PROJECT SUMMARY Early detection of cognitive and functional decline is a major goal of the NIA in its fight against Alzheimer's Disease and Related Dementias (ADRD). Physical, physiological, and cognitive changes associated with ADRD may emerge years prior to clinical manifestations, thus there is an urgent need for novel, cost-effective, noninvasive, and scalable tools to improve detection of ADRD risk. Older adults with cognitive impairment often exhibit changes in movement, sleep, and heart rhythms, suggesting possible shared vascular or neurodegenerative pathways. Emerging research from our group and others links digital signals from movement, sleep, and heart rhythms with brain and cognitive health; however, knowledge gaps remain in the associations among these signals and brain and cognitive health across the cognitive spectrum. In response to NOT-AG-20- 017, this application will directly address these gaps using existing and ongoing/new data collection from wearable technology (7-day accelerometry and 14-day ambulatory electrocardiogram (ECG)), cognitive assessments (neuropsychological battery, adjudicated cognitive diagnosis), and neuroimaging (florbetapir PET for beta amyloid (Aβ), 3T brain MRI for neurodegeneration and white matter disease) from ≥1000 older adults participating in the Atherosclerosis Risk in Communities (ARIC) Neurocognitive Study. We will use novel analytic approaches to integrate movement, sleep, and heart rhythm features and assess their individual and joint associations with brain and cognitive health. Our overarching goal is to identify clinically relevant digital biomarkers that combine movement, sleep, and heart rhythm signals as sensitive indicators of cognitive function, cognitive trajectories, ADRD pathology, and cognitive diagnosis. To this end, this research will inform the future use of wearable devices in large-scale studies, provide novel targets for screening and early detection of ADRD in disease stages during which intervention and treatment are more likely to be effective, and aid in identifying high-risk participants for prevention trials.