Using Digital Data to Predict CHD

University of Pennsylvania

Status

Active, not recruiting

Conditions

Cardiovascular Diseases

Treatments

Other: Survey

Study type

Observational

Funder types

Other

Identifiers

NCT04574882

833699

Details and patient eligibility

About

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.

Full 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.

Enrollment

780 estimated patients

Sex

All

Ages

30 to 74 years old

Volunteers

Accepts Healthy Volunteers

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

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