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

EIGER：定义动态行为模型以支持即时自适应干预该EIGER支持开发一种新的方法来创建人类健康行为的动态计算模型的探索性工作。这个高风险项目的目标是应用一种实验设计，融合来自多个学科的方法来生成必要的数据，以开发人类健康行为的动态系统模型。理论上，移动技术有能力提供适合个人的实时健康干预措施，但在实践中，准确确定何时、何地和如何干预所需的具体理论模型和决策规则并不存在。标准的卫生方法使用理论框架来确定和选择目标行为和方法或干预措施。通过创建人类行为的动态模型，可以开发实时自适应干预并对其进行经验评估，从而建立计算行为的基础科学。虽然这个项目关注的是创建动态计算模型来增加锻炼行为，但这种方法可能会在更广泛的人类健康问题上得到更广泛的应用。这个渴望项目的目标是创建一个数学模型，为制定关于何时、何地以及如何实施适宜性mHealth体育活动干预的决策提供证据。创建这种动态行为模型是一个具有挑战性的问题，需要来自不同学科的见解，因为行为科学提供了关于要衡量什么以及可以动态使用的行为干预策略的见解；然而，当前的行为理论无法提供关于何时、何地以及如何在适当时机进行干预的任何真正的见解。控制系统工程为创建动态数学模型和决策提供了一种方法论，但这种方法论在人类行为环境中的应用很少。开发动态行为模型的关键第一步是收集“有信息的”经验数据来估计模型。这些数据将通过在人类环境中进行的信息丰富的系统识别实验来生成，该实验建立在行为科学关于实验设计的经验教训的基础上，并充分利用了mHealth技术提供的时间丰富的数据。我们将使用这些数据来开发一个基本的、但有经验支持的动态行为模型，以理解我们的目标行为。

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.