Using Digital Data to Predict CHD

NCT04574882 | Completed Results

• 1 other identifier: 833699
• Study Type: observational
• Target: 781
• Locations: 1 country
• Sites: 1

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.

Conditions

Cardiovascular Diseases

Keywords

digital data; digital health

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (2)

• CHD Event

  Through study completion, an average of 3 years

• Health Care Utilization

  Through study completion, an average of 3 years

Study Arms (2)

Case

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

Other: Survey

Control

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

Other: Survey

Interventions

Survey 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.

Case; Control

Eligibility Criteria

• Age: 30 Years - 74 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• 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)

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• University of Pennsylvania: lead

Study Sites (1)

University of Pennsylvania Health System

Philadelphia, Pennsylvania, 19101, United States

Location

MeSH Terms

Conditions

Cardiovascular Diseases

Interventions

Surveys and Questionnaires

Intervention Hierarchy (Ancestors)

Data Collection; Epidemiologic Methods; Investigative Techniques; Health Care Evaluation Mechanisms; Quality of Health Care; Health Care Quality, Access, and Evaluation; Public Health; Environment and Public Health

Results Point of Contact

• Title: Director of Research
• Organization: University of Pennsylvania

digitalhealth@pennmedicine.upenn.edu

2674280125

Publication Agreements

• PI is Sponsor Employee: No
• Restrictive Agreement: No

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 28, 2020
• First Posted: October 5, 2020
• Study Start: September 25, 2020
• Primary Completion: May 30, 2025
• Study Completion: June 1, 2025
• Last Updated: November 4, 2025
• Results First Posted: November 4, 2025

Record last verified: 2025-10

Locations

US (1)