Research Projects Directory

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

CHIP, CAD, Sleep, Physical activities (v8)

To learn the basics of how to work with Fitbit data To prepare data related to sleep and physical activities from Fitbit To analyze role of CHIP, sleep duration and intensity of physical activities in CAD

Scientific Questions Being Studied

To learn the basics of how to work with Fitbit data
To prepare data related to sleep and physical activities from Fitbit
To analyze role of CHIP, sleep duration and intensity of physical activities in CAD

Project Purpose(s)

Ancestry

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:

Md Mesbah Uddin - Research Fellow, Broad Institute

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:

Jesse Valliere - Other, Broad Institute

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 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

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

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

Insomnia and Sleep

his study aims to explore why insomnia develops in adults as they age by identifying key risk factors like stress, lifestyle changes, and biological aging. Insomnia affects both mental and physical health, increasing the risk of anxiety, depression, and heart…

Scientific Questions Being Studied

his study aims to explore why insomnia develops in adults as they age by identifying key risk factors like stress, lifestyle changes, and biological aging. Insomnia affects both mental and physical health, increasing the risk of anxiety, depression, and heart disease. By analyzing data, I hope to uncover patterns and correlations between aging and sleep disturbances to better understand their underlying causes. These findings could help improve prevention strategies and treatment options, ultimately contributing to better public health outcomes.

Project Purpose(s)

Social / Behavioral
Educational

Scientific Approaches

The study will utilize datasets containing information on adult sleep patterns, age, and lifestyle factors to identify potential causes of late-onset insomnia. Research methods will include analyzing survey data, sleep tracking records, and health histories to examine correlations between lifestyle changes, stress, and biological aging with insomnia development. Statistical analysis tools and data visualization software will be used to detect trends and associations between these factors. By leveraging these approaches, the research aims to uncover key risk factors that contribute to insomnia in adults and inform prevention strategies.

Anticipated Findings

The study is expected to identify common risk factors and triggers for late-onset insomnia, such as accumulated stress, lifestyle changes, and biological aging processes. By analyzing these factors, the research may reveal patterns that contribute to sleep disturbances in older adults. These findings could improve early screening methods and lead to more targeted prevention and treatment strategies for insomnia. Ultimately, this study would add to the scientific understanding of how aging affects sleep and help develop better interventions to improve sleep health in adults.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Madison Jemmott - Undergraduate Student, Spelman College

Types of Sleep Apnea and Relation to BMI and Neurological Disorders

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

HEB 117: Sleep as an energy allocator (Registered only)

HEB 117 is a small upper-level research seminar at Harvard University. This semester, we will analyze data from All of Us in R to explore the relationship between sleep and energy allocation. The purpose of this workspace is to explore…

Scientific Questions Being Studied

HEB 117 is a small upper-level research seminar at Harvard University. This semester, we will analyze data from All of Us in R to explore the relationship between sleep and energy allocation. The purpose of this workspace is to explore possible cross tabulations that could be used for student projects. During sleep, energy is used to cleanse the brain via the glymphatic syndrome and to recharge the immune system. In adults, research has shown that sleep deprivation is associated with poorer mental and cardiovascular health outcomes, and with an increased risk of neurodegenerative disease and metabolic syndrome. Are these related to the effect of sleep on the brain and immune system? Additionally, pro athletes Usain Bolt, LeBron James, and Roger Federer have been reported to sleep 12 hours a day when training and competing. Is this related to their high energy expenditure, and are athletes only sleeping the recommended 7-9 hours per night sleep deprived?

Project Purpose(s)

Educational

Scientific Approaches

Fitbit sleep and activity datasets for all ages and sexes assigned at birth. For the initial analysis, groups will be assigned according to activity level (high, moderate, sedentary), sex assigned at birth and age group (18-29, 30-39. 40-49, 50-59, 60-69, 70-70, and 80+). Standard statistical methods (three-way ANOVA) will be used to determine if sleep duration varies according to activity level for each age group. Factors that can confound sleep will be considered as exclusionary criteria e.g. diagnosis with a sleep disorder.

Data on Parkinson's disease, Alzheimer's disease, depression, sleep and fatigue from Electronic Health Records will be used for exploratory data analyses (EDA) to help teach the fundamentals of data visualization, summary statistics and correlation analyses.

Anticipated Findings

The exploratory data analysis is expected to confirm the literature findings on associations between short sleep and PD, AD, depression and fatigue. Our prior research examining the sleep patterns of student athletes at Harvard demonstrated that student athletes exhibited longer sleep duration than non-athletes and we are interested to see if this finding is replicated in a larger cohort.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

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

Collaborators:

Zoe Kim - Undergraduate Student, Harvard Faculty of Arts and Sciences
Ye Won Ham - Undergraduate Student, Harvard Faculty of Arts and Sciences
Monae Stancil - Undergraduate Student, Harvard Faculty of Arts and Sciences
Lennox London - Undergraduate Student, Harvard Faculty of Arts and Sciences
Lauren Choy - Undergraduate Student, Harvard Faculty of Arts and Sciences
Kristine N - Undergraduate Student, Harvard Faculty of Arts and Sciences
Jennifer Kim - Undergraduate Student, Harvard Faculty of Arts and Sciences
Collin Richardson - Undergraduate Student, Harvard Faculty of Arts and Sciences
Anika Christensen - Undergraduate Student, Harvard Faculty of Arts and Sciences
Barisere Tuka - Undergraduate Student, Harvard Faculty of Arts and Sciences
Alyssa Perren - Undergraduate Student, Harvard Faculty of Arts and Sciences

ADHD and Sleep

Literature exists indicating there is a relationship between ADHD and difficulty sleeping. My objective is to determine whether these sleep difficulties are caused purely by ADHD, or whether they are exacerbated with common co-occurrences such as anxiety disorders or stimulant…

Scientific Questions Being Studied

Literature exists indicating there is a relationship between ADHD and difficulty sleeping. My objective is to determine whether these sleep difficulties are caused purely by ADHD, or whether they are exacerbated with common co-occurrences such as anxiety disorders or stimulant usage.

Project Purpose(s)

Educational

Scientific Approaches

I plan to use All of Us data to investigate the existence and strength of a correlation between various sleep disorders and the presence of ADHD, presence of anxiety disorder, and presence and dosage of Adderall.

Anticipated Findings

I anticipate to find evidence as to whether factors other than ADHD, specifically common co-occurring conditions such as disordered anxiety and the use of prescription stimulants, may cause or exacerbate sleep difficulties in adults with ADHD. If these non-ADHD conditions are causing sleep challenges in ADHD individuals (rather than the ADHD itself being the primary cause), this information could be important in ameliorating sleep difficulties in such individuals.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Anika Christensen - Undergraduate Student, Harvard Faculty of Arts and Sciences

Activity and sleep differences in wearable data

We wish to characterize sleep, activity, and other wearable data within the All of Us Research Program Cohort, to better understand population averages and differences across demographic groups. This may include time series analyses to determine how different life events…

Scientific Questions Being Studied

We wish to characterize sleep, activity, and other wearable data within the All of Us Research Program Cohort, to better understand population averages and differences across demographic groups. This may include time series analyses to determine how different life events (e.g. the COVID-19 pandemic, an individual diagnosis, seasonality) affect wearable data.

Project Purpose(s)

Population Health

Scientific Approaches

We will begin with basic characterization of the distributions of wearable data across different demographic groups and expand as time and billing credits allow.

Anticipated Findings

To our knowledge, the WEAR study includes the largest study-sponsored distribution of wearable devices to participants. This presents a unique opportunity to understand the data of people who would not otherwise purchase the devices for themselves.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Julia Vogel - Project Personnel, Scripps Research

Reproductive Hormones and Sleep

Sleep disorders and challenges are common experiences faced by a majority of the general population. Unfortunately, previous studies have found that women report more sleep-related disturbances than their male counterparts. Therefore, there is a critical need to understand why women…

Scientific Questions Being Studied

Sleep disorders and challenges are common experiences faced by a majority of the general population. Unfortunately, previous studies have found that women report more sleep-related disturbances than their male counterparts. Therefore, there is a critical need to understand why women encounter a greater number of sleep-related difficulties, and one of the main explanations for this disparity in sleep between women and men could be due to female reproductive hormone levels. Therefore, this study attempts to better understand the relationship between reproductive hormones and sleep by looking at the association between a woman’s ratio of estradiol to estrone and her sleep duration and wakefulness after sleep onset (WASO).

Project Purpose(s)

Educational

Scientific Approaches

This study will use a longitudinal approach to investigate the association between a woman’s estradiol/estrone ratio and sleep. This will involve looking at datasets with information about a woman’s estradiol and estrone concentration, hours of sleep, and wakefulness after sleep onset. A statistical analysis will be done to determine whether repeated measurements of sleep quality and duration are significantly associated with and dependent on a woman’s estradiol/estrone ratio. The appropriate statistical analyses will be conducted and visualized through the use of R.

Anticipated Findings

This study aims to establish a significant relationship between the ratio of estradiol and estrone in a woman and her sleep duration and quality. Doing so would be impactful on the current understanding of the relationship between a woman’s estrogen production and sleep because it would demonstrate that different types of estrogen—in this case, estradiol and estrone—have separate effects on a woman’s sleep and play different roles in the female body. This would have huge consequences for future research on female reproductive hormones and sleep because it would encourage further studies that assess the ways that hormones of the same family can still interact differently with the body.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Lauren Choy - Undergraduate Student, Harvard Faculty of Arts and Sciences

Comparing the Efficacy of Treatment Modalities in Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) is a common sleep disorder that is characterized by repetitive collapse of the pharynx (throat) during sleep, which can ultimately lead to poor outcomes over time. OSA is the most common form of disordered breathing during…

Scientific Questions Being Studied

Obstructive sleep apnea (OSA) is a common sleep disorder that is characterized by repetitive collapse of the pharynx (throat) during sleep, which can ultimately lead to poor outcomes over time. OSA is the most common form of disordered breathing during sleep, and it may be accompanied by noticeable symptoms or may be asymptomatic. Continuous positive airway pressure (CPAP) is highly effective in treating OSA; however, many patients with OSA who are prescribed CPAP do not regularly use the machine. Several other therapies are available for patients with OSA. These treatment options include promising new surgical interventions and newly approved drug therapies, such as GLP-1 (glucagon-like peptide) receptor agonists.

This project aims to examine and compare the efficacies of CPAP, surgical procedures, including but not limited to uvulopalatoplasty (UPPP) and tonsillectomy, and GLP-1 receptor agonists in reducing the occurrence of heart attack and stroke in patients with OSA.

Project Purpose(s)

Disease Focused Research (Obstructive Sleep Apnea)

Scientific Approaches

Patients with diagnosed obstructive sleep apnea will be identified through the ICD-10 code G47.33, which corresponds to the condition. Upon identifying all patients with diagnosed OSA, they will be categorized into three separate groups based on the OSA treatment modality they received: CPAP, a sleep surgical procedure, or a GLP-1 receptor agonist. Incidence of stroke (ICD-10 codes I60-I69) and heart attack (ICD-10 code I21) will be identified, excluding events occurring within 30 days of treatment initiation. To adjust for baseline differences in patient characteristics, propensity score matching will be performed based on age, BMI, comorbidities, and sex. Additionally, subgroup analyses will be performed within these variables to assess potential differences in outcomes. The number of stroke and heart attack episodes will be summed, converted to percentages for each treatment group, and compared across modalities to evaluate differences in cardiovascular risk.

Anticipated Findings

In patients diagnosed with OSA, there are several treatment modalities available, but there is little research that directly compares the efficacy of the different treatment options available in reducing major negative outcomes associated with poorly managed OSA. Information provided by conducting a retrospective study will help to identify which treatment modalities are most effective in preventing the onset of heart attack and stroke in patients with diagnosed OSA. The findings gained from this study could provide healthcare providers and patients with more information that would aid in the process of making an informed decision regarding treatment and overall management of OSA. Ultimately, more informed decision-making regarding the treatment of OSA could lead to a decrease in the incidence of stroke amongst patients diagnosed with the condition.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Christian Turner - Graduate Trainee, Wayne State University

Sleep, Social Factors, and CVD

This study will examine how social and neighborhood factors affect sleep patterns and, in turn, how changes in sleep impact the risk of conditions like high blood pressure, diabetes, and obesity. We are particularly interested in how neighborhood conditions (e.g.…

Scientific Questions Being Studied

This study will examine how social and neighborhood factors affect sleep patterns and, in turn, how changes in sleep impact the risk of conditions like high blood pressure, diabetes, and obesity. We are particularly interested in how neighborhood conditions (e.g. cohesion) and personal support systems (e.g., social support, loneliness, stress) influence sleep quality, consistency, and duration.
Poor sleep is a well-known contributor to heart disease and other chronic health conditions, but less is known about how social and environmental factors shape sleep behaviors. Understanding these relationships could help identify modifiable factors that improve sleep and reduce the risk of cardiovascular disease. These insights could inform public health efforts, community planning, and individual interventions to promote better sleep and overall health.

Project Purpose(s)

Disease Focused Research (cardiovascular disease)

Scientific Approaches

The research will involve three main datasets: (1) Fitbit sleep data, which tracks sleep duration, quality, and consistency; (2) survey responses, which include information on neighborhood conditions, social support, loneliness, and stress; and (3) electronic health records (EHRs), which provide medical diagnoses for CVD risk factors.
The study will use statistical methods to analyze how different social and neighborhood conditions are linked to sleep patterns. It will also examine whether poor sleep explains the connection between social factors and CVD risk and test whether strong social support reduces the impact of poor neighborhoods on sleep and health. The analysis will be conducted using the All of Us Researcher Workbench and R software. The findings could help identify modifiable factors that improve sleep and reduce chronic disease risk.

Anticipated Findings

We anticipate that (1) people with lower neighborhood safety, fewer social connections, and higher stress levels will have poorer sleep patterns, such as shorter sleep duration and more irregular sleep schedules; (2) poor sleep patterns will be linked to an increased risk of high blood pressure, diabetes, and obesity; and (3) strong social support may reduce the negative effects of poor neighborhood conditions on sleep and health, helping individuals maintain better sleep and lower disease risk.
These findings could provide new insights into how social and environmental conditions shape sleep behaviors and cardiovascular health. If confirmed, this study could help guide public health efforts to improve neighborhood conditions, strengthen social support systems, and promote better sleep health as a way to prevent chronic diseases.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Yashika Sharma - Early Career Tenure-track Researcher, University of Connecticut

Collaborators:

Danayit Gebreyohannes - Student, Columbia University

Duplicate of Assessing Sleep In cancEr Development acroSs mulTiple Ancestries

Diverse data in epidemiological studies is lacking, resulting in poor insight into ancestry-specific causal pathways for modifiable traits of interest and disease outcomes. As such, more research is needed to explore these differences. Aim: To explore the relationships between sleep…

Scientific Questions Being Studied

Diverse data in epidemiological studies is lacking, resulting in poor insight into ancestry-specific causal pathways for modifiable traits of interest and disease outcomes. As such, more research is needed to explore these differences.

Aim: To explore the relationships between sleep traits and risk of cancers across diverse ethnicities/ancestries.

Objectives:
1. Explore the effect of sleep traits (dyssomnia, hypersomnia, able to sleep, difficulty sleeping) on risk of breast cancer in Caucasian, African American, Hispanic and Asian populations.
2. Using ‘fitbit’ data, explore the effects of objective measures of sleep on breast cancer in Caucasian, African American, Hispanic and Asian populations.
3. Generate novel ancestry-specific sleep trait instruments for use in two-sample MR analyses, with outcome data from the breast cancer association consortium or the Pan-UK Biobank.

Project Purpose(s)

Disease Focused Research (breast cancer)
Population Health
Social / Behavioral
Ancestry

Scientific Approaches

The study will be divided into 3 main approaches:

> Observational analyses. Using individual-level data and a female-only cohort, stratified by ancestry, we will conduct multivariable regression analysis to explore the effects of each of the sleep measures on incident breast cancer outcomes.

> Genetic analyses. Using genetic data and a female-only cohort, stratified by ancestry, we will conduct one-sample MR to explore the effects of each of the sleep measures on incident breast cancer outcomes.

> Genetic analyses. Using genetic data from combined male and female data (to maximise power), we will conduct ancestry-specific GWAS of each of the sleep traits and extract genome-wide significant SNPs (p < 5×10−8). Female-specific effect estimates will then be extracted for these SNPs, and these will then be used to conduct two-sample MR of sleep trait effect on breast cancer risk.

Anticipated Findings

Surveys from GLOBOCAN 2020 have demonstrated variability in both incidence and mortality rates for cancer across different geographic populations, and this variability can also be found between different ethnicities/ancestries within a given population. In addition, several studies have reported that sleep duration in East Asian countries, particularly Japan, is substantially shorter than in many Western countries. A study comparing sleep across 10 countries found that in Japan, 28.5% of subjects reported insomnia symptoms (as determined by an Athens insomnia scale score of ≥6), with similar rates found in countries from Western Europe (17.4 – 36.0%). Together, these suggest that genetic causal pathways may differ between ancestries. Therefore, we would expect our findings to reflect this.

This study will help to emphasize the importance of inclusivity in healthcare research and the need for tailored policy to ensure equitable treatment regardless of ethnicity or ancestry.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Bryony Hayes - Graduate Trainee, University of Bristol

Sleep Disturbances and Glaucoma

Are sleep disorders associated with increased risk of glaucoma?

Scientific Questions Being Studied

Are sleep disorders associated with increased risk of glaucoma?

Project Purpose(s)

Disease Focused Research (glaucoma, sleep apnea, sleep disturbances)

Scientific Approaches

Using All-of-Us datasets, we plan to use case-control and retrospective cohort study designs to study associations between various sleep disturbances and glaucoma. We plan to study the association of glaucoma with various metrics of sleep disturbance, including EHR data, sleep survey data, and Fitbit data.

We will use univariate and multivariable logistic regression models to evaluate whether these factors are associated with increased risk of glaucoma.

Anticipated Findings

We anticipate to find that sleep disturbances are associated with higher risk of glaucoma. Understanding the potential association between sleep disturbances and glaucoma can inform the development of effective strategies to improve ocular health and facilitate earlier diagnoses of glaucoma through encouraging collaboration between sleep specialists and ophthalmologists.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Sophia Zhang - Graduate Trainee, University of Pennsylvania
Jiamu He - Project Personnel, University of Pennsylvania

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

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