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Originalia

Die Identifikation früher Veränderungsmuster in der ambulanten Psychotherapie

Published Online:May 31, 2007https://doi.org/10.1026/1616-3443.36.2.93

Abstract

Zusammenfassung.Theoretischer Hintergrund: Im Rahmen einer patientenorientierten Psychotherapieforschung werden Patientenausgangsmerkmale und Veränderungsmuster in einer frühen Therapiephase genutzt, um Behandlungsergebnisse und Behandlungsdauer vorherzusagen. Fragestellung: Lassen sich in frühen Therapiephasen verschiedene Muster der Veränderung (Verlaufscluster) identifizieren und durch Patientencharakteristika vorhersagen? Erlauben diese Verlaufscluster eine Vorhersage bezüglich Therapieergebnis und -dauer? Methode: Anhand des Growth Mixture Modeling Ansatzes wurden in einer Stichprobe von N = 2206 ambulanten Patienten einer US-amerikanischen Psychotherapieambulanz verschiedene latente Klassen des frühen Therapieverlaufs ermittelt und unter Berücksichtigung unterschiedlicher Patientenausgangscharakteristika als Prädiktoren der frühen Veränderungen mit dem Therapieergebnis und der Therapiedauer in Beziehung gesetzt. Ergebnisse: Für leicht, mittelschwer und schwer beeinträchtigte Patienten konnten je vier unterschiedliche Verlaufscluster mit jeweils spezifischen Prädiktoren identifiziert werden. Die Identifikation der frühen Verlaufsmuster ermöglichte weiterhin eine spezifische Vorhersage für die unterschiedlichen Verlaufscluster bezüglich des Therapieergebnisses und der Therapiedauer. Schlussfolgerungen: Frühe Psychotherapieverlaufsmuster können einen Beitrag zu einer frühzeitigen Identifikation günstiger sowie ungünstiger Therapieverläufe leisten.


Patterns of early change in outpatient therapy

Abstract.Background: Patient-focused psychotherapy research aims to predict the course of patients’ progress and outcomes in psychotherapy based on their initial characteristics and information about early change. Objective: In this study we examine two research questions: Is it possible to identify different patterns of early change and to predict them by initial characteristics of the patients? Can these patterns of change be used to predict treatment outcome as well as the length of treatment. Methods: Growth mixture modeling was used to identify latent classes of early change in a sample of N = 2206 outpatients from a US-counseling center. Those early change clusters were associated to initial patient characteristics and used to predict treatment outcome and length of treatment. Results: For each of three patient groups with low, medium, and severe initial impairment, four different patterns of early change were identified. Furthermore, those patterns were related to specific patient variables at intake and they were also associated with differential probabilities for treatment outcome and duration. Conclusions: The modeling of change patterns in psychotherapy can be helpful in identifying negative and positive treatment developments in an early phase of treatment.

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