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.

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.

Aims Prior studies of the relationship between physical activity and incident type 2 diabetes mellitus (T2DM) relied primarily on questionnaires at a single time point. 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, BMI, and sedentary time using multiplicative interaction terms. Results From 5,677 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 of 3.8 years between 2010-2021. 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 6,000. Similar benefits were seen comparing groups based on average duration of various intensities of activity (e.g., lightly active, fairly active, very active). There was no evidence for effect modification by age, sex, body mass index (BMI) or sedentary time. Conclusions Greater time in any type of physical activity intensity was associated with lower risk of T2DM irrespective of age, sex, BMI or sedentary time.

In response to the rapidly evolving 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 six 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 dataset with longitudinal COVID-19 survey data in a large, diverse population that will enable researchers to address important questions related to the pandemic, a dataset which is of additional scientific value when combined with the program's other data sources.

In the version of this article initially published, the statement now reading "The All of Us Research Program Resource Access Board (RAB) has granted a post-hoc exception to the program's Data and Statistics Dissemination Policy for reporting exact participant counts of less than 20 in some of the analyses reporting in this study, due to the very low risk to participant privacy and potential for re-identification" was mistakenly omitted from the "Study participants" subsection within the Methods. The error has been corrected in the HTML and PDF versions of the article.

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.