Current clinical guidelines for diabetes care encourage self-monitoring of blood glucose (SMBG) to improve glycemic control. Specific protocols remain variable, however, particularly among non-insulin-using patients. This is due in part to efficacy studies that neglect to consider (1) the performance of monitoring equipment under real-world conditions, (2) whether or how patients have been taught to take action on test results, and (3) the physiological, behavioral, and social circumstances in which SMBG is carried out. As such, a multidisciplinary group of specialists, including several endocrinologists, a health psychologist, a diabetes nurse practitioner, and a patient advocate (the Panel), discuss within this review article how the potential of SMBG might be fully realized in today's healthcare environment. The resulting recommendations cover technological, clinical, behavioral, and research considerations with the aim of achieving short- and long-term benefits, ranging from fewer hypoglycemic episodes to lower complication-related costs. The panel also made suggestions for designing future studies that increase the ability to discern optimal models of SMBG utilization for individuals with diabetes who may, or may not, use insulin.

Objective:The objective of this study was to determine inaccuracies of miscoded blood glucose (BG) meters and potential errors in insulin dose based on values from these meters. Research Design:Fasting diabetic subjects at three clinical centers participated in a 2-hour meal tolerance test. At various times subjects' blood was tested on five BG meters and on a Yellow Springs Instruments laboratory glucose analyzer. Some meters were purposely miscoded. Using the BG values from these meters, along with three insulin dose algorithms, Monte Carlo simulations were conducted to generate ideal and simulated-meter glucose values and subsequent probability of insulin dose errors based on normal and empirical distribution assumptions. Results:Maximal median percentage biases of miscoded meters were +29% and -37%, while maximal median percentage biases of correctly coded meters were only +0.64% and -10.45% (p = 0.000, c 2 test, df = 1). Using the low-dose algorithm and the normal distribution assumption, the combined data showed that the probability of insulin error of ±1U, ±2, ±3, ±4, and ±5U for miscoded meters could be as high as 49.6, 50.0, 22.3, 1.4, and 0.04%, respectively. This is compared to manually, correctly coded meters where the probability of error of ±1, ±2, and ±3U could be as high as 44.6, 7.1, and 0.49%, respectively. There was no instance of a ±4 or ±5U insulin dose error with a manually, correctly coded meter. For autocoded meters, the probability of ±1 and ±2U could be as high as 35.4 and 1.4%, respectively. For autocoded meters there were no calculated insulin dose errors above ±2U. The probability of insulin misdosing with either manually, correctly coded or autocoded meters was significantly lower than that with miscoded meters. Results using empirical distributions showed similar trends of insulin dose errors. Conclusions:Blood glucose meter coding errors may result in significant insulin dosing errors. To avoid error, patients should be instructed to code their meters correctly or be advised to use an autocoded meter that showed superior performance over manually, correctly coded meters in this study.

Objectives: The proper use of many types of self-monitored blood glucose (SMBG) meters requires calibration to match strip code. Studies have demonstrated the occurrence and impact on insulin dose of coding errors with SMBG meters. This paper reflects additional analyses performed with data from Raine et al. (JDST, 2:205–210, 2007). It attempts to relate potential insulin dose errors to possible adverse blood glucose outcomes when glucose meters are miscoded. Methods: Five sets of glucose meters were used. Two sets of meters were autocoded and therefore could not be miscoded, and three sets required manual coding. Two of each set of manually coded meters were deliberately miscoded, and one from each set was properly coded. Subjects ( n = 116) had finger stick blood glucose obtained at fasting, as well as at 1 and 2 hours after a fixed meal (Boost®; Novartis Medical Nutrition U.S., Basel, Switzerland). Deviations of meter blood glucose results from the reference method (YSI) were used to predict insulin dose errors and resultant blood glucose outcomes based on these deviations. Results: Using insulin sensitivity data, it was determined that, given an actual blood glucose of 150–400 mg/dl, an error greater than +40 mg/dl would be required to calculate an insulin dose sufficient to produce a blood glucose of less than 70 mg/dl. Conversely, an error less than or equal to −70 mg/dl would be required to derive an insulin dose insufficient to correct an elevated blood glucose to less than 180 mg/dl. For miscoded meters, the estimated probability to produce a blood glucose reduction to less than or equal to 70 mg/dl was 10.40%. The corresponding probabilities for autocoded and correctly coded manual meters were 2.52% ( p < 0.0001) and 1.46% ( p < 0.0001), respectively. Furthermore, the errors from miscoded meters were large enough to produce a calculated blood glucose outcome less than or equal to 50 mg/dl in 42 of 833 instances. Autocoded meters produced zero (0) outcomes less than or equal to 50 mg/dl out of 279 instances, and correctly coded manual meters produced 1 of 416. Conclusions: Improperly coded blood glucose meters present the potential for insulin dose errors and resultant clinically significant hypoglycemia or hyperglycemia. Patients should be instructed and periodically reinstructed in the proper use of blood glucose meters, particularly for meters that require coding.

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