Abstract
Hofmann et al. argued that “[w]hile the clinical field has produced a dizzying number of treatment models and treatment protocols for virtually every psychiatric and psychological problem imaginable, increases in understanding of the processes of change in psychotherapy has been slow to arrive.” We propose that one of the reasons for the slow progress is that prior psychotherapy research conflates trait-like and state-like components of mechanisms of change. Trait-like components can serve as prescriptive or prognostic variables, whereas state-like components reflect within-client processes of change, and may highlight active ingredients of successful treatment. Distinguishing between the two is essential for clarifying the underlying processes of change in psychotherapy, and ultimately identifying empirically-derived individualized treatment targets. We review studies that implement methodological and statistical approaches for disentangling the two. These studies clarified particular mechanisms of change that may operate in a given treatment, highlighted differences in the processes of change between different treatments, and explored the within-individual interplay between different mechanisms of change during treatment. Examples include studies investigating the therapeutic role of behavioral, cognitive, and interpersonal skills, as well as emotional processing. We conclude with suggestions for future research, including attention to diversity, improved measurement to facilitate a reliable and valid estimation of trait-like and state-like components, the use of appropriate statistical approaches to adequately disentangle the two components, integration of theory-driven and data-driven methods of analysis, and the need to experimentally manipulate the state-like changes in a given mechanism of change to strengthen causal inferences.
Highlights
Theoretical conceptualizations of the mechanisms underlying psychotherapeutic change refer to dynamic, multivariable processes which unfold over the course of treatment [1]
The original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding author/s
All authors contributed to the article and approved the submitted version
Summary
Theoretical conceptualizations of the mechanisms underlying psychotherapeutic change refer to dynamic, multivariable processes which unfold over the course of treatment [1]. Webb et al [23] found that clientreported use of behavioral – but not cognitive – skills predicted symptom change in CBT for depressed adolescents The latter finding emerged when using conventional analyses (i.e., not disaggregating state-like and trait-like components). Promising results have been obtained in a recent study using a machine learning approach to predict client-specific skill-affect associations based on baseline clinical and demographic characteristics [59] These preliminary findings on the implementation of machine leaning approaches to identifying predictors of state-like effects stress the importance of thoughtful selection of relevant predictors in future trial designs, as well as consideration of a variety of machine leaning-related analytical approaches. Examples of direct manipulation of mechanisms of change include the administration of D-cycloserine and hydrocortisone as facilitators of inhibitory learning in exposure therapy [18], as well as the direct modulation of brain function connectivity using approaches such as transcranial magnetic stimulation (TMS) of the cerebellar midline [69]
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