Research Projects Directory

Duplicate of v8 Sleep Outcomes and Geographical Locations

The study will explore how geographical location and changes in daylight savings impact sleep patterns, using wearable Fitbit data. We aim to understand the relationship between sleep quality, duration, and disturbances with various regions and their climates, alongside examining the…

Scientific Questions Being Studied

The study will explore how geographical location and changes in daylight savings impact sleep patterns, using wearable Fitbit data. We aim to understand the relationship between sleep quality, duration, and disturbances with various regions and their climates, alongside examining the potential influence of illnesses through a PheWAS analysis. Investigating how daylight savings affects sleep across regions is also a central question. These insights are crucial for understanding how external environmental factors influence public health through sleep, a known critical component of overall well-being.

Project Purpose(s)

Population Health

Scientific Approaches

We will use de-identified Fitbit sleep data linked to participants' geographical locations and medical history, conducting a PheWAS to identify associations between sleep disorders and illnesses. The study will leverage geographic information systems (GIS) to analyze location-specific variables, such as altitude, latitude, and climate. Analytical tools like regression models and machine learning will be employed to examine correlations, while time-series analysis will help assess daylight savings effects. Our study will also account for confounders like age, gender, and lifestyle factors.

Anticipated Findings

We anticipate identifying significant relationships between sleep disturbances and specific geographical factors like altitude or latitude, alongside finding patterns in the impact of daylight savings. The PheWAS may reveal associations between sleep disturbances and chronic illnesses. These findings could advance our understanding of environmental and geographical influences on sleep health and inform public health interventions to improve sleep outcomes across diverse populations.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Mason Manetta - Graduate Trainee, Arizona State University

HS, Depression, and Sleep Disturbance

(1) assess the relationship between hidradenitis suppurativa (HS) and sleep disturbances (obstructive sleep apnea, insomnia, hypersomnia) and depression by treatment type. While prior studies have linked HS to OSA and Depression, none have examined how treatment variation influences this relationship…

Scientific Questions Being Studied

(1) assess the relationship between hidradenitis suppurativa (HS) and sleep disturbances (obstructive sleep apnea, insomnia, hypersomnia) and depression by treatment type. While prior studies have linked HS to OSA and Depression, none have examined how treatment variation influences this relationship

(2) Investigate how associations between HS and hypersomnia and insomnia differ across various sociodemographic groups. Though links between HS and both insomnia and hypersomnia exist, few studies have explored how these vary by race or gender. This is important given HS’s disproportionate impact on women and Black individuals.

(3) Assess the relationship between HS, depression, and sleep, given the strong association between depression and sleep disturbance.

(4) Evaluate disparities in access to care among patients with HS using a larger dataset. Previous research has found that patients with HS often face significant barriers to timely diagnosis, treatment, and cost.

Project Purpose(s)

Disease Focused Research (Hidradenitis Suppurativa)
Population Health
Social / Behavioral
Drug Development

Scientific Approaches

Baseline characteristics will be summarized using means and standard deviations for continuous variables, and frequencies with percentages for categorical variables.

Differences in sociodemographic characteristics will be evaluated using chi-square (χ²) tests for categorical data and Analysis of Variance (ANOVA) tests for continuous data.

Logistic regression and multivariable analysis models will be employed for the main analysis.

Stratified analyses will also be performed to further explore subgroup differences.

Anticipated Findings

1.The association between HS and sleep disturbances (obstructive sleep apnea, insomnia, hypersomnia) and depression will vary based on treatment type. Given that HS treatments vary widely it is crucial to assess whether specific treatment modalities mitigate or exacerbate sleep disturbances in HS patients. Identifying potential treatment-related differences can inform clinical decision-making.

2.The relationship between HS and insomnia/hypersomnia will differ across sociodemographic factors, particularly race, highlighting potential disparities in sleep health among HS patients. A deeper understanding of these disparities could inform targeted interventions to improve sleep outcomes and reduce health inequities in affected populations.

3. The association between HS and sleep distrubances will be moderated by depression

4. Non-White participants with HS are expected to experience delays in accessing care.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Sydney Barlow - Graduate Trainee, University of Pittsburgh

JZ Duplicate of Nighttime sleep of Individuals with Inflammatory Bowel Disease

Individuals with inflammatory bowel disease have high rates of poor sleep quality which can impact disease activity and quality of life. The aims are to examine the co-occurring sleep disorders of those with inflammatory bowel disease, and explore the relationship…

Scientific Questions Being Studied

Individuals with inflammatory bowel disease have high rates of poor sleep quality which can impact disease activity and quality of life. The aims are to examine the co-occurring sleep disorders of those with inflammatory bowel disease, and explore the relationship between nighttime sleep (e.g., fitbit) and individual level (e.g., fatigue, overall pain, quality of life, fecal calprotectin) and environmental (e.g., neighborhood characteristics) factors in those with inflammatory bowel.

Project Purpose(s)

Disease Focused Research (inflammatory bowel disease)

Scientific Approaches

We plan to use the fibit data, surveys, and EHR. We will use R software to analyze the data (e.g., descriptive, inferential statistics).

Anticipated Findings

We anticipate that we will find that there are factors that are associated with poor sleep in those with inflammatory bowel disease. By looking at not only individual level factors, this study can help inform current sleep interventions and create new ones that help those with IBD improve their sleep.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Linda Yoo - Graduate Trainee, University of Washington
Jessica Ziemek - Graduate Trainee, University of Washington

How Diet, Specifically Fiber, Affect Sleep

How does diet, precisely the amount of fiber ingested, affect sleep? Do other aspects of diet affect sleep? Does sleep affect diet, specifically hunger? Do effects on gut microbiome change the quality of sleep? A higher-fiber diet positively affects sleep…

Scientific Questions Being Studied

How does diet, precisely the amount of fiber ingested, affect sleep? Do other aspects of diet affect sleep? Does sleep affect diet, specifically hunger? Do effects on gut microbiome change the quality of sleep? A higher-fiber diet positively affects sleep in omnivorous mammals, specifically humans, compared to a meat-heavy or fiber-lacking diet.

Project Purpose(s)

Educational

Scientific Approaches

Look at data showing what hormones and compounds are present in high-fiber diets and whether those same compounds are measured in sleep studies. If they are, compare sleep quality and length. Look at data involving dietary habits and sleep habits.

Anticipated Findings

Higher fiber diets lead to better sleep

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Collin Richardson - Undergraduate Student, Harvard Faculty of Arts and Sciences

Collaborators:

Joanne Clark-Matott - Other, Harvard Faculty of Arts and Sciences

Religiosity and Sleep

Poor sleep health is highly prevalent in the US, particularly among racially and ethnically minoritized groups, and is influenced by social determinants of health (SDOH). Engaging in religious practices may buffer against the negative effects of adverse social conditions on…

Scientific Questions Being Studied

Poor sleep health is highly prevalent in the US, particularly among racially and ethnically minoritized groups, and is influenced by social determinants of health (SDOH). Engaging in religious practices may buffer against the negative effects of adverse social conditions on health outcomes. Few studies have investigated the relationship between religiosity and sleep among diverse socioeconomic status (SES) and racial and ethnic groups. Specific questions: 1. What is the association between religiosity and sleep health? 2. Do these associations vary by sex or gender? 3. Do these associations vary by SES? 4. Do these associations vary by race and ethnicity? We hypothesize that low religiosity will be associated with poorer sleep health. These associations will be stronger among (1) women and gender minoritized groups compared to men (2) lower SES groups compared to higher SES groups, and (3) minoritized racial and ethnic groups compared to White persons.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

Poor sleep health is highly prevalent in the US, particularly among racially and ethnically minoritized groups, and is influenced by social determinants of health (SDOH). Engaging in religious practices may buffer against the negative effects of adverse social conditions on health outcomes. Few studies have investigated the relationship between religiosity and sleep among diverse socioeconomic status (SES) and racial and ethnic groups. Specific questions: 1. What is the association between religiosity and sleep health? 2. Do these associations vary by sex or gender? 3. Do these associations vary by SES? 4. Do these associations vary by race and ethnicity? We hypothesize that low religiosity will be associated with poorer sleep health. These associations will be stronger among (1) women and gender minoritized groups compared to men (2) lower SES groups compared to higher SES groups, and (3) minoritized racial and ethnic groups compared to White persons.

Anticipated Findings

Our findings will advance scientific knowledge by emphasizing the role of religiosity as an important social determinant of sleep health. We anticipate identifying novel relationships between religiosity and sleep health, highlighting the need to consider culturally and contextually relevant strategies in public health interventions aimed at addressing sleep health disparities.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Kate Christenbury - Other, DLH LLC.

Duplicate of Sleep PheWAS RTv7 Community Workspace

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:

menglu liang - Teacher/Instructor/Professor, University of Maryland, College Park

Sleep and Alzheimer's

This work seeks to explore if and what sleep patterns determined via wearables are related to and/or predictive of the development of Alzheimer's disease. This work is important to public health as it could lead to the development of a…

Scientific Questions Being Studied

This work seeks to explore if and what sleep patterns determined via wearables are related to and/or predictive of the development of Alzheimer's disease. This work is important to public health as it could lead to the development of a method for physicians to assess the risk of their patients developing Alzheimer's disease, potentially allowing for early intervention.

Project Purpose(s)

Disease Focused Research ( Alzheimer's disease)

Scientific Approaches

The AoU fitbit and EHR datasets will be critical to this work. Sleep data from the fitbit dataset will be used to predict diagnostic status in the EHR dataset with machine learning models.

Anticipated Findings

It is anticipated that a reduction in sleep later in life will be predictive of a future Alzheimer's disease diagnosis. This finding will add to the scientific body of knowledge by showing the ability of real world data to predict Alzheimer's onset.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Clayton Barnes - Graduate Trainee, Yale University

Seasonal variations in sleep and activity CTDv8

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

Data Set Used

Controlled Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

Collaborators:

Peyton Coleman - Graduate Trainee, Vanderbilt University

Duplicate of Saxena Lab Demo on Sleep Phenotype GWAS analyses

Through this workspace, the Saxena Lab at Massachusetts General Hospital intends to study the application of All Of Us data in our genetic analyses on different sleep phenotypes humans experience. We aim to be able to conduct genome wide association…

Scientific Questions Being Studied

Through this workspace, the Saxena Lab at Massachusetts General Hospital intends to study the application of All Of Us data in our genetic analyses on different sleep phenotypes humans experience. We aim to be able to conduct genome wide association studies (GWAS) of different phenotypes so we learn more about the underlying mechanisms involved in sleep phenotypes and diseases (such as CFS, ME, insomnia). These findings can then be applied and referenced in development of treatments for phenotypes.

Project Purpose(s)

Disease Focused Research (Sleep phenotypes)
Educational
Methods Development
Ancestry

Scientific Approaches

The scientific approaches we plan to use involve developing cohorts reflecting our phenotypes of interest, and using their genetic data to conduct GWAS studies in order to find genetic associations. We plan to use the All Of Us workbench as well as Python, R, and REGENIE.

Anticipated Findings

We are anticipating to find useful associations across the phenotypes of interest that can then be compared to other GWAS studies performed on other cohorts. These findings can help provide statistical power to the analyses we perform, which in turn can help us have more statistically sound findings.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Matthew Maher - Project Personnel, Mass General Brigham
Jesse Valliere - Other, Broad Institute

New Analysis: Everyday Discrimination & Sleep Analysis

We are interested in understanding the relationship between everyday discrimination and sleep outcomes.

Scientific Questions Being Studied

We are interested in understanding the relationship between everyday discrimination and sleep outcomes.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will use the everyday discrimination data from the SDOH survey and sleep data from the Fitbit dataset to study this question. We expect to use generalized linear models to characterize the relationship between these two variables.

Anticipated Findings

We anticipate that exposure to more everyday discrimination will lead to poorer sleep quality. Our findings will contribute to the growing literature describing the effect of racism and discrimination on health in minority populations.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Sarah Lee - Graduate Trainee, University of Massachusetts Medical School
Owen Leary - Graduate Trainee, Brown University

Continuation of Everyday Discrimination & Sleep Analysis

We are interested in understanding the relationship between everyday discrimination and sleep outcomes.

Scientific Questions Being Studied

We are interested in understanding the relationship between everyday discrimination and sleep outcomes.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will use the everyday discrimination data from the SDOH survey and sleep data from the Fitbit dataset to study this question. We expect to use generalized linear models to characterize the relationship between these two variables.

Anticipated Findings

We anticipate that exposure to more everyday discrimination will lead to poorer sleep quality. Our findings will contribute to the growing literature describing the effect of racism and discrimination on health in minority populations.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Sarah Lee - Graduate Trainee, University of Massachusetts Medical School

The genetics of sleep disorders and their relationship to comorbid disease v8

Our goals are to 1) discover new genetic associations with sleep disorders; 2) identify diseases that more common in participants with sleep disorders compared to participants without sleep disorders; and 3) identify non-sleep diseases that share genetic architecture with sleep…

Scientific Questions Being Studied

Our goals are to 1) discover new genetic associations with sleep disorders; 2) identify diseases that more common in participants with sleep disorders compared to participants without sleep disorders; and 3) identify non-sleep diseases that share genetic architecture with sleep disorders.

Sleep is a critical part of our lives, and disordered sleep is known to be associated with a wide range of diseases. However, the molecular processes of sleep are poorly understood. Identifying new genetic associations with sleep disorders will improve our biological understanding of sleep and may guide the development of future interventions to improve disordered sleep. Understanding the the relationship of disordered sleep to other diseases will allow us to develop interventions using a modifiable risk factor.

Project Purpose(s)

Disease Focused Research (sleep disorder)
Ancestry

Scientific Approaches

We will use genome-wide association studies, whole-genome sequence analyses, and related genetic methods (e.g. polygenic risk scores and Mendelian randomization) to study All of Us in conjunction with external clinical biobanks and datasets with objective polysomnography. We will also 'fine-map' the All of Us case/control results using TOPMed Consortium data at whole-genome sequence resolution, and will identify potential objective sleep measures that may contribute to the All of Us case/control associated signals using colocalization.

Anticipated Findings

We anticipate identifying novel genetic associations with sleep disorders, given the large sample size available in All of Us. We also anticipate identifying novel relationships between sleep disorders and comorbid disease, given the breadth of disease diagnoses in EHR-based studies compared to community-based studies with limited survey questions.

Novel genetic associations would improve our understanding of the molecular pathways associated with sleep disorders, which we anticipate would lead to improved molecular interventions to improved disordered sleep. Identifying the patterns of comorbid disease (and possible sub-groups of patients with a given sleep disorder who may be most at risk of developing these comorbidities) may lead to improvements in risk stratification for future patients.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Brian Cade - Early Career Tenure-track Researcher, Mass General Brigham

Collaborators:

Matthew Goodman - Project Personnel, Mass General Brigham

The genetics of sleep disorders and their relationship to comorbid disease

Our goals are to 1) discover new genetic associations with sleep disorders; 2) identify diseases that more common in participants with sleep disorders compared to participants without sleep disorders; and 3) identify non-sleep diseases that share genetic architecture with sleep…

Scientific Questions Being Studied

Our goals are to 1) discover new genetic associations with sleep disorders; 2) identify diseases that more common in participants with sleep disorders compared to participants without sleep disorders; and 3) identify non-sleep diseases that share genetic architecture with sleep disorders.

Sleep is a critical part of our lives, and disordered sleep is known to be associated with a wide range of diseases. However, the molecular processes of sleep are poorly understood. Identifying new genetic associations with sleep disorders will improve our biological understanding of sleep and may guide the development of future interventions to improve disordered sleep. Understanding the the relationship of disordered sleep to other diseases will allow us to develop interventions using a modifiable risk factor.

Project Purpose(s)

Disease Focused Research (sleep disorder)
Ancestry

Scientific Approaches

We will use genome-wide association studies, whole-genome sequence analyses, and related genetic methods (e.g. polygenic risk scores and Mendelian randomization) to study All of Us in conjunction with external clinical biobanks and datasets with objective polysomnography. We will also 'fine-map' the All of Us case/control results using TOPMed Consortium data at whole-genome sequence resolution, and will identify potential objective sleep measures that may contribute to the All of Us case/control associated signals using colocalization.

Anticipated Findings

We anticipate identifying novel genetic associations with sleep disorders, given the large sample size available in All of Us. We also anticipate identifying novel relationships between sleep disorders and comorbid disease, given the breadth of disease diagnoses in EHR-based studies compared to community-based studies with limited survey questions.

Novel genetic associations would improve our understanding of the molecular pathways associated with sleep disorders, which we anticipate would lead to improved molecular interventions to improved disordered sleep. Identifying the patterns of comorbid disease (and possible sub-groups of patients with a given sleep disorder who may be most at risk of developing these comorbidities) may lead to improvements in risk stratification for future patients.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Brian Cade - Early Career Tenure-track Researcher, Mass General Brigham

Collaborators:

Matthew Goodman - Project Personnel, Mass General Brigham

Frailty Index, Aging, Sleep (v8 dataset)

Building on the deficit-accumulation frailty index (AoU-FI) developed by Wong et al. (2023), we aim to explore and identify additional lifestyle and sociodemographic variables that may further improve the utility of this index in predicting health outcomes in older adults.…

Scientific Questions Being Studied

Building on the deficit-accumulation frailty index (AoU-FI) developed by Wong et al. (2023), we aim to explore and identify additional lifestyle and sociodemographic variables that may further improve the utility of this index in predicting health outcomes in older adults. The list of additional variables will be informed by the trauma (e.g., post-fall syndrome) and cognitive aging literature. A better understanding of the factors that contribute to age-related frailty will have important public health implications.

Wong CN, Wilczek M, Smith LH, Bosse JD, Richard EL, Cavanaugh R, Manjourides J, Orkaby AR, Olivieri-Mui B. Frailty Among Sexual and Gender Minority Older Adults: The All of Us Database. The Journals of Gerontology: Series A. 2023; 78 (11), 2111–2118. https://doi.org/10.1093/gerona/glad149

Project Purpose(s)

Population Health
Social / Behavioral
Methods Development

Scientific Approaches

We will use the methods outlined in Wong et al. (2023) and Searle et al (2021) to expand the AoU-FI. We will focus on adults 55 and older and utilize data from surveys, Fitbit, and EHR (upon controlled tier access approval). Additional predictors and outcome variables will be informed by the trauma (e.g., post-fall syndrome) and cognitive aging literature. While the initial AoU-FI validation study focused on frailty among sexual and gender minority older adults, we will also consider other social determinants of health.

Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K. A standard procedure for creating a frailty index. BMC Geriatr. 2008;8(1):1–10. doi:10.1186/1471-2318-8-24

Anticipated Findings

We anticipate the inclusion of factors associated with lifestyle and social determinants of health to improve the predictive power of the original AoU-FI. We also expect the revised accumulation index to be predictive of additional outcome variables (i.e., beyond risk of mortality).

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Irene Kan - Mid-career Tenured Researcher, Villanova University

Collaborators:

Michelle McKay - Early Career Tenure-track Researcher, Villanova University
Margaret Brace - Teacher/Instructor/Professor, Villanova University
Elizabeth Pantesco - Early Career Tenure-track Researcher, Villanova University

Sleep, Race and Cognitive Health in Black Adults

This study investigates the relationship between sleep quality and cognitive function in Black communities using data from the All of Us Research Program. The research aims to determine how race correlates with REM sleep, total sleep duration, and cognitive impairment.…

Scientific Questions Being Studied

This study investigates the relationship between sleep quality and cognitive function in Black communities using data from the All of Us Research Program. The research aims to determine how race correlates with REM sleep, total sleep duration, and cognitive impairment. Special attention is given to the role of structural disparities, environmental stressors, and socioeconomic conditions in shaping sleep outcomes. Fitbit-derived sleep data and cognitive health markers will be analyzed to assess whether poorer sleep quality contributes to higher cognitive impairment risk among Black participants compared to other racial groups.

Project Purpose(s)

Population Health
Social / Behavioral
Other Purpose (Health Disparities)

Scientific Approaches

For this study, I will use a quantitative, cross-sectional analysis within the All of Us Researcher Workbench using Controlled Tier data. I will analyze Fitbit-derived sleep metrics (REM sleep, NREM sleep, total sleep duration, and interruptions) alongside cognitive impairment diagnoses from electronic health records and self-reported memory concerns. Participants will be grouped by race (Black and White), and statistical methods such as one-way ANOVA and multivariate regression will be used to assess associations between sleep quality and cognitive outcomes, adjusting for age, sex, socioeconomic status, and comorbidities. Interaction analyses will explore moderating effects of structural and environmental stressors. Data analysis will be conducted using RStudio within the Workbench environment. This approach aims to uncover structural contributors to cognitive health disparities linked to sleep quality in Black communities

Anticipated Findings

The anticipated findings suggest that Black participants will have shorter sleep durations, lower REM sleep percentages, and more frequent sleep disruptions, which will be significantly associated with higher rates of cognitive impairment compared to White participants. These disparities are expected to be influenced by socioeconomic and environmental stressors. The findings would contribute to the scientific understanding of how structural inequities impact sleep and neurological health in marginalized communities. By highlighting the link between sleep quality and cognitive outcomes, this study may inform future public health interventions focused on sleep hygiene education, early neurological screening, and culturally tailored therapies. This research will also support the broader goal of addressing health disparities through precision medicine and equitable healthcare policy design.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Taylor Washington - Undergraduate Student, Spelman College

Sleep and psychiatric traits

We are interested in exploring how sleep measured by Fit-bit data (objective measure) as daily behavior is associated with psychiatric traits (both questionnaire based and EHR based traits) on a phenotypic level. Meanwhile, we would also alike to explore from…

Scientific Questions Being Studied

We are interested in exploring how sleep measured by Fit-bit data (objective measure) as daily behavior is associated with psychiatric traits (both questionnaire based and EHR based traits) on a phenotypic level. Meanwhile, we would also alike to explore from a genetic perspective and check if the genetic liability differs in different populations.

Project Purpose(s)

Disease Focused Research (Psychiatric disorders)
Ancestry

Scientific Approaches

We plan to process the time-series Fit-bit data on sleep. We plan to demonstrate the descriptive difference of sleep in different sub-populations and link with both genetic data and psychiatric traits data (questionnaire-based and EHR based).

Anticipated Findings

We anticipate to find association between sleep and various psychiatric traits (such as depression, schizophrenia, bipolar, etc.), and we anticipate that genetic liability of sleep will be different in sub-populations and thus the associations may vary in different subgroups based on genetic risks.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Yingzhe Zhang - Graduate Trainee, Mass General Brigham

Collaborators:

Justin Tubbs - Research Fellow, Mass General Brigham
Younga Lee - Research Fellow, Mass General Brigham

The genetics of sleep disorders and their relationship to comorbid disease v7

Our goals are to 1) discover new genetic associations with sleep disorders; 2) identify diseases that more common in participants with sleep disorders compared to participants without sleep disorders; and 3) identify non-sleep diseases that share genetic architecture with sleep…

Scientific Questions Being Studied

Our goals are to 1) discover new genetic associations with sleep disorders; 2) identify diseases that more common in participants with sleep disorders compared to participants without sleep disorders; and 3) identify non-sleep diseases that share genetic architecture with sleep disorders.

Sleep is a critical part of our lives, and disordered sleep is known to be associated with a wide range of diseases. However, the molecular processes of sleep are poorly understood. Identifying new genetic associations with sleep disorders will improve our biological understanding of sleep and may guide the development of future interventions to improve disordered sleep. Understanding the the relationship of disordered sleep to other diseases will allow us to develop interventions using a modifiable risk factor.

Project Purpose(s)

Disease Focused Research (sleep disorder)
Ancestry

Scientific Approaches

We will use genome-wide association studies, whole-genome sequence analyses, and related genetic methods (e.g. polygenic risk scores and Mendelian randomization) to study All of Us in conjunction with external clinical biobanks and datasets with objective polysomnography. We will also 'fine-map' the All of Us case/control results using TOPMed Consortium data at whole-genome sequence resolution, and will identify potential objective sleep measures that may contribute to the All of Us case/control associated signals using colocalization.

Anticipated Findings

We anticipate identifying novel genetic associations with sleep disorders, given the large sample size available in All of Us. We also anticipate identifying novel relationships between sleep disorders and comorbid disease, given the breadth of disease diagnoses in EHR-based studies compared to community-based studies with limited survey questions.

Novel genetic associations would improve our understanding of the molecular pathways associated with sleep disorders, which we anticipate would lead to improved molecular interventions to improved disordered sleep. Identifying the patterns of comorbid disease (and possible sub-groups of patients with a given sleep disorder who may be most at risk of developing these comorbidities) may lead to improvements in risk stratification for future patients.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Brian Cade - Early Career Tenure-track Researcher, Mass General Brigham

Collaborators:

Matthew Goodman - Project Personnel, Mass General Brigham

Sleep Apnea Study

What is the burden of sleep apnea by socioeconomic status? A sleep apnea study, or polysomnography, aims to answer questions about sleep patterns, breathing during sleep, and oxygen levels, helping to diagnose sleep apnea and other sleep disorders.

Scientific Questions Being Studied

What is the burden of sleep apnea by socioeconomic status? A sleep apnea study, or polysomnography, aims to answer questions about sleep patterns, breathing during sleep, and oxygen levels, helping to diagnose sleep apnea and other sleep disorders.

Project Purpose(s)

Disease Focused Research (Sleep Apnea)
Population Health
Social / Behavioral
Drug Development
Methods Development
Control Set
Ancestry
Ethical, Legal, and Social Implications (ELSI)

Scientific Approaches

Sleep Stages and Structure:
What are the different stages of sleep (wake, light sleep, deep sleep, REM sleep), and how long does the person spend in each stage?
Are there any disruptions or abnormalities in the normal sleep architecture?

Anticipated Findings

Sleep Stages and Structure:
What are the different stages of sleep (wake, light sleep, deep sleep, REM sleep), and how long does the person spend in each stage?
Are there any disruptions or abnormalities in the normal sleep architecture?

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Ayodele Akinremi - Early Career Tenure-track Researcher, Hampton University

COVID-19, Sleep, PAL and Lung Function

COVID-19 presents with scarring of flung tissue often resulting in reduced lung compliance, which could compromise the ventilation and quality of life. This study aims to evaluate the association between the COVID-19 profile and selected clinical and functional parameters (such…

Scientific Questions Being Studied

COVID-19 presents with scarring of flung tissue often resulting in reduced lung compliance, which could compromise the ventilation and quality of life. This study aims to evaluate the association between the COVID-19 profile and selected clinical and functional parameters (such as sleep quality, fatigue, cardiorespiratory fitness, and physical activity level). It will also explore the distribution of these outcomes by race and gender.

Project Purpose(s)

Disease Focused Research (COVID-19)
Educational

Scientific Approaches

COVID-19 cohort using clinical and functional datasets.
The research method is a cross-sectional design using secondary data analysis of the All of Us dataset.

Research question: What is the association between COVID-19 Profile (previous infection, severity, vaccination status) and lung function, PAL, sleep, and quality of life; and if this association is different by gender or race?

Anticipated Findings

Hypothesis: Previous history of severe COVID-19 infection could be associated with poor quality of life, reduced PAL, and poor sleep quality; and this association could be different by gender and race.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Ayodele Akinremi - Early Career Tenure-track Researcher, Hampton University

Collaborators:

Tafadzwa Machipisa - Research Fellow, University of Pennsylvania
Joseph Aneke - Early Career Tenure-track Researcher, Hampton University
Graham Chakafana - Early Career Tenure-track Researcher, Hampton University

Relationship Between Sleep Duration and Chronic Conditions

This study will research the degree of correlation between sleep duration and these chronic diseases, specifically Hypertension and Diabetes.

Scientific Questions Being Studied

This study will research the degree of correlation between sleep duration and these chronic diseases, specifically Hypertension and Diabetes.

Project Purpose(s)

Educational

Scientific Approaches

The study will analyze:
- Average sleep duration across demographics.
- Does short/long sleep associate with higher odds of hypertension/diabetes?
- Differences by age, gender, or race/ethnicity.

Anticipated Findings

The findings of study will further inform our knowledge on the connection between sleep habits and risk of chronic diseases.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Luca Yimnai Fotsop Bezietendem - Graduate Trainee, University of New Mexico and University of New Mexico Health Sciences Center

Undergraduate Sleep Study Research

This study hypothesizes that different mental health disorders uniquely impact sleep patterns, with some conditions leading to difficulty falling or staying asleep (e.g., anxiety and depression), while others result in irregular sleep-wake cycles (e.g., bipolar disorder) or excessive sleepiness (e.g.,…

Scientific Questions Being Studied

This study hypothesizes that different mental health disorders uniquely impact sleep patterns, with some conditions leading to difficulty falling or staying asleep (e.g., anxiety and depression), while others result in irregular sleep-wake cycles (e.g., bipolar disorder) or excessive sleepiness (e.g., schizophrenia and PTSD). Additionally, it is expected that individuals with more severe mental health symptoms will experience greater disruptions in sleep quality and duration. Individuals diagnosed with various mental health disorders, including depression, anxiety, bipolar disorder, PTSD, and schizophrenia will be focused on. A comparison group of individuals without mental health conditions will be included to analyze differences. The study will aim to include underrepresented groups to better understand disparities in sleep and mental health.

Project Purpose(s)

Educational

Scientific Approaches

Data from the All of Us database will be used to compare self-reported and device-recorded sleep patterns across different mental health conditions. Statistical analysis will be conducted to examine the relationships between the severity of mental health conditions and disruptions in sleep. Additionally, subgroup analysis will investigate how factors such as age, gender, and socioeconomic status influence sleep patterns in individuals with mental health disorders.

Anticipated Findings

Individuals with more severe mental health symptoms tend to experience greater disruptions in their sleep patterns, creating a cycle where poor sleep worsens mental health over time. Demographic and social factors also play a significant role, as underrepresented populations may face more sleep disruptions due to socioeconomic stressors, healthcare access, and lifestyle factors. Those with limited healthcare access may have untreated sleep disorders that exacerbate mental health issues. This study will provide valuable insights into the impact of mental health on sleep, potentially leading to better sleep-based interventions, personalized treatments, and heightened awareness of the importance of sleep in mental well-being, ultimately improving treatment outcomes and quality of life.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Victoria Otuonye-Otis - Undergraduate Student, Spelman College
India Nichols-Obande - Early Career Tenure-track Researcher, Spelman College

Population Differences in HRR, Sleep and Physical Activity (V8)

The objectives of this study are to assess trajectories of sleep, heart rate reserve (HRR) and trajectories of physical activity separately by race/ethnicity and sex assigned at birth. The secondary objective is to examine these trajectories for those at the…

Scientific Questions Being Studied

The objectives of this study are to assess trajectories of sleep, heart rate reserve (HRR) and trajectories of physical activity separately by race/ethnicity and sex assigned at birth. The secondary objective is to examine these trajectories for those at the intersection of race/ethnicity and sex assigned at birth.
The final aim of this study is to cross-sectionally investigate if physical activity mediates the association between stress and HRR among individuals.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

The present study plans to utilize most of the data available through All of Us which contains electronic health records, biological specimen measurements, and Fitbit measurements. This also includes demographic characteristics of participants such as age, gender identity, sexual orientation, and race. Survey measurement data will also be included such as depressive symptoms, and discrimination. Covariates of cardiovascular disease will also be investigated such as familial history of a heart disease, tobacco use, alcohol use frequency, and other substance use. Heart rate reserve (HRR), physical activity and sleep will be measured through the Fitbit data available and will be treated appropriately with established methods. Researchers will investigate trajectories with a longitudinal perspective.

Anticipated Findings

We hypothesize that racial and ethnic minorities will have faster decreasing trajectories of HRR and physical activity compared to White participants. Additionally, we predict that decreasing trajectories of physical activity will be associated with poorer trajectories of cardiometabolic health, with associations more pronounced for racial end ethnic minorities compared to White individuals.

The findings of this study will contribute to understanding how trajectories of sleep, physical activity and HRR change over time for different groups in the population. Cardiovascular disease remains the leading cause of death in the United States, and this effect is more pronounced for individuals in possession of a minority identity.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Stephanie Cook - Early Career Tenure-track Researcher, New York University
Erica Wood - Graduate Trainee, New York University

Collaborators:

Jingxuan Evelyn Ma - Graduate Trainee, New York University
Danning Tian - Graduate Trainee, New York University
Cindy Patippe - Graduate Trainee, New York University

Sleep Fitbit Data Exploration

Sleep is crucial for our human health - playing a role in psychiatric disorders, cardiovascular disease, and neurological disorder risk. Investigating how specific measures of sleep (like when we sleep, or how long we sleep, or how well we sleep)…

Scientific Questions Being Studied

Sleep is crucial for our human health - playing a role in psychiatric disorders, cardiovascular disease, and neurological disorder risk. Investigating how specific measures of sleep (like when we sleep, or how long we sleep, or how well we sleep) can be incredibly insightful. Understanding which traits of sleep are protective, or risk factors, for a certain disorder can transform the precision medicine space, and bring in insights from lifestyle.

This workspace will be used for the purpose of understanding sleep dimensions in the All of Us research program, their distributions, and data characteristics to then have a vantage point for project planning. Understanding data is key for any quantitative project. The focus will be on sleep measures in Fitbit data - sleep duration, sleep onset, restless sleep duration, sleep irregularity, and % of time in specific sleep stages (REM, light, deep).

Project Purpose(s)

Methods Development

Scientific Approaches

Data visualization tools (seaborn, matplotlib) will be applied to generate histograms, bar charts, and scatter plots. The Python pandas library will be used for summarizing the data in terms of counts and %.

Anticipated Findings

The anticipated findings is understand what sleep dimensions have enough sample size, any data harmonization and quality control efforts that must be undertaken to prepare data for a specific research question understanding sleep dimension X's effect, on Y: some disease outcome, or lifestyle trait. At this point, this is merely data exploration. A specified scientific hypothesis is not underway, as we wish to firstly understand what data is available and what sleep traits have large enough sample size in this dataset. Moreover data harmonization for sleep can be difficult and project-specific, so it is very important to first get an outlook on the data characteristics.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Pavithra Nagarajan - Project Personnel, Mass General Brigham
Heming Wang - Teacher/Instructor/Professor, Mass General Brigham

Trajectories of device-measured movement and sleep behaviors and NCD risk

Global physical activity guidelines are primarily based on survey-derived evidence, leaving critical gaps in our understanding of population-level physical activity as measured by wearable devices. These gaps hinder efforts to refine and update current recommendations. This study aims to examine…

Scientific Questions Being Studied

Global physical activity guidelines are primarily based on survey-derived evidence, leaving critical gaps in our understanding of population-level physical activity as measured by wearable devices. These gaps hinder efforts to refine and update current recommendations. This study aims to examine trajectories of 24-hour movement behaviors—including sleep, physical activity, and sedentary behavior—and their associations with noncommunicable disease risk and common mental disorders. Specifically, we will analyze intraday activity patterns, such as peak 1- and 30-min cadence, time spent in cadence-bands and activity minutes, sleep duration, sleep regularity, captured by commercial wearables to uncover insights into their impact on health.

Project Purpose(s)

Population Health
Social / Behavioral
Educational

Scientific Approaches

We plan to use electronic health records, measurements and wearables (including Fitbit (or other wearable device-based) measurements) and survey data (such as demographic characteristics of participants, overall health outcomes and healthcare utilization). We are interested in exploring the associations of stepping volume and intensity/pattern (i.e., cadence-based metrics) with prospective health outcomes. This will include (1) using step-based metrics to describe the volume, intensity and pattern of step accumulation in relation to health outcomes and (2) whether stepping trajectories over time e.g. increasing, decreasing, consistently high, consistently low) might be associated with changes in various health outcomes.

Anticipated Findings

1) Understanding movement patterns using data from consumer wearables could inform (1) future guidelines - e.g. what patterns of physical activity to recommend for better health, (2) future population surveillance approaches - e.g. how to better monitor population adherence to physical activity guidelines, (3) the design of future interventions.
2) We will provide new insights into longitudinal patterns of physical activity and risk of NCDs e.g., (1) does consistently low step count correlate with poorer health outcomes? (2) do individuals with increasing step count over time show improvements in health (e.g., lower BMI, reduced blood pressure)? (3) is a decline in step count predictive of worsening health conditions? (4) is time spent above a higher step cadence threshold (e.g., ≥100 steps/min) indicative of health benefits?
3) Step-based patterns may also vary by age, sex, race/ethnicity, socioeconomic status, education level, etc.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Jacqueline Mair - Senior Researcher, ETH Singapore SEC Ltd

Sleep Afib

This study aims to investigate the association between sleep duration and quality with the incidence of atrial fibrillation (AFib). Sleep disturbances are increasingly recognized as potential contributors to cardiovascular disease, yet their role in the development of AFib remains unclear.…

Scientific Questions Being Studied

This study aims to investigate the association between sleep duration and quality with the incidence of atrial fibrillation (AFib). Sleep disturbances are increasingly recognized as potential contributors to cardiovascular disease, yet their role in the development of AFib remains unclear. Given AFib’s strong link to stroke and heart failure, identifying modifiable risk factors like sleep patterns could help improve prevention strategies. Exploring this relationship may provide insights into whether optimizing sleep can reduce AFib risk, leading to better public health outcomes.

Project Purpose(s)

Disease Focused Research (Atrial Fibrillation)

Scientific Approaches

We will use data from the All of Us Research Program, which includes self-reported sleep patterns, wearable device data (if available), and electronic health records to identify AFib diagnoses. Statistical methods such as Cox proportional hazards models will be used to examine the association between sleep duration, sleep quality, and incident AFib while adjusting for confounding factors like age, sex, BMI, comorbidities, and lifestyle factors. Sensitivity analyses will assess whether these relationships differ by demographics or underlying health conditions.

Anticipated Findings

We anticipate that short/long or poor-quality sleep will be associated with a higher incidence of AFib. If our findings confirm this link, they would contribute to the growing evidence on sleep as a modifiable risk factor for cardiovascular health. This research could support public health initiatives advocating for better sleep hygiene as part of AFib prevention strategies, leading to improved cardiovascular outcomes and reduced healthcare burden.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

MohammadHossein Yazdanpanah - Research Fellow, Mass General Brigham

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