Research Projects Directory

Environment, Sleep and health Test Project

Epidemiological evidence has shown climate change (including extreme heat and elevated temperature) have adverse effects on population health, including sleep disruption and adverse disease incidence. This study will link temperature data with All of US data to understand heat exposure…

Scientific Questions Being Studied

Epidemiological evidence has shown climate change (including extreme heat and elevated temperature) have adverse effects on population health, including sleep disruption and adverse disease incidence. This study will link temperature data with All of US data to understand heat exposure on sleep, to understand how high temperature influence sleep among All of US participants.

Project Purpose(s)

Disease Focused Research (cardiovascular disease, type 2 diabetes, cardiometabolic disease)
Population Health

Scientific Approaches

We will match All of US participants' ZIPCode with spatial-temporal datasets. Sleep disruption will be collected from Fitbit sleep tracking data. Epidemiological analysis will be conducted to assess heat exposure on sleep disruption and disease outcomes.

Anticipated Findings

We anticipated that we will find heat exposure is adversely affecting participants sleep, and that sleep disruption is associated with increased disease outcomes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Jiawen Liao - Research Fellow, University of Southern California

DST_Sleep_Analysis_2.0

NA

Scientific Questions Being Studied

NA

Project Purpose(s)

Population Health

Scientific Approaches

NA

Anticipated Findings

NA

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Ke Wang - Graduate Trainee, Duke University
Hayoung Jeong - Graduate Trainee, Duke University

Collaborators:

Sarah Jiang - Undergraduate Student, Duke University

AIM-AHEAD - Impact of Heavy Drinking on Sleep

This study seeks to determine if there are sleep metrics from Fitbit data that are correlated with heavy drinking (alcohol use disorder).

Scientific Questions Being Studied

This study seeks to determine if there are sleep metrics from Fitbit data that are correlated with heavy drinking (alcohol use disorder).

Project Purpose(s)

Disease Focused Research (Alcohol Use Disorder)

Scientific Approaches

I will use logistic regression to predict sleep quality as a function of alcohol consumption.

Anticipated Findings

The study will measure the impacts of heavy drinking on sleep patterns, including total sleep, light sleep, deep sleep, and time awake.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Jonathan Holt - Research Associate, All of Us Researcher Academy/RTI International
John McCarthy - Project Personnel, All of Us Researcher Academy/RTI International
Edward Preble - Senior Researcher, All of Us Researcher Academy/RTI International
Angela Gasdaska - Teacher/Instructor/Professor, All of Us Researcher Academy/RTI International
Andrew Burnette - Administrator, All of Us Researcher Academy/RTI International

Collaborators:

Daniel Brannock - Senior Researcher, All of Us Researcher Academy/RTI International
Pooja Gaur - Project Personnel, All of Us Researcher Academy/RTI International
Meghan Hegarty-Craver - Research Associate, All of Us Researcher Academy/RTI International
Hope Davis-Wilson - Research Associate, All of Us Researcher Academy/RTI International

Are Neurodegenerative Diseases and Sleep Deprivation connected

The specific scientific question we intend to study is Does sleep deprivation increase the risk of Neurodegenerative Diseases? We hope to be able to use the All of US research Data set to gain an understanding of the numerical data…

Scientific Questions Being Studied

The specific scientific question we intend to study is Does sleep deprivation increase the risk of Neurodegenerative Diseases? We hope to be able to use the All of US research Data set to gain an understanding of the numerical data associated with diseases such as ALS, Parkinson's Disease, Primary Lateral Sclerosis, Multiple System Atrophy and Multiple Sclerosis. These cause the nerve cells in muscles to act out, leads to stiffness in the arms. The cause of these is still unknown. This is important because we can find possible causes of this disease using this data.

Project Purpose(s)

Educational

Scientific Approaches

We plan to use sleep datasets of patients who had been diagnosed with Neurodegenerative Diseases, and compare them to the average hours of sleep of common people and as a control we will compare them both to the recommended hours of sleep. Using this data we will create a bar graph and create visualizations and use a statistical test to prove our findings.

Anticipated Findings

We expect to see the conclusion that sleep deprivation is a possibility of Neurodegenerative diseases, and this can contribute to the scientific knowledge in the field due to the cause of this disease being unknown can further promote an incentive to sleep better.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Zheyang Wu - Late Career Tenured Researcher, Worcester Polytechnic Institute
Kadhir Vallatharasu - Other, Acton-Boxborough Regional School District
Tushar Sivakumar - Other, Acton-Boxborough Regional School District
Aaron Mathieu - Teacher/Instructor/Professor, Acton-Boxborough Regional School District

Sleep Regularity Analysis

Our primary goal is to obtain a survey on sleep regularity in AOU cohort.

Scientific Questions Being Studied

Our primary goal is to obtain a survey on sleep regularity in AOU cohort.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will compute numeric measurements of sleep regularity for patients using the sleep levels and sleep times.

Anticipated Findings

We expect to obtain a distribution of sleep regularity among people and how this distribution changes in time.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Adnan Cihan Cakar - Project Personnel, Vanderbilt University Medical Center

Typical Sleep Period CTDv7

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

Scientific Questions Being Studied

Our primary goal is to understand the interaction and association between sleep quality and human disease. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking care. Currently, the Fitbit sleep algorithm divides sleep into main sleep and non-main sleep, where main sleep is the longest sleep period during the night. We hope to improve upon this algorithm to better capture sleep periods, which will in turn provide a clearer picture of the development and progression of human disease as they relate to Fitbit sleep metrics.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will examine the relationship between daily sleep patterns over time and the prevalence and progression of coded human diseases. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality). We will be utilizing the controlled tier version of AOU in this workspace. We will develop an algorithm that uses the granular sleep levels (REM, Deep, Light, Wake) as input and as output, the algorithm will provide a flag as to whether the sleep segment is part of the typical sleep period.

Anticipated Findings

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

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

Collaborators:

Adnan Cihan Cakar - Project Personnel, Vanderbilt University Medical Center

Evaluating Sleep and Physical Activity in Ocular Diseases Using Wearable Devices

Scientific Question: Do individuals with common ocular diseases, such as age-related macular degeneration, glaucoma, cataracts, and dry eye disease, exhibit distinct patterns in sleep and physical activity compared to those without these diseases? Importance: The visual system is a key…

Scientific Questions Being Studied

Scientific Question: Do individuals with common ocular diseases, such as age-related macular degeneration, glaucoma, cataracts, and dry eye disease, exhibit distinct patterns in sleep and physical activity compared to those without these diseases?
Importance: The visual system is a key factor in the regulation of circadian rhythms. There is a growing body of literature on the associations between common ocular diseases and both sleep and physical activity. However, the directionality and mechanisms of these relationships have yet to be elucidated. The study addresses a gap in understanding how ocular health impacts circadian rhythms and physical activity and vice versa. Findings could improve understanding of disease mechanisms and evaluate the use of wearable devices in ophthalmic research.

Project Purpose(s)

Disease Focused Research (ocular diseases)

Scientific Approaches

This study will use data from wearable devices (e.g., Fitbit) linked with health outcomes data. Cases are individuals with ocular diseases, and controls are matched by age, gender, race, and ethnicity. Sleep and physical activity patterns collected by Fitbits are the primary outcomes, measured as average daily sleep duration, average daily sleep pattern distribution (restless, sleep), sleep irregularity, sleep stage distribution (REM, deep, light), and daily step count. Linear and/or logistic regression models will be used to assess the associations between these outcomes and ocular diseases, accounting for confounders such as smoking, diabetes, and obesity.

Anticipated Findings

We anticipate that individuals with ocular diseases will have shorter and more irregular sleep patterns and lower physical activity levels than those without such conditions. These findings could enhance our understanding of the role of vision in circadian regulation and physical activity. Additionally, the study would emphasize the utility of wearable devices in providing accessible, objective data for ophthalmic epidemiology research. Results may inform interventions to improve health outcomes in individuals with ocular diseases.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Alan Huang - Graduate Trainee, University of Pennsylvania

Collaborators:

Jiamu He - Project Personnel, University of Pennsylvania
Gui-shuang Ying - Late Career Tenured Researcher, University of Pennsylvania

Duplicate of Sleep Regularity

Our primary goal is to obtain a survey on sleep regularity in AOU cohort.

Scientific Questions Being Studied

Our primary goal is to obtain a survey on sleep regularity in AOU cohort.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will compute numeric measurements of sleep regularity for patients using the sleep levels and sleep times.

Anticipated Findings

We expect to obtain a distribution of sleep regularity among people and how this distribution changes in time.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Adnan Cihan Cakar - Project Personnel, Vanderbilt University Medical Center

Sleep Regularity

Our primary goal is to obtain a survey on sleep regularity in AOU cohort.

Scientific Questions Being Studied

Our primary goal is to obtain a survey on sleep regularity in AOU cohort.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will compute numeric measurements of sleep regularity for patients using the sleep levels and sleep times.

Anticipated Findings

We expect to obtain a distribution of sleep regularity among people and how this distribution changes in time.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Adnan Cihan Cakar - Project Personnel, Vanderbilt University Medical Center

ALS with Sleep Deprivation

The specific scientific question we intend to study is Does sleep deprivation increase the risk of Amyotrophic Lateral Sclerosis? Do we hope to be able to use the All of US research Data set to gain an understanding of the…

Scientific Questions Being Studied

The specific scientific question we intend to study is Does sleep deprivation increase the risk of Amyotrophic Lateral Sclerosis? Do we hope to be able to use the All of US research Data set to gain an understanding of the numerical data associated with Amyotrophic Lateral Sclerosis(ALS). ALS is a disease that causes the nerve cells to die, leads to stiffness in the arms, and soon after results in paralysis and death. The cause of this is still unknown. This is important because we can find possible causes of this disease using this data.

Project Purpose(s)

Educational

Scientific Approaches

We plan to use sleep datasets of patients who had been diagnosed with ALS, and compare them to the average hours of sleep of common people and as a control we will compare them both to the recommended hours of sleep. Using this data we will create a bar graph and create visualizations and use a statistical test to prove our findings. In order to further prove this, we will do the same, however with the amount of calories consumed.

Anticipated Findings

We expect to see the conclusion that sleep deprivation is a possibility of ALS, and this can contribute to the scientific knowledge in the field due to the cause of this disease being unknown can further promote an incentive to sleep better.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Tushar Sivakumar - Other, Acton-Boxborough Regional School District

Sleep, Cardiovascular Disease, and Socioeconomic Interactions

I aim to investigate the associations between sleep patterns, cardiovascular disease prevalence, and mortality across different socioeconomic groups. Additionally, I will examine the influence of physical activity and sedentary behavior, focusing on their interactions with sleep. Understanding these relationships is…

Scientific Questions Being Studied

I aim to investigate the associations between sleep patterns, cardiovascular disease prevalence, and mortality across different socioeconomic groups. Additionally, I will examine the influence of physical activity and sedentary behavior, focusing on their interactions with sleep. Understanding these relationships is critical for informing public health interventions aimed at reducing cardiovascular risks and improving sleep health among disadvantaged groups.

Project Purpose(s)

Disease Focused Research (Cardiovascular disease)
Population Health
Social / Behavioral
Methods Development
Control Set
Ethical, Legal, and Social Implications (ELSI)

Scientific Approaches

I will use the All of Us Controlled Tier v7 to explore sleep-related data, cardiovascular disease prevalence, mortality, and socioeconomic factors. My analysis will involve querying sleep and physical activity measures using accelerometer data, self-reported sleep questionnaires, and cardiovascular health outcomes. Statistical analysis methods such as Cox proportional hazards models and linear regression will be applied to assess the impact of physical activity and sedentary behavior on cardiovascular outcomes, adjusted for socioeconomic status.

Anticipated Findings

I anticipate identifying significant associations between poor sleep patterns, reduced physical activity, and higher cardiovascular disease risks, particularly in lower socioeconomic groups. The findings will contribute to understanding how sleep and activity disparities affect cardiovascular health and highlight the need for targeted interventions to improve health outcomes in underrepresented populations. These insights could inform future public health strategies aimed at reducing health disparities and improving sleep and cardiovascular health.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Sai Rahul Ponnana - Other, Case Western Reserve University

Collaborators:

Skanda Moorthy - Other, Case Western Reserve University
Jean-Eudes Dazard - Other, Case Western Reserve University

Insomnia and associations with sleep and health outcomes

Insomnia is a common sleep disorder that adversely impacts daily functioning and quality of life. Insomnia is comorbid with multiple conditions, but rates of comorbidity are lacking in large cohort data which is an important gap in knowledge to address…

Scientific Questions Being Studied

Insomnia is a common sleep disorder that adversely impacts daily functioning and quality of life. Insomnia is comorbid with multiple conditions, but rates of comorbidity are lacking in large cohort data which is an important gap in knowledge to address to better understand the scale of insomnia comorbidity in clinical practice. This study will examine temporal relationships between insomnia diagnosis and other sleep, physical health, and mental health diagnoses in the All Of Us dataset. A secondary aim is to examine objectively-derived sleep from the Fitbit device and associations with insomnia, and how objectively poor sleep combined with insomnia may lead to worse health outcomes than either alone.

Project Purpose(s)

Disease Focused Research (Insomnia)

Scientific Approaches

This study will be conducted on people who have a diagnosis of insomnia in the electronic health record. Associations and time to diagnosis for multiple health conditions will be explored, including: hypertension, cancer, diabetes, cardiovascular diseases, depression, anxiety, PTSD, OSA, and chronic pain. The secondary analysis will examine whether insomnia combined with objective sleep problems (duration, timing, quality, regularity) impacts these relationships versus insomnia alone.

Anticipated Findings

It is anticipated that this study will yield novel insights on the comorbidity of insomnia and how it impacts risk of developing comorbid disorders. This will help inform risk assessments to prevent or diagnose sooner other health conditions after an insomnia diagnosis.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Hannah Scott - Early Career Tenure-track Researcher, Flinders University

Dual orexin receptor antagonists for sleep and alcohol use

Alcohol use, particularly regular alcohol use, is known to negatively impact sleep. The hypocretin/orexin neurotransmitter system is known to regulate arousal and sleep, and has been identified as a promising target for potential treatment for addiction in preclinical work. The…

Scientific Questions Being Studied

Alcohol use, particularly regular alcohol use, is known to negatively impact sleep. The hypocretin/orexin neurotransmitter system is known to regulate arousal and sleep, and has been identified as a promising target for potential treatment for addiction in preclinical work. The purpose of the present analysis is to test whether a dual orexin receptor antagonist may reduce alcohol use or alcohol craving by testing the following questions:

1. Do dual orexin receptor antagonists improve objective and subjective sleep outcomes (e.g., total sleep time, subjective sleep symptoms) among persons engaged in regular alcohol use.

2.Do dual orexin receptor antagonists improve alcohol-related outcomes.

Project Purpose(s)

Drug Development
Other Purpose (Pilot data supporting grant submission.)

Scientific Approaches

Using data from All of Us Restricted Dataset and results from surveys, EHR, and digital health (e.g., FitBit), we will create a cohort of individuals engaged in risky alcohol use. Within this cohort, we will compare individuals with and without a DORA prescription on outcomes related to sleep measured via FitBit and self report. We will also test whether these groups differ based on alcohol characteristics. We will account for demographic characteristics between those with and without a DORA prescription to control for variability that may impact the outcomes that we are interested in.

Anticipated Findings

We expect that persons who received a DORA will have longer total sleep time and shorter wake after sleep onset, better subjective sleep, and reduced symptoms of problematic alcohol use. If our hypotheses are supported and DORA medications are associated with better sleep in persons engaged in risky drinking as well as reduced symptoms of problematic alcohol use, the we intend to use these findings as pilot data to support a grant application to conduct a double-blind randomized controlled trial to more thoroughly assess this question.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Jennifer Ellis - Early Career Tenure-track Researcher, Johns Hopkins University

Genetic Integration into Sleep Insights

This study aims to explore the relationship between genetics, sleep patterns, disease outcomes, and biomarker levels by leveraging the All of Us dataset, focusing on participants with EHR, wearable, and genomic data (~8,000 records). We will layer genomic data into…

Scientific Questions Being Studied

This study aims to explore the relationship between genetics, sleep patterns, disease outcomes, and biomarker levels by leveraging the All of Us dataset, focusing on participants with EHR, wearable, and genomic data (~8,000 records). We will layer genomic data into an existing All of Us-based sleep study referenced below to investigate genetic links to sleep and health outcomes. Advanced statistical modeling will be applied to integrate genomic, clinical, and phenotypic data.

Reference:
Zheng NS, Annis J, Master H, Han L, Gleichauf K, Ching JH, Nasser M, Coleman P, Desine S, Ruderfer DM, Hernandez J, Schneider LD, Brittain EL. Sleep patterns and risk of chronic disease as measured by long-term monitoring with commercial wearable devices in the All of Us Research Program. Nat Med. 2024 Sep;30(9):2648-2656. doi: 10.1038/s41591-024-03155-8. Epub 2024 Jul 19. PMID: 39030265; PMCID: PMC11405268.

Project Purpose(s)

Ancestry

Scientific Approaches

We plan to use Fitbit data, EHR data, and the Genomic data in a Python environment. We will be testing out various ML models to see if we can find one with good predictive power.

Anticipated Findings

We aim to explore whether sleep patterns (as observed via FitBit data) and clinical outcomes (as observed via EHR data) have any correlation to specific sleep-associated genetic variants.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Talia Haller - Graduate Trainee, Johns Hopkins University

Collaborators:

Nitish Aswani - Graduate Trainee, Johns Hopkins University
Levon Galstyan - Graduate Trainee, Johns Hopkins University

An Exploratory Study on Psychedelic Use and Sleep

The questions we intend to answer with this research study are: (1) Does use of psychedelics contribute to changes in total sleep time and amount of time spent in each stage of sleep (REM, N1, N2, N3)? (2) Does the…

Scientific Questions Being Studied

The questions we intend to answer with this research study are: (1) Does use of psychedelics contribute to changes in total sleep time and amount of time spent in each stage of sleep (REM, N1, N2, N3)? (2) Does the frequency of psychedelic use contribute to changes in total sleep time and amount of time spent in each stage of sleep (REM, N1, N2, N3)? and (3) Do psychedelic users have differences in total sleep time and amount of time spent in each sleep stage (REM, N1, N2, N3) compared to those who do not use? This research may help inform public health policy as it relates to psychedelic use by increasing our knowledge of sleep detriments related to its use.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

This research study will utilize a cross-sectional method to determine relationships between psychedelic use, total sleep time and time spent in each stage of sleep (REM, N1, N2, N3). We will first asses for normality by analyzing descriptives and frequencies. Following that we will employ correlational analysis, regression analysis, and independent t-tests to test our hypotheses. All data will be analyzed using SPSS.

Anticipated Findings

We anticipate that we will find psychedelic use is associated with changes in total sleep time as well as the amount of time spent in each sleep stage (NREM, N1, N2, N3). We also expect to find group differences between psychedelic users and non-users on total sleep time and time spent in each stage of sleep (NREM, N1, N2, N3). This research study will be the first of its kind to assess associations between total sleep time and amount of time spent in each stage of sleep (NREM, N1, N2, N3) in users of psychedelics, and to assess how the frequency of use affects these patterns. This could lead to advances in practice that enhance our understanding of the sleep consequences of psychedelic use.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

April Roeper - Graduate Trainee, Delaware State University

Sleep phenotypes and psychosocial functioning of orofacial pain conditions

The aim of this study is to explore differences in objective sleep parameters among different orofacial pain conditions (headache, TMD, TACs, and neuropathic pain), and their correlation with psychosocial functioning (fatigue, pain, emotional distress, and social functioning).

Scientific Questions Being Studied

The aim of this study is to explore differences in objective sleep parameters among different orofacial pain conditions (headache, TMD, TACs, and neuropathic pain), and their correlation with psychosocial functioning (fatigue, pain, emotional distress, and social functioning).

Project Purpose(s)

Disease Focused Research (Orofacial Pain, Neuropathic Pain)
Social / Behavioral

Scientific Approaches

This study is assembling cohorts from the All of Us Registered data tier to identify participants with a diagnosis of orofacial pain. EHR data will be screened to identify participants with records relating to (1) tempromandibular disorders (TMD), (2) neuropathic pain to the trigeminal nerve, (3) migraine, and (4) trigeminal autonomic cephalagia. We will then crossreference participants with more than one of these diagnoses with the available Fitbit Sleep Daily Summary.

Anticipated Findings

We anticipate finding increased reporting of psychosocial functioning problems (fatigue, pain, emotional distress, and social functioning) with participants who have orofacial pain conditions. We anticipate finding differences in tracked sleep patterns with those participants diagnosed with orofacial pain conditions.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Linda Sangalli - Teacher/Instructor/Professor, Midwestern University
Gabriel Kowalczyk - Project Personnel, University of Pittsburgh

Frailty Index, Aging, Sleep

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

Race and disparities in sleep and activity for autoimmune skin disease

Purpose is to investigate if the negative effects of autoimmune skin diseases on sleep and activity varies with race. If results are significant, the hope is to publish findings to a peer reviewed journal and raise awareness of this disparity.

Scientific Questions Being Studied

Purpose is to investigate if the negative effects of autoimmune skin diseases on sleep and activity varies with race. If results are significant, the hope is to publish findings to a peer reviewed journal and raise awareness of this disparity.

Project Purpose(s)

Other Purpose (Purpose is to investigate if the negative effects of autoimmune skin diseases on sleep and activity varies with race. If results are significant, the hope is to publish findings to a peer reviewed journal. )

Scientific Approaches

Purpose is to investigate if the negative effects of autoimmune skin diseases on sleep and activity varies with race. Fitbit data will be used to assess activity and sleep disparities between different populations.

Anticipated Findings

We anticipate finding a disparity between different racial groups on the impact of autoimmune skin conditions on sleep and activity.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Christopher Reagen - Graduate Trainee, University of Missouri

Afib and sleep

1.Is there an association between sleep patterns and the prevalence of atrial fibrillation? 2.Is there an association between sleep cycle duration and prevalence of atrial fibrillation

Scientific Questions Being Studied

1.Is there an association between sleep patterns and the prevalence of atrial fibrillation?
2.Is there an association between sleep cycle duration and prevalence of atrial fibrillation

Project Purpose(s)

Disease Focused Research (atrial fibrillation)

Scientific Approaches

we will extract the sleep cycles information from the Fitbit dataset. Using the ICD-10 codes we will extract the prevalence of atrial fibrillation and use descriptive statistics and logistics regression to explore the associatioons.

Anticipated Findings

One recent review highlights that sleep disorders generally, including interruptions in REM sleep due to obstructive sleep apnea, may lead to increased sympathetic nervous system activity and systemic inflammation, both of which are factors in AF development. This supports the hypothesis that reduced or fragmented REM sleep can contribute to AF by increasing autonomic instability and cardiovascular stress. Additionally, a study from the American Heart Association has linked sleep disturbances in REM stages, particularly in patients with insomnia or stress-related sleep issues, with a higher likelihood of AF in certain populations, such as postmenopausal women and young adults with high-stress occupations. Future research is needed to understand REM sleep’s precise role in AF risk and whether improving REM sleep duration or quality could serve as a preventative measure.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Erfan Tasdighi - Research Fellow, Johns Hopkins University

Activity and Sleep During Cancer Progression

We are interested in the use of wearable devices to better personalise cancer care, treatment and interventions. Patients with metastatic bone cancer are at risk of fracture and may undergo radiotherapy or surgery to address metastasis. Fracture risk How does…

Scientific Questions Being Studied

We are interested in the use of wearable devices to better personalise cancer care, treatment and interventions. Patients with metastatic bone cancer are at risk of fracture and may undergo radiotherapy or surgery to address metastasis. Fracture risk

How does the site of primary tumors or the site of metastatic bone disease change patient activity and functionality?
Does prophylactic fixture of a potential pathological fracture improve functionality?
Does baseline activity vary across demographics and what factors must we try to account for when personalising risk scores based on wearable data?
Does engagement with digital health vary across cancer patients other demographics and how can we improve patient access to clinical trials using wearables?

Project Purpose(s)

Disease Focused Research (Metastatic Bone Disease)
Methods Development

Scientific Approaches

Data:
Demographic data (sex, gender, race and ethnicity)
Pain and functionality questionnaires
Fitbit activity data
Treatment and Surgical information

Anticipated Findings

We anticipate evaluating baseline activity differences in patient cohorts to build stratified risk models and associations of activity changes with disease progression in MBD. This data would ideally support future trials in clinical contexts that allow us to improve, accelerate, and tailor patient care.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Rowan Barker-Clarke - Research Fellow, Cleveland Clinic
Alexya Jankowski - Project Personnel, University of Arizona

cancer_sleep_quality

The primary scientific question we aim to study is: How can wearable device data (e.g., sleep patterns) and clinical data (e.g., treatment side effects, psychological factors) be integrated to develop accurate AI models for predicting sleep quality in cancer patients?…

Scientific Questions Being Studied

The primary scientific question we aim to study is: How can wearable device data (e.g., sleep patterns) and clinical data (e.g., treatment side effects, psychological factors) be integrated to develop accurate AI models for predicting sleep quality in cancer patients? This question is highly relevant because sleep disturbances are prevalent in up to 70% of cancer patients and negatively impact fatigue, pain, depression, and treatment outcomes. Currently, a significant gap exists in research exploring how clinical conditions affect wearable data accuracy in this population. By examining this intersection, we can create predictive models to better understand the unique sleep challenges faced by cancer patients. Ultimately, addressing this gap is crucial for developing personalized, data-driven interventions aimed at improving sleep quality, enhancing overall health, and optimizing treatment responses in cancer care.

Project Purpose(s)

Population Health
Methods Development

Scientific Approaches

Our study will use a multidisciplinary approach, integrating wearable device data and clinical data from cancer patients to predict sleep quality. We will leverage datasets from wearable devices (e.g., Fitbit, smartwatches) that provide continuous sleep metrics like total sleep time, sleep stages, and sleep efficiency. These will be paired with Electronic Health Records (EHR) containing clinical information such as cancer type, treatment regimens, pain levels, psychological distress, and medication use.

We will employ machine learning (ML) techniques, including supervised learning models such as random forests and neural networks, to develop predictive models of sleep quality. Feature engineering will be performed on both wearable and clinical data to capture relevant patterns. Additionally, statistical methods will be used to analyze the associations between clinical conditions and sleep metrics.

Anticipated Findings

We anticipate that the study will uncover significant relationships between clinical factors (e.g., cancer type, treatment side effects, psychological distress) and sleep patterns measured through wearable devices. Specifically, we expect to develop machine learning models that can accurately predict sleep disturbances in cancer patients based on a combination of clinical and wearable data.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Sheida Habibi - Research Fellow, Emory University
Selen Bozkurt - Early Career Tenure-track Researcher, Emory University
Jung In Park - Early Career Tenure-track Researcher, University of California, Irvine

Sleep Medications and Wearables

Do sleep aids results in longer or “better” sleep. Or more generically, how to sleep aids affect sleep? These data exist in clinical trials, but real world data are limited.

Scientific Questions Being Studied

Do sleep aids results in longer or “better” sleep. Or more generically, how to sleep aids affect sleep? These data exist in clinical trials, but real world data are limited.

Project Purpose(s)

Disease Focused Research (sleep disorders)
Population Health

Scientific Approaches

The cohort will consist of people who have Fitbit monitoring before and after being newly prescribed one of the following. We will organize medications by class:

1. Benzodiazepines
2. Non-Benzodiazepine Hypnotics (Z-drugs)
3. Melatonin Receptor Agonists
4. Orexin Receptor Antagonists
5. Antidepressants (used off-label for sleep)
6. Antihistamines
7. Melatonin Supplements

We will pull basic demographics and clinical characteristics of the people who do and don’t get these medications prescribed. In our initial analysis, we will provide summary statistics of all the sleep metrics before and after exposure to each class. Follow-up analyses will include time windowing and more sophisticated modeling.

Anticipated Findings

While sleep medications are intended to increase the quantity and quality of sleep, real world data are limited. We may see a change in quantity of sleep (i.e. minutes asleep) and a change in quality (i.e. increased deep sleep), or we may see a change in one and not the other. Lastly, we may see no change in either.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

Sleep PheWAS CTDv7

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

Scientific Questions Being Studied

Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will examine the relationship between daily sleep patterns over time and the prevalence and progression of coded human diseases. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality). We will be utilizing the controlled tier version of AOU in this workspace.

Anticipated Findings

We expect to find that lower levels of sleep and certain sleep patterns are associated with a higher prevalence and more rapid progression of chronic diseases. We may find clustering in activity and disease prevalence/severity which would motivate studies/interventions to reduce these health disparities. We may also find patterns in seasonal, weekly, or daily patterns in physical activity lead to differences in outcomes.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

Collaborators:

Peyton Coleman - Graduate Trainee, Vanderbilt University
Hiral Master - Project Personnel, All of Us Program Operational Use
Lide Han - Project Personnel, Vanderbilt University Medical Center

Seasonal variations in sleep and activity

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

Scientific Questions Being Studied

Our primary goal is to understand the interaction between seasonal variations of activity and sleep quality with the development and progression of human disease. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will examine the relationship between seasonal variations in daily sleep patterns over time and the prevalence and progression of coded human diseases. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality). We will be utilizing the controlled tier version of AOU in this workspace.

Anticipated Findings

We expect to find that lower levels of sleep and certain sleep patterns are associated with a higher prevalence and more rapid progression of chronic diseases. We also expect to find seasonal variations in sleep patterns and will examine the extent to which these may play a role in the progression of chronic disease. We may find clustering in activity and disease prevalence/severity which would motivate studies/interventions to reduce these health disparities. We may also find patterns in seasonal, weekly, or daily patterns in physical activity lead to differences in outcomes.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

Collaborators:

Lide Han - Project Personnel, Vanderbilt University Medical Center
Christopher Chen - Undergraduate Student, Vanderbilt University

Sleep Apnea - Delays to Care (General Socio Disp)

In this study, we seek to examine the impact of sleep apnea on QoL, healthcare access, and utilization, with special focus on race and ethnicity.

Scientific Questions Being Studied

In this study, we seek to examine the impact of sleep apnea on QoL, healthcare access, and utilization, with special focus on race and ethnicity.

Project Purpose(s)

Disease Focused Research (sleep apnea)
Population Health
Social / Behavioral

Scientific Approaches

We will use a propensity-matched cohort to study the impact of sleep apnea diagnosis on and quality of life.

Anticipated Findings

We expect to find that those of lower socioeconomic status, minority background limited education will be less likely to have had a CPAP prescription with delays in care and more significant loss to follow up.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Oluwatobiloba Ayo-Ajibola - Graduate Trainee, University of Southern California

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