Technology-Based Intervention
HeartMapp: A Mobile Phone app for
Telemonitoring Support to improve Heart
Failure Outcomes
Pon Athilingam, PhD, ACNP, MCH, FAANP
Miguel Labrador, PhD. Computer Science & Engineering
Transforming Healthcare Transforming Lives
HeartMapp: A mobile App for Heart Failure
Copyrighted by USF: Ponrathi R. Athilingam
Miguel Labrador
Development was Funded by:
The CS Draper Laboratory
Florida High-Tec Corridor
No financial Conflict
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Heart Failure Symptoms
• HF: Complex syndrome
• 5.1 million people in US
(Go et al., 2014)
• Symptom clusters varies
individually (Moser et al., 2014 )
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Heart Failure Self Care
Heart failure patients are expected to:
• Remember complex information
• Practice Daily self-care
• Daily weight
• Follow complex medication regimen
• Follow low salt diet
• Understand and manage heart failure symptoms
• Exercise regularly
• Manage other diseases (Yancy et al., 2013)
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Why is Self-care Important?
Heart Failure Management and Self-care are aimed to:
• Prevent remodeling: Prevent heart failure from getting
• Increase lifespan
• Improve quality of life
• Thereby reduce costly readmissions
(Mann & Bristow, 2005)
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Current Strategies to Improve Outcomes
• Get with guidelines (Go et al., 2014)
• Improve performance measures (Heidenreich et al., 2013)
• Focused on best practice models
• Follow Pathways and algorithms
• Have discharge order set
• Patient education tools
• However: no sustained benefit in reducing readmission rate
• 25% in 30-days
• 50% within 6-months
(Go et al., 2014)
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Technology Use by Older Adults
• 79% of Internet users accessed the internet through their mobile phone in
• 98% of text messages are read, compared to 22% of emails on the computer
• Text messages are usually read within the first five seconds of being received
• Once older adults >65 years join the online world, digital technology often
becomes an integral part of their daily lives
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Mobile Technology Application
• Mobile technology is defined as devices (Mobile phones and tablets) that
are intended to be always be on and carried by the person throughout the
day for the purpose of communication during normal daily activities.
• Thus available whenever and wherever the individual is
• Potential to enhance persistent use of materials presented unlike current
paper version of educational materials
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Mobile Phone Application to
Improve Self-care and HF Outcomes
• Technology has exploded within the last decade.
• HeartMapp, a Mobile Phone Application for Heart Failure is developed in
collaboration with Dr. Miguel Labrador, Professor, Department of Computer
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Theoretical Model for HeartMapp (mHealth)
• 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).
• Research on adult learning and engagement suggests that an adult’s
engagement in learning activities could increase persistence and
involvement (Illeris, 2002).
• Yang’s holistic theory of knowledge and learning that suggests
engagement and learning are integrated processes by which adult
learners gain a deeper understanding and make meaning of the activities
he or she is exposed to in a given learning environment (Yang, 2003)
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Integrated mHealth Theoretical Model
The integrated mHealth theoretical model builds on the currently available
models and theories and expands to include
• The Mayer’s Cognitive Theory of Multimedia Learning and theory of
problem-based learning promotes engagement by working collaboratively
with patient and family.
• Self-care influenced by individual, family, community, and systems.
• Self-care as a process in the contexts of knowledge of pathophysiology of HF
• Self-care skills needed in HF
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HeartMapp Architecture
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Hypothesis for Developing HeartMapp
• Mobile technology, particularly mobile health (mHealth) for education and
self-care skill development could offer persistent engagement and utility of
educational materials presented over a prolonged period.
• HeartMapp encourages persistent involvement and engagement of patients’
daily self-care activities to potentially improve HF outcomes.
• HeartMapp could offer constant companion and will be like a coach or buddy
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Method/Design Used
• Patient centered approach was utilized
• Alpha and beta settings were done during the development
• Based on feedback from patients and providers -refinements
were added to the HeartMapp
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Features of HeartMapp
• HeartMapp is Copyrighted
• Accept the disclaimer
• Register to use the HeartMapp
• Once registered, patients will
have access to all features of
HeartMapp Disclaimer
Create Account
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Features of HeartMapp
Heart failure symptoms
2. Exercises
3. Vital Sign Monitoring
Heart rate
Respiratory rate
Heart rate variability
4. CHF educational information- 10 modules
that are audio enabled
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Clinical Features of HeartMapp
• Patient engagement tool including audio/video enabled
HF information to enhance knowledge
• Automation of clinical protocol
• HF symptom assessment utilizing the NYHA
Classification to determine severity of HF and feedback
using colored zones
• Feedback on physical activity utilizing distance walked
in six minutes based on age, gender, and BMI
• Biofeedback for deep breathing exercise
• Remote physiological monitoring of heart rate,
respiratory rate, and heart rate variability utilizing
Bluetooth sensors
• Clinical support decisions by offering feedback to patients
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as well asMaking
based on real time data.
Assessment: Memory Testing
• Test of memory/exercise
• Presents picture of the Day
Three pictures
Occur randomly
Different ones each day
• HeartMapp Includes a Library of pictures
over 50
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Assessment: Weight and BP
• Check weight and Blood pressure and record it
• Bluetooth enabled devises will transfer data
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HF Symptom Assessment
Heart failure symptom questions; audio enabled; answers appear randomly
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HF Symptom Assessment
Heart failure symptom questions; audio enabled; answers appear randomly
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Assessment: Memory Testing
• Nine pictures appear randomly from the library of 50 pictures
• If heart function is normal, they may be able to remember all
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Feedback on Symptom Assessment
Colored Zones are classified utilizing New York Heart Association classification
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HeartMapp: Exercises Module
Breathing Exercise
Biofeedback with 6 breath/min
It aims to reset the autonomic system
Measures Predicted distance walked
in 6 minutes- based on the Algorithm
of Age, Gender, BMI
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HeartMapp: Vital Sign Monitoring
BioHarness Strap with
Bluetooth Sensor
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Heart Rate
Resp. rate
HeartMapp: CHF Info Module on Education
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Alpha and Beta Testing of HeartMapp
Beta testing (N=10): mean age of 63 years
• 60% were 65 years of age or older.
• All ten of the participants were employed full time
• 60% men
• 70% Caucasian
• 40% lived alone
• Ejection fraction is 32.3 ± 12.05%
• 90% having systolic HF with ejection fraction <40%
• 60% were in New York Heart Association functional classification II
• All 100% owned a mobile phone, 50% smartphones
• 50% used text messaging
• 60% reported using mobile phone very well and 40% fairly well.
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Beta Testing of HeartMapp
Table 3: Beta Testing Results
Heart Failure tutorial was educational and engaging
Learned something new from this tutorial that they did not previously know.
Interactive tutorial found them to be engaging and helpful.
Interactive activities were related to the Heart Failure subject.
Navigation through the modules are easy
Addition of Avatar is found to be helpful
The scenarios are found to be helpful and engaging
The audio was found to be helpful and engaging
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Research Goal and Clinical Implication
• Currently HeartMapp is being refined
• Pilot testing HeartMapp in a clinical trial to compare with
existing Telemonitoring service:
• explore safety and efficacy of HeartMapp
• explore if these technology-based interventions will improve
self-care, knowledge, adherence, and quality of life.
• explore if HeartMapp interventions will reduce costly
hospital readmissions by early intervention
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Research Goal and Clinical Implication
Design: Proposed a randomized control clinical trial
Compare HeartMapp with existing telemonitoring services
Follow patient at 30-days, 3- and 6-months
• Self-care
• Medication adherence
• HF knowledge test
• Quality of life
• Physiological parameters
• Self-confidence in using HeartMapp
• Usability questionnaire of HeartMapp
• Hospital readmission and ER visit data
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Allen MW, Sites RH. (2012). Leaving ADDIE for SAM. An Agile Model for Developing the Best Learning Experiences.
Danvers, MA: American Society of Training and Development Press.
Enright PL, Sherrill DL. Reference equations for the six-minute walk in healthy adults. Am J Resp Crit Care Med. 1998;
Go AS, Mozaffarian D, Roger VL, et al. Heart disease and stroke statistics--2014 update: a report from the American Heart
Association. Circulation. 2014;129:e28-e292.
Heidenreich PA, Albert NM, Allen LA, et al. Forecasting the impact of heart failure in the United States: a policy statement
from the American Heart Association. Circ Heart Fail. 2013;6:606-19.
Illeris K. The three dimensions of learning: Contemporary learning theory in the tension field between the cognitive, the
emotional and the social. Copenhagen: Roskilde University. : Niace Publication; 2002.
Mann DL, Bristow MR. Mechanisms and models in heart failure: the biomechanical model and beyond. Circulation
Moser DK, Lee KS, Wu JR, et al. Identification of symptom clusters among patients with heart failure: An international
observational study. Int J Nurs Stud. 2014;51:1366-72.
Yancy CW, Jessup M, Bozkurt B, et al. 2013 ACCF/AHA guideline for the management of heart failure: executive summary: A
report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines.
Circulation 2013;128:1810-52.
Yang B. Toward a holistic theory of knowledge and adult learning. Human Res Develop Rev. 2003;2:106-29.
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