Advertisement

eDiab: A System for Monitoring, Assisting and Educating People with Diabetes

  • L. Fernández-Luque
  • J. L. Sevillano
  • F. J. Hurtado-Núñez
  • F. J. Moriana-García
  • F. Díaz del Río
  • D. Cascado
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4061)

Abstract

In this paper, a system developed for monitoring, assisting and educating people with diabetes, named eDiab, is described. A central node (PDA or mobile phone) is used at the patient’s side for the transmission of medical information, health advices, alarms, reminders, etc. The software is adapted to blind users by using a screen reader called Mobile Speak Pocket/Phone. The glucose sensor is connected to the central node through wireless links (Zigbee/Bluetooth) and the communication between the central node and the server is established with a GPRS/GSM connection. Finally, a subsystem for health education (which sends medical information and advice like treatment reminder), still under development, is briefly described.

Keywords

Mobile Phone Central Node Glucose Sensor Disease Severity Index Screen Reader 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    King, H., Aubert, R.E., Herman, W.H.: Global Burden of Diabetes 1995-2025: Prevalence, Numerical Estimates and Projections – World Health Organization. Diabetes Care 21, 1414–1431 (1998)CrossRefGoogle Scholar
  2. 2.
    Gan, D., et al.: Diabetes Atlas. International Diabetes Federation (2003), http://www.eatlas.idf.org/
  3. 3.
    Roglic, G., et al.: The Burden of Mortality Attributable to Diabetes World Health Organization. Diabetes Care 28, 2130–2135 (2005)CrossRefGoogle Scholar
  4. 4.
    Rosenzweig, J.L., Weinger, K., et al.: Use of a disease severity index for evaluation of healthcare costs and management of comorbidities of patients with diabetes mellitus. In: Office for Disease Management, Joslin Diabetes Center, Boston (2002)Google Scholar
  5. 5.
    Rahmani, B., Tielsch, J.M., et al.: The cause-specific prevalence of visual impairment in an urban population. The Baltimore Eye Survey. Ophthalmology 03, 1721–1726 (1996)Google Scholar
  6. 6.
    Haffner, S.M., et al.: Mortality from coronary heart disease in subjects with type 2 diabetes and in nondiabetic subjects with and without prior myocardial infarction. New England Journal of Medicine 339, 229–234 (1998)CrossRefGoogle Scholar
  7. 7.
    Lin, E.H., Katon, W., et al.: Relationship of depression and diabetes self-care, medication adherence, and preventive care. Diabetes Care 27, 2154–2160 (2004)CrossRefGoogle Scholar
  8. 8.
    Norris, S.L., Engelgau, M.M., et al.: Effectiveness of self-management training in type 2 diabetes. A systematic review of randomized controlled trials. Diabetes Care 24, 561–587 (2001)CrossRefGoogle Scholar
  9. 9.
    Norris, S.L., Lau, J., et al.: Self-Management Education for Adults with Type 2 Diabetes: A meta-analysis of the effect on glycemic control. Diabetes Care Journal (25), 1159–1171 (2002)Google Scholar
  10. 10.
    Montani, S., Bellazzi, R., Quaglini, S., D’Annunzio, G., et al.: Meta-analysis of the effect of the use of computerbased systems on the metabolic control of patients with diabetes mellitus. Diabetes Technol. Ther., 347–356 (2001)Google Scholar
  11. 11.
    Jackson, Chandra, L., Bolen, et al.: A Systematic Review of Interactive Computer-assisted Technology in Diabetes Care. Interactive Information Technology in Diabetes Care. Journal of General Internal Medicine 21(2), 105–110 (2006)Google Scholar
  12. 12.
    Lewis, D.: Computer-based Approaches to Patient Education A Review of the Literature. Journal of American Medical Informatics Association, 272–282 (July–August 1999)Google Scholar
  13. 13.
    McMahon, G.T., Gomes, H.E., et al.: Web-Based Care Management in Patients with Poorly Controlled Diabetes. Diabetes Care Journal 28, 1624–1629 (2005)CrossRefGoogle Scholar
  14. 14.
    Klonoff, D.C.: Diabetes and telemedicine. Is the technology sound, effective, cost-effective and practical? Diabetes Care 26, 1626–1928 (2003)CrossRefGoogle Scholar
  15. 15.
    Starren, J., et al.: Columbia University’s Informatics for Diabetes Education and Telemedicine (IDEATel) Project. Journal of American Medical Informatics Association 9, 25–36 (2002)Google Scholar
  16. 16.
    Bellazi, R., Arcelloni, M., et al.: Design, methods, and evaluation directions of a multi-access service for the management of diabetes mellitus patients. Diabetes Technology & Therapeutics 5(4), 621–629 (2003)CrossRefGoogle Scholar
  17. 17.
    European Project Inca, Intelligent Control Assistant for Diabetes, 5th EC Framework Programme, http://www.ist-inca.org
  18. 18.
    Cafazzo, J.: Tele-management of Diabetic Hypertension: The use of the digital medical diary. In: eHealth Conference (2005)Google Scholar
  19. 19.
    Williamson, T.H., et al.: Telemedicine and computers in diabetic retinopathy screening. British Journal of Ophthalmology 82, 5–6 (1998)CrossRefGoogle Scholar
  20. 20.
    Tejedor, J., Bolaños, D., et al.: Multimedia Medicine Consultant for Visually Impaired People. In: Miesenberger, K., Klaus, J., Zagler, W., Burger, D. (eds.) ICCHP 2004. LNCS, vol. 3118, pp. 564–570. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  21. 21.
    Khan, A.H.M.N., Chowdhury, S.N.: Computer for Persons with Visually Impairment: A Door to Education, Information and Employment. In: Miesenberger, K., Klaus, J., Zagler, W., Burger, D. (eds.) ICCHP 2004. LNCS, vol. 3118, pp. 571–575. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  22. 22.
    Mobile Speak Software, http://www.codefactory.es/
  23. 23.
    Telegesis Inc., http://www.telegesis.com
  24. 24.
    Nokia 6630 Technical Characteristics, http://www.nokia.co.uk/nokia/0,,59880,00.html
  25. 25.
    Gutierrez, J.A., Callaway, E.H., Barrett, R.: Low-Rate Wireless Personal Area Networks: Enabling Wireless Sensor Networks with IEEE 802.15.4. IEEE Press, Los Alamitos (2004)Google Scholar
  26. 26.
    Holden, M.K., Dyar, T., Schwamm, L., et al.: Home-based Telerehabilitation using a virtual environment system. In: Burdea, G.C., Thalmann, D., Lewis, J.A. (eds.) Proceedings of the 2nd International Workshop on Virtual Rehabilitation, 2003, pp. 4–12 (2003)Google Scholar
  27. 27.
    Hirst, G., DiMarco, C., Hovy, E., Parsons, K.: Authoring and generating health-education documents that are tailored to the needs of the individual patient. In: Proceedings of the Sixth International Conference on User Modeling, Sardinia, Italy, pp. 107–118 (1997)Google Scholar
  28. 28.
    DiMarco, C., Bray, P., Covvey, D., Cowan, D., DiCiccio, V., Lipa, J., Hovy, E.H.: Authoring and Generation of Tailored Preoperative Patient Education Materials. In: Proceedings of the Workshop on Personalisation for e-Health at the User Modeling (UM) Conference, Edinburgh, UK (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • L. Fernández-Luque
    • 1
  • J. L. Sevillano
    • 1
  • F. J. Hurtado-Núñez
    • 2
  • F. J. Moriana-García
    • 3
  • F. Díaz del Río
    • 1
  • D. Cascado
    • 1
  1. 1.Robotics & Computer Technology for Rehabilitation LaboratoryUniversity of SevillaSpain
  2. 2.Diabetes Education Study GroupClinical Psychologist – Specialist in Diabetic Education. Spanish Society of Diabetes (SED) 
  3. 3.MSc Computer Science – eHealth Consultant 

Personalised recommendations