A Remote Patient Monitoring System for Congestive Heart Failure

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.

[1]  B. Riegel,et al.  Psychometric Testing of the Heart Failure Somatic Awareness Scale , 2006, The Journal of cardiovascular nursing.

[2]  C. Mohan,et al.  An efficient and flexible method for archiving a data base , 1993, SIGMOD Conference.

[3]  John D. McGregor,et al.  Community , 2016, physiopraxis.

[4]  C. Mohan,et al.  Coordinating backup/recovery and data consistency between database and file systems , 2002, SIGMOD '02.

[5]  Akshay S. Desai,et al.  Connecting the circle from home to heart-failure disease management. , 2010, The New England journal of medicine.

[6]  Natalya F. Noy,et al.  Semantic integration: a survey of ontology-based approaches , 2004, SGMD.

[7]  Majid Sarrafzadeh,et al.  An automated vital sign monitoring system for congestive heart failure patients , 2010, IHI.

[8]  J. Larson.  The MOS 36-Item Short form Health Survey , 1997, Evaluation & the health professions.

[9]  D. Levy,et al.  Impact of high-normal blood pressure on the risk of cardiovascular disease. , 2002, The New England journal of medicine.

[10]  G. Lamas,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. , 2007, Journal of cardiac failure.

[11]  G. Schuler,et al.  Exercise Capacity in Patients With Chronic Heart Failure Regular Physical Exercise Corrects Endothelial Dysfunction and Improves , 1998 .

[12]  Steve Pettifer,et al.  Visualising biological data: a semantic approach to tool and database integration , 2009, BMC Bioinformatics.

[13]  D. Mozaffarian,et al.  Heart disease and stroke statistics--2009 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. , 2009, Circulation.

[14]  Anil Sethi,et al.  Matching records in a national medical patient index , 2001, CACM.

[15]  Daniel Levy,et al.  Systolic Blood Pressure, Diastolic Blood Pressure, and Pulse Pressure as Predictors of Risk for Congestive Heart Failure in the Framingham Heart Study , 2003, Annals of Internal Medicine.

[16]  E K Antonsson,et al.  The frequency content of gait. , 1985, Journal of biomechanics.

[17]  M. Zile,et al.  Transition From Chronic Compensated to Acute Decompensated Heart Failure: Pathophysiological Insights Obtained From Continuous Monitoring of Intracardiac Pressures , 2008, Circulation.

[18]  D. Levy,et al.  Obesity and the risk of heart failure. , 2003, The New England journal of medicine.

[19]  H. Krumholz,et al.  Telemonitoring in patients with heart failure. , 2010, The New England journal of medicine.

[20]  John Spertus,et al.  Randomized trial of Telemonitoring to Improve Heart Failure Outcomes (Tele-HF): study design. , 2007, Journal of cardiac failure.

[21]  P. Whelton,et al.  Risk factors for congestive heart failure in US men and women: NHANES I epidemiologic follow-up study. , 2001, Archives of internal medicine.

[22]  Inger Ekman,et al.  Reasons for seeking acute care in chronic heart failure , 2007, European journal of heart failure.

[23]  Douglas W Mahoney,et al.  Burden of systolic and diastolic ventricular dysfunction in the community: appreciating the scope of the heart failure epidemic. , 2003, JAMA.

[24]  Shingo Oda,et al.  Light-intensity activities are important for estimating physical activity energy expenditure using uniaxial and triaxial accelerometers , 2008, European Journal of Applied Physiology.

[25]  Robert H. G. Schwinger,et al.  Epidemiology and economic burden of chronic heart failure , 2004 .

[26]  D. Mozaffarian,et al.  Heart disease and stroke statistics--2009 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. , 2009, Circulation.

[27]  G. Lamas,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. , 2010, Journal of cardiac failure.

[28]  N. Jones.  Clinical Exercise Testing , 1982 .

[29]  Barbara Liskov,et al.  Keynote address - data abstraction and hierarchy , 1988, OOPSLA '87.

[30]  Matsuki Yamamoto,et al.  THE RELATIONSHIP BETWEEN BODY WEIGHT REDUCTION AND INTENSITY OF DAILY PHYSICAL ACTIVITIES ASSESSED WITH 3-DIMENSION ACCELEROMETER , 2006 .

[31]  Nigel H. Lovell,et al.  Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring , 2006, IEEE Transactions on Information Technology in Biomedicine.

[32]  Majid Sarrafzadeh,et al.  WANDA B.: Weight and activity with blood pressure monitoring system for heart failure patients , 2010, 2010 IEEE International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM).

[33]  G R HERRMANN,et al.  Congestive heart failure. , 1962, Geriatrics.

[34]  C. Sherbourne,et al.  The MOS 36-item Short-Form Health Survey (SF-36): III. Tests of data quality, scaling assumptions, and reliability across diverse patient groups. , 1994 .