From physical activity patterns to cognitive status: development and validation of novel digital biomarkers for cognitive assessment in older adults.

ABSTRACT:

Background

This study aims to investigate the associations between signal-level physical activity (PA) features derived from wrist accelerometry data and cognitive status in older adults, and to evaluate their potential predictive value when combined with demographics.

Methods

We analyzed PA data from 3,363 older adults (NHATS: n = 747; NHANES: n = 2,616), with each participant contributing a complete 3-day continuous activity sequence. We extracted the most relevant PA features associated with cognitive function using feature engineering and recursive feature elimination. Demographic characteristics were also incorporated, and multimodal data fusion was achieved through canonical correlation analysis. We then developed explainable machine learning models, primarily random f

SUBMITTER: Fan LJ 

PROVIDER: S-EPMC11748278 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature