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 forest, optimized with hyperparameters, to predict individual cognitive function status. RESULTS: Using recursive feature elimination, we identified the top 20 PA features from each dataset and combined them with demographic features for modeling. The models achieved AUCs of 0.84 and 0.80 for NHATS and NHANES. Change quantiles and FFT coefficients emerged as the consistently top-ranked PA features across datasets, ranking 1st and 2nd respectively in their predictive importance for cognitive function. Models based on the top 10 PA features common to both datasets, along with demographic features, achieved AUCs above 0.8. CONCLUSIONS: This study identifies novel time-frequency domain features of physical activity that show robust associations with cognitive status across two independent cohorts. These features, particularly those capturing activity variability and rhythmicity, provide complementary information beyond traditional cumulative PA measures. Based on these findings, we developed a proof-of-concept application that demonstrates the feasibility of translating these PA analytics into practical monitoring tools in real-world settings.