Remote Patient Monitoring System Research Articles

IoT-Based Heartbeat Rate-Monitoring Device Powered by Harvested Kinetic Energy.

Remote patient-monitoring systems are helpful since they can provide timely and effective healthcare facilities. Such online telemedicine is usually achieved with the help of sophisticated and advanced wearable sensor technologies. The modern type of wearable connected devices enable the monitoring of vital sign parameters such as: heart rate variability (HRV) also known as electrocardiogram (ECG), blood pressure (BLP), Respiratory rate and body temperature, blood pressure (BLP), respiratory rate, and body temperature. The ubiquitous problem of wearable devices is their power demand for signal transmission; such devices require frequent battery charging, which causes serious limitations to the continuous monitoring of vital data. To overcome this, the current study provides a primary report on collecting kinetic energy from daily human activities for monitoring vital human signs. The harvested energy is used to sustain the battery autonomy of wearable devices, which allows for a longer monitoring time of vital data. This study proposes a novel type of stress- or exercise-monitoring ECG device based on a microcontroller (PIC18F4550) and a Wi-Fi device (ESP8266), which is cost-effective and enables real-time monitoring of heart rate in the cloud during normal daily activities. In order to achieve both portability and maximum power, the harvester has a small structure and low friction. Neodymium magnets were chosen for their high magnetic strength, versatility, and compact size. Due to the non-linear magnetic force interaction of the magnets, the non-linear part of the dynamic equation has an inverse quadratic form. Electromechanical damping is considered in this study, and the quadratic non-linearity is approximated using MacLaurin expansion, which enables us to find the law of motion for general case studies using classical methods for dynamic equations and the suitable parameters for the harvester. The oscillations are enabled by applying an initial force, and there is a loss of energy due to the electromechanical damping. A typical numerical application is computed with Matlab 2015 software, and an ODE45 solver is used to verify the accuracy of the method.

Impact of Remote Patient Monitoring Systems on Nursing Time, Healthcare Providers, and Patient Satisfaction in General Wards.

Remote patient monitoring systems (RPMS) are increasingly integrated into hospital wards to improve patient safety and reduce the workload on healthcare professionals (HCPs). This study evaluates the efficacy of RPMS in general wards, focusing on their impact on nursing efficiency, patient care, HCPs, and patient satisfaction. A comprehensive time-motion study was conducted along with surveys targeting HCPs and patients in M.S. Ramaiah Memorial Hospital, Bangalore, India, which has implemented RPMS in general wards. The study involved observing and comparing nursing activities in RPMS-equipped wards versus control wards without RPMS across various shifts. In addition, feedback on the system's impact on patient safety, overall care quality, and usability was gathered through a survey form. RPMS decreases the amount of time nurses spend on routine monitoring, communication, and coordination, enabling a 43.11% increase in time available for patient care. More than 89% ofHCPs noted improvements in the level of care and overall patient safety. More than 80% of the HCPs also noted improvement in the patient's experience. More than 50% of HCPs find RPMS easy to use and user-friendly.More than 60% of the patients noted an overall improvement in care quality. RPMS has proven to be a valuable asset in hospital wards, enhancing patient monitoring and safety while reducing the workload on staff. In addition, significant time savings on routine tasks and high satisfaction levels from both staff and patients underscore the system's benefits.

Telemedicine for the Underserved Racial and Ethnic Minorities During COVID-19 and Beyond.

Objective: To demonstrate that a culturally and linguistically appropriate telehealth protocol can be implemented to improve the glycemic control of patients as an extension of regular clinical services and provide continuity of care. Methods: A telehealth platform was established during COVID-19 pandemic and from numerous telehealth encounters we sampled 498 patients who received telehealth intervention over a 12-month period for specific services: Rx refill, consultation for laboratory results, wellness evaluation and education, and acute or sick visits with appropriate referrals. This telehealth platform was integrated with a remote patient monitoring system utilizing a Bluetooth-enabled glucometer for patients with diabetes compared to their abnormal baseline hemoglobin A1C (HgA1C). The Blood sugar values were recorded at predefined intervals to monitor controls for diabetes. The ethnic diversity and level of education of patients required addressing the digital divide, language interpretation, and navigation at each monitoring step. Results: This method demonstrated that a culturally and linguistically appropriate telehealth protocol can be implemented to improve the glycemic control of patients in an intervention group compared with a control group. Validation of the glycemic control was based on 70 patients identified as eligible for participation based on the inclusion criteria: a HgA1C level of 7% or higher obtained within the last 10 months. Informed consent was obtained for 42 participants based on patient participation constraints during the COVID-19 pandemic. Conclusions: We conclude that telemedicine procedures utilized for patients with little or no prior knowledge of remote self-monitoring methods can support their treatment of chronic diseases, such as diabetes. The outcomes from the implementation of telemedicine services were observed in a well-defined group of underserved racial and ethnic minority patients at our clinic. We now have a protocol to expand this to other chronic diseases and used as a regular clinical procedure.

Delay of Transmitted Data in the Remote Patient Monitoring System through AMQP and CoAP

Remote Patient Monitoring (RPM) is a healthcare solution that uses technology to monitor patients outside conventional healthcare settings. It is especially useful for people with chronic conditions or needing regular monitoring. One of the main reasons for the increase in the number of deaths each year is the increase in cardiovascular diseases, including hypertension. Online blood pressure monitoring offers many advantages but also potential challenges. This work reviews the key communication technologies and research challenges in the real-time transmission of measured blood pressure data. Delay in these systems is not tolerated as it involves human lives. To conduct the experimental studies, a prototype of an experimental intelligent system was created to study the delay and processor load of the RPI4 gateway. The measured blood pressure data is sent to the Things Board cloud using the AMQP and CoAP protocols. The experimental results are particularly useful for RPM system designers. The results of this research facilitate an informed decision on the choice of protocol that transmits the data from the gateway to the cloud in the process of designing remote patient monitoring systems.

Abstract PO1-10-10: Leveraging technology to optimize symptom management and patient empowerment in routine breast cancer care: implementation of a remote patient monitoring (RPM) pathway across 24 hospitals in Europe

Abstract Background: In clinical trials, RPM has been proven to improve the quality of life and toxicity management for patients receiving systemic treatment and is recommended by international guidelines for routine clinical practice (Di Maio et al., Ann Oncol 2022). Resilience is an oncology-specific RPM system (CE marked, class IIa medical device - mobile application [app] or web interface) which prompts patients to complete a weekly survey including core symptoms from NCI’s PRO-CTCAE questionnaire. Severe or worsening symptoms trigger an alert notification sent to the patient’s care team. In addition to the RPM system, the mobile app offers a large pool of breast cancer-specific educational, self-management, and patient empowerment resources that are personalized according to symptoms reported, disease stage, and treatments received. Here we present the implementation of the Resilience RPM pathway in routine breast cancer care across 24 highly diverse cancer centers. Methods: The implementation process followed a phasic process of Exploration, Preparation, Implementation, and Sustainability that involved close collaboration with technology providers, hospital managers, and care teams at participating cancer centers to coordinate, train, provide technical assistance, and respond to site-specific needs. In addition, monthly webinars offered participating centers an opportunity to share best practices and ways to overcome implementation barriers. A dedicated platform with educational RPM resources for healthcare providers was also available. A process evaluation according to the RE-AIM framework was performed assessing the reach, adoption, implementation, and maintenance of the RPM pathway in routine care. The effectiveness of the pathway was assessed as the ability of the care team to timely respond to alerts. Aggregated system level metric of RPM was collected covering patients with breast cancer under active systemic treatment followed by Resilience from 01-11-2021 to 29-06-2023. Usage data of mobile app resources was tracked from 01-01-2023 to 29-06-2023. Qualitative assessments with focus groups complemented this evaluation. Results: 985 patients, 1% male, 99% female, with a median age of 57y (range: 21-95y; P25-75 38.5-64.5y), were registered across 24 cancer centers. In the last 6 months, the predominant RPM interface chosen by patients was the mobile app (64.1 vs 35.9% for the web platform). Adherence to weekly ePRO reporting was 89.4% in the mobile app and 86.6% in the web platform. The median time from prompt receipt to survey completion was 5.6 hours. The median time for the nurse navigator (NN) to process an alert was 3h50min. Most alerts (91%) were handled directly by NN with 9% requiring the support of a physician. The mean number of individual content consumed per patient was 13 (6744 resources/523 pts, 100% of app users accessed resources). Preferred formats of content delivery were written articles (66.5%), followed by videos (23.1%) and podcasts (10.4%). Most assessed contents referred to: symptom management advice (16%), followed by information about breast cancer disease characteristics (12.5%), supportive care strategies (11.5%), self-guided exercises of mindfulness-based stress reduction (9.4%), and breast cancer treatment journey (8.3%). Qualitative assessments of the app interface, educational content, and self-management programs revealed high levels of satisfaction. The RPM pathway is sustained in the 24 cancer centers and implementation is expanding to additional centers. Conclusion: The Resilience RPM pathway was successfully implemented in routine care across a diverse group of cancer centers with elevated levels of patient engagement and responsiveness from NN. In addition, patients accessed evidence-based educational and empowerment resources, adapted to their medical profile according to their preferences and learning style. Citation Format: Maria Alice Franzoi, Arlindo R Ferreira, Juliette Fanton d'Andon, Thomas Grellety, Eliane Ithurbide-Dachary, Laura Polastro, Laurence Caravella, Joseph Rodriguez, Nathanaelle Leprevier, Elsa Blanchard, Stéphane Remy, Belen d' Ythurbide, Romain Rivoirard, Raoudha Boughzala, Joana M Ribeiro, Ines Vaz Luis. Leveraging technology to optimize symptom management and patient empowerment in routine breast cancer care: implementation of a remote patient monitoring (RPM) pathway across 24 hospitals in Europe [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-10-10.

IoT-Based Virtual Primary Clinic: Remote Patient Monitoring and Consultation System

Using Internet of Things technology, the Virtual Primary Clinic project is a cutting-edge healthcare endeavor that enables remote patient monitoring and consultation. The project collects essential health metrics including body temperature, oxygen saturation, heartbeat, and ECG signals by integrating a variety of sensors, including NodeMCU ESP32, SpO2, DHT11, and AD8232 ECG. The information collected by these sensors is sent via API to a specially created website and an online platform called ThingSpeak. With features including patient registration, appointment scheduling, access to medical histories, and doctor-patient contact, the website acts as a comprehensive interface between patients and physicians. Patients can register on the website, provide their medical information, and get medications sent to them remotely based on their conditions. However, without having to see patients in person, doctors can examine patient data, diagnose ailments, and write prescriptions or offer medical advice.The Virtual Primary Clinic project intends to improve healthcare accessible, especially in remote or underserved locations, by enabling remote monitoring and consultation. In addition to providing healthcare providers with an effective platform for remote patient care, it gives patients the ability to take charge of their health. This project is a major step toward using cutting-edge IoT-based technologies to improve patient outcomes and democratize healthcare.

IoT based Remote Patient Monitoring System

Remote Patient Monitoring (RPM) systems have emerged as transformative tools in healthcare, leveraging technological advancements to enhance patient care beyond traditional clinical settings. This abstract explores the design and implementation of a Remote Patient Monitoring System aimed at bridging the gap between patients and healthcare providers. The system employs a network of wearable devices and sensors to continuously collect and transmit relevant patient data to a secure cloud-based platform. Through real- time monitoring, healthcare professionals can remotely assess vital signs, medication adherence, and other crucial health metrics, enabling early intervention and personalized care. The integration of data analytics and machine learning algorithms further enhances the system's ability to detect patterns, predict potential health issues, and optimize treatment plans. This abstract delves into the key features, benefits, and challenges associated with the Remote Patient Monitoring System, emphasizing its potential to revolutionize healthcare delivery by fostering proactive, patient-centered care and improving overall health outcomes.

Interfering sensed input classification model using assimilated whale optimization and deep Q-learning for remote patient monitoring

The Internet of Medical Things (IoMT)-based Remote Patient Monitoring (RPM) systems provide real-time data and insights about patients' conditions without the need for constant physical visits to healthcare facilities. The wearable sensors are responsible for sensing the psychological vitals of humans at different intervals to detect their precise health status. This article introduces an Interfering Input Classification Model (IICM) for addressing the continuous and discrete data extraction issues in RPM. The proposed model hybridizes whale optimization and deep-Q-learning for classification and connectivity identification between different interval data. The sensed information is segregated as discrete and continuous using the search process termination of the whales. Such classified data is validated for the maximum discreteness and its interconnection between the previous sequences. This interconnection is performed using Q-learning based on different state changes through continuous clinical range correlations. The proposed model exemplifies continuous and discrete signals (data) for abnormality detection and diagnosis recommendation. Thus the proposed model is reliable in improving the classification ratio and accuracy with fewer errors. The experimental values of the proposed model are as follows: IICM enhances classification by 6.65%, signal identification by 6.63%, and classification accuracy by 10.08%. This model reduces classification time by 9.51% and error by 10.14% for different ranges of inputs.

Development and research of a remote patient monitoring system

Development and research of a remote patient monitoring system

Telemedicine in the age of the pandemics: The prospects of web-based remote patient monitoring systems for orthopaedic ambulatory care management in the developing economies

Objective The goal of this research was to demonstrate the efficacy of telemedicine via design, implementation and evaluation of a web-based remote patient monitoring system (WB-RPMS) across the tertiary/university teaching hospitals in a developing country Nigeria, as a tool to continue to expand access to an affordable and resilient tertiary healthcare system through the challenging times of the COVID-19 pandemic or any future disruptions. Methods This research employed an agile and human-centred design thinking philosophy, which saw the researchers iteratively collaborate with clinicians across the system development value chain. It also employed qualitative and quantitative research methods for new system evaluations. After the system's development, a 20-patient sample was randomly selected from members of the National Youth Service Corp to evaluate the WB-RPMS Patient Portal for usability and user experience through a survey based on the system usability scale. Again, the COREQ standards for reporting research result were adopted for this study. Results The evaluation of the WB-RPMS Patient Portal by a select patient sample showed that 95.0% of the respondents believed that they would like to use the system frequently. It was also discovered that 90.0% of all respondents also indicated that they found the Patient Portal to be simple; 85.0% of the respondents believed and indicated that the WB-RPMS Patient Portal was easy to use. Conclusions The result of the usability evaluation of the developed WB-RPMS Patient Portal showed that it was well received by the select patient sample and by the clinicians who participated in the development process. In fact, the performance of the system shows that it has the potential to remotely support and sustain improved access to affordable healthcare for outpatients in developing countries even during times of uncertainties and disruptions as recently occasioned by COVID-19 pandemic.

Real-Time Remote Patient Monitoring and Alarming System for Noncommunicable Lifestyle Diseases.

Telemedicine and remote patient monitoring (RPM) systems have been gaining interest and received adaptation in healthcare sectors since the COVID-19 pandemic due to their efficiency and capability to deliver timely healthcare services while containing COVID-19 transmission. These systems were developed using the latest technology in wireless sensors, medical devices, cloud computing, mobile computing, telecommunications, and machine learning technologies. In this article, a real-time remote patient monitoring system is proposed with an accessible, compact, accurate, and low-cost design. The implemented system is designed to an end-to-end communication interface between medical practitioners and patients. The objective of this study is to provide remote healthcare services to patients who need ongoing care or those who have been discharged from the hospital without affecting their daily routines. The developed monitoring system was then evaluated on 1177 records from MIMIC-III clinical dataset (aged between 19 and 99 years). The performance analysis of the proposed system achieved 88.7% accuracy in generating alerts with logistic regression classification algorithm. This result reflects positively on the quality and robustness of the proposed study. Since the processing time of the proposed system is less than 2 minutes, it can be stated that the system has a high computational speed and is convenient to use in real-time monitoring. Furthermore, the proposed system will fulfil to cover the lower doctor-to-patient ratio by monitoring patients from remote locations and aged people who reside in their residences.

A secure framework for IoT‐based healthcare using blockchain and IPFS

AbstractSmart healthcare, also known as IoT (Internet of Things) based healthcare, utilizes IoT technology to enhance the healthcare industry. The use of IoT‐enabled medical equipment enables remote monitoring of patients, allowing for in‐home care and alerting healthcare providers of any changes. This can lead to improved patient outcomes and better disease management. However, it is important to implement robust security measures to protect patient data when using IoT in healthcare. One potential solution is to use blockchain technology to secure data storage and sharing with medical providers. Blockchain's decentralized structure and cryptographic techniques make it difficult for hackers to access or tamper with patient information. Additionally, IPFS (InterPlanetary file system) can be used for efficient data storage and sharing with authorized medical professionals, while smart contract functionality can automate the process of granting and revoking access to patient data. This article proposes a blockchain‐based secure remote patient monitoring system, specifically for chronic disease patients. The system utilizes distributed blockchain for data security, IPFS for data storage and sharing, and DApp for data collection and connection to the blockchain, along with encryption for added security. The proposed approach is compared to existing solutions and found to be a superior option for IoT‐based healthcare.

Design Remote Monitoring System for Patients at Real-Time based on Internet of Things (IoT)

Design Remote Monitoring System for Patients at Real-Time based on Internet of Things (IoT)

Distributed Ledger Technologies for Electronic Health Care: IOTA-Based Remote Patient Monitoring and Telemedicine System

Distributed Ledger Technologies for Electronic Health Care: IOTA-Based Remote Patient Monitoring and Telemedicine System

Continuous Remote Monitoring in Moderate and Severe COVID-19 Patients.

Background COVID-19 steadily built up the pressure on healthcare systems worldwide, creating the need for novel methods to alleviate the burden. Continuous remote monitoring of vital parameters reduces morbidity and mortality in hospitals by providing real-time disease data that can be analyzed through web portals. It enables healthcare workers to identify which patients require prompt administration of healthcare. Patients remain under the purview of their doctors and can be notified early if there are any deteriorations in the parameters being monitored. Aims To evaluate the use of remote monitoring in moderate and severe COVID-19 patients and to correlate the Dozee Early Warning Score (DEWS) with severity and outcome in moderate and severe COVID-19 patients. Materials and methods We conducted a prospective study on adult (>18 years old) moderate and severe COVID-19 patients during the second wave of COVID-19. The vitals of the subjects were continuously monitored using Dozee, a contactless remote patient monitoring system enabled with DEWS that reflects the overall patient condition based on respiratory rate (RR), heart rate (HR), and oxygen saturation (SpO2). We assessed the correlation of DEWS with patients' clinical outcomes: deteriorated or recovered. Results Thirty-nine COVID-19 patients were recruited for the study, of whom 29 were discharged after recovery and 10 deteriorated and died. Respiratory rate trend, respiratory rate DEWS, SpO2 DEWS, and total DEWS showed a significant reduction in recovered patients, while the same parameters showed a significant increase followed by consistently high scores in patients who deteriorated and died due to the disease. Total DEWS was proportional to the risk of mortality in a patient. Conclusion We concluded that continuous vitals monitoring and the resulting DEWS in moderate and severe COVID-19 patients were indicators of their improvement or deterioration. DEWS uses continuous remote monitoring of routinely collected vitals (HR, RR, and SpO2) to serve as a predictor of patient outcome.

Improving Remote Patient Monitoring Systems Using a Fog-Based IoT Platform With Speech Recognition

Due to the recent shortage of resources in the healthcare industry, Remote Patient Monitoring (RPM) systems arose to establish a convenient alternative for accessing healthcare services remotely. However, as the usage of this system grows with the increase of patients and sensing devices, data and network management becomes an issue. As a result, wireless architecture challenges in patient privacy, data flow, and service interactability surface that need addressing. We propose a fog-based Internet of Things (IoT) platform to address these issues and reinforce the existing RPM system. The introduced platform can allocate resources to alleviate server overloading and provide an interactive means of monitoring patients through speech recognition. We designed a testbed to simulate and test the platform in terms of accuracy, latency, and throughput. The results show the platform’s potential as a viable RPM system for sound-based healthcare services.

Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation.

Medical devices (MDs) have been designed for monitoring the parameters of patients in many sectors. Nonetheless, despite being high-performing and reliable, they often turn out to be expensive and intrusive. In addition, MDs are almost exclusively used in controlled, hospital-based environments. Paving a path of technological innovation in the clinical field, a very active line of research is currently dealing with the possibility to rely on non-medical-graded low-cost devices, to develop unattended telemedicine (TM) solutions aimed at non-invasively gathering data, signals, and images. In this article, a TM solution is proposed for monitoring the heart rate (HR) of patients during sleep. A remote patient monitoring system (RPMS) featuring a smart belt equipped with pressure sensors for ballistocardiogram (BCG) signals sampling was deployed. A field trial was then conducted over a 2-month period on 24 volunteers, who also agreed to wear a finger pulse oximeter capable of producing a photoplethysmography (PPG) signal as the gold standard, to examine the feasibility of the solution via the estimation of HR values from the collected BCG signals. For this purpose, two of the highest-performing approaches for HR estimation from BCG signals, one algorithmic and the other based on a convolutional neural network (CNN), were retrieved from the literature and updated for a TM-related use case. Finally, HR estimation performances were assessed in terms of patient-wise mean absolute error (MAE). Results retrieved from the literature (controlled environment) outperformed those achieved in the experimentation (TM environment) by 29% (MAE = 4.24 vs. 5.46, algorithmic approach) and 52% (MAE = 2.32 vs. 3.54, CNN-based approach), respectively. Nonetheless, a low packet loss ratio, restrained elaboration time of the collected biomedical big data, low-cost deployment, and positive feedback from the users, demonstrate the robustness, reliability, and applicability of the proposed TM solution. In light of this, further steps will be planned to fulfill new targets, such as evaluation of respiratory rate (RR), and pattern assessment of the movement of the participants overnight.

Security In Wireless Body Area Network (WBAN) Using Blockchain

In this era of increased technological and medical advancements, remote monitoring of patients and elderly people has become more popular. Even in situations like the Covid'19 pandemic, the hospital and medical facilities are in demand. In such scenarios, elderly people who need medical care and patients who need round-the-clock medical attention can be made to stay in their homes and can be monitored remotely with the help of a remote patient monitoring system. Such a system can be built by incorporating Wireless Body Area Networks (WBAN). WBAN encompasses various biological wearable sensors which monitor the patient’s health around the clock. The accumulated sensor data is then shared with hospitals and medical professionals through wireless sensor networks. This data includes personnel details, various vital parameters, medical images, etc. which are highly confidential. So providing security and confidentiality in such networks is mandatory. Several cryptographic mechanisms and algorithms are available for ensuring security in WBANs. In recent days several works have been proposed to incorporate the emerging Blockchain technology to improve the privacy, security, and confidentiality in WBANs. In this survey, we discuss various research that proposed blockchain-based solutions for ensuring security, integrity, and data privacy in WBAN.

A pathway to In-Memory Computing driven Wellness Digital Twin utilizing Remote Patient Monitoring System (WDT-RPMS)

With fast-paced digital transformation, precision medicine has been an essential part to elevate the conscience of healthcare with respect to the biopsychosocial model with the help of Remote Patient Monitoring Systems. Digital twins have been successful in many industries, in healthcare, it can optimize the wellness of the human body to improve physical and mental wellness including the non-diagnosed diseased populations and rural populations.
 In this paper, we discuss the challenges in terms of computing to implement a complex system called Wellness Digital Twin-based Remote Patient Monitoring System following Model-Based Systems Engineering (MBSE), we present the definition, needs of the digital twin in the context of wellness and healthcare, a feasibility study followed by a concept of operations. Subsequently, we present the requirements and architecture including In-memory computing, (Emerging NonVolatile Memories, memristive devices) that will enhance sensing, analytics, and levers of action.

Remote Monitoring of Patients with Implantable Devices-Telemonitoring and Chronic Diseases.

The high health care costs and poor quality associated with heart failure have led to the development of remote patient monitoring (RPM or RM) systems and cost-effective disease management strategies. RM of cardiac implantable electronic devices (CIEDs) is the application of communication technology to patients carrying a pacemaker (PM), an implantable cardioverter defibrillator (ICD) for cardiac resynchronization therapy (CRT) or an implantable loop recorder (ILR). The aim of this study is to define and analyze the benefits of modern telecardiology to provide patients with remote clinical assistance especially for patients with implantable devices for early detection of the development of heart failure as well as the limitations inherent therein. Furthermore, the study examines the benefits of telemonitoring in chronic and cardiovascular diseases proposing holistic care. A systematic review was conducted using Preferred Reporting Items for Systematic Reviews and Meta-Analyses-PRISMA methodology. The results highlight that telemonitoring has augmented beneficial effects on clinical outcomes of heart failure, including, a reduction in mortality, heart failure hospitalization, all cause hospitalization and also an improvement in Quality of Life.