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Objectives: Within a randomized controlled trial contrasting the outcome of manualized cognitive-behavioral (CBT) and short term psychodynamic therapy (PDT) compared to a waiting list condition (the SOPHO-Net trial), we set out to test whether self-reported attachment characteristics change during the treatments and if these changes differ between treatments.
Research design and methods: 495 patients from the SOPHO-Net trial (54.5% female, mean age 35.2 years) who were randomized to either CBT, PDT or waiting list (WL) completed the partner-related revised Experiences in Close Relationships Questionnaire (ECR-R) before and after treatment and at 6 and 12 months follow-up. The Liebowitz Social Anxiety Scale (LSAS) was administered at pre-treatment, post-treatment, and at 6-month and 1-year follow-up. ECR-R scores were first compared to a representative healthy sample (n = 2508) in order to demonstrate that the clinical sample differed significantly from the non-clinical sample with respect to attachment anxiety and avoidance.
Results: LSAS scores correlated significantly with both ECR-R subscales. Post-therapy, patients treated with CBT revealed significant changes in attachment anxiety and avoidance whereas patients treated with PDT showed no significant changes. Changes between post-treatment and the two follow-ups were significant in both conditions, with minimal (insignificant) differences between treatments at the 12- month follow-up.
Conclusions: The current study supports recent reviews of mostly naturalistic studies indicating changes in attachment as a result of psychotherapy. Although there were differences between conditions at the end of treatment, these largely disappeared during the follow-up period which is line with the other results of the SOPHO-NET trial.
Trial registration: Controlled-trials.com ISRCTN53517394
In psychotherapy, movement synchrony seems to be associated with higher patient satisfaction and treatment outcome. However, it remains unclear whether movement synchrony rated by humans and movement synchrony identified by automated methods reflect the same construct. To address this issue, video sequences showing movement synchrony of patients and therapists (N = 10) or not (N = 10), were analyzed using motion energy analysis. Three different synchrony conditions with varying levels of complexity (naturally embedded, naturally isolated, and artificial) were generated for time series analysis with windowed cross-lagged correlation/ -regression (WCLC, WCLR). The concordance of ratings (human rating vs. automatic assessment) was computed for 600 different parameter configurations of the WCLC/WCLR to identify the parameter settings that measure movement synchrony best. A parameter configuration was rated as having a good identification rate if it yields high concordance with human-rated intervals (Cohen’s kappa) and a low amount of over-identified data points. Results indicate that 76 configurations had a good identification rate (IR) in the least complex condition (artificial). Two had an acceptable IR with regard to the naturally isolated condition. Concordance was low with regard to the most complex (naturally embedded) condition. A valid identification of movement synchrony strongly depends on parameter configuration and goes beyond the identification of synchrony by human raters. Differences between human-rated synchrony and nonverbal synchrony measured by algorithms are discussed.