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 reduce functional and cognitive decline and maintain
independent functional living in an aging population.
