PROJECT SUMMARY/ABSTRACT Markers of functional decline begin years before clinical symptoms of Alzheimer’s disease and related dementias (ADRD). It is essential to capture these functional changes as early as possible to intervene before symptoms arise to prevent further deterioration and loss of independence. Resources to assess cognitive changes and detect the progression to mild cognitive impairment (MCI) and ADRD are limited among older adults with health disparities. The digital technology-based assessment of cognitively complex activities, such as life-space mobility (LSM), has the potential to identify those at risk for cognitive decline. The proposed longitudinal, case-control study aims to develop sensitive, practical, and ecologically acceptable LSM digital markers that could be clinically relevant markers of subsequent cognitive decline among older adults, including those with health disparities. Additionally, we will examine the moderating role of social health factors (e.g., social isolation, loneliness) in the relationship between sensor-based LSM features and cognitive function. We will create a Real-Life Activity and Life-Space Mobility Monitoring Solution (RAMS), consisting of a GPS data logger and wrist-worn actigraphy to assess real-life mobility performance objectively. The RAMS measures will include spatial and temporal mobility measures (e.g., movement size, outdoor time) and physical activity measures (e.g., sedentary time, activity fragmentation). We propose to recruit individuals aged ≥ 65 from a racially and socioeconomically diverse group. Participants will be classified as cognitively normal (CN; n=157) or MCI (n=157) based on a neuropsychological battery at baseline and will be prospectively followed up for 3 years to collect 7-day RAMS data and neuropsychological evaluations every 6 months. The specific aims are to (1) Compare baseline and longitudinal trajectories of RAMS measures between CN and MCI groups and determine the impact of social health factors on RAMS indicators and cognitive function; (2) Determine RAMS indicators that classify CN and MCI groups at baseline and evaluate the ability of RAMS indicators to predict the subsequent onset of MCI and dementia over a 3-year period; and (3) Evaluate older adults’ attitudes towards and willingness to use digital health technology for monitoring risks of cognitive decline using a mixed- methods approach. The public health impact of this study will provide unique insights into clinically meaningful digital measures and modifiable risk factors to support the need for early treatment and prevention of progression into MCI and dementia in older adults, including low-income ethnic minority older adults. Understanding digital health monitoring acceptance will inform the translation of RAMS markers as a clinically relevant tool for early detection of cognitive decline. Results will support our long-term goal to implement RAMS as a platform for real-time monitoring to redu...