Predicting goal attainment in process-oriented behavioral interventions using a data-driven system identification approach.

Behavioral interventions (such as those developed to increase physical activity, achieve smoking cessation, or weight loss) can be represented as dynamic process systems incorporating a multitude of factors, ranging from cognitive (internal) to environmental (external) influences. This facilitates the application of system identification and control engineering methods to address questions such as: what drives individuals to improve health behaviors (such as engaging in physical activity)? In this paper, the goal is to efficiently estimate personalized, dynamic models which in turn will lead to control systems that can optimize this behavior. This problem is examined in system identification applied to the Just Walk study that aimed to increase walking behavior in sedentary adults. The paper presents a Discrete Simultaneous Perturbation Stochastic Approximation (DSPSA)-based modeling of the Goal Attainment construct estimated using AutoRegressive with eXogenous inputs (ARX) models. Feature selection of participants and ARX order selection is achieved through the DSPSA algorithm, which efficiently handles computationally expensive calculations. DSPSA can search over large sets of features as well as regressor structures in an informed, principled manner to model behavioral data within reasonable computational time. DSPSA estimation highlights the large individual variability in motivating factors among participants in Just Walk , thus emphasizing the importance of a personalized approach for optimized behavioral interventions. [Display omitted] This paper demonstrates a comprehensive data-driven methodology for modeling physical activity through the synergism of behavior change theory, process modeling, and control. • Increasing physical activity in individuals can be described as a process system. • Behavioral science, process modeling, and system identification can be applied. • Benefits are obtained from novel experiment design and model validation procedures. • Stochastic search via DSPSA enables determining the best features in limited time. • Estimated models can contribute to personalized "just in time" adaptive interventions. [ABSTRACT FROM AUTHOR]