Using Digital Data to Predict Cardiovascular Health and Health Care Utilization
Trial Snapshot
- Phase
- Not Applicable
- Status
- Completed
- Sponsor
- University of Pennsylvania
- Enrollment
- 781
- Locations
- 1
- Primary Endpoint
- Latent Dirichlet Allocation (LDA) Topics - Topics / Themes Discussed Between Patients With and Without Heart Disease
Study Overview
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.
Study Design
- Study Type
- Observational
- Observational Model
- Case Control
- Time Perspective
- Cross Sectional
Eligibility Criteria
- Ages
- 30 Years to 74 Years (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- Yes
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
Outcomes
Primary Outcomes
Latent Dirichlet Allocation (LDA) Topics - Topics / Themes Discussed Between Patients With and Without Heart Disease
Time Frame: Through study completion, an average of 3 years
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.
Secondary Outcomes
No secondary outcomes reported