Objective: Current approaches of routine outcome monitoring (session-by-session measures) expect that trajectories of change should move on a standard track. Patients moving out of standard tracks are assumed to be at risk of deterioration. From a nonlinear dynamic systems perspective, there is not any assumption regarding a supposed standard track a patient should follow. Individual trajectories should be more complex than averaged tracks, highly individual, and characterised by pattern transitions. Method: We tested if high-frequency (daily) trajectories of change are moving on standard tracks, if there are different complexity levels of high- versus low-frequency time series, if ‘not on track' dynamics will be correlated with poor outcome and if complexity peaks representing the critical instabilities of a process will be correlated with the outcome. The patients included in the data analysis (N = 88) used the Therapy Process Questionnaire (TPQ) for daily self-assessments and the ICD-10-based Symptom Rating (ISR) for outcome evaluation. Results: High-frequency trajectories are not running on standard tracks and are not necessarily correlated with poor outcome. Locally increased complexity may be associated with good outcome. Conclusion: It may be useful to move beyond the concept of standard tracks and expected treatment outcomes. Routine feedback procedures should use the information that is given by the nonlinear dynamics of multiple change criteria.

Complex individual pathways or standard tracks? A data-based discussion on the trajectories of change in psychotherapy

Gelo O.;
2020

Abstract

Objective: Current approaches of routine outcome monitoring (session-by-session measures) expect that trajectories of change should move on a standard track. Patients moving out of standard tracks are assumed to be at risk of deterioration. From a nonlinear dynamic systems perspective, there is not any assumption regarding a supposed standard track a patient should follow. Individual trajectories should be more complex than averaged tracks, highly individual, and characterised by pattern transitions. Method: We tested if high-frequency (daily) trajectories of change are moving on standard tracks, if there are different complexity levels of high- versus low-frequency time series, if ‘not on track' dynamics will be correlated with poor outcome and if complexity peaks representing the critical instabilities of a process will be correlated with the outcome. The patients included in the data analysis (N = 88) used the Therapy Process Questionnaire (TPQ) for daily self-assessments and the ICD-10-based Symptom Rating (ISR) for outcome evaluation. Results: High-frequency trajectories are not running on standard tracks and are not necessarily correlated with poor outcome. Locally increased complexity may be associated with good outcome. Conclusion: It may be useful to move beyond the concept of standard tracks and expected treatment outcomes. Routine feedback procedures should use the information that is given by the nonlinear dynamics of multiple change criteria.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11587/435847
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