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.

Diabetes connect: an evaluation of patient adoption and engagement in a web-based remote glucose monitoring program

BACKGROUND

We determine whether Diabetes Connect (DC), a Web-based diabetes self-management program, can help patients effectively manage their diabetes and improve clinical outcomes.

METHODS

Diabetes Connect is a 12-month program that allows patients with type 2 diabetes mellitus to upload their blood glucose readings to a database, monitor trends, and share their data with their providers. To examine the impact of the program, we analyzed patient utilization and engagement data, clinical outcomes, as well as qualitative feedback from current and potential users through focus groups.

RESULTS

We analyzed 75 out of 166 patients. Mean age was 61 years (range 27-87). Patients engaged in DC had an average hemoglobin A1c (HbA1c) change of 1.5%, while nonengaged patients had a HbA1c change of 0.4% (p = .05). Patients with the best outcomes (HbAlc decline of at least 0.8%) typically took less than 10 days to upload, while patients with the worst outcomes (a rise in HbAlc) took an average of 65 days to upload. Patients with more engaged providers had a better HbA1c change (1.39% versus 0.87%) for practices with an average of 74 versus 30 logins/providers.

CONCLUSIONS

Patient engagement in the program has a positive impact on the outcomes of this collaborative Web-based diabetes self-management tool. Patients who engage early and remain active have better clinical outcomes than unengaged patients. Provider engagement, too, was found critical in engaging patients in DC.

The impact of depression on self-care activities and health care utilization among people with diabetes in Taiwan

Using cross-sectional analyses of a nationally representative community sample (n=1260), we found that in people with diabetes, depression was associated with lower rates of reducing or quitting smoking and alcohol, less exercise, less regular lifestyle, but more health care utilization and a higher rate of foot care.

Advanced meter features improve postprandial and paired self-monitoring of blood glucose in individuals with diabetes: results of the Actions with the CONTOUR Blood Glucose Meter and Behaviors in Frequent Testers (ACT) study

BACKGROUND

This study evaluated whether education and use of the advanced meter features of the CONTOUR(®) (Bayer HealthCare LLC, Diabetes Care, Tarrytown, NY) blood glucose monitoring system (BGMS) affect the frequency and pattern of blood glucose testing in insulin-using subjects with diabetes who routinely perform self-monitoring of blood glucose (SMBG).

SUBJECTS AND METHODS

Insulin-using subjects with type 1 or type 2 diabetes were enrolled in this 6-month, multicenter, prospective study and randomized to one of two groups. The basic meter features group (BMF group) received basic instruction in the use of the BGMS, whereas the advanced meter features group (AMF group) also received training in the use of advanced features, including the meal marker and audible reminder, and were instructed to use these features. Both groups received education on the importance of postprandial testing.

RESULTS

The AMF group (n=105) had significantly greater average weekly postprandial blood glucose testing than the BMF group (n=106) at each follow-up visit (P<0.001) and significantly increased the frequency of paired blood glucose testing (P<0.001) as well. In both groups, glycated hemoglobin decreased significantly as postprandial testing frequency increased (P<0.05). Subject reports indicated that use of advanced features made postmeal SMBG considerably easier to remember, helped them better understand how to make decisions on their own, and increased their confidence in meal choices.

CONCLUSIONS

Study findings showed that advanced features of the CONTOUR BGMS increased structured testing as measured by postprandial and paired SMBG and were perceived as useful by patients.

Patient perspectives on personalized glucose advisory systems for type 1 diabetes management

BACKGROUND

Diabetes technology is rapidly advancing toward fully automated glucose control systems, but little is known about patient perspectives on these systems. This study aimed to gather qualitative and quantitative data on patient attitudes and concerns about using a personalized glucose advisory system (PGASystem) for diabetes management.

SUBJECTS AND METHODS

Fifty-six adults with type 1 diabetes on insulin pump therapy participated in focus group interviews following use of an insulin pump and continuous glucose monitoring for 4 weeks in a parent study to develop a PGASystem. Focus groups were transcribed and coded for thematic content.

RESULTS

All participants endorsed the desire to use a PGASystem, and the majority wanted advice from the system on all aspects of insulin delivery. However, participants indicated that they might be reluctant to follow such advice because of the following concerns: how the advice was generated, relinquishing control to automated technology, and inadequate personalization of the system. Participants believed the system would need to consider numerous factors related to their food, activities, and other personal information to provide optimally individualized advice. The majority also reported difficulties with behavioral event recording on their insulin pumps, and approximately one-third endorsed difficulty with accurate carbohydrate counting.

CONCLUSIONS

Adults with type 1 diabetes appear to be enthusiastic about using a PGASystem system for their diabetes management but also have significant concerns affecting their overall willingness to follow such a system's advice. Addressing these concerns will be crucial in the future development of glucose advisory and control technology.

Integrated self-monitoring of blood glucose system: handling step analysis

Self-monitoring of blood glucose (SMBG) implicates a number of handling steps with the meter and the lancing device. Numerous user errors can occur during SMBG, and each step adds to the complexity of use. This report compares the required steps to perform SMBG of one fully integrated (the second generation of the Accu-Chek® Mobile), three partly integrated (Accu-Chek Compact Plus, Ascensia® Breeze®2, and Accu-Chek Aviva), and six conventional (Bayer Contour®, Bayer Contour USB, BGStar™, FreeStyle Lite®, OneTouch® Ultra® 2, and OneTouch Verio™Pro) systems. The results show that the fully integrated system reduces the number of steps to perform SMBG. The mean decrease is approximately 70% compared with the other systems. We assume that a reduction of handling steps also reduces the risk of potential user errors and improves the user-friendliness of the system.

Value and utility of structured self-monitoring of blood glucose in real world clinical practice: findings from a multinational observational study

BACKGROUND

The Structured Testing Program (STeP) study, a cluster-randomized, controlled trial, showed that a structured self-monitoring of blood glucose (SMBG)-based intervention improves clinical outcomes. It is important to determine whether this intervention can be adapted for use in general medical practice. This study examined the feasibility and effects of a modified version of the STeP intervention on clinical and attitudinal outcomes in real world clinical settings.

METHODS

In this 3-month, observational, multinational study, 375 type 1 and type 2 diabetes patients in 11 countries were asked to generate a blood glucose (bG) profile once per month for 3 consecutive months, using a paper-based bG analysis tool (Accu-Chek® 360° View® bG analysis system, Roche Diagnostics, Mannheim, Germany). Measurements were to be performed before and 2 h after main meals and before bedtime on 3 consecutive days. End points included change from baseline in glycated hemoglobin (HbA1c) and other parameters of diabetes complications. Patient and physician attitudes toward use of the structured testing form were also assessed.

RESULTS

Reductions in mean (SD) HbA1c from baseline were significant, from 9.2% (1.6%) to 8.0% (1.4%) (Δ -1.2% [1.6%], P<0.001). Reductions in mean (SD) average bG from baseline were significant, from 189.5 mg/dL (55.5 mg/dL) to 153 mg/dL (39.6 mg/dL) (Δ-36.4 mg/dL [52.5 mg/dL], P<0.001). Significant (P<0.001) improvements in body mass index, lipids, and blood pressure were also observed. Patients and physicians were generally positive about the utility of the structured testing form.

CONCLUSIONS

Use of the structured SMBG intervention is practical in real world clinical settings and is associated with improved diabetes management.

Frequency of glycated hemoglobin monitoring was inversely associated with glycemic control of patients with Type 2 diabetes mellitus

BACKGROUND

The frequency of monitoring glycated hemoglobin (HbA(1c)) and its impact on glycemic control of Chinese Type 2 diabetes mellitus (T2DM) patients have not been well understood.

AIM

To explore the current status of the glycemic control, the frequency of HbA(1c) monitoring, and their relationship in T2DM outpatients in urban China.

SUBJECTS AND METHODS

A cross-sectional study was carried out in 15 hospitals purposely sampled from 4 cities of China. T2DM outpatients were consecutively recruited, and underwent a face-to-face interview in outpatient consulting rooms using a self-developed structured questionnaire to collect information. All consented patients were invited to have a free HbA(1c) test.

RESULTS

Among 1511 subjects, the average level of HbA(1c) was 8.1±1.6% with the ideal percents of 13.6% and 24.8% (HbA(1c)<6.5% and <7.0%, respectively). Less than 1/3 (339/1157) had received 2 or more HbA(1c) tests per yr, and they had a significantly lower average of HbA(1c) than those having only 1 or no test per yr (F=5.012, p=0.007). After adjustment for possible confounders including age, gender, and city, there was a significantly inverse association with adjusted odds ratios of 2.56 [95% confidence interval (CI): 1.71, 3.86] and 1.67 (95% CI: 1.11, 2.50), respectively, between the frequency of monitoring HbA(1c) (null, once vs ≥2 times per yr) and worse glycemic control (HbA(1c)≥7.0%).

CONCLUSIONS

Glycemic control of T2DM outpatients was poor in urban China. Frequency of HbA(1c) monitoring is seriously insufficient in majority of patients. Lower frequency of HbA(1c) monitoring is significantly associated with poor glycemic control.

A comparison of average daily risk range scores for young children with type 1 diabetes mellitus using continuous glucose monitoring and self-monitoring data

BACKGROUND

Young children with type 1 diabetes are vulnerable to glycemic excursion. Continuous glucose monitoring (CGM), combined with variability statistics, can offer a richer and more complete picture of glycemic variability in young children. In particular, we present data for the Average Daily Risk Range (ADRR) and compare ADRR scores calculated using CGM versus self-monitoring of blood glucose (SMBG) data for young children.

METHODS

CGM and SMBG data from 48 young children with type 1 diabetes (mean age, 5.1 years) were used to calculate two separate ADRR scores, using SMBG data (ADRRs) and CGM data (ADRRc), for each child. Additionally, we calculated mean amplitude of glycemic excursion (MAGE) scores for children to examine the concurrent validity of the ADRRs and ADRRc.

RESULTS

Young children's mean ADRRc score was significantly greater than their ADRRs score (55±12 and 46±11, respectively; P<0.001). In addition, 74% of the time the children's ADRRc score reflected greater variability risk than their ADRRs score. Examining the concurrent validity, children's ADRRc scores correlated positively with MAGE scores calculated using their CGM and SMBG data, whereas their ADRRs scores only correlated with MAGE scores calculated using SMBG.

CONCLUSIONS

ADRR scores generated for young children with type 1 diabetes demonstrate a high risk for glucose variability, but ADRR scores generated from CGM data may provide a more sensitive measure of variability than ADRR scores generated from SMBG. In young children with type 1 diabetes, ADRR scores calculated from CGM data may be superior to scores calculated from SMBG for measuring risk of excursion.