Exploring the predictive value of different affect dynamics for psychological treatment outcome

In psychotherapy research, many studies have focussed on generating expected treatment response (ETR, Howard et al., 1996) curves at the onset of treatment to identify patients at risk of treatment failure (e.g., Delgadillo et al., 2016; Lutz et al., 2006; Lutz et al., 2019). This line of research resulted in establishing predictive models for ETR based on cross-sectional data, including patient characteristics like problem chronicity, previous treatment, treatment expectations, and global assessment of functioning (Lutz et al., 1999). Notably, initial impairment has consistently emerged as a robust and reliable predictor of treatment outcomes (Beutler et al., 2018; Lutz et al., 2018; Zimmerman et al., 2017).
In recent years, the field has sought to advance the accuracy of data assessment and analysis to refine prediction models. To improve data quality, researchers have explored the implementation of Ecological Momentary Assessment (EMA; Stone & Shiffman, 1994). This method aims to mitigate retrospective bias, enhance ecological validity, and capture dynamic aspects of patient experiences (e.g., Hamaker & Wichers, 2017; Shiffman et al., 2008; Trull & Ebner-Priemer, 2013). Affect dynamics and their interplay, in particular, have been suggested as valuable predictors for treatment outcomes, with recent pilot studies demonstrating an incremental explanation of variance (Hehlmann et al., 2024; Lutz et al., 2018). However, the methodological approaches employed to capture affect dynamics demonstrate heterogeneity across studies, with each emphasizing distinct dynamic aspects.
A prior study, Dejonckheere and colleagues (2019) investigated multiple approaches for the analysis of affect dynamics,and concluded that most of the indicators derived from these approaches had limited added value over the mean level of affect for predicting psychological well-being. Nonetheless, it remains uncertain whether any of these analytic indicators, including the mean level, have the potential to enhance predictive value beyond the initial impairment. To this end, our study aims to contribute to the existing literature by adopting an approach similar to that of Dejonckheere's study. We plan to apply a broad spectrum of analytic approaches on intensive longitudinal affect data, to examine the additional value the derived indicators may offer in predicting treatment outcomes. Through this examination, we aim to refine and advance our understanding of predictive indicators for psychotherapeutic outcomes.

在心理治疗研究领域，众多研究致力于在治疗初期生成预期治疗反应曲线（ETR，Howard 等人，1996年），以识别可能面临治疗失败风险的患者（例如，Delgadillo 等人，2016年；Lutz 等人，2006年；Lutz 等人，2019年）。此研究方向促使建立了基于横断面数据的ETR预测模型，包括患者的病情慢性化、既往治疗、治疗期望和功能评估等方面的特征（Lutz 等人，1999年）。值得注意的是，初始损伤一直被证明是治疗结果的一个稳健且可靠的预测指标（Beutler 等人，2018年；Lutz 等人，2018年；Zimmerman 等人，2017年）。
近年来，该领域力求提升数据评估和分析的准确性，以精炼预测模型。为了提高数据质量，研究人员探讨了生态瞬间评估（EMA；Stone & Shiffman，1994年）的实施。该方法旨在减轻回顾性偏差，增强生态效度，并捕捉患者体验的动态方面（例如，Hamaker & Wichers，2017年；Shiffman 等人，2008年；Trull & Ebner-Priemer，2013年）。特别是情感动态及其相互作用，已被建议为治疗结果的有价值预测指标，近期的小型研究表明，它能够增加变异的解释程度（Hehlmann 等人，2024年；Lutz 等人，2018年）。然而，用于捕捉情感动态的方法学在研究中显示出异质性，每种方法都强调不同的动态方面。
前期研究，Dejonckheere 及其同事（2019年）调查了分析情感动态的多种方法，并得出结论，这些方法中大多数指标相对于情感的平均水平在预测心理健康方面增加的价值有限。尽管如此，这些分析方法指标，包括平均水平，是否有可能在初始损伤之外提升预测价值仍然是个未知数。为此，我们的研究旨在通过采用与Dejonckheere研究类似的方法，为现有文献做出贡献。我们计划对密集纵向情感数据进行广泛的分析方法应用，以检验所得指标可能为预测治疗结果提供的额外价值。通过这一研究，我们旨在精炼和推进我们对心理治疗效果预测指标的理解。