Using Digital Data to Predict CHD

Using Digital Data to Predict Cardiovascular Health and Health Care Utilization

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.

Study Overview

• Status: Completed
• Conditions: Cardiovascular Diseases
• Intervention / Treatment: Other: Survey

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.

• Study Type: Observational
• Enrollment (Actual): 781

Contacts and Locations

Study Locations

• United States
  • Pennsylvania
    • Philadelphia, Pennsylvania, United States, 19101
      • University of Pennsylvania Health System

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 30 years to 74 years (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

We will identify patients ages 30-74 with and without CHD (ICD 9:414.0, ICD 10: I63, I20-I25)

Description

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

Study Plan

How is the study designed?

Design Details

• Observational Models: Case-Control
• Time Perspectives: Cross-Sectional

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Case; Patients ages 30-74 with and without CHD (IICD 10: I63, I20-I25 ) within the last 5 years.	Other: 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.
Control; Patients aged 30-74 who have non-cardiovascular-related history	Other: 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Latent Dirichlet Allocation (LDA) Topics - Topics / Themes Discussed Between Patients With and Without Heart Disease	The primary outcome is topics and features (derived using the LDA method for clustering language data). For each participant, we included all available Facebook wall posts from the start of their account history through data collection, regardless of whether they occurred before or after a CHD diagnosis. We examined associations between linguistic features (unigrams, LIWC categories, LDA topics) and cardiovascular case status (CHD presence vs absence) using Pearson correlation and logistic regression. Latent LDA, a systematic method to identify text-based themes, was applied to generate 200 clusters of co-occurring words ("topics"). For each feature type (unigram, LIWC category, LDA topic), we fit separate logistic regression models and calculated Pearson correlation coefficients to assess predictive value for case status. Each language-derived feature was encoded as a normalized frequency count per user to enable consistent comparison across participants.	Through study completion, an average of 3 years

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
CHD Event	Reliability in predicting CHD related event in patient as measured by Framingham Risk Score. The Framingham Risk Score (FRS) is a validated means of predicting cardiovascular disease (CVD) risk. Input variables include age, cigarette smoking, total cholesterol, HDL cholesterol, systolic blood pressure measurement and treatment for hypertension. Point values are calculated based on each of these risks. A 10-year risk score can be derived as a percentage. Risk scores range from 0-20%. Low Risk: Less than 10% risk that you will develop a heart attack or die from coronary disease in the next 10 years. Intermediate risk: A 10 to 20% risk that you will develop a heart attack or die from coronary disease in the next 10 years. High Risk: A greater than 20% risk that you will develop a heart attack or die from coronary disease in the next 10 years.	Through study completion, an average of 3 years
Health Care Utilization	Prediction of cost for health care utilization between heart disease and non- heart disease subjects measured by insurance claims data	Through study completion, an average of 3 years

Collaborators and Investigators

• Sponsor: University of Pennsylvania

Study record dates

Study Major Dates

• Study Start (Actual): September 25, 2020
• Primary Completion (Actual): May 30, 2025
• Study Completion (Actual): June 1, 2025

Study Registration Dates

• First Submitted: September 28, 2020
• First Submitted That Met QC Criteria: September 28, 2020
• First Posted (Actual): October 5, 2020

Study Record Updates

• Last Update Posted (Estimated): November 4, 2025
• Last Update Submitted That Met QC Criteria: October 8, 2025
• Last Verified: October 1, 2025

More Information

Terms related to this study

• Keywords: digital health; digital data
• Additional Relevant MeSH Terms: Cardiovascular Diseases; Health Care Quality, Access, and Evaluation; Investigative Techniques; Epidemiologic Methods; Data Collection; Health Care Evaluation Mechanisms; Quality of Health Care; Public Health; Environment and Public Health; Surveys and Questionnaires
• Other Study ID Numbers: 833699

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No