N/A
N=781
Using Digital Data to Predict CHD
Cardiovascular Diseases
Bottom Line
View on ClinicalTrials.gov: NCT04574882 ↗Enrolled (actual)
781
Serious AEs
—
Results posted
Nov 2025
Primary outcome: Primary: Latent Dirichlet Allocation (LDA) Topics - Topics / Themes Discussed Between Patients With and Without Heart Disease — 0.85; 0.87; 0.55; 0.67 proportion probability AUC (Area Under t
Study Design & Population
- Study type
- Observational
- Phase
- N/A
- Interventions
- Survey (Other)
- Age
- Adult, Older Adult · 30+ yrs
- Sex
- All
- Sponsor
- University of Pennsylvania
- Primary completion
- May 2025
Outcome Measures
| Outcome | Result | p-value |
|---|---|---|
| PRIMARY Latent Dirichlet Allocation (LDA) Topics - Topics / Themes Discussed Between Patients With and Without Heart Disease |
0.85; 0.87; 0.55; 0.67; 0.61; 0.71 | — |
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.
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
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
Data sourced from ClinicalTrials.gov (NCT04574882). Outcome figures and adverse-event rates are extracted automatically from the registry's posted results and are provided for clinician reference, not as a substitute for the primary publication.