HeartMApp: A self-care mobile telemedicine application to improve heart failure outcomes
Ponrathi Athilingam, RN, ARNP, FAANP; Miguel A. Labrador, MSc
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Session presented on Saturday, November 7, 2015:
Background: Heart failure (HF) is a major public health problem affecting 5.7 million people, costing $35 billion annually, with over 17 billion spent on hospitalizations in the United States (Dharmarajan et al., 2013; Go et al., 2014). Treatment and intervention for HF include drug therapy to treat pathophysiology and the use of clinical and educational interventions to improve knowledge, self-care and adherence (Yancy et al., 2013). Despite increasing compliance with treatment guidelines, hospital readmission rates remain high at 25% within 30-days (Dharmarajan et al., 2013). Current intervention strategies only reduce hospital readmission rates by a combined 2% representing a critical barrier to progress in this field (Bradley et al., 2013). Patients with HF are expected to learn and remember intricate information on disease management and follow complex self-care practices on their own (Moser et al., 2012). Telemonitoring intervention and home health care to improve HF outcomes demonstrated inconsistent results in reducing readmissions (Inglis et al., 2010). Persistent care coordination and prolonged engagement is recommended for effective behavior change to develop knowledge and self-care skills (Lainscak et al., 2011). Even when knowledge of HF is improved by multiple strategies, accompanying changes in self-care practice and readmission rates are not evident, indicating that significant reductions in HF readmissions may be outside the reach of current management approaches (Davis et al., 2014). Evidence supports that despite challenges faced by older adults who often resist using mobile technology, once they join the online world, digital technology often becomes an integral part of their daily lives (Seto et al., 2012). Currently, 91% of adults 65 years or older in the US own a mobile phone, 58% of those are smart phones, and 52% of adults use mobile apps. A recent survey of patients with a variety of chronic diseases (N=2000) reported that 24% of patients are more willing to accept prescriptions for a mHealth app than a pill or invasive devices like FitBits and wireless weighing scales to better manage daily self-care; thus designing a mobile app to improve self-care seemed a viable option (PmLive, 2014). Therefore our interdisciplinary team designed a patient-centered intervention utilizing a mobile platform to offer persistent engagement to develop self-care skills, improve knowledge, and quality of life, and thus potentially reduce costly HF readmission rates. The HeartMapp Features and Architecture The heart mobile application (HeartMapp) is an easy to use non-pharmacological, non-invasive intervention developed with four main features: the assessment, exercises, vital signs and CHF info. A) The assessment feature prompts the user to check daily weight and complete a short questionnaire on HF symptoms. B) Vital sign utilizes wearable Bluetooth sensors with built in algorithms to remotely measure physiological parameters in real-time including heart rate, heart rate variability (HRV), and respiratory rate. C) Exercises includes animated deep breathing to improve HRV and calculate predicted distance walked in six minutes based on age, gender, height, and weight to monitor functional improvement. D) CHF infoincludes audio enabled interactive educational modules and reference resources to enhance HF knowledge. The HeartMapp is designed based on the client-server architecture. The client consists of an Android application, tested on the Nexus 4 and Nexus 5 running the Android 4.4.0 platform. A Zephyr BioHarness TM3 strap that connects to the Android phone via Bluetooth sensor and accelerometer data and the built-in algorithms in the HeartMapp calculates vital data on heart rate, HRV, respiration rate. We believe HeartMapp offers a novel multi-dimensional approach specially tailored for self-care management, while the patient is not under direct medical supervision. The multiple dimensions included are: A) Patient Engagement:HeartMapp has been carefully designed to incorporate human-centered interfaces such as self-care and symptom questionnaire, interactive educational modules on HF facts, vital signs monitoring and visualization charts, and step-by-step exercise guides to augment breathing and physical activity. B) Automation of Clinical Protocols. Existing protocols that are part of traditionally prescribed treatments have been implemented into the HeartMapp for ease of execution by the patients. For example, controlled deep breathing at 6 breaths per minute compared reduced blood pressure and significantly increased baro-reflex sensitivity among 81 patients with HF (p<0.0025) compared to 21 healthy controls (Jerath, Edry, Barnes, & Jerath, 2006). Other clinical protocols that are automated through this architecture include the personalized six-minute walking goal predicted for the individuals' age, gender, height, and weight (Enright & Sherrill, 1998). HF Self-care questionnaire is weighed based on the New York Heart Association (NYHA) class to determine HF severity (Lindenfeld et al., 2010) and automated to provide feedback to patients with color coded zones: [Green: Stable with no change in HF symptoms, continue current treatment plan, Yellow: symptoms mildly worse and warrant treatment (an extra dose of water pill) and prompts to call physician, Orange: Symptoms moderately worse and needs to seek help immediately and prompts to call physician, and Red: Symptoms are grave and prompts to call 911 for urgent medical care]. C) Remote Physiological Monitoring. HeartMapp utilizes wearable Bluetooth sensors that monitor electrocardiogram (ECG). The built-in algorithm in the HeartMapp calculates heart rate, HRV, and respiratory rate (Pinna et al., 2005). D) Clinical Decision Support. In the context of HF patients at home under a prescribed treatment, clinical decisions translate into understanding changes to seek early and timely medical help by the patients. One way to accomplish this is to monitor negative changes in the physiological data collected via the wearable sensors as well as the data on weight, blood pressure, and the incorporated patient symptom assessments that resemble the type of questions and/or examinations that a doctor will perform to assess HF severity to determine a treatment plan (Bosl et al., 2013). The data entered in HeartMapp including weight, blood pressure, HF symptom questionnaire, distance walked in six minutes, and breathing exercises performed are stored in a secured server and can be viewed by the patient and the clinical team that trigger prompt intervention. The providers will have access to these data for triage and early treatment. The ultimate goal is to encourage persistent utility of HeartMapp by the patients and clinical team to potentially improve daily self-care, knowledge, and symptom assessment and thus potentially reduce the costly readmission rates.
Method and Design of HeartMapp Development: The Successive Approximation Model (SAM) was used throughout the design and development phases of the application, for both the instructional content and media (Allen & Sites, 2012). The HeartMapp was developed utilizing a patient-centered approach with funding from the CS Draper Laboratory and the Florida High Tech Corridor. After obtaining approval from the Institutional Review Board (IRB), patients with HF and currently practicing six clinical cardiologists, nurse practitioners, and nurses were interviewed utilizing a step-by-step questionnaire to make an accurate assessment of HF symptoms and the specific needs of patients and health care providers. The alpha testing was performed primarily to test the prototype on three students and three patients with HF to identify any initial design problems for making improvements. Beta testing took place with 10 HF patients from the USF Center for Advanced Health care to understand usability of the HeartMapp.
Results of Beta Testing: Mean age of the ten patients was 63.0 14.1 years, ranged from 43 to 81 years of age, 70% were Caucasian, 40% lived alone, 40% had less than high school education, 80% had HF diagnosis for four or more years, mean ejection fraction was 32.3 12.05%, 60% were in NYHA class II, all 100% owned a mobile phone, 50% were smart phones, and 60% reported using mobile phones very well. Results on the usability of HeartMapp using yes or no questions are provided in table 1. Self-confidence in using the HeartMapp was measured using the validated questionnaire using 10 Likert scale (1=not at all confident to 5=extremely confident) with a total score of 50. The mean self-confidence score was 42.2 12.08 indicating very confident in using the HeartMapp.
Discussion and Conclusion: The Beta testing of the HeartMapp is promising, particularly with respect to the comfort level of using mobile devices by HF patients who were between the ages 43 to 81 years and all of whom owned a mobile phone. Almost 80% of patients had HF for over 4 years, thus only 67% of participants reported obtaining new information from HeartMapp. However, all of them reported that they would use the app and recommend to it others especially newly diagnosed HF patients. Currently the HeartMapp is undergoing refinement for a proposed clinical trial to test feasibility comparing with home health care and telemonitoring service.
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