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This study leverages the rich diversity of the All of Us Research Program (All of Us)'s dataset to devise a predictive model for cardiovascular disease (CVD) in breast cancer (BC) survivors. Central to this endeavor is the creation of a robust data integration pipeline that synthesizes electronic health records (EHRs), patient surveys, and genomic data, while upholding fairness across demographic variables.

Summaries of health research can be a complementary way to return value to participants. We assess how research participants engage with summaries via email communication and how this can be improved.

Despite easy-to-use tools like the Cohort Builder, using All of Us Research Program data for complex research questions requires a relatively high level of technical expertise. We aimed to increase research and training capacity and reduce barriers to entry for the All of Us community through an R package, allofus. In this article, we describe functions that address common challenges we encountered while working with All of Us Research Program [...]

We aimed to evaluate the feasibility of using ChatGPT as programming support for nursing PhD students conducting analyses using the All of Us Researcher Workbench.

To evaluate the NIH All of Us Research Program database as a potential data source for studying allostatic load and stress among adults in the United States (US).

The National Institutes of Health's All of Us Research Program addresses gaps in biomedical research by collecting health data from diverse populations. Pregnant individuals have historically been underrepresented in biomedical research, and pregnancy-related research is often limited by data availability, sample size, and inadequate representation of the diversity of pregnant people. All of Us integrates a wealth of health-related data, providing a unique opportunity to conduct comprehensive pregnancy-related research. We [...]

We introduce a widely applicable model-based approach for estimating individual-level Social Determinants of Health (SDoH) and evaluate its effectiveness using the All of Us Research Program.

We describe new curriculum materials for engaging secondary school students in exploring the "big data" in the NIH All of Us Research Program's Public Data Browser and the co-design processes used to collaboratively develop the materials. We also describe the methods used to develop and validate assessment items for studying the efficacy of the materials for student learning as well as preliminary findings from these studies.

This study aims to develop machine learning models that provide both accurate and equitable predictions of 2-year stroke risk for patients with atrial fibrillation across diverse racial groups.

To illustrate the utility of the All of Us Research Program for studying military and veteran health.