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.

Heart failure is a leading cause of death in the United States, with around 5 million Americans currently suffering from congestive heart failure. The WANDA B. wireless health technology leverages sensor technology and wireless communication to monitor heart failure patient activity and to provide tailored guidance. Patients who have cardiovascular system disorders can measure their weight, blood pressure, activity levels, and other vital signs in a real-time automated fashion. The system was developed in conjunction with the UCLA Nursing School and the UCLA Wireless Health Institute for use on actual patients. It is currently in use with real patients in a clinical trial.

Congestive heart failure (CHF) is a cardiovascular disorder that affects approximately 4.6 million Americans and is a leading cause of death in the United States. Current research shows that strategies to promote early recognition and treatment of symptoms and enhance self-care management behaviors reduce unnecessary hospitalizations. However, mechanisms to monitor patients' health status and behaviors are limited by constraints imposed by the patient's geography, infirmity, or resources. Remote monitoring supports a more dynamic connection between healthcare providers and patients, improves health promotion and patient care through monitoring of health data, communicates health reminders, and makes provisions for patient feedback. This paper will describe two versions of Weight and Activity with Blood Pressure Monitoring System (WANDA [22]) that leverages sensor technology and wireless communication to monitor health status of patients with CHF. The WANDA system is built on a three-tier architecture consisting of sensors, a web server, and back-end database tiers. 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 in a real-time automated fashion and allows health professionals to offer surveillance, advice, and continuity of care and triggers early implementation of strategies to enhance adherence behaviors. The small study has enabled patients to reduce or maintain the number of readings which are out of the acceptable range. For diastolic, systolic, and heart rate values, the t-test results show that the WANDA study is effective for patients with CHF.

Recent advances in wireless sensors, mobile technologies, and cloud computing have made continuous remote monitoring of patients possible. In this paper, we introduce the design and implementation of WANDA, an end-to-end remote health monitoring and analytics system designed for heart failure patients. The system consists of a smartphone-based data collection gateway, an Internet-scale data storage and search system, and a backend analytics engine for diagnostic and prognostic purposes. The system supports the collection of data from a wide range of sensory devices that measure patients' vital signs as well as self-reported questionnaires. The main objective of the analytics engine is to predict future events by examining physiological readings of the patients.We demonstrate the efficiency of the proposed analytics engine using the data gathered from a pilot study of 18 heart failure patients. In particular, our results show that the advanced analytic algorithms used in our system are capable of predicting the worsening of patients' heart failure symptoms with up to 74% accuracy while improving the sensitivity performance by more than 45% compared to the commonly used thresholding algorithm based on daily weight change. Moreover, the accuracy attained by our system is only 9% lower than the theoretical upper bound. The proposed framework is currently deployed in a large ongoing heart failure study that targets 1500 congestive heart failure patients.

Diabetes is the seventh leading cause of death in the United States, but careful symptom monitoring can prevent adverse events. A real-time patient monitoring and feedback system is one of the solutions to help patients with diabetes and their healthcare professionals monitor health-related measurements and provide dynamic feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the domain of remote health monitoring. This paper presents a wireless health project (WANDA) that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. The WANDA dynamic task management function applies data analytics in real-time to discretize continuous features, applying data clustering and association rule mining techniques to manage a sliding window size dynamically and to prioritize required user tasks. The developed algorithm minimizes the number of daily action items required by patients with diabetes using association rules that satisfy a minimum support, confidence and conditional probability thresholds. Each of these tasks maximizes information gain, thereby improving the overall level of patient adherence and satisfaction. Experimental results from applying EM-based clustering and Apriori algorithms show that the developed algorithm can predict further events with higher confidence levels and reduce the number of user tasks by up to 76.19 %.