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Category: Project

Description: 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.