Nonverbal synchrony: A new approach to better understand psychotherapeutic processes and drop-out

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 out- patient 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. Further- more, 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

Randomized controlled trial to evaluate the effects of personalized prediction and adaptation tools on treatment outcome in outpatient psychotherapy: study protocol

Abstract Background Psychotherapy is successful for the majority of patients, but not for every patient. Hence, further knowledge is needed on how treatments should be adapted for those who do not profit or deteriorate. In the last years prediction tools as well as feedback interventions were part of a trend to more personalized approaches in psychotherapy. Research on psychometric prediction and feedback into ongoing treatment has the potential to enhance treatment outcomes, especially for patients with an increased risk of treatment failure or drop-out. Methods/design The research project investigates in a randomized controlled trial the effectiveness as well as moderating and mediating factors of psychometric feedback to therapists. In the intended study a total of 423 patients, who applied for a cognitive-behavioral therapy at the psychotherapy clinic of the University Trier and suffer from a depressive and/or an anxiety disorder (SCID interviews), will be included. The patients will be randomly assigned either to one therapist as well as to one of two intervention groups (CG, IG2). An additional intervention group (IG1) will be generated from an existing archival data set via propensity score matching. Patients of the control group (CG; n = 85) will be monitored concerning psychological impairment but therapists will not be provided with any feedback about the patients assessments. In both intervention groups (IG1: n = 169; IG2: n = 169) the therapists are provided with feedback about the patients self-evaluation in a computerized feedback portal. Therapists of the IG2 will additionally be provided with clinical support tools, which will be developed in this project, on the basis of existing systems. Therapists will also be provided with a personalized treatment recommendation based on similar patients (Nearest Neighbors) at the beginning of treatment. Besides the general effectiveness of feedback and the clinical support tools for negatively developing patients, further mediating and moderating variables on this feedback effect should be examined: treatment length, frequency of feedback use, therapist effects, therapist’s experience, attitude towards feedback as well as congruence of therapist’s and patient’s evaluation concerning the progress. Additional procedures will be implemented to assess treatment adherence as well as the reliability of diagnosis and to include it into the analyses. Discussion The current trial tests a comprehensive feedback system which combines precision mental health predictions with routine outcome monitoring and feedback tools in routine outpatient psychotherapy. It also adds to previous feedback research a stricter design by investigating another repeated measurement CG as well as a stricter control of treatment integrity. It also includes a structured clinical interview (SCID) and controls for comorbidity (within depression and anxiety). This study also investigates moderators (attitudes towards, use of the feedback system, diagnoses) and mediators (therapists’ awareness of negative change and treatment length) in one study. Trial registration Current Controlled Trials NCT03107845 . Registered 30 March 2017

Interpersonal clarification effects in Cognitive-Behavioral Therapy for depression and how they are moderated by the therapeutic alliance

Background: Although a wide body of research links depression to interpersonal deficits, Cognitive-Behavioral Therapy (CBT), considered the gold standard in the treatment of this condition, has not been developed to specifically address interpersonal difficulties. However, cognitive changes on a relational level occurring during CBT might play an important role in the treatment of depression. Interpersonal clarification refers to the process of better understanding the nature of one´s interpersonal patterns during therapy. The aim of this study is to analyze the effects of interpersonal clarification in CBT and how they are moderated by the therapeutic alliance. Methods: A sample of 621 patients diagnosed with depression were treated with CBT by 126 therapists in a university outpatient clinic. Patients completed measures of interpersonal problems and depression severity at baseline, measures of symptomatic evolution before each session and process measures (assessing interpersonal clarification and alliance) after each session. Multilevel models separating between-patient (BP) and within-patient (WP) effects of interpersonal clarification, and including BP and WP alliance effects as covariates and moderators of the interpersonal clarification effects were conducted. Results: Analyses showed both significant BP and WP effects interpersonal clarification, even when adjusting for alliance effects. Furthermore, significant interactive effects were found between outcome of WP interpersonal clarification with both BP alliance and WP alliance. Limitations: Interpersonal clarification was measured with one single-item and adherence to CBT was not explicitly measured. Conclusions: The results present preliminary evidence for considering interpersonal clarification a meaningful change process in CBT for depression, especially in the context of a stronger therapeutic alliance.Fil: Gómez Penedo, Juan Martín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universitat Trier; Alemania. Universidad de Buenos Aires. Facultad de Psicología; ArgentinaFil: Schwartz, Brian. Universitat Trier; AlemaniaFil: Deisenhofer, Anne Katharina. Universitat Trier; AlemaniaFil: Rubel, Julian. Justus Liebig Universitat Giessen; AlemaniaFil: Babl, Anna M.. University of Bern; SuizaFil: Lutz, Wolfgang. Universitat Trier; Alemani

For whom should psychotherapy focus on problem coping? A machine learning algorithm for treatment personalization

Objective: We aimed to develop and test an algorithm for individual patient predictions of problem coping experiences (PCE) (i.e., patients’ understanding and ability to deal with their problems) effects in cognitive–behavioral therapy. Method: In an outpatient sample with a variety of diagnoses (n=1010), we conducted Dynamic Structural Equation Modelling to estimate within-patient cross-lagged PCE effects on outcome during the first ten sessions. In a randomly selected training sample (2/3 of the cases), we tried different machine learning algorithms (i.e., ridge regression, LASSO, elastic net, and random forest) to predict PCE effects (i.e., the degree to which PCE was a time-lagged predictor of symptoms), using baseline demographic, diagnostic, and clinically-relevant patient features. Then, we validated the best algorithm on a test sample (1/3 of the cases). Results: The random forest algorithm performed best, explaining 14.7% of PCE effects variance in the training set. The results remained stable in the test set, explaining 15.4% of PCE effects variance. Conclusions: The results show the suitability to perform individual predictions of process effects, based on patients’ initial information. If the results are replicated, the algorithm might have the potential to be implemented in clinical practice by integrating it into monitoring and therapist feedback systems.Fil: Gómez Penedo, Juan Martín. University Of Trier; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Schwartz, Brian. University Of Trier; AlemaniaFil: Giesemann, Julia. University Of Trier; AlemaniaFil: Rubel, Julian A.. Justus Liebig University Giessen; AlemaniaFil: Deisenhofer, Anne-Katharina. University Of Trier; AlemaniaFil: Lutz, Wolfgang. University Of Trier; Alemani

Physical Activity of Young Patients following Minimally Invasive Lateral Unicompartmental Knee Replacement

Unicompartmental knee replacement (UKR) has increased in popularity in recent years, especially in young patients with high demands on their athletic ability. To date, there are no data available on the physical activity of young patients following lateral UKR. The aim of this study was to demonstrate return-to-activity rate and sporting activity of patients aged 60 years or younger following lateral UKR with a fixed-bearing (FB) prosthesis. Thirty-seven patients aged 60 years or younger after lateral FB-UKR were included. Sporting activities were assessed using the University of California Los Angeles activity scale (UCLA) and the Tegner activity score (TAS). Clinical outcome was measured using the Oxford Knee Score (OKS), range of motion (ROM) and visual analogue scale (VAS). The mean follow-up (FU) was 3.1 ± 1.5 years and the mean age at surgery was 52.8 ± 3.1 years. The return-to-activity rate was 87.5% and 49% of patients were highly active postoperatively as defined by an UCLA score of 7 or higher. All clinical parameters increased significantly postoperatively. We demonstrated a high return-to-activity rate with nearly half of the patients achieving high activity levels. Longer FU periods are necessary to evaluate the effect of activity on implant survival

Identification of movement synchrony : validation of windowed cross-lagged correlation and -regression with peak-picking algorithm

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

Identification of movement synchrony: Validation of windowed cross-lagged correlation and -regression with peak-picking algorithm.

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