Using Digital Data to Predict CHD

NCT ID: NCT04574882

Last Updated: 2025-11-04

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 781 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2020-09-25
• Study Completion Date: 2025-06-01

Brief Summary

This project seeks to identify and characterize features derived from digital data (e.g. social media, online search, mobile media) which are associated with coronary heart disease (CHD) and related risk factors, and develop models that use digital data and conventional predictive models to predict CHD risk and health care utilization.

Detailed Description

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. This project seeks to identify and characterize features derived from digital data (e.g. social media, online search, mobile media) which are associated with coronary heart disease (CHD) and related risk factors, and develop models that use digital data and conventional predictive models to predict CHD risk and health care utilization.

Conditions

Cardiovascular Diseases

Study Design

• Observational Model Type: CASE_CONTROL
• Study Time Perspective: CROSS_SECTIONAL

Study Groups

Case

Patients ages 30-74 with and without CHD (IICD 10: I63, I20-I25 ) within the last 5 years.

Survey

Intervention Type: OTHER

Interested participants may complete the informed consent online. After informed consent, the participant will be asked to share the digital data types that they use (Facebook, Instagram, Twitter, Google search, step data) and then participants will complete a cross-sectional survey.

Control

Patients aged 30-74 who have non-cardiovascular-related history

Survey

Intervention Type: OTHER

Interested participants may complete the informed consent online. After informed consent, the participant will be asked to share the digital data types that they use (Facebook, Instagram, Twitter, Google search, step data) and then participants will complete a cross-sectional survey.

Interventions

Survey

Interested participants may complete the informed consent online. After informed consent, the participant will be asked to share the digital data types that they use (Facebook, Instagram, Twitter, Google search, step data) and then participants will complete a cross-sectional survey.

Intervention Type: OTHER

Eligibility Criteria

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

• 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

• Minimum Eligible Age: 30 Years
• Maximum Eligible Age: 74 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

University of Pennsylvania

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

University of Pennsylvania Health System

Philadelphia, Pennsylvania, United States

Countries

United States

Provided Documents

Document Type: Study Protocol and Informed Consent Form

View Document

Document Type: Statistical Analysis Plan

View Document

Other Identifiers

833699

Identifier Type: -

Identifier Source: org_study_id