Jeffrey Annis

Vanderbilt University Medical Center

47 active projects

Polygenic Risk Scores and Physical Activity CW

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

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

Typical Sleep Period CTDv7

Our primary goal is to understand the interaction and association between sleep quality and 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 Questions Being Studied

Our primary goal is to understand the interaction and association between sleep quality and 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. Currently, the Fitbit sleep algorithm divides sleep into main sleep and non-main sleep, where main sleep is the longest sleep period during the night. We hope to improve upon this algorithm to better capture sleep periods, which will in turn provide a clearer picture of the development and progression of human disease as they relate to Fitbit sleep metrics.

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. We will develop an algorithm that uses the granular sleep levels (REM, Deep, Light, Wake) as input and as output, the algorithm will provide a flag as to whether the sleep segment is part of the typical sleep period.

Anticipated Findings

We expect the typical sleep period algorithm will provide different sleep periods than the main sleep algorithm provided by default by Fitbit. The typical sleep period algorithm takes into account the entire monitoring period of sleep for each user and tries to build a typical sleep period from that. We anticipate that the main sleep algorithm might, for example, be underestimating the amount of wakefulness for certain populations such as patients with insomnia. The typical sleep algorithm is intended to correct for this.

Demographic Categories of Interest

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

Causal Inference with Wearables CTDv7

Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease utilizing causal inference and other related 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 causal inference and other related 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

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

  • Nikhil Khankari - Early Career Tenure-track Researcher, Vanderbilt University Medical Center
  • Jeffrey Annis - Other, Vanderbilt University Medical Center

ReHospitalization for Various ICDs

Our primary goal is to investigate re-hospitalization occurrences for certain ICD codes.

Scientific Questions Being Studied

Our primary goal is to investigate re-hospitalization occurrences for certain ICD codes.

Project Purpose(s)

  • Population Health
  • Social / Behavioral

Scientific Approaches

We will examine the relationship between admission diagnosis and re-hospitalization incidences.

Anticipated Findings

We expect to find differences in occurrences in different diagnosis during initial hospitalization.

Demographic Categories of Interest

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Peyton Coleman - Graduate Trainee, Vanderbilt University

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

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Hiral Master - Project Personnel, All of Us Program Operational Use
  • Lide Han - Project Personnel, Vanderbilt University Medical Center

Exercise Habits with Diabetes

Our primary goal is to compare morbidity and morbidity in diabetic patients with different habits regarding the exercise time during the day. These analyses will generate hypotheses guiding clinical and research interventions focused on exercise time in the day to…

Scientific Questions Being Studied

Our primary goal is to compare morbidity and morbidity in diabetic patients with different habits regarding the exercise time during the day. These analyses will generate hypotheses guiding clinical and research interventions focused on exercise time in the day to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

  • Population Health
  • Social / Behavioral

Scientific Approaches

We will examine the relationship between the time of the day patients are active and the later in life outcomes. We will divide the patients into categories depending on when during the day they are most active using Fitbit records and build a model to compare outcomes in different categories.

Anticipated Findings

We expect to find differences in outcomes between patients who are active during different times of the day.

Demographic Categories of Interest

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

PAH Prediction Algorithm v7

Our primary purpose at this stage is to test whether we can reliably identify patients with pulmonary hypertension in the All of Us resource. We have developed and internally validated a machine learning algorithm to identify patients with pulmonary hypertension…

Scientific Questions Being Studied

Our primary purpose at this stage is to test whether we can reliably identify patients with pulmonary hypertension in the All of Us resource. We have developed and internally validated a machine learning algorithm to identify patients with pulmonary hypertension using administrative data. We first want to test the performance of that algorithm in All of Us. If the performance is satisfactory, we will then begin addressing research questions centered around the epidemiology and outcomes of patients with pulmonary hypertension. We will examine the geographic distributions, trends in the diagnosis over time, hospitalization and mortality rates, medication use, and medication exposures that may suggest drug-induced PH.

Project Purpose(s)

  • Disease Focused Research (pulmonary hypertension)

Scientific Approaches

We will use ICD and CPT codes, and medications exposures to test our machine learning algorithm. If the algorithm performs well, we will then use basic demographics, medications, and outcomes data to study the epidemiology of pulmonary hypertension in All of Us.

Anticipated Findings

We anticipate that our study will provide a contemporary "real world" view of pulmonary hypertension patients in the United States. This is important and new because most epidemiological studies of pulmonary hypertension in the United States to date derive from either single centers or registries that enroll patients from select centers.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Cardiovascular Risk Calculator

Our primary goal is to compare how fitbit data compare with traditional risk scores for cardiovascular events.. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking…

Scientific Questions Being Studied

Our primary goal is to compare how fitbit data compare with traditional risk scores for cardiovascular events.. 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 cardiovascular risk calculators, Fitbit data and the prevalence and progression of coded human diseases. We will perform variable/model selection to study the degree to which risk scores defined by Fitbit vs. traditional risk score 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

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

Pregnancy and Wearable CTDv7

Our primary goal is to understand the interaction between activity levels and sleep quality with pregnancy. 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 sleep quality with pregnancy. 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

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

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

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Registered Tier

Research Team

Owner:

Collaborators:

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

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Laleh Jalilian - Mid-career Tenured Researcher, University of California, Los Angeles
  • 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

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

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

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

Collaborators:

  • Samuel Butensky - Research Fellow, Yale University

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

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Registered Tier

Research Team

Owner:

Collaborators:

  • Omar Costilla Reyes - Research Fellow, Massachusetts Institute of Technology

Sepsis and Wearables CTDv7

Our primary goal is to understand the interaction between activity levels and sleep quality with sepsis. 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 sleep quality with sepsis. 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

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

Bariatric Surgery and Wearables CTDv7

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

Scientific Questions Being Studied

Our primary goal is to understand the interaction between activity levels and sleep quality with bariatric surgery. 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

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

Physical Activity and Sedentary Time CTDv7

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

Scientific Questions Being Studied

Our primary goal is to understand the interaction between activity levels and the sedentary time with the development and progression of human disease. Both physical activity and sedentary time 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

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, and clinical outcomes (hospitalizations/mortality).

Anticipated Findings

We expect to find that certain patterns of activity and sedentary time 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 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

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

Polygenic Risk Scores and Physical Activity (Extended) 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

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

  • Lide Han - Project Personnel, Vanderbilt University Medical Center
  • Jeffrey Annis - Other, Vanderbilt University Medical Center

Seasonal variations in sleep and activity

Our primary goal is to understand the interaction between seasonal variations of activity 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…

Scientific Questions Being Studied

Our primary goal is to understand the interaction between seasonal variations of activity 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 seasonal variations in 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 also expect to find seasonal variations in sleep patterns and will examine the extent to which these may play a role in the progression of chronic disease. 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

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

Pregnancy and Wearables RTDv7

Our primary goal is to understand the interaction between activity levels and sleep quality with pregnancy. 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 sleep quality with pregnancy. 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

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Registered Tier

Research Team

Owner:

Causal Inference with Wearables RTDv7

Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease utilizing causal inference and other related 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 causal inference and other related 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

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Registered Tier

Research Team

Owner:

  • Douglas Ruderfer - Mid-career Tenured Researcher, Vanderbilt University Medical Center
  • Peyton Coleman - Graduate Trainee, Vanderbilt University
  • Nikhil Khankari - Early Career Tenure-track Researcher, Vanderbilt University Medical Center
  • Jeffrey Annis - Other, Vanderbilt University Medical Center

Hospitalization and Wearables CTD7

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

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 leading to hospitalization. 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 risk of hospitalization. We may find clustering in activity and disease prevalence/severity which would motivate studies/interventions to reduce these health disparities.

Demographic Categories of Interest

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

ECG PheWAS CTDv7

Our primary goal is to understand the interaction between ECG measures 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…

Scientific Questions Being Studied

Our primary goal is to understand the interaction between ECG measures 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 ECG measures over time and the prevalence and progression of coded human diseases. We will use ECG measures, 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 ECG 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

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

Hospitalization and Activity RTDv7

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

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 leading to hospitalization. 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 risk of hospitalization. We may find clustering in activity and disease prevalence/severity which would motivate studies/interventions to reduce these health disparities.

Demographic Categories of Interest

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Registered Tier

Research Team

Owner:

AHA Essential 8 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

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Registered Tier

Research Team

Owner:

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

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

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
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