Clinical Manifestations

Area of Science:

• Neuroscience
• Gerontology
• Biomedical Engineering

Background:

• Alzheimer's disease and related dementias (ADRD) significantly impact sleep-wake cycles.
• Commercial wearable devices offer scalable passive actigraphy for real-world monitoring.
• Characterizing rest-activity patterns can reveal differences between healthy adults and ADRD cohorts.

Purpose of the Study:

• To characterize continuous rest-activity patterns using Fitbit™ data.
• To examine differences in these patterns between healthy adults and various ADRD cohorts.
• To investigate associations between activity patterns and cognitive/functional decline.

Main Methods:

• Collected tri-axial actigraphy, clinical, cognitive, functional, and mood data from healthy adults, mild cognitive impairment (MCI), Alzheimer's disease (AD), and frontotemporal lobar degeneration (FTLD) cohorts.
• Quantified activity patterns using rest-activity aggregates and minute-level step count features.
• Applied Principal Component Analysis (PCA) for data reduction and identified five components explaining >85% of variance.

Main Results:

• Principal Component 1 (PC1), representing activity variability and amplitude, was negatively associated with cognitive and functional decline scores (CDR®+FTLD-NACC).
• PC1 also showed significant diagnostic group differences, with FTLD patients exhibiting reduced PC1 scores compared to healthy and MCI groups.
• PC3, related to rest-activity start timing, was negatively associated with global cognitive scores.

Conclusions:

• Activity variability and amplitude derived from Fitbit™ data highlight distinct behavioral profiles in FTLD syndromes.
• These distinct activity profiles differentiate FTLD patients from those with MCI or who are functionally intact.
• Passive actigraphy via wearables provides valuable insights into neurodegenerative disease progression.