Quantifying Time-Varying Physical Activity Intervention Effects via Functional Regression

2026-05-09

AI Summary

Traditional approaches often reduce physical activity data to scalar summaries, failing to capture the time-varying dynamics of intervention effects. This study addresses this limitation by employing a functional regression framework that treats entire activity trajectories as functional observations, thereby directly modeling the temporal evolution of intervention effects. By replacing conventional two-stage methods with function-on-scalar regression (FoSR) and further extending it to function-on-function regression (FoFR), combined with functional principal component analysis (FPCA), the approach substantially enhances the ability to analyze high-dimensional outcome data. Applied to the STEP UP trial, this method successfully identified three distinct intervention strategies that exerted significant, interpretable, and differentially sustained time-varying effects on daily step counts.

Abstract

Physical activity (PA) intervention studies often collect repeated intensity measurements over long observation periods. Quantifying the variation in intervention effects over the study period is critical to evaluating and improving intervention strategies, yet many analyses reduce PA data into scalar summary measures, resulting in limited insights. We propose a functional regression framework, which captures time-varying intervention effects by modeling the entire PA trajectory as a functional observation. From both methodological and practical perspectives, we demonstrate the advantages of function-on-scalar regression (FoSR) over the traditional two-step approach of applying functional principal components analysis (FPCA) followed by regressing scores on covariates. The FoSR is further extended to a function-on-function regression (FoFR) for studying the association of PA across time periods. Methods are applied to daily step counts from the Social incentives to Encourage Physical Activity and Understand Predictors (STEP UP) study, revealing distinct and highly interpretable time-varying effects of three intervention strategies on PA and differences in their sustainability. Our case study highlights the feasibility of functional data analysis techniques for uncovering novel insights in intervention studies with high-dimensional endpoints.

Problem

• time-varying intervention effects
• physical activity
• functional data analysis
• high-dimensional endpoints
• longitudinal intervention studies

Innovation

• functional regression
• time-varying intervention effects
• function-on-scalar regression
• physical activity trajectory
• functional data analysis