Using Digital Data to Predict Cardiovascular Health and Health Care Utilization
Conditions: Cardiovascular Diseases
Sponsor: University of Pennsylvania

 
 
 
 
 
 
 
Digital Phenotyping and Cardiovascular Health 
NCT04574882 (IRB #: 833699) 
December 7, 2021 

Page 3 of 32
Protocol Details
Resubmission*
Yes
Hospital Sites
Will any research activities and/or services be conducted at a Penn Medicine afﬁliated hospital site?
Yes
Active Hospital Sites
Hospital of the University of Pennsylvania Pavilion (HUP) - East
Penn Presbyterian Medical Center (PPMC)
Hospital of the University of Pennsylvania (HUP) ***Primary***
Chester County Hospital
Pennsylvania Hospital (PAH)
Study Personnel
Principal Investigator
Name:
MERCHANT, RAINA M
Dept / School / Div:
4302 - EG-Emergency Medicine
Campus Address
Mail Code
4283
Address:
HUP
3400 SPRUCE ST
City State Zip:
PHILADELPHIA PA 19104-4283
Phone:
215-615-3408
Fax:
215-573-2742
Pager:
Email:
raina.merchant@pennmedicine.upenn.edu
HS Training Completed:
Yes
Training Expiration Date:
Name of course completed :
CITI Protection of Human Subjects Research Training - ORA
GCP Training Completed:
Yes
Training Expiration Date:
11/27/2021
Name of course completed :
Good Clinical Practice: An Introduction to ICH (GCP) Guidelines

Page 4 of 32
Study Contacts
Name:
SOUTHWICK, LAUREN E
Dept / School / Div:
4302 - EG-Emergency Medicine
Campus Address
Mail Code
4283
Address:
HUP
3400 SPRUCE ST
City State Zip:
PHILADELPHIA PA 19104-4283
Phone:
215-349-8382
Fax:
4283
Pager:
Email:
Lauren.Southwick@uphs.upenn.edu
HS Training Completed:
Yes
Training Expiration Date:
Name of course completed :
CITI Protection of Human Subjects Research Training - ORA
GCP Training Completed:
Yes
Training Expiration Date:
08/11/2024
Name of course completed :
Good Clinical Practice: An Introduction to ICH (GCP) Guidelines
Name:
TORRENTE, DENA M
Dept / School / Div:
4302 - EG-Emergency Medicine
Campus Address
Mail Code
4283
Address:
HUP
3400 SPRUCE ST
City State Zip:
PHILADELPHIA PA 19104-4283
Phone:
215-898-0339
Fax:
-
Pager:
Email:
Dena.Torrente@uphs.upenn.edu
HS Training Completed:
Yes
Training Expiration Date:
Name of course completed :
CITI Protection of Human Subjects Research Training - ORA
GCP Training Completed:
Yes
Training Expiration Date:
09/29/2024
Name of course completed :
Good Clinical Practice: An Introduction to ICH (GCP) Guidelines

Page 5 of 32
Name:
MITRA, NANDITA
Dept / School / Div:
4635 - BE-Biostatistics Division
Campus Address
Mail Code
6021
Address:
Department of Biostatistics
University of Pennsylvania
212 Blockley Hall
City State Zip:
Philadelphia PA 19104-4865
Phone:
215-573-4467
Fax:
-
Pager:
Email:
nanditam@pennmedicine.upenn.edu
HS Training Completed:
Yes
Training Expiration Date:
Name of course completed :
CITI Protection of Human Subjects Research Training - ORA
GCP Training Completed:
No
Training Expiration Date:
Name of course completed :
Other Investigator
Name:
ASCH, DAVID A
Dept / School / Div:
4002 - SM-MG-Holding
Campus Address
Mail Code
6218
Address:
423 Guardian Drive
1223 Blockley Hall
City State Zip:
PHILADELPHIA PA 19104-6021
Phone:
215-898-0102
Fax:
-
Pager:
Email:
ASCH@WHARTON.UPENN.EDU
HS Training Completed:
Yes
Training Expiration Date:
Name of course completed :
CME Credit for POR Expedited Review - SOM
GCP Training Completed:
No
Training Expiration Date:
Name of course completed :
Responsible Org (Department/School/Division):
4302 - EG-Emergency Medicine

Page 6 of 32
Key Study Personnel
Name:
MURGULESCU, VERONICA
Department/School/Division:
Health System
HS Training Completed:
No
Training Expiration Date:
Name of course completed:
GCP Training Completed:
No
Training Expiration Date:
Name of course completed:
Name:
MENG, SABRINA
Department/School/Division:
Health System
HS Training Completed:
Yes
Training Expiration Date:
Name of course completed:
CITI Protection of Human Subjects Research Training - ORA
GCP Training Completed:
Yes
Training Expiration Date:
12/26/2027
Name of course completed:
CITI Good Clinical Practice (GCP) - OCR
Name:
SEHGAL, NEIL K
Department/School/Division:
School of Engineering
HS Training Completed:
Yes
Training Expiration Date:
Name of course completed:
CITI Protection of Human Subjects Research Training - ORA
GCP Training Completed:
No
Training Expiration Date:
Name of course completed:
Name:
WEISSMAN, GARY E
Department/School/Division:
DM-Pulmonary, Allergy and Critical Care
HS Training Completed:
Yes
Training Expiration Date:
Name of course completed:
CITI Protection of Human Subjects Research Training - ORA
GCP Training Completed:
No
Training Expiration Date:
Name of course completed:
Name:
SELTZER, EMILY
Department/School/Division:
Health System
HS Training Completed:
Yes
Training Expiration Date:
Name of course completed:
CITI Protection of Human Subjects Research Training - ORA
GCP Training Completed:
No
Training Expiration Date:
Name of course completed:

Page 7 of 32
Name:
GROENEVELD, PETER W
Department/School/Division:
DM-General Internal Medicine
HS Training Completed:
Yes
Training Expiration Date:
Name of course completed:
CITI Protection of Human Subjects Research Training - ORA
GCP Training Completed:
Yes
Training Expiration Date:
11/07/2024
Name of course completed:
Good Clinical Practice: An Introduction to ICH (GCP) Guidelines
Name:
FRANCISCO, ASHLEY
Department/School/Division:
PS-Neuropsychiatry
HS Training Completed:
Yes
Training Expiration Date:
Name of course completed:
CITI Protection of Human Subjects Research Training - ORA
GCP Training Completed:
No
Training Expiration Date:
Name of course completed:
Name:
KLINGER, ELISSA
Department/School/Division:
Health System
HS Training Completed:
Yes
Training Expiration Date:
Name of course completed:
CITI Protection of Human Subjects Research Training - ORA
GCP Training Completed:
No
Training Expiration Date:
Name of course completed:
Name:
MANCHENO, CHRISTINA
Department/School/Division:
Health System
HS Training Completed:
Yes
Training Expiration Date:
Name of course completed:
CITI Protection of Human Subjects Research Training - ORA
GCP Training Completed:
Yes
Training Expiration Date:
04/15/2024
Name of course completed:
CITI Good Clinical Practice (GCP) - OCR
Name:
GUNTUKU, SHARATH CHANDRA
Department/School/Division:
Computer and Information Science
HS Training Completed:
Yes
Training Expiration Date:
Name of course completed:
CITI Protection of Human Subjects Research Training - ORA
GCP Training Completed:
No
Training Expiration Date:
Name of course completed:

Page 8 of 32
Name:
KAPADIA, ARYAN RAMCHANDRA
Department/School/Division:
Research Services
HS Training Completed:
Yes
Training Expiration Date:
Name of course completed:
CITI Protection of Human Subjects Research Training - ORA
GCP Training Completed:
No
Training Expiration Date:
Name of course completed:
Name:
PELULLO, ARTHUR
Department/School/Division:
Health System
HS Training Completed:
Yes
Training Expiration Date:
Name of course completed:
CITI Protection of Human Subjects Research Training - ORA
GCP Training Completed:
No
Training Expiration Date:
Name of course completed:
Name:
KIMMEL, STEPHEN E
Department/School/Division:
Health System
HS Training Completed:
Yes
Training Expiration Date:
Name of course completed:
POR Recertiﬁcation Quiz - Full Board Review - SOM
GCP Training Completed:
No
Training Expiration Date:
Name of course completed:
Name:
UNGAR, LYLE H
Department/School/Division:
Computer and Information Science
HS Training Completed:
Yes
Training Expiration Date:
Name of course completed:
CITI Protection of Human Subjects Research Training - ORA
GCP Training Completed:
No
Training Expiration Date:
Name of course completed:
Name:
YANG, LINLIN
Department/School/Division:
SM-DN-Biomedical Graduate Studies
HS Training Completed:
Yes
Training Expiration Date:
Name of course completed:
CITI Protection of Human Subjects Research Training - ORA
GCP Training Completed:
Yes
Training Expiration Date:
05/16/2025
Name of course completed:
CITI Good Clinical Practice (GCP) - OCR

Page 9 of 32
Name:
SNIDER, CHRISTOPHER
Department/School/Division:
Health System
HS Training Completed:
Yes
Training Expiration Date:
Name of course completed:
CITI Protection of Human Subjects Research Training - ORA
GCP Training Completed:
No
Training Expiration Date:
Name of course completed:
Name:
JOSHI, SUKANYA M
Department/School/Division:
Research Services
HS Training Completed:
Yes
Training Expiration Date:
Name of course completed:
CITI Protection of Human Subjects Research Training - ORA
GCP Training Completed:
No
Training Expiration Date:
Name of course completed:
Name:
SCHROEDER, DEVON
Department/School/Division:
OB-OBGYN RESEARCH-ADMIN
HS Training Completed:
No
Training Expiration Date:
Name of course completed:
GCP Training Completed:
Yes
Training Expiration Date:
10/03/2026
Name of course completed:
CITI Good Clinical Practice (GCP) - OCR
Name:
DISCHER, ADRIANA
Department/School/Division:
Health System
HS Training Completed:
Yes
Training Expiration Date:
Name of course completed:
CITI Protection of Human Subjects Research Training - ORA
GCP Training Completed:
Yes
Training Expiration Date:
12/17/2024
Name of course completed:
Penn CR: Full Onboarding: Good Clinical Practice: An Introduction to ICH GCP Guidelines (2HRS)
Penn Intellectual Property*
To the best of the Principal Investigator's knowledge, does this protocol involve the testing, 
development or evaluation of a drug, device, product, or other type of intellectual property (IP) that is 
owned by or assigned to the University of Pennsylvania? Please refer to the Patent and Tangible 
Research Property Policies and Procedures.
No
Certiﬁcation
I have reviewed the Financial Disclosure and Presumptively Prohibited Conﬂicts for Faculty 
Participating in Clinical Trials and the Financial Disclosure Policy for Research and Sponsored 
Projects  with all persons who are responsible for the design, conduct, or reporting of this research; and 
all required Disclosures have been attached to this application.

Page 10 of 32
Yes
Social and Biological Sciences
Study Instruments
Discuss the particulars of the research instruments, questionnaires and other evaluation instruments in 
detail. Provide validation documentation and or procedures to be used to validate instruments. For well 
know and generally accepted test instruments the detail here can be brief. More detail may be required 
for a novel or new instrument. For ethnographic studies identify any study instruments to be used (i.e. 
for deception studies) and describe in detail where, when and how the study will be conducted and who 
or what are the subjects of study. Note: For more information on how to conduct ethical and valid 
ethnographic research, follow the link For oral histories or interviews provide the general framework 
for questioning and means of data collection. If interviews or groups settings are to be audio taped or 
video taped describe in detail the conditions under which it will take place. Include a copy of any novel 
or new test instruments with the IRB submission. 
After consenting, participants will complete a survey on a research laptop or on their own laptop at 
home (ThinkPad or MacBook air) computer (e.g. NHANES: SES [income, education], diet behavior 
and nutrition, physical activity and physical ﬁtness, smoking and tobacco use; digital data use [Pew 
questionnaire]). Survey questions and citations are attached. For in-person enrollment, the survey will 
be collected on an incognito browser, https:// donate.centerfordigitalhealth.upenn.edu. The data 
collection platform was reviewed by Penn Medicine IS team. After they complete the survey, they will 
be asked to donate their social media and digital data on the ThinkPad laptop computer or their personal 
laptop computer using the same browser and url. All data extraction will be consistent with the terms of 
service of the digital platform. Any social media posts collected on Facebook, Instagram or other social 
media platforms will not be linked to the participant's name. A unique code links the participant's 
survey responses to the de-identiﬁed social media posts. All data will be kept in a protected ﬁeld in a 
password-protected database. All participants will receive a unique ID to track survey responses. 
Participants will receive a copy of consent-related documents as well as detailed information about 
what data are being extracted, how those data will be used and shared among the research team and 
instructions on how to permanently delete any study apps that provide ongoing access. The study team 
will not communicate with participants via social media or publicly post that the participant is involved 
in the study. The study team will not have the ability to post any messages or content on behalf of the 
user. We received clearance from the Ofﬁce of Clinical Research to not collect participants' Social 
Security Numbers, since compensation is $50. After the patient has completed enrollment, we will send 
them a follow-up summary about the status of the study (ie. how many participants are enrolled and the 
number of different data sources shared. After participants completed enrollment, we will re-contact 
participants to complete a short survey as related to objective 1. The study instrument will include 
questions related to health status, social media phenotypes, and AI perceptions, and patient activation. 
In addition to the above cohort, we offer the opportunity for survey respondents to enroll in a short 
interview (20-30 minutes) that will evaluate perceptions about use and engagement phenotypes, details 
about individual use of social media platforms, concerns and ways to mitigate concerns about 
phenotyping, potential uses of phenotypes, opportunities for patient centered design and 
implementation. Interviews will occur over 20-30 min in subgroups of 15-20 patients. Patients will 
consent to participate, and participation will be anonymous. Interviews will occur via virtual conference 
call and audio-recorded.
Group Modiﬁcations
Describe necessary changes that will or have been made to the study instruments for different groups.
No group modiﬁcations necessary.
Method for Assigning Subjects to Groups
Describe how subjects will be randomized to groups.
No randomization will occur.
Administration of Surveys and/or Process
Describe the approximate time and frequency for administering surveys and/or evaluations. For surveys, 
questionnaires and evaluations presented to groups and in settings such as high schools, focus group 

Page 11 of 32
sessions or community treatment centers explain how the process will be administered and who will 
oversee the process. For instance, discuss the potential issues of having teachers and other school 
personnel administer instruments to minors who are students especially if the content is sensitive in 
nature. Describe the procedure for audio and videotaping individual interviews and/or focus groups and 
the storage of the tapes. For instance, if audio tape recording is to be used in a classroom setting, 
describe how this will be managed if individuals in the class are not participating in the study. Explain 
if the research involves the review of records (including public databases or registries) with identiﬁable 
private information. If so, describe the type of information gathered from the records and if identiﬁers 
will be collected and retained with the data after it is retrieved. Describe the kinds of identiﬁers to be 
obtained, (i.e. names, social security numbers) and how long the identiﬁers will be retained and 
justiﬁcation for use. 
Participation should take 15-60 minutes during one session, but could take up to a week or more 
depending on the completion of all enrollment steps by the participant. Enrollment will take longer for 
participants opting to participate in the additional parts of the study (ie. additional questionnaire and/or 
focus group). Subjects may participate by having a research assistant approach them in person, via 
email, or over the phone. For in-person recruitment, participants will be approached during an in-patient 
visit stay following admission from the emergency department at HUP, HU pavilion, PAH, or PPMC or 
at outpatient settings like Penn Medicine Primary care clinics or Cardiology clinics in Philadelphia. 
Additionally, we will be reaching out to patients presenting to a Penn Medicine Chester County 
location, such as Lancaster General and Chester County hospital, remotely. Regardless of recruitment 
site, in-person recruitment, or remote recruitment, all patients will read and e-sign an online consent 
form, answer survey questions, and share their digital data. The survey and data donation platform lives 
within 
the 
existing 
structure 
of 
the 
Penn 
WayToHealth 
(WTH) 
platform, 
(https://
donate.centerfordigitalhealth.upenn.edu/capture/) created by Co-Investigator Dr. Asch. Additionally, 
survey information will be captured through REDcap as well. WTH allows for secure patient consent 
and subject interaction.. All this data will be stored on our HIPAA secure shared drive and patient 
tracking information will be stored in Penn Box HIPAA compliant feature as well as REDcap.  After 
the patient has completed enrollment, we will send them a follow-up summary about the status of the 
study (ie. how many participants are enrolled and the number of different data sources shared. For the 
Objective 1 subgroup participants, they will complete a short survey and have the option to opt-into a 
brief recorded Zoom interview on digital data norms and beliefs. Interviews will occur over 20-30 min 
in subgroups of 15-20 patients. Patients will consent to participate, and participation will be 
anonymous.
Data Management
Describe how and who manages conﬁdential data, including how and where it will be stored and 
analyzed. For instance, describe if paper or electronic report forms will be used, how corrections to the 
report form will be made, how data will be entered into any database, and the person(s) responsible for 
creating and maintaining the research database. Describe the use of pseudonyms, code numbers and 
how listing of such identiﬁers will be kept separate from the research data. 
All study data will be stored on the Health Service Research Data Center (HSRDC). As per the HSRDC 
Data Management Policy and Procedures Manual, password protection is used at the server and web 
portal levels for all transactions that allow entry and editing of data, or provide access to sensitive 
subject data or administrative privileges. Passwords will be managed to require all users to change their 
password within 90 days and strict rules will be implemented to require strong passwords. Additionally, 
all PHI data will be encrypted and linked to survey data through a study ID number, with linkage and 
de-encryption keys activated only by a user password for which a member of the research team has 
been given permission to access these sensitive data (Principal Investigator Dr. Merchant and project 
staff who are under the direct supervision of the PI). As per the HSRDC Data Management Policy and 
Procedures Manual, the database management on these servers is built with multiple layers of security 
and follows best practices for securing sensitive data. The main levels of security are fourfold and 
include machine physical security at the IT facility at which the servers are housed, physical server 
security in the highly restricted server containment room, electronic server security (ﬁrewalls, 
passwords, encryption) restricting access to the machine, and directory access controls restricting 
access to these particular data.
Radiation Exposure*
Are research subjects receiving any radiation exposure solely because they are enrolled in this protocol? 
(e.g. X-rays, CT, Fluoroscopy, DEXA, pQCT, FDG, Tc-99m, etc.)?IF YES, the protocol must be 
approved by the RRSC (Radiation Research Safety Committee). Consult EHRS web site: 

Page 12 of 32
www.ehrs.upenn.edu/protocols/radiohuman.html for more information.If you have questions, email 
jjesik@ehrs.upenn.edu or kavyap@upenn.eduIf your protocol includes Nuclear Medicine Procedures, 
the protocol must be reviewed by the Nuclear Med Operations Committee: https://redcap.link/NMOPS
No
Human Source Material*
Does this research include collection or use of human source material (i.e., human blood, blood 
products, tissues or body ﬂuids)? IF YES, consult the EHRS web site: www.ehrs.upenn.edu/programs/
bio/bbpathogens.html for information on OSHA Bloodborne Pathogens requirements (training, 
vaccination, work practices and Exposure Control Plan). If you have questions, call 215-898-4453. 
No
Image Guided Biopsies*
Does the research involve imaging guided biopsy? IF YES, please contact the Clinical Imaging Core. 
See https://www.med.upenn.edu/cbi for more details. Any questions should be directed to the Director 
of Research Operations, Dept of Radiology, Kathleen Thomas. 
No
Computerized Tomography (CT) Studies*
Does the protocol involve CT scans that are not considered standard of care and are being performed for 
research purposes?IF YES, complete the CACTIS Committee Application: https://is.gd/CACTIS and 
consult CACTIS website: http://www.uphs.upenn.edu/radiology/research/labs/cactis/ for application 
requirements.
No
CAMRIS and MRI Studies*
Is an MRI scan being performed for research only and NOT considered standard of care (example: 
speciﬁc scanner, parameters or solely for the purposes of research)?NOTE: Research/non-standard use 
of MRI may include but is not limited to any of the following: Situations in which MRI results may 
impact subjects current clinical care plan or treatment decisions, such as:The study requires a 
customized report with speciﬁcs regarding the study protocol (i.e., speciﬁc measurements or details); 
Introduction of a device of any kind during the MRI that is not used during a 'standard of care' type 
scan.Your MRI is not consistent with standard care time points for MRI imaging.Your MRI is not paid 
for by insurance.IF YES, consult CAMRIS website: https://www.med.upenn.edu/camris/application-
and-faq.html for application requirements and required institutional consent form language.
No
Cancer Related research not being conducted by an NCI cooperative group*
Does this protocol involve cancer-related studies in any of the following categories? Therapeutic, 
Prevention, Supportive Care, Screening, Early Detection, or Diagnostic, Epidemiologic, Observational, 
Outcome, Ancillary or Correlative. For a description of these categories, see http://www.ctsrmc.org/
submitting_a_protocol.php NCI Cooperative Groups are as follows:Alliance for Clinical Trials in 
OncologyNCI Clinical Trials Group (Canadian Cancer Society) (NCCTG)Children's Oncology Group 
(COG)NRG Oncology GroupECOG-ACRIN Cancer Research GroupSouthwest Oncology Group 
(SWOG) IF YES, the protocol must be submitted to the Cancer Center's Clinical Trials Scientiﬁc 
Review Committee for scientiﬁc review and approval prior to obtaining IRB approval. Consult the 
CTSRMC website: www.ctsrmc.org for application requirements 
No
HIPAA / Protected Health Information
Does the research proposal involve accessing (viewing / using), collecting, or disclosing of protected 
health information (PHI) directly from participants or their medical or dental record for research 
purposes?
Yes
CHPS Resources*
Does the research involve CHPS resources?
No

Page 13 of 32
HUP Inpatient Nursing Resources
Does this research include an inpatient admission at HUP?
No
If the answer is YES, indicate which items is is provided with this submission:
Modiﬁed research informed consent document that incorporates HIPAA requirements
Use of UPHS services*
Does your study require the use of University of Pennsylvania Health System (UPHS) services, tests or 
procedures , whether considered routine care or strictly for research purposes? (UPHS includes all Penn 
hospitals and clinical practices, including the Clinical Care Associates network of community 
practices). Examples of UPHS services/tests/procedures includes the Clinical Translational Research 
Center (CTRC), laboratory tests, use of the pathology lab, cardiovascular imaging tests or radiology 
imaging tests (whether being billed via the Service Center or through UPHS), other diagnostic tests & 
procedures and associated professional services, etc. 
No
Veteran's Affairs (VA) Patients or Subjects
Does your study involve data from Veteran's Affairs (VA) patients or subjects?
No
If yes, was this approved by the Philadelphia VA?
No
Out of State Research
Will any Penn personnel conduct any research activities outside of the State of Pennsylvania?
No
Research involving Virtua Health
Will any Penn personnel conduct any research activities at a Virtua Health site location, OR in 
collaboration with Virtua Health System personnel, OR using any Virtua Health System resources (e.g., 
medical records)?
No
Primary Focus*
Research on human data sets (e.g. medical records, clinical registries, existing research data sets, 
medical administrative data, etc.)
Protocol Interventions
x Sociobehavioral (i.e. cognitive or behavioral therapy)
Drug
Device - therapeutic
Device - diagnostic (assessing a device for sensitivity or speciﬁcity in disease diagnosis)
Surgical
Diagnostic test/procedure (research-related diagnostic test or procedure)
Obtaining human tissue for basic research or biospecimen bank
x Survey instrument
None of the above
The following documents are currently attached to this item:
There are no documents attached for this item.

Page 14 of 32
Sponsors
Business Administrator
Name:
RIZIO, SUZANNE
Dept / School / Div:
4302 - EG-Emergency Medicine
Phone:
215-898-9108
Fax:
215-898-0868
Pager:
Email:
rizio@pennmedicine.upenn.edu
Department budget code
400 - 400 - 4 - 577869 - 5316 - 2000 - 3520
Funding Sponsors
Name:
NATIONAL INSTITUTES OF HEALTH
Type:
UPENN Federal
Funding sponsors billing address
If you have selected a commercial or industry sponsor, please provide the appropriate address and 
contact information for the Sponsor for the purposes of billing for IRB review fees (initial review, 
continuing review and convened modiﬁcation fees apply here). If the Sponsor is not industry/
commercial, this information is not necessary to provide with your application.
National Heart, Lung, and Blood Institute Building 31 31 Center Drive, Bethesda, MD 20814 United 
States
Funding sponsors gift
Is this research being funded by a philanthropic gift?
No
Regulatory Sponsor
IND/IDE Sponsor
none
400 - 400 - 4 - 577869 - 5316 - 2000 - 3520
Industry Sponsor
None
Project Funding*
Is this project funded by or associated with a grant or contract?
Pending
Sponsor Funding
Is this study funded by an industry sponsor?
No
Status of contract
The following documents are currently attached to this item:

Page 15 of 32
There are no documents attached for this item.
Multi-Center Research
Penn as lead
1. Is this a multi-center study where Penn is serving as the Lead Site or the Penn PI is serving as the 
Lead Investigator? 
No
Management of Information for Multi-Center Research
Penn irb of record
2. Is this a multi-center study where the Penn IRB will be asked to serve as the IRB of Record for other 
external study sites? 
No
Other Sites
No other sites
Protocol
Abstract
Cardiovascular disease is the leading cause of death in the US. While prevention approaches have 
improved longevity of patients, risk factors and adverse health behaviors (e.g., physical inactivity, 
smoking) are highly prevalent. The logistics and practicalities of meeting the goal of ideal CV health 
have not been clearly elucidated. Practice guidelines recommend using the Framingham risk score 
(FRS) or other risk prediction tools to classify patients risk of CV disease. These models however are 
imprecise and there is increasing focus on identifying markers that provide better measures of risk. As 
digital platforms are increasingly used to document lifestyle and health behaviors, data from digital 
sources may provide a window into manifestations of novel risk factors and potentially a better 
characterization of existing risk factors.
Objectives
Overall objectives
This study has three main objectives. (1) Identify and characterize topics and features derived from 
digital data (e.g., social media, online search, mobile media) which are associated with CHD and 
related risk factors and develop phenotypes of how patients use and engage with social media and then 
determine the association of these phenotypes with health status, social media perceptions, and patient 
activation. (2) Quantify and validate the incremental beneﬁt of adding variables from patients' digital 
data to conventionally-derived predictive models of CHD (e.g. Framingham risk score/American 
College of Cardiology ASCVD Risk Estimator Plus) using a retrospective cohort of patients with and 
without heart disease. (3) In a cohort of patients with and without heart disease, develop and evaluate 
models that use digital media data to predict health care utilization (e.g. cost). (4) Evaluate the impact 
of dynamic health events (e.g. COVID-19) on modiﬁable CV health behaviors (smoking, diet, activity) 
using population level-digital media data (e.g. Facebook, Twitter)
Primary outcome variable(s)
The primary outcome variable for the ﬁrst objective will be topics and features (derived using the Latent 
Dirichlet Allocation [LDA] method for clustering language data) as well as a model-based approach of 
latent class analysis (LCA). We hypothesize: H1: There will be a difference in themes and topics posted 
by patients with heart disease and for patients without heart disease and use and engagement 

Page 16 of 32
phenotypes reﬂecting quantity, quality, and interaction of posts will be associated with health status, 
social media perceptions, and patient activation. The second objective outcome variable will be any 
CHD event. We hypothesize: H2: Adding digital data variables to conventionally-derived predictive 
models will improve the AUC for forecasting/predicting a CHD related event in a patient. The third 
objective outcome measurewill be health care utilization measured by cost. We hypothesize: H3: 
Unique digital data variables identiﬁed for patients with heart disease and without heart disease will 
better predict future health care utilization costs for the health system. The fourth objective outcome 
measure will be the impact of dynamic health events on CV health behaviors. We have the following 
three hypotheses: H4,1: Digital media data will provide valid measures of health behaviors when 
compared with traditional sources. H4,2: Variations in digital data about modiﬁable CV health 
behaviors will correlate with dynamic health events. H4,3: Modiﬁable CV health behaviors (smoking, 
poor diet, and reduced physical activity) will increase more in response to dynamic health events in low 
income and minority communities.
Secondary outcome variable(s)
Given the correlational nature of this research the distinction between primary and secondary outcome 
variables is not particularly apparent.
Background
Cardiovascular disease is the leading cause of death in the US. While secondary prevention approaches 
have improved longevity of patients, risk factors and adverse health behaviors (e.g., physical inactivity, 
smoking) are highly prevalent, and in most contemporary series, less than 1% of adults meet all factors 
of ideal CV health. The logistics and practicalities of meeting the goal of ideal CV health have not been 
clearly elucidated. Practice guidelines recommend using the Framingham risk score (FRS) or other risk 
prediction tools to classify patients' risk of CV disease. These models however are imprecise and there 
is increasing focus on identifying markers that provide better measures of risk. As digital platforms are 
increasingly used to document lifestyle and health behaviors, data from digital sources may provide a 
window into manifestations of novel risk factors and potentially a better characterization of existing risk 
factors. While it seems like a cliche to mention the profound impact of digital data on everyday lives, 
there is indeed great substance in the opportunities these new media provide for understanding 
behavioral, social, and environmental determinants of health. Health is shaped most by the behaviors, 
events, and circumstances that occur outside of traditional health care contact. Even a patient with a 
chronic illness may spend only a few hours a year in front of a doctor, but the same patient will spend 
over 5,000 waking hours a year doing everything else. And it is during those other 5,000 hours that so 
many of the determinants of people's health play out. Digital footprint data offer a window into what 
people do in those 5,000 hours. Social and digital information in the form of posts, photo likes, Internet 
searches, steps walked, and other online footprints can reveal a detailed narrative about an individuals 
day-to-day activities and behaviors. Google search queries have been used to measure trends in 
inﬂuenza, predict prevalence of disease, and estimate congestive heart failure admissions. Social media 
has been used to track infectious disease outbreaks, cardiac arrest geography, heart disease mortality 
rates, access to healthy food, and depression. Steps data from sensors (e.g. iPhone, Android 
accelerometers) have been used for real-time tracking and as a tool to improve physical activity in 
adults with cardiac disease. The widespread interest in precision medicine based largely on people's 
genomic identities took hold once we had a way to capture genomic information from DNA 
sequencing. Digital data may be complementary and provide new signal as: [1] social information is 
now more accessible, not requiring the bio samples that limit the reach and generalizability of genomic 
data; [2] digital platforms often come with their own 'return to sender' pathway that offers opportunities 
for intervention, making information derived from it more actionable. Digital platform users are self-
selected and thus reﬂections of what people reveal organically can't represent the population as a whole, 
but it is clear that the digital divide is narrowing, and that utilization of digital platforms is growing 
exponentially. The fastest growing group of users of social media is over the age of 65. African- 
Americans, Latinos, and those in urban populations are in fact overrepresented on some social 
platforms (e.g., Twitter) relative to the general population. Google use is ubiquitous with more than 
1.17 billion people performing more than 2 trillion searches per year. More than 50% of individuals in 
the US are iPhone users and 1 in 5 Americans owns a wearable device. Of central importance to this 
proposal is that individuals from diverse backgrounds are increasingly using these platforms for 
communication about health and healthcare. The speed with which data are generated creates potential 
for earlier signal about disease development or exacerbation. The regular production of data also allows 
for tracking of health in real-time or over expanded periods time. The variety of data offers promise as 
this information provides different insights than data from traditional surveys or claims. Another 

Page 17 of 32
opportunity is in the ability of digital platforms to not only provide markers of health but also serve as 
platforms that can be used for direct intervention. For example, Facebook now allows users to ﬂag posts 
within their network that they think may suggest suicidal ideation. Facebook then anonymously 
provides resources for potential individuals at risk. To expand the scale of this type of intervention for 
mental health and other disorders would require calibrated algorithms with high sensitivity and 
speciﬁcity. The potential in CV health is in tracking, codifying, and better understanding the hard to 
measure lifestyle choices and exposures related to diet, exercise, smoking and others which can 
signiﬁcantly contribute to development and progression of heart disease. At present measuring many of 
these behaviors is dependent on self-report and recall. Yet, posts or images from digital media could 
better inform a patient-provider discussion about how to change for example actual dietary choices and 
consumption. Standing in the way of this potential promise are a host of questions that require further 
scientiﬁc study about the social acceptability and logistic feasibility of collecting ongoing streams of 
data from individuals, about the analytic approaches essential for identifying associations between those 
data and more conventional representations of individual health, and about the application of those 
associations for research and for individual and population health. Further needed is a better 
understanding of the accuracy of the data, motivations for posting, representativeness (are posts actually 
by the person assigned), missing data (what behaviors are not captured intentionally or unintentionally) 
and others. The proposed project represents an entirely new area of study in looking at multiple digital 
media outputs and their association with CV risk and health care utilization. Understanding the 
collection, interpretation, and use of these new data streams represents a pioneering exploration of 
potentially great and enduring value.  Ideal cardiovascular health (CVH) is based on 7 modiﬁable CV 
factors and behaviors (smoking, BMI, diet, physical activity, smoking status).74 Low risk with respect 
to these collective measures is associated with reduced CV and stroke incidence and mortality.87 Only 
5% of US Adults have 6 metrics in the ideal range.73 Modiﬁable CV behaviors are tracked through 
different validated nationally representative surveys (e.g. NHANES) but the output from these surveys 
often lag signiﬁcantly in their availability for researchers and providers. Therefore, there are signiﬁcant 
limitations in being able to develop public health interventions to address population level changes in 
health behaviors in near real time. Less is known about how modiﬁable health behaviors change in 
response to dynamic public health events. On March 11, the World Health Organization declared novel 
coronavirus (COVID-19) a global pandemic and identiﬁed that it was associated with an infodemic of 
information online.88 As millions of Americans were required to shelter in place, health behaviors were 
notably altered and social media use exponentially increased.89 Quarantine and sheltering in place have 
been associated with increased stress, anxiety, exacerbation of mental health conditions, and alterations 
in diet, weight, and physical activity.90 In the era of COVID-19, disparities in care and outcomes have 
already been described.91, 92 The impact of COVID-19 is predicted to be longstanding due to 
alterations in the economy, education, and other areas of society which impact health. We will use 
advanced computational models for identifying and codifying health behaviors by region over time and 
in real time. This can inform strategies that health systems and public health departments can deploy for 
addressing the immediate and long-term CV health of the patients and communities they serve.  Ideal 
CV health is ideal but making lifestyle and behavioral changes is hard.99, 100 Digital interventions can 
be used to enhance self-management of diabetes and modiﬁable behaviors like smoking 
cessation.101-103 Of increased focused, engagement phenotypes that help explain how individuals use 
and engage with technology can be used for tailoring behavioral interventions and sustained behavioral 
change.76 While marketers routinely use this information for increasing interest in products, there is 
limited data on how patient phenotypes (developed from consenting patients) could be used for 
personalized health interventions using social media data and social media platforms.
Study Design
Phase*
Not applicable
Design
We will identify patients ages 30-74 with and without CVD ) within the last 5 years who receive care 
through the University of Pennsylvania Health System or receive care in another health system in the 
United States (U.S.). For patients with CVD, we will be using the following ICD codes for 
identiﬁcation: ICD 10: I63, I20-I25. For patients without CVD, please ﬁnd attached the list of ICD 10 
codes we will use for identiﬁcation. UPHS is the largest health system in Eastern Pennsylvania with a 
diverse patient population of more than 500,000 patients with CV disease. We will recruit both in and 
out-patients as study participants. For both the inpatient population and outpatient population within 

Page 18 of 32
UPHS, we will use Epic workbench and other Epic screening tools such as the Best Practice Alerts 
(BPA) to screen for eligible patients. BPAs can be conﬁgured to identify patients given a set of 
inclusion criteria, in real-time, and send a silent in-basket message to allocated study personnel. Users 
gain access to these alerts through an in-basket message pool within PennChart, BPA distribution and 
modiﬁcation privileges can be controlled (and limited) based on security conﬁgurations. Penn Medicine 
sites identiﬁed in the BPA include PCAM, HUP, PPMC, PAH, CCH, Cardiology HVC Consultative 
Clinic, Cardiology HVC Clinic, PCAM, Cooper/PIMA Internal Med, Penn Cardiology Bucks County, 
Penn Cardiology South Philly, Penn Cardiology Valley Forge, Penn Medicine Bala Cynwyd, Penn Heart 
and Vascular Center Washington Square, Penn Cedar and Penn Heart and Vascular Center Radnor, PA. 
For patients who are identiﬁed outside of UPHS, we will solely rely on self-report diagnosis for 
identiﬁcation. Potentially eligible patients will either be approached or emailed/called about the 
opportunity to participate in the study either in person during an in-patient visit (4,734,948/annually) or 
outpatient visit at Penn Medicine hospital, including the Emergency Rooms at the Hospital of the 
University of Pennsylvania (HUP), Penn Presbyterian Medical Center, and Pennsylvania Hospital, or at 
one of the Outpatient Internal Medicine Clinics in Philadelphia, or contacted remotely (over a secure 
calling phone application or emailed). Additionally, we will contact patients remotely at various Penn 
Medicine Chester County locations such as Lancaster General and Chester County hospital. For initial 
interest participation emails and follow-up emails, we will be using a shared Penn Medicine email 
account through our Center (dydhearthealth@pennmedicine.upenn.edu) to reach out to those folks. In 
addition to emailing and calling patients, we will be using My Penn Medicine (MPM) which is UPHS's 
secure patient portal to message identiﬁed patients about interest in our study. Our message will use the 
same template as our initial outreach email. Another form of remote outreach we will be using is Way 
to Health (W2H), which is a HIPAA secure two-way texting platform, where we will text message 
identiﬁed patients our initial outreach message about our study (reference the text message automated 
ﬂow attached). If patients are interested, they will be texted our eligibility survey link to complete. We 
will also use this HIPAA secure text messaging platform to follow up with participants related to 
various study participation (ie. re-sharing of data through a secure link, scheduling conﬁrmation). 
Interested participants who are eligible will be asked to ﬁll out a short survey asking what days and 
times work best for a team member to give them a call to walk through what is involved in participating 
in the study. We will use the Academic Associate (AA) Program in the Emergency Department (ED) at 
the Hospital of the University of Pennsylvania, Presbyterian Medical Center to identify patients. The 
AA Program provides premedical and pre-health post-baccalaureate students an opportunity to engage 
in clinical and translational research activities. The AAs have Immunization records (measles, mumps, 
and rubella [MMR] and varicella [chickenpox]) and TB screening (PPD test or equivalent), and 
inﬂuenza (in season) and complete a web-based course sponsored by the University of Pennsylvania on 
the protection of human subjects in research. Using Epic tools and AAs, we will screen all patients aged 
30-74 examining their chief complaint recorded in Epic. If patients present cardiovascular-related chief 
complaints, the patient will be noted in a screening log, and if admitted, note their in-patient ICD-10 
admission code. Similarly, for patients aged 30-74 who have a non-cardiovascular-related chief 
complaint, the patient will be noted in a screening log and if admitted, note their in-patient ICD-10 
admission code.  For in-person recruitment and enrollment in the outpatient setting, outpatient Penn 
Medicine clinics (e.g. Internal Medicine, Primary Care, Cardiology), RAs will review the weekly 
appointment list to review if any patients meet either case and control requirements. RAs will be in 
communication regularly with the nursing coordinator, ofﬁce manager, charge nurse, nurse/ physician 
team to determine the daily census and appropriateness of approaching patients. In both in and out-
patient settings, the AAs or CDH research staff will approach and will introduce the study in brief and if 
the patient is interested in enrolling. They query eligible patients about their interest in study enrollment 
(i.e. sharing their social media, online search, or step tracking data and allowing access to their EMR 
data and health insurance data). For remote recruitment and enrollment, identiﬁed patients via the BPA 
and EPIC reporting workbench will either be emailed, called, sent an MPM message, or a W2H 
message in regards to interest in participating in this research study. Initial interest participation emails 
and all follow-up emails will be sent using our Centers shared Penn Medicine email account 
(dydhearthealth@pennmedicine.upenn.edu), MPM messaging, or W2H messaging. All recruitment and 
follow-up emails will be A/B tested, also known as split testing, the process of comparing two identical 
versions of an email. Potential variations include the order of information, email headers, and 
signatures. NOTE: In addition to the research team reaching out to potentially eligible patients, we will 
be using ﬂyers, we will post our study on Penn Medicine Lancaster General Health's various social 
media sites (ie. Facebook, Instagram, Twitter, LinkedIn, etc.), and we will be posting our study on 
iConnect for interested patients to reach out to us regarding participation in the research study. We will 
be using iConnects pre-screener feature to assess patients for pre-eligibility for our study. These pre-

Page 19 of 32
screener questions will be the same as the questions already built into our eligibility survey. If the 
patient is interested, they will receive a link to an eligibility survey to complete either over the phone 
with a team member or on their own time. The link will be sent via our secure shared Penn medicine 
email, MPM, or W2H on our HIPAA secure survey platform. If the patient is eligible and still 
interested, they will be asked to ﬁll out a baseline survey at a time that works best for them. At that 
time, interested participants will be asked to complete the informed consent HIPAA authorization 
process at that time. The HIPAA authorization will also include detailed information about the request 
for permission to collect cost data. For example We are asking permission to collect information on 
your Medicare and/or insurance coverage and on your cost of care. This information will be collected 
directly from your insurance and medical record. Information will be collected for up to 5 years. If you 
agree to participate in this study the following information will be collected: type of health insurance, 
health insurance provider, health insurance policy, health insurance group number, and the 
policyholder's name and date of birth will be collected. If we aren't able to obtain your health insurance 
policy number, we will either call you to obtain your policy number or you can ﬁll out a secure survey 
through REDcap that will allow you to input your policy number. For in-person enrollment, after the 
patient signs the informed consent the AA or CDH staff will complete the baseline survey and continue 
the enrollment process. Patients will complete several surveys on a research laptop (ThinkPad or 
MacBook air) and/or an iPad (e.g. NHANES: SES [income, education], diet behavior and nutrition, 
physical activity and physical ﬁtness, smoking, and tobacco use; digital data use [Pew questionnaire]). 
For patients outside of UPHS, we will ask them to upload screenshots and/or self-report information 
from their ofﬁce visit summaries, labs, vital signs, ED and inpatient hospital admission information 
since we will not have access to their EHR. For in-person enrollment, the survey will be collected on an 
incognito browser through either https:// donate.centerfordigitalhealth.upenn.edu or REDcap. Once the 
survey is completed, the patient will reach a donate data page on the same browser, https:// 
donate.centerfordigitalhealth.upenn.edu or be taking to another survey to share their digital data, https:// 
donate.centerfordigitalhealth.upenn.edu, on one of the research laptops. For remote enrollment, 
participants will receive a link via email from our HIPAA secure survey platform to complete the 
baseline survey either on their own time or over the phone with a study team member. Interested 
participants will be asked to ﬁll out a brief survey, via Redcap, asking what days and times work best 
for them to set-up a phone call with one of our team members to go over what is involved in 
participating in the study. They can either complete the enrollment process on the phone with the team 
member or on their own time.  Subjects who meet case or control criteria, complete the consent process, 
and opt to share their social media data with the research team, will be instructed on how to share this 
information by logging into apps for Facebook and Instagram that have been created solely for the 
purpose of research. They will ﬁrst be asked to create an account on our secure digital data platform/
application (https:// donate.centerfordigitalhealth.upenn.edu). Once they have conﬁrmed information in 
the data collection survey, they will be directed to a Facebook application called Penn Image Station 
that was designed for the purposes of this study. The Penn Image Station application will gather 
information from their Facebook public proﬁle, status updates, personal description, and likes. The 
application will not affect their usual use of Facebook nor will it post on their page. Facebook data will 
be collected prospectively for 60 days. If the participant shares their Instagram posts, they will be 
directed to an Instagram application called Penn Social Mediome. They will read a brief statement, 
either agree or disagree with the statement, and then log on to their Instagram account. Their Instagram 
posts and photos will be extracted by the Penn Social Mediome application and will be collected for 60 
days prospectively. If our data collection application is not working, there will be a back-up plan set in 
place. We will ask participants to share their Instagram usernames so we to pull data manually from 
their account. If there are technological difﬁculties with our data collection application, we will reach 
out to participants at a later time to share their Instagram and Facebook data through the participants 
unique data sharing url. If there are technical difﬁculties with our application pulling Facebook data, we 
will ask participants to manually download a ﬁle of their Facebook data from their account. No 
passwords would be collected by the study team. If the patient has Twitter or Reddit accounts and wish 
to share that data as well, they will be asked to provide their Twitter handle (username) and/or Reddit 
handle (username).The participants IP addresses from their devices will be collected.  If subjects opt 
and consent to share Google take-out information with us, they will download a zip ﬁle of their google 
searches and YouTube activity. Google take-out data is only retrospective. They will upload the zip ﬁle 
to the browser https:// donate.centerfordigitalhealth.upenn.edu.  If subjects opt and consent to share step 
tracking data with our research team and the patient is eligible and has Apple Health, S Health, Garmin, 
or Fitbit, they will export the data from the speciﬁc step tracking platform and the patient will upload 
this data to https:// donate.centerfordigitalhealth.upenn.edu.  Data collection manual outlines all data 
extraction procedures [See Appendix].  Data extracted from Facebook will include: age, gender, 

Page 20 of 32
language, photo, work history, education history, hometown, interests, relationship status, current city, 
religious or political views, number of friends, items "liked", and number and status updates. Data from 
Twitter will include: handle, number of followers/following, tweets, geography, time/date, proﬁle. Data 
extracted from Reddit will include: Reddit posts, comments to posts, your votes "up" or "down" to 
posts, and the sub-reddits(forums) that youve joined. Data from Instagram will include image posts, 
time of posting, and image captions. Data extracted from Google takeout includes the search terms, 
website visited with the URL, YouTube search history, date, and Google ﬁt that includes activities and 
daily aggregations. If sharing Apple Health, data extracted will include: any data stored or gathered by 
the Health app and any associated devices, including any Medical ID data, the native iPhone step 
counter and distance tracker, any data from an Apple Watch, and any data gathered from anythird 
partydevices that are syncing to Health app, like a smart scale or blood pressure monitor. (http://
osxdaily.com/2019/05/20/export-health-data-from-iphone/). If sharing Fitbit, data extracted will 
include: Activity, Exercise, Social, Sleep, Coach, Corporate, Logs, Proﬁle, Direct messages, Female 
health, Sleep score, Friends, Subscriptions. If sharing Garmin, data extracted will include: steps per 
month for the past 12 months. If sharing S Health, data extracted will include: Daily step count for 34 
days prior to export date(https://medium.com/@dimshik100/how-to-extract-your-personal-samsung-
health-data-514bbe2331f7). If sharing MapMyFitness, data extracted will include: Date Submitted, 
Workout Date, Activity Type, Calories Burned, Distance, Workout Time, Average Pace, Max Pace, 
Average Speed, Max Speed, Average Heart, Rate, Steps, Source, Link *we do not use this as it requires 
user login to view link results*  Patients who donated at least one of the following digital data sources: 
social media, google takeout zip ﬁle, or step tracking data will receive $50 for survey completion. For 
in-person enrollment, the entire process will take place during one encounter lasting approximately 
15-60 minutes. For remote enrollment, the process can take 15-60 minutes in one session or up to a 
week or more depending on completion of all enrollment steps. If participants are unable to donate 
desired digital data sources, we will contact them up to 4 times in a month to download and donate all 
relevant sources as well as complete the baseline survey if they havent already done so.  All patient 
enrollment status tracking will be documented and saved in either our Penn Medicine secure shared 
drive, Penn Box (HIPAA compliant feature), or REDcap.  From the above cohort we will additionally 
consent these patients to complete several surveys, if they opt to participate in this additional part of the 
study. These include: perceptions of health status and perceptions about social media will be assessed as 
this could impact engagement with a digital intervention.  In addition to recontacting the above cohort, 
we will also identify an additional cohort from iConnect (Penn Medicine research registry), who opt to 
participate in focus groups that will evaluate perceptions about use and engagement phenotypes, details 
about individual use of social media platforms, concerns and ways to mitigate concerns about 
phenotyping, potential uses of phenotypes, opportunities for patient centered design and 
implementation. Patients will complete a separate informed consent to participate and participation will 
be anonymous. Interviews will occur via virtual conference call and recorded. After the patient has 
completed enrollment, we will send them a follow-up summary about the status of the study (ie. how 
many participants are enrolled and the number of different data sources shared. This application and 
methodology have been previously used by investigators in this protocol for research within the World 
Well Being Project at the Positive Psychology Center at the University of Pennsylvania (prior Penn IRB 
protocols #816091, 813820, 819442, 824493).
Study duration
It will take approximately 15-60 minutes during one session, but could take up to a week or more 
depending on completion of all enrollment steps for a participant to complete the study. For remote 
enrollment, it could take up to a week or more for participants to complete the eligibility survey, 
baseline survey, and share their digital data. If there are technical difﬁculties with our data collection 
application, we will ask participants who are opting to share Instagram if we can record their Instagram 
username for manual data extraction. Additionally, we will reach back out to them at a later time to 
share through our data collection application if there were any technical difﬁculties. All recruitment and 
follow up emails will be A/B tested, also known as split testing, the process of comparing two identical 
versions of an email. Potential variations include the order of information, email headers, and 
signatures. We aim to recruit 1000 participants over 4 years.  Additionally, participants who agree to 
participate in the optional survey and interview will have to wait until a date and time is determined for 
the focus group to occur. Follow-up is necessary in this case.
Resources necessary for human research protection
Describe research staff and justify that the staff are adequate in number and qualiﬁcations to conduct 
the research. Describe how you will ensure that all staff assisting with the research are adequately 

Page 21 of 32
informed about the protocol and their research related duties. Please allow adequate time for the 
researchers to conduct and complete the research. Please conﬁrm that there are adequate facilities for 
the research.
The study team will not have direct access to any participants password for any digital media accounts 
and will not have the ability to post any messages or content on behalf of the user. If there are issues 
with the data collection application, we will ask participants to share their Instagram username with us 
for manual data extraction. Participants will receive a copy of consent-related documents as well as 
detailed information about what data are being extracted, how those data will be used and shared among 
the research team and instructions on how to permanently delete any study apps that provide ongoing 
access. We will observe best practice protocols for information security within our study team, practices 
that are already well-established for minimum-necessary access from environmentally, physically, and 
digitally secured repositories. Digital data will be reported only in aggregate and not the level of an 
individual, aside from excerpts of illustrative postings in the same way written questionnaires are 
reported only in aggregate with the exception of excerpts of illustrative free text comments. The study 
team will not communicate with participants via social media or publicly post that the participant is 
involved in the study. Participants will be informed that they should not have any expectation of 
surveillance or medical advice/medical response or that any posts, images, searches, physical activity 
data will be responded to in real time or at any time by the study team. Lexical analysis will be 
completed by the coinvestigators listed, whom have prior experience analyzing social media data and 
using the Facebook application used in this study to extract language, photos, and other social media 
data from Facebook, as well as applying the described lexical analysis. All aspects of the study will be 
overseen by the principal investigator, Raina Merchant, MD MSHP, assistant professor in Emergency 
Medicine, who has extensive experience analyzing social media data and large data analysis. Together 
this research team is part of the Penn Medicine Center for Digital Health at the Penn Medicine Center 
for Health Care Innovation. Directed by Dr. Merchant, this multidisciplinary team has a rich history of 
collaboration and we believe this is the perfect environment to successfully apply the described 
protocol.
Characteristics of the Study Population
Target population
We will identify patients ages 30-74 with and without CVD ) who receive care in the University of 
Pennsylvania Health System or in another health system in the United States (U.S.). For patients with 
CVD, we will be using the following ICD codes for identiﬁcation :ICD 10: I63, I20-I25. For patients 
without CVD, please ﬁnd attached the list of ICD 10 codes we will use for identiﬁcation
Subjects enrolled by Penn Researchers
1000
Subjects enrolled by Collaborating Researchers
0
Accrual
Patients will be contacted via email/phone call to participate in the study or in person during an 
inpatient admission from the Emergency Department at HUP, PAH, or PPMC or an out-patient visit at 
Penn Medicine Primary care clinics, Internal Medicine, Cardiology clinics or if they are a patient in 
another health system, we will reach out to them via email or phone call
Key inclusion criteria
- 30-74 years of age - Willing to sign informed consent - Primarily English speaking (for language 
analysis) - Has an account on any of the following digital data platforms (Facebook, Instagram, Twitter 
Reddit, Google (gmail), or smartphone or wearable device such as Apple Health, Fitbit, Samsung 
Health, MapMyFitness or Garmin) and willing to share data - If has social media account, Instagram or 
Facebook, willing to share historical and prospective data (60 days) If has Google (gmail) account, 
willing to download and share google takeout zip ﬁle - If has smartphone or wearable device, willing to 
share step data - Willing to share access to medical health records  - Willing to share healthcare 
insurance information For the Objective 1 subgroup:  - 30-74 years of age - Willing to sign informed 

Page 22 of 32
consent Willing to complete an online survey
Key exclusion criteria
- Patient does not meet age inclusion criteria above - Does not use and post on digital data sources we 
are studying or unwilling to donate data  - Patient is in severe distress, e.g. respiratory, physical, or 
emotional distress  - Patient is intoxicated, unconscious, or unable to appropriately respond to questions 
- Patient has a history of heart attack, stroke, or CAD more than 5 years ago.  For the Objective 1 
subgroup:  - Not between 30-74 years of age - Unwilling to sign informed consent Unwilling to 
complete an online survey
Vulnerable Populations
Children Form
Pregnant women (if the study procedures may affect the condition of the pregnant woman or 
fetus) Form
Fetuses and/or Neonates Form
Prisoners Form
Other
x None of the above populations are included in the research study
The following documents are currently attached to this item:
There are no documents attached for this item.
Populations vulnerable to undue inﬂuence or coercion
This study will not intentionally enroll any vulnerable populations, however pregnant women may be 
enrolled if they meet eligibility requirements as described above. All the research assistants have 
completed the CITI-Protection of Human Subjects Research Training courses which fulﬁll the 
University's requirement for training in human research protections. RAs will ensure that subjects' 
understand that participation in this study is voluntary and will not effect their care in any way
Participant recruitment
Please describe the plan to fairly identify and recruit a group of participants that is reﬂective of the 
population under study. If this is a multicenter protocol, the recruitment plan should describe the local 
(Penn) site's plan. Describe: how potential participants may be identiﬁed (review of medical records, 
Slicer Dicer, DAC reports including referrals from physician ofﬁces and clinics);who may approach 
potential participants;methods to achieve a sample representative of the population being studied;what 
information may be presented to or discussed with them; andthe context and setting in which 
recruitment will happen.
Recruitment will occur in-person and remotely. For in-person recruitment, patients will be directly 
approached by a research assistant (RA) or AAs. For remote recruitment, patients will be reached out to 
via email or over the phone by a study team member. For in-person recruitment, if necessary AAs or 
RAs will communicate directly with patients providers (physicians, residents or nurses) to determine 
whether it is appropriate to approach the patient. For remote recruitment, RAs and AAs will conduct a 
brief chart review to ensure that the patient is able to consent to participate in studies. Potentially 
eligible patients will be approached or reached out to remotely to verify they meet all inclusion criteria 
and will then be asked to participate. An AA or RA will approach or call/email/MPM/W2H participants 
and explain the study in brief. Interested participants may complete the informed consent online via 
REDcap or https://donate.centerfordigitalhealth.upenn.edu/capture/ process at that time, or ask the 
research assistant to return or call at a, more convenient time. After informed consent, the participant 
will be asked to share the digital data types that they use (Facebook, Instagram, Twitter, Google search, 
step data). Finally, participants will complete the survey. All recruitment and follow up emails will be 
A/B tested, also known as split testing, the process of comparing two identical versions of an email. 
Potential variations include the order of information, email headers, and signatures.  For patients that 
opt to participate in the focus group, they will receive a date and time to for the virtual conference call 
and it is a one-time occurrence.  Additionally, we will use ﬂyers and post our study on the University of 
Pennsylvanias research registry (iConnect) for interested patients to reach out to us regarding 
participation in our study. Within iConnect, we will use their pre-screener feature to assess patients for 
pre-eligibility. These questions will be the same questions as we have already built in our eligibility 

Page 23 of 32
survey. For interested patients reaching out about the research study, our RAs and AAs will assess for 
eligibility and reach back out to the patient if eligible or ineligible. If patients are eligible and consent to 
participate, RAs and AAs will start the enrollment process.  We will also post our study on Penn 
Medicine Lancaster General Healths various social media sites (ie. Facebook, Instagram, Twitter, 
LinkedIn, etc.) with a link to our iConnect study site. For interested patients reaching out about the 
research study, our RAs and AAs will assess for eligibility and reach back out to the patient if eligible 
or ineligible. If patients are eligible and consent to participate, RAs and AAs will start the enrollment 
process.  Lastly, we will be using Research Matchs (https://www.researchmatch.org/) site to reach out 
to interested individuals. For interested patients reaching out about the research study, our RAs and AAs 
will assess for eligibility and reach back out to the patient if eligible or ineligible. If patients are eligible 
and consent to participate, RAs and AAs will start the enrollment process.  After the patient has 
completed enrollment, we will send them a follow-up summary about the status of the study (ie. how 
many participants are enrolled and the number of different data sources shared. We recontact study 
participants and offer them the opportunity to complete 1 additional survey and opt-in interview. In 
addition to recontacting study participants, we will recruit participants (speciﬁc to Objective 1) using 
ResearchMatch and iConnect registries.
Recruitment Materials
Is the research team using any recruitment materials? These may include but are not limited to: phone 
call scripts, radio/video scripts, ﬂyers/brochures, internet postings, email, letters to potential 
participants, letters to patient physicians, My Penn Medicine (MPM), other direct messaging, etc. For 
guidance regarding recruitment materials, please review the IRB's guidance on Participant Recruitment 
Materials online:https://irb.upenn.edu/recruitment
Yes
Use of Penn Media & Social Media Services
Will the recruitment plan propose to use any Penn media services (communications, marketing, etc.) for 
outreach via social media avenues (examples include: Facebook, Twitter, blogging, texting, etc.) or does 
the study team plan to directly use social media to recruit for the research?
Yes
Please identify which method(s) of social media you will utilize, the content of the text to be used, and 
the method(s) for posting this information (i.e., using Penn supported communication services). When 
proposing the text to utilize, please be aware of any social media limitations (i.e., number of characters 
allowed in a tweet) and any appropriate conﬁdentiality practices necessary to be compliant with posting 
research recruitment text.NOTE: Penn Medicine must utilize one of the centralized PM Facebook 
Pages:ClinicalResearch@Penn Facebook pagePenn Medicine Facebook page@PennCancer Facebook 
pageAll clinical research paid Facebook ads must be listed on Clinical Research @ Penn Facebook 
page,www.facebook.com/ClinicalTrialsAtPenn. Exceptions to the above must get approval from the 
Penn Medicine Social Media Committee: pennmedicinesocialmediacommittee@uphs.upenn.edu. 
This study uses the Penn Medicine iConnect Facebook page to recruit participants.
The following documents are currently attached to this item:
There are no documents attached for this item.
Subject compensation*
Will subjects be ﬁnancially compensated for their participation?
Yes
The following documents are currently attached to this item:
There are no documents attached for this item.
If there is subject compensation, provide the schedule for compensation per study visit or session 
and total amount for entire participation, either as text or separate document
Patients who consent to share digital data from 1. Social media, 2. Online search, and/or 3. Step data) 4. 
Electronic medical record, and 5.  Their health insurance information will receive $50 for completing 
the survey and data donation.  Patients who consent to complete additional surveys will receive an extra 

Page 24 of 32
$10.  Participants who complete the additional survey will receive $20 compensation and $30 for 
interview completion. All payments will be loaded on a Virtual Visa Greenphire ClinCard.  Patients who 
consent will be entered into a rafﬂe to win an iPad or equivalent device valued at approximately $500. 
Rafﬂe winners will be chosen at random for every 300 study participants enrolled and notiﬁed by email 
and/or phone
Study Procedures
Suicidal Ideation and Behavior
Does this research qualify as a clinical investigation that will utilize a test article (ie- drug or biological) 
which may carry a potential for central nervous system (CNS) effect(s)? Centeral nervous system(CNS) 
effect: the ability of a test article to enter into and potentially interact with the central nervoous system 
(brain and spinal cord). Clinical Investigation: Any experiment that involves a test article and one or 
more human subjects that either is subject to requirements for prior submission to the Food and Drug 
Adminstration (FDA) under section 505(i) or 520(g) of the Federal Food, Drug, and Cosmetic Act, or is 
not subject to the requirements for prior submission to the FDA under these sections of the act, but, the 
results of which are intended to be submitted later to, or held for inspection by, the FDA as part of an 
application for a research or marketing permit. 
No
Procedures
The study will be completed within 1-5 encounters when the participant is at UPHS or remote. Should 
the encounter be interrupted before participants share their social media or digital data information, or 
should participants forget social media login credentials during the encounter, they will receive up to 5 
emails within 1-2 weeks of consenting reminding them to log in and share their social media data 
remotely on https:// donate.centerfordigitalhealth.upenn.edu. Each participant will have a unique ID. 
For 
example 
1304, 
is 
the 
unique 
ID 
associated 
with 
a 
participant, 
(https://
donate.centerfordigitalhealth.upenn.edu/socialshare?activity=1304&survey=69)   For patients that opt 
to participate in the focus group, they will receive a date and time to for the virtual conference call and 
it is a one-time occurrence.  After the patient has completed enrollment, we will send them a follow-up 
summary about the status of the study (ie. how many participants are enrolled and the number of 
different data sources shared.
The following documents are currently attached to this item:
There are no documents attached for this item.
Deception
Does your project use deception? Deception could be considered any direct misinformation presented to 
the subject or omission of key information pertaining to the design or nature of the project. 
No
International Research
Are you conducting research outside of the United States? 
No
Analysis Plan
As digital data exist in multiple formats (e.g. posts, photos, searches, steps) we will analyze each source 
according to its speciﬁc attributes. We will systematically apply robust data-driven approaches to 
parsing and stratifying data. This step requires signiﬁcant data pre-processing and annotation as raw 
digital data is not directly interpretable. This will also occur over multiple iterations over the course of 
the grant as new terms (e.g., the term "vaping" describes use of e-cigarettes) and digital sources emerge. 
To automatically extract meaningful variables from the text, we will use both dictionary-based (e.g., 
counting words from previously constructed word lists, lexica) and data-driven "open vocabulary" 
methods which automatically generate lexica and identify important words and phrases based on the 
data itself rather than a priori categories. For dictionary analyses, we will use the Consumer Health 
Vocabulary, an online open source thesaurus of colloquial and technical medical terms and the Uniﬁed 
Medical Language System which contains lists of medical terms and vocabulary to identify disease 

Page 25 of 32
discussions. We will speciﬁcally focus on words and words associated with previously identiﬁed 
predictors of CHD (e.g. demographics, family history, lifestyle, comorbidities, blood pressure) as 
identiﬁed in a systematic review of prediction models for cardiovascular risk by Damen et al. BMJ 
2016. We will create classiﬁers to determine if a given instance of a disease term is referring to the 
disease or something unrelated (e.g. "If I see a celebrity I will have a heart attack" vs. "I am having 
chest pain, SOB and had a prior heart attack"). Our prior work has demonstrated that, as one might 
guess, some CV diseases, like "hypertension", are almost always used in their medical context while 
others, like "heart attack", are often used colloquially. We will also create classiﬁers to categorize 
language by broad grouping such as risk factors, treatment, medication adherence, symptoms, and by 
more granular categories such as active/sedentary, healthy food/unhealthy food, smoking. We will 
compare the performance of several machine-learning algorithms (e.g. support vector machines, 
random forests, and, in cases with adequate data, deep learning) and assess the accuracy of our disease 
term classiﬁer against hand-annotated data. For multiword phrases we will use approaches for 
determining the probability that words occur together (e.g., pointwise mutual information PMI).  We 
will also use additional linguistic models to account for more subtle language complexities and nuance 
such as parts of speech, and disambiguated words. We will use automated computational methods (e.g., 
deep learning-based object recognition) combined with manual coding to classify features (e.g., healthy 
or unhealthy food, physical activity, smoking, and drinking) in images. To determine nutritional content 
of food images, for example, we will use the imagga.com image classiﬁcation software, combined with 
analysis of the words used in the text associated with images to recognize food images. Those images 
that are food images will then, to the best of our ability, be compared to a list of healthy and unhealthy 
groupings (e.g. high salt, high fat) and also compared to a USDA database of precise nutritional values 
for over 30 nutrients for 8600 food items. We will also construct a convolutional neural network based 
on labeled food and health behavior-related images to train a model to recognize images in the most 
frequent categories which our ﬁrst pass fails to ﬁnd. We will analyze the labeled images to better 
understand the nature of food posting, and how they vary with the time of day, demographics of the 
person posting the image We will compare summary data from smartphones with self-reported data. 
Time series data from sensors will be used to analyze presumed changes in behavior using a previously 
validated health behavior activity change detection framework which segments time series data by time 
periods, identiﬁes changes and their signiﬁcance, and identiﬁes the presumed locus of changes. The 
strength of using different types of data (text, images, sensors) is that each provides a unique type of 
information. Sensors provide direct measurements of protective and risk factors for CV, but they only 
measure what they are intended to measure. On the other hand, text and image data have been shown to 
capture a wide variety of information about people from demographics and personality to mood and 
beliefs. Further, each patient may use differing proportions of each modality such that, for example, text 
may make up for a lack of images and vice-versa. As digital media data may over-represent, under- 
represent or not represent health behaviors we will compare survey data and clinical data with digital 
data to quantify differences in behaviors and risk factors. We will explore statistical and machine 
learning methods for combining heterogeneous variables into predictive models. For our main analysis, 
we will both explore the correlations between the different modalities to better understand when they 
provide redundant or non-redundant information and we will, more importantly, combine the different 
modalities into a single predictive model. Standard machine learning methods such as random forests 
make it easy to add heterogeneous features to a single predictive model, and give measures of variable 
signiﬁcance' indicating how much each feature contributes to predictive accuracy. We will also test 
more sophisticated machine learning methods for combining multiple modalities in a single model, 
such as using extensions of the 'elastic net' regression and dimensionality reduction framework that 
account for the fact that different modalities require different regularization penalties. We will also 
explore imputation and "back-off" methods that account for the fact that different patients will have 
substantially different amounts of data available in different modalities. These methods work by either 
estimating ("imputing") values for the missing data or automatically adjusting the weights given to 
different modalities for each patient based on the amount of data available for that patient. For 
Objective 1: Sample characterization: We will use summary statistics to describe patient demographics, 
survey response data, and digital data usage (e.g. frequency of posting, number of social media 
platforms). Descriptive data will be presented as mean (SD) for continuous variables and frequencies of 
participants for categorical variables. 5b Extracting topics and features: We will use the Mallet package 
for implementation of the Latent Dirichlet Allocation (LDA) method for clustering language data of all 
participants. The LDA probabilistic model assumes that documents (e.g. Facebook status updates) 
contain a distribution of topics which then contain a distribution of words. Ultimately, words are 
grouped together by considering the other words they appear with. Descriptive statistics, t-tests 
(continuous measures) and chi-squared tests (categorical measures) will be used to compare topics and 

Page 26 of 32
features with survey data. For example, NHANES survey questions (e.g. Do you now smoke 
cigarettes?) would be compared with images of cigarette smoking. For Objective 2: We will compare 
prediction models with and without digital variables. Probability weighted Cox proportional hazard 
analysis with robust variance estimates will be used to assess the association between each variable 
from digital media and CHD in univariable and multivariable models. We will use graphical and 
analytical methods to test the proportional hazards assumption for each analysis. To evaluate the 
predictive strength of digital data, we will build four predictive machine learning models using a 
stepwise sequence to select the most parsimonious model using combinations of risk factors. (1. 
Framingham predictors / American College of Cardiology ASCVD Risk Estimator Plus alone, 2. 
Framingham predictors / American College of Cardiology ASCVD Risk Estimator Plus + SES, 3. 
Framingham predictors / American College of Cardiology ASCVD Risk Estimator Plus + digital topics/
features, 4. Framingham predictors / American College of Cardiology ASCVD Risk Estimator Plus + 
digital topics/features+ SES) For Objective 3: We will use Super Learner, the ensemble machine 
learning approach which allows for the speciﬁcation of multiple plausible candidate prediction models. 
Each of the candidate models is applied to a training set and outcomes are predicted using the validation 
set. A loss function is calculated within each validation set and then averaged across validation sets, 
which provides the estimated cross-validated risk score for each method. Calibration will be assessed 
using a calibration plot comparing the predicted probabilities (obtained using ﬁxed regression 
coefﬁcients of the prediction models) with the observed probabilities. The Super Learner algorithm 
ﬁnds the optimal weighted combination across all of the speciﬁed methods. Van der Laan et al. proved 
the asymptotic efﬁciency of the Super Learner algorithm and demonstrated that the optimal 
combination performs at least as well as the best estimators from the candidate models. We will 
implement Super Learner in R (The R Foundation for Statistical Computing). In this approach 
generalized estimating equation (GEE) and generalized linear mixed effects models will be explored to 
account for repeated measures per patient over time. One of the challenges of cost analyses is 
censoring, since cost may be incomplete and available for some patients and not others. As the 
dependent variable will be cost, we will be attentive to informative censoring and right skewness, 
potentially using generalized linear models under a gamma distribution and log link. To account for 
informative sensoring we will use inverse probability of sensoring weighting. We will also explore the 
application of our new nested g-computation approach that allows for informative censoring and time-
varying covariates to obtain future cost predictions. Additionally, we will generate summary statistics to 
describe the cohort and survey responses. We will use the model-based approach of latent class analysis 
(LCA)- to classify individuals into previously unmeasured subgroups. Variables to include in the LCA 
will include: demographics, years of platform use, posting: volume, quality, retransmission, and 
engagement. We will use the Bayesian information criterion to assess goodness of model ﬁt, entropy to 
assess variations between classes, and the parametric bootstrapped likelihood ratio test to assess if a 
model with k classes has better performance than k-1 classes. We will then use a regression model to 
compare with measures of health status, social media perceptions, and readiness/activation for each 
latent class. Models will be adjusted for demographics.
The following documents are currently attached to this item:
There are no documents attached for this item.

Page 27 of 32
Data conﬁdentiality
x Paper-based records will be kept in a secure location and only be accessible to personnel involved 
in the study.
x Computer-based ﬁles will only be made available to personnel involved in the study through the 
use of access privileges and passwords.
Prior to access to any study-related information, personnel will be required to sign statements 
agreeing to protect the security and conﬁdentiality of identiﬁable information.
x Wherever feasible, identiﬁers will be removed from study-related information.
A Certiﬁcate of Conﬁdentiality will be obtained, because the research could place the subject at 
risk of criminal or civil liability or cause damage to the subject's ﬁnancial standing, 
employability, or liability.
A waiver of documentation of consent is being requested, because the only link between the 
subject and the study would be the consent document and the primary risk is a breach of 
conﬁdentiality. (This is not an option for FDA-regulated research.)
x Precautions are in place to ensure the data is secure by using passwords and encryption, because 
the research involves web-based surveys.
x Audio and/or video recordings will be transcribed and then destroyed to eliminate audible 
identiﬁcation of subjects.
Subject Conﬁdentiality
Study personnel will have access to patients' electronic medical records for screening, eligibility 
conﬁrmation purposes. Study personnel will also have access to patients past medical history and 
demographic data. The information used for screening or enrollment purposes and study analysis. This 
data will be extracted using Clarity. On the servers that host the Facebook language-extraction 
application, we will not collect identiﬁable information. Instead, we will use the Facebook/Instagram 
mechanism to generate a unique random ID. This ID will be used to track users and will be stored 
together with their survey results on the client-facing server. If disclosed, the unique random ID cannot 
be traced back to a given individual (unlike the Facebook/Instagram user ids, which can comparatively 
simply be resolved into identiﬁable information). In addition to this user-facing server (application 
server), we will also have a Secure Data Repository, which will receive the Facebook user id from the 
application server and will then proceed to query the Facebook/Instagram Application Programming 
Interface (API) for (identiﬁable) user information and Facebook/Instagram statuses the users have 
written on Facebook/Instagram. This we can only do once the user has joined our application, these 
privileges are managed through Facebook's/Instagram's systems of access tokens. The Secure and the 
Application server would use the same access token, but only the Secure Server would pull identiﬁable 
user information, whereas only the Application server would interact directly with the users (and is thus 
more at risk for code injection and other hacking attacks). The secure data repository will be housed on 
servers under our control at the University of Pennsylvania, such that no data we collect will be stored 
out of our control. All language data will be stored on the Health Service Research Data Center 
(HSRDC). As per the HSRDC Data Management Policy and Procedures Manual, password protection 
is used at the server and web portal levels for all transactions that allow entry and editing of data, or 
provide access to sensitive subject data or administrative privileges. Passwords will be managed to 
require all users to change their password within 90 days and strict rules will be implemented to require 
strong passwords. Additionally, all PHI data will be encrypted and linked to survey data through a study 
ID number, with linkage and de-encryption keys activated only by a user password for which a member 
of the research team has been given permission to access these sensitive data (Principal Investigator Dr. 
Merchant and project staff who are under the direct supervision of the PI). As per the HSRDC Data 
Management Policy and Procedures Manual, the database management on these servers is built with 
multiple layers of security and follows best practices for securing sensitive data. The main levels of 
security are fourfold and include machine physical security at the IT facility at which the servers are 
housed, physical server security in the highly restricted server containment room, electronic server 
security (ﬁrewalls, passwords, encryption) restricting access to the machine, and directory access 
controls restricting access to these particular data.  We will consult the PI, ED physician to manage 
participant safety concerns and serve as the clinical contact point person. The data security of the 
applications is as follows: Facebooks General Data Protection Regulation - https://www.facebook.com/
business/gdpr Instagram Data privacy webpage, https://help.instagram.com/519522125107875 Google 

Page 28 of 32
takeouts security center - https://www.google.com/safetycenter/everyone/start/security-help/, Reddits 
Data protection webpage, https://www.reddit.com/r/dataprotection/.We would report to the speciﬁc 
digital platform anything that violates their community standards. We will also apply existing ﬁlters 
(e.g. not safe for work) before processing image and text data. Facebook community standards: https:// 
www.facebook.com/communitystandards 
Instagram 
community 
standards: 
https:// 
help.instagram.com/477434105621119 Google takeout standards, https://www.google.com/+/policy/
content.html. Our data scientist will manage the collection of these social media platforms. Only our 
research team will have access to the data through the applications. For the experimental group, the 
patient and provider will seem summary trends from the social media platforms. If study participants do 
not uninstall the app after the study ended, the apps will be closed and no additional information will be 
used or collected through the app. All patient information used for screening, recruitment, and 
enrollment purposes, survey responses, and focus group input will be stored on either the Penn 
Medicine secure shared drive, Penn Box (HIPAA compliant feature, or REDcap.
Sensitive Research Information*
Does this research involve collection of sensitive information about the subjects that should be excluded 
from the electronic medical record? [NOTE: This does not apply to: 1) research information that would 
not normally be included in the electronic medical record or 2) information that is in the electronic 
medical record as part of clinical care.]
No
Subject Privacy
Privacy refers to the person's desire to control access of others to themselves. Privacy concerns people, 
whereas conﬁdentiality concerns data. Describe the strategies to protect privacy giving consideration to 
the following: The degree to which privacy can be expected in the proposed research and the safeguards 
that will be put into place to respect those boundaries. The methods used to identify and contact 
potential participants. The settings in which an individual will be interacting with an investigator. The 
privacy guidelines developed by relevant professions, professional associations and scholarly 
disciplines (e.g., psychiatry, genetic counseling, oral history, anthropology, psychology). 
All encounters will take place in private rooms, via the online platform, via email communication, via 
secure text messaging, or through a secure phone call. We will offer patients the option of having the 
survey completed without any other people present in their room or they can complete it on their own 
time. Patients have the option of the research assistants assisting them in completing the survey or 
ﬁlling out the survey by themselves. Patients who choose to share their Facebook, Twitter, Instagram or 
other digital data will be given a unique ID that will be linked to identiﬁable information. Participants 
who install the application to share their Facebook posts will be able to opt in and out of the application 
at their discretion. Patients will be asked by a RA if they would be willing to anonymously share their 
Facebook/Instagram posts for health research (Example description: "Penn researchers are trying to 
learn if certain language is associate with certain health outcomes. Would you be willing to share your 
Facebook posts with researchers? Your posts will be anonymous -- that is, not connected to you name -- 
and you can choose to stop sharing your posts at any time.") The Facebook/Instagram application will 
access data that is stored in the user proﬁles in the Facebook/Instagram platform. When participants ﬁrst 
click on the link to use the app, they will ﬁrst be greeted by the Facebook/Instagram consent screen that 
will ask the user permission for the application to have access to their proﬁle information and to consent 
to the terms and service of the application. Participants would have to agree to this screen in order to 
use the application. They will have to click agree to access the application (at least at the ﬁrst time). All 
language data will be stored on secure severe using only the unique study ID which can be used to link 
this 
data 
to 
Center 
for 
Digital 
Health 
data 
collection 
platform, 
(https://
donate.centerfordigitalhealth.upenn.edu/capture/). This linkage will be stored in a double locked fashion 
on a password protected computer will access only by the principal investigator and key study 
personnel. For the participants who select to enroll in an interview, interview audio-recordings will be 
transcribed verbatim, and all identiﬁers will be removed. Transcripts and audio ﬁles will be saved on a 
Penn Medicine managed, HIPAA compliant server. Interview audio-recordings will be transcribed 
verbatim and all identiﬁers will be removed. The recording and transcript will be kept a secure and 
locked area with access limited to designated researchers. After we analyze the recordings, we will 
destroy recordings after data analysis or completion of the study. All encounters will take place on Penn 
Medicine Zoom videoconference. When utilizing Zoom for audio recordings, additional steps will be 
made by the research team to ensure participant safety. Based on guidelines, researchers will ensure the 
following settings for privacy and compliance for Penn Zoom. 1) Researchers will turn off the Personal 
Meeting ID setting and instead opt for the meeting ID to be generated automatically, allowing for new 

Page 29 of 32
links and meeting IDs to be created for each meeting. 2) Research staff will turn off Cloud Recordings 
and ensure only local recordings that are HIPAA compliant are used as they are encrypted and managed 
by PMACS/DART or UPHS. The recording and transcript will be kept a secure and locked area with 
access limited to designated researchers. After we analyze the recordings, we will destroy recordings 
after data analysis or completion of the study. All survey data will be stored in a secure, web-based 
database (Way to Health or REDCap) where a study ID number will be generated for each patient. A 
link between the study ID number and the patient PHI will need to be maintained to ensure that the 
study staff can track recruitment efforts to potential participants and to avoid contacting any patients 
who have previously declined to participate. To ensure that patient conﬁdentiality is preserved, 
individual identiﬁers (such as name) are stored in a single password protected system that is accessible 
to study research, analysis and IT staff only. This system is hosted on site at UPenn and is protected by 
a secure ﬁrewall. Once a participant is in this system, they will be given a unique study ID number. Any 
datasets and computer ﬁles that leave the ﬁrewall will be stripped of all identiﬁers besides the study ID 
and individuals will be referred to by their study ID only. The study ID will also be used on all 
analytical ﬁles. Way to Health and REDCap are secure web applications for building and managing 
online surveys and databases. Privacy of all study data will be maintained by restricting access to the 
identiﬁable information only to approved study staff who have received subject conﬁdentiality and 
privacy training. Study coordinators will access patient contact information from the database to 
conduct recruitment phone calls. The study coordinator will review the consent script, which will 
include a description of the voluntary nature of participation, the study procedures, risks and potential 
beneﬁts in detail. Participants will be told that all information will be kept strictly conﬁdential, except 
as required by law. Subjects will be provided a copy of the consent document. All efforts will be made 
by study staff to ensure subject privacy. This database is hosted on a secure server as detailed in the 
subject conﬁdentiality section.
Disclosures
Will any data or specimens from Penn participants OR other research generated work product (e.g., 
intellectual property) be disclosed to any individuals, entities, or vendors, etc. outside of Penn?
No
Data Protection*
x Name
x Street address, city, county, precinct, zip code, and equivalent geocodes
All elements of dates (except year) for dates directly related to an individual and all ages over 89
x Telephone and fax number
x Electronic mail addresses
x Social security numbers
x Medical record numbers
x Health plan ID numbers
Account numbers
Certiﬁcate/license numbers
Vehicle identiﬁers and serial numbers, including license plate numbers
Device identiﬁers/serial numbers
x Web addresses (URLs)
x Internet IP addresses
Biometric identiﬁers, incl. ﬁnger and voice prints
x Full face photographic images and any comparable images
x Any other unique identifying number, characteristic, or code
None
Does your research request both a waiver of HIPAA authorization for collection of patient information 
and involve providing Protected Health Information ("PHI") that is classiﬁed as a "limited data set" 

Page 30 of 32
(city/town/state/zip code, dates except year, ages less than 90 or aggregate report for over 90) to a 
recipient outside of the University of Pennsylvania covered entity?
No
Tissue Specimens Obtained as Part of Research*
Are Tissue Specimens being obtained for research?
No
Tissue Specimens - Collected during regular care*
Will tissue specimens be collected during regulator clinical care (for treatment or diagnosis)?
No
Tissue Specimens - otherwise discarded*
Would specimens otherwise be discarded?
No
Tissue Specimens - publicly available*
Will tissue specimens be publicly available?
No
Tissue Specimens - Collected as part of research protocol*
Will tissue specimens be collected as part of the research protocol?
No
Tissue Specimens - Banking of blood, tissue etc. for future use*
Does research involve banking of blood, tissue, etc. for future use?
No
Genetic testing
If genetic testing is involved, describe the nature of the tests, including if the testing is predicative or 
exploratory in nature. If predictive, please describe plan for disclosing results to subjects and provision 
of genetic counseling. Describe how subject conﬁdentiality will be protected Note: If no genetic testing 
is to be obtained, write: "Not applicable."
Not applicable
Consent
1. Consent Process
Overview
We will enroll patients in person and remotely. For in-person enrollment, we will meet patients during 
their in-patient stays at HUP, PAH, or PPMC or an outpatient primary care visit at Penn Medicine who 
are between the ages of 30-74. For remote enrollment, we will call/email/text message potentially 
eligible patients. Potentially eligible patients will be approached or called/emailed/text messaged to 
verify they meet all inclusion criteria and will then be asked to participate. An AA or CDH RA will ﬁrst 
ask participants if they use Social Media, have a Gmail account, and what type of step data. For those 
who do not use any, the encounter ends as these patients are not eligible. Patients who are potentially 
eligible, they will be told that they may be eligible for a research study looking at digital data and 
coronary heart disease (CHD). Subjects will be asked to conﬁrm whether they will share their digital 
data with health researchers as described in this protocol. The language will be similar to (but not 
verbatim): "Penn researchers are trying to learn if digital data is associated with cardiovascular health. 
Would you be willing to share your Facebook posts with researchers? Your posts will be anonymous -- 
that is, not connected to your name -- and you can choose to stop sharing your posts at any time." The 
RA will obtain informed consent using the attached consent form via a HIPAA-compliant online 
survey/data collection platform (https://donate.centerfordigitalhealth.upenn.edu/capture/), or REDcap. 
The participants will sign an electronic version of the consent form (stored on the Center for Digital 
Health data collection platform,( https://donate.centerfordigitalhealth.upenn.edu/capture/) or REDcap 
and will be either emailed a copy of their signed informed consent or handed a copy of the informed 

Page 31 of 32
consent for their records. The RA or AA will describe the study in detail, including the use of 
information from their medical record, the survey, the use of Google takeout, how their steps are 
calculated, and the use of the Facebook/Instagram app to extract their posts for language analysis and 
how Reddit and Twitter data is extracted. The participant may refuse to answer any questions for any 
reason, can skip any question, and can stop participation at any time. Subjects may contact the research 
team as described in the consent to have their information destroyed. The research presents no more 
than minimal risk of harm to subjects and involves no procedures for which written consent is normally 
required outside of the research context. All study activities are conducted either while the patient is the 
in the hospital during this brief encounter, over the phone using a HIPAA secure telemedicine 
application (Doximity), via email using our Centers shared pennmedicine.upenn.edu email address, or 
via MPM or W2H. The research presents no more than minimal risk of harm to subjects and does not 
involve procedures for which written consent is normally required outside of the research context. The 
study is not intervening or currently changing patient care. Participants who complete the additional 
survey will complete a separate informed consent document on RedCap. All participants will have the 
option to complete the document with a research coordinator.
Children and Adolescents
N/A
Adult Subjects Not Competent to Give Consent
N/A
2. Waiver of Consent
Waiver or Alteration of Informed Consent*
No Waiver Requested
Minimal Risk*
Impact on Subject Rights and Welfare*
Waiver Essential to Research*
Additional Information to Subjects
Written Statement of Research*
No
If no written statement will be provided, please provide justiﬁciation
The following documents are currently attached to this item:
There are no documents attached for this item.
Risk / Beneﬁt
Potential Study Risks
The potential risks associated with participating in the study are minimal. Regarding unforeseen safety 
issues, the researchers will take every step to ensure these do not occur. Participants will have every 
opportunity to stop the survey at any time or discontinue participation.
Potential Study Beneﬁts
There are no direct beneﬁts to participation. Participants are contributing to health research in general 
by providing information which could improve ability to use language and social media data to predict 
certain health outcomes and health care utilization.
Alternatives to Participation (optional)
N/A

Page 32 of 32
Data and Safety Monitoring
The principal investigator will be responsible for monitoring the data of the study. The safety, privacy, 
and data integrity will be maintained as described in the conﬁdentiality section. The PI will ensure all 
researchers are adhering to the study protocol and applicable research regulations and Penn 
requirements. This includes weekly lab meetings and monthly discussions of data integrity and study 
progression. Interim analysis will be completed regularly for the duration of the study. All digital media 
data, focus group, and survey data will be collected and stored on servers that sit behind the health 
system ﬁrewall. Subject names and other relevant PHI will be removed if data are to be shared with 
auditors and other investigators for whom this information is not required. Consent materials and 
documentation will be kept as part of the secured study ﬁles. All study data will be carefully structured 
and maintained such that participants will be protected. Data will be kept in locked research ofﬁces and 
stored on the secure Penn server. Computer ﬁles containing data will be password-protected, and 
records will only be made available to research personnel who are IRB approved.
The following documents are currently attached to this item:
There are no documents attached for this item.
Risk / Beneﬁt Assessment
Based on the potential risks and beneﬁts described above, we believe this study may be considered 
minimal risk.
General Attachments
The following documents are currently attached to this item:
Cover Letter (letter.docx)
Additional forms (screenshot2025-08-01at1.42.438239pm.png)

e-copy of the informed consent 
document you signed virtually

e-copy of the informed consent 
document you signed virtually

e-copy of the informed consent 
document you signed virtually

e-copy of the informed consent 
document you signed virtually

e-copy of the informed consent 
document you signed virtually

e-copy of the informed consent 
document you signed virtually

e-copy of the informed consent 
document you signed virtually

e-copy of the informed consent 
document you signed virtually

e-copy of the informed consent 
document you signed virtually

e-copy of the informed consent 
document you signed virtually