EAGER: Defining a Dynamical Behavioral Model to Support a Just in Time Adaptive Intervention

点击翻译按钮获取中文摘要

EAGER: Defining a Dynamical Behavioral Model to Support a Just in Time Adaptive Intervention This EAGER supports exploratory work to develop a novel approach to the creation of a dynamical computational model of human health behavior. The goal of this high risk project is to apply an experimental design that merges methods from multiple disciplines to generate the necessary data to develop a dynamical systems model of human health behavior. In theory, mobile technologies have this capacity to provide health interventions in real-time that are adapted to the individual, but in practice the specific theoretical models and decision rules required to determine exactly when, where, and how to intervene do not exist. Standard health approaches use theoretical frameworks to identify and select target behaviors and approaches or intervention. By creating dynamical models of human behavior, real-time adaptive interventions can be developed and empirically assessed building the foundational science of computational behavior. While this project is concerned with creating dynamic computational models for increasing exercise behavior, the approach may find applications more broadly with a wide range of human health issues.The goal of this EAGER project is to create a mathematical model that will provide the evdience for making decisions about when, where, and how a "just in time" adaptive mHealth physical activity intervention should be implemented. Creating this dynamical behavioral model is a challenging problem that requires insights from different disciplines because behavioral science provides insights regarding what to measure, and behavioral intervention strategies that could be used dynamically; however, current behavioral theories fail to provide any real insights on when, where, and how to intervene at the opportune moment. Control systems engineering provides a methodology for creating dynamic mathematical models and decision-making, but this methodology has only sparsely been applied in a human behavioral context. A key first step for developing a dynamical behavioral model is to gather "informative" empirical data to estimate the model. These data will be generated with an informative system identification "informative" experiment within a human context that builds on lessons from behavioral science about experimental designs and that takes full advantage of the temporally rich data available from mHealth technologies. We will use these data to develop a fundamental yet empirically- supported dynamical behavioral model for understanding our target behavior.