Prozessfeedback in der Psychotherapie

Schiepek, Günter; Aichhorn, Wolfgang; Schöller, Helmut; Kronberger, Helmut

doi:10.1007/s00278-018-0272-6

Cited by 20 publications

(10 citation statements)

References 22 publications

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“…The male patient (diagnosis: Obsessive-Compulsive Disorder, OCD) of example four was treated in an inpatient setting (combining Cognitive Behavior Therapy with different group and creative therapies) and underwent a sudden gain in OCD-and depressive symptoms after about the first third of the therapy. The assessment was realized by daily self-ratings of the items of the Therapy Process Questionnaire (TPQ, Schiepek et al, 2017) presented by the internet-based Synergetic Navigation System (SNS, Schiepek et al, 2018). The TPQ included 47 items distributed on 7 subscales.…”

Section: Time Series Of Model and Empirical Systems Used For The Idenmentioning

confidence: 99%

“…The assessment also was realized by daily self-ratings using the TPQ. The case and the synergies of different therapeutic experiences preparing the phase transition were described in detail in a single case study (Schiepek et al, 2018). In Figure 1(6A) the dynamics of the factor S ("success/therapeutic progress") is shown, which corresponds to the factor S of the mathematical simulation model [Figure 1(2A)].…”

Section: Time Series Of Model and Empirical Systems Used For The Idenmentioning

confidence: 99%

“…Most of the methods used in this convergent validity test seem to be promising and are used in routine process monitoring of psychotherapy. In the SNS, DC, PE, RP, and SPA are implemented (Schiepek et al, 2018). Beyond this, many other methods are available for the identification of critical transitions and related early warnings, which were published in different disciplines (e.g., climate research, ecology, brain dynamics, and physiology).…”

Section: Perspectivesmentioning

confidence: 99%

“…Here we focus on methods which can be applied to short time series (100 measurement points or less) and are able to identify pattern transitions and/or precursors of those transitions. Further criteria for the selection of methods is that they should not be restricted by mathematical or parametric assumptions, applicable to real-world time series, and available in computer-based tools for routine process monitoring (e.g., the Synergetic Navigation System, SNS; Schiepek et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Convergent Validation of Methods for the Identification of Psychotherapeutic Phase Transitions in Time Series of Empirical and Model Systems

et al. 2020

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In many cases, the dynamics of psychotherapeutic change processes is characterized by sudden and critical transitions. In theoretical terms, these transitions may be "phase transitions" of self-organizing nonlinear systems. Meanwhile, a variety of methods is available to identify phase transitions even in short time series. However, it is still an open question if different methods for timeseries analysis reveal convergent results indicating the moments of critical transitions and related precursors. Methods and Procedures: Seven concepts which are commonly used in nonlinear time series analysis were investigated in terms of their ability to identify changes in psychological time series: Recurrence Plots, Change Point Analysis, Dynamic Complexity, Permutation Entropy, Time Frequency Distributions, Instantaneous Frequency, and Synchronization Pattern Analysis, i.e., the dynamic inter-correlation of the system's variables. Phase transitions were simulated by shifting control parameters in the Hénon map dynamics, in a simulation model of psychotherapy processes (one by an external shift of the control parameter and one created by a simulated control parameter shift), and three sets of empirical time series generated by daily self-ratings of patients during the treatment. Results: The applied methods showed converging results indicating the moments of dynamic transitions within an acceptable tolerance. The convergence of change points was confirmed statistically by a comparison to random surrogates. In the three simulated dynamics with known phase transitions, these could be identified, and in the empirical cases, the methods converged indicating one and the same transition (possibly the phase transitions of the cases). Moreover, changes that did not manifest in a shift of mean or variance could be detected.

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Section: Time Series Of Model and Empirical Systems Used For The Idenmentioning

confidence: 99%

Section: Time Series Of Model and Empirical Systems Used For The Idenmentioning

confidence: 99%

Section: Perspectivesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Convergent Validation of Methods for the Identification of Psychotherapeutic Phase Transitions in Time Series of Empirical and Model Systems

et al. 2020

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“…Beispiele für Systemmodelle und Prozesserfassungen liegen für Patient*innen mit der Hauptdiagnose Depression oder aber mit depressiver Belastung bei anderen Hauptdiagnosen vor (Kronberger & Aichhorn, 2015;Schiepek et al, 2013a;, 2018b. Die Effekte eines solchen personalisierten Vorgehens sind vielfältig: Bereits die Modellierung in Kooperation mit Patient*innen eröffnet neue und vielfältige Perspektiven auf das Problem und macht systemische Zusammenhänge zwischen unterschiedlichen Aspekten deutlich, womit eine kognitiv-emotionale Umstrukturierung angeregt wird.…”

Section: Personalisierte Psychotherapieunclassified

Depression – ein komplexes dynamisches System

Schiepek¹

2020

PTW

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Der vorliegende Beitrag befasst sich mit neueren Befunden zur Depressionsforschung, vor allem zur psychotherapeutischen Behandlung von Depression. Auf Wunsch der Redaktion sollte damit auch eine Synopse der wissenschaftlichen und praktischen Entwicklungen gegeben werden, die am Institut für Synergetik und Psychotherapieforschung der Paracelsus Medizinischen Privatuniversität Salzburg und der kooperierenden Forschergruppen stattfinden. Ein Schwerpunkt liegt in der Personalisierung der Psychotherapie und betrifft sowohl neuere Methoden der Fallkonzeption (z.B. idiografische Systemmodellierung und deren computerbasierte Visualisierung) als auch Verfahren des hochfrequenten Prozessmonitorings und Prozessfeedbacks. Hierfür wurde das Internetund App-basierte Synergetische Navigationssystem (SNS) entwickelt und erprobt. Die Verläufe der Psychotherapie depressiver Patient*innen zeigen diskontinuierlich Sprünge dynamischer Muster (sogenannte Ordnungsübergänge), die von einer erhöhten kritischen Instabilität (dynamische Komplexität) und zeitlich begrenzter Synchronisation der Items eines Prozessbogens eingeleitet werden. Erhöhte kritische Instabilität ist zudem prädiktiv für besseren Therapieerfolg. In einer Studie an komorbid depressiven Patient*innen mit Zwangsstörungen wurde deutlich, dass sich neben klinischen Verbesserungen auch die funktionelle Konnektivitätsdynamik neuronaler Netze verändert. Wiederholte fMRT-Scans zeigten, dass Flexibilität und Fluidität der Neurodynamik mit der Psychopathologie (z.B. Depressionsintensität, BDI) negativ assoziiert sind, aber im Verlauf von Psychotherapie zunehmen. Abschliessend erfolgt eine Einschätzung von Möglichkeiten, nichtinvasive Neurostimulation mit Psychotherapie zu kombinieren.

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The Therapy Process Questionnaire ‐ Factor analysis and psychometric properties of a multidimensional self‐rating scale for high‐frequency monitoring of psychotherapeutic processes

Schiepek

Stöger‐Schmidinger

Kronberger

et al. 2019

Clin Psychology and Psychoth

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Many outcome measures and session‐related questionnaires in psychotherapy are designed for weekly or biweekly administration. Yet, today, technical developments allow for higher frequency assessments to monitor human change dynamics more closely by daily assessments. For this purpose, the Therapy Process Questionnaire (TPQ) was developed, with a specific focus on inpatient psychotherapy. In this article, we present an explorative and confirmative factor analysis of the TPQ on the basis of the time series data of 150 patients collected during their hospital stay (mean time series length: 69.1 measurement points). A seven‐factor solution was identified, which explains 68.7% of variance and associates 43 items onto the factors, which are “well‐being and positive emotions,” “relationship with fellow patients,” “therapeutic relationship and clinical setting,” “emotional and problem intensity,” “insight/confidence/therapeutic progress,” “motivation for change,” and “mindfulness/self‐care.” The internal consistency (Cronbach's α), the inter‐item correlations of the subscales, and the discriminative power of the items are excellent. The TPQ can be applied in practice and research for creating time series with equidistant measurement points and time series lengths, which are appropriate for the application of nonlinear analysis methods. Especially in clinical practice, it is important to identify precursors of phase transitions, changing synchronization patterns, and critical or instable periods of a process, which now is possible by internet‐ or app‐based applications of this multidimensional questionnaire.

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Prozessfeedback in der Psychotherapie

Cited by 20 publications

References 22 publications

Convergent Validation of Methods for the Identification of Psychotherapeutic Phase Transitions in Time Series of Empirical and Model Systems

Convergent Validation of Methods for the Identification of Psychotherapeutic Phase Transitions in Time Series of Empirical and Model Systems

Depression – ein komplexes dynamisches System

The Therapy Process Questionnaire ‐ Factor analysis and psychometric properties of a multidimensional self‐rating scale for high‐frequency monitoring of psychotherapeutic processes

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