharmaceuticals America, Inc., Sanofi, Novo Nordisk Inc.. Research Support; Self; Astellas Pharma US, Inc., MSD K.K., Ono Pharmaceutical Co., Ltd., Shionogi & Co., Ltd., Takeda Pharmaceuticals U.S.A., Inc., Daiichi Sankyo Company, Limited, Nippon Boehringer Ingelheim Co. Ltd., Novartis Pharma K.K., Novo Nordisk Inc., Sanofi, Mitsubishi Tanabe Pharma Corporation, Sumitomo Dainippon Pharma Co., Ltd., Taisho Toyama?Pharmaceutical Co. M. Lind: Consultant; Self; AstraZeneca. Research Support; Self; AstraZeneca. Consultant; Self; Novo Nordisk Inc.. Advisory Panel; Self; Novo Nordisk Inc.. Research Support; Self; Novo Nordisk Inc.. Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc.. Consultant; Self; Eli Lilly and Company. Advisory Panel; Self; MSD K.K.. Research Support; Self; Pfizer Inc.. Consultant; Self; Medtronic. S.C. Bain: Research Support; Self; Novo Nordisk Inc., AstraZeneca. S. Jabbour: None. N. Arya: Employee; Self; AstraZeneca. F.A. Thoren: Employee; Self; AstraZeneca. A. Langkilde: Employee; Self; AstraZeneca. Stock/Shareholder; Self; AstraZeneca.

Five Simple Steps to Optimize Bolus Insulin Therapy in Type 2 Diabetes.

The chronic consequences of hyperglycemia in diabetes are well documented. There is evidence that aggressive glycemic control is beneficial in certain populations. Therapy optimization occurs when ineffective medications are adjusted or new medications are added to achieve target glucose levels with minimal adverse events, specifically, hypoglycemia. Therapy selection and glycemic goals vary depending on diabetes type, duration, and comorbidities. Insulin is paramount in reaching A1C goals for many patients with type 2 diabetes. Relying on sliding-scale insulin, a method in which the patient reactively takes a predetermined number of units of bolus insulin per blood glucose level in response to hyperglycemic self-monitored blood glucose readings, as an effective means of controlling blood glucose is not supported by evidence-based medicine. Often, basal insulin alone is not sufficient because of declining β-cell function. Establishing a basal-bolus insulin regimen can achieve glycemic control even with daily fluctuations in carbohydrate intake. There are numerous anecdotal methods for initiating such therapy, but no consensus has developed. Health care providers must review recent literature on methods of optimizing bolus insulin to assist their patients in reaching glycemic targets and reducing the risk of hypoglycemia. Five simple steps to optimize bolus insulin therapy, as discussed below and summarized in Table 1, may provide people with diabetes and their health care teams with a guide for success. View this table: TABLE 1. Five Steps of Bolus Insulin Optimization In the landmark U.K. Prospective Diabetes Study (UKPDS), people with newly diagnosed type 2 diabetes in the intensive therapy arm were treated with sulfonylurea, metformin, or insulin and attained an A1C of 7% compared to 7.9% with standard therapy. Obtaining a lower A1C reduced microvascular complications by 25% over a median period of 11 years (1). Additionally, a long-term cohort follow-up demonstrated reduced cardiovascular events in the intensive therapy group (2). Results from …

Impact of Self-monitoring of Blood Glucose Log Reliability on Long-term Glycemic Outcomes in Children with Type 1 Diabetes.

Introduction:Logbooks of self-monitoring of blood glucose (SMBG) are useful in the modulation of insulin regimens, which aid in achieving glycemic control in type 1 diabetes mellitus (T1DM). However, discrepancies in SMBG charting may impede its utility. This study aimed to assess the accuracy of log entries and its impact on long-term glycemic control.Methods:SMBG in logbooks was compared with readings in glucometer memory and discrepancies between the two were evaluated in 101 children with T1DM. The relationship between these discrepancies and glycated hemoglobin (HbA1c) over 44 months was assessed.Results:Errors in glucose charting were observed in 32.67% children. The most common observed error was omission (42.42%), followed by fabrication (27.27%), erroneous (18.18%), and others (12.12%). Age was not significantly different among children having accurate versus inaccurate SMBG logs. During follow-up of 44 months, children with accurate SMBG logs consistently had lower HbA1c as compared to children having inaccurate logs, which was statistically significant at 4, 16, 20, and 28 months' follow-up. The same was reflected in the proportion of children achieving HbA1c <7% and 7%–9%. Of the 14 children who had omissions, 9 had omission of high values only, 3 patients had omission of low values only, 1 had omission of both high and low values, and 1 had omission of normal values. Among logs with fabrication, parents were responsible in 2 of 9 incidents. In the remaining 7, it was the child himself/herself. Children with fabrication consistently had the highest HbA1c values among the different types of inaccurate blood glucose chartings, which was statistically significant at 32 and 36 months of follow-up.Conclusions:Reliability of SMBG logs is a significant problem among children with T1DM at our center. Children with accurate logs of SMBG readings were more likely to have better glycemic control on long-term follow-up.

Glycated Hemoglobin (HbA1c): Clinical Applications of a Mathematical Concept.

Background and purpose:Glycated hemoglobin (HbA1c) reflects the cumulative glucose exposure of erythrocytes over a preceding time frame proportional to erythrocyte survival. HbA1c is thus an areal function of the glucose-time curve, an educationally useful concept to aid teaching and clinical judgment.Methods:An ordinary differential equation is formulated as a parsimonious model of HbA1c. The integrated form yields HbA1c as an area-under-the-curve (AUC) of a glucose-time profile. The rate constant of the HbA1c model is then derived using the validated regression equation in the ADAG study that links mean blood glucose and HbA1c with a very high degree of goodness-of-fit.Results:This model has didactic utility to enable patients, biomedical students and clinicians to appreciate how HbA1c may be conceptually inferred from discrete blood glucose values using continuous glucose monitoring system (CGMS) or self-monitored blood glucose (SMBG) glucometer readings as shown in the examples. It can be appreciated how hypoglycemia can occur with rapid HbA1c decline despite poor glycemic control.Conclusions:Being independent of laboratory assay pitfalls, computed ‘virtual’ HbA1c serves as an invaluable internal consistency cross-check against laboratory-measured HbA1c discordant with SMBG readings suggestive of inaccurate/fraudulent glucometer records or hematologic disorders including thalassemia and hemoglobinopathy. This model could be implemented within portable glucometers, CGMS devices and even smartphone apps for deriving tentative ‘virtual’ HbA1c from serial glucose readings as an adjunct to measured HbA1c. Such predicted ‘virtual’ HbA1c readily accessible via glucometers may serve as feedback to modify behavior and empower diabetic patients to achieve better glycemic control.

Are late-night eating habits and sleep duration associated with glycemic control in adult type 1 diabetes patients treated with insulin pumps?

Aims/IntroductionLittle is known about the impact of sleep duration and late-night snacking on glycemic control in patients with type 1 diabetes using insulin pumps. The aim of the present study was to examine whether late-night eating habits and short sleep duration are associated with glycemic control in continuous subcutaneous insulin infusion-treated type 1 diabetic patients.Materials and MethodsWe included 148 consecutive adult type 1 diabetic subjects using an insulin pump (100 women and 48 men). Participants completed a questionnaire regarding sleep duration (classified as short if ≤6 h) and late-night snacking. Other sources of information included medical records and data from blood glucose meters. Glycemic control was assessed by glycated hemoglobin (HbA1c) levels and mean self-monitoring of blood glucose (SMBG) readings.ResultsThe mean age of patients was 26 years, mean type 1 diabetes duration was 13.4 years and mean HbA1c level was 7.2%. In a univariate regression analysis, sleep duration was a predictor of both HbA1c (β = 0.51, P = 0.01) and SMBG levels (β = 11.4, P = 0.02). Additionally, an association was found between frequent late-night snacking and higher SMBG readings (often snacking β = 18.1, P = 0.05), but not with increased HbA1c levels. In the multivariate linear regression, independent predictors for HbA1c and SMBG were sleep duration and patient age. In a univariate logistic regression, sleep duration and frequency of late-night snacking were not predictors of whether HbA1c target levels were achieved.ConclusionsShort sleep duration, but not late-night snacking, seems to be associated with poorer glycemic control in type 1 diabetic patients treated with continuous subcutaneous insulin infusion.

Improvements in illness self-management and psychological distress associated with telemonitoring support for adults with diabetes

ObjectiveThe objective of this observational open label trial was to characterize changes in diabetes self-management and psychological distress associated with a mobile health (mHealth) interactive voice response (IVR) self-management support program. MethodsFor 3–6 months, 301 patients with diabetes received weekly IVR calls assessing health status and self-care and providing tailored pre-recorded self-management support messages. Patients could participate together with an informal caregiver who received suggestions on self-management support, and patients’ clinicians were notified automatically when patients reported significant problems. ResultsPatients completed 84% of weekly calls, providing 5682 patient-weeks of data. Thirty-nine percent participated with an informal caregiver. Outcome analyses adjusted for study design factors and sociodemographics indicated significant pre–post improvement in medication adherence, physical functioning, depressive symptoms, and diabetes-related distress (all p values <0.001). Analyses of self-management problems indicated that as the intervention proceeded, there were significant improvements in patients’ IVR-reported frequency of weekly medication adherence, SMBG performance, checking feet, and frequency of abnormal self-monitored blood glucose readings (all p values <0.001). ConclusionsWe conclude that the combined program of automated telemonitoring, clinician notification, and informal caregiver involvement was associated with consistent improvements in medication adherence, diabetes self-management behaviors, physical functioning, and psychological distress. A randomized controlled trial is needed to verify these encouraging findings.

Value and Utility of Self-Monitoring of Blood Glucose in Non-Insulin-Treated Patients With Type 2 Diabetes Mellitus

Self-monitoring of blood glucose (SMBG) levels provides important information regarding glycemic control for patients with diabetes, and is recommended by European and American diabetes organizations as an essential adjunct to periodic glycated hemoglobin (HbA) level monitoring. The benefits of SMBG in improving glycemic control in patients with type 1 diabetes and those with type 2 diabetes (T2DM) who are being treated with insulin are well recognized. In contrast, the potential role of SMBG in patients with T2DM not treated with insulin remains controversial, which may lead to underutilization of SMBG in this population. Structured SMBG, introduced aspart of a treatment intervention, has been associated with modest but significant improvements in HbA1c levels in patients with T2DM who are not taking insulin as part of their management plan. Patient-obtained readings provide valuable real-time feedback on glucose responses to meals and exercise, and provide the patient with guidance on the day-to-day management of their diabetes. Studies have shown that when patients perform self-monitoring as part of their treatment interventions, support through appropriate educational initiatives is critical to ensure that patients understand the rationale for SMBG. Patients should be trained in correct testing technique and data recording for SMBG, as well as target blood glucose and goal HbA1c levels so that they will know when their SMBG readings are out of range. Technology has a potential role in facilitating SMBG-based interventions by improving patient-physician communication and optimizing glycemic control through the use of remote data uploading, data analysis tools, and, perhaps, even text messaging. This review outlines the benefits of SMBG in the management of patients with T2DM not treated with insulin, and highlights strategies for improving the effectiveness of SMBG-based treatment interventions in this population.

Performance Variability of Seven Commonly Used Self-Monitoring of Blood Glucose Systems: Clinical Considerations for Patients and Providers

Blood glucose data are frequently used in clinical decision making, thus it is critical that self-monitoring of blood glucose (SMBG) systems consistently provide accurate results. Concerns about SMBG accuracy have prompted the development of newly proposed International Organization for Standardization (ISO) standards: ≥ 95% of individual glucose results shall fall within ± 15 mg/dl of the results of the manufacturer's reference procedure at glucose concentrations <100 mg/dl and within ± 15% for values ≥ 100 mg/dl. We evaluated seven marketed systems against the current and proposed ISO criteria (criterion A). Capillary blood samples were collected from 100 subjects and tested on seven systems: Accu-Chek Aviva Plus, Advocate Redi-Code, Element, Embrace, Prodigy Voice, TRUEbalance, and WaveSense Presto. Results were compared with manufacturer's documented reference system, YSI or perchloric acid hexokinase; three different strip lots from each system were tested on each subject, in duplicate. Compared against current ISO criteria (≥ 95% within ± 15 mg/dl for values <75 mg/dl and ± 20% for values ≥ 75 mg/dl) the Accu-Chek Aviva Plus, Element, and WaveSense Presto systems met accuracy criteria. However, only the Accu-Chek Aviva Plus met the proposed ISO criteria (criterion A) in all three lots. The other six systems failed to meet the criteria in at least two of the three lots, showing lot-to-lot variability, high/low bias, and variations due to hematocrit. Inaccurate SMBG readings can potentially adversely impact clinical decision making and outcomes. Clinicians can reduce controllable variables by prescribing accurate SMBG systems. Adherence to the proposed ISO criteria should enhance patient safety by improving the accuracy of SMBG systems.

Utilization of a Reminder Mailing to Improve Blood Glucose Log Reporting in an Outpatient Diabetes Clinic

Self-monitored blood glucose (SMBG) offers a strategy used to achieve glycemic control in diabetic patients. However, if SMBG readings are unavailable to clinicians, this strategy will have a limited effect. This study assessed the impact of a reminder mailing on response rates to requests for SMBG logs. Patients were asked to mail completed SMBG logs to the clinic in 2 weeks. For the intervention, a reminder mailing was sent to each patient 1 week before SMBG logs were to be returned. Compliance rates pre and postinterventions were compared. The primary outcome was the percentage of all SMBG logs returned on time. Secondary outcomes included the percentage of SMBG logs returned, percentage fulfilled, percentage of clinic appointments kept, percentage of SMBG logs brought to follow-up appointments, and number of interventions made to antidiabetic therapy. Twenty SMBG requests were made in the preintervention cohort versus 19 in postintervention cohort. A trend toward more on time and fulfilled SMBG requests was observed post vs. preintervention. Overall return rates were similar between groups. A nonsignificant increase in clinic appointments kept and a nonsignificant decrease in interventions made were observed postintervention. Receipt of a reminder mail was not a significant predictor of patients bringing an SMBG log to follow-up appointments. In conclusion, the use of a reminder mail was not associated with an increase in the return rate of SMBG logs, although there were nonsignificant trends toward more on time and fulfilled SMBG logs received during the postintervention period.

Combination exenatide–sitagliptin therapy used with glipizide in a patient with type 2 diabetes mellitus

The case of a patient with type 2 diabetes mellitus who received combination exenatide-sitagliptin with glipizide is reported. A 55-year-old, 204-lb Caucasian woman arrived at a clinic with polydipsia. Her blood glucose concentration was 450 mg/dL and her glycosylated hemoglobin (HbA(1c)) value was 13.4%. She was diagnosed with type 2 diabetes mellitus and started on metformin hydrochloride 500 mg orally twice daily. Metformin was later discontinued due to elevated liver function test values. Sitagliptin 100 mg daily was substituted, and glipizide was later added and its dosage adjusted over the next several months. After six months, her HbA(1c) value had decreased to 9.3% and she had gained 14 lb. Exenatide was then added to her regimen, and the dosage was adjusted to 10 μg subcutaneously twice daily. Two months after the initiation of sitagliptin, glipizide, and exenatide, the patient had lost 10 lb, reported significant improvements in self-monitored blood glucose readings, and required a reduction in glipizide dosage despite no reported therapeutic lifestyle changes. Seven months after the initiation of exenatide, sitagliptin, and glipizide, her HbA(1c) value was 7.4%. Triple therapy resulted in a total HbA(1c) value reduction of 1.9%, a weight loss of 11 lb, and normalized liver function test values. The patient's high blood pressure was treated with losartan and remained at goal throughout the duration of this report. In a patient with type 2 diabetes mellitus, the addition of the incretin mimetic exenatide and the dipeptidyl peptidase-4 inhibitor sitagliptin to glipizide therapy appeared effective and safe.

An Innovative Approach for Predicting Severe Hypoglycemia

Cox DJ, Gonder-Frederick L, Ritterband L, Clarke W, Kovatachev BP: Prediction of severe hypoglycemia. Diabetes Care 30:1370–1373, 2007 Objective. Research demonstrates the importance of achieving near-normal glycemia in the prevention of diabetes-related complications.1 However, in an effort to attain euglycemia, one runs the risk for hypoglycemia.2 Severe hypoglycemia (SH), a low blood glucose resulting in stupor, seizure, or unconsciousness, prohibits self-treatment. To prevent SH, one must be able to anticipate when an event may occur to initiate treatment steps or identify warning signs and take action to stop further progression. The objective of this study was to test methods for predicting SH by using blood glucose meter readings. Design and methods. One hundred adults with type 1 diabetes and 79 insulin-using adults with type 2 diabetes participated in this study. Their self-monitoring of blood glucose (SMBG) readings were stored on memory meters, and the patients were asked biweekly about the occurrence of SH. Results. Relative risk (RR) for SH, quantified by the ratio of an individual's low blood glucose index (LBGI) based on the previous 150 SMBG readings to the LBGI based on recent SMBG readings, increased significantly in the 24-hour period before SH episodes in individuals with either type 1 or type 2 diabetes. An algorithm detected 58% of impending SH episodes when three SMBG readings …

A survey of blood glucose monitoring in patients with type 2 diabetes: are recommendations from health care professionals being followed?

Objective:To survey the self-reported use of self-monitoring of blood glucose (SMBG) among patients with type 2 diabetes (T2DM), both insulin users (IUs) and non-insulin users (NIUs), in the United States and to examine: how often patients test; what SMBG instructions patients report receiving from their health care providers (HCPs); how the frequency of testing conforms with reported HCP recommendations for testing; and what is done with the results of testing. Differences between IUs and NIUs were also investigated.Methods:A convenience sample of 886 T2DM participants at a series of one-day conferences across the United States completed a survey on current and recommended SMBG frequency, how SMBG results were used, and how HCPs reportedly talked about SMBG issues with the patient. IUs (65% of the sample) and NIUs (35%) were examined separately.Results:IUs and NIUs reported testing significantly less frequently than was recommended (in both cases, p < 0.001), with wide variations within both groups. Many IUs (42%) and NIUs (50%) did not bring SMBG data regularly to medical visits, and 54% of IUs and 56% of NIUs did not respond regularly to out-of-range SMBG readings. HCPs were generally supportive and responsive to SMBG data. More frequent SMBG was associated with more regular HCP attention to SMBG records, for IUs (p = 0.02) and NIUs (p = 0.004).Conclusions:Self-reported SMBG use is common in T2DM, though frequency is lower than HCP recommendations. Wide variations in actual and recommended SMBG were observed. HCP support for SMBG is reportedly common, and is associated with greater SMBG frequency. While SMBG data can be valuable, recommendations are often not followed and data often goes unused by both HCPs and patients.

Impact of High Altitudes on Glucose Control

Airplane flight results in exposure to hypobaric conditions. At cruising altitude, usually 10,000 to 13,000 meters (∼30,000 to 42,000 feet), the pressure in the cabin is 0.75 atm, which is 75% of the pressure measured at sea level. The difference in pressure between 1.0 and 0.75 atm is what passengers experience as temporary ear discomfort at take-off and landing What does elevation have to do with diabetes then? It would be anticipated that people with diabetes travel as frequently as everybody else. It has been described in the literature1 that some glucose meters are affected with changes in altitude. The glucometers studied underestimated glucose levels by approximately 1–2% for each 300 meters/1000 feet of elevation after correlation for changes in temperature and humidity. The lower the oxygen partial pressure, the lower the glucose value. Most glucometers use glucose-oxygenase methods, which are dependent on oxygen in the surrounding environment.2 The partial pressure of oxygen in the airplane cabin is 16 kPa compared to 21 kPa at sea level, thus there is a risk that a glucometer can show a false hypo- or normoglycemia while in the air, when the true value in fact could be higher. These effects of high altitudes do not apply only to flying conditions. There are many cities around the world situated at high altitudes: Colorado Springs, United States, 1840 meters (∼6040 feet); Mexico City, Mexico, 2200 meters (∼7220 feet), and La Paz, Bolivia, 3640 meters (∼11,940 feet). It might be that people with diabetes living in these cities are taking much lower insulin doses than necessary because of false, low self-monitoring of blood glucose readings and therefore have a higher HbA1c level compared to a similar population living close to sea level. For instance a comparison between residents of Colorado Springs and Boston would be interesting to see. Hypoglycemia has been reported, in connection to flying, in people with insulin-treated diabetes using continuous subcutaneous insulin infusion.3 This may be due to trapped air within the ampule/infusion set or related to a direct effect on the pump's insulin delivery system, but this has not been completely investigated. With the increasing use of continuous glucose monitoring (CGM), especially in sensor-augmented pumps,4 it is of importance that sensor systems as well as insulin pumps be evaluated in various pressure conditions.5 The glucose oxygenase method is used by CGM systems and by most glucometers. However, the accuracy and performance of one CGM system was minimally affected when challenged under hypobaric conditions at 0.5 and 0.75 atm.6 The Food and Drug Administration has advocated that all sensors being used for glucose sensing be evaluated under relevant pressure conditions. What then are the implications for our patients with diabetes? It is commonly suggested to refrain from calibrating a CGM during flights. This should be done prior to take-off and/or after landing; however, this has not been investigated. Further studies are needed to assess accuracy and performance of both CGM systems as well as insulin pumps of patients with diabetes under in-flight conditions.