Projects by Researcher – Jeffrey Annis

HRV PheWAS CTDv7

Our primary goal is to understand the interaction between activity levels and heart rate variability with the development and progression of human disease. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce…

Scientific Questions Being Studied

Our primary goal is to understand the interaction between activity levels and heart rate variability with the development and progression of human disease. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will examine the relationship between daily heart rate variability patterns over time and the prevalence and progression of coded human diseases. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality). We will be utilizing the controlled tier version of AOU in this workspace.

Anticipated Findings

We expect to find that certain patterns of heart rate variability are associated with a higher prevalence and more rapid progression of chronic diseases. We may find clustering in activity and disease prevalence/severity which would motivate studies/interventions to reduce these health disparities. We may also find patterns in seasonal, weekly, or daily patterns in physical activity lead to differences in outcomes.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

Polygenic Risk Scores and Physical Activity CTDv7

Our primary goal is to understand the interaction between activity levels and polygenic risk score with the development and progression of human disease. Both physical activity and polygenic risk scores have been shown to be associated with prevalence and outcomes…

Scientific Questions Being Studied

Our primary goal is to understand the interaction between activity levels and polygenic risk score with the development and progression of human disease. Both physical activity and polygenic risk scores have been shown to be associated with prevalence and outcomes in many human diseases. These analyses will generate hypotheses guiding clinical and research interventions focused on activity to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

Population Health
Social / Behavioral
Ancestry

Scientific Approaches

We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence and progression of coded human diseases, which may be modified by genetics. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, clinical outcomes (hospitalizations/mortality), and polygenic risk scores derived from the WGS dataset in AoU.

Anticipated Findings

We expect to find that lower levels of activity are associated with a higher prevalence and more rapid progression of certain diseases and that this risk may be modified by polygenic risk score. These data will provide the rationale to link wearables data with electronic health records nationwide as a window into behavioral activity choice as a modifiable risk factor for chronic diseases. We may find substantial variation in activity and disease prevalence/severity by socioeconomic status, which would motivate studies/interventions to reduce these health disparities.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

Collaborators:

Lide Han - Project Personnel, Vanderbilt University Medical Center

Ruderfer - Brittain Collaboration (Pulm. Circ. Billing - CTDv7)

The demo project aims to explore the strategies to leverage Fitbit along with genomics, survey and EHR data on the cloud-based platform in a cost-efficient fashion. These strategies can lay the foundation to multiple research studies which can drive evidence-based…

Scientific Questions Being Studied

The demo project aims to explore the strategies to leverage Fitbit along with genomics, survey and EHR data on the cloud-based platform in a cost-efficient fashion. These strategies can lay the foundation to multiple research studies which can drive evidence-based care for all. Specifically, the project aims to develop the workspace on Researcher Workbench to develop use cases for digital biomarker development for the Fitbit data and its integration with other AoU data types. One existing challenge is how to ensure that information about multiple streams of health data can be conveyed appropriately to enable fit-for-purpose analyses. Therefore, the intent of the demonstration projects is to understand the challenges that users might face who would like to leverage Fitbit data in tandem with surveys, measurements, genomics and EHR data.

Project Purpose(s)

Population Health
Social / Behavioral
Educational
Ancestry

Scientific Approaches

Data wrangling strategies to meaningfully combine Fitbit data with EHR and genomics data on the Researcher Workbench. Develop strategy in R and Python (RMarkdown and Jupyter Notebooks), including calculation of summary statistics and data visualizations, for users of varying levels of digital health literacy.

Develop educational materials to acquaint researchers with the benefits and limitations of combining Fitbit, EHR and genomics data. Materials to be developed include peer-reviewed manuscript, articles/blogs, videos, and user guides.

Anticipated Findings

All of Us Research program (AoURP) currently provides multiple streams of health data (i.e., genomics, surveys, electronic healthcare records (EHR) and Fitbit) to registered users on Researcher Workbench - cloud based platform. This in turn provides a unique opportunity to answer clinically relevant questions. Wearable devices enable continuous monitoring of physiological signals, which may be used for discovery, diagnostic, and prognostic purposes. The Fitbit study as a part of the AoURP includes Fitbit data from approximately 12,000 patients. The information available from the Fitbits (such as activity, heart rate, sleep patterns and device metadata) can be used to develop new digital biomarkers by exploring their correlations with clinical measurements, genetic risk scores and information from surveys such as social determinants of health.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

Collaborators:

Douglas Ruderfer - Mid-career Tenured Researcher, Vanderbilt University Medical Center
Lide Han - Project Personnel, Vanderbilt University Medical Center
Peyton Coleman - Graduate Trainee, Vanderbilt University
Hiral Master - Project Personnel, All of Us Program Operational Use

Sleep PheWAS CTDv7

Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity…

Scientific Questions Being Studied

Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will examine the relationship between daily sleep patterns over time and the prevalence and progression of coded human diseases. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality). We will be utilizing the controlled tier version of AOU in this workspace.

Anticipated Findings

We expect to find that lower levels of sleep and certain sleep patterns are associated with a higher prevalence and more rapid progression of chronic diseases. We may find clustering in activity and disease prevalence/severity which would motivate studies/interventions to reduce these health disparities. We may also find patterns in seasonal, weekly, or daily patterns in physical activity lead to differences in outcomes.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

Collaborators:

Peyton Coleman - Graduate Trainee, Vanderbilt University

Ruderfer - Brittain Collaboration (V7)

The demo project aims to explore the strategies to leverage Fitbit along with genomics, survey and EHR data on the cloud-based platform in a cost-efficient fashion. These strategies can lay the foundation to multiple research studies which can drive evidence-based…

Scientific Questions Being Studied

The demo project aims to explore the strategies to leverage Fitbit along with genomics, survey and EHR data on the cloud-based platform in a cost-efficient fashion. These strategies can lay the foundation to multiple research studies which can drive evidence-based care for all. Specifically, the project aims to develop the workspace on Researcher Workbench to develop use cases for digital biomarker development for the Fitbit data and its integration with other AoU data types. One existing challenge is how to ensure that information about multiple streams of health data can be conveyed appropriately to enable fit-for-purpose analyses. Therefore, the intent of the demonstration projects is to understand the challenges that users might face who would like to leverage Fitbit data in tandem with surveys, measurements, genomics and EHR data.

Project Purpose(s)

Population Health
Social / Behavioral
Educational
Ancestry

Scientific Approaches

Data wrangling strategies to meaningfully combine Fitbit data with EHR and genomics data on the Researcher Workbench. Develop strategy in R and Python (RMarkdown and Jupyter Notebooks), including calculation of summary statistics and data visualizations, for users of varying levels of digital health literacy.

Develop educational materials to acquaint researchers with the benefits and limitations of combining Fitbit, EHR and genomics data. Materials to be developed include peer-reviewed manuscript, articles/blogs, videos, and user guides.

Anticipated Findings

All of Us Research program (AoURP) currently provides multiple streams of health data (i.e., genomics, surveys, electronic healthcare records (EHR) and Fitbit) to registered users on Researcher Workbench - cloud based platform. This in turn provides a unique opportunity to answer clinically relevant questions. Wearable devices enable continuous monitoring of physiological signals, which may be used for discovery, diagnostic, and prognostic purposes. The Fitbit study as a part of the AoURP includes Fitbit data from approximately 12,000 patients. The information available from the Fitbits (such as activity, heart rate, sleep patterns and device metadata) can be used to develop new digital biomarkers by exploring their correlations with clinical measurements, genetic risk scores and information from surveys such as social determinants of health.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Douglas Ruderfer - Mid-career Tenured Researcher, Vanderbilt University Medical Center
Hiral Master - Project Personnel, All of Us Program Operational Use
Jeffrey Annis - Other, Vanderbilt University Medical Center

Collaborators:

Peyton Coleman - Graduate Trainee, Vanderbilt University
Lide Han - Project Personnel, Vanderbilt University Medical Center
Brandon Lowery - Other, Vanderbilt University Medical Center

Polygenic Risk Scores and Physical Activity CTDv6

Our primary goal is to understand the interaction between activity levels and polygenic risk score with the development and progression of human disease. Both physical activity and polygenic risk scores have been shown to be associated with prevalence and outcomes…

Scientific Questions Being Studied

Our primary goal is to understand the interaction between activity levels and polygenic risk score with the development and progression of human disease. Both physical activity and polygenic risk scores have been shown to be associated with prevalence and outcomes in many human diseases. These analyses will generate hypotheses guiding clinical and research interventions focused on activity to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

Population Health
Social / Behavioral
Ancestry

Scientific Approaches

We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence and progression of coded human diseases, which may be modified by genetics. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, clinical outcomes (hospitalizations/mortality), and polygenic risk scores derived from the WGS dataset in AoU.

Anticipated Findings

We expect to find that lower levels of activity are associated with a higher prevalence and more rapid progression of certain diseases and that this risk may be modified by polygenic risk score. These data will provide the rationale to link wearables data with electronic health records nationwide as a window into behavioral activity choice as a modifiable risk factor for chronic diseases. We may find substantial variation in activity and disease prevalence/severity by socioeconomic status, which would motivate studies/interventions to reduce these health disparities.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

Collaborators:

Brandon Lowery - Other, Vanderbilt University Medical Center
Joshua Halevi - Undergraduate Student, Vanderbilt University Medical Center
Lide Han - Project Personnel, Vanderbilt University Medical Center

Sleep PheWAS RTDv7

Our primary goal is to develop a model based on the American Heart Association's (AHA) Essential 8 in order to understand the interaction between activity levels and sleep quality with the development and progression of human disease. These analyses will…

Scientific Questions Being Studied

Our primary goal is to develop a model based on the American Heart Association's (AHA) Essential 8 in order to understand the interaction between activity levels and sleep quality with the development and progression of human disease. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will examine the relationship between derived heart scores from the AHA's Essential 8 and the prevalence and progression of coded human diseases. We will perform variable/model selection to study the degree to which each of the AHA's Essential 8 factors impacts outcomes. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality).

Anticipated Findings

We expect to find that lower levels of activity and sleep are associated with a higher prevalence and more rapid progression of chronic diseases. We may find clustering in activity and disease prevalence/severity which would motivate studies/interventions to reduce these health disparities. We may also find patterns in seasonal, weekly, or daily patterns in physical activity lead to differences in outcomes.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

Collaborators:

Neil Zheng - Graduate Trainee, Yale University
Hiral Master - Project Personnel, All of Us Program Operational Use

Fitbit Clustering CTDv7

This is an extension of the Fitbit Phenotyping notebook in which the controlled tier data set will be used to determine daily, weekly, and seasonal patterns in physical activity. Our primary goal is to understand the interaction between activity levels…

Scientific Questions Being Studied

This is an extension of the Fitbit Phenotyping notebook in which the controlled tier data set will be used to determine daily, weekly, and seasonal patterns in physical activity.
Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease utilizing clustering and other machine learning techniques. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence and progression of coded human diseases. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality). We will be utilizing the controlled tier version of AOU in this workspace.

Anticipated Findings

We expect to find that lower levels of activity are associated with a higher prevalence and more rapid progression of chronic diseases. We may find clustering in activity and disease prevalence/severity which would motivate studies/interventions to reduce these health disparities. We may also find patterns in seasonal, weekly, or daily patterns in physical activity lead to differences in outcomes.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

Fitbit Clustering RTDv7

Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease utilizing clustering and other machine learning techniques. These analyses will generate hypotheses guiding clinical and research interventions focused…

Scientific Questions Being Studied

Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease utilizing clustering and other machine learning techniques. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence and progression of coded human diseases. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality).

Anticipated Findings

We expect to find that lower levels of activity are associated with a higher prevalence and more rapid progression of chronic diseases. We may find clustering in activity and disease prevalence/severity which would motivate studies/interventions to reduce these health disparities.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

AMP-HYP CTv7

The specific scientific question being studied in this case is the potential relationship between pulmonary hypertension (PH) and physical activity levels, as measured by Fitbit monitoring. The healthy controls will serve as a reference group for comparison to the PH…

Scientific Questions Being Studied

The specific scientific question being studied in this case is the potential relationship between pulmonary hypertension (PH) and physical activity levels, as measured by Fitbit monitoring. The healthy controls will serve as a reference group for comparison to the PH cohort in order to determine if there are any significant differences in physical activity levels between the two groups. Understanding the factors that contribute to the development and progression of PH is essential for improving treatment options and outcomes for patients.

Project Purpose(s)

Disease Focused Research (pulmonary hypertension)
Control Set

Scientific Approaches

Data wrangling strategies to meaningfully combine Fitbit data with EHR and genomics data on the Researcher Workbench. Develop strategy in R and Python (RMarkdown and Jupyter Notebooks), including calculation of summary statistics and data visualizations, for users of varying levels of digital health literacy. Develop educational materials to acquaint researchers with the benefits and limitations of combining Fitbit, EHR and genomics data. Materials to be developed include peer-reviewed manuscript, articles/blogs, videos, and user guides.

Anticipated Findings

Physical activity has been shown to have numerous health benefits, including improving cardiovascular health and reducing the risk of certain diseases. However, the relationship between physical activity and pulmonary hypertension is not well understood. By studying the relationship between physical activity levels and pulmonary hypertension, we may be able to identify potential interventions or lifestyle modifications that could help prevent or manage the condition. Overall, this research has the potential to make an important contribution to public health by improving our understanding of pulmonary hypertension and identifying strategies for improving patient outcomes.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

Sepsis and Activity

We intend to investigate whether regular physical activity is associated with a lower risk of developing sepsis or acute respiratory failure, or whether physical activity can improve outcomes in patients with these conditions. The importance of studying the relationship between…

Scientific Questions Being Studied

We intend to investigate whether regular physical activity is associated with a lower risk of developing sepsis or acute respiratory failure, or whether physical activity can improve outcomes in patients with these conditions. The importance of studying the relationship between physical activity and sepsis/acute respiratory failure lies in the potential for identifying modifiable risk factors that could reduce the burden of these conditions. Exploring the available data on physical activity, sepsis, and acute respiratory failure may help us understand the potential impact of physical activity on these conditions and identify areas for further investigation. It could also help inform public health policies and interventions aimed at promoting physical activity and reducing the risk of sepsis and acute respiratory failure.

Project Purpose(s)

Disease Focused Research (sepsis)

Scientific Approaches

We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence and progression of coded human diseases, which may be modified by genetics. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, clinical outcomes (hospitalizations/mortality).

Anticipated Findings

We expect to find that lower levels of activity are associated with a higher prevalence and more rapid progression of certain diseases. These data will provide the rationale to link wearables data with electronic health records nationwide as a window into behavioral activity choice as a modifiable risk factor for sepsis and acute respiratory failure. We may find substantial variation in activity and disease prevalence/severity by socioeconomic status, which would motivate studies/interventions to reduce these health disparities.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

COVID-19 and Wearables CTDv6

Our primary goal is to understand the interaction between activity levels and the development, progression, and societal effects of COVID-19. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality…

Scientific Questions Being Studied

Our primary goal is to understand the interaction between activity levels and the development, progression, and societal effects of COVID-19. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence of COVID-19. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality).

Anticipated Findings

We may find substantial variation in activity and disease prevalence/severity by socioeconomic status and/or location which would motivate studies/interventions to reduce these health disparities.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Runqi Yuan - Graduate Trainee, Vanderbilt University
Jeffrey Annis - Other, Vanderbilt University Medical Center

Collaborators:

STACY DESINE - Project Personnel, Vanderbilt University Medical Center
Hiral Master - Project Personnel, All of Us Program Operational Use
Aymone Kouame - Other, All of Us Program Operational Use

Ruderfer - Brittain Collaboration

The demo project aims to explore the strategies to leverage Fitbit along with genomics, survey and EHR data on the cloud-based platform in a cost-efficient fashion. These strategies can lay the foundation to multiple research studies which can drive evidence-based…

Scientific Questions Being Studied

The demo project aims to explore the strategies to leverage Fitbit along with genomics, survey and EHR data on the cloud-based platform in a cost-efficient fashion. These strategies can lay the foundation to multiple research studies which can drive evidence-based care for all. Specifically, the project aims to develop the workspace on Researcher Workbench to develop use cases for digital biomarker development for the Fitbit data and its integration with other AoU data types. One existing challenge is how to ensure that information about multiple streams of health data can be conveyed appropriately to enable fit-for-purpose analyses. Therefore, the intent of the demonstration projects is to understand the challenges that users might face who would like to leverage Fitbit data in tandem with surveys, measurements, genomics and EHR data.

Project Purpose(s)

Population Health
Social / Behavioral
Educational
Ancestry

Scientific Approaches

Data wrangling strategies to meaningfully combine Fitbit data with EHR and genomics data on the Researcher Workbench. Develop strategy in R and Python (RMarkdown and Jupyter Notebooks), including calculation of summary statistics and data visualizations, for users of varying levels of digital health literacy.

Develop educational materials to acquaint researchers with the benefits and limitations of combining Fitbit, EHR and genomics data. Materials to be developed include peer-reviewed manuscript, articles/blogs, videos, and user guides.

Anticipated Findings

All of Us Research program (AoURP) currently provides multiple streams of health data (i.e., genomics, surveys, electronic healthcare records (EHR) and Fitbit) to registered users on Researcher Workbench - cloud based platform. This in turn provides a unique opportunity to answer clinically relevant questions. Wearable devices enable continuous monitoring of physiological signals, which may be used for discovery, diagnostic, and prognostic purposes. The Fitbit study as a part of the AoURP includes Fitbit data from approximately 12,000 patients. The information available from the Fitbits (such as activity, heart rate, sleep patterns and device metadata) can be used to develop new digital biomarkers by exploring their correlations with clinical measurements, genetic risk scores and information from surveys such as social determinants of health.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Douglas Ruderfer - Mid-career Tenured Researcher, Vanderbilt University Medical Center
Hiral Master - Project Personnel, All of Us Program Operational Use
Lide Han - Project Personnel, Vanderbilt University Medical Center
Jeffrey Annis - Other, Vanderbilt University Medical Center

Collaborators:

Brandon Lowery - Other, Vanderbilt University Medical Center

RF Duplicate of COVID-19 and Wearables CTDv6

Our primary goal is to understand the interaction between activity levels and the development, progression, and societal effects of COVID-19. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality…

Scientific Questions Being Studied

Our primary goal is to understand the interaction between activity levels and the development, progression, and societal effects of COVID-19. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence of COVID-19. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality).

Anticipated Findings

We may find substantial variation in activity and disease prevalence/severity by socioeconomic status and/or location which would motivate studies/interventions to reduce these health disparities.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Runqi Yuan - Graduate Trainee, Vanderbilt University
Jeffrey Annis - Other, Vanderbilt University Medical Center

Social Determinants of Health and Disease

Social determinants garner increasing attention for the contributions to health and disease. Our group identified heterogeneity in heart failure incidence rates, with the highest rates among individuals of lower socioeconomic position, greatest neighborhood deprivation, and those residing in rural areas.…

Scientific Questions Being Studied

Social determinants garner increasing attention for the contributions to health and disease. Our group identified heterogeneity in heart failure incidence rates, with the highest rates among individuals of lower socioeconomic position, greatest neighborhood deprivation, and those residing in rural areas. Whether this pattern is specific to heart failure or is evident more broadly is uncertain. Therefore, we will investigate differential associations between social determinants and disease.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

Data: controlled tier.
Exposure(s) : social determinants of health (from survey)
Outcome(s): Phecodes
Covariates: basics, overall health, lifestyle, personal medical history, healthcare access/utilization, family history, drug exposures, labs/measurements, physical measurements/wearables, etc

Analytic approach:
Data structure/correlations between social determinants will be examined
Multivariable regression models will be used to examine cross sectional and longitudinal associations between social determinants and disease(s).
Interaction terms will be included to assess for variation in the associations between social determinants and disease by age, sex, race, ethnicity, prevalent comorbid disease.

Anticipated Findings

We expect to find heterogeneity in the associations between social determinants and disease with variation by demographic groups and comorbidity. Identifying where heterogeneity exists will inform subsequent stratified analyses for deeper investigation and understanding of potentially modifiable drivers of disease.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Deepak Gupta - Mid-career Tenured Researcher, Vanderbilt University Medical Center
Jeffrey Annis - Other, Vanderbilt University Medical Center

AHA Essential 8 RTDv6

Our primary goal is to develop a model based on the American Heart Association's (AHA) Essential 8 in order to understand the interaction between activity levels and sleep quality with the development and progression of human disease. These analyses will…

Scientific Questions Being Studied

Our primary goal is to develop a model based on the American Heart Association's (AHA) Essential 8 in order to understand the interaction between activity levels and sleep quality with the development and progression of human disease. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will examine the relationship between derived heart scores from the AHA's Essential 8 and the prevalence and progression of coded human diseases. We will perform variable/model selection to study the degree to which each of the AHA's Essential 8 factors impacts outcomes. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality).

Anticipated Findings

We expect to find that lower levels of activity and sleep are associated with a higher prevalence and more rapid progression of chronic diseases. We may find clustering in activity and disease prevalence/severity which would motivate studies/interventions to reduce these health disparities. We may also find patterns in seasonal, weekly, or daily patterns in physical activity lead to differences in outcomes.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

Heart Failure and Wearables CTDv6

We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence and progression of coded human diseases with a primary focus on heart failure. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality…

Scientific Questions Being Studied

We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence and progression of coded human diseases with a primary focus on heart failure. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality).

Project Purpose(s)

Disease Focused Research (heart failure)
Population Health
Social / Behavioral
Other Purpose (Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease with a primary focus on heart failure. Higher physical activity is associated with lower prevalence and better outcomes in virtually every human disease. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking care.)

Scientific Approaches

We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence and progression of coded human diseases with a primary focus on heart failure. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality).

Anticipated Findings

We expect to find that lower levels of activity are associated with a higher prevalence and more rapid progression of heart failure and other diseases. These data will provide the rationale to link wearables data with electronic health records nationwide as a window into behavioral activity choice as a modifiable risk factor for chronic diseases. We may find substantial variation in activity and disease prevalence/severity by socioeconomic status, which would motivate studies/interventions to reduce these health disparities.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

Fitbit Clustering RTDv6

Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease utilizing clustering and other machine learning techniques. These analyses will generate hypotheses guiding clinical and research interventions focused…

Scientific Questions Being Studied

Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease utilizing clustering and other machine learning techniques. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence and progression of coded human diseases. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality).

Anticipated Findings

We expect to find that lower levels of activity are associated with a higher prevalence and more rapid progression of chronic diseases. We may find clustering in activity and disease prevalence/severity which would motivate studies/interventions to reduce these health disparities.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

Fitbit Clustering CTDv6

This is an extension of the Fitbit Phenotyping notebook in which the controlled tier data set will be used to determine daily, weekly, and seasonal patterns in physical activity. Our primary goal is to understand the interaction between activity levels…

Scientific Questions Being Studied

This is an extension of the Fitbit Phenotyping notebook in which the controlled tier data set will be used to determine daily, weekly, and seasonal patterns in physical activity.
Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease utilizing clustering and other machine learning techniques. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence and progression of coded human diseases. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality). We will be utilizing the controlled tier version of AOU in this workspace.

Anticipated Findings

We expect to find that lower levels of activity are associated with a higher prevalence and more rapid progression of chronic diseases. We may find clustering in activity and disease prevalence/severity which would motivate studies/interventions to reduce these health disparities. We may also find patterns in seasonal, weekly, or daily patterns in physical activity lead to differences in outcomes.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

Fitbit Clustering RTDv5

Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease utilizing clustering and other machine learning techniques. These analyses will generate hypotheses guiding clinical and research interventions focused…

Scientific Questions Being Studied

Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease utilizing clustering and other machine learning techniques. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence and progression of coded human diseases. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality).

Anticipated Findings

We expect to find that lower levels of activity are associated with a higher prevalence and more rapid progression of chronic diseases. We may find clustering in activity and disease prevalence/severity which would motivate studies/interventions to reduce these health disparities.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

Wearables and Medications RTDv6

Our primary goal is to understand the interaction between activity levels and medications. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking care.

Scientific Questions Being Studied

Our primary goal is to understand the interaction between activity levels and medications. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will examine the relationship between daily activity (steps, activity intensity) over time and the number and type of medications prescribed. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality).

Anticipated Findings

We expect to find that lower levels of activity are associated with a higher number of prescription medications. These data will provide the rationale to link wearables data with electronic health records nationwide as a window into behavioral activity choice as a modifiable risk factor for chronic diseases. We may find substantial variation in activity and disease prevalence/severity by socioeconomic status, which would motivate studies/interventions to reduce these health disparities.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

Heart Failure and Wearables RTDv6

Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease with a primary focus on heart failure. Higher physical activity is associated with lower prevalence and better outcomes…

Scientific Questions Being Studied

Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease with a primary focus on heart failure. Higher physical activity is associated with lower prevalence and better outcomes in virtually every human disease. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

Disease Focused Research (heart failure)
Population Health
Social / Behavioral

Scientific Approaches

We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence and progression of coded human diseases with a primary focus on heart failure. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality).

Anticipated Findings

We expect to find that lower levels of activity are associated with a higher prevalence and more rapid progression of heart failure and other diseases. These data will provide the rationale to link wearables data with electronic health records nationwide as a window into behavioral activity choice as a modifiable risk factor for chronic diseases. We may find substantial variation in activity and disease prevalence/severity by socioeconomic status, which would motivate studies/interventions to reduce these health disparities.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

Type 2 DM and Wearables Data RTDv5

Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease with a primary focus on type 2 diabetes mellitus. Higher physical activity is associated with lower prevalence and…

Scientific Questions Being Studied

Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease with a primary focus on type 2 diabetes mellitus. Higher physical activity is associated with lower prevalence and better outcomes in virtually every human disease. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

Disease Focused Research (type 2 diabetes mellitus)
Population Health
Social / Behavioral

Scientific Approaches

We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence and progression of coded human diseases with a primary focus on Type 2 DM. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality).

Anticipated Findings

We expect to find that lower levels of activity are associated with a higher prevalence and more rapid progression of Type 2 DM and other diseases. These data will provide the rationale to link wearables data with electronic health records nationwide as a window into behavioral activity choice as a modifiable risk factor for chronic diseases. We may find substantial variation in activity and disease prevalence/severity by socioeconomic status, which would motivate studies/interventions to reduce these health disparities.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

COVID-19 and Wearables CTDv5

Our primary goal is to understand the interaction between activity levels and the development, progression, and societal effects of COVID-19. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality…

Scientific Questions Being Studied

Our primary goal is to understand the interaction between activity levels and the development, progression, and societal effects of COVID-19. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence of COVID-19. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality).

Anticipated Findings

We may find substantial variation in activity and disease prevalence/severity by socioeconomic status and/or location which would motivate studies/interventions to reduce these health disparities.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

Type 2 DM and Wearables Data RTDv6

Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease with a primary focus on type 2 diabetes mellitus. Higher physical activity is associated with lower prevalence and…

Scientific Questions Being Studied

Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease with a primary focus on type 2 diabetes mellitus. Higher physical activity is associated with lower prevalence and better outcomes in virtually every human disease. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

Disease Focused Research (type 2 diabetes mellitus)
Population Health
Social / Behavioral

Scientific Approaches

We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence and progression of coded human diseases with a primary focus on Type 2 DM. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality).

Anticipated Findings

We expect to find that lower levels of activity are associated with a higher prevalence and more rapid progression of Type 2 DM and other diseases. These data will provide the rationale to link wearables data with electronic health records nationwide as a window into behavioral activity choice as a modifiable risk factor for chronic diseases. We may find substantial variation in activity and disease prevalence/severity by socioeconomic status, which would motivate studies/interventions to reduce these health disparities.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Hiral Master - Project Personnel, All of Us Program Operational Use
Aymone Kouame - Other, All of Us Program Operational Use
Jeffrey Annis - Other, Vanderbilt University Medical Center

Jeffrey Annis

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