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This study investigated symptom change trajectory for patients with persistent somatic symptoms (PSS) during psychotherapy and the association of these patterns with pre-treatment characteristics and long-term outcome. Growth mixture modeling was used to identify trajectory curves in a sample of N = 210 outpatients diagnosed with PSS and treated either with conventional cognitive behavioral therapy (CBT) or CBT enriched with emotion regulation training (ENCERT). We identified three subgroups of patients with similar symptom change patterns over the course of treatment (a "no change," "strong response," and "slow change" subgroup). Higher initial anxiety symptoms were significantly associated with the no change and strong response subgroups; symptom-related disability in daily routine with no changes. Patients with a strong response had the highest proportion of reliable improvement at termination and at six-month-follow-up. Our results indicate that, instead of one common change pattern, patients with PSS respond differently to treatment. Due to the high association of symptom curves with long-term outcome, the identification and prediction of an individual's trajectory could provide important information for clinicians to identify non-responding patients that are at risk for failure. Selecting personalized treatment interventions could increase the effectiveness of psychotherapy. Trial registration: ClinicalTrials.gov identifier: NCT01908855.. [ABSTRACT FROM AUTHOR]

Objective: Because individual patients with persistent somatic symptoms (PSS) respond differently to treatments, a better understanding of the factors that predict therapy outcomes are of high importance. Aggregating a wide selection of information into the treatment-decision process is a challenge for clinicians. Using the Personalized Advantage Index (PAI) this study aims to deal with this. Methods: Data from a multicentre RCT comparing CBT (N = 128) versus CBT enriched with emotion regulation training (ENCERT) (N = 126) for patients diagnosed with somatic symptom disorder were used to identify based on two machine learning approaches predictors of therapy outcomes. The identified predictors were used to calculate the PAI. Results: Five treatment unspecific predictors (pre-treatment somatic symptom severity, depression, symptom disability, health-related quality of life, age) and five treatment specific moderators (global functioning, early childhood traumatic events, gender, health anxiety, emotion regulation skills) were identified. Individuals assigned to their PAI-indicated optimal treatment had significantly lower somatic symptom severity at the end of therapy compared to those randomised to their non-optimal condition. Conclusion: Allowing patients to choose a personalised treatment seems to be meaningful. This could help to improve outcomes for PSS and reduce its high costs to the health care system. [ABSTRACT FROM AUTHOR]