Self-monitoring of blood glucose (SMBG) is an important part of the management of diabetes. Glucose results generated from glucose testing at home and sent to a medical care provider impowers the patient to adjust insulin doses according to glucose levels with medical supervision. Also, this data enables the medical care provider to modify insulin and other diabetic medications between clinic visits in order to achieve treatment goals. SMBG can be performed with a meter that measures glucose from a fingerstick (or alternate site) drop of blood, or with a continuous glucose monitor (CGM), which is an external sensor with a filament that is inserted under the skin.
According to the American Diabetes Association 2019 Standards of Care (1) for optimizing self-monitoring of blood glucose (SMBG):
SMBG and CGM [continuous glucose monitoring] accuracy is dependent on the instrument and user, so it is important to evaluate each patient’s monitoring technique, both initially and at regular intervals thereafter. Optimal use of SMBG and CGM requires proper review and interpretation of the data, by both the patient and the provider, to ensure that data are used in an effective and timely manner. For patients with type 1 diabetes using CGM, the greatest predictor of A1C [average glucose] lowering for all age-groups was frequency of sensor use, which was highest in those aged ≥25 years and lower in younger age-groups. Similarly, for SMBG in patients with type 1 diabetes, there is a correlation between greater SMBG frequency and lower A1C. Among patients who check their blood glucose at least once daily, many report taking no action when results are high or low. Patients should be taught how to use SMBG and/or CGM data to adjust food intake, exercise, or pharmacologic therapy to achieve specific goals. The ongoing need for and frequency of SMBG should be reevaluated at each routine visit to avoid overuse, particularly if SMBG is not being used effectively for self-management.
The Standards of Care recommends:
7.6 Most patients using intensive insulin regimens (multiple daily injections or insulin pump therapy) should assess glucose levels using self-monitoring of blood glucose (or continuous glucose monitoring) prior to meals and snacks, at bedtime, occasionally postprandially, prior to exercise, when they suspect low blood glucose, after treating low blood glucose until they are normoglycemic, and prior to critical tasks such as driving. B
7.7 When prescribed as part of a broad educational program, self-monitoring of blood glucose may help to guide treatment decisions and/or self-management for patients taking less frequent insulin injections. B
7.8 When prescribing self-monitoring of blood glucose, ensure that patients receive ongoing instruction and regular evaluation of technique, results, and their ability to use data from self-monitoring of blood glucose to adjust therapy. Similarly, continuous glucose monitoring use requires robust and ongoing diabetes education, training, and support. E
The quality of this data depends upon the accuracy of the glucose monitor.
Patients assume their glucose monitor is accurate because it is FDA cleared, but often that is not the case. There is substantial variation in the accuracy of widely used blood glucose monitoring systems. The Diabetes Technology Society Blood Glucose Monitoring System Surveillance Program provides information on the performance of devices used for SMBG (https://www.diabetestechnology.org/surveillance.shtml). In a recent analysis, the program found that only 6 of the top 18 glucose meters met the accuracy standard.
Klonoff et al evaluated the accuracy of 18 personal glucose meters for the Diabetes Technology Society Blood Glucose Monitor System (BGMS) Surveillance Program , and found: (2)
Self-testing of blood glucose (BG) using a personal blood glucose monitor (BGM) is a cornerstone of diabetes treatment. BGMs are used for 1) measuring BG to determine therapeutic decisions, 2) calibrating continuous glucose monitoring systems, and 3) detection or confirmation of hypoglycemia. To be both safe and of clinical value, BGM systems should measure BG levels accurately.
In conclusion, 6 of the 18 best-selling personal BGMs met a protocol-specified accuracy standard similar to current ISO and FDA standards on three of three studies. These same six meters ranked highest according to four other metrics. Since patients depend on their BGMs for day-to-day management, lack of accuracy may put patients at risk for both hypoglycemia and hyperglycemia. We believe that this study points out the varying degrees to which commonly used BGMs do or do not give accurate information. We hope that this study will provide objective and validated information for patients, health care professionals, and payers to make informed product selection. We also hope that this study will provide important information that will lead regulators to consider introducing a mechanism to evaluate postmarket performance of these types of analytical products.
Of the 18 meters tested, the Bayer Contour Next was the most accurate. Specifically, 100% of the tests performed by the Contour Next were compliant with test standards (within 15% of reference value if >=100 (5.55 mmol/L) or 15 mg/dL [0.83 mmol/L] of reference value if <=100 mg/dL [5.55 mmol/L]. The coefficient of variation (SD of the % difference = (BGM reading 2 reference value)/(reference value) was 5.4%. Bias (the average difference as a percent of the reference value) was 1.2% below the reference value. 95% limits of agreement (the range that included 95% of values around the reference value) was -11 to +10.
The Bayer Contour Next One glucose meter fascilitates sending the glucose data to a medicalcare provider. It integrates the glucose test result with a smartphone app so that results are automatically synced and logged, and can be reviewed by a patient and/or sent to a medical care provider for analysis.
Diabetic mobile phone apps in combination with self glucose testing appears to improve diabetes care. Hou et al reviewed published data and stated: (3)
Participants from 14 studies (n = 1,360) were included and quality assessed.Although there may have been clinical diversity, all type 2 diabetes studies reported a reduction in HbA1c. The mean reduction in participants using an app compared with control was 0.49% (95% Cl 0.30, 0.68; I2 = 10%), with a moderate GRADE of evidence. Subgroup analyses indicated that younger patients were more likely to benefit from the use of diabetes apps, and the effect size was enhanced with health care professional feedback. There was inadequate data to describe the effectiveness of apps for type 1 diabetes.
Apps for diabetes have been evaluated by Chavez et al: (4)
We looked at the highest scoring apps, with respect to all four MARS [Mobile App Rating Scale] sections (engagement, functionality, aesthetics, and information), app subjective score, and diabetes management tasks score, for a total possible score of 31. The top scoring app (Tactio Health: My Connected Health Logbook) scored 28.61 points and integrated all six diabetes management tasks. The second app (ACCU-CHEK 360° Diabetes Mgmt) scored 25.94. Both were in the top percentile for all subscales.
In conclusion, self-monitoring of blood glucose can improve diabetes care. It is important that the glucose meter be accurate and testing technique be correct. Glucose data should be sent to a medical care provider for review. Diabetic care apps that are linked to the meter may provide addtional benefit.
1. American Diabetes Association. 7. Diabetes technology: Standards of Medical Care in Diabetes—2019. Diabetes Care 2019;42(Suppl. 1):S71–S80
2. Investigation of the Accuracy of 18 Marketed Blood GlucoseMonitors. David C. Klonoff,1 Joan Lee Parkes,2Boris P. Kovatchev,3 David Kerr,4Wendy C. Bevier,4 Ronald L. Brazg,5Mark Christiansen,6 Timothy S. Bailey,7James H. Nichols,8 and Michael A. Kohn9. Diabetes Care. 2018 Aug;41(8):1681-1688. doi: 10.2337/dc17-1960. Epub 2018 Jun 13
3. Do Mobile Phone ApplicationsImprove Glycemic Control (HbA1c) in the Self-management of Diabetes? A Systematic Review,Meta-analysis, and GRADE of14 Randomized Trials. Can Hou,1 Ben Carter,1,2 Jonathan Hewitt,1Trevor Francisa,1 and Sharon Mayor. Diabetes Care 2016;39:2089–2095 | DOI: 10.2337/dc16-0346
4. Mobile Apps for the Management of Diabetes. Chavez S, Fedele D, Guo Y, Bernier A, Smith M, Warnick J, and Modave F. Diabetes Care 2017;40:e145–e146 | https://doi.org/10.2337/dc17-0853
Disclaimer: Since healthcare is complicated and personal, you should discuss these topics with your healthcare provider before applying this information to your own health. This website does not intend to diagnose or treat any disease or medical condition. Its only purpose is to assist people to monitor their health at home under the supervision of their healthcare provider.