Nonlinear Dynamics, Psychology, and Life Sciences, Vol. 20, No. 2, pp. 145-166. © 2016 Society for Chaos Theory in Psychology & Life Sciences Movement Coordination in Psychotherapy: Synchrony of Hand Movements is Associated with Session Outcome. A Single-Case Study Fabian Ramseyer,1 and Wolfgang Tschacher, University of Bern, Switzerland Abstract: Previous work has shown that nonverbal behavior was associated with both session-level outcome and global outcome in psychotherapy. Nonverbal synchrony – here the coordination between patient's and psychotherapist's movement behavior – is a facet of nonverbal behavior that has recently been studied with video-based motion energy analysis (MEA). The present study aimed to replicate and extend these findings by using direct acquisition of movement data. In a single-case analysis, we monitored patient's and therapist's hand movements with a high-resolution accelerometric measurement system (Vitaport (r)). In addition to these behavioral data, both patient and therapist provided session-level ratings of various factors relevant to the psychotherapy process, which were assessed with post-session questionnaires. The patient-therapist coordination of hand movements, i.e. nonverbal synchrony, in (N = 27) sessions of this dyadic psychotherapy was positively associated with progress reported in post-session questionnaires. Sessions with good evaluations concerning the quality of therapeutic alliance were characterized by high movement coordination. Thus, accelerometric data of this therapy dyad confirmed previous findings gained through video analyses: The coordination of nonverbal behavior shown by patient and therapist was an indicator of beneficial processes occurring within sessions. This replication study showed that nonverbal synchrony embodies important aspects of the alliance. Its assessment and quantification may provide therapists important additional information on processes that usually occur outside conscious awareness, but that nevertheless influence core aspects of the therapy. Key Words: nonverbal synchrony, movement coordination, accelerometer, psychotherapy INTRODUCTION Coordination is a commonality of any kind of social interaction – the degrees of coordination influence the quality and outcome of interactions 1 Correspondence address: Fabian Ramseyer, Ph.D., University of Bern Department for Clinical Psychology and Psychotherapy, Gesellschaftsstrasse 49 CH – 3012 Bern, Switzerland. E-mail: fabian.ramseyer@psy.unibe.ch 145 146 NDPLS, 20(2), Ramseyer & Tschacher between two or more persons (Schmidt & Richardson, 2008; Strogatz, 2003). Coordination may emerge with or without the interactants' conscious goal of achieving coordination (Di Paolo & De Jaegher, 2012), and may occur on multiple levels and in different communication modalities that are active during interpersonal exchanges (Delaherche et al., 2012). A common characteristic of these incidents of coordination is their association with interpersonal processes: Numerous studies have demonstrated that the coordination in different modalities has a positive impact on interactants taking part in social exchange (Chartrand & Lakin, 2013). This connection between the physical manifestations of coordinative dynamics and their effect on a person‘s cognitive and emotional state is focused by the stance of embodied cognition (Tschacher & Bergomi, 2011; Wachsmuth, Lenzen, & Knoblich, 2008). Precursors of contemporary embodiment research can be recognized in the James-Lange theory of emotions, in the perception-action cycles of ecological psychology (Gibson, 1966; 1986), and in phenomenological philosophy (Merleau-Ponty, 2002). In the course of the introduction we will argue that and how coordinative phenomena in social interaction can be integrated with a dynamical-systems view. This approach considers coordination as the emergence of self-organized patterns. The present empirical study was an extended case study (27 sessions of one therapeutic dyad) focusing on nonverbal behavior in psychotherapy. Previous research has shown, in a randomized sample, that the relationship quality of patients and therapists is embodied by the coordination of their movement behavior (Ramseyer & Tschacher, 2011) and that the degree of a therapist's empathic reaction was associated with vocal synchrony (Imel et al., 2014; Reich, Berman, Dale, & Levitt, 2014). Physiological signals were also found to converge in empathic dyads (Marci, Ham, Moran, & Orr, 2007; Guastello, Pincus, & Gunderson, 2006) and work teams (Guastello et al., in press). There is increasing evidence of synchronization processes occurring in psychotherapy dyads assessed by e.g. skin conductance levels (Kleinbub et al., 2012; Itävuori et al., 2015; Messina et al., 2012), and heart rate activity (Marci et al., 2007). Human Movement Broadly speaking, human movement provides information that includes signals necessary for the individual's navigation of its social environment (Coey, Varlet, & Richardson, 2012). At a neurobiological level, the detection of movement occurs in regions of the brain specialized to process and interpret the movement of animated objects, so-called biological movement (Grosbras, Beaton, & Eickhoff, 2012). Very early in development, humans are capable of not only recognizing but also of accurately differentiating various signatures of human movement (Blake & Shiffrar, 2007). Even with limited information available, correct inferences are possible, showing that viewers can efficiently use the information unfolding in movement dynamics. This was demonstrated in research using "point-light displays" (Johansson, 1973), where the moving body is reduced to the movement of few points that are fixed at limbs and joints of the body. A related phenomenon is quite frequent in everyday life: People we know NDPLS, 20(2), Movement Coordination in Psychotherapy 147 well may be identified by minimal cues such as e.g. the noise-patterns generated by their specific way of walking. Decades ago, Allport described similar phenomena (1937) and recently the recognition of people based on their movement signatures was found empirically (Loula, Prasad, Harber, & Shiffrar, 2005). Clarke et al. (Clarke, Bradshaw, Field, Hampson, & Rose, 2005) presented videos of point-light sequences of two people in social interaction to participants, who were able to correctly classify the main emotional theme displayed in these sequences. This was interpreted as evidence for accurate inference of emotion based on visible motion stimuli – a finding on a par with Grahe and Bernieri's (1999) observation that rapport, the quality of a relationship, may be derived by observers who viewed, but not overheard, social situations. “The most parsimonious reason may be that rapport is primarily a physically manifested construct; it is a construct that is visible at the surface and readily apparent. (...) In other words, rapport simply may be visible” (1999, p. 265). Understanding others' actions and goals is an important skill, often called 'theory of mind', that is demanded in social environments. This aspect of social cognition is correlated to the brain employing a specific capacity of neurons in the motor cortex: motor neurons are not only involved in executing motor action but also the mirroring of perceived action – they are the components of the mirror-neuron system (Iacoboni, 2009; Overwalle & Baetens, 2009). The perception-action model (Preston & de Waal, 2002) and the shared circuits model (Hurley 2008) both emphasize the biological foundations of behavioral coordination. Irregularities in the mirror-neuron system have been associated with autism spectrum symptoms (Rizzolatti & Fogassi, 2014) and other psychiatric disorders (Mehta, Thirthalli, Basavaraju, Gangadhar, & Pascual-Leone, 2014). Taken together, human movement provides essential information that enables an observer to decipher intentions and feelings of the observed subject – information that is crucial for the social survival of an individual (Blake & Shiffrar, 2007). Thus evolution theory (Lakin, Jefferis, Cheng, & Chartrand, 2003) is a general background in this respect: The social advantages are, among others, improved perception and encoding of actions (internal simulation) as well as improved inferences about the other person (understanding and empathy), providing important cues about intentions of others, and their relation to the own social group (Carr, Iacoboni, Dubeau, Mazziotta, & Lenzi, 2003). In terms of nonlinear systems theory, this capability is an example of pattern recognition and pattern completion (Haken, 1996; Haken & Tschacher, 2010). The brain acts as a complex non-equilibrium system that generates selforganized patterns in such a way as to reduce environmental gradients or, in Friston's (2011) words, minimize free energy. According to this view, the brain works like a Bayesian inference engine that is continuously trying to optimize probabilistic representations of what caused its sensory input, and thereby arrives at an intentionalistic understanding of its (social) environment. In multi-person interaction, this perception-action view involves also coordinative dynamics. One may distinguish rhythmic coordination (Keller, 148 NDPLS, 20(2), Ramseyer & Tschacher Novembre, & Hove, 2014) from coordination of complex movements such as e.g. dynamic movements during conversation (Ramseyer & Tschacher, 2006; Tschacher, Rees, & Ramseyer, 2014). In recent years, ample evidence has accumulated for rhythmic coordination in social interaction (Chartrand & Lakin, 2013), with movements as diverse as dancing (Brick & Boker, 2011; Reddish, Fischer, & Bulbulia, 2013), stepping (Miles, Griffiths, Richardson, & Macrae, 2010), finger-tapping (Valdesolo & DeSteno, 2011), or drumming (Kokal, Engel, Kirschner, & Keysers, 2011). Baimel, Severson, Baron, and Birch (2015) have argued that these different kinds of behavioral synchronization are effectively fostering theory of mind. From an evolutionary standpoint, advantages to survival are evident both phylogenetically (e.g. a synchronized flight response in threatening situations; Preston & de Waal, 2002), as well as ontogenetically (e.g. positive developmental outcomes; Harrist & Waugh, 2002). A systems-theoretical background of rhythmic behavioral coordination was provided in the seminal paper of Haken, Kelso and Bunz (1985) who modeled the phase coupling (i.e. synchronization) of a person's two index fingers. Their "HKB model" put a specific focus on the observable phase transitions, i.e. changes of the movement pattern to a different attractor. The HKB model was repeatedly applied to further coordinative movements in the individual and to social coordination between individuals, and has become a major approach in sports science and more generally movement science. Hand Movement The HKB model and many experiments inspired by it usually explore highly artificial movement coordinations in the laboratory. In another train of research, hand movements were investigated in naturalistic contexts in relation to speech production, specifically as gesturing (Kendon, 2004). Such co-speech gestures have been analyzed for the occurrence of mimicry, and in the context of collaborative face-to-face dialogue, the imitation of co-speech gestures was reported (Holler & Wilkin, 2011). Gesture was found to be a predictor of the veracity of statements in investigative interviews (Broaders & Goldin-Meadow, 2010), and gesturing increased the comprehension of communicative meanings in a discourse (Cutica & Bucciarelli, 2011; Goldin-Meadow & Alibali, 2013). Even in a non-communicative task and on a highly abstract level, hand movement has been shown to be indicative of psychological processes, e.g. when moving a computer mouse on a choice-task (Freeman, Dale, & Farmer, 2011). In the psychotherapy context of the data presented here, the communicative character accompanying speech production (McNeill 1985), together with its significance for a person's emotion regulation (Lausberg 2011; Lausberg & Kryger, 2011) are especially relevant. In the present article, we were less interested in the description of exactly which hand movements the patient and therapist made, but more in the temporal dynamics generated by the interplay of the two interacting individuals. This interplay was thus conceived as an instance of interpersonal movement coordination in a naturalistic context. On the background of nonlinear dynamical systems and self-organization, one NDPLS, 20(2), Movement Coordination in Psychotherapy 149 should expect the emergence of movement synchronization (Salvatore & Tschacher, 2012; Tschacher, 1997). In the extended case study reported here, we intended to show that movement coordination, operationalized as nonverbal synchrony of two person's hand movements, was present in naturalistic psychotherapy sessions. The aim was to test a direct measurement procedure using movement sensors in a psychotherapy setting, and to explore possible associations between movement characteristics and process measures of the session-level dynamics in the therapy system. We expected to support previous findings of a link between relationship quality and synchrony. In addition, we wished to explore the computational details by performing sensitivity analyses on the parameters of the method (segment size, lag length). We finally regarded complementary methods of interest such as surrogate analysis, and Fourier decomposition of the movement time series. METHODS Measurement of Hand Movement Video-based algorithms allow the monitoring of movement activity displayed in video recordings (Ramseyer & Tschacher, 2014). Motion energy analysis (MEA, Ramseyer & Tschacher, 2011) is convenient when direct assessment of movement is ruled out, such as in archival material or when the attachment and wearing of devices is considered too invasive. Direct assessments using accelerometric devices, with potentially increased accuracy and validity, have been employed in empirical research on human movement for a considerable time (Freedson & Miller, 2000), and their use has been refined in recent years (Aparicio-Ugarriza et al., 2015; Trost, McIver, & Pate, 2005). Previous work was primarily conducted in experimental settings, in the context of physical movement assessment and health-related studies (Ruiz et al., 2011; Hooker & Masters, 2014), and in psychiatric research (Walther, Horn, Koschorke, Müller, & Strik, 2009). Actimetry was found useful in the diagnostics and prediction of psychiatric symptoms (Walther, Ramseyer, Horn, Strik, & Tschacher, 2014). To our knowledge, an extended use of high-resolution accelerometric recordings in psychotherapy sessions has not yet been documented in the literature. Nonverbal Coordination: Synchrony Nonverbal synchrony is conceived as a dynamic quality capturing movement characteristics irrespective of the type of movement or posture displayed, and irrespective of which part of the body is involved in movement, as long as it is inside the region of interest defined in the video (Ramseyer & Tschacher, 2006). Nonverbal synchrony thereby reflects an objective quantification of the dynamic movement characteristics displayed by patient and therapist. 150 NDPLS, 20(2), Ramseyer & Tschacher Participants This study employed a single-case, repeated measures design in a psychotherapy setting. The patient was female, 36 years of age and suffered from depressive symptoms (DSM-IV diagnosis of major depression – single episode) as well as interpersonal problems. During therapy, the patient was in psychiatric day treatment, i.e. there was a structured schedule for the duration of the day on weekdays. For the evening and night, as well as for week-ends, the patient lived at home. The therapist was female as well, 40 years of age and treated the patient with a modern (eclectic) form of psychodynamic therapy. Therapy Sessions The therapy sessions were held over a time-period of 12 months, with sessions usually occurring once weekly. During the one-year treatment, a total of 42 sessions were administered, each with a duration of 50 to 60 minutes. This variant of psychodynamic therapy was administered with patient and therapist in seating positions and in a face-to-face orientation. Measurements Post-session Questionnaire Post-session questionnaires (BPSR-P/BPSR-T: Flückiger, Regli, Zwahlen, Hostettler, & Caspar, 2010) were administered to both the patient and the therapist after each therapy session. These self-report measures comprised 15 (patient) and 17 (therapist) items covering six global factors determined by factor analysis, which was conducted on a questionnaire with more items (Tschacher, Ramseyer, & Grawe, 2007). Three factors captured the patient’s view of the therapy process (quality of therapeutic bond, patient’s well-being, patient's progress in therapy) and three factors reflected the therapist’s perspective (quality of therapeutic bond, therapist’s evaluation of patient's cooperation, therapist’s evaluation of progress in therapy). The questionnaires were handed out after each session and completed by both interaction partners independently. Vitaport Data Both patient and therapist were instructed to continuously wear sensors of the Vitaport mobile monitoring system. The sensors used in the present study were attached to both wrists of each person. These sensors are lightweight and their fixation on the wrist is straightforward. The sensors were attached by cable to an adjacent mobile recording unit worn by each participant. This setup was repeated in each session throughout the course of therapy. Apart from recording hand movements, breathing activity and heart-rate were also monitored. The data from the four actimetric wrist sensors were collected on two Vitaport systems – one for the patient and one for the therapist. The actimetry channel of the device was set at 25 Hz, i.e. 25 acceleration measurements per second. Raw data of the two recording devices was temporally aligned by using a manually generated marker that was initiated at the beginning of each therapy session. NDPLS, 20(2), Movemen nt Coordinatioon in Psychoth herapy 1151 For the pressent analyses, data from thee left- and righht-hand channels were summed up intto one generaal movement pparameter per person that w was indicatiive of overall hand-movemen h nt. This simpliification was chhosen in orderr to reduce the complexitty of the biman nual setup andd in light of thee limited numbber of sessions, which alrready put consstraints on statiistical power aand the statisticcal modelss available. Fig gure 1 providess an example oof patient and th therapist data aand a graph hical illustratio on how the two channels of movement daata (left and rigght wrists) were summed d up. Movement analysis a was in nitiated at a tiime-point that was 10 minuttes into thee therapy, and the analyzed duration of moovement data was restricted to 40 min nutes. Thus in each session, minutes numbber 10 to 50 w were used in tthe analysees presented heere. The late in nitiation of thee analysis was based on careful visual inspection of raw r data, whicch revealed a hhigh number oof unusual movvement activity a occurrring during the first 5 to 1 0 minutes. It was decided to excludee this accomm modation phasee from later annalysis, as the ensuing minuttes reflecteed more naturaal behavior than n the initial phhase where patiient and therappist probably needed som me getting-accu ustomed to the recording setuup. The limitatiion to 40 minutes m of eacch recorded sesssion was chossen because soome sessions ddid not lastt more than 55 minutes. Fig. 1. Raw data of the t Vitaport sy ystem. Segmen nts of 60 seco onds duration a are depicte ed in different grays. g Magnifie ed area to the right: Exempla ary raw data fro om the patient's left and right r wrists, and d summed movvement time-se eries (sum). Cross-Correlation Time-seriess of motion recordings (F Fig. 1) were cross-correlatted (Bokerr, Xu, Rotondo o, & King, 200 02; Derrick & Thomas, 20044) in segments of 152 NDPLS, 20(2), Ramseyyer & Tschach her wo one miinute duration. In each segment, the cross- correlation funnction of the tw time seeries was comp puted, so that th he non-stationaarity of movem ment behaviorss is consideered by the segment-wisee computationns. All crosss-correlations of movem ment up to the time t lag of ±5 seconds were uused in order tto allow for exaact synchro onization (lag of 0) and delay yed synchronizzation (lags off, in steps of 0..04 second ds, up to 5 seeconds). The absolute valuues of cross-ccorrelations weere computted separately in all segments and then agggregated over thhe entire intervval of 40 minutes. m The resulting glob bal measure m mean of cross-ccorrelations thhus compriised the absolu ute values of po ositive as well as negative coorrelations andd is consideered to indicatte the general association bettween patient‘‘s and therapisst‘s movem ment, their nonv verbal synchrony. Fig. 2. Cross-correlattion plot of one e exemplary se ession (duratio on = 40 minute es). Time ru uns on the X-a axis, from left to right. Lags are shown on n the Y-axis, w with positive e lags from the e top to the mid ddle and negattive lags from tthe middle to tthe bottom. Lag Leength on of an approp priate time-spaan is an imporrtant decision ffor The selectio the assessment of no onverbal coordiination. Guasteello, Reiter, annd Malon (20115) recently y provided vaarious solution ns for choosiing lag lengthh. We used aand describ be a different method m based on a bootstraap-approach. T The utilization of bootstrraps for the ev valuation of pseudosynchron p ny (see below w) has been prreviously y described by various authorrs (e.g. Bernierri, Reznick, & Rosenthal, 19888; Tschaccher, 1997) and d it provides a practical estiimation for thee strength of tthe phenom menon (see Raamseyer and Tschacher, T 201 0 for details). We did not fo for- NDPLS, 20(2), Movement Coordination in Psychotherapy 153 mulate an a priori analysis plan, but the choice of lag lengths was mainly driven by empirical data gained with motion energy analysis. We were thus particularly interested whether our previously determined lag length of ±5 seconds (Ramseyer & Tschacher, 2011) could once again be determined to provide the best differentiation between genuine synchrony versus pseudosynchrony. Segment Size Based on our previous research conducted with MEA, we assumed that the movement patterns captured by the Vitaport system would not represent stationary time-series. This assumption may already be observed in the raw data (see Fig. 1), and it is clearly evident in the windowed cross-correlation plots (see Fig. 2). Research on human communication has identified periodicities in human speech (Warner & Mooney, 1988; Warner, Malloy, Schneider, Knoth, & Wilder, 1987). According to Warner and Mooney, "the periodogram can be viewed as a partition of the variance of the original time series into the amount of variance that is accounted for by ... periodic components."(1988, p. 105). Pseudosynchrony The phenomenon of synchronization between interacting persons has been repeatedly shown in previous research, but a control condition serving as an empirical point of reference is of substantial help in estimating the magnitude of the phenomenon. An elegant and simple method for such estimation has been proposed by Bernieri et al. (1988), who altered split-screen video clips in such a way that subjects were paired with other subjects that they never interacted with. They called these recombined video clips "pseudosynchrony" and used them for the evaluation of how strongly real synchrony differentiated from these pseudointeractions. In the present analysis, we employed the same technique: Surrogate datasets (N = 1404) were produced by the permutation of patient's and therapist's time-series. In our single-case study the patient and therapist did interact with each other, but the permuted recombination resulted in segments that where randomized with respect to time across all sessions. We used all possible combinations, i.e. also included patient-patient as well as therapist-therapist recombinations. The 27 sessions available for our analysis thus provided a maximum of 1404 recombinations (based on the total of 54 time-series). Pseudosynchrony in shuffled datasets was calculated identically to the synchrony of the original data. For the comparison of nonverbal synchrony versus pseudosynchrony, the mean value of the 1404 shuffled surrogate data was computed as well as its standard deviation. Subtracting pseudosynchrony from real synchrony and dividing it by the standard deviation of pseudosynchronies provided an analog to Cohen's effect-size d, where values of 0.3 to 0.5 represent small effects, 0.5 to 0.8 moderate effects, and values > 0.8 are considered large effects (Cohen, 1988). In previous studies, we permuted the time-series in a more restricted way: A segment-wise shuffling was used, where only the temporal sequence was 154 NDPLS, 20(2), Ramseyer & Tschacher permuted, while the interaction partner was not substituted, i.e. no pairings of interactants who never interacted were allowed in this earlier algorithm (Ramseyer & Tschacher, 2010). Statistical Models To determine the general association between synchrony and outcome, Pearson correlations were calculated for each factor of the post-session questionnaire. Furthermore, stepwise backward regressions were used to determine relevant predictors of synchrony. RESULTS Global Movement Characteristics On a general level, we assessed patient‘s and therapist‘s movement characteristics across the therapy sessions. In terms of whether the subjects moved or remained static, we quantified the percentage of movement for both interaction partners: Overall, the therapist moved significantly more than the patient (MTH = 53.04%, SDTH = 14.43; MPAT = 36.82% SDPAT = 16.38; t(26) = 4.8 p < .0001; Cohen‘s d = 1.05). For the patient, movement frequency was strongly associated with session number (r = .54; p < .01), and to a lesser degree also for the therapist (r = .37; p = .06). Between patient and therapist, movement percentages were positively associated (r = .36; p = .07). Visual inspection of the evolution of movement percentage indicated different associations between patient and therapist for the initial (< session #20) and final (sessions 20-40) phases of therapy and in terms of movement percentage, there was more movement in the later phase of therapy for both patient (d = 1.38) and therapist (d = 1.52). In the first half of therapy, patient and therapist had positively associated patterns in their amounts of movement (r = .264), while in the second phase of therapy, a trend for a compensatory pattern could be detected (r = -.174), i.e. initially patient and therapist tended to match their movement activity, while later on, one person's movement was associated with less movement from the other person. Pseudosynchrony The comparison between hand-movement synchrony and pseudosynchrony was conducted with the maximal possible number of combinations (N = 1404) from the 27 available sessions. Each session's patient and therapist data (n = 54) was combined with all other available data (i.e. inclusive of patient-patient and therapist-therapist combinations). The effect-size of this comparison was d = 0.48 (p < .05) for a segment size of 1 minute and a lag length of ±5 seconds. The choice of segment size and lag length was on one hand based on the explorative comparison described below and on the other hand with respect to previous choices in psychotherapy data, thus allowing an easier comparison of data from different research projects. NDPLS, 20(2), Movemen nt Coordinatioon in Psychoth herapy 1155 gth, Segment Size, S Pseudosyynchrony, Outtcome Lag Leng Different len ngths of lags and a different ssegment sizes were tested aand The parallel liines (one line ffor comparred based on their respectivee effect-sizes. T each laag length of 3, 5, 8, and 10 0 seconds) shoown in Fig. 3 indicate that tthe magnittude of the effect e from th he comparisonn of synchronny with pseuddosynchro ony peak at sim milar segment sizes across ddifferent lag lenngths. A lag off 3 second ds provided th he highest effeect-size acrosss all segmentss, with a clearrly distingu uishable peak at a the segmentt size of 60 secoonds. Fig. 3. Lag length (3, 5, 8, and 10 se econds), segm ment size (in se econds), and th heir associa ations with sync chrony vs. pse eudosynchrony effect-size. Another posssibility for the graphical insppection of an opptimal lag lenggth may be b derived from f the distribution of genuine syynchrony verssus pseudo osynchrony: Fiigure 4 depictts average corrrelations for lags up to ± 5 second ds (for the sellected segmen nt size of 60 seconds). Thee average crosscorrelaation of the reaal dyads crossees the grand avverage of the rrandom dyadss at roughly y ± 75 lags, which equals ± 3 seconds.. This means the duration of significcant synchrony y in this psycho otherapy coursee was 6 secondds. Calculating cross-correlatiions within a ttime-frame of 60 seconds thhus deliverred the highest contrast between synchronyy and pseudosyynchrony. In liine with our o rationale to t use segmen nted cross-corr rrelations to aaccount for noonstationaarity, longer seegment sizes co orresponded w with lower effecct-sizes. 156 NDPLS, 20(2), Ramseyyer & Tschach her Fig. 4. Average (abs solute) cross co orrelation valu es of real (N = 27, black) a and random m (N = 1404, grray) dyads. Rhythm m and Periodiccity As shown in Fig. 3, th he signal-strenngth for the comparisons of synchro ony versus psseudosynchrony y peaked at a segment sizee of 60 seconnds duratio on, and increased once more at 240 secondss. This pattern was observedd in all fourr lags being co ompared, which h points to thee possibility thaat the peaks m may represeent natural rhytthms occurring g in psychotheerapeutic exchaange. We furthher exploreed this possibiility using specctral analysis ((Fast Fourier Transformation), and wee received high her periodic power p at these time-scales (ii.e. at periods of around d 90 seconds). Synchrony S and d Post-Session n Outcome The synchro ony values of all a available 277 sessions weree correlated w with factors of post-sesssion questionn naires from bboth patient's and therapisst's perspecctives. An overrview of the asssociations is fo found in Table 1, which lists tthe correlaation coefficien nts aggregated d over all 27 ssessions. Diffeerent lag lengtths and seg gment sizes aree provided. In addition to t this simple correlative c meaasure, multiple regressions w with synchro ony as the outccome variable and post-sessiion questionnaiires as predictoors were caarried out: Step pwise backwarrd elimination was chosen annd the remainiing factors were entered d into models. Session num mber was addeed to the moddel, NDPLS, 20(2), Movement Coordination in Psychotherapy 157 because it was strongly associated with both patient-rated alliance as well as with synchrony. The whole model was not statistically significant at the 5%level [F(7,26) = 2.17; p = 0.85; R2 = 0.24]. However, stepwise elimination of factors resulted in a model with two predictors, namely "session number" and "alliance PAT", which explained more than 25% of the variance [F(2,26) = 5.25; p = .01; R2 = 0.26]. Table 1. Associations between nonverbal synchrony and process measures at different lag lengths and segment sizes. Post-session Factor Alliance PAT Alliance TH Progress PAT Progress TH Well-being PAT Collaboration TH session # lag 3 / 60s .39* .18 .19 .10 .23 .12 .40* lag 5 / 60s .39* .14 .20 .05 .24 .10 .42* synchrony @ lag 3 / lag 5 / 90s 90s .48** .50** .27 .29 .03 .01 .13 .11 .12 .15 .31 .30 .41* .40* lag 8 / 90s .54** .27 -.04 .02 .19 .31 .38* lag 3 / 240s .55** .20 -.06 -.17 .31 .26 .33 *p < .05; **p < .01 Table 2. Associations within process measures and between nonverbal synchrony and session number. Post-session session # Factor synchrony @ .42* (lag 5 / 60s) Alliance PAT .49** Alliance TH -.21 Progress PAT .39* Progress TH -.18 Well-being PAT .31 Collaboration TH -.19 ALTH ALPAT .39* . .03 -.06 -.10 -.01 .40* PROGTH PROGPAT .14 .20 . .00 . .56** .54** .41* .34 .40* -.18 COLLTH WBPAT .05 .24 .10 . .21 .30 . .14 . *p < .05; **p < .01 Lag length, Segment size and Outcome The same explorative strategy for different lag lengths and segment sizes described above was performed on the associations of synchrony with post-session outcome. For the sake of simplicity, we limited this comparison to patient-reported relationship quality, which had the strongest and most reliable association with synchrony of all five post-session factors. Figure 5 shows the distribution of the correlation between synchrony and relationship quality for the 158 NDPLS, 20(2), Ramseyyer & Tschach her variouss combinationss tested in this sample (the saame combinations were usedd in the com mparison of syn nchrony versuss pseudosynchrrony). Fig. 5. Lag length (3, 5, 8, and 10 se econds), segm ment size (in se econds), and th heir associa ations with outc come (alliance rated by the pa atient). The differen nt lag lengths once o more folloow a parallel ppattern which w we already y found in th he effect-size comparison. Lag 3 proviides the highest correlaations across many m segment sizes s and there is a clear peakk at the segmenntsize off 90 seconds. Another high value is founnd at the segm ment size of 2240 second ds. If we were to t derive the optimal o parameeters for lag annd segment froom their asssociations witth outcome, th he selection woould thus be ddifferent from tthe one gained in the syn nchrony-pseud dosynchrony coomparison: A longer segmenntsize off 240 seconds and a lag-llength of 3 sseconds proviided the highest correlaation with outco ome (r = .55). However, connsidering the reesults in Tablee 1, the asssociations witth patient's alliance rating remains highhly stable acrooss differen nt settings. Basing B the seelection of pparameters on the effect-siize comparrison thus seem med to be the most m robust meethod. DIISCUSSION Nonverbal synchrony was detected in haand-movementt data assessedd in naturalistic psychoth herapy session ns. Nonverbal synchrony w was shown to be presentt at a level welll above chancee and with a m medium effect-ssize. This findiing not onlly confirmed prrevious finding gs from a largee, randomized sample that weere gained by non-invasiive video anallysis (Ramseyeer & Tschacheer, 2011), it allso cross-v validated the indirect i assesssment methodoology used inn motion enerrgy NDPLS, 20(2), Movement Coordination in Psychotherapy 159 analysis (MEA). The amount of synchrony increased with session number, a finding that fits previous research showing an increase of order throughout psychotherapy courses (Tschacher & Grawe, 1996; Tschacher et al., 2007; Tschacher, Scheier, & Grawe, 1998). Various parameters for lag length and segment size were explored and different strategies confirmed that a lag length of 5 seconds – which was previously used in various studies on nonverbal synchrony – was an appropriate time-span that sufficiently captures the phenomenon. The high values found with lags of 3 seconds provided stronger effects in some cases and one might argue that this shorter duration would be sufficient to capture the phenomenon. This may be true, but our decision to stick with the previously chosen lag length of 5 seconds may be viewed as a conservative solution, because it comprises sections at the limits of synchrony and pseudosynchrony. The distribution of cross-correlations across lags and in front of the background of pseudosynchronies supports this interpretation, as one may easily make out the crossing of synchronies at lags around ±4 seconds. Furthermore, a time-span of approximately 5 seconds has been previously identified as an indicator of social presence (Tschacher, Ramseyer, & Bergomi, 2013). For the chosen segment size, a similar result emerged: We were able to confirm the nonstationary nature of this kind of movement-based behavioral time-series, and for the psychotherapy setting, nonverbal synchrony seems to be best captured by segments of 60 to 90 seconds, a size that proved effective in previous studies (Ramseyer & Tschacher, 2011). By exploring different data-analytical strategies, we found a consistent pattern of associations between the quality of the therapeutic alliance, session number and synchrony. The patient's subjective assessment of the alliance after each session was the best predictor of synchrony. The results from this single-case study are in line with our previous findings based on automated video-analyses (MEA) of recorded therapy sessions. In a single-case analysis of N = 21 sessions of dyadic psychotherapy (Ramseyer & Tschacher, 2008), which was conducted with MEA, relationship quality from the patient's (r = .60) as well as from the therapist's perspective (r = .69) were found to be the best predictors of synchrony. The associations found in hand-movement data presented here thus complement this previous finding, although the therapist's evaluation did not correlate with the phenomenon of synchrony. The lack of association with the therapist's evaluations was however consistent with our previous larger, randomized sample (Ramseyer & Tschacher, 2011), where we also predominantly found associations with the patient's perspective only. This may in part be due to the fact that in this singlecase, the convergence of alliance ratings from patient and therapist was exceptionally low (r = .03). In terms of alliance-ratings found elsewhere, this low agreement does neither align with meta-analytic results (Shick Tryon, Collins Blackwell, & Felleman Hammel, 2007) reporting considerably higher associations (r = .36), nor with our previous, larger sample (r = .38). Other factors of this single-case were more in line with these other findings, such as e.g. the progress ratings from patient and therapist (r = .54) or the alliance from the patient's perspective with collaboration from the therapist's perspective (r = .40, 160 NDPLS, 20(2), Ramseyer & Tschacher see Table 2 for details). Taken together, it may be concluded that both the direct assessment of hand-movement by sensors, as well as the indirect quantification of nonverbal movement by video-analysis, provided convergent evidence for (a) the existence of the phenomenon, and (b) its consistent association with relevant facets of the therapeutic relationship. In the single-case described here, the relevant factor was the patient's assessment of the relationship. Leaving aside the therapist's perspective, on may conclude that nonverbal synchrony in psychotherapy indeed embodies the relationship between patient and therapist. The present analysis provides evidence for associations between synchrony and important aspects of psychotherapy. The causal nature or these associations is not yet decided, but there are theoretical and empirical arguments speaking for a reciprocal relationship between synchrony and therapeutic alliance as perceived by the patient. A general point is the bidirectionality of embodiment – it was repeatedly found that bodily phenomena entail mental changes and also, in reverse, that mental phenomena are expressed by the body. This bidirectionality, the hallmark of embodiment, is reflected in the systemstheoretical concept of circular causality (Haken, 1996). Limitations and Future Research The statistical modeling used in this research was of a rather simple nature, because the power to perform more sophisticated analyses was not given with the low number of sessions available. However, in two different models (correlation, stepwise regression), the quality of the therapeutic alliance from the patient's perspective always was the single best indicator of synchrony. Future analyses should be conducted with more dyads, which would enable disentangling dyad-specific factors from general associations between variables. The fact that we successfully replicated the main findings of studies employing completely different methodological approaches (such as MEA) speaks to the reliability and generalizability of these results: Relationship quality is embodied by the coordination of nonverbal behavior between patient and therapist. This coordination may rest on indirect observations of movement via videorecordings, or on global assessments of movement activity, or – as was presented here – on direct measurements of hand-movement using sensors. The explorative nature of our calculations on lag length and segment size should not be mistaken as an effort to maximize effects: The aim of reporting these different results is to provide an empirical account of how associations between our target variables are affected by methodological decisions. The main findings reported in the results section adhere to the previously established parameters used in the motion energy analyses. The similarities found with the Vitaport data reported here actually support the validity of these settings. For psychotherapy practitioners, these conclusions may not be very surprising because it has long been assumed that relationship formation and maintenance is strongly influenced by nonverbal aspects. 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