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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 (

Premature termination is a problem in psychotherapy. In addition to the examination of demographic and clinical variables as predictors of dropout, research indicates the importance of dyadic variables. Nonverbal synchrony (e.g., movement synchrony) operationalizes the coordination of patient and therapist and is a promising candidate for predicting premature termination. This secondary data analysis included data on patients with social anxiety disorder (N ϭ 267) that were treated with Ͼ20 sessions of cognitive-behavioral therapy or psychodynamic therapy. Therapy outcome was measured by the Inventory of Interpersonal Problems and the Beck Depression Inventory. Individual movements in the third session were assessed by motion energy analysis. Movement synchrony was identified with a windowed cross-lagged correlation and peak-picking algorithm. We performed logistic regressions and mixed effects Cox regressions to investigate synchrony as a predictor of premature termination. Therapistpatient dyads that included a patient who terminated psychotherapy prematurely had significantly lower movement synchrony at the beginning of therapy than patients who completed therapy. Gender-matching and therapeutic approach had a (marginally) significant effect in moderating the relationship. Therefore, low movement synchrony in early therapy sessions may contain clues to premature termination and reflect a mismatch between the patient and therapist or problems in the therapeutic alliance. Clinical Impact StatementQuestion: Movement synchrony at the beginning of therapy, measured by frame-by-frame coding, was investigated as a predictor of premature termination of psychotherapy. Findings: High nonverbal synchrony was associated with a low rate of premature therapy termination in patients with social anxiety disorder. Meaning: Because premature termination is generally viewed as a failure in psychotherapy, information on nonverbal aspects may help therapists prevent premature termination. Next Steps: Nonverbal elements should be explicitly integrated into psychotherapy practice.

Early change is an increasing area of investigation in psychotherapy research. In this study, we analyzed patterns of early change in interpersonal problems and their relationship to nonverbal synchrony and multiple outcome measures for the first time. We used growth mixture modeling to identify different latent classes of early change in interpersonal problems with 212 patients who underwent cognitive-behavioral treatment including interpersonal and emotion-focused elements. Furthermore, videotaped sessions were analyzed using motion energy analysis, providing values for the calculation of nonverbal synchrony to predict early change in interpersonal problems. The relationship between early change patterns and symptoms as well as overall change in interpersonal problems was also investigated. Three latent subgroups were identified: 1 class with slow improvement (n ϭ 145), 1 class with fast improvement (n ϭ 12), and 1 early deterioration class (n ϭ 55). Lower levels of early nonverbal synchrony were significantly related to fast improvement in interpersonal change patterns. Furthermore, such patterns predicted treatment outcome in symptoms and interpersonal problems. The results suggest that nonverbal synchrony is associated with early change patterns in interpersonal problems, which are also predictive of treatment outcome. Limitations of the applied methods as well as possible applications in routine care are discussed.Editor's Note. Sigal Zilcha-Mano served as the action editor for this article.-DMK Jr.

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.