# Thinking about walking: Can digital phenotyping of mobility improve the prediction of Alzheimer's dementia and inform on the pathologies and proteins contributing to this association?

> **NIH NIH R01** · RUSH UNIVERSITY MEDICAL CENTER · 2024 · $698,969

## Abstract

ABSTRACT
In its earliest stage Alzheimer’s disease does not manifest cognitive impairment while dementia is a late
manifestation. A biomarker to identify preclinical Alzheimer’s dementia is crucial for treatments aimed at its
prevention. Alzheimer’s disease can also degrade non-cognitive functions like mobility that precedes and
predicts cognitive impairment in many older adults. To use mobility as a biomarker, it is crucial to identify the
metrics that best predict Alzheimer’s dementia and the mechanisms that account for this association.
We must think to move. Mobility requires motor and cognitive abilities that derive from distinct brain regions.
This may explain why mobility is an early predictor of dementia. Yet, motor testing usually only quantifies
movement duration. So, the role of cognitive abilities in the association of mobility with Alzheimer’s dementia is
unclear. Unobtrusive sensors can be used to assess cognitive and motor metrics crucial for mobility.
This study will use novel digital mobility phenotyping to improve the prediction of Alzheimer’s disease
dementia and identify brain pathologies and proteins that inform on this association.
This study responds to NOT-AG-20-053 and will add new resources to those available from 1000 older adults
in the Rush Memory and Aging Project (R01AG17917). To improve the prediction of Alzheimer’s dementia, we
will add cognitive mobility metrics e.g., motor planning and attentional metrics to a single-testing session. To
capture the varied cognitive demands during everyday mobility, we will also add new multi-day mobility metrics
obtained from a wrist sensor. Motor planning is related to supplementary motor area (SMA) and task attention
and executive function are regulated by dorsolateral prefrontal cortex (DLPFC). So, we focus on these regions
to identify mechanisms shared by mobility and Alzheimer’s disease dementia. In 200 decedents with available
brain pathologies, we will collect new proteome data from SMA to complement the available DLPFC proteome.
Aim 1 will add new digital cognitive mobility metrics to motor metrics obtained from a single-testing session as
well as novel multi-day mobility metrics to improve the prediction of Alzheimer’s dementia. Sensors yield large
numbers of mobility metrics. Aim 1 will isolate individual metrics that predict Alzheimer’s dementia. Aim 2 will
analyze these novel metrics with a second approach to identify different mobility subgroups that may have
varied risks of Alzheimer’s dementia. To inform on the mechanisms underlying the association of mobility and
Alzheimer’s dementia, Aim 3 will use brain pathologies to determine the pathologic bases for these mobility
subgroups. Aim 4 will collect proteome from SMA and DLPFC to identify cortical proteins independently
related to mobility subgroups when controlling for ADRD pathologies. From the set of proteins related to
mobility, we will identify a subset that are also related to Alzheimer’s dementia. This stud...

## Key facts

- **NIH application ID:** 10893361
- **Project number:** 5R01AG079133-03
- **Recipient organization:** RUSH UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** ARON S BUCHMAN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $698,969
- **Award type:** 5
- **Project period:** 2022-09-01 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10893361

## Citation

> US National Institutes of Health, RePORTER application 10893361, Thinking about walking: Can digital phenotyping of mobility improve the prediction of Alzheimer's dementia and inform on the pathologies and proteins contributing to this association? (5R01AG079133-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10893361. Licensed CC0.

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