The integration of mobile health (mHealth) devices into behavioral health research has fundamentally changed the way researchers and interventionalists are able to collect data as well as deploy and evaluate intervention strategies. In these studies, researchers often collect intensive longitudinal data (ILD) using ecological momentary assessment methods which aim to capture psychological, emotional and environmental factors that may relate to a behavioral outcome in near real time. In order to investigate ILD collected in a novel, smartphone-based smoking cessation study, we propose a Bayesian variable selection approach for time-varying effect models, designed to identify dynamic relations between potential risk factors and smoking behaviors in the critical moments around a quit attempt. We use parameter-expansion and data-augmentation techniques to efficiently explore how the underlying structure of these relations varies over time and across subjects. We achieve deeper insights into these relations by introducing nonparametric priors for regression coefficients that cluster similar effects for risk factors while simultaneously determining their inclusion. Results indicate that our approach is well positioned to help researchers effectively evaluate, design and deliver tailored intervention strategies in the critical moments surrounding a quit attempt.

中文翻译：

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行为 mHealth 数据的贝叶斯时变效应模型

将移动健康 (mHealth) 设备集成到行为健康研究中，从根本上改变了研究人员和干预专家收集数据以及部署和评估干预策略的方式。在这些研究中，研究人员经常使用生态瞬时评估方法收集密集的纵向数据 (ILD)，这些方法旨在近乎实时地捕捉可能与行为结果相关的心理、情感和环境因素。为了调查在一项基于智能手机的新型戒烟研究中收集的 ILD，我们提出了一种用于时变效应模型的贝叶斯变量选择方法，旨在识别戒烟关键时刻潜在危险因素与吸烟行为之间的动态关系试图。我们使用参数扩展和数据增强技术来有效地探索这些关系的底层结构如何随时间和跨学科而变化。我们通过为回归系数引入非参数先验来更深入地了解这些关系，这些回归系数将风险因素的相似效应聚集在一起，同时确定它们的包含。结果表明，我们的方法可以很好地帮助研究人员在戒烟尝试的关键时刻有效地评估、设计和提供量身定制的干预策略。我们通过为回归系数引入非参数先验来更深入地了解这些关系，这些回归系数将风险因素的相似效应聚集在一起，同时确定它们的包含。结果表明，我们的方法可以很好地帮助研究人员在戒烟尝试的关键时刻有效地评估、设计和提供量身定制的干预策略。我们通过为回归系数引入非参数先验来更深入地了解这些关系，这些回归系数将风险因素的相似效应聚集在一起，同时确定它们的包含。结果表明，我们的方法可以很好地帮助研究人员在戒烟尝试的关键时刻有效地评估、设计和提供量身定制的干预策略。