There is no longer any doubt about the general effectiveness of psychotherapy. However, up to 40% of patients do not respond to treatment. Despite efforts to develop new treatments, overall effectiveness has not improved. Consequently, practice-oriented research has emerged to make research results more relevant to practitioners. Within this context, patient-focused research (PFR) focuses on the question of whether a particular treatment works for a specific patient. Finally, PFR gave rise to the precision mental health research movement that is trying to tailor treatments to individual patients by making data-driven and algorithm-based predictions. These predictions are intended to support therapists in their clinical decisions, such as the selection of treatment strategies and adaptation of treatment. The present work summarizes three studies that aim to generate different prediction models for treatment personalization that can be applied to practice. The goal of Study I was to develop a model for dropout prediction using data assessed prior to the first session (N = 2543). The usefulness of various machine learning (ML) algorithms and ensembles was assessed. The best model was an ensemble utilizing random forest and nearest neighbor modeling. It significantly outperformed generalized linear modeling, correctly identifying 63.4% of all cases and uncovering seven key predictors. The findings illustrated the potential of ML to enhance dropout predictions, but also highlighted that not all ML algorithms are equally suitable for this purpose. Study II utilized Study I’s findings to enhance the prediction of dropout rates. Data from the initial two sessions and observer ratings of therapist interventions and skills were employed to develop a model using an elastic net (EN) algorithm. The findings demonstrated that the model was significantly more effective at predicting dropout when using observer ratings with a Cohen’s d of up to .65 and more effective than the model in Study I, despite the smaller sample (N = 259). These results indicated that generating models could be improved by employing various data sources, which provide better foundations for model development. Finally, Study III generated a model to predict therapy outcome after a sudden gain (SG) in order to identify crucial predictors of the upward spiral. EN was used to generate the model using data from 794 cases that experienced a SG. A control group of the same size was also used to quantify and relativize the identified predictors by their general influence on therapy outcomes. The results indicated that there are seven key predictors that have varying effect sizes on therapy outcome, with Cohen's d ranging from 1.08 to 12.48. The findings suggested that a directive approach is more likely to lead to better outcomes after an SG, and that alliance ruptures can be effectively compensated for. However, these effects
were reversed in the control group. The results of the three studies are discussed regarding their usefulness to support clinical decision-making and their implications for the implementation of precision mental health.
Imagery-based techniques have received increasing interest in psychotherapy research. Whereas their effectiveness has been shown for various psychological disorders, their underlying mechanisms remain unclear. Current research predominantly investigates intrapersonal processes, while interpersonal processes have received no attention to date. The aim of the current dissertation was to fill this lacuna. The three interrelated studies comprising this dissertation were the first to examine the effectiveness of imagery-based techniques in the treatment of test anxiety, relate physiological arousal to emotional processing, and investigate the association between physiological synchrony and multiple process measures.
Study I investigated the feasibility of a newly developed protocol, which integrates imagery-based and cognitive-behavioral components, to treat test anxiety in a sample of 31 students. The results indicated the protocol as acceptable, feasible, and effective in the treatment of test anxiety. Additionally, the imagery-based component was positively associated with therapeutic bond, session evaluation, and emotional experience.
Study II shifted the focus from the effectiveness of imagery-based techniques to client-therapist physiological synchrony as a putative mechanism of change in the same sample. The results suggested that physiological synchrony was greater than chance during both imagery-based and cognitive-behavioral components. Variability of physiological synchrony on the session-level during the imagery-based components and variability on both levels (session and dyad) during the cognitive-behavioral components were demonstrated. Furthermore, physiological synchrony of the imagery-based segments was positively assocatied with therapeutic bond. No association was found for the cognitive-behavioral components.
Study III examined both intrapersonal (i.e., clients’ electrodermal activity) and interpersonal (i.e., client-therapist electrodermal activity synchrony) processes and their associations with emotional processing in a sample of 49 client-therapist-dyads. The results suggested that higher client physiological arousal and a moderate level of physiological synchrony were associated with deeper emotional processing.
Taken together, the results highlight the effectiveness of imagery-based techniques in the treatment of test anxiety. Furthermore, the results of Studies II and III support the idea of physiological synchrony as a mechanism of change in imagery with and without rescripting. The current dissertation takes an important step towards optimizing process research within psychotherapy and contributes to a better understanding of the potency and mechanisms of change of imagery-based techniques. We hope that these studies’ implications will support everyday clinical practice.
Vor dem Hintergrund eines fachlichen und gesellschaftlichen Interesses an psychologischen Forschungsthemen wurde der Einsatz von Topic Modeling zu deren automatisierten und validen Identifikation erprobt und mit einem traditionellen Ansatz verglichen, der auf Klassifikationskategorien von Fachdatenbanken beruht. Kern der Arbeit sind drei Studien mit unterschiedlichen Anwendungsszenarien: (1) Die Ermittlung psychologischer Brennpunktthemen, (2) das Verhältnis zwischen gesellschaftlichen Herausforderungen und Themen der psychologischen Fachliteratur sowie (3) die Forschungsinteressen von Psychologinnen und Psychologen. In jeder dieser Studien wurden sukzessive neue methodische Schwerpunkte gesetzt. Die Erkenntnisse daraus flossen in die Entwicklung einer nutzerfreundlichen App zur Exploration und Analyse von psychologischen Forschungsthemen ein. Im Vergleich mit dem klassifikationsbasierten Ansatz erwies sich Topic Modeling als Methode der Wahl zur automatisierten und differenzierten Identifikation psychologischer Forschungsthemen. Zur externen Validierung der Themeninhalte stellte sich ein Klassifikationssystem für psychologische Fachliteratur jedoch als geeignet heraus. Einschränkungen, Implikationen für weitere Forschung sowie Folgerungen für die Weiterentwicklung digitaler Produkte von forschungsbasierten Infrastruktureinrichtungen in der Psychologie werden diskutiert. Abschließend wird eine Entscheidungsheuristik zur Auswahl geeigneter Topic-Modeling-Varianten im Kontext von Forschungsthemen vorgelegt.