Hayoung Jeong
Graduate Trainee, Duke University
4 active projects
DST_Sleep_Analysis_2.0
Scientific Questions Being Studied
NA
Project Purpose(s)
- Population Health
Scientific Approaches
NA
Anticipated Findings
NA
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierResearch Team
Owner:
- Ke Wang - Graduate Trainee, Duke University
- Hayoung Jeong - Graduate Trainee, Duke University
Demo Project: State-level Activity Inequality [Published Work]
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 TierResearch Team
Owner:
- Hiral Master - Project Personnel, All of Us Program Operational Use
- Hayoung Jeong - Graduate Trainee, Duke University
- Christopher Lord - Project Personnel, All of Us Program Operational Use
- Aymone Kouame - Other, All of Us Program Operational Use
DST_Sleep_Analysis
Scientific Questions Being Studied
NA
Project Purpose(s)
- Population Health
Scientific Approaches
NA
Anticipated Findings
NA
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierResearch Team
Owner:
- Ke Wang - Graduate Trainee, Duke University
- Hayoung Jeong - Graduate Trainee, Duke University
Duplicate of Wearables and The Human Phenome (Published Work)
Scientific Questions Being Studied
Our primary goal is to understand the relation between activity levels 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 to reduce morbidity and mortality in patients seeking care.
This workspace is replication workspace for Wearables and The Human Phenome project. We replicated the workspace to provide a clean and reduced version of code that was used to generate the findings, which were published in Nature Medicine (https://www.nature.com/articles/s41591-022-02012-w).
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, and survey results.
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
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