Understanding “Just-in-Time” States in Behavioral Interventions using System Identification and Data Science Methods

Abstract

Insufficient physical activity (PA) is highly prevalent in society, despite its negative impact on personal health. Effective interventions are clearly needed. However, improvements to behavioral interventions face many challenges, from noisy and missing measurements to inadequate understanding of the dynamics of behavior change in context. In this paper, we present a comprehensive, data-driven, system identification approach aimed at overcoming challenges to better understand the dynamics of PA behavior change, which in turn can improve the efficacy of these interventions. The proposed approach consists of an innovative input signal design (aimed at providing informative data sets to study the concept of “just-in-time” dynamics), Singular Spectrum Analysis (SSA; for noise reduction and exploring the separability of the measured output signal), and Model-on-Demand (MoD) estimation, a hybrid data-driven modeling approach, which allows identifying dynamics under changing operating conditions. The proposed approach is evaluated on data for a representative participant from the JustWalk JITAI study. The results demonstrate significant potential of the methodology in enhancing the understanding of the dynamics of behavior change in context.