Mobile Health Intervention for Rural Atrial Fibrillation
Phase: NA
Conditions: Atrial Fibrillation, Familial Atrial Fibrillation, Arrhythmia, Cardiac, Heart Diseases, Pathologic Processes
Sponsor: University of Pittsburgh

 
 
 
 
 
Atrial Fibrillation Health Literacy and Information 
Technology Trial 
NCT04076020 
Document Date: April 30, 2023 

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Mobile Health Intervention for Rural 
Atrial Fibrillation 
Study Protocol 
April 2023 
 
Funding Organization: 
National Heart, Lung, and Blood Institute 
R01HL143010 
Principal Investigator: 
Jared W Magnani, MD, MSc 
Telephone: (412) 383 0611 
E-mail: magnanijw@upmc.edu 
Co-Investigator 
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: 
Samar R. El Khoudary, PhD, MPH 
Telephone: (412) 624-5543 
Email: sae25@pitt.edu 
 
Maria Mori Brooks, PhD 
Telephone: (412) 624-1618 
Email: mbrooks@pitt.edu 

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List of Abbreviations 
 
 
 
AE 
Adverse Event 
AF 
Atrial Fibrillation 
AFEQT 
Atrial Fibrillation Effect on QualiTy of life 
CDSM 
Chronic disease self-management 
DOAC 
Direct oral anticoagulant 
EDC 
Epidemiology Data Center 
EHR 
Electronic Health Record 
HCU 
Health Care Utilization 
HrQOL 
Health-related Quality Of Life 
IRB 
Institutional Review Board 
NVS 
Newest Vital Sign 
PHQ 
Patient Health Questionnaire 
PI 
Principal Investigator 
PROMIS 
Patient-Reported Outcomes Measurement Information System 
QOL 
Quality Of Life 
UPMC 
University of Pittsburgh Medical Center 

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1. ABSTRACT 
 
Atrial fibrillation (AF) is a common, morbid condition. Anticoagulation to prevent thromboembolic 
strokes is a foremost priority in AF but adherence is challenging for patients and lapses in 
anticoagulation are common. Chronic disease self-management (CDSM) is a recognized program 
to enhance self-efficacy and improve adherence, quality of life, and patient-centered health 
outcomes. Rural patients with AF experience increased vulnerability to adverse outcomes due to 
geographic and social isolation, poor health care access, and limited health literacy. Study team 
will conduct a single-center parallel group randomized clinical trial to test the efficacy of a 
relational agent to improve patient-centered care in AF delivered by smartphone. The Principal 
Investigator and study team have used the agent – a computer character that simulates face-to-
face conversation using voice, hand gesture and gaze cues to provide education, monitoring and 
problem-solving – to improve health behaviors and outcomes in individuals with limited computer 
and health literacy. Here, the team will expand its successful 30-day relational agent pilot to 
develop a novel CDSM program for AF. This study will combine the relational agent with the 
AliveCor Kardia smartphone heart rate and rhythm monitor. The team will implement a 4-month 
CDSM curriculum and assess its sustainability at 8 and 12 months. The trial randomizes 264 
patients with AF who are receiving anticoagulation for stroke prevention to (1) the relational agent 
and Kardia intervention, accompanied by provider alerts for prespecified results; or (2) the control, 
consisting of an AF educational session and a smartphone with a general health application 
(WebMD). Our aims are: (1) To assess the effect of the relational agent and Kardia intervention 
on anticoagulant adherence, as determined by the proportion of days covered (PDC), and self-
report. (2) To identify the effect of the intervention on health care utilization, hospitalizations, 
emergency visits, and AF-related procedures at 1-year follow up. (3) To determine the effect of 
the intervention on the patient-centered outcomes of quality of life and symptoms, as measured 
by the AF Effect on QualiTy of life (AFEQT) measure, specific to AF, and the general Patient-
Reported Outcomes Measurement Information System-29 Profile at baseline, 4, 8 and 12 months. 
(4) To examine mechanisms for the effect of the intervention by symptom burden, AF 
classification, health literacy and intervention use. The trial will additionally follow the cohort 
through the study period for exploratory analyses of rurality and outcomes associated with AF. 
Expected Results: In this project we will evaluate a scalable patient-centered intervention to 
improve anticoagulation adherence, health care utilization and patient-centered outcomes in 
vulnerable rural individuals with chronic AF. If proven successful, this intervention can be broadly 
disseminated to improve the care of patients with AF. 
 
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. This study team has developed a novel, practical mHealth 
intervention that is accessible to high-risk patients such as those with limited social resources or 
health literacy. 

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2. STUDY RATIONALE AND SIGNIFICANCE OF THE RESEARCH 
a) AF prevalence is estimated to reach 12 million US adults 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 
Individuals with AF have 4.7-times greater annual days of hospitalization than those 
without,9 and expenditures for Medicine beneficiaries with AF have increased 1.6-fold 
since 1999.10 Among Medicare beneficiaries, the mortality rate following a diagnosis of AF 
is 19.5% at 1 year and 48.8% at 5 years.11 Even on optimal therapy, patients experience 
a stroke rate of 1.5%, heart failure of 4-5%, and mortality of 3% per year.12 This project 
aims to reduce the morbidity and social costs of AF. 
b) Poor medication adherence results from the complex interaction between patients, their 
social environment, and the health care system and professionals.13 Patient-level barriers 
to adherence have been characterized as limited health literacy and behavioral obstacles 
to successful medication management.14 Rural individuals have additional risk for poor 
adherence because of limited health literacy, lower social resources, poverty, and 
geographic distance from care providers.15,16 They also experience disparities in 
cardiovascular outcomes, attributable in part to social determinants of health.17,18 Rural 
regions have higher unemployment and poverty rates, lower educational attainment,19 
higher rates of low health literacy, less access to health care services,20 and worse control 
of cardiovascular disease and AF risk factors (hypertension, obesity, diabetes).21,22 Our 
program promotes longitudinal, patient-centered strategies for rural patients to overcome 
adherence challenges.23 
c) Chronic disease self-management (CDSM) provides a paradigm for patients to establish 
practical strategies to negotiate the long-term self- care required to succeed with a disease 
such as AF.24-29 CDSM incorporates education and behavior change strategies. The 
general tenets of CDSM are: (1) patient-centered education on the causes and 
complications of chronic disease; (2) understanding medications; (3) managing common 
symptoms; (4) skills for partnership with physicians and the care team; and (5) self- 
assessment of goals for health-related quality of life (HRQoL). CDSM is available in only 
8% of rural counties.30 The relational agent addresses the limitations of prior interventions 
to provide virtual CDSM and bridge the rural health divide. 
d) AF is a chronic disease with extensive symptoms, adverse outcomes, and resulting poor 
HRQoL. Anticoagulation is a mainstay of AF treatment but demands long-term – likely 
lifelong – daily adherence with concomitant monitoring for bleeding. AF symptoms are 
reported to diminish HRQoL, subjective health, and functional status. 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. 
Our intervention addresses the poor HRQoL in AF that stems from symptoms, treatment 
burden, and clinical uncertainty. It is designed to empower patients, ameliorate health 
literacy related barriers to self-management, and improve patient-centered outcomes for 
vulnerable patients with AF. 
e) The relational agent is a mobile health (mHealth) 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 delivered by relational agent to promote self-management and HRQoL (e.g., 

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R01AG028669, 1R01HL081307, NCI 5R21CA127511, R01HL116448). The agent speaks 
with synthetic speech accompanied by animation to provide health education, empathic 
counseling, and monitoring. The patient engages by listening to didactic 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 CDSM. 
The relational agent (a) elicits symptoms and (b) gestures to enhance educational content. 
The intervention here integrates the relational agent with the AliveCor Kardia (Mountain 
View, CA) smartphone heart rate and rhythm monitor to guide Kardia use and enhance 
CDSM in AF. 
f) Our relational agent/Kardia intervention improves the current paradigm for rural AF care24 
by aiming to: (1) Enhance patient-centered resources. AF is a complex condition that 
requires long-term patient engagement31-34 and the relational agent serves as an empathic 
coach to improve the patient experience of this chronic disease. We provide a non- 
pharmacologic, non-invasive approach that is accessible to patients, patient-centered, and 
disease-specific. (2) Synergize the relational agent/Kardia. Relational agent content 
prompts use of the Kardia when users report symptoms. The relational agent can correlate 
symptoms with heart rate and rhythm, improve symptom characterization, and guide 
CDSM to improve multiple patient-centered domains assessed by the Patient-Reported 
Outcomes Measurement Information System (PROMIS) instrument used here.35 (3) 
Increase health-related attention. Rural individuals often have less access to resources 
for adherence support and disease-specific education.36 Patients will benefit from AF- 
specific education, symptom monitoring, self-management content, and encouragement 
of engagement and activation.  
3. 0 STUDY AIMS 
3.1 Aim 1 
To assess the effect of the relational agent/Kardia intervention on anticoagulant 
adherence. 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 identify the effect of the intervention on health care utilization (HCU). We will quantify 12-
month HCU for intervention administration. Hypothesis: Intervention participants will have lower 
HCU events compared to control arm participants. 
3.3 Aim 3 
To determine the effect of the intervention on HRQoL and symptoms. We will compare 
HRQoL and symptoms between the intervention and control arms at 4, 8, and 12 months with the 
AF Effect on QualiTy of life (AFEQT) instrument (specific to HRQoL in AF) and the Patient- 
Reported Outcomes Measurement Information System (PROMIS)-29 Profile. Hypothesis: 

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Intervention participants will have superior HRQoL and lower symptom burden compared to 
control arm participants at 4, 8, and 12 months. 
3.4 Aim 4 
To examine mechanisms for the effect of the intervention. We will determine how patient- 
centered baseline factors including sex, race, health literacy, AF classification (paroxysmal 
versus chronic), and symptom burden moderate the intervention effect, and how intervention 
engagement and use, change in symptom burden, and AF therapies mediate the intervention 
effect on our primary and secondary outcomes. 
4. STUDY DESIGN 
4.1 Study Overview 
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 264 
patients residing in rural municipalities (defined as “rural” by the Center for Medicaid and Medicare 
Services Rural Health Clinics Program) 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, they 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 receive a smartphone with a general health application 
(WebMD) and they will not receive an AliveCor Kardia device. 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 quality of life, medical adherence and health care utilization resulting from the 
intervention. 
5.0 STATISTICAL CONSIDERATIONS 
We propose a two-arm RCT to evaluate the efficacy of the relational agent and AliveCor Kardia 
heart rhythm monitor to improve patient-centered outcomes in patients with the debilitating chronic 
condition atrial fibrillation (AF). We will recruit 264 patients with AF receiving anticoagulation over 
a 30-month period, prioritizing recruitment in socioeconomically depressed regions in rural 
Northwest Pennsylvania. We will randomize participants 1:1 to receive the mobile relational agent 
and Kardia (n=132) or control (n=132) 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 direct acting oral anticoagulant). We will assess the impact of 
our intervention at 4, 8 months and then at 12 months to determine sustainability. Our primary 
hypothesis will test whether the relational agent and Kardia intervention can improve adherence 
to anticoagulation as quantified by the PDC and by participant self- report. Our secondary 
hypotheses will evaluate 1) assessments of health care utilization between the intervention and 
control; 2) the improvement in health-related quality of life at 4, 8, and 12 months compared to the 
control, as measured by instruments specific to AF and general, validated quality of life 
assessments; and 3) the mechanisms for the effect of the intervention, as evaluated by 
assessments of moderation and mediation. All participants will have return visits at 4, 8, and 12 
months. The outcomes will all be calculated without reference to intervention arm. 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 for Aim 1 is anticoagulant adherence as 
assessed by PDC. We will use the intention-to-treat principle for all primary and secondary 

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analyses. We will analyze PDC as continuous (range 0-1, higher ratio indicating better adherence) 
and binary (optimally adherent if ≥0.8) variables. We will assess differences between the 2 study 
arms in PDC at 12 months with linear regression (for continuous PDC) or logistic regression (for 
binary PDC) adjusting for trial stratification factor (as suggested by Kahan and Morris37). We will 
conduct our secondary assessments of adherence at 4 8, and 12 months similarly. We will 
categorize individuals scoring ≥2 on any of the 3 self-report adherence items (range 1-5, Voils et 
al.38) as nonadherent. To assess whether the effect of the intervention changes over time, we will 
use linear (for continuous measures) or non-linear (for categorical measures) mixed-effect models 
with random intercepts with adherence measures modeled as a function of study arm, study visit 
and their interaction. We calculated the statistical power to detect a range of difference in PDC (our 
primary outcome) between the intervention and control arms. After accounting for 10% attrition 
through 12 months (consistent with our prior RCTs), we determined that a sample size of 119 in 
each study arm will enable us to detect a minimum difference in PDC between the two groups as 
small as 11.7% with 85% power (assuming a SD=30%). Our power calculations assume use of 
2-sided tests with 0.05 significance level. With an additional, unexpected 10% attrition (n=106 per 
arm, to accommodate for relocation, loss-to- follow-up, institutionalization, or death), we will be 
able to detect a minimum difference in PDC as small as 12.4% (0.41 standardized mean 
difference) with 85% power. Both are considered small effect sizes per Cohen’s guidelines.39 
Statistical approach for Aim 2. We will compare HCU across trial arms in models that again 
account for trial stratification factor.37 We will use the electronic health record (EHR) in concert 
with 4-, 8-, and 12-month self-report to catalog events (hospitalization, emergency room and 
ambulatory visits, medications, and procedures) and classify them as related to AF. We will 
determine person-year of follow-up as starting from the date of randomization to the 12-month 
visit. We will then classify HCU in aggregate and by item as count outcomes. We will use 
generalized estimating equations (GEE) negative binomial regression models (chosen instead of 
Poisson regression due to expected over-dispersion in the data). Similar approaches will be used 
to assess counts for each HCU component. Based on preliminary data from an outpatient registry 
of individuals with chronic AF, we expect there will be 42.0 events for every 100 person-years in 
the control arm of the trial.40We estimate that we have 85% power to detect a minimum absolute 
reduction of 21 events per 100 person-years in the intervention group, assuming an equivalent 
number of events in the control group at 12 months with a sample size of 132 per arm. A 20% 
attrition in study participation (n=106 per arm) at 12 months will still allow us to detect a minimum 
of absolute reduction of 23 events per 100 person-years in the intervention group with 85% 
statistical power. As exploratory analyses, we will perform survival analyses to compare the time 
to event between the intervention and control arms for preselected outcomes: AF-related 
hospitalization, emergency visit, or procedure; myocardial infarction; heart failure; stroke; and all- 
cause mortality. Participants will contribute 18 to 51 months of follow-up; those without the event 
of interest will be censored at the end of follow-up. We will examine the assumption of proportional 
hazards and use Cox regression models to estimate the hazards ratio for assigned treatment for 
each outcome. 
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 demonstration 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, greater scores indicating 
superior HRQoL). We will use linear regression adjusted for trial stratification factors and the 
baseline score as per Harrell41 to evaluate the differences in AFEQT global and domain-specific 
scores between study arms at 4 months. We will similarly assess the effect of the intervention for 

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each of the 4 AFEQT domains and the 8 symptom and HRQoL domains measured by the 
PROMIS-29 at 4 months. Next, we will assess sustainability of the intervention effect at 8 and 12 
months using linear mixed models. Our power calculations assume a 90% 4-month assessment 
completion rate (to accommodate for 10% attrition), 2-tailed α=0.05, and standard deviation of 17 
(representing the upper limit of the 95% confidence interval for the SD and consistent with the 
data used to determine the minimum important difference42). Based on these assumptions, we 
expect our sample size (119 per arm) will enable us to detect a minimum difference in AFEQT 
between the two study arms at 4 months of at least 6.3, corresponding to 0.37 SD, a small effect 
size.39 A 20% sample size attrition (n=106 per arm) at 12 months will allow us to detect a difference 
in AFEQT of at least 0.41 SD. 
Statistical approach for Aim 4. Subgroup analyses will be performed to assess moderation of the 
intervention effect by baseline patient-centered factors: (1) sex; (2) race (white vs non-white); (2) 
health literacy, assessed using the Newest Vital Sign (NVS); (3) AF classification (paroxysmal vs 
permanent), (4) symptom burden, measured by the AFEQT; (5) anticoagulant type (warfarin or 
direct oral anticoagulant (DOAC)); (6) self-reported anticoagulant nonadherence (<2 or ≥2); and 
(7) duration of anticoagulation (<1 or ≥1 year). We will formally test for effect modification by 
creating a regression model for each outcome including the subgroup variable, the intervention 
assignment and the interaction between the subgroup variable and intervention and evaluating 
the significance of the interaction term. The false discovery rate43 will be used to adjust for multiple 
comparisons. Causal mediation analyses will be used to determine the degree to which 
intermediate patient-level factors explain the intervention effect as potential mediators of the 
primary and secondary outcomes.44-46 We will estimate the direct and indirect effects of the 
intervention on outcomes accounting for (1) changes in symptom burden during follow-up, 
measured by the AFEQT; (2) introduction of new AF therapies over 12-month study participation 
(antiarrhythmic, cardioversion or electrophysiologic study); (3) relational agent and Kardia usage; 
and (4) EHR alerts. Instead of emphasizing p-values, we will focus on estimation of mediation 
effect sizes. 
Race and sex. We will ensure our study cohort is 50% women and 30% non-white race, of whom 
80% (24% overall) will be black race. (1) We have reviewed47 AF is less prevalent in blacks48-50 
but associated with worse outcomes54 (2) Women have worse HRQoL and increased stroke risk 
than men.51-53 (3) We have developed strategies to oversample participants of black race and 
female sex. Although the current trial is not specifically design to assess racial/sex variability in 
the intervention effect, since the overall effect has not yet been established, subgroups analyses 
among all race/sex groups will set the stage for further studies that focus on racial/sex effects. 
We note our statistical power for such trial participation: (1) if we randomize 64 study participants 
of black race, then we will have 85% power to detect a 22.8% PDC difference between trial arms. 
(2) Similarly, if we randomize 135 women, 51% of trial participants, then we will have 85% power 
to detect a 15.6% difference between trial arms. 
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. 
Study staff will promote awareness of our study through multiple ways. Recruitment will be 
conducted at numerous University of Pittsburgh Medical Center sites located in rural 
Pennsylvania counties. The PI and PM will contact cardiologist, admin staff, PCPs, nursing staff 

9 
 
 
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. 
Study team 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 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 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.  
5) 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. 
6) 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 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. 
Recruitment will be conducted by trained individuals serving as study research assistants and 
study staff. 

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6.2 Inclusion Criteria 
1. 
Adult, age ≥21. 
2. 
Diagnosis of AF, identified from the EHR and confirmed by either an ECG, 
Holter, or a clinical note. 
3. 
Resides in a municipality defined as rural by the Center for Medicaid and 
Medicare Services Rural Health Clinics Program. 
4. 
Prescribed use of warfarin or DOAC (formerly NOAC) for AF stroke prevention. 
5. 
English-speaking well enough to participate in informed consent and this study. 
6. 
No plans to relocate from the area within 12 months of enrollment. 
 
6.3 Exclusion Criteria 
1. 
Previous catheter ablation procedure for treatment of AF (pulmonary vein 
isolation, AF ablation). 
2. 
Previous 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.56 
12. 
PIs or study team’s judgement as to the ability of the participant to comply and 
complete the study protocol.

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7. STUDY DESIGN AND METHODS 
Fig 1. Participant screening through Randomization Process 
 
 
7.1 Pre-Screening 
 
To identify potentially eligible participants: 
 
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 talk 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 review. If the non-UPMC medical records are available in 

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"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 either meet with a study recruiter 
(Research Assistant/ Project Manager) or have a scheduled phone call with a study recruiter. 
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 state correctly 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 eConsent 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, if 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 REDCap system, 
etc.). Or, if a participant gives verbal consent over the phone to a study team member, they will 
sign and return a paper version of the consent for to the study team. If a paper version is 
completed, either in place of a virtual consent or if given verbally, then this paper 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 

13 
 
 
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 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 REDCap 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 Form 
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 (in person or over 
the phone). 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 type of 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 

14 
 
 
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) 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) 
application 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 study staff 
will be blinded to assignment to intervention or control arm. The baseline and 4-follow-up visits 
will be completed either in person or over the phone with a study staff member. 
7.7 Intervention 
 
 
FIG 2. RELATIVE AGENT 
An example of a screen shot 
of the relational agent and a 
clinical encounter menu 
 
Relational Agent 
The relational agent is a mobile health (mHealth)57 
application for patient education monitoring and problem- 
solving. It has had extensive use in multiple contexts and has 
been developed in the lab of Timothy Bickmore, PhD, 
Northeastern University. Dr. Bickmore has developed over 
25 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. 

15 
 
 
Kardia 
 
The AliveCor (Kardia) is a 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 study provided iPhone from the App Store. Study staff will create 
their Kardia account using the the participant’s first name, and substituting their last name with 
“####” (where #### 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 3. KARDIA 
 
FIG 4: DATA from KARDIA 
 
 
 
 
8. STUDY PROCEDURES 
 
8.1 Assessments 
Study team will conduct research assessments with each study subject at baseline, and then at 
4, 8 and 12 month 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. Study team will administer the 
assessments as portrayed in table below. 

17 
 
 
 
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 

17 
 
 
 
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. 
 
8.1.1.4 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. 

18 
 
 
 
 
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: 
8.1.13.2 Warfarin 
8.1.13.3 Novel oral anticoagulants 
8.1.13.4 Medications for blood pressure 
8.1.13.5 
Medications for cardiovascular disease, e.g. beta blockers, ACE inhibitors, 
ARBs, dihyropyridine and non-dihydropyridine calcium channel blockers. 
8.1.13.6 Other antiplatelet agents, e.g. clopidogrel, prasugrel, ticagrelor 
8.1.13.7 Medications for diabetes (insulins and oral agents) 
8.1.14 
Medical History 
Medical history will be obtained from EHR problem lists and include: 
8.1.14.2 Congestive heart failure 
8.1.14.3 Hypertension 
8.1.14.4 Diabetes 
8.1.14.5 History of stroke or TIA 
8.1.14.6 
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. 
8.1.14.7 
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. 
Study staff will maintain relational agent and Kardia results daily on a web-based dashboard 
developed by our collaborators at Northeastern University with the help of the Epidemiology Data 
Center (EDC). Agent dashboard results will consist of use statistics, reported symptoms, and self- 

19 
 
 
 
reported adherence to anticoagulation. Kardia results include date and time of use and heart rate 
and rhythm. Dashboard results will be reviewed daily. 
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 a feedback survey as well as 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). Study iPhones will be returned during the 4-month visit, either in person or mailed 
back to the study team in a postage prepaid box provided to the participant. 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 
Medication fill data, for purposes of calculating the proportion of days covered (PDC), a common 
measure of medication adherence, will also be extracted from the EHR and from the 
pharmacist(s). Study staff will assess the EHR for incidence of adverse cardiovascular event and 
mortality. Study staff will also use the National Death Index to identify participant deaths and their 
dates. 

20 
 
 
 
10 Clinical Trial Oversight and Monitoring 
10.1 Data Safety Monitoring 
This study will recruit subjects at the University of Pittsburgh Medical Center (UPMC) who have a 
chronic heart rhythm disease, atrial fibrillation (AF), and are receiving anticoagulation for stroke 
prevention. Participants are expected to constitute a high-risk population, as they will live in rural 
settings, have limited socioeconomic resources, and likely have low health literacy. The study will 
consist of 264 adults who will be randomized to an intervention or control cohort. The intervention 
cohort will receive (1) a smartphone with a relational agent application, described in the 
application as an embodied conversational agent, and (2) the 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 and a smartphone with the WebMD application installed. 
participants will use the intervention 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 study team will follow up on the results of the 
smartphone agent and 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 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 3 individuals. 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 

21 
 
 
 
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 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). 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 
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. 
Identification of AE, SAE, and unanticipated problems. The study team will learn of AE, SAE, 
and unanticipated problems by participant interview at the 4-, 8- and 12-month assessments; 
direct contact by participants during the scheduled check-in calls; 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. Study staff will follow guidelines set 
forth by the University of Pittsburgh’s Institutional Review Board (IRB) and the Human Research 
Protection Office. The University of Pittsburgh Epidemiology Data Center, serving as the Data 
Coordinating Center (EDC) 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); 
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 and signed by the 
PI. In accordance with the University of Pittsburgh IRB, all internal AEs which are (1) unexpected, 
fatal or life-threatening, and (2) related or possibly related to the relational agent /Kardia 
intervention will be reported to the IRB within 24 hours 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 or possibly related to the study procedure (relational agent 
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. In accordance with NHLBI regulations, SAEs 

22 
 
 
 
will be reported within 7 days of initial receipt of information to the NHLBI. SAE that are non-fatal 
and non-life-threatening will be reported to the NHLBI within 15 days of the receipt of information. 
The project manager, co-investigators, PI and statistician will review aggregate data on AEs and 
SAEs regularly. The DSMB will be notified simultaneously with the IRB and NHLBI and 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 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. 
Risk assessment. Participation in the proposed research may be considered moderate risk. This 
is because (1) the investigation may have direct implications for clinical care; (2) findings identified 
by use of the relational agent and Kardia may enter the electronic health record and thereby result 
in modified care; and (3) recruitment of high-risk populations (i.e., individuals with limited 
socioeconomic resources, low health literacy, and/or racial/ethnic minorities). As stated in the 
Informed Consent, use of the relational agent does not replace routine, urgent, or emergent 
clinical care. Participants in this trial will be instructed NOT to delay seeking care because they 
are using the relational agent to address a medical problem, question, concern or issue. All AE 
will be closely evaluated in order to ascertain if the AE is related or possibly related to participation 
in this research. Participant use of the relational agent and Kardia in proximity to the AE will be 
scrutinized for whether or not use of the intervention delays participants from seeking care. If 
necessary, participants will be directly interviewed to appreciate their understanding of the relation 
of their participation in this research and the occurrence of the AE. The project manager, co- 
investigators, and PI will review the AE and evaluate for their association with the intervention. 
The results of such examination will be recorded on a unique report form as prepared by the 
Epidemiology Data Center and submitted routinely for review to the DSMB. 
An additional risk for participants is loss of confidentiality. The risk of breach of confidentiality is 
best addressed by appropriate study procedures. The PI will be responsible for assuring that 
study procedures are adhered to regarding data security, transfer, and communications in 
tracking participants by meeting regularly with study staff, reviewing procedures and performing 
quality control reviews of study forms. 
Data and safety monitoring for continuation, modification, or termination of participation 
or the clinical trial. The DSMB will 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. 
Data Management and Security to Protect Privacy. The University of Pittsburgh Epidemiology 
Data Center (EDC) has extensive experience with supervising the conduct and data management 
of clinical trials and will serve as the data coordinating center (DCC) for this study under the 
direction of Dr. El Khoudary. Study data will be maintained on University of Pittsburgh servers. 
Security and integrity of study resources will be achieved via strictly enforced policies, standards 
and guidelines which are compliant with federal regulations and guidelines including the Health 
Insurance Portability and Accountability Act (HIPAA), the Federal Information Security 

23 
 
 
 
Management Act of 2002 (FISMA) and Department of Health and Human Services (HHS). Access 
to the data system requires training and certification with respect to protocol, procedure and 
security. Access to data on EDC servers is managed by EDC server administrators via Active 
Directory and Windows Authentication. Study data will be stored on an EDC SQL Server that is 
behind the University of Pittsburgh enterprise firewall system and is protected in a server VLAN. 
Direct access to this database server is restricted and is only available to authorized EDC 
personnel with appropriate credentials and data privileges. Regularly scheduled backups and 
archives protect central and local information from hard disk failures. Permanent archives of 
critical project files are created and are stored in a secure off-site facility to prevent data loss due 
to catastrophic events. 
Data collection forms. The EDC will support the research team to develop required protocols, 
data collection forms and venues for the data collection (e.g., telephone interview, clinic visit). 
Data collection forms will be designed to ensure protocol compliance, optimize interview flow with 
branching logic, reduce missing data elements, minimize participant burden in self-reports, and 
support efficient data collection and analysis processes. 
Data Management Manual of Operations. The EDC will assist in developing a Manual of 
Operations (MOP) to document all data collection and management procedures for the study. The 
MOP will include an overall description of the project, the study protocol, data collection 
procedures (including question by question instructions), and detailed descriptions of all other 
procedures required to conduct the study. In addition, an internal DCC Manual of Operations 
(DCC MOP) will be developed. The DCC MOP will provide documentation of the internal 
procedures, such as the creation of analytic data sets, the generation of reports, and the data 
freeze. The MOPs will facilitate consistency in protocol implementation and provide some 
protection against potential disruptions due to personnel turnover. 
Quality control/Data Collection Monitoring. The DCC will monitor all aspects of study 
performance and protocol compliance and will be responsible for monitoring the data from all 
study entities as well as adherence to established adverse event reporting. Electronic data 
capture methods will be implemented for this study. Study data will be subjected to extensive 
checks for completeness, accuracy, and consistency including data validations to verify the 
authenticity of participant IDs, prohibit duplicate record entries, and incorporate range, format, 
data type checks and alert values. To facilitate monitoring of study performance, data status 
reports will be generated and reviewed by study leadership on a regular basis. These reports will 
include recruitment and retention, protocol adherence, data quality, and study participant 
characteristics. Creation of analytic datasets for statistical programming. The statistical 
programmer and analyst of the data management team will be responsible for cleaning the 
datasets and preparing the corresponding codebooks and statistical summaries. These datasets 
will be used to carry out the full range of data analysis activities needed to prepare monitoring 
reports, abstracts and manuscripts. This will include planning and developing methods for data 
analysis, statistical programming, and interpreting and summarizing data for internal and DSMB 
reports. 
10.0 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 

24 
 
 
 
analysis. We will upload the full data set to BioLINCC prior to study closure. 
11.0 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.

25 
 
 
 
12.0 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 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 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, meet monthly with the study PI, and participate in the abstracts and manuscripts 
presenting study results. 
Samar R. El Khoudary, PhD, MPH, BPharm (Co-Investigator; Study Statistician) is an Associate 
Professor of Epidemiology and Clinical and Translational Science Institute and a core faculty 
member of the Epidemiology Data Center (EDC). She has extensive experience in multi-center 
longitudinal studies and statistical methodologies to analyze longitudinal data and clinical trials. 
She will provide primary statistical leadership of the study and oversee the routine operations of 
the EDC as pertinent to this study. Dr. El Khoudary will lead the Data Coordinating Center (EDC) 
of this study and will supervise the data management and statistical organization of this study. Dr. 
El Khoudary will supervise the preparation of all data reports and will work closely with the 
statistical programmer regarding on-going analysis. Dr. El Khoudary will collaborate with Dr. 
Magnani and the other study investigators in manuscript writing and abstracts’ preparation for 
scientific meetings. She will work with study investigators to frame hypotheses, propose 
appropriate analytic methods, interpret the analytic results, and formulate appropriate 
conclusions. 
Maria M. Brooks, PhD (Co-Investigator; Study Statistician) is a Professor of Epidemiology and 
Biostatistics and a director of the Epidemiology Data Center (EDC). Dr. Brooks has an extensive 
portfolio of experience in the statistical design and analysis of clinical trials. She will provide 
robust, high-level input on clinical trial design, study implementation, the study protocol, and she 
will interface with our DSMB. Dr. Brooks provides senior oversight on statistical and analytical 
issues. She will collaborate on manuscript writing and presentations at scientific meetings. 

26 
 
 
 
Timothy W. Bickmore, Ph.D. (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 agent development, participant recruitment, and interpretation of study 
results. 

27 
 
 
 
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