# Variability and Volume of Day-to-Day Lifestyle Activity in Sustaining Cognitive Function among Insufficiently Active Older Adults at Risk for Alzheimer's Disease and Related Dementias

> **NIH NIH R21** · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · 2024 · $179,586

## Abstract

The escalating incidence of Alzheimer’s disease and related dementias (ADRD) places a tremendous economic
burden on society and families, making up the largest healthcare cost expenditure in the US. To date, medical
approaches for preventing and treating ADRD have yielded limited success. Non-pharmacological preventive
strategies, such as physical activity, hold promise for sustaining cognition and thus reducing ADRD risks in older
adults. Unfortunately, it is not feasible and practical for many older adults to engage in the recommended higher-
intensity physical activities (or aerobic exercise), especially for those with mobility limitations, physical barriers,
and predisposing health conditions. Existing evidence suggests that even engaging in lower-intensity lifestyle
activities, such as walking, may benefit cognitive health. However, this literature has exclusively focused on the
total amount or mean volume of light-intensity physical activity (LPA) or steps and identified small-to-moderate
effects on cognition. Beyond the total volume performed, the day-to-day variability (or stability) of LPA or steps
also represents a behavioral target that potentially can amplify the cognitive benefits, but this variable has not
been tested in the literature. Using an experimental medicine approach, this proposal will bridge the knowledge
gap by determining whether older adults’ daily variability and volume of LPA/steps jointly predict their
proximal and distal cognitive function. This study will recruit ethnically diverse older adults (ages 60+) who
are insufficiently active and have higher risks for developing ADRD. Eligible participants will wear an
accelerometer for 30 days to estimate each person’s typical variability and volume of their daily LPA/steps. They
will complete daily e-surveys during the same 30-day period to assess their daily stress, sleep, and social
engagements. These factors influence older adults’ daily physical activity and cognition, so they will be
considered (modeled) when estimating each person’s typical LPA and step patterns. After the 30-day monitoring
period, participants will complete three follow-up cognitive measures on Day 31, at 2.5 months, and 4 months.
These cognitive measures involve validated in-lab and smartphone-based tools (MoCA, scales, NIH-funded
M2C2 app). Lastly, this study will examine whether subgroup differences (based on demographics) exist in the
proposed associations between daily lifestyle activity patterns (variation and volume of LPA/steps) and cognitive
function. To enhance the rigor of our study findings, we will apply the novel MixWILD statistical program in our
analysis. This program accounts for uncertainties in unbalanced datasets, which can enhance estimation
precision and provide reliable results. The first study aim is to determine if both variation and volume of daily
LPA/steps are joint targets for sustaining proximal and distal cognitive function in older adults at risk for ADRD.
The seco...

## Key facts

- **NIH application ID:** 10841038
- **Project number:** 5R21AG077515-02
- **Recipient organization:** UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
- **Principal Investigator:** Chih-Hsiang Yang
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $179,586
- **Award type:** 5
- **Project period:** 2023-05-15 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10841038

## Citation

> US National Institutes of Health, RePORTER application 10841038, Variability and Volume of Day-to-Day Lifestyle Activity in Sustaining Cognitive Function among Insufficiently Active Older Adults at Risk for Alzheimer's Disease and Related Dementias (5R21AG077515-02). Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nih/10841038. Licensed CC0.

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