An increasingly popular form of data collection in health psychology research is Ecological Momentary Assessment (EMA); that is, using diaries or smartphones to collect intensive longitudinal data. This method is increasingly applied to the study of relationships between state-based aspects of individuals’ functioning and health outcomes (e.g., binge eating, alcohol use). Analysis of such data is challenging and regression tree modelling (RTM) may be a useful alternative to multilevel modelling for investigating the association between a set of explanatory variables and a continuous outcome. Furthermore, RTM outputs ‘decision trees’ that could be used by health practitioners to guide assessment and tailor intervention. In contrast to regression, RTM is able to easily accommodate many complex, higher-order interactions between predictor variables (without the need to create explicit interaction terms). These benefits make the technique useful for those interested in monitoring and intervening upon health and psychological outcomes (e.g., mood, eating behaviour, risky alcohol use, and treatment adherence). Using real data, this paper demonstrates both the benefits and limitations of RTM and how to extend these models to accommodate analysis of nested data; that is, data that arise from EMA where repeated observations are nested within individuals.

在健康心理学研究中，越来越流行的数据收集形式是生态矩评估（EMA）；也就是说，使用日记或智能手机收集密集的纵向数据。这种方法越来越多地用于研究个人功能的基于状态的方面与健康结果（例如暴饮暴食，饮酒）之间的关系。对此类数据的分析具有挑战性，并且回归树建模（RTM）可能是多级建模的有用替代方法，用于研究一组解释变量与连续结果之间的关联。此外，RTM输出“决策树”，医务人员可以使用它们来指导评估和定制干预措施。与回归相反，RTM可以轻松容纳许多复杂的，预测变量之间的高级交互（无需创建显式交互项）。这些好处使该技术对于有兴趣监视和干预健康和心理结果（例如情绪，进食行为，高危饮酒和坚持治疗的人）有用。本文使用真实数据演示了RTM的优点和局限性，以及如何扩展这些模型以适应嵌套数据分析。也就是说，来自EMA的数据，其中重复的观察嵌套在个人内。本文展示了RTM的优点和局限性，以及如何扩展这些模型以适应嵌套数据分析。也就是说，来自EMA的数据，其中重复的观察嵌套在个人内。本文展示了RTM的优点和局限性，以及如何扩展这些模型以适应嵌套数据分析。也就是说，来自EMA的数据，其中重复的观察嵌套在个人内。