Using Digital Data to Predict CHD

Recruiting

• Conditions: Cardiovascular Diseases
• Interventions: Other: Survey

• Registration Number: NCT04574882
• Lead Sponsor: University of Pennsylvania

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.

Basic Information
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Recruitment & Eligibility
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Study & Design
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Trial Locations

Study Timeline

• Study Start: 2020-09-25
• Study First Submitted: 2020-09-28
• Study First Submitted QC: 2020-09-28
• Study First Posted: 2020-10-05
• Status Verified: 2024-07
• Last Update Submitted: 2024-07-01
• Last Update Posted: 2024-07-03
• Primary Completion: 2025-05-30
• Study Completion: 2025-06-01

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1000

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

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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

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Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

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

Primary Outcome Measures

Name	Time	Method
Latent Dirichlet Allocation Topics - topics / themes discussed between patients with and without heart disease	Through study completion, an average of 3 years	The primary outcome is topics and features (derived using the Latent Dirichlet Allocation \[LDA\] method for clustering language data).

Trial Locations

Locations (1)

University of Pennsylvania Health System; 🇺🇸 Philadelphia, Pennsylvania, United States