Abstract
Abstract
Background
Personalizing physical activity recommendations for older adults requires understanding not only which dimensions of physical activity and sedentary behaviors (24-h movement behaviors) influence health outcomes but also when, within an individual’s everyday life, these dimensions are most relevant. Current observational and interventional approaches rarely capture the temporal dynamics linking everyday patterns of 24-hour movement behaviors to cognitive and mental health trajectories, 2 key determinants of healthy aging.
Objective
This study introduces and evaluates PEPHA (Personalized Phenotyping for Aging), an interpretable artificial intelligence (AI) framework designed to identify which dimensions of behavior and when within an observation window are strongly associated with cognitive functioning and depressive symptoms in older adults.
Methods
We introduce PEPHA, an interpretable AI framework that integrates passive, high-frequency wearable data (physical activity and sleep) with periodic, active, validated cognitive and affective assessments (waves). Using longitudinal data from the
Providemus alz
cohort (n=67, up to 6 waves and 528 days of wearable data per person), we examined 2 key outcomes representing cognitive functioning and mental health (processing speed and depressive symptoms). PEPHA summarizes daily physical activity and sedentary patterns into slope-based temporal features, optimizes support vector regression parameters through Bayesian optimization, and applies individualized analyses including time order-swap testing and change point detection to identify “potential temporal association windows” (ie, periods within an observation window during which outcomes appear more sensitive to changes in behavioral patterns).
Results
Personalized analyses showed that roughly 40% of individuals exhibited moderate or large temporal order effects of 24-hour movement behavior in the outcomes. For 1 exemplar participant (male, above the mean sample age), we localized 2 potential temporal association windows approximately 60 days and 30 days before the assessment of his processing speed, suggesting periods during which this individual may have been more sensitive to favorable or unfavorable behavioral configurations. Across participants, PEPHA revealed distinct 24-hour movement behavioral patterns correlated with cognitive functioning and mental health. Processing speed was best explained by locomotor activity, while depressive symptoms were best explained by sedentary behavior. Five control variables (education, cognitive reserve, diet, sex, and subjective age difference) were noninformative, whereas chronological age had predictive power regarding depressive symptoms.
Conclusions
PEPHA demonstrates that continuous passive wearable data can uncover individualized, time-specific behavioral patterns associated with cognitive functioning and mental health. Although exploratory, this framework transforms observational data into interpretable, timing-aware insights that can help identify periods of increased behavioral sensitivity or association, thereby informing future personalized, AI-supported physical activity interventions in aging.