The All of Us Data and Research Center: Creating a Secure, Scalable, and Sustainable Ecosystem for Biomedical Research.

The All of Us Research Program's Data and Research Center (DRC) was established to help acquire, curate, and provide access to one of the world's largest and most diverse datasets for precision medicine research. Already, over 500,000 participants are enrolled in All of Us, 80% of whom are underrepresented in biomedical research, and data are being analyzed by a community of over 2,300 researchers. The DRC created this thriving data ecosystem by collaborating with engaged participants, innovative program partners, and empowered researchers. In this review, we first describe how the DRC is organized to meet the needs of this broad group of stakeholders. We then outline guiding principles, common challenges, and innovative approaches used to build the All of Us data ecosystem. Finally, we share lessons learned to help others navigate important decisions and trade-offs in building a modern biomedical data platform.

Design and Implementation of the All of Us Research Program COVID-19 Participant Experience (COPE) Survey.

In response to the rapidly evolving coronavirus disease 2019 (COVID-19) pandemic, the All of Us Research Program longitudinal cohort study developed the COVID-19 Participant Experience (COPE) survey to better understand the pandemic experiences and health impacts of COVID-19 on diverse populations within the United States. Six survey versions were deployed between May 2020 and March 2021, covering mental health, loneliness, activity, substance use, and discrimination, as well as COVID-19 symptoms, testing, treatment, and vaccination. A total of 104,910 All of Us Research Program participants, of whom over 73% were from communities traditionally underrepresented in biomedical research, completed 275,201 surveys; 9,693 completed all 6 surveys. Response rates varied widely among demographic groups and were lower among participants from certain racial and ethnic minority populations, participants with low income or educational attainment, and participants with a Spanish language preference. Survey modifications improved participant response rates between the first and last surveys (13.9% to 16.1%, P < 0.001). This paper describes a data set with longitudinal COVID-19 survey data in a large, diverse population that will enable researchers to address important questions related to the pandemic, a data set that is of additional scientific value when combined with the program's other data sources.

How Fitbit data are being made available to registered researchers in All of Us Research Program.

The National Institutes of Health's (NIH) All of Us Research Program aims to enroll at least one million US participants from diverse backgrounds; collect electronic health record (EHR) data, survey data, physical measurements, biospecimens for genomics and other assays, and digital health data; and create a researcher database and tools to enable precision medicine research [1]. Since inception, digital health technologies (DHT) have been envisioned as essential to achieving the goals of the program [2]. A "bring your own device" (BYOD) study for collecting Fitbit data from participants' devices was developed with integration of additional DHTs planned in the future [3]. Here we describe how participants can consent to share their digital health technology data, how the data are collected, how the data set is parsed, and how researchers can access the data.

Association of Longitudinal Activity Measures and Diabetes Risk: An Analysis From the National Institutes of Health All of Us Research Program.

CONTEXT: Prior studies of the relationship between physical activity and incident type 2 diabetes mellitus (T2DM) relied primarily on questionnaires at a single time point. OBJECTIVE: We sought to investigate the relationship between physical activity and incident T2DM with an innovative approach using data from commercial wearable devices linked to electronic health records in a real-world population. METHODS: Using All of Us participants' accelerometer data from their personal Fitbit devices, we used a time-varying Cox proportional hazards models with repeated measures of physical activity for the outcome of incident T2DM. We evaluated for effect modification with age, sex, body mass index (BMI), and sedentary time using multiplicative interaction terms. RESULTS: From 5677 participants in the All of Us Research Program (median age 51 years; 74% female; 89% White), there were 97 (2%) cases of incident T2DM over a median follow-up period of 3.8 years between 2010 to 2021. In models adjusted for age, sex, and race, the hazard of incident diabetes was reduced by 44% (95% CI, 15%-63%; P = 0.01) when comparing those with an average daily step count of 10 700 to those with 6000. Similar benefits were seen comparing groups based on average duration of various intensities of activity (eg, lightly active, fairly active, very active). There was no evidence for effect modification by age, sex, BMI, or sedentary time. CONCLUSION: Greater time in any type of physical activity intensity was associated with lower risk of T2DM irrespective of age, sex, BMI, or sedentary time.

Association of step counts over time with the risk of chronic disease in the All of Us Research Program.

The association between physical activity and human disease has not been examined using commercial devices linked to electronic health records. Using the electronic health records data from the All of Us Research Program, we show that step count volumes as captured by participants' own Fitbit devices were associated with risk of chronic disease across the entire human phenome. Of the 6,042 participants included in the study, 73% were female, 84% were white and 71% had a college degree, and participants had a median age of 56.7 (interquartile range 41.5-67.6) years and body mass index of 28.1 (24.3-32.9) kg m-2. Participants walked a median of 7,731.3 (5,866.8-9,826.8) steps per day over the median activity monitoring period of 4.0 (2.2-5.6) years with a total of 5.9 million person-days of monitoring. The relationship between steps per day and incident disease was inverse and linear for obesity (n = 368), sleep apnea (n = 348), gastroesophageal reflux disease (n = 432) and major depressive disorder (n = 467), with values above 8,200 daily steps associated with protection from incident disease. The relationships with incident diabetes (n = 156) and hypertension (n = 482) were nonlinear with no further risk reduction above 8,000-9,000 steps. Although validation in a more diverse sample is needed, these findings provide a real-world evidence-base for clinical guidance regarding activity levels that are necessary to reduce disease risk.

Comparing medical history data derived from electronic health records and survey answers in the All of Us Research Program.

OBJECTIVE: A participant's medical history is important in clinical research and can be captured from electronic health records (EHRs) and self-reported surveys. Both can be incomplete, EHR due to documentation gaps or lack of interoperability and surveys due to recall bias or limited health literacy. This analysis compares medical history collected in the All of Us Research Program through both surveys and EHRs. MATERIALS AND METHODS: The All of Us medical history survey includes self-report questionnaire that asks about diagnoses to over 150 medical conditions organized into 12 disease categories. In each category, we identified the 3 most and least frequent self-reported diagnoses and retrieved their analogues from EHRs. We calculated agreement scores and extracted participant demographic characteristics for each comparison set. RESULTS: The 4th All of Us dataset release includes data from 314 994 participants; 28.3% of whom completed medical history surveys, and 65.5% of whom had EHR data. Hearing and vision category within the survey had the highest number of responses, but the second lowest positive agreement with the EHR (0.21). The Infectious disease category had the lowest positive agreement (0.12). Cancer conditions had the highest positive agreement (0.45) between the 2 data sources. DISCUSSION AND CONCLUSION: Our study quantified the agreement of medical history between 2 sources-EHRs and self-reported surveys. Conditions that are usually undocumented in EHRs had low agreement scores, demonstrating that survey data can supplement EHR data. Disagreement between EHR and survey can help identify possible missing records and guide researchers to adjust for biases.