Skip to main content

Towards Harmonized Data Processing in SMBG

  • Conference paper
  • First Online:
Precision Medicine Powered by pHealth and Connected Health (ICBHI 2017)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 66))

Included in the following conference series:


Self-monitoring of blood glucose (SMBG) is the key activity in diabetes management. Patients are required to take measurements and act accordingly, while the physicians use measured data to adjust the therapy. Though the accuracy of individual glucose meters used for SMBG is limited, the main difficulty in interpretation of the recorded data is due to inaccuracy of the records made by patients themselves into the paper diabetic diary. Oftentimes, patients do not record data properly and therefore the data is not reliable for use in determining long-term changes and trends or to use it for further analysis. Therefore, analysis and decision making should rely on the values recorded and stored in glucose memory. The large variety of glucometer models on the market introduce a large problem in using the recorded values since companies which produce and sale glucometers do not necessarily base their data transmission code on accepted standards but they embed custom made code. Data from 37 models of glucometers is transferred into a cloud based platform using previously developed system and available for immediate analysis and for saving into an appropriate health registry in a harmonized structure despite differences in protocols and data structure of different meters. Immediate statistics are given to the physicians upon patient’s checkup. However, general statistical metrics usually do not include metrics on glucose variability, which is one of the most important measurements of glycemic control. We added glycemic variability metrics, including other metrics into tool for data analysis using MATLAB. The output of the analysis can be stored in the system and can be combined with the existing healthcare registries to develop multidimensional analysis for new knowledge discovery. This paper describes the system for acquisition of SMBG data, MATLAB analysis software and the notes on the analysis of the previously discussed data set.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others


  1. Dailey G (2007) Assessing glycemic control with self-monitoring of blood glucose and hemoglobin A(1c) measurements. In: Mayo clinic proceedings. Mayo Clinic, vol 82, no 2, pp 229–235; quiz 236.

    Article  Google Scholar 

  2. Benjamin EM (2002) Self-monitoring of blood glucose: the basics. Clin Diabetes 20(1):45–47.

    Article  MathSciNet  Google Scholar 

  3. Prašek M (2011) Self-control diary—challenges of new technological possibilities, pp 972–973. Springer, Berlin, Heidelberg.

    Google Scholar 

  4. Parkin CG, Davidson JA, Affiliations A (2009) Value of self-monitoring blood glucose pattern analysis in improving diabetes outcomes. J Diabetes Sci Technol 33(33):500–508

    Article  Google Scholar 

  5. Rodbard D (2009) Display of glucose distributions by date, time of day, and day of week: new and improved methods. J Diabetes Sci Technol 3(6):1388–1394.

    Article  Google Scholar 

  6. Clar C, Barnard K, Cummins E, Royle P, Waugh N, Aberdeen Health Technology Assessment Group (2010) Self-monitoring of blood glucose in type 2 diabetes: systematic review. Health Technol Assess 14(12):1–140.

  7. Žulj S, Celić L, Grgurević M, Prašek M, Magjarević R (2016) Pilot project: ICT system for management and self- management of diabetes, 29 June

    Google Scholar 

  8. Greenwood DA, Blozis SA, Young HM, Nesbitt TS, Quinn CC (2015) Overcoming clinical inertia: a randomized clinical trial of a telehealth remote monitoring intervention using paired glucose testing in adults with type 2 diabetes. J Med Internet Res 17(7):e178.

    Article  Google Scholar 

  9. Wojcicki JM, Ladyzynski P, Foltynski P (2013) What we can really expect from telemedicine in intensive diabetes treatment: 10 years later. Diabetes Technol Ther 15(3):260–268.

    Article  Google Scholar 

  10. Rodbard D (2007) Optimizing display, analysis, interpretation and utility of self-monitoring of blood glucose (SMBG) data for management of patients with diabetes. J Diabetes Sci Technol (Online) 1(1):62–71.

    Article  Google Scholar 

  11. Kovatchev B, Cobelli C (2016) Glucose variability: timing, risk analysis, and relationship to hypoglycemia in diabetes. Diabetes Care 39(4):502–510.

    Article  Google Scholar 

  12. Kubiak T, Mann CG, Barnard KC, Heinemann L (2016) Psychosocial aspects of continuous glucose monitoring: connecting to the patients’ experience. J Diabetes Sci Technol 10(4):859–863.

    Article  Google Scholar 

Download references

Conflict of Interest

The authors declare that they have no conflict of interest.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Sara Zulj .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zulj, S., Seketa, G., Magjarevic, R. (2018). Towards Harmonized Data Processing in SMBG. In: Maglaveras, N., Chouvarda, I., de Carvalho, P. (eds) Precision Medicine Powered by pHealth and Connected Health. ICBHI 2017. IFMBE Proceedings, vol 66. Springer, Singapore.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7418-9

  • Online ISBN: 978-981-10-7419-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics