Skip to main content

A Remote Patient Monitoring System for Congestive Heart Failure

Abstract

Congestive heart failure (CHF) is a leading cause of death in the United States affecting approximately 670,000 individuals. Due to the prevalence of CHF related issues, it is prudent to seek out methodologies that would facilitate the prevention, monitoring, and treatment of heart disease on a daily basis. This paper describes WANDA (Weight and Activity with Blood Pressure Monitoring System); a study that leverages sensor technologies and wireless communications to monitor the health related measurements of patients with CHF. The WANDA system is a three-tier architecture consisting of sensors, web servers, and back-end databases. The system was developed in conjunction with the UCLA School of Nursing and the UCLA Wireless Health Institute to enable early detection of key clinical symptoms indicative of CHF-related decompensation. This study shows that CHF patients monitored by WANDA are less likely to have readings fall outside a healthy range. In addition, WANDA provides a useful feedback system for regulating readings of CHF patients.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

References

  1. Keith, J. D., Congestive heart failure. Pediatrics 18(3):491–500, 1956.

    MathSciNet  Google Scholar 

  2. Lloyd-Jones, D., Adams, R., et al., Heart Disease and Stroke Statistics 2009 update: a report from the American Heart Association statistics committee and stroke statistics subcommittee. Circulation 119:e21–e181, 2009.

    Article  Google Scholar 

  3. Bundkirchen, A., Epidemiology and economic burden of chronic heart failure. Eur. Heart J. Suppl., D57–D60, 2004.

  4. Suh, M. K., Evangelista, L., et al., An Automated Vital Sign Monitoring System for Congestive Heart Failure Patients. ACM International Health Informatics Symposium, 2010.

  5. Suh, M. K., Evangelista, L., et al., WANDA B.: Weight and Activity with Blood Pressure Monitoring System for Heart Failure Patients, IEEE Workshop on Interdisciplinary Research on E-Health Services and Systems, 2010.

  6. Kenchaiah, S., Evans, J. C., Levy, D., et al., Obesity and the risk of heart failure. N. Engl. J. Med. 34(5):305–313, 2002.

    Article  Google Scholar 

  7. Vasan, R. S., Impact of high-normal blood pressure on the risk of cardiovascular disease. N. Engl. J. Med. 345(18):1291–1297, 2001.

    Article  Google Scholar 

  8. Haider, A. W., Systolic blood pressure, diastolic blood pressure, and pulse pressure as predictors of risk for congestive heart failure in the Framingham Heart Study. Ann. Intern. Med. 138(1):10–16, 2003.

    Google Scholar 

  9. Redfield, M. M., Burden of systolic and diastolic ventricular dysfunction in the community: appreciating the scope of the heart failure epidemic. JAMA 289(2):194–202, 2003.

    Article  Google Scholar 

  10. He, J., Risk factors for congestive heart failure in US men and women: NHANES I epidemiologic follow-up study. Arch. Intern. Med. 161(7):996–1002, 2001.

    Article  Google Scholar 

  11. Hambrecht, R., Regular physical exercise corrects endothelial dysfunction and improves exercise capacity in patients with chronic heart failure. Circulation 98(24):2709–2715, 1998.

    Google Scholar 

  12. Jurgens, C. Y., Psychometric testing of the heart failure somatic awareness scale. J. Cardiovasc. Nurs. 21(2):95–102, 2006.

    Google Scholar 

  13. Chaudhry, S. I., et al., Randomized trial of telemonitoring to improve heart failure outcomes (Tele-HF): study design. J. Card. Fail. 13(9):709–714, 2007.

    Article  Google Scholar 

  14. Chaudhry, S. I., et al., Telemonitoring in patients with heart failure. N. Engl. J. Med. 363(24):2301–2309, 2010.

    Article  Google Scholar 

  15. Pharos Innovations. Pharos Innovations. http://www.pharosinnovations.com/, 2011.

  16. Soran, O. Z., et al., Cost of medical services in older patients with heart failure: those receiving enhanced monitoring using a computer-based telephonic monitoring system compared with those in usual care: the heart failure home care trial. J. Card. Fail. 16(11):859–866, 2010.

    Article  Google Scholar 

  17. Soran, O. Z., et al., A randomized clinical trial of the clinical effects of enhanced heart failure monitoring using a computer-based telephonic monitoring system in older minorities and women. J. Card. Fail. 14(9):711–717, 2008.

    Article  Google Scholar 

  18. Alere. Alere. http://www.alere.com/, 2011.

  19. Desai Akshay, S., et al., The circle from home to heart-failure disease management. N. Engl. J. Med. 363:2364–2367, 2010.

    Article  Google Scholar 

  20. Zile, M. R., Bennett, T. D., St John Sutton, M., et al., Transition from chronic compensated to acute decompensated heart failure: patho physiological sights obtained from continuous monitoring of intracardiac pressures. Circulation 118:1433–1441, 2008.

    Article  Google Scholar 

  21. UCLA Wireless Health Community. UCLA. http://www.wirelesshealth.ucla.edu/, 2011.

  22. McHorney, C. A., The MOS 36-item Short-Form Health Survey (SF-36): III. Tests of data quality, scaling assumptions, and reliability across diverse patient groups. Med. Care 32(1):40–66, 1994.

    Article  Google Scholar 

  23. Ideal Life. Ideal Life. http://www.ideallifeonline.com/, 2011.

  24. A&D. A&D Engineering, Inc. http://www.andonline.com/.

  25. Roving Networks. Roving Networks, Inc. http://www.rovingnetworks.com/.

  26. Jones, N. L., Clinical exercise testing, 2nd edition. Saunders, Philadelphia, 1982.

    Google Scholar 

  27. Yamada, Y., Light-intensity activities are important for estimating physical activity energy expenditure using uniaxial and triaxial accelerometers. Eur. J. Appl. Physiol. 105(1):141–152, 2009.

    Article  Google Scholar 

  28. Hara, T., The relationship between body weight reduction and intensity of daily physical activities assessed with 3-dimension accelerometer. Jpn. J. Phys. Fitness Sports Med. 55(4):385–391, 2006.

    Google Scholar 

  29. Patel, H., Reasons for seeking acute care in chronic heart failure. Eur. J. Heart Fail. 9(6–7):702–708, 2007.

    Article  Google Scholar 

  30. Antonsson, E. K., The frequency content of gait. J. Biomech. 18(1):39–47, 1985.

    Article  Google Scholar 

  31. Karantonis, D. M., Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Trans. Inf. Technol. Biomed. 10(1):156–167, 2006.

    Article  Google Scholar 

  32. Google Maps API Family. Google. http://code.google.com/apis/maps/, 2011.

  33. Liskov, B., Keynote address-data abstraction and hierarchy. SIGPLAN Not. 23(5):17–34, 1988.

    Article  Google Scholar 

  34. Pettifer, S., Visualising biological data: a semantic approach to tool and database integration. BMC Bioinform. 10(suppl 6):S19, 2009.

    Article  Google Scholar 

  35. Bell, G. B., Matching records in a national medical patient index. Commun. ACM 44(9):83–88, 2001.

    Article  Google Scholar 

  36. Noy, N. F., Semantic integration: a survey of ontology-based approaches. SIGMOD Rec. 33(4):65–70, 2004.

    Article  Google Scholar 

  37. SOPHI. UCLA. http://cs.ucla.edu/∼ani/SOPHI/, 2010.

  38. Mohan, C., An efficient and flexible method for archiving a data base. SIGMOD Rec. 22(2):139–146, 1993.

    Article  MathSciNet  Google Scholar 

  39. Bhattacharya, S., Coordinating backup/recovery and data consistency between database and file systems. In Proceedings of the 2002 ACM SIGMOD international conference on Management of data, 500–511, 2002.

  40. WANDA. UCLA.http://www.wandab.net, 2011.

Download references

Acknowledgements

The authors would like to acknowledge the funding sources: NIH/National Library of Medicine Medical Informatics Training Program Grant T15 LM07356, National Institute of Health-National Heart, Lung, and Blood Institute Grant 1R01HL093466-01, and NetScienctific. Dr. Evangelista also received support from the University of California, School of Nursing Intramural Research Grant and the University of California, Los Angeles, Resource Centers for Minority Aging Research/Center for Health Improvement of Minority Elderly (RCMAR/CHIME) under NIH/NIA Grant P30-AG02-1684. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Aging or the National Institutes of Health. We would like to thank Wen-Sao Hong, Victor Chen, Professor William Kaiser and Professor Alex Bui for their help.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Myung-kyung Suh.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Suh, Mk., Chen, CA., Woodbridge, J. et al. A Remote Patient Monitoring System for Congestive Heart Failure. J Med Syst 35, 1165–1179 (2011). https://doi.org/10.1007/s10916-011-9733-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-011-9733-y

Keywords

  • Health monitoring
  • Telemedicine
  • Wireless health
  • Congestive heart failure patients monitoring
  • Real-time feedback
  • Data integrity
  • Database backup