[Evaluation on the status quo of self monitoring of blood glucose and self-efficacy of diabetes patients in community]

OBJECTIVE

To investigate the status quo and influence factors of self monitoring of blood glucose (SMBG) and self-efficacy of diabetes patients' that participated in community diabetes self management group.

METHODS

Beijing, Shanghai, Chongqing, Jiangsu, Guangdong, and Zhejiang were selected as the study sites considering patients management experiences they had. 1 401 adult diabetes patients were recruited from communities via health records system screening, telephone notification, poster advertisement, letters invitation ways. Face to face questionnaire survey was applied to obtain patients' general information, diabetes history, diabetes knowledge awareness, SMBG, and self-efficacy information. Multiple linear regression was used to analyze the relationship between factors and self efficacy.

RESULTS

There were 519 male patients (37.0%) and 882 female patients (63.0%) with an average age of (64.9 ± 8.9) years old. Patients lived in city accounted for 48.0% (672/1 401) and rural patients accounted for 52.0% (729/1 401). Patients who conducted SMBG accounted for 79.9% (1 120/1 401) and 33.3% (446/1 401) patients conducted blood glucose monitoring 1-3 times per month. Rural patients, primary school educated, and new rural cooperative medical system (NCMS) covered patients had a higher proportion of never conducting SMBG which were 21.9% (160/729), 24.2% (160/662), and 26.3% (125/475) , respectively. Scores of self-efficacy was (69.24 ± 16.30) (hundred-mark system) with a relative lower score in monitoring of blood glucose (64.09 ± 20.08) and foot care (63.63 ± 21.40), as well as a highest score in taking medicine and insulin injections (76.10 ± 22.00). Multiple regression analysis on self-efficacy and its related factors show a negative correlation between patients' place of residence and self-efficacy (β' = -0.076) and a positive correlation between education and self-efficacy (β' = 0.114) as well as between diabetes knowledge awareness and self-efficacy (β' = 0.193)(t = -2.46, 3.71, 7.18, P < 0.05).

CONCLUSION

Community diabetes patients had a low self-efficacy and it was even lower among low economic and education degree patients. The worst parts were SMBG and foot care. Place of residence, education, and diabetes knowledge awareness are factors that influence patients' self efficacy.

[Association of health literacy with health management among diabetics]

OBJECTIVE

To understand status of health literacy among diabetics and their health management behaviors, and analyze the relationship of health literacy and health management.

METHODS

A two-staged cluster randomized sampling method was used to investigate 1 130 diabetics in Beijing, Ningbo and Xiamen from October to November in 2012. All participants should be diagnosed by primary hospital and above and have lived in the community over six months. Diabetic patients who indicated that they had severely impaired vision or cognitive disorder, or had severe physical deterioration, or did not live in the address provided were excluded. A total of 1 130 questionnaires were sent out and 1 083 eligible questionnaires were taken back, accounting for 96.87%. Multivariate logistic regression was adopted to analyze the association between health literacy and health management behaviors and blood glucose level.

RESULTS

Among those participants, 47.7% (517) were men, 52.3% (566) were women, the age was (67.0 ± 9.5). According to diabetes health literacy scores, 73.7% (798/1 083) of them were classified as poor health literacy and 26.1% (283/1 083) as essential health literacy. Health literacy was associated with health management behaviors independently, demonstrating that the probability of utilizing health education, free physical examination, lifestyle guidance, monitoring blood glucose on their own, measuring blood glucose more than once a week and taking hypoglycemic agent regularly among diabetics with essential health literacy were 1.40 (95%CI:1.03-1.91), 1.65 (95%CI: 1.19-2.28), 2.70 (95%CI:1.98-3.69), 2.05 (95%CI:1.34-3.15), 2.56 (95%CI:1.85-3.56) , 1.48 (95%CI:1.07-2.06) times of those in diabetics with poor health literacy (P < 0.05).

CONCLUSION

Health literacy may affect health management behaviors among diabetics. More activities targeted on diabetics with low health literacy were suggested to improve their' health literacy and their skills about diabetes mellitus management.

The surveillance error grid

Currently used error grids for assessing clinical accuracy of blood glucose monitors are based on out-of-date medical practices. Error grids have not been widely embraced by regulatory agencies for clearance of monitors, but this type of tool could be useful for surveillance of the performance of cleared products. Diabetes Technology Society together with representatives from the Food and Drug Administration, the American Diabetes Association, the Endocrine Society, and the Association for the Advancement of Medical Instrumentation, and representatives of academia, industry, and government, have developed a new error grid, called the surveillance error grid (SEG) as a tool to assess the degree of clinical risk from inaccurate blood glucose (BG) monitors. A total of 206 diabetes clinicians were surveyed about the clinical risk of errors of measured BG levels by a monitor. The impact of such errors on 4 patient scenarios was surveyed. Each monitor/reference data pair was scored and color-coded on a graph per its average risk rating. Using modeled data representative of the accuracy of contemporary meters, the relationships between clinical risk and monitor error were calculated for the Clarke error grid (CEG), Parkes error grid (PEG), and SEG. SEG action boundaries were consistent across scenarios, regardless of whether the patient was type 1 or type 2 or using insulin or not. No significant differences were noted between responses of adult/pediatric or 4 types of clinicians. Although small specific differences in risk boundaries between US and non-US clinicians were noted, the panel felt they did not justify separate grids for these 2 types of clinicians. The data points of the SEG were classified in 15 zones according to their assigned level of risk, which allowed for comparisons with the classic CEG and PEG. Modeled glucose monitor data with realistic self-monitoring of blood glucose errors derived from meter testing experiments plotted on the SEG when compared to the data plotted on the CEG and PEG produced risk estimates that were more granular and reflective of a continuously increasing risk scale. The SEG is a modern metric for clinical risk assessments of BG monitor errors that assigns a unique risk score to each monitor data point when compared to a reference value. The SEG allows the clinical accuracy of a BG monitor to be portrayed in many ways, including as the percentages of data points falling into custom-defined risk zones. For modeled data the SEG, compared with the CEG and PEG, allows greater precision for quantifying risk, especially when the risks are low. This tool will be useful to allow regulators and manufacturers to monitor and evaluate glucose monitor performance in their surveillance programs.

Comparison between one-point calibration and two-point calibration approaches in a continuous glucose monitoring algorithm

The purpose of this study was to investigate the effect of using a 1-point calibration approach instead of a 2-point calibration approach on the accuracy of a continuous glucose monitoring (CGM) algorithm. A previously published real-time CGM algorithm was compared with its updated version, which used a 1-point calibration instead of a 2-point calibration. In addition, the contribution of the corrective intercept (CI) to the calibration performance was assessed. Finally, the sensor background current was estimated real-time and retrospectively. The study was performed on 132 type 1 diabetes patients. Replacing the 2-point calibration with the 1-point calibration improved the CGM accuracy, with the greatest improvement achieved in hypoglycemia (18.4% median absolute relative differences [MARD] in hypoglycemia for the 2-point calibration, and 12.1% MARD in hypoglycemia for the 1-point calibration). Using 1-point calibration increased the percentage of sensor readings in zone A+B of the Clarke error grid analysis (EGA) in the full glycemic range, and also enhanced hypoglycemia sensitivity. Exclusion of CI from calibration reduced hypoglycemia accuracy, while slightly increased euglycemia accuracy. Both real-time and retrospective estimation of the sensor background current suggest that the background current can be considered zero in the calibration of the SCGM1 sensor. The sensor readings calibrated with the 1-point calibration approach indicated to have higher accuracy than those calibrated with the 2-point calibration approach.

Computing the surveillance error grid analysis: procedure and examples

The surveillance error grid (SEG) analysis is a tool for analysis and visualization of blood glucose monitoring (BGM) errors, based on the opinions of 206 diabetes clinicians who rated 4 distinct treatment scenarios. Resulting from this large-scale inquiry is a matrix of 337 561 risk ratings, 1 for each pair of (reference, BGM) readings ranging from 20 to 580 mg/dl. The computation of the SEG is therefore complex and in need of automation. The SEG software introduced in this article automates the task of assigning a degree of risk to each data point for a set of measured and reference blood glucose values so that the data can be distributed into 8 risk zones. The software's 2 main purposes are to (1) distribute a set of BG Monitor data into 8 risk zones ranging from none to extreme and (2) present the data in a color coded display to promote visualization. Besides aggregating the data into 8 zones corresponding to levels of risk, the SEG computes the number and percentage of data pairs in each zone and the number/percentage of data pairs above/below the diagonal line in each zone, which are associated with BGM errors creating risks for hypo- or hyperglycemia, respectively. To illustrate the action of the SEG software we first present computer-simulated data stratified along error levels defined by ISO 15197:2013. This allows the SEG to be linked to this established standard. Further illustration of the SEG procedure is done with a series of previously published data, which reflect the performance of BGM devices and test strips under various environmental conditions. We conclude that the SEG software is a useful addition to the SEG analysis presented in this journal, developed to assess the magnitude of clinical risk from analytically inaccurate data in a variety of high-impact situations such as intensive care and disaster settings.

Personality traits, self-care behaviours and glycaemic control in type 2 diabetes: the Fremantle diabetes study phase II

AIMS

To determine whether the personality traits of conscientiousness and agreeableness are associated with self-care behaviours and glycaemia in Type 2 diabetes.

METHODS

The Big Five Inventory personality traits Agreeableness, Conscientiousness, Extraversion, Neuroticism and Openness were determined along with a range of other variables in 1313 participants with Type 2 diabetes (mean age 65.8 ± 11.1 years; 52.9% men) undertaking their baseline assessment as part of the community-based longitudinal observational Fremantle Diabetes Study Phase II. Age- and sex-adjusted generalized linear modelling was used to determine whether personality was associated with BMI, smoking, self-monitoring of blood glucose and medication taking. Multivariable regression was used to investigate which traits were independently associated with these self-care behaviours and HbA1c .

RESULTS

Patients with higher conscientiousness were less likely to be obese or smoke, and more likely to perform self-monitoring of blood glucose and take their medications (P ≤ 0.019), with similar independent associations in multivariate models (P ≤ 0.024). HbA1c was independently associated with younger age, indigenous ethnicity, higher BMI, longer diabetes duration, diabetes treatment, self-monitoring of blood glucose (negatively) and less medication taking (P ≤ 0.009), but no personality trait added to the model.

CONCLUSIONS

Although there was no independent association between personality traits and HbA1c , the relationship between high conscientiousness and low BMI and beneficial self-care behaviours suggests an indirect positive effect on glycaemia. Conscientiousness could be augmented by the use of impulse control training as part of diabetes management.

Frequency of blood glucose testing among insulin-treated diabetes mellitus patients in the United Kingdom

BACKGROUND

Through a retrospective database analysis, this study seeks to provide an understanding of the utilization of SMBG by insulin therapy and diabetes type and to estimate healthcare costs of blood glucose monitoring in the UK diabetes population.

METHODS

Data were obtained from the IMS LifeLink Electronic Medical Record-Europe (EMR-EU) Database, a longitudinal database containing anonymized patient records from physician-practice data systems of office-based physicians in the UK. Depending on the insulin types used for type 1 and type 2 diabetes, patients were sub-categorized into one of four insulin regimen groups (basal, bolus, pre-mixed, or basal-bolus). Frequency of blood glucose testing was assessed descriptively throughout the 12-month post-index period, and generalized linear models were used to evaluate the effect of baseline characteristics, including insulin type, on the likelihood of blood glucose test utilization. Healthcare resource utilization and costs for all-cause services were assessed by insulin type.

RESULTS

This study identified 8322 type 1 and type 2 diabetes patients with two insulin pharmacy records between January 1, 2009 and December 31, 2010. After applying study inclusion and exclusion criteria, a total of 2676 (32.2%) insulin-treated diabetes mellitus patients in the UK were identified, with the number of pharmacy blood glucose test strips averaging 771.1 (median 600). The glucose testing frequency was lowest among basal-only insulin patients and pre-mixed insulin patients (mean=576.2 [median=450] and mean=599.5 [median=500], respectively; non-significantly different) compared to other insulin types.

CONCLUSION

Although the data did not capture the glucose frequency comprehensively, it varied significantly by insulin types, and was higher than what is recommended in the guidelines for patients with type 2 diabetes.

Self-monitoring of blood glucose in type 2 diabetic patients in China: current status and influential factors

BACKGROUND

Self-monitoring of blood glucose (SMBG) by individuals with type 2 diabetes (T2D) is crucial for long-term health, yet numerous cultural, economic and health factors can reduce SMBG. Most studies on SMBG adherence have come out of the US and Europe, and their relevance to Asia is unclear. The aims of the present study were to assess the current state of SMBG in China and analyze demographic and diabetes-related characteristics that may influence it.

METHODS

In this multi-center, cross-sectional study, 5 953 individuals with T2D from 50 medical centers in 29 provinces across China filled out a standardized questionnaire that requested information on demographic characteristics, education level, occupation, income, lifestyle risk factors, duration of diabetes, chronic complications, and frequency of SMBG. Respondents were also asked whether their glycosylated hemoglobin (HbA1c) had been checked in the past 6 months. The most recent values for fasting plasma glucose, 2-hour postprandial blood glucose and HbA1c were recovered from medical records.

RESULTS

Only 1 130 respondents (18.98%) performed SMBG with the recommended frequency, while 4 823 (81.02%) did not. In fact, nearly 2 105 (35.36%) reported never performing SMBG. In the subset of 3 661 individuals on insulin therapy, only 266 (7.27%) performed SMBG at least once a day, while 1 210 (33.05%) never performed it. In contrast, 895 of 2 292 individuals (39.05%) on diet/exercise therapy or oral hypoglycemic therapy never performed it. Multivariate Logistic regression identified several factors associated with SMBG adherence: female gender, higher education level, higher income, longer T2D duration and education about SMBG.

CONCLUSIONS

SMBG adherence in our Chinese population with T2D was less frequent than that in developed countries. Several factors influence SMBG adherence: gender, education level, income, T2D duration, therapy regimen and exposure to education about SMBG.

What is so tough about self-monitoring of blood glucose? Perceived obstacles among patients with Type 2 diabetes

AIMS

To identify patient-reported obstacles to self-monitoring of blood glucose among those with Type 2, both insulin users and non-insulin users, and to investigate how obstacles are associated with frequency of self-monitoring and use of self-monitoring data.

METHODS

Patients with Type 2 diabetes (n = 886, 65% insulin users) who attended a 1-day diabetes education conference in cities across the USA completed a survey on current and recommended self-monitoring of blood glucose frequency, how they used self-monitoring results and perceived obstacles to self-monitoring use. Exploratory factor analysis examined 12 obstacle items to identify underlying factors. Regression analyses examined associations between self-monitoring of blood glucose use and the key obstacle factors identified in the exploratory factor analysis.

RESULTS

Three obstacle factors emerged: Avoidance, Pointlessness and Burden. Avoidance was the only significant independent predictor of self-monitoring frequency (β = -0.23, P < 0.001). Avoidance (β = -0.12, P < 0.01) and Pointlessness (β = -0.15, P < 0.001) independently predicted how often self-monitoring data were shared with healthcare professionals and whether or not data were used to make management adjustments (Avoidance: odds ratio = 0.74, P < 0.001; Pointlessness: odds ratio = 0.75, P < 0.01). Burden was not associated with any of the self-monitoring behavioural measures. Few differences between insulin users and non-insulin users were noted.

CONCLUSIONS

Obstacles to self-monitoring of blood glucose use, both practical and emotional, were common. Higher levels of Avoidance and Pointlessness, but not Burden, were associated with less frequent self-monitoring use. Addressing patients' self-monitoring-related emotional concerns (Avoidance and Pointlessness) may be more beneficial in enhancing interest and engagement with self-monitoring of blood glucose than focusing on day-to-day, behavioural issues (Burden).

Associations among health literacy, diabetes knowledge, and self-management behavior in adults with diabetes: results of a dutch cross-sectional study

Various studies have examined the association between health literacy and self-management behavior, but few have explored ways through which this occurs. The present study examines to what extent health literacy is associated with diabetes self-management behavior and to what extent diabetes knowledge is a mechanism in this association. The study was based on cross-sectional data retrieved from patient registrations and questionnaires completed in 2010. The sample included 1,714 predominantly type 2 diabetes patients, with a mean age of 67 years. Diabetes self-management was indicated by HbA1c level, glucose self-control and self-reported monitoring of glucose levels, physical activity, and smoking. Multilevel analyses were applied based on multiple imputed data. Lower health literacy was significantly associated with less diabetes knowledge, higher HbA1c level, less self-control of glucose level, and less physical activity. Participants with more diabetes knowledge were less likely to smoke and more likely to control glucose levels. Diabetes knowledge was a mediator in the association between health literacy and glucose self-control and between health literacy and smoking. This study indicates that higher health literacy may contribute to participation in certain self-management activities, in some cases through diabetes knowledge. Diabetes knowledge and health literacy skills may be important targets for interventions promoting diabetes self-management.

Self-management of diabetes in children and young adults using technology and smartphone applications

Treatment compliance and adherence are often a challenge in patients with type 1 diabetes, particularly for adolescent and young adult patients. With the availability of the internet and smart phone applications (apps) there is a hope that such technology could provide a means to encourage treatment adherence in this group of patients. This review focuses on whether telemedicine and smartphone technology in diabetes can influence self-management in young people with diabetes. A large number of smartphone apps are targeted at people with diabetes, but a limited number of well designed evaluation studies have been performed. As our review shows, the evidence base for efficacy of most of these applications is minimal and improvement in hard outcomes such as HbA1c and complication development is largely lacking.

Effects of patient-reported non-severe hypoglycemia on healthcare resource use, work-time loss, and wellbeing in insulin-treated patients with diabetes in seven European countries

PURPOSE

Hypoglycemia is a frequent side effect induced by insulin treatment of type 1 (T1DM) and type 2 diabetes (T2DM). Limited data exist on the associated healthcare resource use and patient impact of hypoglycemia, particularly at a country-specific level. This study investigated the effects of self-reported non-severe hypoglycemic events (NSHE) on use of healthcare resources and patient wellbeing.

METHODS

Patients with T1DM or insulin-treated T2DM diabetes from seven European countries were invited to complete four weekly questionnaires. Data were collected on patient demographics, NSHE occurrence in the last 7 days, hypoglycemia-related resource use, and patient impact. NSHE were defined as events with hypoglycemia symptoms, with or without blood glucose measurement, or low blood glucose measurement without symptoms, which the patient could manage without third-party assistance.

RESULTS

Three thousand, nine hundred and fifty-nine respondents completed at least one wave of the survey, with 57% completing all four questionnaires; 3827 respondents were used for data analyses. Overall, 2.3% and 8.9% of NSHE in patients with T1DM and T2DM, respectively, resulted in healthcare professional contact. Across countries, there was a mean increase in blood glucose test use of 3.0 tests in the week following a NSHE. Among respondents who were employed (48%), loss of work-time after the last hypoglycemic event was reported for 9.7% of NSHE. Overall, 10.2% (daytime) and 8.0% (nocturnal) NSHE led to work-time loss, with a mean loss of 84.3 (daytime) and 169.6 (nocturnal) minutes among patients reporting work-time loss. Additionally, patients reported feeling tired, irritable, and having negative feelings following hypoglycemia.

LIMITATIONS

Direct comparisons between studies must be interpreted with caution because of different definitions of hypoglycemia severity, duration of the studies, and methods of data collection.

CONCLUSIONS

NSHE were associated with use of extra healthcare resources and work-time loss in all countries studied, suggesting that NSHE have considerable impact on patients/society.

Correlates of treatment patterns among youth with type 2 diabetes

OBJECTIVE To describe treatment regimens in youth with type 2 diabetes and examine associations between regimens, demographic and clinical characteristics, and glycemic control. RESEARCH DESIGN AND METHODS This report includes 474 youth with a clinical diagnosis of type 2 diabetes who completed a SEARCH for Diabetes in Youth study visit. Diabetes treatment regimen was categorized as lifestyle alone, metformin monotherapy, any oral hypoglycemic agent (OHA) other than metformin or two or more OHAs, insulin monotherapy, and insulin plus any OHA(s). Association of treatment with demographic and clinical characteristics (fasting C-peptide [FCP], diabetes duration, and self-monitoring of blood glucose [SMBG]), and A1C was assessed by χ(2) and ANOVA. Multiple linear regression models were used to evaluate independent associations of treatment regimens and A1C, adjusting for demographics, diabetes duration, FCP, and SMBG. RESULTS Over 50% of participants reported treatment with metformin alone or lifestyle. Of the autoantibody-negative youth, 40% were on metformin alone, while 33% were on insulin-containing regimens. Participants on metformin alone had a lower A1C (7.0 ± 2.0%, 53 ± 22 mmol/mol) than those on insulin alone (9.2 ± 2.7%, 77 ± 30 mmol/mol) or insulin plus OHA (8.6 ± 2.6%, 70 ± 28 mmol/mol) (P < 0.001). These differences remained significant after adjustment (7.5 ± 0.3%, 58 ± 3 mmol/mol; 9.1 ± 0.4%, 76 ± 4 mmol/mol; and 8.6 ± 0.4%, 70 ± 4 mmol/mol) (P < 0.001) and were more striking in those with diabetes for ≥2 years (7.9 ± 2.8, 9.9 ± 2.8, and 9.8 ± 2.6%). Over one-half of those on insulin-containing therapies still experience treatment failure (A1C ≥8%, 64 mmol/mol). CONCLUSIONS Approximately half of youth with type 2 diabetes were managed with lifestyle or metformin alone and had better glycemic control than individuals using other therapies. Those with longer diabetes duration in particular commonly experienced treatment failures, and more effective management strategies are needed.

Self-monitoring blood glucose--non-insulin-treated type 2 diabetes in Australian general practice

BACKGROUND

Benefits of self-monitoring blood glucose (SMBG) in non-insulin-treated type 2 diabetes (T2D) are questionable. We investigated proportions of general practitioner (GP) patients who self-monitor, and associations between SMBG, glycosylated haemoglobin (HbA1c) and body mass index (BMI).

METHODS

Sub-studies of the Bettering the Evaluation and Care of Health (BEACH) program, involving 5 730 patients from 194 GPs. Outcomes Type 2 diabetes prevalence; HbA1c; BMI; blood glucose (BG) monitoring routine.

RESULTS

Prevalence of non-insulin-treated T2D was 6.7%. Mean HbA1c level was 7.1%. Almost half (47.5%) of T2D patients were obese compared with 26.7% of patients at all BEACH encounters in 2011-2012. Of 344 non-insulin-treated T2D patients, 79.4% self-monitored fasting BG and 69.7% of 314 self-monitored post-prandial BG. For both groups, mean HbA1c was significantly higher for those who tested daily than for those who never tested.

CONCLUSION

Patients with non-insulin-treated T2D who test BG daily may be those struggling for control. For others, benefits seem minimal for the proportion utilising self-monitoring.

Frequency of blood glucose testing in well educated patients with diabetes mellitus type 1: how often is enough?

AIMS

Self-monitored blood glucose (SMBG) and knowledge of insulin requirements are pivotal for good metabolic control in patients with diabetes mellitus type 1. However, the SMBG-frequency needed for optimal glycaemic control especially in well educated patients is unclear.

METHODS

In patients with type 1 diabetes treated with flexible intensified insulin therapy, we evaluated HbA1c values and the directly preceding computerised SMBG-frequencies over a 12 months period. To estimate the association between HbA1c and SMBG-frequency, we fitted a piecewise linear spline model with a change in slope at 4 SMBGs per day which is the recommended minimal SMBG-frequency at our institution.

RESULTS

A total of 150 patients were available for analysis, with a median baseline HbA1c of 7.1% (interquartile range 6.6, 7.8). In the multivariable analysis (adjusted for gender and psychological problems), each additional SMBG measurement was associated with an estimated difference in HbA1c of -0.19% (95% confidence interval (CI) -0.42, 0.05) for ≤4 SMBGs per day and of -0.02% (95% CI -0.10, 0.06) for >4 SMBGs per day.

CONCLUSIONS

Good diabetes control can be achieved in routine diabetes care with flexible intensified insulin therapy based on continuing patients' education and with a minimum of 4 SMBGs per day.

Performance evaluations of continuous glucose monitoring systems: precision absolute relative deviation is part of the assessment

BACKGROUND

Even though a Clinical and Laboratory Standards Institute proposal exists on the design of studies and performance criteria for continuous glucose monitoring (CGM) systems, it has not yet led to a consistent evaluation of different systems, as no consensus has been reached on the reference method to evaluate them or on acceptance levels. As a consequence, performance assessment of CGM systems tends to be inconclusive, and a comparison of the outcome of different studies is difficult.

MATERIALS AND METHODS

Published information and available data (as presented in this issue of Journal of Diabetes Science and Technology by Freckmann and coauthors) are used to assess the suitability of several frequently used methods [International Organization for Standardization, continuous glucose error grid analysis, mean absolute relative deviation (MARD), precision absolute relative deviation (PARD)] when assessing performance of CGM systems in terms of accuracy and precision.

RESULTS

The combined use of MARD and PARD seems to allow for better characterization of sensor performance. The use of different quantities for calibration and evaluation, e.g., capillary blood using a blood glucose (BG) meter versus venous blood using a laboratory measurement, introduces an additional error source. Using BG values measured in more or less large intervals as the only reference leads to a significant loss of information in comparison with the continuous sensor signal and possibly to an erroneous estimation of sensor performance during swings. Both can be improved using data from two identical CGM sensors worn by the same patient in parallel.

CONCLUSIONS

Evaluation of CGM performance studies should follow an identical study design, including sufficient swings in glycemia. At least a part of the study participants should wear two identical CGM sensors in parallel. All data available should be used for evaluation, both by MARD and PARD, a good PARD value being a precondition to trust a good MARD value. Results should be analyzed and presented separately for clinically different categories, e.g., hypoglycemia, exercise, or night and day.

A consensus perceived glycemic variability metric

OBJECTIVE

Glycemic variability (GV) is an important component of overall glycemic control for patients with diabetes mellitus. Physicians are able to recognize excessive GV from continuous glucose monitoring (CGM) plots; however, there is currently no universally agreed upon GV metric. The objective of this study was to develop a consensus perceived glycemic variability (CPGV) metric that could be routinely applied to CGM data to assess diabetes mellitus control.

METHODS

Twelve physicians actively managing patients with type 1 diabetes mellitus rated a total of 250 24 h CGM plots as exhibiting low, borderline, high, or extremely high GV. Ratings were averaged to obtain a consensus and then input into two machine learning algorithms: multilayer perceptrons (MPs) and support vector machines for regression (SVR). In silica experiments were run using each algorithm with different combinations of 12 descriptive input features. Ten-fold cross validation was used to evaluate the performance of each model.

RESULTS

The SVR models approximated the physician consensus ratings of unseen CGM plots better than the MP models. When judged by the root mean square error, the best SVR model performed comparably to individual physicians at matching consensus ratings. When applied to 262 different CGM plots as a screen for excessive GV, this model had accuracy, sensitivity, and specificity of 90.1%, 97.0%, and 74.1%, respectively. It significantly outperformed mean amplitude of glycemic excursion, standard deviation, distance traveled, and excursion frequency.

CONCLUSIONS

This new CPGV metric could be used as a routine measure of overall glucose control to supplement glycosylated hemoglobin in clinical practice.

Importance of manually entering blood glucose readings when wireless-compatible meters are not being used with an insulin pump

BACKGROUND

The objective was to determine if there were differences in blood glucose monitoring (BGM) data downloaded from insulin pumps of patients who use meters that wirelessly transmit data to their insulin pumps (i.e., wireless group) and those who do not (i.e., nonwireless group).

METHODS

Blood glucose monitoring data were downloaded from the meters and insulin pumps of 47 children and adolescents with type 1 diabetes mellitus. Independent and paired t tests compared BGM data downloaded from meters and BGM data downloaded from insulin pumps.

RESULTS

There were significant differences in BGM data downloaded from the insulin pumps of patients using wireless meters compared to those using nonwireless meters. Wireless patients appeared to engage in more BGM, had more low and in-range BG readings and fewer very high BG readingss than nonwireless patients. However, a comparison of BGM data downloaded from meters and insulin pumps of nonwireless patients indicated that their insulin pump data significantly underestimated the number of BGM readings conducted, as well as the number of low and in-range readings, while overestimating the number of very high BGM readings.

CONCLUSIONS

Because patients who use nonwireless-compatible meters do not manually enter their low and in-range BGM readings into the insulin pump, BGM data downloaded only from pumps may provide an incomplete representation of BGM frequency or results. It is recommended that patients use meters that directly communicate with pumps or perform bolus calculations. Patients should be educated about the importance of manually entering all BGM readings if they do not use a wireless-compatible meter with their insulin pump.

A nine country study of the burden of non-severe nocturnal hypoglycaemic events on diabetes management and daily function

AIMS

The purpose of this study was to explore the burden and impact of non-severe nocturnal hypoglycaemic events (NSNHEs) on diabetes management, patient functioning and well-being in order to better understand the role that NSNHEs play in caring for persons with diabetes and facilitate optimal diabetes treatment management strategies.

METHODS

A 20-min survey assessing the impact of NSNHEs was administered to patients with self-reported diabetes age 18 or older via the Internet in nine countries (USA, UK, Germany, Canada, France, Italy, Spain, The Netherlands and Sweden) who experienced an NSNHE in the last month. Questions captured reasons for and length of the event, and impacts on diabetes management, daily function, sleep and well-being.

RESULTS

A total of 20 212 persons with Type 1 diabetes mellitus (T1DM) and Type 2 diabetes mellitus (T2DM) were screened of which 2108 respondents were eligible. Respondents initiated, on average, an additional 3.6 glucose monitoring tests, and did not resume usual functioning for an average of 3.4 hours after the NSNHE. Of the respondents using insulin, 15.8% decreased their insulin dose over an average of 3.6 days. NSNHEs also impacted sleep, with 10.4% not returning to sleep that night. Next day functioning was affected with 60.3% (n = 1273) feeling the need to take a nap and/or rest (with 65.5% of those actually taking a nap/rest) and 40.2% (n = 848) wanting to go to bed earlier than usual. A total of 21.4% were restricted in their driving the next day. These events also resulted in decreased well-being with 39.6% of respondents feeling 'emotional low' the following day.

CONCLUSIONS

NSNHEs have serious consequences for patients. Greater attention to patient and physician education regarding the burden of NSNHEs and incorporation of corrective actions in treatment plans is needed to facilitate patients reaching optimal glycaemic control.

Practical use of self-monitoring of blood glucose data

Self-monitoring of blood glucose provides information about blood glucose control. The data become useful information and knowledge through careful analysis for patterns that are appropriate or can be corrected. Some analyses can be performed on newer blood glucose meters, but most often, this needs to be done on a computer, tablet, or smartphone. There are a few established methods of presenting the data that make analysis easier. In this article, we discuss four types of data presentations and the methods for utilizing them.

Frequency of mealtime insulin bolus as a proxy measure of adherence for children and youths with type 1 diabetes mellitus

BACKGROUND

Electronic measures of adherence can be superior to patient report. In type 1 diabetes, frequency of blood glucose monitoring (BGM), as measured by patients' home blood glucose meters, has already been identified as a valid proxy of adherence. We present methodology to calculate adherence using insulin pump records and evaluate the reliability and validity of this methodology.

SUBJECTS AND METHODS

Blood glucose meter data, insulin pump records, and corresponding hemoglobin A1c (HbA1c) levels were randomly gathered from clinical and research databases for 100 children and youths (referred to hereafter as youths) with type 1 diabetes (mean±SD age, 12.7±4.6 years). Youths' mean frequency of daily BGM was calculated. Additionally, we calculated a mean mealtime insulin bolus score (BOLUS): youths received 1 point each for a bolus between 0600 and 1000 h, 1100 and 1500 h, and 1600 and 2200 h (maximum of 1 point/meal or 3 points/day).

RESULTS

Simple correlations between youths' HbA1c level, age, frequency of BGM, and insulin BOLUS scores were all significant. Partial correlations and multiple regression analyses revealed that insulin BOLUS scores better explain variations in HbA1c levels than the electronically recorded frequency of daily blood glucose measures.

CONCLUSIONS

Our procedures for calculating insulin BOLUS scores using insulin pump records demonstrate better concurrent validity with youths' HbA1c levels than that of the frequency of BGM with youths' HbA1c levels. Our analyses have shown that insulin bolus scoring was superior to the frequency of BGM in predicting youths' HbA1c levels.

TeMaD system: telecare for managing diabetes in Saudi Arabia

This paper briefly describes the main characteristics of the TeMaD system, developed for the Saudi National Guard Hospital in Riyadh. TeMaD attempts to improve current healthcare services for diabetic patients, and assists healthcare givers in disease management. It strengthens communication channels between patients and their healthcare givers, possibly leading to better health.

Causality in scale space as an approach to change detection

Kernel density estimation and kernel regression are useful ways to visualize and assess the structure of data. Using these techniques we define a temporal scale space as the vector space spanned by bandwidth and a temporal variable. In this space significance regions that reflect a significant derivative in the kernel smooth similar to those of SiZer (Significant Zero-crossings of derivatives) are indicated. Significance regions are established by hypothesis tests for significant gradient at every point in scale space. Causality is imposed onto the space by restricting to kernels with left-bounded or finite support and shifting kernels forward. We show that these adjustments to the methodology enable early detection of changes in time series constituting live surveillance systems of either count data or unevenly sampled measurements. Warning delays are comparable to standard techniques though comparison shows that other techniques may be better suited for single-scale problems. Our method reliably detects change points even with little to no knowledge about the relevant scale of the problem. Hence the technique will be applicable for a large variety of sources without tailoring. Furthermore this technique enables us to obtain a retrospective reliable interval estimate of the time of a change point rather than a point estimate. We apply the technique to disease outbreak detection based on laboratory confirmed cases for pertussis and influenza as well as blood glucose concentration obtained from patients with diabetes type 1.

Glucose control in diabetes: the impact of racial differences on monitoring and outcomes

Type 2 diabetes is the seventh leading cause of death in the US and is projected to increase in prevalence globally. Minorities are disproportionately affected by diabetes and data suggest that clinical outcomes consistently fall below American Diabetes Association recommendations. The purpose of this systematic review was to examine ethnic differences in self-monitoring and outcomes in adults with type 2 diabetes. Medline was searched for articles published between January 1990 and January 2012 by means of a reproducible strategy. Inclusion criteria included (1) published in English, (2) targeted African Americans, Hispanic, or Asian adults, ages 18+ years with type 2 diabetes, (3) cross-sectional, cohort, or intervention study, and (4) measured change in glycemic control, BP, lipids, or quality of life by race. Twenty-two papers met the inclusion criteria and were reviewed. Overall, significant racial differences and barriers were found in published studies in diabetes management as it pertains to self-monitoring and outcomes. African Americans tend to consistently exhibit worse outcomes and control when compared to other minority populations and non-Hispanic Whites. In conclusion, significant racial differences and barriers exist in diabetes management as it pertains to self-monitoring and outcomes when compared to non-Hispanic Whites. Explanatory and intervention studies are needed to determine the mechanisms and mediators of these differences and strategies to reduce these disparities. In addition, more research is needed to investigate the impact of racial differences in self-monitoring and outcomes on quality of life.