Hiral Master

Project Personnel, All of Us Program Operational Use

15 active projects

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

Pregnancy and postpartum

Maternal mortality is responsible for 700 deaths annually in the United States. It has been demonstrated that 80% of pregnancy related deaths are preventable, highlighting the need for improved pregnancy and postpartum patient care. This study seeks to investigate digital…

Scientific Questions Being Studied

Maternal mortality is responsible for 700 deaths annually in the United States. It has been demonstrated that 80% of pregnancy related deaths are preventable, highlighting the need for improved pregnancy and postpartum patient care. This study seeks to investigate digital biomarkers from wearables in patients during pregnancy and the postpartum period with several conditions to facilitate early disease detection and enhanced patient monitoring.

Project Purpose(s)

  • Commercial

Scientific Approaches

We will create cohorts of individuals with different conditions that impact patients during pregnancy and postpartum. Then, we will use statistical models to determine whether digital biomarkers from wearables may be used for early disease prediction, detection, and patient monitoring.

Anticipated Findings

The anticipated findings are potentially developing detection algorithms for severe conditions that impact patients during pregnancy and postpartum.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Eric Hurwitz - Research Fellow, University of North Carolina, Chapel Hill
  • Hiral Master - Project Personnel, All of Us Program Operational Use

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

Demo Project: State-level Activity Inequality [Published Work]

How is physical activity distributed within states in the US? Analysis of such activity distributions and inequality can reveal important relationships between physical activity disparities, health outcomes, and modifiable factors, as Althoff et al. studied in their paper, "Large-scale physical…

Scientific Questions Being Studied

How is physical activity distributed within states in the US? Analysis of such activity distributions and inequality can reveal important relationships between physical activity disparities, health outcomes, and modifiable factors, as Althoff et al. studied in their paper, "Large-scale physical activity data reveal worldwide activity inequality" (2017).

Project Purpose(s)

  • Educational

Scientific Approaches

The cohort will consist of Fitbit users in the US, with analysis being subdivided to the state level. Various graphs will be utilized to help visualize the low- and high-activity trends across states. Well-defined measures such as the Gini coefficient will be used to aid in the analysis of activity inequality.

Anticipated Findings

The study aims to find relationships between activity inequality and health outcomes, such as obesity levels. With the growing accessibility of fitness trackers and activity sensors built into personal devices, this study hopes to leverage the volume of available data and potentially inform measures to improve population activity and health.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

V7 PASC Workspace

This project will explore the scope of patients with COVID-19 and the characteristics of patients with PASC.

Scientific Questions Being Studied

This project will explore the scope of patients with COVID-19 and the characteristics of patients with PASC.

Project Purpose(s)

  • Educational
  • Ancestry
  • Other Purpose (practice notebook to familiarize with RW)

Scientific Approaches

We will apply algorithms developed by the RECOVER PCORnet Adult Cohort and compare the overlap in cohorts with the set derived though the N3C algorithm

Anticipated Findings

We expect to find a high degree of concordance between the RECOVER Adult Cohort algorithm and the N3C algorithm, even though the approaches were developed through different machine learning methods on different source patient data sets

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Lina Sulieman - Other, All of Us Program Operational Use

AOU_Recover_Long_Covid_v6

The purpose of this workspace was to implement the published XGBoost machine learning (ML) model, which was developed using the National COVID Cohort Collaborative’s (N3C) EHR repository to identify potential patients with PASC/Long COVID in All of Us Research Program.

Scientific Questions Being Studied

The purpose of this workspace was to implement the published XGBoost machine learning (ML) model, which was developed using the National COVID Cohort Collaborative’s (N3C) EHR repository to identify potential patients with PASC/Long COVID in All of Us Research Program.

Project Purpose(s)

  • Disease Focused Research (Long COVID)

Scientific Approaches

To achieve this objective, data science workflows were used to apply ML algorithms on the Researcher Workbench. This effort allowed an expansion in the number of participants used to evaluate the ML models used to identify risk of PASC/Long COVID and also serve to validate the efforts of one team and providing insight to other teams. These models were implemented within the All of Us Controlled Tier data (C2022Q2R2), which was last refreshed on June 22, 2022. We intend to provide a step-by-step guide for the implementation of N3C's ML Model for identification of PASC/Long COVID Phenotype in the All of Us dataset.

Anticipated Findings

We intend to provide a step-by-step guide for the implementation of N3C's ML Model for identification of PASC/Long COVID Phenotype in the All of Us dataset.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • WeiQi Wei - Other, All of Us Program Operational Use
  • Vern Kerchberger - Early Career Tenure-track Researcher, Vanderbilt University Medical Center
  • Srushti Gangireddy - Project Personnel, Vanderbilt University Medical Center
  • Mark Weiner - Mid-career Tenured Researcher, Cornell University
  • Hiral Master - Project Personnel, All of Us Program Operational Use
  • Gabriel Anaya - Administrator, National Heart, Lung, and Blood Institute (NIH - NHLBI)
  • David Mohs - Other, All of Us Program Operational Use
  • Christopher Lord - Project Personnel, All of Us Program Operational Use
  • Chenchal Subraveti - Project Personnel, All of Us Program Operational Use

Collaborators:

  • Jun Qian - Other, All of Us Program Operational Use
  • Chris Lunt - Other, All of Us Program Operational Use

Practice Notebook to Explore AoU dataset

This project will explore the scope of patients with COVID-19 and the characteristics of patients with PASC.

Scientific Questions Being Studied

This project will explore the scope of patients with COVID-19 and the characteristics of patients with PASC.

Project Purpose(s)

  • Educational
  • Other Purpose (practice notebook to familiarize with RW)

Scientific Approaches

We will apply algorithms developed by the RECOVER PCORnet Adult Cohort and compare the overlap in cohorts with the set derived though the N3C algorithm

Anticipated Findings

We expect to find a high degree of concordance between the RECOVER Adult Cohort algorithm and the N3C algorithm, even though the approaches were developed through different machine learning methods on different source patient data sets

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Srushti Gangireddy - Project Personnel, Vanderbilt University Medical Center
  • Mark Weiner - Mid-career Tenured Researcher, Cornell University
  • Hiral Master - Project Personnel, All of Us Program Operational Use

Collaborators:

  • Aashri Aggarwal - Undergraduate Student, Cornell University

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

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

Collaborators:

  • Brandon Lowery - Other, Vanderbilt University Medical Center

Duplicate of Skills Assessment Training Notebooks For Users

This workspace contains multiple notebooks that assess users' understanding of the workbench and OMOP. These notebooks are meant to help users check their knowledge not only on Python, R, and SQL, but also on the general data structure and data…

Scientific Questions Being Studied

This workspace contains multiple notebooks that assess users' understanding of the workbench and OMOP. These notebooks are meant to help users check their knowledge not only on Python, R, and SQL, but also on the general data structure and data model used by the All of Us program.

Project Purpose(s)

  • Educational

Scientific Approaches

There are no scientific approach used in this workspace because it is meant for educational purposes only. We will cover all aspects of OMOP, and hence will use most datasets available in the workbench.

Anticipated Findings

We do not anticipate to have any findings. Instead, we are educating people on the use of the workbench and the common data model OMOP used by the program.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Collaborators:

  • Michael Lyons - Project Personnel, All of Us Program Operational Use
  • Hunter Hollis - Project Personnel, All of Us Program Operational Use
  • Christopher Lord - Project Personnel, All of Us Program Operational Use

Wearables and The Human Phenome (v3)

This replicates the workspace Wearables and The Human Phenome. We would like to create a clean and reduced version of our prior workspace for public facing code that was requested from us by Nature Medicine. Our primary goal is to…

Scientific Questions Being Studied

This replicates the workspace Wearables and The Human Phenome. We would like to create a clean and reduced version of our prior workspace for public facing code that was requested from us by Nature Medicine.

Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease. 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)

  • 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. 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:

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

Work with All of Us Physical Measurements Data - Class Teaching

How to navigate around physical measurements?

Scientific Questions Being Studied

How to navigate around physical measurements?

Project Purpose(s)

  • Other Purpose (Testing and operations purposes)

Scientific Approaches

N/A

Anticipated Findings

N/A

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Hunter Hollis - Project Personnel, All of Us Program Operational Use
  • Hiral Master - Project Personnel, All of Us Program Operational Use
  • Aymone Kouame - Other, All of Us Program Operational Use

Wearables Data and the Human Phenome

Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease. Higher physical activity is associated with lower prevalence and better outcomes in virtually every human disease. These analyses…

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. 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)

  • 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. 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:

  • Shi Huang - Other, Vanderbilt University Medical Center
  • Hiral Master - Project Personnel, All of Us Program Operational Use
  • Evan Brittain - Mid-career Tenured Researcher, Vanderbilt University Medical Center
  • Jeffrey Annis - Other, Vanderbilt University Medical Center

Implementing Recover Algorithm on AOU Data

Identify potential long COVID patients among three groups in the database: All COVID-19 patients, patients hospitalized with COVID-19, and patients who had COVID-19 but were not hospitalized. The models proved to be accurate, as people identified as at risk for…

Scientific Questions Being Studied

Identify potential long COVID patients among three groups in the database: All COVID-19 patients, patients hospitalized with COVID-19, and patients who had COVID-19 but were not hospitalized. The models proved to be accurate, as people identified as at risk for long COVID were similar to patients seen at long COVID clinics.

Project Purpose(s)

  • Disease Focused Research (Long COVID)

Scientific Approaches

XGBoost machine learning model is developed to identify potential patients with long COVID.
Base population is defined as any non-deceased adult patient (age ≥18 years) with either an International Classification of Diseases-10-Clinical Modification COVID-19 diagnosis code (U07.1) from an inpatient or emergency visit, or a positive SARS-CoV-2 PCR or antigen test, and for whom at least 90 days have passed since COVID-19 index date.
The model examines demographics, health-care utilization, diagnoses, and medications for adults with COVID-19.

Anticipated Findings

Identify with high accuracy, patients who potentially have long COVID. Find the important features.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • WeiQi Wei - Other, All of Us Program Operational Use
  • Vern Kerchberger - Early Career Tenure-track Researcher, Vanderbilt University Medical Center
  • Srushti Gangireddy - Project Personnel, Vanderbilt University Medical Center
  • Hiral Master - Project Personnel, All of Us Program Operational Use
  • Gabriel Anaya - Administrator, National Heart, Lung, and Blood Institute (NIH - NHLBI)

Collaborators:

  • Chris Lunt - Other, All of Us Program Operational Use

RECOVER+AoU

The goal of this initial cross-platform testing effort is focused on expanding the analytical capability of available data sources that have collected data on SARS-CoV-2. As we gather data across the US, we can use independent data sources to better…

Scientific Questions Being Studied

The goal of this initial cross-platform testing effort is focused on expanding the analytical capability of available data sources that have collected data on SARS-CoV-2. As we gather data across the US, we can use independent data sources to better understand PASC in our population and identify possible interventions. As a first step, we hope to leverage available RECOVER data tools and apply within the All of Us Researcher Workbench to assess cross-platform interoperability and analytical equivalence. This would provide a path to engage our research community and guide research towards our understanding of PASC.

Project Purpose(s)

  • Population Health
  • Methods Development
  • Control Set
  • Other Purpose (Testing PASC ML Algorithm from N3C-RECOVER in AoU Platform)

Scientific Approaches

Bring existing data query code and data analytics code from the RECOVER researcher team into the All of Us Researcher Workbench. Use “equivalent” code sets to explore and expand our understanding of PASC and its effects on the US population. Share reproducible findings through programming “notebook” and analysis of standardized datasets (OMOP).

Anticipated Findings

This research activity will be developed in conjunction with an awareness campaign of the collaborative efforts undertaken by both RECOVER and AoU. We intend to highlight the available datasets with SARS-CoV-2 data, as well as the cloud-based researcher workspaces (RECOVER, AoU). With the awareness campaign and cross-platform testing, we intent to create an on-ramp for experienced and young researchers within two large and diverse datasets.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • WeiQi Wei - Other, All of Us Program Operational Use
  • Vern Kerchberger - Early Career Tenure-track Researcher, Vanderbilt University Medical Center
  • Srushti Gangireddy - Project Personnel, Vanderbilt University Medical Center
  • Hiral Master - Project Personnel, All of Us Program Operational Use
  • Gabriel Anaya - Administrator, National Heart, Lung, and Blood Institute (NIH - NHLBI)
  • Chris Lunt - Other, All of Us Program Operational Use

Test - Work With Wearable Device Data

Testing and operational use

Scientific Questions Being Studied

Testing and operational use

Project Purpose(s)

  • Other Purpose (Testing and operational use)

Scientific Approaches

This Tutorial Workspace contains one Jupyter Notebook written in Python. The notebook contains information on how to extract and work with the current set of All of Us Fitbit data. What are the anticipated findings from the study? How would your findings contribute to the body of scientific knowledge in the field? By reading and running the notebook in this Tutorial Workspace, researchers will learn how to query information about steps, heart rate, and daily activity summary.

Anticipated Findings

By reading and running the notebook in this Tutorial Workspace, researchers will understand how to work with Fitbit CDR data from the workbench. They will learn how to query information about steps, heart rate, and daily activity summary.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Hiral Master - Project Personnel, All of Us Program Operational Use

Collaborators:

  • Francis Ratsimbazafy - Other, All of Us Program Operational Use
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