Quantifying Time-Varying Physical Activity Intervention Effects via Functional Regression

TL;DR

This paper introduces a functional regression framework to analyze time-varying effects of physical activity interventions, providing more detailed insights than traditional scalar summaries.

Contribution

It develops and demonstrates the advantages of function-on-scalar and function-on-function regression methods for analyzing PA trajectories in intervention studies.

Findings

Revealed distinct time-varying effects of intervention strategies on physical activity.

Showed the feasibility of functional data analysis for high-dimensional intervention data.

Identified differences in sustainability of intervention effects over time.

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.