Q-Score: development of a new metric for continuous glucose monitoring that enables stratification of antihyperglycaemic therapies

BACKGROUND

Continuous glucose monitoring (CGM) has revolutionised diabetes management. CGM enables complete visualisation of the glucose profile, and the uncovering of metabolic 'weak points'. A standardised procedure to evaluate the complex data acquired by CGM, and to create patient-tailored recommendations has not yet been developed. We aimed to develop a new patient-tailored approach for the routine clinical evaluation of CGM profiles. We developed a metric allowing screening for profiles that require therapeutic action and a method to identify the individual CGM parameters with improvement potential.

METHODS

Fifteen parameters frequently used to assess CGM profiles were calculated for 1,562 historic CGM profiles from subjects with type 1 or type 2 diabetes. Factor analysis and varimax rotation was performed to identify factors that accounted for the quality of the profiles.

RESULTS

We identified five primary factors that determined CGM profiles (central tendency, hyperglycaemia, hypoglycaemia, intra- and inter-daily variations). One parameter from each factor was selected for constructing the formula for the screening metric, (the 'Q-Score'). To derive Q-Score classifications, three diabetes specialists independently categorised 766 CGM profiles into groups of 'very good', 'good', 'satisfactory', 'fair', and 'poor' metabolic control. The Q-Score was then calculated for all profiles, and limits were defined based on the categorised groups (<4.0, very good; 4.0-5.9, good; 6.0-8.4, satisfactory; 8.5-11.9, fair; and ≥12.0, poor). Q-Scores increased significantly (P <0.01) with increasing antihyperglycaemic therapy complexity. Accordingly, the percentage of fair and poor profiles was higher in insulin-treated compared with diet-treated subjects (58.4% vs. 9.3%). In total, 90% of profiles categorised as fair or poor had at least three parameters that could potentially be optimised. The improvement potential of those parameters can be categorised as 'low', 'moderate' and 'high'.

CONCLUSIONS

The Q-Score is a new metric suitable to screen for CGM profiles that require therapeutic action. Moreover, because single components of the Q-Score formula respond to individual weak points in glycaemic control, parameters with improvement potential can be identified and used as targets for optimising patient-tailored therapies.

The impact of policies to reduce blood glucose test strip utilization and costs in Canada

OBJECTIVES

Several strategies have been proposed to manage the utilization of blood glucose test strips (BGTS) in Canada; however their potential impacts on utilization and costs of publically funded test strips are unknown.

METHODS

We investigated the impact of three potential policies that would restrict the number of test strips reimbursed by the public drug plans in Ontario and British Columbia (BC), and incorporated negotiated price reductions. These policies were based on recommendations from the Canadian Agency for Drugs and Technologies in Health, a briefing document by the Canadian Diabetes Association, and a new policy introduced by the Ontario Ministry of Health and Long-Term Care. BGTS utilization rates were assessed in two cross-sectional analyses among adults aged 18 years or older in BC and 65 or older in Ontario who received publicly-funded BGTS between January 2004 and December 2012. We modeled the 5-year utilization and cost implications of the three policies using time-series analysis.

RESULTS

In 2012, there were 317,130 test strip recipients in Ontario and 136,659 recipients in BC, at a cost of $104.4 million and $22.6 million respectively. Under the scenarios of reduced BGTS quantities, 5-year cost savings ranged between $98.8 million (18.2% reduction) and $224.1 million (41.4% reduction) in Ontario and between $23.1 million (19.2% reduction) and $51.1 million (42.4% reduction) in BC. Price reductions of 15% resulted in annual savings of $14.4 million (13.7% reduction) in Ontario and $3.4 million (14.1% reduction) in BC.

CONCLUSIONS

Policies that align with evidence and expert guidance could impart substantial cost savings in multiple jurisdictions despite different public drug plans.

Impact of socioeconomic deprivation on the development of diabetic retinopathy: a population-based, cross-sectional and longitudinal study over 12 years

OBJECTIVE

To study the association between socioeconomic deprivation and prevalence of diabetic retinopathy (DR).

DESIGN

Population-based, cross-sectional observational study and retrospective longitudinal analysis over 12 years.

SETTING

Primary care, East of Scotland.

METHODS

Outcome data from DR screening examinations (digital retinal photography) were collected from the Scottish regional diabetes electronic record from inception of database to December 2012. The overall Scottish Index of Multiple Deprivation (SIMD) 2012 score for each patient was obtained using their residential postcode. Multiple binary logistic regression was used to analyse the relationship between overall SIMD score and prevalence of DR, adjusting for other variables: age, gender, glycated haemoglobin, cholesterol levels and duration of disease.

PRIMARY OUTCOME

Any retinopathy (R1 and above) in either eye.

RESULTS

A total of 1861 patients with type 1 diabetes mellitus (DM) and 18,197 patients with type 2 DM were included in the study. Prevalence of DR in type 1 and type 2 DM were 56.3% and 25.5%, respectively. Increased prevalence of DR in type 1 DM was associated with higher overall SIMD score (p=0.002), with an OR for the most deprived relative to the least deprived of 2.40 (95% CI 1.36 to 4.27). In type 2 DM, the overall SIMD score was not significantly associated with increased prevalence of DR, with an OR for the most deprived relative to the least deprived of 0.85 (95% CI 0.71 to 1.02, p=0.07).

CONCLUSIONS

Socioeconomic deprivation is associated with increased prevalence of DR in patients with type 1 DM and this occurs earlier. This highlights the need for targeted interventions to address inequalities in eye healthcare.

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

BACKGROUND AND OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Important determinants of diabetes control in insulin pump therapy in patients with type 1 diabetes mellitus

BACKGROUND

Insulin pumps are equipped with advanced functions. Intensive training and adherence are required for optimum use of the technology. We aimed to assess the association of various key elements in insulin pump functions on blood glucose control.

PATIENTS AND METHODS

Patients on insulin pump therapy were enrolled. Insulin pumps were downloaded (CareLink(®) Pro 3 software; Medtronic Minimed, Northridge, CA), and data were collected over an 8-12-week period. Glycemic control of patients was classified as controlled (hemoglobin A1c [HbA1c] level of 7.5% or less in adults and 8% or less in children) and uncontrolled based on HbA1c level at enrollment. Variables studied were use of sensors and duration, frequency of blood glucose monitoring, Bolus Wizard (Medtronic Minimed) use, frequency of correction boluses, and frequency of cannula changing.

RESULTS

Seventy-two patients were enrolled (50 children). Median age was 12 years for children and 27.5 years for adults. Respective median numbers of blood glucose checks were 4.4 and 3.2 for controlled and uncontrolled children (P<0.021) and 3.1 and 2.8 for controlled and uncontrolled adults, respectively. Respective frequency of Bolus Wizard use per day showed a median of 6 and 4.15 for controlled and uncontrolled children (P<0.001) and 3.8 and 3.5 for controlled and uncontrolled adults. Controlled children wore sensors for longer (5 vs. 2.9 days/week) and did more corrections (3.9 vs. 2.5). There was no difference in the frequency of changing the infusion cannula in children's or adults' groups.

CONCLUSIONS

We conclude that the frequency of blood glucose monitoring and Bolus Wizard use have a favorable association with glycemic control. These observations were more significant in the children's groups. Our data shows that patients with better control tend to bolus more for correction and wear sensors longer.

Multinight "bedside" closed-loop control for patients with type 1 diabetes

BACKGROUND

Studies of closed-loop control (CLC) systems have improved glucose levels in patients with type 1 diabetes. In this study we test a new CLC concept aiming to "reset" the patient overnight to near-normoglycemia each morning, for several consecutive nights.

SUBJECTS AND METHODS

Ten insulin pump users with type 1 diabetes (mean age, 46.4±8.5 years) were enrolled in a two-center (in the United States and Italy) randomized crossover trial comparing 5 consecutive nights of CLC (23:00-07:00 h) in an outpatient setting versus sensor-augmented insulin pump therapy of the same duration at home. Primary end points included time spent in 80-140 mg/dL as measured by continuous glucose monitoring overnight and fasting blood glucose distribution at 7:00 h.

RESULTS

Compared with sensor-augmented pump therapy, CLC improved significantly time spent between 80 and 140 mg/dL (54.5% vs. 32.2%; P<0.001) and between 70 and 180 mg/dL (85.4% vs. 59.1%; P<0.001); CLC reduced the mean glucose level at 07:00 h (119.3 vs. 152.9 mg/dL; P<0.001) and overnight mean glucose level (139.0 vs. 170.3 mg/dL; P<0.001) using a marginally lower amount of insulin (6.1 vs. 6.8 units; P=0.1). Tighter overnight control led to improved daytime control on the next day: the overnight/next-day control correlation was r=0.52, P<0.01.

CONCLUSIONS

Multinight CLC of insulin delivery (artificial pancreas) results in significant improvement in morning and overnight glucose levels and time in target range, with the potential to improve daytime control when glucose levels were "reset" to near-normoglycemia each morning.

Patients who self-monitor blood glucose and their unused testing results

OBJECTIVES

To investigate the prevalence, predictors, and costs associated with unused results from self-monitoring of blood glucose (SMBG).

STUDY DESIGN

Observational cohort study.

METHODS

We studied 7320 patients with type 2 diabetes mellitus who were not prescribed insulin and who reported SMBG. Patients reported whether they used SMBG results to make adjustments to diet, exercise, or medicines; and whether their physician/provider reviewed their SMBG results. We categorized SMBG results as "used" (by patient and/or provider) or "unused" (not used by either patient or provider).

RESULTS

SMBG results were unused by patient and provider in 15.2% of patients. In separate models adjusted for demographic and clinical differences, major predictors of SMBG without patient or physician using the results included a patient reporting that diabetes was not a high priority (relative risk [RR], 1.81; 95% CI, 1.58-2.07); the physician not engaging in shared decision making (RR, 1.66; 95% CI, 1.46-1.90); and no healthcare professional teaching the patient how to adjust diet/medicines based on SMBG results in the past year (RR, 2.27; 95% CI, 2.00-2.57). Patients with unused results were dispensed 171 ± 191 test strips per year at an estimated annual cost of $168.

CONCLUSIONS

Nearly 1 in 6 non-insulin-treated patients practiced SMBG without either the patient or physician using the results. This represents a wasteful and ineffective practice for patients and health systems alike. Our results suggest that the decision to initiate and continue SMBG must be made in concert with the patient's own priorities, and, if prescribed, SMBG requires effective patient provider communication and patient education.

Blood glucose self-monitoring patterns in Mexican Americans: further lessons from the Starr County Border Health Initiative

BACKGROUND

The purpose was to describe patterns of home self-monitoring of blood glucose (SMBG) in Mexican Americans with type 2 diabetes mellitus enrolled in a diabetes self-management education protocol. Research questions were as follows: (1) What were the patterns and rates of home glucose self-monitoring over the 6-month course of the study? (2) What were the differences in monitoring rates between experimental and control groups? (3) What were the relationships between rates of monitoring and glycosylated hemoglobin (A1C), gender, and years with diabetes?

SUBJECTS AND METHODS

We used a randomized (by group) repeated-measures pretest/posttest control group design. Glucometer data from an experimental group (diabetes self-management education plus nurse case management) and a comparison group (diabetes self-management education only) were analyzed. Data were collected at baseline and at 3 and 6 months.

RESULTS

Overall average SMBG rates were low. Experimental and control group monitoring levels were not significantly different. More females than males never monitored glucose values, but more females than males checked at least one time per week. Those participants who checked their glucose levels more than once per week had diabetes for a longer period of time. Rates of monitoring were not strongly associated with A1C levels at 3 and 6 months, but at 6 months A1C levels were statistically significantly different based on whether or not individuals monitored their glucose levels (P=0.03, n=71).

CONCLUSIONS

SMBG rates were low in this study despite SMBG education and access to free glucometers and test strips. The lower rates of SMBG may reflect the effects of unexpected environmental challenges, but exact causes remain unclear. Reasons for low rates of SMBG need to be explored further, especially in underserved communities.

Hypoglycemia prediction using machine learning models for patients with type 2 diabetes

Minimizing the occurrence of hypoglycemia in patients with type 2 diabetes is a challenging task since these patients typically check only 1 to 2 self-monitored blood glucose (SMBG) readings per day. We trained a probabilistic model using machine learning algorithms and SMBG values from real patients. Hypoglycemia was defined as a SMBG value < 70 mg/dL. We validated our model using multiple data sets. In addition, we trained a second model, which used patient SMBG values and information about patient medication administration. The optimal number of SMBG values needed by the model was approximately 10 per week. The sensitivity of the model for predicting a hypoglycemia event in the next 24 hours was 92% and the specificity was 70%. In the model that incorporated medication information, the prediction window was for the hour of hypoglycemia, and the specificity improved to 90%. Our machine learning models can predict hypoglycemia events with a high degree of sensitivity and specificity. These models-which have been validated retrospectively and if implemented in real time-could be useful tools for reducing hypoglycemia in vulnerable patients.

Evaluating quality of glycemic control: graphical displays of hypo- and hyperglycemia, time in target range, and mean glucose

There is need for readily understandable graphical displays of glucose data to facilitate interpretation by clinicians and researchers. (1) Display of the percentage of glucose values above a specified threshold for hyperglycemia (%High) versus percentage of glucose values below a specified threshold for hypoglycemia (%Low). If all glucose values fell within the target range, then all data points would fall at the origin. (2) After an intervention, one can plot the change in percentage of glucose values above a specified threshold for hyperglycemia versus the change in percentage of glucose values below a specified threshold defining hypoglycemia: The quadrants of this graph correspond to (a) increased risk of both hyper- and hypoglycemia, (b) decreased hyperglycemia but increased risk of hypoglycemia, (c) decreases in both hypo- and hyperglycemia, and (d) decreased hypoglycemia but increased hyperglycemia. (3) A 2-dimensional triangular graph can be used for simultaneous display of %High, %Low, and percentage in target range. (4) Display of risk of hyper- versus risk of hypoglycemia based on both frequency and severity of departures from the target range can be used. (5) Graphs (1) and (4) can also be presented using percentile scores relative to a reference population. (6) It is also useful to analyze %Hypoglycemia or risk of hypoglycemia versus mean glucose. These methods are illustrated with examples from representative cases and shown to be feasible, practical, and informative. These new types of graphical displays can facilitate rapid analysis of risks of hypo- and hypoglycemia simultaneously and responses to therapeutic interventions for individuals or in clinical trials.

Self-monitoring of blood glucose: use, frequency drivers, and cost in Argentina

Although test strips for self-monitoring of blood glucose (SMBG) represent around 50% of diabetes treatment cost in Argentina, little is known about their current use and relationship with different types of treatment. We therefore aimed to estimate the current use of test strips and identify the major use drivers and the percentage they represent of total prescription costs in 2 entities of the social security system (SSS) of Argentina. Observational retrospective study measuring test strip prescriptions delivered by pharmacies from the province of Buenos Aires (8115 records collected during 3 months provided by the Colegio de Farmacéuticos de la Provincia de Buenos Aires) of affiliates with type 2 diabetes (T2DM) from 2 large entities of the SSS system. The average monthly test strips/patient used for SMBG was 97.5 ± 70.1. This number varied according to treatment: monotherapy with oral antidiabetic drugs (OAD) < combined OAD therapy < insulin treatment. Test strips represented a higher percentage of the total prescription cost in people under OAD monotherapy (84.6%) and lower in those with insulin analogs (46.9%). In our population, the type of hyperglycemia treatment was the main driver of test strip use for SMBG and its impact on the total prescription cost depends on the kind of such treatment. Since it has been shown that patients' education and prescription audit can optimize test strip use and treatment outcomes, implementation of such strategies could appropriately support, optimize, and reduce ineffective test strip use in people with T2DM.

The fractal nature of blood glucose fluctuations

AIMS

Fluctuations of blood glucose are generated by multiple external and internal factors continuously modifying glucose concentrations through complex feedback loops. This equilibrium may be perturbed during physiological or pathological conditions. The traditional theory suggests that physiological systems achieve homeostasis when disturbed and restore equilibrium through linear feedback loops. Complex systems on the other hand, may function nonlinearly with feedback loops that operate at different time scales, exhibiting chaotic or fractal behavior. We hypothesized that blood glucose fluctuations recorded for prolonged time periods show chaotic, fractal-like behavior that may be altered in diabetes.

METHODS

We applied nonlinear analytical methods such as detrended fluctuation analysis to glucose data derived from continuous glucose monitoring devices for prolonged time periods in healthy volunteers, diabetes type 1 and pregnant diabetes type 1 patients.

RESULTS

Glucose fluctuations extracted for prolonged time periods show fractal-like behavior and power law behavior of the system.

CONCLUSIONS

Hidden features underlying glucose fluctuations in health and in disease were revealed by using dynamic nonlinear analyses methods to discrete glucose readings extracted from continuous glucose monitoring devices. By using such methods we can enhance our understanding of the dynamics of blood glucose fluctuations in health and disease.

Prevalence, correlates and management of type 2 diabetes mellitus in Lebanon: findings from a national population-based study

AIMS

This study aims to examine the prevalence, associated risk factors and complications of diabetes, as well management and preventive care in Lebanon, a small, middle-income country of the Mediterranean region.

METHODS

Using a comprehensive multi-dimensional questionnaire, a cross-sectional national survey of 2195 Lebanese adults aged ≥25 years was conducted based on the WHO STEPwise guidelines. The outcome variable, diabetes, was self-reported. Measures for diabetes management included frequency of blood glucose testing and regular eye and foot exams. Macrovascular and microvascular complications were also recorded.

RESULTS

The prevalence of type 2 diabetes was 8.5% (95%CI=7.3-9.7). Factors associated with an increased risk of having diabetes were: being divorced or widowed (OR=2.56; 95%CI=1.07-5.42) compared to single, being obese (OR=1.50, 95%CI=1.00-2.57), and having a family history of diabetes (OR=3.40;95%CI=2.48-5.19). Vigorous physical activity significantly decreased the odds of diabetes (OR=0.42; 95%CI=0.24-0.72). Diabetes management and self-care goals were as follows: 82% were not measuring their blood sugar via dextro on a daily basis, 64.2% did not have a foot exam within the past year, and 52.4% did not obtain the recommended yearly eye exam. The most common complications included heart disease (27.8%) and retinopathy (16.6%).

CONCLUSIONS

Prevalence of diabetes in Lebanon was comparable to that found in the West, yet remained lower than estimates in other resource-rich neighboring countries. Adherence to management and self-care measures was sub-optimal resulting in high complication rates. Contextual factors play a role in increasing diabetes risk. Population-based interventions to enhance and promote self-management behaviors are essential to improve complication rates.

Diabetes self-care assessment

Diabetes is a common diagnosis for home care patients. Conducting an assessment of diabetes self-care management knowledge and skills can identify areas for improvement and support and pinpoint individual self-care barriers. This article will provide targeted questions that may be used by the home care nursing professional when conducting such an assessment.

Check it! A randomized pilot of a positive psychology intervention to improve adherence in adolescents with type 1 diabetes

PURPOSE

The purpose of the current study was to pilot-test a positive psychology intervention to improve adherence to diabetes management in adolescents with type 1 diabetes.

METHODS

A total of 39 adolescents (ages, 13-17 years) with type 1 diabetes and their caregivers were randomized to a positive psychology intervention (n = 20) or an attention control (education) intervention (n = 19). The intervention condition used positive psychology exercises (eg, gratitude, self-affirmation), small gifts, and parent affirmations to boost positive affect. Outcomes included frequency of blood glucose monitoring, quality of life, and glycemic control.

RESULTS

No main effects for treatment were observed at the 6-month follow-up. However, there was a significant association between adolescents' levels of positive affect and measures of adherence, including self-report and meter downloads of glucose monitoring.

CONCLUSIONS

The results from the current study support the assertion that positive affect in the context of diabetes education is an important factor to consider in adolescents with type 1 diabetes.

[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.