Cardiovascular DiseasesNCT04574882
Using Digital Data to Predict CHD
An observational study of Cardiovascular Diseases, run by University of Pennsylvania at 1 site in United States.
Trial 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… Read more about this study
- Who is it for?
- Ages 30 to 74 yearsHealthy volunteers can take part
- Conditions the study is for
- Cardiovascular Diseases
- What is being tested?
- Survey
- How many people
- 781 participants
- Where
- 1 siteUnited States
- Study dates
- 25 Sep 2020to 1 Jun 2025
- Phase
- Observational
- Are there results?
- Yes, posted in the registrySummary in the doctors' view
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 30 to 74 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: We will identify patients ages 30-74 with and without CHD (ICD 9:414.0, ICD 10: I63, I20-I25)
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
United States1 site
University of Pennsylvania Health System
Philadelphia, Pennsylvania, 19101
About this study
Official title: 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.
Read the 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.
- Conditions
- Cardiovascular Diseases
- Being tested
- Survey
- Keywords
- digital data, digital health
- Medical subject headings
- Cardiovascular Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
SurveyOther
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.
| Study group | Role | What they receive |
|---|---|---|
| Case | Patients ages 30-74 with and without CHD (IICD 10: I63, I20-I25 ) within the last 5 years. Receives: Other: Survey | |
| Control | Patients aged 30-74 who have non-cardiovascular-related history Receives: Other: Survey |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Through study completion, an average of 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Oct 2020
- Study start25 Sep 2020
- Primary completion30 May 2025
- Study completion1 Jun 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Posted resultsParticipant flow, outcome measures and adverse events reported by the sponsor.
- Other trials for Cardiovascular DiseasesStudies of the same condition, open and under way.
- More trials run by University of PennsylvaniaThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Cross-sectional
- Enrolment
- 781 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Pennsylvania
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04574882
- Sponsor's protocol ID
- 833699
- Status last verified
- Oct 2025
- First submitted
- 28 Sep 2020
- Last updated
- 4 Nov 2025
- Results posted
- Yes, on ClinicalTrials.gov
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 4 Nov 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Using Digital Data to Predict Cardiovascular Health and Health Care Utilization
- ClinicalTrials.gov ID
- NCT04574882
- Acronym / sponsor ID
- 833699
- Conditions
- Cardiovascular Diseases
- Keywords
- digital data, digital health
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 781 participants (actual)
- Lead sponsor
- University of PennsylvaniaUniversity, hospital or other
- Study start
- 25 Sep 2020 (actual)
- Primary completion
- 30 May 2025 (actual)
- Study completion
- 1 Jun 2025 (actual)
- First posted
- 5 Oct 2020 (actual)
- Results
- Posted 4 Nov 2025
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
- Observational model
- Case-control
- Time perspective
- Cross-sectional
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Case | – | Other: SurveyPatients ages 30-74 with and without CHD (IICD 10: I63, I20-I25 ) within the last 5 years. |
| Control | – | Other: SurveyPatients aged 30-74 who have non-cardiovascular-related history |
Who may qualify
- Ages
- 30 to 74 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- We will identify patients ages 30-74 with and without CHD (ICD 9:414.0, ICD 10: I63, I20-I25)
Key inclusion criteria (8 listed)
- 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
Key exclusion criteria (4 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| SurveyInterested 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. | Other | Case, Control |
Primary outcome
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.
Results and safety
Loading the posted results from ClinicalTrials.gov…
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Nov 2025.
- Posted resultsParticipant flow, baseline, all outcome measures and adverse events.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Cardiovascular Diseases, run by University of Pennsylvania at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 8 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 4 Nov 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Yes. Results are posted in the registry (first posted 4 Nov 2025). A summary is in the For doctors view, and the full results are on ClinicalTrials.gov.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 4 Nov 2025.