A Mobile Relational Agent to Enhance Atrial Fibrillation Self-care
Phase: NA
Conditions: Atrial Fibrillation, Familial Atrial Fibrillation, Arrythmia, Cardiac, Heart Diseases, Pathologic Processes
Sponsor: University of Pittsburgh

 
 
 
 
 
 
 
 
A Mobile Relational Agent to Enhance Atrial Fibrillation 
Self-Care 
NCT04075994 
Document Date: April 30, 2023 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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A Mobile Relational Agent to Enhance Atrial 
Fibrillation Self-Care 
 
Funding Organization: 
National Heart, Lung, and Blood Institute 
R61HL144669 
Principal Investigator: 
Jared W Magnani, MD, MSc  
Telephone: (412) 383 0611 
E-mail: magnanijw@upmc.edu 
Co-Investigators  
Bruce Rollman, MD, MPH 
Telephone: (412) 692-2659 
Email: rollmanbl@upmc.edu 
 
Tim Bickmore, PhD 
Telephone: (617) 373-5477 
Email: bickmore@ccs.neu.edu 
 
Michael Paasche-Orlow, MD, MA, MPH 
Telephone: (617) 414-5877 
Email: Michael.PaascheOrlow@bmc.org  
Co-I/Data Coordinating Center: 
Kaleab Abebe, PhD 
Telephone: (412) 246-6931 
Email: kza3@pitt.edu  
 
 
 

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List of Abbreviations 
AE 
 
 
 
Adverse Event 
AF 
 
 
 
Atrial Fibrillation 
AFEQT 
 
 
Atrial Fibrillation Effect on QualiTy of life 
BPA 
 
 
 
Best Practice Alert  
 
 
 
 
 
CVD   
 
    
Cardiovascular Disease 
                       CCDC   
 
 
Center for Clinical Trials and Data Coordination 
DCC 
 
 
 
Data Coordinating Center  
DOCA  
 
 
Direct oral anticoagulant 
DSMB  
 
 
Data and Safety Monitoring Board 
ECG 
 
 
 
Electrocardiogram  
EHR 
 
 
 
Electronic Health Record 
HCU 
 
 
 
Health Care Utilization 
HrQoL  
 
 
Health-related Quality of Life 
IRB  
 
 
 
Institutional Review Board 
NHLBI  
 
 
National Heart Lung Blood Institute  
NIH 
 
 
 
National Institutes of Health 
NVS   
 
 
Newest Vital Sign  
OIT 
 
 
 
Office of Information Technology 
PDC 
 
 
 
Proportion of Days Covered 
PHQ 
 
 
 
Patient Health Questionnaire 
PI  
 
 
 
Principal Investigator 
PM 
 
 
 
Project Manager  
QC   
 
 
 
Quality Control  
QOL 
 
 
 
Quality of Life 
RCT                                        Randomized Clinical Trials 
SAE 
 
 
 
Serious Adverse Event  
SSL 
 
 
 
Secure Socket Layering  
UPMC  
 
 
University of Pittsburgh Medical Center 
 
 
 
 

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1. ABSTRACT 
Atrial fibrillation (AF) is a common, morbid condition. Symptoms, complex management, and 
significant adversity contribute to poor health-related quality of life (HRQoL). Social 
determinants of health exacerbate morbidity in AF, and limited health literacy compounds the 
poor patient experience of AF. We propose a single-center parallel group randomized clinical 
trial to test the efficacy of a relational agent to improve patient-centered care in AF. The 
relational agent is a computer character that simulates face-to-face conversation using voice, 
hand gesture, and gaze cues to provide education, monitoring and problem-solving. We have 
used the relational agent in multiple health contexts for self-care and demonstrated its success 
to improve health behaviors and outcomes in individuals with limited computer and health 
literacy. Here we propose to expand our successful 30-day pilot (n=31) of the relational agent in 
order to evaluate the effect of a 4-month self-care curriculum and assess its 8- and 12-month 
sustainability. All participants will receive an AliveCor Kardia smartphone heart rate and rhythm 
monitor. We will randomize 240 patients with AF who are receiving anticoagulation to either (1) 
the relational agent intervention provided by smartphone, a Kardia and accompanying EHR 
alerts, or (2) the control, consisting of an AF educational session, a Kardia, and a smartphone 
with a general health application for self-care (WebMD). Our trial will leverage the clinical 
infrastructure of the University of Pittsburgh Medical Center (UPMC) by recruiting at 8 UPMC 
clinics that share a common electronic health record. We will focus recruitment on individuals 
with limited socioeconomic resources, low health literacy, or racial/ethnic minorities. Our aims 
are: (1) To evaluate the effect of the relational agent and EHR alert intervention on 
anticoagulation adherence. We will quantify adherence using the Proportion of Days Covered 
(PDC) standard and pharmacy contact at 12 months, and complementary measures of self-
reported non-adherence at baseline, 4, 8, and 12 months. (2) To determine the effect of the 
intervention on health care utilization at 4, 8, and 12 months using participant interview and the 
common EHR. (3) To examine the effect of the relational agent intervention on patient-centered 
outcomes. We will evaluate HRQoL with the AF-specific AF Effect on QualiTy of life (AFEQT) 
measure and general HRQoL with the Patient-Reported Outcomes Measurement Information 
System-29 Profile at baseline, 4, 8, and 12 months. Our trial will engage an 8-member patient 
advisory committee comprised of individuals with chronic AF to guide the intervention’s cultural 
acceptability, recruitment, and presentation of results. Expected Results: In this project we will 
evaluate a scalable patient-centered intervention to improve HRQoL, improve anticoagulation 
adherence, and reduce health care utilization in vulnerable individuals with chronic AF. If proven 
successful, this intervention can be broadly disseminated to improve the care of patients with 
AF. 
2. BACKGROUND 
Standard care is insufficient to address the combined challenges of AF, its complex symptoms 
and treatments, and the social determinant and health literacy obstacles that exacerbate 
outcomes with the condition. We propose an innovative and accessible mHealth intervention to 
improve self-care for patients with this complex and chronic condition. We have developed a 
novel, practical mHealth intervention that is accessible to high-risk patients such as those with 
limited social resources or health literacy. 
3. STUDY RATIONALE AND SIGNIFICANCE OF THE RESEARCH 
a) AF prevalence is estimated to reach 6 to 12 million adults in the US by 2030.1,2 AF 
increases risks of stroke 5-fold,3 heart failure 3-fold,4,5 dementia 2-fold,6,7 and death 1.5- 
to 2-fold.8 Even on optimal therapy, patients in randomized clinical trials (RCT) still 

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experience a stroke rate of 1.5%, heart failure 4-5%, and mortality 3% per year.9 
Individuals with AF have 4.7-times greater days of hospitalization compared to those 
without.10 There has been a 1.6-fold increased expenditure per Medicare beneficiary 
with AF since 1999.11 This project aims to reduce the morbidity and social costs of AF. 
b) AF is a chronic disease with extensive symptoms, adverse outcomes, and resulting poor 
health-related quality of life (HRQoL). Anticoagulation is a mainstay of AF treatment but 
demands long-term – likely lifelong – daily adherence with concomitant monitoring for 
bleeding. We have reported that symptoms diminish HRQoL, subjective health, and 
functional status.12 In our pilot intervention, patients described the effects of AF on 
general HRQoL: “It’s miserable…you never know when it’s going to hit…you’re 
physically drained…it scares me to death every day…I could have a stroke, it’s just a 
scary thing.” National and international guidelines emphasize improved social 
determinants exacerbate worse HRQoL and outcomes in AF. (1) Racial and ethnic 
minorities. AF is reported as less prevalent in blacks but racial disparities in AF are well-
evidenced. Blacks with AF have worse HRQoL, and we identified 1.5- to 2-fold greater 
rates of heart failure, coronary disease, stroke, and mortality in blacks with AF compared 
to whites in the community-based Atherosclerosis Risk in Communities Study. (2) 
Income. Lower income decreases access to health care and medications and increases 
Health Care Utilization (HCU) and likelihood of poor outcomes. Lower income in 
combination with limited health literacy challenges adherence, self-care, and patient 
activation. In our preliminary University of Pittsburgh Medical Center (UPMC) cohort 
(n=213), the association of lower income with significantly worse HRQoL was sustained 
following adjustment for demographics and comorbidities. (3) Health literacy. We have 
summarized the adverse relation of limited health literacy to outcomes, treatment 
access, symptom reporting, and adherence in AF.13,14 Our intervention is designed to 
empower patients, ameliorate health literacy related barriers to self-care, and improve 
patient-centered outcomes for vulnerable patients with AF. 
c) Middle-range theory of self-care has articulated self-care as essential for individuals to 
promote health and manage chronic illness.15,16 Self-care encompasses: (1) 
maintenance, disease-specific knowledge acquisition, adherence to treatments, and 
daily behavior to promote health; (2) monitoring, identifying and tracking common 
symptoms and disease-specific metrics; (3) management, the ability to distinguish 
symptom severity, follow and evaluate action plans, and initiate contact with health 
providers. The self-care model has been demonstrated to improve patient-centered and 
clinical outcomes in cardiovascular disease (CVD) such as heart failure, hypertension, 
coronary disease, and stroke.15 Self-care research in AF has been limited by studies 
being short-term with poor patient retention, absence of a patient-centered approach, or 
lacking a self-efficacy focus. More successful self-care interventions for AF have had 
longitudinal and individualized, patient-facing content.17-19 Accordingly, we propose to 
provide a longitudinal, patient-centered program that will engage patients and provide 
sustained improvement to self-care. 
d) The relational agent is a mobile health (mHealth)20 application for patient education, 
monitoring and problem-solving. It is a virtual agent that uses interactive conversation for 
health counseling and guidance. We have extensive experience developing health 
interventions for the relational agent to promote self-care HRQoL (e.g., R01AG028669, 
1R01HL081307, NCI 5R21CA127511, R01HL116448).21-24, 25-26 The relational agent 
speaks with synthetic speech accompanied by animation to provide health education, 
empathic counseling, and monitoring. The patient engages by listening to didactic 

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content or questions and selecting responses on the touch screen. Patients converse 
with the agent, develop an empathic therapeutic alliance, and report/record across 
domains of self-care. The relational agent (a) elicits symptoms and (b) gestures to 
enhance educational content. We integrate the relational agent with the AliveCor Kardia 
(Mountain View, CA) smartphone heart rate and rhythm monitor to guide Kardia use and 
enhance AF self-care monitoring. 
e) HRQoL as a benchmark goal for AF treatment.13,14 Our intervention addresses the poor 
HRQoL in AF that stems from symptoms, treatment burden, and clinical uncertainty.  
3.0 STUDY AIMS 
3.1 Aim 1  
To evaluate the effect of the relational agent intervention on adherence to 
anticoagulation. We will quantify adherence to anticoagulation with (a) proportion of days covered 
(PDC) obtained from pharmacy data, and (b) self-report, at 4, 8, and 12 months. Hypothesis: Intervention 
participants will have better anticoagulant adherence than control arm participants as measured by 
objective and self-reported assessments of adherence. 
3.2 Aim 2 
To determine the effect of the intervention on HCU. We will evaluate 4-, 8- and 12-month 
HCU (emergency visit, hospitalizations and number of days hospitalized) between the 
intervention and control arms by review of the EHR and participant interview. We hypothesize 
that intervention participants will have superior rates of HCU compared to control arm 
participants. As an exploratory analysis, we will compare the 12-month incidence of 
cardiovascular outcomes (myocardial infarction, heart failure, stroke) and mortality by trial arm. 
3.3 Aim 3 
To examine the effect of the relational agent intervention on patient-reported outcomes. 
We will compare patient-reported outcomes between the intervention and control arms at 4 
months and assess sustainability at 8 and 12 months. We hypothesize that the intervention vs. 
the control at 4 months will produce improvement in: (1) AF-specific HRQoL (secondary 
outcome, measured by the AF Effect on QualiTy of life, or AFEQT33,34 and (2) general HRQoL 
(secondary outcomes, measured by the Patient-Reported Outcomes Measurement Information 
System, PROMIS-29 Profile35). 
4. STUDY DESIGN 
This is a randomized clinical trial to evaluate the effect of a smartphone-based intervention on 
health outcomes in people with the heart disease called atrial fibrillation. The study will enroll 
240 patients at UPMC sites with this condition and randomize them to the intervention or 
control. Intervention participants will receive a smartphone with an application (or app) called a 
relational agent, which simulates conversation. In addition, intervention participants will receive 
an AliveCor Kardia for heart rate and rhythm monitoring, an FDA-approved, widely used 
instrument that pairs with the smartphone. Control participants will also receive a smartphone 
with the application WebMD installed on the phone and the AliveCor Kardia. The intervention 
will last 4 months, and participants will have visits at baseline, 4, 8 and 12 months. The study 
will evaluate the improvement in adherence to anticoagulation, health care utilization, and 
patient-reported outcomes resulting from the intervention.  
 
 

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5.0 STATISTICAL CONSIDERATIONS 
We propose a two-arm RCT to evaluate the efficacy of the relational agent to improve 
anticoagulation adherence in patients with the debilitating chronic condition AF. We will recruit 
240 participants with AF receiving anticoagulation over a 30-month period, prioritizing 
recruitment in socioeconomically depressed regions in the Pittsburgh, PA, metropolitan area. 
We will randomize participants 1:1 to receive the intervention, a mobile Health relational agent 
(n=120) or control, an AliveCor Kardia device (n=120) for 4 months, conducting randomization 
with a web-based data management system that we have used in prior RCTs. Randomization 
will be stratified by type of oral anticoagulant (warfarin or DOAC) and the Newest Vital Sign 
score. We will assess the impact of our intervention at 4, and then at 8 and 12 months to 
determine sustainability.  
Our primary hypothesis will test whether the relational agent intervention can improve 
adherence to anticoagulation as measured by PDC and self-report. Our secondary hypotheses 
will evaluate whether the intervention can reduce rates of HCU and produce the recognized 
minimally important or greater improvement in HRQoL vs. the control, as measured by the 
AFEQT Instrument. Prespecified secondary analyses are listed in the approach and include the 
effect of the intervention by sex, race, income, and health literacy. All participants will have 
return visits at 4, 8, and 12 months. We will use the intention-to-treat principle for all primary and 
secondary analysis. All analyses will be blinded to trial randomization arm.  
Statistical approach for Aim 1. The primary outcome is anticoagulant adherence as assessed by 
PDC. PDC will be analyzed as both continuous (range 0-1 with higher ratio indicating better 
adherence) and binary (optimally adherent if PDC ≥0.8) variables. Primary analysis to assess 
differences in PDC measures at 12 months between study arms will be adjusted for trial 
stratification factors (type of anticoagulant treatment)36 using linear regression (for continuous 
PDC) or logistic regression (for binary PDC). For secondary assessments of adherence at 4, 8, 
and 12 months, we will analyze self-reported extent of non-adherence (Voils et al.37) as a binary 
variable, categorizing individuals with any of 3 items ≥2 (range 1-5) as reporting nonadherence. 
These models will be adjusted additionally for trial stratification factors.36 We calculated the 
statistical power to detect a range of difference in PDC between the intervention and control 
arms. We determined that a sample size of 120 in the intervention group and 120 in the control 
group will enable us to detect a minimum difference in PDC between the two groups as small as 
12.6% with 90% power. Our power calculations assume use of 2-sided tests with 0.05 
significance level. With 15% attrition of the sample size (n=204), we will be able to detect a 
minimum difference in PDC between the 2 study arms as small as 14.1% (0.47 standardized 
mean difference) with 80% power. 
Statistical approach for Aim 2. We will compare the difference in rate of HCU across trial arms. 
Person-year of follow-up will be calculated from the date of randomization to the 12-month visit 
date. The annualized rate of in-patient HCU will then be estimated by dividing the aggregate 
count of inpatient days by person-year of follow-up. Rates and rate ratios of HCU comparing 
intervention and control groups will be calculated. We will use generalized linear models with a 
Poisson distribution as our initial approach. If overdispersion is observed, we will utilize negative 
binomial distribution. Next, if observed data frequently display a higher relative frequency of 
zeros, then zero-inflated count models will be utilized. To better inform which model to use, we 
will conduct (1) a likelihood-ratio test for the overdispersion parameter α in the negative binomial 
specification against the Poisson model specification and (2) a Vuong test of the standard count 
model against the zero inflated count model.38-41 Similar approaches will be used to assess 
counts for each HCU component. All models will account for trial stratification factors.36 For the 

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exploratory analyses, we will compare the 12-month combined adverse cardiovascular 
outcomes and mortality rates by study arm using stratified Mantel-Haenszel Chi-square 
statistics accounting for trial stratification factors.36 Our best estimates of HCU in AF patients are 
based on HCU rates observed in a community-based study that similarly ascertained HCU with 
health services claims.10 The average number of days hospitalized was 13.2 days over 12 
months. Given our sample size, we estimate that we have 80% power to detect a minimum of 
9.7% reduction in HCU in the intervention group, assuming annual rates of 13.2 annual days of 
hospitalization in the control group. A 15% attrition in study participation (n=204) will allow us to 
detect a minimum of 10.6% reduction in HCU in the intervention group with 80% statistical 
power. 
Statistical approach for Aim 3. We estimate that patients randomized to our intervention will 
have a clinically relevant improvement in HRQoL as demonstrated by AFEQT global score at 4-
months follow-up vs. control. Our limited-sized AF pilot cohort (N=31) had a mean increase in 
AFEQT global score of 12±16 (from 64±23 to 76±19) over 30 days (range 0-100, with greater 
scores indicating superior HRQoL). We will use linear regression to model 4-month changes in 
AFEQT global score as a function of study arm, health literacy, and anticoagulation type. 
Similarly, we will assess the effect of the intervention for each of the 8 symptom and HRQoL 
domains measured by the PROMIS-29 at 4 months. Next, we will assess sustainability of the 
intervention effect at 12 months using linear mixed models. Our power calculations assume an 
85% 4-month assessment completion rate (to accommodate for 15% attrition), 2-tailed α=0.05, 
and 19-point improvement in global AFEQT score between our intervention arm and control arm 
(consistent with the data used to determine the minimum important difference34) as well as 
standard deviation of 28 (representing the upper limit of the 95% confidence interval for the SD). 
The minimally important difference in AFEQT global scores has been determined elsewhere as 
a 19-point difference.34 Based on these assumptions, we will have greater than 85% power to 
detect the minimally important difference in the 4-month improvement of the AFEQT global 
score, our primary outcome. We will have more than 85% power to detect similar effect sizes in 
our secondary outcome measures (e.g., AFEQT domains, PROMIS-29) between our 
intervention and control arms. A 15% sample size attrition (n=204) will allow us to detect the 
minimally important difference in AFEQT with 80% power.  
Race and sex. We will ensure our study cohort comprises 51% women and at least 30% non-
white race, of whom 80% (24% overall) will be black race. (1) These demographics reflect the 
Pittsburgh, PA, region. (2) We have reviewed42 that multiple studies have reported AF as less 
prevalent in blacks.43,44-46 Yet we and others have identified consistent data on racial disparities 
in AF. (3) There are sex-specific differences in AF, as women are more likely to present with 
atypical symptoms, report worse HRQoL than men, and have increased stroke risk in AF.47-49 (4) 
Hence, we have developed strategies to oversample participants of black race and female sex, 
detailed in our Inclusion of Women and Minorities. We note our statistical power for such trial 
participation: (1) if we randomize 56 study participants of black race, then we will have 80% 
power to detect a 25.0-point difference in AFEQT between trial arms. (2) Similarly, if we 
randomize 122 women, 51% of trial participants, then we will have 80% power to detect a 17.0-
point difference between trial arms.  
Subgroup analyses. We will conduct secondary subgroup analyses for (1) health literacy (NVS 
<4 or ≥4); (2) type of anticoagulant (warfarin or DOAC); (3) sex; (4) race (white vs non-white); 
(5) self-reported anticoagulant nonadherence37 (<2 or ≥2); (6) duration of anticoagulation (<1 or 
≥1 year); (7) AF classification13 as per the EHR at study enrollment (paroxysmal or 
persistent/permanent); and (8) introduction of new therapies for AF over 12-month study 
participation (antiarrhythmic, cardioversion or electrophysiologic study). We will test whether 

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each of these variables modifies the intervention effect on study outcomes in regression 
models. Our significance level will account for multiple comparisons. 
6.0 SUBJECT SELECTION 
6.1 Study Population and Recruitment 
Individuals with a diagnosis of atrial fibrillation who meet the inclusion and none of the exclusion 
criteria will be eligible for participation in this study.  
We will promote awareness of our study through multiple ways. Recruitment will be conducted 
at numerous University of Pittsburgh Medical Center sites located in Pittsburgh. The Principal 
Investigator (PI) and Project Manager (PM) will contact cardiologist, admin staff, primary care 
physicians, nursing staff and care managers at the UPMC clinics. They will visit these UPMC 
sites to introduce the study and present several educational discussions about the Atrial 
Fibrillation and the importance of this study for these patients as well as develop personal 
relationships with these clinic personnel. During these visits, the PI and PM will give the 
providers a newsletter which will briefly describe the study and will do a short presentation to 
clinicians and their staff.  
We will also include the following recruitment strategies: 
1) Study staff will screen the EHR to identify potentially eligible participants. Study staff will then 
notify clinic practice managers and/or clinicians which patients with upcoming appointments are 
potentially eligible for the study. On the day of scheduled appointment, clinic staff will notify 
potential participants that study staff will meet with them to introduce the study. After learning 
about the study, those wishing to participate will be consented and enrolled.  
2) Study staff will mail recruitment letters to eligible participants. They will follow-up with a phone 
call and/or email these potentially eligible participants. If the eligible participants are interested, 
study staff will conduct the 6-item screening to ensure that the participant meets eligibility 
criteria. Those who meet all eligibility criteria will be mailed a hard copy of the consent form, 
medical and pharmacy release of information forms, and the baseline survey. Study staff will 
schedule their baseline visit phone call at a time that is preferred by the eligible participant. 
Those eligible patients who are interested will be informed about the current study, and for 
those wishing to participate, verbal informed consent will be administered by study staff.  
3) Patient-centered brochures and posters (with and without pull tabs) will be placed in clinic 
waiting areas and in patient examination rooms. These materials will summarize the research 
study and will include study contact information so that participants will know how to reach the 
study team.  
4) The study team will have a study website (https://aflitt.pitt.edu) which will provide a direct 
portal for candidate participants and referring providers to communicate with study staff. 
Individuals will have the opportunity via the website to receive an electronic survey for first-pass 
eligibility assessment.  
5) Study team will leverage the web-based research portal of the University of Pittsburgh 
(Pitt+me, pittplus.com, https://www.facebook.com/pittplusme/)/ which provides an accessible 
listing of research studies accompanied by limited eligibility screening. The CTSI Pitt+Me team 
can screen candidate participants and direct them to study group if they are eligible. Participants 

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who contact the study team via Pitt+Me will be scheduled for their baseline visit. Pitt+Me will 
also mail newsletters to candidates living in Pittsburgh by targeted zip codes.  
6) The study team will receive lists of potentially eligible patients from R3, which is a service of 
the Department of Biomedical Informatics. Potentially eligible patients will also be accumulated 
from a UPMC AFib diagnosis list, and will use those lists to screen the EHR of said patients for 
eligibility.  
 
Recruitment will be conducted by trained individuals serving as study research assistants and 
study staff.  
6.2 Inclusion Criteria 
1. 
Adult, age ≥21; 
2. 
Diagnosis of AF, identified from the EHR and confirmed by either an AF 
monitoring event (Electrocardiogram (ECG), Holter or event monitor) or a clinical 
note;  
3. 
Prescribed use of warfarin or Direct Oral Anticoagulant (DOAC) (formerly NOAC)  
for AF stroke prevention; 
4. 
English-speaking well enough to participate in informed consent and this study; 
5. 
No plans to relocate from the area within 12 months of enrollment.  
 
6.3 Exclusion Criteria  
1. 
Prior catheter ablation procedure for treatment of AF (pulmonary vein isolation, 
AF ablation); 
2.  
Prior AV nodal (atrioventricular nodal) ablation procedure; 
3.  
Conditions other than AF that require anticoagulation, such as mechanical 
prosthetic valve, deep vein thrombosis, or pulmonary embolism; 
4. 
Heart failure necessitating hospital admission ≤3 months prior to study inclusion; 
5. 
Acute coronary syndrome (defined as at least 2 of the following: chest pain, 
ischemic electrocardiographic changes, or troponin ≥0.1 ng/mL) ≤3 months prior 
to study inclusion; 
6. 
Untreated hyperthyroidism or ≤3 months euthyroidism before inclusion; 
7. 
Foreseen pacemaker, internal cardioverter defibrillator, or cardiac 
resynchronization therapy; 
8. 
Cardiac surgery ≤3 months before inclusion; 
9. 
Planned cardiac surgery; 
10. 
Presence of non-cardiovascular conditions likely to be fatal within 12 months 
(e.g., cancer);  
11. 
Inability to comprehend the study protocol, defined as failing three times to 
answer correctly a set of questions during the consent process;51 
12. 
A medical disorder, condition, or history that would impair the participant’s ability 
to participate or complete the study. 
 
 
 

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7. STUDY DESIGN AND METHODS 
Fig 1. Participant screening through Randomization Process  
 
 
 
7.1 Pre-Screening  
1) Screening for eligibility will be performed by using rosters and schedules of clinical visits. 
Study staff will screen the EHR to identify these potentially eligible participants. 
 
2) The study may also use R3, which is a service of the Department of Biomedical Informatics. 
R3 will create a list of patients who meet the study’s inclusion criteria and provide the list to 
study staff. Study staff will then verify the participant’s eligibility by checking their medical 
history in EPIC and following the “EHR Screening Form” checklist. 
3) Participants can also contact study staff directly (via phone call, email, Pitt+Me, study 
website) to express interest in participating in the study. Study staff will then screen the EHR to 
determine if the participant is eligible. Study staff can then schedule an appointment to meet 
with potential study candidate to provide more information about the study/consent participant.  
4) If non-UPMC patients contact the study team and are interested in participating in the study, 
they will confirm with the patient that they will need to check the patient’s eligibility by medical 

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review. If the non-UPMC medical records are available in "EPIC Care Anywhere" (an option in 
EPIC that links to other healthcare systems that are also using EPIC), study staff will check their 
medical history to confirm eligibility. However, if their medical records are not available in EPIC 
Care Anywhere, study staff will request the participant to sign a medical release form to receive 
the necessary medical information to determine eligibility. 
In-person recruitment: Study staff will then notify clinic practice managers and/or clinicians 
which patients with upcoming appointments are potentially eligible for the study. On the day of 
scheduled appointment, clinic staff will notify potential participants that study staff will meet with 
them to introduce the study. After learning about the study, those wishing to participate will be 
consented and enrolled.  
Virtual recruitment: Study staff will also mail recruitment letters to these potentially eligible 
participants and will follow up with call and/or email. If they are interested, they will provide their 
verbal consent and complete the 6-item screening item to confirm eligibility. Study staff will then 
mail hard copies of the consent forms, medical and pharmacy release forms, and the baseline 
survey. Study staff will then schedule their baseline visit phone call at a time that is preferred by 
the eligible participant. Those eligible patients who are interested will provide their verbal 
consent and complete the baseline survey.  
7.2 Screening at Baseline  
Participants who are screened to be eligible will be scheduled to meet with a study recruiter 
(Research Assistant/ Project Manager). Individuals who agree to participate will provide their 
verbal agreement and this will be noted in the “Clinic Screening Form” and will undergo a 6-
item screener consisting of being asked to repeat and remember 3 words, and to correctly state 
the day of the week, month, and year. Implementation of basic memory assessment for 
screening is a standard component of clinical research studies.   
7.3 Consent Process  
The Investigator will prepare the informed consent form and the authorization of medical release 
form and submit to Institutional Review Board (IRB) for approval. The written consent document 
will embody the elements of informed consent as described in the International Conference on 
Harmonisation and will also comply with local regulations. The informed consent will be 
completed online using a tablet that is provided by the research team, or will be given by 
the participant verbally over the phone to a study team member during their baseline call. 
However, a paper version will also be made available for use only when the online version 
cannot be accessed (e.g. no available tablets, no internet access, unable to access the tracking 
system, etc.). If a paper version is completed, then this consent form will be scanned and saved 
as a PDF and attached to the participant’s record. The paper version of the form will also be 
stored in a locked filing cabinet, behind two locked office doors.  
In-person consent: The informed consent will be conducted in a private location to respect 
subject privacy. Following the briefing of the research study, the study recruiter will provide the 
subject ample time to read the consent and study recruiter will answer any of their questions 
regarding the document. Prior to the subject’s participation in the study, the study recruiter will 
ensure that the participant understand the research study and their role in the study. They will 
be made aware of their responsibilities during the baseline visit, after randomization 
assignment, and throughout the study period. The importance of continued follow-up should be 
stressed and balanced with a discussion of the effect of withdrawal on the study. The participant 

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will sign and date the econsent in REDCap using their finger or stylus on the study tablet, 
include their first and last name, as well as DOB and a security question. Designated study staff 
member will then e-sign and date the consent form. The copy of the signed econsent will be 
saved in tracking and a copy of it will be provided to the participant, upon request. Informed 
consent will be obtained in accordance with the Declaration of Helsinki, ICH GCP, US Code of 
Federal Regulations for Protection of Human Subjects (21 CFR 50.25[a,b], CFR 50.27, and 
CFR Part 56, Subpart A), the Health Insurance Portability and Accountability Act (HIPAA, if 
applicable), and local regulations.  
Verbal consent: Potentially eligible participants who are interested in participating in the study 
but unable to meet with a study recruiter, will be able to complete the consent and the baseline 
survey over the phone. Study staff will mail the consent form prior to calling the participant so 
that the participant will have ample time to read the consent form and understand the research 
study.  
Medical Release Forms 
After the participant has e-signed the informed consent form, the study staff will review the 
Authorization to Use and Disclose Health Information form with the participant. This form is to 
be signed by the participant for permission to obtain pharmacy records from their preferred 
pharmacy during the course of their study participation. This Authorization to Use and Disclose 
Health Information form has an expiration date of 12 months. Therefore, we will ask participants 
to sign 2 copies of the Authorization to Use and Disclose Health Information forms (one with the 
current date and another post-dated) to ensure that we get a whole year of pharmacy record 
information.  
The study participant will also be asked to sign two copies of the Authorization for Release of 
Protected Health Information form, in the event that the participant is admitted to a non-UPMC 
hospitals during the study period.  
7.4 Survey Process 
Once the participant has provided their written or verbal consent, the participant will complete 
the survey. The surveys are to be completed at baseline, 4-, 8-, and 12-months after 
randomization. The surveys are to be completed online using the study tablet or over the phone 
with a study staff. Paper versions of the surveys (see Table 1 for assessments completed at 
each visit) will be mailed to participants who prefer to complete the surveys over the phone with 
a study staff. Each survey will have a “Q by Q” guide (specifying responses to sample questions 
participants may have for each question/item) for staff to refer to if participants raise questions 
during form completion supporting consistency.  
7.5 Randomization 
Participants meeting all of the inclusion and none of the exclusion criteria will be randomized to 
one of 2 groups, a control and an intervention group. To ensure flexibility in achieving the 
proposed allocation of patients between study arms, permuted block randomization will be used. 
Additionally, randomization will be stratified by the following factor that may influence outcomes 
with AF: (1) Health literacy (low versus higher) based on the NVS (<4 or ≥4), because of the 
prevalence of limited health literacy in lower income individuals, its relevance to health 
outcomes and disparities, and necessity for success as a patient with AF. (2) Type of 

13 
 
anticoagulant, (warfarin or DOAC) as DOACs do not require monitoring and are associated with 
fewer major bleeding events compared to warfarin.  
If the participant is randomized to the treatment group, he or she will receive the ECA+Kardia 
app/hardware that will come pre-loaded onto a study iPhone that will be returned at the end of 
the study. These study iPhones will either be mailed or provided to the participant in person.  
Participants randomized to control will receive an educational session (over the phone or in 
person), Kardia and a brochure published by the American Heart Association that describes AF 
and the relevance of anticoagulation. Control participants will receive an Apple smartphone 
(identical to that received by intervention participants) with the WebMD 
(www.webmd.com/mobile) and Kardia applications installed and directions for its use. The 
WebMD app provides general health content and can be used for enhanced self-care. We will 
inform control participants that they may use the WebMD app to track symptoms and record and 
learn about their medications for AF and other conditions. 
7.6 Blinding 
This is a parallel-arm, randomized clinical trial. Neither the study participants nor the recruitment 
study staff will be blinded to assignment to intervention or control arm. 
7.7 Relational Agent  
The relational agent is a mobile health (mHealth)20 
application for patient education monitoring and problem-
solving. It has had extensive use in multiple contexts and 
has been developed in the lab of Dr. Timothy Bickmore, 
Northeastern University. Dr. Bickmore has developed over 
25 relational agent -based health interventions in which the 
relational agent is designed to foster a sense of therapeutic 
alliance. The relational agent speaks with synthetic speech 
accompanied by animation to provide health education, 
emphatic counseling, and monitoring. The patient engages 
by listening to didactic content or questions and selecting 
responses on the touch screen (FIG 2). Patients thereby 
converse with the agent, develop empathic therapeutic 
alliance, and report/record across domains of self-care. To 
adapt the relational-agent intervention specifically to this 
study population, in-depth interviews will be conducted with 
potential study participants prior to the start of recruitment. 
The interviews will capture anecdotal evidence regarding 
clinical encounters, barriers to care and medication 
adherence, and general experience living with atrial 
fibrillation that will inform content development and 
delivery. In addition, preferences for the relational agent’s 
physical persona will be queried.  
We integrate the relational agent with the AliveCor Kardia 
(Mountain View, CA) smartphone heart rate and rhythm 
monitor to guide Kardia use and enhance AF self-care 
monitoring.  
 
FIG 2. RELATIVE AGENT 
An example of a screen shot 
of the relational agent and a 
clinical encounter menu 

14 
 
7.8 Kardia  
The AliveCor (Kardia) is an FDA-approved, heart-rhythm monitor that is accessed via 
smartphone application. The device is attached to the smartphone and provides a lead I ECG 
rhythm strip with 30 seconds of finger placement on two poles (FIG 3), like metallic buttons 
(there is no electric current and participants have an experience analogous to the performance 
of a standard 10-second, 12-lead electrocardiogram used in routine clinical practice). The 
tracings are uploaded to a secure web-based portal with time stamps of use, duration of use, 
and heart rate and rhythm (sinus of AF). The Kardia has been principally used for AFib 
detection. The Kardia app will be downloaded to the participant’s smartphone (or study provided 
iPhone) from the App Store or Play Store. Study staff will create their Kardia account using the 
participant’s first name and substituting their last name with “XXXX” (where XXXX represents 
the participant’s assigned study ID). The date of birth, height and weight for every participant will 
be the same information (to ensure that study staff won’t enter identifying information). The 
study staff will assist the participant in running a sample EKG on their smartphone by asking the 
participant to place the Kardia device close to their smartphone and then to lightly place the 
index and middle fingers on the pads for 30 seconds (pictured below). They will also explain to 
the participant that he/she will not be able to see the first results immediately; however, the 
results of all other EKGs taken forward will be recorded immediately. We will instruct 
participants to use the Kardia a minimum of once daily. Kardia use is tracked automatically, and 
review of the results will be completed by the PI, who is a cardiologist and has the expertise to 
oversee Kardia interpretation (FIG 4). 
 
               
 
 FIG 4: DATA from KARDIA 
 
 
8.  STUDY PROCEDURES  
8.1 Assessments 
We will conduct research assessments with each study subject at baseline, 4mth, 8mth and 
12mth follow up visits. Sociodemographic, clinical and outcome information will be obtained 
directly from subjects and through review of their medical records including hospital databases, 
outpatient records, and insurance records. We will administer our assessments as portrayed in 
table below. 
 
FIG 3. KARDIA
 

15 
 
Table 1 
Assessment 
(In order of study procurement) 
Screening/ 
Consent (in clinic 
or by phone) 
Baseline 
(in clinic or 
by phone) 
4mth Follow-up 
(in clinic or 
by phone) 
8mth Follow-up 
(by phone) 
12mth Follow-up 
(by phone) 
Role to assessment 
Clinic Screening Form (includes 6-item screener) 
X 
 
 
 
 
 
Consent 
X 
 
 
 
 
 
Sociodemographic Characteristics 
 
 
 
 
 
 
 
 
 
 
X 
Exploratory Outcome 
Demographics 
X 
Transportation 
X 
Kaiser: Your Current Living Situation 
X 
Smoking 
X 
Alcohol Use 
Montreal Cognitive Assessment 
X 
X 
Quality of Life Assessment 
 
 
 
 
 
Secondary Outcome 
AFEQT 
X 
X 
X 
X 
 
PROMIS-29 Profile v2.0 
X 
X 
X 
X 
Anticoagulant Adherence 
 
 
 
 
 
Primary Outcome 
VOILS: Medication Nonadherence 
X 
X 
X 
X 
 
VOILS: Medication - Reasons for nonadherence 
X 
X 
X 
X 
Self-Efficacy 
 
 
 
 
 
Exploratory Outcome 
PROMIS Self-Efficacy for Managing Medications &Treatments 
X 
X 
X 
X 
PROMIS Self-Efficacy Managing Symptoms 
X 
X 
X 
X 
Social Measures 
 
 
 
 
 
X 
X 
 
 
 
 
 
 
Exploratory Outcome 
BRIEF Health Literacy Screener 
X 
Newest Vital Sign (NSV) 
Connor-Davidson Resilience Scale (CD-RISC-10)  
Berkman-Syme Social Network Index 
X 
 
Psychiatric Symptoms 
 
 
X 
 
X 
 
X 
 
X 
Covariate 
Patient Health Questionnaire (PHQ-8) 
Medical Co-Morbidity Variables 
 
 
 
 
 
Covariate 
AF History 
X 
 
 
 
Vital Signs (BMI) 
X 
 
 
 
Antiarrhythmic Medications 
X 
X 
X 
X 
Other Medications 
X 
 
 
 
Other Medical History 
X 
 
 
 
New AF Therapies (Cardioversion, pacemaker) 
 
X 
X 
X 
Proportion of Days Covered (PDC) 
 
 
 
 
X 
Primary Outcome 
Health care utilization (events) 
 
 
X 
X 
X 
Secondary Outcome 
RELATIONAL AGENT/Kardia Information 
 
 
 
 
 
Exploratory Outcome 
Relational Agent usage 
Study team Kardia review time 
X 
X 
AliveCor Tracking 
Satisfaction in using Relational Agent and Kardia 
X 
X 
Clinical events 
Cardiac hospitalizations (heart failure, myocardial infarction, 
hospitalization for AF) 
 
 
 
X 
 
X 
 
X 
 
AS NEEDED FORMS: 
 
 
 
 
 
 
Adverse Event and Serious Adverse Event monitoring 
Protocol Deviations 
Unanticipated Problems 
Withdrawal 
Death notification 

16 
 
8.1.1 Sociodemographic Characteristics 
Demographic information (date of birth, sex) will be recorded at screening. Race/ethnicity, 
education, employment, marital status, insurance, financial resources and transportation 
status will be determined by self-report. 
8.1.1.1 Your Current Living Situation 
Five items from Kaiser’s Your Current Living Situation Questionnaire. 
8.1.1.2 Smoking 
Six items from the NHANES Smoking questionnaire. 
8.1.1.3 Alcohol 
Five items from the NHANES Alcohol questionnaire. 
8.1.1.2 Montreal Cognitive Assessment (added June 2021) 
The entire Montreal Cognitive Assessment (telephone version). 
8.1.2 Symptoms of Atrial Fibrillation 
The 20-item AF Effect on QualiTy of life (AFEQT) is a validated instrument for measuring AF 
quality of life (QOL). 
8.1.3 PROMIS-29 Profile v2.0 
A generic health-related quality of life survey ranked on a 5-point Likert Scale. There is also 11- 
point rating scale for pain intensity. 
8.1.4 Medication Nonadherence 
Voils two-part scale of self-reported measure of medication non-adherence. 
8.1.5 PROMIS Self-efficacy Managing Medications and Treatments 
An eight-item tool to assess confidence in managing medication schedules of different 
complexity. Managing medication and other treatments in challenging situations such as when 
travelling, when running out of medication, and when adverse effects are encountered. 
8.1.6 BRIEF Health 
Validated 4-item instrument for quantifying health literacy. 
8.1.7 
Newest Vital Sign Health Literacy 
Validated 6-item instrument for quantifying health literacy. 
8.1.8 
Connor-Davidson Resilience Scale (CD-RISC-10) (added June 2021) 
A validated 10-item social factors quality of life survey ranked on a 5-point Likert Scale.  
8.1.9 
Berkman-Syme Social Network Index (added June 2021) 
A validated 11-item social factors quality of life survey.  
8.1.10 Patient Health Questionnaire-8 (PHQ-8) 
An 8-item validated self-administered instrument to measure symptoms of depression in 
primary care settings and has been used in heart failure populations. 
8.1.11 History of Atrial Fibrillation 
Six items describing individual history of AF 

17 
 
8.1.12 
Anthropometrics 
Anthropometrics will consist of weight, height, and BMI as determined from the most recent 
data available in the electronic health record. Data recorded >1 year prior to study enrollment 
will not be used. 
8.1.13 Medications 
Medications will be obtained from the electronic health record. Specific medications recorded will 
be: 
8.1.13.1 
Warfarin 
8.1.13.2 
Novel oral anticoagulants 
8.1.13.3 
Medications for blood pressure 
8.1.13.4 
Medications for cardiovascular disease, e.g. beta blockers, ACE 
inhibitors, ARBs, dihyropyridine and non-dihydropyridine calcium channel blockers. 
8.1.13.5 
Other antiplatelet agents, e.g. clopidogrel, prasugrel, ticagrelor 
8.1.13.6 
Medications for diabetes (insulins and oral agents) 
8.1.14 Medical History 
Medical history will be obtained from EHR problem lists and include: 
8.1.14.1 
Congestive heart failure 
8.1.14.2 
Hypertension 
8.1.14.3 
Diabetes 
8.1.14.4 
History of stroke or TIA 
8.1.14.5 
Vascular disease, as determined by history of myocardial infarction as a clinical 
event; coronary angiography with stenosis documented as >50%; peripheral arterial 
disease, as documented by symptomatic claudication, ankle-brachial index 
documented as ≤0.90, carotid stenosis >80%, or abdominal aortic aneurysm measured 
at ultrasound by >5 cm. 
8.1.14.6 
Cardioversion procedures, including electrical cardioversion, pharmacologic 
cardioversion, and pulmonary vein isolation (AF ablation) 
8.1.15 Health Care Utilization 
Information on health services utilization, including hospitalizations, outpatient visits, and other 
specialty referrals, and medication usage by abstracting patients’ medical records. We will also 
advise patients to report any hospitalizations or other medical events (especially those that 
occur at non-UPMC locations) to our research team by telephone any time. All reports of 
utilization will be investigated using hospital records. 
9. Follow-up Visit Schedule  
Throughout the duration of baseline to 4 months  
All participants will receive scheduled check-in calls at study day 7, 14, 30, 60 and 90. These 
calls will serve to support participants and attend to any difficulties they may be experiencing 
with the study protocol and/or device(s) they are asked to use daily. Participants who haven’t 
mailed back their medical release forms will be reminded to do so during these calls.   
We will maintain relational agent and Kardia results daily on a web-based dashboard developed 
with the help of the Center for Clinical Trials and Data Coordination (CCDC). Relational agent 
dashboard results will consist of use statistics, reported symptoms, and adherence 
anticoagulation. Kardia results include date and time of use and heart rate and rhythm.  

18 
 
Dashboard results will be reviewed every weekday except federal holidays. Particular attention 
is given to the fact that this intervention does not replace or preclude usual and routine care. 
Hence the dashboard does not preclude urgent or emergency assessments as would be 
obtained were an individual not participating in this study. The informed consent documents that 
individuals should pursue care as they would if not participating in this study and participants 
are likewise instructed of this when they enter the study. 
2nd Study follow-up: - +~4 months  
Participants will complete the 2nd visit at 4 months in person or over the phone if the participant 
is unable to meet with study staff. Study staff will send reminder cards/e-cards to the 
participants prior to their follow-up visit. Participants will complete assessments and 
questionnaires identical to several of those done at the baseline visit. Medical records will be 
reviewed to assess health care utilization (emergency visit, hospitalization, and number of days 
hospitalized). If the participant used his or her personal phone for the study, study apps will be 
removed. If the participant used sponsor-supplied smartphones, the devices will be returned. 
This visit will take participants 45-60 minutes to complete. 
3rd Study follow-up: - +~8 months  
The 3rd follow up will be conducted over the phone by trained interviewers.  
Medical records will be reviewed to assess health care utilization (emergency visit, 
hospitalization, and number of days hospitalized). Participants will complete assessments and 
questionnaires identical to several of those done at the baseline and month 4 visits.  
4th Study follow-up: - +~12 months 
The 4th follow up will be conducted over the phone by trained interviewers. Participants will 
complete assessments and questionnaires identical to several of those done at the baseline and 
month 4 and 8 visits. Medical records will be reviewed to assess health care utilization 
(emergency visit, hospitalization, and number of days hospitalized).  
At time- +~12 months 
Medical records will be reviewed to assess health care utilization (emergency visit, 
hospitalization, and number of days hospitalized). Medication fill data, for purposes of 
calculating medication possession ratio, a common measure of medication adherence, will also 
be extracted from the EHR and from the pharmacist(s). We will assess the EHR for incidence of 
adverse cardiovascular event and mortality. We will also use the National Death Index to 
identify participant deaths and their dates. 
10 Clinical Trial Oversight and Monitoring  
10.1 Data Safety Monitoring  
The study team will recruit subjects at the University of Pittsburgh Medical Center (UPMC) who 
have a chronic heart rhythm disease, atrial fibrillation, and are receiving anticoagulation for 
stroke prevention. Participants are expected to constitute a high-risk population, as they will 
have limited socioeconomic resources, low health literacy, and/or be racial/ethnic minorities. 
The study will consist of 240 adults who will be randomized to an intervention or control cohort. 

19 
 
The intervention cohort will receive a smartphone with a relational agent application and an 
AliveCor Kardia heart rate and rhythm monitor, described here as Kardia for simplicity. Topics 
and content provided by the relational agent will focus on selfcare in AF, including symptom 
monitoring, maintenance, and health-promoting behaviors. The relational agent will also provide 
education about AF and its treatments and outcomes, and health-related quality of life. The 
Kardia is an FDA-approved device that also uses the smartphone. It provides 30 continuous 
seconds of ECG monitoring similar to a Lead I rhythm strip of a 12-lead ECG. Both devices are 
non-invasive and may be described as smartphone applications, or apps. Participants 
randomized to the control arm will receive an educational session about AF, a Kardia device 
and a smartphone with the WebMD application installed. Participants will use the apps and 
Kardia for 4 months and have return visits at 4-, 8- and 12-months with assessments as 
documented in the application. Intervention participants will use the smartphone in order to have 
interactive exchanges with the relational agent and to check their heart rates and rhythm with 
the Kardia. The control participants will also use the Kardia to check their heart rates and 
rhythm. The study team will follow up on the results of the smartphone agent and/or the Kardia, 
as these mobile health applications can provide important information that can be used to guide 
and enhance patient care. If results meet specified criteria, then the study team will send alerts 
through the electronic health record to the physicians and care teams that provide care for 
intervention participants. The research questions being investigated by this study consequently 
have direct implications for clinical care and patient management. 
10.2 Data Safety Monitoring Board 
To ensure the safety of the participants and the validity and integrity of the data, the clinical trial 
will have a Data and Safety Monitoring Board (DSMB). The DSMB will be charged with 
evaluating the quality of trial administration, monitoring safety issues, and providing guidance on 
scientific, methodological and ethical issues. As its first priority, the DSMB will approve and 
codify the DSMB charter. Following approval by the DSMB, the charter will be submitted to the 
National Heart Lung Blood Institute (NHLBI) for review and approval. The DSMB charter will 
describe the study protocol, data and safety monitoring plan, operation and format of DSMB 
meetings, Adverse Event (AE) definitions, AE reporting templates and case report forms, 
stopping rules for the trial, and a schedule for conducting blinded assessments of study benefit 
and safety. 
The DSMB will be valuable for ensuring the quality and scientific validity of the study. It will be 
comprised of 4 individuals and chaired by Leslie McClure, PhD, MS. The remaining members of 
the DSMB have a combination of expertise in clinical trials, health services research, and 
biostatistics. Members will adhere to the University of Pittsburgh’s policy on conflict of interest 
and are expected to participate for the duration of this clinical trial. The DSMB will have 
scheduled meetings every 6 months to evaluate and review safety, review any potential 
breaches in protocol, and discuss summaries of the interim AE and serious adverse event 
(SAE). The DSMB will make recommendations concerning (1) participant safety, (2) the benefit 
and risk ratio of the study, (3) the efficacy of the study intervention, (4) any possible 
amendments to the study protocol or consent, and (5) proposed ancillary studies and their 
impact on participant burden. Following each meeting, the DSMB will make recommendations 
on continuation, modification, or termination of this trial. The DSMB will further evaluate 
adherence to the protocol and the recruitment and retention of participants. DSMB meeting 
format will be open or closed session, as determined by Dr. McClure, DSMB Chair, with regard 
to NHLBI participation. Additional meetings will be arranged as per the charter for the need to 
review events or issues arise (such as an unexpected number or severity of AE).  

20 
 
For safety monitoring, discussion will take place on whether or not any reported incident is 
unanticipated and/or places subjects or others at greater risk of harm, and if protocols or 
consent processes need to be modified. The DSMB will review the annual progress report on 
(1) confirmation of adherence to the data and safety monitoring plan, (2) a summary of data and 
safety monitoring issues for the since the last report, (3) changes in the research protocol or 
data and safety monitoring plan, and (4) IRB status and approvals.  
10.3 Adverse events 
An AE is any untoward medical occurrence, which does not necessarily have a causal 
relationship with the trial intervention, after providing written informed consent for participation in 
the study. Therefore, an AE may or may not be temporally or causally associated with the use of 
the study intervention.   
Individuals enrolled in this trial will have a chronic disease, atrial fibrillation. This trial will use 
standard definitions for adverse events that accord with Federal mandates for human subjects’ 
protection and adverse event reporting.   
All AEs will be reported with an event diagnosis (including the term and grade based on CTCAE 
grading), start/stop dates, action taken, any corrective measures taken, outcome, and other 
possible causes. For all events, the relationship to study intervention and the attrition of the 
event will be determined by the investigator. Attribution is an assessment of the relationship 
between the AE and the medical intervention. After naming and grading the event, the clinical 
investigator or study staff must assign an attribution to the AE using the following attribution 
categories: 
RELATIONSHIP 
ATTRIBUTION 
DESCRIPTION 
Unrelated to the study 
intervention 
Unrelated 
The AE is clearly NOT 
related to the intervention 
Unlikely 
The AE is doubtfully related 
to the intervention 
Related to the study 
intervention 
Possible 
The AE may be related to 
the intervention 
Probable 
The AE is likely related to 
the intervention 
Definite 
The AE is clearly related to 
the intervention 
For each AE, the seriousness will be determined according to the criteria given below.  
Definition of serious adverse event (SAE)  
An SAE is classified as any untoward medical occurrence that, at any dose, meets any of the 
following criteria (1–6):  
1. Results in death.  
2. Is life-threatening.   
3. Requires inpatient hospitalization or prolongation of existing hospitalization.  A planned 
hospitalization or prolongation of hospitalization for a pre-existing condition will not be 
regarded as an SAE.  
4. Results in persistent or significant disability / incapacity.  

21 
 
5. Is a congenital anomaly / birth defect.  
6. Is another medically important serious event as judged by the investigator.  
The SAE form will capture information on the setting where the event occurred, the intensity 
(mild, moderate, severe), relevant lab results and other tests, and the concomitant medications. 
Study staff will also record a detailed description of the event, evaluation, assessment and any 
applicable treatment.  
Each serious adverse event will be followed up until resolution or stabilization, by submission of 
updated reports to the designated person.  
Relatedness of the adverse event to the research study 
The study team will make a determination as to the relatedness of an AE to the intervention. 
AEs will be categorized as one of the following (1-6): 
1. Unrelated (would have occurred regardless of the study and/or intervention) 
2. Unlikely related (likely to have occurred regardless of the study and/or intervention) 
3. Possibly related (may have occurred due to the study and/or intervention) 
4. Probably related (likely to have occurred due to the study and/or intervention) 
5. Definitely related (would not have occurred outside of the study and/or intervention) 
6. Indeterminate 
Unanticipated Problems (UAP) 
An unanticipated problem is any incident, experience, or outcome that meets each of the 
following criteria: 
1. Unexpected (in terms of nature, severity, or frequency) given (a) the research 
procedures that are described in the protocol-related documents, such as the IRB-
approved research protocol and informed consent document; and (b) the characteristics 
of the subject population being studied; 
2. Related or possibly related to participation in the research (in this guidance document, 
possibly related means there is a reasonable possibility that the incident, experience, or 
outcome may have been caused by the procedures involved in the research); and 
3. Suggests that the research places subjects or others at a greater risk of harm (including 
physical, psychological, economic, or social harm) than was previously known or 
recognized. 
The Unanticipated Problem form captures information about the date of the event and when the 
site became aware of the event, subject(s) affected, if the event was expected or unexpected, 
whether it is related to the research study, if it places the subject(s) or others at greater risk of 
harm, type of unanticipated problem. Study staff will also include a general description of the 
problem, and corrective actions taken. This form will be completed for each unique event.  
Reporting of adverse events, serious adverse events and unanticipated problems 
All serious adverse events will be reported to the Data Coordinating Center (DCC), which will 
regularly summarize their frequency and quantity. The DSMB, all investigators, and IRB will also 
be promptly notified in accordance with the respective policies and procedures. 

22 
 
The study team will learn of AE, SAE, and unanticipated problems by participant interview at the 
4-, 8- and 12-month assessments; direct contact with participants during the 4-month 
intervention phase of the study or following; review of the electronic health record; 
communication from referring physicians or participants’ providers; or contact with the 
study team initiated by family or other participant surrogates.   
Responding to AE, SAE, and unanticipated problems. We will follow guidelines set forth by 
the University of Pittsburgh’s Institutional Review Board (IRB) and the Human Research 
Protection Office. The Center for Clinical Trials and Data Coordination, serving as the Data 
Coordinating Center (DCC) for the trial, will develop AE case report forms specific to this trial. 
The project manager and PI will prospectively record all incidents that meet any of the above 
definitions, and then classify AE within 24 hours of identification using the following criteria:   
1. Severity (mild, moderate, severe, life threatening);  
2. Relationship to study intervention (not related, unlikely, possibly, probably, or 
 
definitely related, as defined above);  
3. Action take regarding study intervention (none, medical intervention, hospitalization, 
intervention discontinued, or other);  
4. Outcome of AE (resolved, recovered with minor sequelae, recovered with major 
sequelae, ongoing or continuing treatment, condition worsening, death, or unknown);  
5. Expected AE (yes or no); and  
6. Whether the AE constitutes an SAE.   
All AEs and classification according to these criteria will be maintained in a log. In accordance 
with the University of Pittsburgh IRB, all internal AEs which are (1) unexpected, fatal or life-
threatening, and (2) related (as per criteria above, defined by being possibly, probably, or 
definitely related to the study and intervention) to the relational agent and/or Kardia will be 
reported to the IRB within 24 hours of learning of the event. All other reportable AEs will be 
reported to the IRB within 10 working days of learning of the event.   
For this clinical trial, an example of an AE that would merit classification as an SAE would be 
hospitalization. AEs that are classified as SAEs will be further reviewed to determine if (1) they 
are unexpected, and (2) related (possibly, probably, or definitely) to the study procedure 
(relational agent and AliveCor). SAEs meeting these criteria will be reported to the IRB within 2 
business days (48 hours) of learning of the event. Those SAEs that meet such criteria will also 
be reported to the NHLBI within 7 calendar days of initial receipt of information. SAE that are (1) 
non-fatal or non-life-threatening, (2) unexpected, and (3) determined as related to the study 
procedure will be reported to the IRB and NHLBI within 15 calendar days of the receipt of 
information. Any unanticipated adverse effect related to the study procedure will be reported to 
the IRB and NHLBI within 10 working days of the receipt of information.  
The project manager, co-investigators, PI, and statistician will review aggregate data on AEs 
and SAEs regularly. SAEs that merit reporting to the IRB and NHLBI will also be reported to the 
DSMB, which will receive case report forms and documentation with a request for timely 
response to the SAE.  
Unanticipated problems will likewise be reported to the IRB. within 24 hours of the PI learning of 
the incident if they are fatal or life-threatening. The report will explain why the incident is 
considered an unanticipated problem and how the protocol will be modified. Incidents that do 
not meet the three criteria for unanticipated problems (unexpected, given the research 
procedures; related or possibly related to participation in the research; and suggest that the 

23 
 
research places participants at a greater risk of harm than previously known or recognized) will 
be considered as either potential AE or classified as SAE as defined above. Unanticipated 
problems that are not SAEs will be reported to the IRB and the NHLBI within 14 days of the PI 
becoming aware of the problem.  
 
Data and safety monitoring for continuation, modification, or termination of participation 
or the clinical trial. The DSMB will regularly review summaries of AEs, SAEs, and 
Unanticipated Problems and make recommendations for the continuation, modification or 
termination of the trial. The trial is not expected to have an early termination.    
Expected events. Participants in this study have atrial fibrillation, a chronic heart condition 
associated with significant morbidity. Individuals are likely to be older age, have multiple 
additional conditions, and have frequent hospitalizations. The following items are categorized as 
expected events given their high likelihood of occurring in this population: 
• 
Chest pain or stable angina 
• 
New unstable angina, requiring clinical evaluation such as stress testing with or 
without accompanying hospitalization 
• 
New or suspected myocardial infarction 
• 
Cardiac catheterization of the left or right heart  
• 
Percutaneous Coronary Intervention (PCI)  
• 
Coronary Artery Bypass Graft (CABG)  
• 
Mechanical complications of MI (e.g., rupture of intraventricular septum, left 
ventricular free wall, mitral valve papillary muscle or left ventricular aneurysm) 
• 
Signs, symptoms or treatment suggestive of heart failure, either preserved or 
reduced ejection fraction 
• 
Cardiomyopathy, i.e. new onset heart failure 
• 
Cardiogenic shock, requiring vasopressor or circulatory support 
• 
Transient Ischemic Attack (TIA) 
• 
Stroke, ischemic or hemorrhagic 
• 
Bleeding event, identified as cerebral or gastrointestinal 
• 
Mobitz type 1 AV block  
• 
Mobitz type 2 AV block  
• 
Complete (3rd degree) AV block  
• 
Right bundle branch block  
• 
Left bundle branch block  
• 
Bifascicular block  
• 
Pacemaker or defibrillator implantation 
• 
Electrophysiologic procedure (pulmonary vein isolation or other ablation electrical 
cardioversion) 
• 
Admission for initiation of antiarrhythmic medication requiring in-patient 
monitoring (e.g., sotalol, dofetilide) 
• 
Asystole  
• 
Syncope 
• 
Fall with traumatic injury (e.g., concussion, fracture) 
11 Data Management and Quality Monitoring Plan   
Data Management and Security to Protect Privacy. The Center for Clinical Trials and Data 
Coordination will serve as the DCC at the University of Pittsburgh will supervise the 

24 
 
data management plan for this study. The DCC will employ procedures for data management 
that have been established and validated by other studies. The DCC will assure high quality 
forms, monitor data quality, and track and link the multiple data sources. The DCC will develop 
data collection forms, design the database management system for data entered and for subject 
tracking, implement procedures for quality control, and provide statistical programming and 
collaborate in report writing and presentation of study results. The DCC will devise a system 
such that data from all sources are linked and entered using multiple checks.   
DCC databases are located on secure, password-protected servers, behind the University of 
Pittsburgh firewalls. The web and database servers use Secure Socket Layering (SSL) to 
ensure data security and confidentiality. Servers incorporate RAID hard drives for data 
redundancy. A separate web server dedicated for Cold Fusion applications is also available. 
The servers are part of the UPMC network, only connections from users authenticated from the 
domain controller are accepted, thus providing a secure environment for all data. Specifically, 
the policies for computer systems security implemented at UPMC are as follows:  
• 
Provide physical security of data. The server resides in the same building as the UPMC 
Office of Information Technology (OIT) servers. The lobby of the building in which the 
systems reside is under the security purview of the University of Pittsburgh General 
Services Security Office and is under surveillance. All central systems are physically 
secured behind two card-access doors with access to the primary door restricted to key 
personnel in the OIT. Access through the primary door is also protected by a keypad 
alarm system that is tied directly into the on-site central emergency response security 
control center. Written policies exist for contingencies to provide access to the room to 
those not explicitly authorized.  
• 
Provide virtual security via connectivity.  Internal access to all systems is done via 
MicroSoft Challenge Handshake Authentication Protocol. With the exception of internet 
provider-based services, external client access must first gain access to the internal 
network before connecting to the systems. This connection is initiated via a Virtual 
Private Network connection using Point-to-Point Tunneling Protocol or through the 
University's modem pool which require Kerberos authentication. All web-based mail is 
encrypted with high-encryption domestic SSL.  
All data are protected with disaster recovery via several methods:  
• 
Hardware redundancy: Several stages of redundancy exist at the hardware level to 
minimize failure:  Dual-redundant power supplies exist on each disk array; hot-spare disk 
is configured to automatically self-heal in the event of a disk failure in the array; 
emergency power generators ensure a 100% electrical uptime; and uninterrupted power 
supplies present the systems with conditioned steady-state power.  
• 
Data backup: Backups are completed daily over the UPMC network using both on-site 
and off-site disk-based backup devices.  
• 
Data Security: All data are stored on servers that are password-protected. To protect 
against security breaches, data will be electronically encrypted so that only the intended 
recipient can decode.  
Creation of analytic datasets for statistical programming. The Statistical Analyst of the data 
management team will be responsible for cleaning the datasets, construction of the analytical 

25 
 
variables, and writing the computer code to format and label each variable in the datasets. 
Documentation will be developed that will include data dictionaries defining the field names, 
location, and formats of all variables, the data collection forms, coding manuals, and 
documentation for computed variables and scale construction. Data will be cleaned in “batches” 
and cleaned batches appended to the master database. Statistical summaries will be provided. 
Data collection and form development. The CCDC supports the research team in designing, 
piloting, and implementing data collection forms by ensuring that the data fields are 
unambiguous, and the systems for recording information function smoothly. 
Each case report form (CRF) has been developed by the CCDC in conjunction with the clinical 
study team. In order to make sure that important study forms are not overlooked, CRFs will be 
considered across the following categories: 1) screening & baseline information; 2) 4-, 8- and 
12- month follow up visits and their blinded assessments; 3) 7-, 14-, 30-, 60-, and 90- day check 
in calls with the participants; 4) results and adherence to the ECA and Kardia; 5) electronic 
health record alert and alert tracking to resolution; 6) modifications to medications and 
interventions specific to AF during the 12-month study participation; 7) medication possession 
ratio; 8) health care utilization; and 9) clinical endpoints and vital status. This approach 
facilitates CRF development for each of the categories and prevents particular forms from being 
missed.  
Database tracking system. The tracking system will generate a schedule of data collection for 
each participant and, where applicable, automatically send reminders to the study team when a 
data collection visit or call is due. The study coordinator or research assistant will log into the 
data entry and tracking system, enter the participant’s study ID, then choose the visit to be 
viewed; the appropriate set of forms will be generated for that participant based on the visit 
number. The CCDC has a primary goal to minimize missing data, so all of the study forms will 
have certain key fields that are required before form submission. Pop-up windows upon form 
submission will remind the study team that particular fields are empty and give reasons as to 
why incomplete data was submitted. Each CRF will be made available on the study website for 
review and download by study personnel at any time. 
A subset of the CRFs will include the ability for the study team to electronically “sign off” on the 
submitted information. These CRFs include those that are a part of the screening visit, and 
CRFs at baseline and beyond that are deemed critical enough (i.e., concomitant medications, 
symptoms, adverse events, etc.). Select study personnel will have log-in credentials for a 
separate “PI Portal,” which facilitates this process.  
Data entry. Study data will be entered electronically via a password-protected Web-based data 
entry system. However, study team members will have paper versions of forms for manual entry 
in case of technical issues. The system will be created using ASP.NET programming, with the 
data stored using MS SQL Server. When the study team logs in and enters the participant study 
ID for which data are being entered, the screens generated will match the paper form(s) most 
recently created for that study ID. The study staff member will not be able to enter data for forms 
not generated for that study ID or visit. Similarly, the Web-based data entry screens will have 
out-of-range and other limits that will not permit the entry of inappropriate data. 
Participant eligibility & randomization. The data entry process will begin during the online 
participant enrollment into the clinical trial. Study team personnel will generate a participant ID, 

26 
 
which will unlock screening forms that will be completed to assess the study inclusion and 
exclusion criteria detailed in the protocol. The CCDC will utilize an “eligibility checklist” which is 
pre-populated with information from all questions that directly relate to inclusion and exclusion 
criteria. By not having a separate form with checkboxes for each criterion, this will prevent any 
data entry errors which may result in ineligible randomizations. 
Study design tracking. An important function of any data management system is its ability to 
track the progress of study participants through the various phases of the study. Given the 
richness of our cohort, it is critical to retain all participants in the study for the duration of the 
trial. The tracking and reporting systems are integrated within our web-based data management 
system. The tracking system includes programmed follow-up time periods so that the study 
team can ascertain which participants are due for milestone visits or contacts. In addition, we 
will create a calendar so that study staff can schedule follow-up visits. Study staff will be able to 
maneuver through the tracking system for individual participants. The CCDC team tracks hits to 
the tracking system to monitor how frequently study staff are using the tracking system and to 
monitor the overall performance of study staff members and determine individual use and 
concomitant effects on study follow-up, retention, and tracking system use. This enables the 
CCDC to partner with the study team to better coordinate study staffing to ensure recruitment 
and follow-up rates remain consistent. The tracking system will further permit the CCDC to 
generate reports to monitor study progress and for presentation at steering committee meetings. 
These reports will include detailed recruitment numbers, SAEs, and reports of outstanding 
forms. 
Quality control procedures for data collection and data entry. The CCDC has several 
systems programmers and data managers who design and maintain the data entry/checking 
programs as well as maintain the databases, generate reports, and provide on-site support for 
the all levels of study personnel. In addition, CCDC staff generate appropriate documentation 
and other training materials that will be made available on the study website for data entry 
personnel. Being located in close proximity to the study team, the CCDC staff are able to have 
regular meetings to review any problems with CRF, data entry, or troubleshoot use of the 
website.  
The CCDC institutes multiple strategies to ensure data is high quality: use of standard methods 
of data collection and recording, careful programming of the data management system, detailed 
documentation of computer operations and data editing procedures, and regular meetings with 
project staff to review any changes in procedure. The CCDC will verify all data, program out-of-
range data checks into data entry fields and evaluate the full data process within and across 
forms.  
A typical variable may be subjected to two kinds of range checking: impossible values (e.g., 
negative blood pressure) and suspicious values (e.g., SBP > 300). The former will be coded into 
the data entry system, restricting such values from being entered. CCDC personnel check 
suspicious values from the enrollment of the first participant to the data-cleaning phase, at 
which point logical checks will be performed, and outliers will be analyzed.  
Audit logs track changes to information previously submitted and recorded on electronic forms 
and will be used to ensure data integrity. Information on the person responsible for the change, 
the date of the change, the previous entry in the data field, the new entry in the data field, and 
the reason for the change are recorded and displayed in the electronic forms audit trail. 

27 
 
Database backup. The EWI (Enterprise Web Infrastructure) employed by the CCDC is backed 
up with the Symantec NetBackup system. The backup images are processed through an IBM 
ProtectTier appliance and stored on IBM DS8870 storage arrays. All EWI servers receive daily 
operating system backup with a retention period of 60 days. Server content is replicated to a 
secondary IBM DS8870 storage array located at Posvar Hall on the main campus of the 
University of Pittsburgh. This protects the backup images in the event of a catastrophic event at 
the data center. 
EWI sites with a back-end database server also receive a daily backup. All EWI database 
servers have a daily full backup and daily log backups. EWI database servers running MS SQL 
Server utilize a specialized NetBackup SQL backup agent. Any EWI database server running 
MySQL has a daily backup in the form of daily local dump. The exported database files are then 
backed up during the daily operating system backup with replication again in the Posvar Hall 
site on the University of Pittsburgh’s main campus. The backup methods described here have 
been tested and demonstrated to provide comprehensive successful recovery of data.  
The CCDC is compliant with FDA 21 CFR part 11 protocols. Access to data is controlled 
through password and authentication policies. Only approved individuals have access to data. 
Password policies control the length and variability of the user password selection. Audit trails 
are implemented to log date, time, individual, changed values, and rationale for all data 
changes. 
Archival of data. Throughout the study, data will be predominantly recorded and stored 
electronically. Sources of data include screening records, assessments, summaries from the 
electronic health record, and compilation from web sites such as the relational agent dashboard. 
All such tangible electronic data will be archived for a minimum of seven years following the 
publication of the primary analysis. Second, the paper format informed consents from the study 
will likewise be stored for seven years. We will upload the full data set to BioLINCC prior to 
study closure. Finally, the web-based relational app will continue to be available for public 
download for at least a year after study closure.  
12.0 Costs and payments  
Each study subject will be provided $25 for the baseline visit, $50 for the 4-month follow-up visit, 
$25 for the 8-month follow up telephone assessment and $50 for the last 12-month telephone 
assessment as compensation for his/her time. This totals $150 of participant compensation if 
the entire study is completed. 
13.0 Qualifications of Investigators  
Jared W. Magnani, MD, MSc (Principal Investigator) is an Associate Professor of Medicine in 
the Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine. 
Dr. Magnani is a clinical cardiologist and cardiovascular epidemiologist with a focus on health 
services research. He has served as a Framingham Heart Study investigator, having completed 
a clinical research fellowship in that study and received a Master of Science in epidemiology 
from Boston University. He has led multiple investigations of novel atrial fibrillation risk factors in 
the Framingham Heart Study and other cohorts, and been supported by the American Heart 
Association, the Doris Duke Foundation, and the National Institutes of Health (NIH). Dr. 
Magnani is responsible for initiating and developing the study. He will develop and supervise 
content for the smartphone-based intervention. He will establish study infrastructure, develop a 

28 
 
manual of operations, meet routinely with study staff, and supervise all aspects of study 
administration. He will have overall responsibility for implementing and monitoring all phases of 
the proposed research plan.  
Bruce L. Rollman, MD, MPH (Co-Investigator) is a Professor of Medicine, Psychiatry, 
Biomedical Informatics, and Clinical and Translational Science. He is the UPMC Endowed Chair 
in General Internal Medicine and the Director of the Center for Behavioral Health and Smart 
Technology. Dr. Rollman has expertise and leadership in the development and implementation 
of randomized clinical trials at UPMC. He has been principal investigator on six NIH-funded R01 
clinical trials, which have used multidisciplinary approaches to improve outcomes for mood 
disorders and cardiovascular disease. Dr. Rollman serves as local mentor for Dr. Magnani’s 
Doris Duke Clinical Scientist Development Award, and has guided the completion of the 
preliminary data concerning this project as presented in the proposal. He will provide guidance 
and expertise for the development and successful implementation of this trial. As such, Dr. 
Rollman will provide senior level advisement on trial execution and implementation. Dr. Rollman 
will attend regular study meetings, serve on the Steering Committee, meet monthly with the 
study PI, and participate in the abstracts and manuscripts presenting study results.  
Kaleab Abebe, PhD (Co-Investigator; Study Statistician) is an Associate Professor of Medicine, 
Biostatistics and Clinical and Translational Science at the University of Pittsburgh. Dr. Abebe 
directs the CCDC located within the Center for Research on Health Care in the Department of 
Medicine. Dr. Abebe's collaborative research focuses on design, conduct, and analysis of 
multicenter, randomized, controlled trials. As founding director of the CCDC, Dr. Abebe has 
contributed to the design, conduct, and analysis of multiple trials. He has an extensive portfolio 
of experience in the statistical design and analysis of clinical trials. Accordingly, he will provide 
input on clinical trial design, study implementation, and statistical analyses over the course of 
the trial. He will participate on the Steering Committee for the trial and will collaborate on 
manuscript writing and presentations at scientific meetings.  
Timothy W. Bickmore, PhD (Co-Investigator) is a Professor of Computer and Information 
Science at Northeastern University. His background is in artificial intelligence, natural language 
processing and health communications. He has over 20 years’ experience managing advanced 
software technology R&D projects and has spent the last ten years developing natural language 
dialogue systems for health education, counseling and behavior change. Dr. Bickmore will be 
responsible for all technical development on the project and all aspects of the project conducted 
at Northeastern University.  
Michael Paasche-Orlow, MD, MA, MPH (Co-Investigator) is Professor of Medicine at Boston 
University School of Medicine. He is a primary care clinician and a nationally recognized expert 
in the field of health literacy. He has served as primary mentor for Dr. Magnani’s Doris Duke 
clinical Scientist Development Award, and has an extensive contribution to investigations of 
health literacy and interactive behavioral informatics programs. He has collaborated extensively 
with Drs. Magnani and Bickmore (co-investigator, Northeastern University) to develop the 
relational agent system used in this trial. Dr. Paasche-Orlow will participate in the development 
and implementation of the relational agent and its content as relevant for doctor-patient 
communication. He will oversee the assessment and implementation of health literacy 
throughout the study, spanning the relational agent development, participant recruitment, and 
interpretation of study results. Dr. Paasche-Orlow will serve on the Steering Committee. 

29 
 
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APPENDIX A: CONSENT FORM 
APPENDIX B: MEDICAL RELEASE FORM_PHARMACY  
APPENDIX C: MEDICAL RELEASE FORM_NON-UPMC