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Video-based measurement methods are new to psychotherapy research and provide new opportunities to investigate mechanisms of psychotherapeutic change related to nonverbal synchrony (movement coordination between patient and therapist). In this study, we validated the applied video-based procedures and evaluated nonverbal synchrony in association with the therapeutic relationship, therapy outcome, and drop-out. The naturalistic analysis sample consisted of 143 patients (136 videotaped sessions), who were treated with integrative cognitive–behavioral therapy at an outpatient clinic in southwest Germany. The videos were analyzed using Motion Energy Analysis (MEA), which provided a value for nonverbal synchrony. Patients routinely completed questionnaires assessing the therapeutic relationship and treatment success. We tested various confounding variables using multilevel modeling and investigated nonverbal synchrony in relation to measures of the therapeutic relationship. Furthermore, we compared different types of outcomes with regard to nonverbal synchrony by means of multilevel modeling. The video-based procedures were shown to be highly valid. We found a link between the amount of nonverbal synchrony and therapeutic success; patients with nonimprovement and consensual termination showed the highest level, improved patients a medium level, and nonimproved patients with drop-out the lowest level of synchrony at the beginning of therapy, even when controlling for the therapeutic relationship. The study applied and evaluated a novel video-based approach in psychotherapy research and related it to common factors and the therapeutic process. Limitations of the automatic measurement methods and opportunities for the future routine prediction of drop-out are discussed.

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Nonverbal synchrony describes coordination of the nonverbal behavior of two interacting partners. Additionally, it seems to be important in human interactions, such as during psychotherapy. Currently, there are several options for the automated determination of synchrony based on linear time series analysis methods (TSAMs). However, investigations into whether the different methods measure the same construct have been missing. In this study, N = 84 patient-therapist dyads were videotaped during psychotherapy sessions. Motion energy analysis was used to assess body movements. We applied seven different TSAMs and recorded multiple output scores (average synchrony, maximum synchrony, and frequency of synchrony; in total, N = 16 scores). Convergent validity was examined using correlations of the output scores and exploratory factor analysis. Additionally, two criterion-based validations were conducted: investigations of concordant validity with a more generalized nonlinear method, and of the predictive validity of the synchrony scores for improvement in interpersonal problems at the end of therapy. We found that the synchrony measures only partially correlated with each other. The factor analysis did not support a common-factor model. A three-factor model with a second-order synchrony variable showed the best fit for eight of the selected synchrony scores. Only some synchrony scores were able to predict improvement at the end of therapy. We concluded that the considered TSAMs do not measure the same synchrony construct, but different facets of synchrony: the strength of synchrony of the total interaction, the strength of synchrony during synchronization intervals, and the frequency of synchrony. Keywords Nonverbal behavior. Movement synchrony. Motion energy analysis. Time series analysis. Convergent validity Currently, body movements can be assessed fully automatically and with high time resolution (e.g., 25 times per second) using either motion-tracking, motion capture devices, or video-based algorithms (Delaherche et al., 2012). Motion energy analysis (MEA) is a method that quantifies the intensity of videotaped movements frame-wise (Grammer, Honda, Juette, & Schmitt, 1999). By determining a region of interest (ROI) for each of two videotaped individuals (e.g., a patient and therapist during a psychotherapy session), two time series can be generated displaying the time course of the individuals' body movements. This technique has several advantages: (1) it is less timeconsuming than collecting human ratings; (2) it is highly objective, reliable, and valid; and (3) in comparison to motion capture devices, no high-resolution camera equipment is necessary, and no sensors are attached to the patient's body (Altmann, 2010; Ramseyer & Tschacher, 2011). Therefore, during the past few years, the use of MEA has become enormously widespread. In behavioral and social science, MEA has been used to assess movements in mother-child interactions (

Objective: Thus far, most applications in precision mental health have not been evaluated prospectively. This article presents the results of a prospective randomized-controlled trial investigating the effects of a digital decision support and feedback system, which includes two components of patient-specific recommendations: (a) a clinical strategy recommendation and (b) adaptive recommendations for patients at risk for treatment failure. Method: Therapist-patient dyads (N = 538) in a cognitive behavioral therapy outpatient clinic were randomized to either having access to a decision support system (intervention group; n = 335) or not (treatment as usual; n = 203). First, treatment strategy recommendations (problem-solving, motivationoriented, or a mix of both strategies) for the first 10 sessions were evaluated. Second, the effect of psychometric feedback enhanced with clinical problem-solving tools on treatment outcome was investigated. Results: The prospective evaluation showed a differential effect size of about 0.3 when therapists followed the recommended treatment strategy in the first 10 sessions. Moreover, the linear mixed models revealed therapist symptom awareness and therapist attitude and confidence as significant predictors of an outcome as well as therapist-rated usefulness of feedback as a significant moderator of the feedback-outcome and the not on track-outcome associations. However, no main effects were found for feedback. Conclusions: The results demonstrate the importance of prospective studies and the highquality implementation of digital decision support tools in clinical practice. Therapists seem to be able to learn from such systems and incorporate them into their clinical practice to enhance patient outcomes, but