Medical & Biological Engineering & Computing

, Volume 53, Issue 12, pp 1295–1303 | Cite as

Performance assessment of a closed-loop system for diabetes management

  • A. Martinez-Millana
  • G. Fico
  • C. Fernández-Llatas
  • V. Traver
Special Issue - Original Article

Abstract

Telemedicine systems can play an important role in the management of diabetes, a chronic condition that is increasing worldwide. Evaluations on the consistency of information across these systems and on their performance in a real situation are still missing. This paper presents a remote monitoring system for diabetes management based on physiological sensors, mobile technologies and patient/doctor applications over a service-oriented architecture that has been evaluated in an international trial (83,905 operation records). The proposed system integrates three types of running environments and data engines in a single service-oriented architecture. This feature is used to assess key performance indicators comparing them with other type of architectures. Data sustainability across the applications has been evaluated showing better outcomes for full integrated sensors. At the same time, runtime performance of clients has been assessed spotting no differences regarding the operative environment.

Keywords

mHealth SOA Diabetes Sensors Telemonitoring Performance KPI 

Notes

Acknowledgments

The authors wish to acknowledge the consortium of the METABO project (funded by the European Commission, Grant nr. 216270) for their commitment during concept development and trial execution.

References

  1. 1.
    Bellazzi R, Larizza C, Montani A et al (2002) A telemedicine support dor diabetes management: the T-IDDM project. Comput Methods Programs Biomed 69:147–161Google Scholar
  2. 2.
    Boloor K, Chirkova R, Salo T, Viniotis Y (2011) Analysis of response time percentile service level agreements in soa-based applications. IEEE global telecommunications conference (GLOBECOM 2011), dec. 2011, pp 1–6Google Scholar
  3. 3.
    Cartwright M et al (2013) Effect of telehealth on quality of life and psychological outcomes over 12 months: nested study of patient reported outcomes in a pragmatic, cluster randomised controlled trial. BMJ 346:f653Google Scholar
  4. 4.
    Chen I-Y et al (2008) Pervasive digital monitoring and transmission of pre-care patient biostatics with an OSGi, MOM and SOA based remote health care system. In: Proceedings of the 6th annual IEEE international conference on PerCom. Hong KongGoogle Scholar
  5. 5.
    Fico G, Fioravanti A, Arredondo MT, Leuteritz JP, Guillén A, Fernandez D (2011) A user centered design approach for patient interfaces to a diabetes IT platform. Conf Proc IEEE Eng Med Biol Soc 2011:1169–1172PubMedGoogle Scholar
  6. 6.
    Fioravanti A, Fico G, Arredondo MT, Salvi D, Villalar JL (2010) Integration of heterogeneous biomedical sensors into an ISO/IEEE 11073 compliant application. In: Engineering in medicine and biology society (EMBC), 2010 Annual international conference of the IEEE, pp 1049–1052Google Scholar
  7. 7.
    García Saez G et al (2009) Architecture of a wireless personal assistant for telemedical diabetes care. Int J Med Inform 9(78):391–403Google Scholar
  8. 8.
    Gómez EJ, Hernando ME et al (2008) The INCA system: a further step towards a telemedical artificial pancreas. IEEE Trans Inf Technol Biomed 12(4):470–479Google Scholar
  9. 9.
    Harrison’s Principles of Internal Medicine (2011) McGraw-Hill. ISBN:978-0071748896. Ed. July 2011Google Scholar
  10. 10.
    Ke X, Li W et al (2010) WCDMA KPI framework definition methods and applications. ICCET proceedings V4-471–V4-475Google Scholar
  11. 11.
    Klonof D (2013) Twelve modern digital technologies that are transforming decision making for diabetes and all areas of health care. J Diabetes Sci Technol 7(2):291–295CrossRefGoogle Scholar
  12. 12.
    Lanzola G et al (2007) Going mobile with a multiaccess service for the management of diabetic patients. J Diabetes Sci Technol 1(5):730–737Google Scholar
  13. 13.
    Ma C et al (2006) Empowering patients with essential information and communication support in the context of diabetes. Int J Med Inform 75(8):577–596Google Scholar
  14. 14.
    Müller AJ, Knuth M, Nikolaus KS, Krivánek R, Küster F, Hasslacher C (2013) First clinical evaluation of a new percutaneous optical fiber glucose sensor for continuous glucose monitoring in diabetes. J Diabetes Sci Technol 7:13Google Scholar
  15. 15.
    Nundy S et al (2012) Using mobile health to support chronic care model: developing an institutional model. Int J Telemed Appl 2012, Art Id 871925. doi:10.1155/2012/871925
  16. 16.
    Obstfelder A, Engeseth KH, Wynn R (2007) Characteristic of succesfully implemented telemedical applications. Implement Sci 2:25Google Scholar
  17. 17.
    Pravin P et al (2012) A framework for the comparison of mobile patient monitoring systems. J Biomed Inf 45:544–556Google Scholar
  18. 18.
    Reichel A, Rietzsch H, Ludwig B, Röthig K, Moritz A, Bornstein S (2013) Self-adjustment of insulin dose using graphically depicted self-monitoring of blood glucose measurements in patients with type 1 diabetes mellitus. J Diabetes Sci Technol 7(1):156–162PubMedCentralCrossRefPubMedGoogle Scholar
  19. 19.
    Ryan D et al (2012) Clinical and cost effectiveness of mobile phone supported self-monitoring of asthma: multicenter randomized controlled trial. BMJ 344:e1756Google Scholar
  20. 20.
    Schade DS et al (2005) To pump or not to pump. Diabetes Technol Therapeutics 7:845–848Google Scholar
  21. 21.
    Stravroula G, Bartsocas CS et al (2010) SMARTDIAB: a communication and information technology approach for the intelligent monitoring, management and follow-up of type 1 diabetes patients. IEEE Trans Inf Technol Biomed 14(3):622–633Google Scholar
  22. 22.
    The Diabetes Control and Complications Trial Research Group (1993) The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med 329(14):977–986CrossRefGoogle Scholar
  23. 23.
    Trief PM, Morin PC, Izquierdo R, Teresi JA, Eimicke JP, Goland R, Starren J, Shea S, Winstock RS (2006) Depression and glycaemic control in elderly etchnically diverse patients with diabetes: the IDEATel project. Diabetes Care 29(4):830–835Google Scholar
  24. 24.
    van der Weegentres S et al (2013) The development of a mobile monitoring and feedback tool to stimulate physical activity of people with a chronic disease in primary care: a user-centered design. JMIR 1(2):e8Google Scholar
  25. 25.
    Wakefield BJ et al (2014) Effect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes. Telemed e-Health 20(3):199–205. doi:10.1089/tmj.2013.0151
  26. 26.
    Winkler S et al (2011) A new telemonitoring system intended for chronic heart failure patients using mobile technology—Feasibility Study. Int J Cardiol 153:55–58Google Scholar
  27. 27.
    Zhou YY, Kanter MH, Wang JJ, Garrido T (2010) Improved quality at kaiser permanente through e-mail between physicians and patients. Health Aff 29(7):1370–1375Google Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2015

Authors and Affiliations

  • A. Martinez-Millana
    • 1
  • G. Fico
    • 2
  • C. Fernández-Llatas
    • 1
  • V. Traver
    • 1
  1. 1.ITACA, Health and Wellbeing TechnologiesUniversidad Politécnica de ValenciaValenciaSpain
  2. 2.Life Supporting TechnologiesUniversidad Politécnica de MadridMadridSpain

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