Self-monitoring of blood glucose: from theory to practice in effective diabetes management

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

Background: This clinical case demonstrates the value of a participatory approach in the treatment of patients with type 2 diabetes with long-term decompensation. The case demonstrates that even with initially low treatment adherence, patient engagement in disease management using digital technologies, education, and lifestyle modification can significantly improve metabolic parameters.

Description of the clinical case: A 61-year-old patient with a long history of type 2 diabetes mellitus and severe decompensation consulted an endocrinologist. The initial data indicated a significant disturbance of carbohydrate metabolism (HbA1c – 12.5%, fasting glucose – 13.2 mmol/L), stage 1 obesity (BMI 30.2 kg/m2). At the time of presentation, the patient received fixed-dose hypoglycemic therapy: metformin 1000 mg twice daily, insulin glargine 40 U, insulin glulisine 10 U before meals. Self-monitoring of blood glucose was performed irregularly. The therapeutic intervention consistently included: structured self-monitoring of blood glucose using a glucometer and a mobile app; training in the rules of calculating BE and adjusting insulin therapy; dietary correction and introduction of regular physical activity; transition to an intensified insulin therapy regimen. After three months of follow-up, significant positive dynamics were recorded: HbA1c decreased to 8.5%, fasting glucose to 5.3 mmol/L. A 3 kg weight loss was noted (BMI 29.3 kg/m2).

Conclusion: Regular structured self-monitoring of blood glucose using modern digital technologies significantly increases patient awareness and adherence to the therapeutic regimen. Active patient involvement in disease management is a crucial factor for success, even in patients with a long history of diabetes and low initial motivation. The use of mobile apps for blood glucose and nutrition monitoring makes diabetes management more transparent, convenient, and analyzable, which contributes to the stabilization of the condition.

About the authors

D. M. Antsiferova

Endocrinology Dispensary of the Moscow Healthcare Department; Russian Medical Academy of Continuous Professional Education

Author for correspondence.
Email: cifrenda@yandex.ru
ORCID iD: 0000-0002-3920-5914

Endocrinologist, Endocrinology Dispensary of the Moscow Healthcare Department; Postgraduate Student, Department of Endocrinology, Russian Medical Academy of Continuous Professional Education

Russian Federation, Moscow; Moscow

References

  1. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in diabetes prevalence and treatment from 1990 to 2022: a pooled analysis of 1108 population-representative studies with 141 million participants. Lancet. 2024;404(10467):2077-2093.
  2. Дедов И.И., Шестакова М.В., Викулова О.К. и др. Сахарный диабет в Российской Федерации: динамика эпидемиологических показателей по данным Федерального регистра сахарного диабета за период 2010–2022 гг. Сахарный диабет. 2023;26(2):104–123. [Dedov I.I., Shestakova M.V., Vikulova O.K., et al. Diabetes mellitus in the Russian Federation: dynamics of epidemiological indicators according to the Federal Register of Diabetes Mellitus for the period 2010–2022. Diabetes mellitus. 2023;26(2):104–123. (In Russ.)].
  3. Sarol J.N. Jr., Nicodemus N.A. Jr., Tan K.M., Grava M.B. Self-monitoring of blood glucose as part of a multi-component therapy among non-insulin requiring type 2 diabetes patients: a meta-analysis (1966-2004). Curr Med Res Opin. 2005;21(2):173–184. https://dx.doi.org/10.1185/030079904X20286
  4. Parkin C.G., Buskirk A., Hinnen D.A., Axel-Schweitzer M. Results that matter: structured vs. unstructured self-monitoring of blood glucose in type 2 diabetes. Diabetes Res Clin Pract. 2012;97(1):6–15. https://dx.doi.org/10.1016/j.diabres.2012.03.002.S0168-8227(12)00098-8
  5. Mannucci E., Antenore A., Giorgino F., Scavini M. Effects of structured versus unstructured self-monitoring of blood glucose on glucose control in patients with non-insulin-treated type 2 diabetes: A meta-analysis of randomized controlled trials. J Diabetes Sci Technol. 2018;12(1):183–189. https://dx.doi.org/10.1177/1932296817719290
  6. Bosi E., Scavini M., Ceriello A. et al. Intensive structured self-monitoring of blood glucose and glycemic control in noninsulin-treated type 2 diabetes: The Prisma randomized trial. Diabetes Care. 2013;36(12):e218. https://dx.doi.org/10.2337/dc13-1683
  7. Khamseh M.E., Ansari M., Malek M. et al. Effects of a structured self-monitoring of blood glucose method on patient self-management behavior and metabolic outcomes in type 2 diabetes mellitus. J Diabetes Sci Technol. 2011;5(2):388–393. https://dx.doi.org/10.1177/193229681100500228.
  8. Li C.L., Wu Y.C., Kornelius E. et al. Comparison of different models of structured self-monitoring of blood glucose in type 2 diabetes. Diabetes Technol Ther. 2016;18(3):171–177. https://dx.doi.org/10.1089/dia.2015.0082
  9. Nishimura A., Harashima S.I., Fujita Y. et al. Effects of structured testing versus routine testing of blood glucose in diabetes selfmanagement: A randomized controlled trial. J Diabet Complicat. 2017;31(1):228–233. https://dx.doi.org/10.1016/j.jdiacomp.2016.08.019
  10. Polonsky W.H., Fisher L., Schikman C.H. et al. Structured self-monitoring of blood glucose significantly reduces A1C levels in poorly controlled, noninsulin-treated type 2 diabetes: results from the Structured Testing Program study. Diabetes Care. 2011;34(2):262–267. https://dx.doi.org/10.2337/dc10-1732
  11. Parsons S.N., Luzio S.D., Harvey J.N. et al. Effect of structured self-monitoring of blood glucose, with and without additional TeleCare support, on overall glycaemic control in non-insulin treated Type 2 diabetes: the SMBG Study, a 12-month randomized controlled trial. Diabet Med. 2019;36(5):578–590. doi: 10.1111/dme.13899
  12. Davidson P.C., Hebblewhite H.R., Bode B.W., et al. Statistically fitted curve for A1c as a function of the SMBG tests per day. Program and abstracts from the 64th Scientific Sessions of the American Diabetes Association. Orlando, Florida, June 4–8 2004. Abstract 430-P.
  13. Chang Y.T., Tu Y.Z., Chiou H.Y., et al. Real-world benefits of diabetes management app use and self-monitoring of blood glucose on glycemic control: retrospective analyses. JMIR Mhealth Uhealth. 202215;10(6):e31764. https://dx.doi.org/10.2196/31764
  14. Bailey T.S. Wallace J.F., Pardo S., et al. Accuracy and user performance evaluation of a new, wireless-enabled blood glucose monitoring system that links to a smart mobile device. J Diabetes Sci Technol. 2017;11(4):736–743. https://dx.doi.org/10.1177/1932296816680829R
  15. Shaginian R., Pardo S., Trifonova I., Richardson J. Glycemic control of patients with diabetes in Russia who were using the Contour®Plus One BGMS with Contour®Diabetes app. Diabetes Technology & Therapeutics. 2022;24(S1) https://doi.org/10.1089/dia.2022.2525.abstractsА

Supplementary files

Supplementary Files
Action
1. JATS XML

Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).